CHAPTER 1 INTRODUCTION

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "CHAPTER 1 INTRODUCTION"

Transcription

1 1 CHAPTER 1 INTRODUCTION 1.1 GENERAL The vertically integrated power system around the world is undergoing a transformation towards deregulated environment. The objectives of deregulation are aiming to establish a more competitive market in order to achieve lower rates for consumer and higher profit for the suppliers. The other benefits of deregulation are better service, reliable operation and competitive rates. The main participants of the new electricity market are Generating Companies (GENCOS), Power Markets (PM), Power Exchange (PX), Scheduling Coordinators (SC), Transmission Owners (TO), Ancillary Services (AS), Retail Service Providers(R) and Distribution Companies (DISCOS). These participants are coordinated by Independent System Operator (ISO). The function of ISO is to ensure free and non-discriminatory access to transmission system for all the market participants. In this deregulated system, effective management of transactions, to meet the active power requirements of the consumers can be done through pool and bilateral / multilateral markets. One of the main functions of ISO in a day ahead market is assessment of Available Transfer Capability (ATC) and updating this value in Open Access Same time Information Systems (OASIS). The market participants use ATC for their transaction planning. In a deregulated system, the generation and distribution companies (i.e. market players) carry out transactions (i.e. selling or buying electricity) through a competitive bidding process administered by an agency known as

2 2 power exchange apart from the transactions through bilateral negotiations. Every intended transaction is communicated to the ISO. The transactions are evaluated by ISO on the basis of ATC. ATC, which refers to the capacity and ability of a transmission network to allow for a reliable movement of electric power from the areas of supply to the areas of need, is an issue of concern to both system planners as well as system operators. ATC indicates how much power can be transferred without compromising system security. Accurate identification of this capacity provides vital information for both planning and operation of bulk power market. 1.2 FACTORS INFLUENCING ATC The calculation of ATC is generally based on computer simulations of the operation of the interconnected transmission network under a specific set of assumed operating conditions. The simulations are typically performed offline well before the systems approach the operational state. The factors (ftp.nerc.com/pub/sys/all_updl/pc/atcwg/atc96-1.pdf) considered in these simulations are i) Projected Customer Demands Base case demand levels should be appropriate to the system conditions and customer demand levels under study and may be representative of peak, off-peak or light demand conditions. ii) Generation dispatch Utility and non-utility generators should be realistically dispatched for the system conditions being simulated. iii) System configuration The base case configuration of the interconnected system should be representative of the conditions being simulated, including any generation and transmission outages that are expected. The activation of any

3 3 operation procedures normally expected to be in effect should also be included in the simulations. iv) Base scheduled transfers The scheduled electric power transfers that should be modeled are those that are generally considered to be representative of the base system conditions being analyzed and which the parties involved agree upon. v) System contingencies A significant number of generation and transmission system contingencies should be screened, consistent with individual electric system, to ensure that the facility outage most restrictive to the transfer being studied is identified and analyzed. The contingencies evaluated may in some instances include multiple contingencies. The conditions on the interconnected network continuously vary in real time. Therefore, the transfer capability of the network will also vary from one instant to the next. For this reason, transfer capability calculations may need to be updated periodically for application in the operation of the network. 1.3 LIMITS TO TRANSFER CAPABILITY The ability of interconnected transmission networks to reliably transfer electric power may be limited by the physical and electrical characteristics (ftp.nerc.com/pub/sys/all_updl/pc/atcwg/atc96-1.pdf) of the system including any one or more of the following: i) Thermal limits Thermal limits establish the maximum amount of electrical current that a transmission line or electrical facility can conduct over a specified time period

4 4 before it sustains permanent damage by overheating or before it violates public safety requirements. ii) Voltage limits System voltage and changes in voltages must be maintained within the range of acceptable minimum and maximum limits. The minimum voltage limits can establish a maximum amount of electric power that can be transferred without causing damage to the electric system or customer facilities. iii) Stability Limits ATC values must be estimated with Angle stability and voltage stability limits. The methods that are adopted in the literature for the calculation of ATC are presented in Table 1.1 Table 1.1 Methods adopted for calculation of ATC S. No. Method Description 1 Linear approximation DC Power Flow Model, Thermal Limits only 2 Optimal Power Flow (OPF) 3 Continuation Power Flow (CPF) AC Power Flow Model, Thermal Limit + Voltage Limit AC Power Flow Model, Thermal Limit = Voltage Limit (Voltage Collapse) 4 Stability constrained Time Domain Simulations with Dynamic Model The DC power flow method is a gross approximation of the AC power flow method as it considers linear models for network topology. It may not be accurate, where VAR flow and voltage deviations are considerable. CPF method considers a series of power system solutions to be solved and tested for limits. The amount of transfer is gradually increased from the base

5 5 case until the binding limit is encountered. OPF method requires formulation of an optimization problem with constraints arising from the power flow. The results obtained through OPF may vary depending upon the objective function and the constraints. Stability constrained methods require transient studies to be carried for a case with anticipated scenario. 1.4 LITERATURE REVIEW Publications on the topic of deregulation, ATC estimation and enhancement are too numerous, so only important publications have been referred which are directly related with the work presented in this thesis. Literature review carried out is presented under the following topics Literature Review on ATC Estimation Power transaction between a source bus and a sink bus can be committed only when sufficient power transfer capability is available. The ISO has to estimate the ATC between interface and update the same on OASIS at regular time intervals. Therefore, the methods required for computation of ATC must be fast and accurate. A frame work for determining ATC of the interconnected transmission networks for a commercially viable wholesale market has been established by the North American Electrical Reliability council (NERC) as per NERC report (1996) Hamoud (2000) proposed a method which uses a DC power flow model and Linear Programming (LP) algorithm for computing the ATC between any two areas in the transmission system. The simplex method is used. The DC power flow based methods are faster than their AC counterparts but only real power flow in the lines is considered.

6 6 Ejebe et al (2000) proposed a method based on power transfer / outage distribution factors for assessment of ATC. The formulation is based on the linear incremental power flow to account for the line flow thermal loading effects. The calculations for ATC utilize three sets of linear sensitivities such as branch (line) outage distribution factors, power transfer distribution factors and generator outage distribution factors, This method can cater to only the scenarios that are close to the base case from which the factors are derived. Li et al (2002) proposed maximum area concept for the allocation of simultaneous ATC. This concept decides maximum area capability inside the security region by considering the shape of security boundary. Kumar et al (2004) presented the development of a simple and noniterative method to calculate ATC for a transmission system using a new set of distribution factors. Sets of power transfer distribution factors and voltage distribution factors have been obtained using the sensitivity based approach for base case as well as contingency cases, and utilized to check line flow limits and voltage limits during ATC determination. Venkatesh et al (2004) described the assessment of ATC using AC Power Transfer Distribution Factors (ACPTDF) in combined economic emission dispatch environment. ACPTDF are derived using sensitivity based approach for the system intact case and utilized to check the line flow limits during ATC determination. The ATC results obtained are compared with Newton Raphson power flow method. Ou and Singh (2002) presented a Repeated Power Flow (RPF) method to determine ATC. Monte Carlo Simulation method is utilized to take one of the factors in Transmission Reliability Margin (TRM), load uncertainty into account. Capacity Benefit Margin (CBM) calculation is based on single

7 7 area generation reliability evaluation using a probabilistic method. The rules and procedure to allocate CBM into individual areas are proposed. Besides these, Transfer Based Security Constrained Optimal Power Flow (TSCOPF) method is proposed as a replacement of conventional SCOPF method for use in the deregulation environment. Milano et al (2005) proposed a method for the proper inclusion of contingencies and stability constraints through the use of a Voltage Stability Constrained Optimal Power Flow (VSC-OPF) for the estimation of ATC. The ATC value is computed based on N-1 contingency criterion for an initial optimal operating condition, and then to solve an OPF problem for the worst contingency case. This process is repeated until the changes in the ATC values are below a minimum threshold. The author also proposed another approach which solves a reduced number of OPF problems associated with contingency cases according to a ranking based on a power transfer sensitivity analysis of the transmission lines. Zhang et al (2004) proposed an optimization method for assessment of ATC, by considering dynamic constraints such as transient rotor angle stability constraint in addition to line thermal overload constraint and bus voltage limit constraints. ATC model is formulated as optimization problem with differential-algebraic equations and solved by applying a Non-Linear Programming (NLP) tool. An algorithm based on Control Variable Parameterization (CVP) is implemented to solve the formulated dynamic constrained optimization problem to handle large dynamic optimization problems without solving very large NLPs. Ning et al (2009) proposed an alternate continuation power flow for the estimation of ATC of a AC/DC transmission system

8 8 Rodrigues and DaSilva (2007) described a probabilistic methodology for assessing chronological variations on the ATC caused by the uncertainties associated with hourly load fluctuations and equipment unavailability s. The system states resulting from these uncertainties are generated using Monte Carlo method with sequential simulation. The ATC associated with each sample system state is evaluated through a linear DC Optimal Power Flow. The results demonstrate that uncertainties associated with hourly load transitions and equipment availability cause significant variations on the ATC. Cheng et al (2006) suggested a method for ATC estimation considering the voltage stability constraints. A new ATC determination scheme using Quasi steady-state (QSS) approximation is proposed. The proposed method estimates ATC with dynamic voltage stability constraints accurately and the calculation speed is also less to meet the real time requirements. Dong-Joon Shin et al (2007) proposed a probabilistic approach to calculate available transfer capability of the interconnected network. The computation of ATC is carried out in three steps. The total transfer capability is calculated using continuation power flow, the transmission reliability margin and capacity benefit margin are evaluated by probabilistic load flow and Monte Carlo simulation. Pandey et al (2010) suggested a Levenberg Marquardt algorithm of neural network (LMANN) based approach for fast and accurate estimation of system ATC. System ATC has been estimated for both varying load condition as well as for single line outage contingency condition by employing distributed computing. Principal component analysis (PCA) has been applied for effective input feature selection. Contingency clusters are

9 9 formed such that each cluster contains almost similar ATC values. For each contingency clusters separate LMANNs has been developed. Rajabi Ghahnavieh et al (2009) proposed a comprehensive approach for allocation of ATC to transmission service requests. The proposed method considers types, tariffs, and priority of requests in a mixedinteger nonlinear optimization which incorporates the allocation rules. The solution to the optimization problem gives the amount of accepted requests and their priority to the system operator while an emergency could result in the curtailment of certain transmission services. Yuan Kang Wu (2007) proposed a novel algorithm for contingency ATC computation and a sensitivity analysis for system uncertainties. It incorporates linear distribution factors and AC load flow sensitivity-based method to calculate ATC efficiently and speedily considering lines outages. Yog Raj Sood (2010) developed an algorithm for assessment of the feasibility of simultaneous bilateral and multilateral transactions. This method also finds out the minimum amount of transacted power to be curtailed if they are not feasible. This analysis will be a great help for the generations-loads pairs to decide whether to withdraw the unfeasible transaction completely or to make it feasible by reducing its size optimally. The proposed algorithm can also be used for determining the transfer capability and hence feasibility of a single bilateral transaction at a time. In addition to above algorithm an efficient, repeated Newton-Raphson power flow based algorithm is also developed to determine transfer capability and hence feasibility for single bilateral transaction. Hamoud et al (2000a) proposed a method for feasibility assessment of simultaneous bilateral transactions. The theory of simultaneous and non-

10 10 simultaneous transactions was also discussed. Yog Raj Sood et al (2004) proposed a method for the assessment of feasibility and pricing of transactions. Jain et al (2009) utilized a bifurcation approach to compute oscillatory stability constrained available transfer capability (ATC) in an electricity market having bilateral as well as multilateral transactions. Oscillatory instability in non-linear systems can be related to Hopf bifurcation. At the Hopf bifurcation, one pair of the critical Eigen values of the system Jacobian reaches imaginary axis. A new optimization formulation, including Hopf bifurcation conditions, has been developed in this paper to obtain the dynamic ATC. Kumar et al (2004a) proposed a method for ATC estimation using bifurcation approach. Lai (2001) presented a detailed study of the basics of restructuring. DaSilva et al (2004) proposed a new methodology to determine the best points in the system to add reinforcements of the transmission network and to add new sellers and buyers, in order to maximize the ATC, without violating a pre-established reliability level. The proposed algorithm uses non sequential Monte Carlo simulation to select system state and Linear Programming with a DC power flow model to analyze and optimize each selected state. Gisin et al (2000) discussed some of the complexities involved in calculating ATC values and performing the sensitivity analysis for the various factors involved, including parallel transfers and generation dispatch patterns. Distribution factors based on DC power flow method are proposed to calculate ATC. Because of the relative ease coupled with the mild computational burden involved in computing these factors they have found

11 11 widespread applications in the industry. However, in view of their limitations, the future of using such methods in the competitive market environment is doubtful. Ejebe et al (1998) proposed Continuation Power Flow (CPF) method for assessment of ATC which uses a full AC power flow solution incorporating the effects of reactive power flows, voltage limits and voltage collapse as well as the traditional line flow thermal loading effects. Since optimization approach with various control measures are not used the ATC obtained will be conservative. David et al (1998) discussed dispatch methodologies for open access transmission systems Min and Abur (2006) presented a method for computation of ATC in a multi area power system using CPF method. This computation takes into account the limits on the line flows, bus voltage magnitude, generator reactive power, voltage stability as well as the effect of line contingencies. The multiarea ATC problem is solved by using a network decomposition approach. Wang et al (1999) proposed a fast computational method to determine the Simultaneous Available Transfer Capability in a power system. The method consists of a fast estimation algorithm and a constrained power flow iteration that is based on fast estimation results. The fast estimation algorithm also includes the modified AC power flow equations, the sensitivity analysis under both normal condition and first line outage condition. The ATC limiting factors considered in the method are line thermal limits, bus voltage limits and generator reactive power limits. When combined with the first line contingency considerations, this method gives the fastest ATC computing. Shaaban et al (2000) proposed a formulation for optimal Power Flow (OPF) to calculate ATC using AC power flow model. The objective is

12 12 to maximize the sum of the sending end generation and receiving-end load of specified buses. The constraints are AC power flow equations and system operating limits such as thermal limits of transmission lines, voltage bounds of buses and upper and low limits of generators power. Sequential Quadratic Programming method is used for the optimization process. De Tuglie et al (2000) introduced a new methodology based on nonlinear technique, for assessing dynamic ATC in real time environment. The main objective is to maximize the ATC over the transmission lines belonging to a fixed interface named Critical Interface considering thermal constraints, dynamic constrain such as transient rotor angle stability constraint. The main feature of the approach is to treat the trajectories of the system as unknowns of an optimization problem and confine them in a time varying domain based on static and dynamic requirements. Goh et al (2003) presented an OPF based approach for transfer capability assessment incorporating a security index which determines the system security level of each transfer. Line stability indices are also implemented to quantify how close the system is to the point of static voltage collapse and to identify the critical lines connecting weak buses in a stressed system. Luo et al (2000) proposed a neural network solution methodology for the problem of ATC calculations. Based on the optimal power flow formulation of the problem, the inputs for a neural network are generator status, line status and load status and the output is the transfer capability. The Quickprop algorithm is used to train the neural network. Khairuddin et al (2004) proposed a fuzzy logic approach for determining ATC in a large deregulated power system. The proposed fuzzy method is tested for computing ATC between a number of source-sink pairs.

13 13 The method is also compared with a full-scale AC power flow based method in terms of accuracy and CPU time for evaluating ATCs considering the same array of transactions, base cases and outages. The CPU time requirement of the proposed method is independent of the system size while the power flow based ATC determination method s CPU time is directly proportional to the size despite exploitation of sparse structure of the system. Berizzi et al (2007) proposed a new methodology to reduce the arbitrariness related to the mid and long term ATC computation using a probabilistic approach. A Monte Carlo method is applied to sample many different reference scenarios in terms of generation patterns to be adopted for the ATC computation. Eventually, the probability density function of the ATC is built. A stochastic calculation of ATC is proposed by Jonathan et al (2007). A stochastic power flow algorithm is used to quantify and evaluate the uncertainties involved in the ATC estimation Othman et al (2005) presented computationally fast and accurate method for evaluating available transfer capability based on curve fitting technique called as the cubic-spline interpolation technique. This method traces the curves of voltage magnitude and power flow variations with respect to the increase of real power transfer. Srinu Naik et al (2010) proposed a method for determination of ATC with PTDF using linear methods in presence of TCSC. IEEE 14 bus system was used to test the fasibility of the model. Othman et al (2006) proposed a new technique which uses evolutionary programming to maximize the total amount of generation so as to determine the capacity benefit margin for each area.

14 14 Gnanadass et al (2004) described an ATC estimation method with capacity benefit and transmission reliability margins of practical power systems with combined economic emission dispatch. In this approach Newton-Raphson power flow method and Evolutionary Programming algorithm were combined for ATC calculation. Tae Kyung Hahn et al (2008) described a fuzzy logic approach to parallelizing contingency-constrained optimal power flow. The fuzzy multi objective problem is formulated for ATC estimation Sung Sukim et al (2008) aimed to determine available transfer capability (ATC) based on the fuzzy set theory for continuation power flow (CPF), thereby capturing uncertainty. Xiong Pan and Guoyu Xu (2005) proposed a model for ATC calculations accorded with trade-off mechanism in electricity market was set up. The impact of branch outage contingency on the static voltage stability margin is analyzed and contingency ranking is performed through sensitivity indices of branch flows with respect to the loading margin. Tomohiko Ichikawa et al (2009) proposed a method for estimation of ATC from the view point of power system transient stability Literature Review on Support Vector Machine (SVM) Abdala and Saeed (2004) used weighted K-nearest neighbor algorithm that exploits the correlation between a missing dimension and available data values from other fields. This algorithm is used to estimate the missing values in clinical laboratory measurements of ICU patients.

15 15 Bo-Luen Chen et al (2004) applied SVM successfully to load forecasting. Some important conclusion about mid term load fore casting is inferred. Iffat (2009) compared all the computational intelligent machine learning algorithms of data mining. The conventional imputation algorithms are briefed. Nivedita (2004), Micro calcification (MC) detection is an important component of breast cancer diagnosis. However visual analysis of mammograms is a difficult task for radiologists. Computer Aided Diagnosis is used effectively to make a final decision. The author made a comparative study of digital mammography using Neural Networks and Support Vector Machine. Pedro et al (2009) proposed a novel KNN imputation algorithm using a feature weighted distance metric based on mutual information. This method provides a missing data estimation technique for solving the imputed data classification problem. Moulin et al (2004) proposed a pattern recognition approach to transient stability analysis (TSA). This paper used learning based non linear classifier the support vector machine. It is proved that it can be used for solving problem of high dimensionality. Ravikumar et al ((2008) proposed a method for improving power system protection co-ordination using intelligent knowledge based systems. SVM is used as intelligence tool to identify the faulted line that is emanating and finding the distance from the substation.

16 16 Robert Salat and Stanislaw (2004) presented a new approach to the location of fault in the high voltage power transmission line using SVM and frequency characteristics of the measured terminal voltage and current transient signals of the system Literature Review on Back Propagation Neural Network (BPNN) Vasudevan, et al (2004) Proposed a new multi-parameter retrieval algorithm based on a back propagation neural network (BPNN) developed for deriving integrated water vapor and cloud liquid water contents over oceans from brightness temperatures measured by the Multi-frequency Scanning Microwave Radiometer launched onboard Indian Remote Sensing satellite IRS-P4. Yu-Chuan et al (2008) proposed various virtual metrology algorithms, including back-propagation neural networks (BPNN), simple recurrent neural networks (SRNN), and multiple regression (MR), are evaluated to see whether they can meet the accuracy and real-time requirements of wafer-to-wafer advanced process control or not. The fifth generation TFT-LCD chemical-vapor deposition process is used to test and verify the requirements. Hosny et al (2009) proposed a novel dynamic nonlinear model for pulsed corona discharge using back propagation neural networks. The Levenberg-Marquardt training algorithm, which is perfectly suitable for fitting functions, is employed. The developed model is based on the voltagecurrent characteristics of an actual hybrid-series reactor and takes the practical constrains associated with a real system into account.

17 17 Azimi-Sadjadi et al (2002) presented a new adaptive underwater target classification system to cope with environmental changes in acoustic backscattered data from targets and non-targets are introduced. The core of the system is the adaptive feature mapping that minimizes the classification error rate of the classifier. The goal is to map the feature vector in such a way that the mapped version remains invariant to the environmental changes. A K- nearest neighbor (K-NN) system is used as a memory to provide the closest matches of an unknown pattern in the feature space. The classification decision is done by a back propagation neural network (BPNN). Xiaofeng et al (2009) presented a back-propagation neural network (BPNN)-aided K*F algorithm and a fuzzy inference-based KF algorithm in order to overcome the time delay of RDSS positioning provided by a doublestar positioning system in China. Traditional KF causes biased solutions, and indeed, leads to filter instability easily since the time delay of RDSS positioning, in an active mode, is hard to be modeled and sometimes suffers from RDSS outages. Therefore, a fuzzy inference is used to correct the variance matrix of KE measurement noises adaptively; and a trained BPNN corrects the outputs of the Kalman filter. Tung-Ho Lin et al (2009) proposed an advanced key-variable selection method, the neural-network-based stepwise selection method, which can enhance the conjecture accuracy of the NN-based virtual metrology algorithms. Multi-regression-based step wise selection method is widely applied in dealing with key-variable selection problems despite the fact that it may not guarantee finding the best model based on its selected variables. Gutierrez-Martinez et al (2011) proposed a new approach to model stability and security constraints in optimal power flow (OPF) problems based on a neural network (NN) representation of the system security boundary (SB). The novelty of this proposal is that a closed form, differentiable

18 18 function derived from the system's SB is used to represent security constraints in an OPF model Literature Review on Generalized Regression Neural Network (GRNN) Das Merces Machado et al (2009) explained the use of the wavelet transform and computational intelligence techniques to quantify voltage shortduration variation in electric power systems, with respect to time duration and magnitude. The wavelet transform is used to determine the event duration, as well as for obtaining a characteristic curve relating the signal norm as function of the number of cycles for a waveform without disturbance that is used as reference for the calculation of the magnitude of the event. A generalized regression neural network (GRNN) is used to interpolate not stored points of the characteristic curve. Goulermas et al (2007) proposed a novel algorithm for function approximation that extends the standard generalized regression neural network. Instead of a single bandwidth for all the kernels, this paper employed a multiple-bandwidth configuration. However, unlike previous works that use clustering of the training data for the reduction of the number of bandwidths, this paper proposed a distinct scheme that manages a dramatic bandwidth reduction while preserving the required model complexity. In this scheme, the algorithm partitions the training patterns to groups, where all patterns within each group share the same bandwidth. Rutkowski (2004) proposed a new class of generalized regression neural networks working in non-stationary environment. The generalized regression neural networks (GRNN) studied in this paper are able to follow changes of the best model, i.e., time-varying regression functions. This paper

19 19 presented an adaptive GRNN tracking time-varying regression functions. The speed of convergence of the GRNN is investigated Kim et al (2004) Silicon carbide (SiC) was etched in a NF 3 /CH 4 inductively coupled plasma. The etch process was modeled by using a neural network called generalized regression neural network (GRNN). For modeling, the process was characterized by a 2 4 full factorial experiment with one center point. To test model appropriateness, additional test data of 16 experiments were conducted. The GRNN prediction performance was optimized by means of a genetic algorithm (GA). Madkour et al (2007) presented an investigation into the comparative performance of an active vibration control (AVC) system using a number of intelligent learning algorithms. Recursive least square (RLS), evolutionary genetic algorithms (GAs), general regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) algorithms are proposed to develop the mechanisms of an AVC system. The controller is designed on the basis of optimal vibration suppression using a plant model. A simulation platform of a flexible beam system in transverse vibration using a finite difference method is considered to demonstrate the capabilities of the AVC system using RLS, GAs, GRNN, and ANFIS. Chaofeng Li Bovik and Xiaojun Wu (2011) developed a noreference image quality assessment (QA) algorithm that deploys a general regression neural network (GRNN). The new algorithm is trained on and successfully assesses image quality, relative to human subjectivity, across a range of distortion types. The features deployed for QA include the mean value of phase congruency image, the entropy of phase congruency image, the entropy of the distorted image, and the gradient of the distorted image. Image quality estimation is accomplished by approximating the functional

20 20 relationship between these features and subjective mean opinion scores using a GRNN Literature Review on ATC Enhancement Ghawghawe and Thakre (2009) proposed a new approach for determination of changes in the reactance of TCSC, required for achieving the desired transfer capability. Also a suitable criterion with a mathematical model for computation of reactance is proposed for selection of a line for the installation of TCSC. Jain et al (2009a) Proposed sensitivity analysis of structure preserving energy margin with respect to the control parameters of FACTS controllers for their optimal placement in the network. Two types of FACTS controllers, viz Static Synchronous Compensator (STATCOM) and Unified Power Flow Controller (UPFC) have been considered. The hybrid approach, combining a structure preserving and a time domain simulation method, has been utilized to compute the dynamic ATC in presence of these controllers and their impact on dynamic ATC has been analyzed. The potential energy, contributed by the STATCOM and the UPFC, has also been included in the structure preserving energy function to include their influence on transient stability. Khaburi et al (2010) proposed a probabilistic modeling based approach to determine the total transfer capability enhancement using FACTS devices Rashidinejad et al (2008) suggested an approach to determine the optimum location and optimum capacity of TCSC in order to improve ATC as well as voltage profile. Real genetic algorithm (RGA) associated with analytical hierarchy process (AHP) and fuzzy sets are implemented as a

21 21 hybrid heuristic technique in this paper to optimize such a complicated problem. Singh et al (2009) proposed a method for dynamic ATC enhancement. This paper proposed a method for optimal placement of FACTS devices for ATC enhancement. Harrinder Sawhney and Jeyasurya (2004) suggested the application of the unified power flow controller (UPFC) to improve the transfer capability of power system. 1.5 OBJECTIVES OF THE THESIS In the restructured power system the feasibility of all the proposed transactions should be evaluated by the ISO. From the literature review it is observed that the conventional methods like RPF, AC OPF and DC OPF estimate the feasibility of the proposed transactions sequentially therefore the computation time taken will be more. So there is a need for a method which can estimate ATC simultaneously. The operation and control of restructured power system is based on real power transactions. But most of the system studies are based on real power loads. So there is a scope to estimate ATC using the transactions. From the literature review it is also observed that the AI techniques that are used to estimate ATC make use of either real power loads or load index and source injection as inputs. Separate AI models are developed to estimate ATC of the outage conditions. So, it is necessary to develop a set of inputs that can be used for estimating the ATC so that the set of inputs will remain the same irrespective of the size of the system. Also the need for using separate AI models for outage condition can be avoided. These issues are

22 22 addressed in this thesis. The motivation and the main objectives of the thesis are as follows: To estimate ATC of a bilateral transaction using SVM and GRNN To estimate ATC using transactions as one of the inputs for AI models. To estimate ATC with load, generation and transaction indices as inputs to AI models. To estimate ATC of multi-lateral transaction with economic constraint. To determine the degree of series and shunt compensation that is necessary to enhance the ATC to the prescribed level using SVM. 1.6 OUTLINE OF THE THESIS This thesis is organized into eight Chapters. Chapter-wise summary of the thesis stating the development reported therein and results obtained from investigations are presented in the sections that follow. Artificial Intelligence (AI) techniques find application for almost all the power system problems. Second Chapter explains the basics of SVM, GRNN, BPNN and Fuzzy logic. These are the techniques used for ATC estimation in the subsequent Chapters. The third Chapter proposes a method for ATC estimation using SVM and GRNN. The real power loads are used as inputs. The method is

23 23 applied on IEEE 24 bus RTS and IEEE 118-bus systems. The test results are compared with the RPF results. The fourth Chapter explains the method of ATC estimation using the transactions as one of the inputs. It is observed that, in the restructured power system the operation and control is based on transactions. For this reason transaction is used for ATC estimation. The method is applied on IEEE 24 bus RTS and IEEE 118-bus systems. The test results are compared with the RPF results. The fifth Chapter aims at determining the ATC using few indices. The inclusion of outage conditions either by developing a separate AI model or by including the status of lines in the input vector increases the computation time significantly. This chapter proposes the transactions as an index for outage conditions. This chapter aims at defining few operator friendly indices to estimate ATC with lesser number of inputs. The method is applied on IEEE 24 bus RTS and IEEE 118-bus systems. The test results are compared with the RPF results. The sixth Chapter describes a method for the ATC estimation of multi-lateral transactions. This chapter also aims at estimating the economic schedule of generators involved in the multi lateral transactions. An OPF based method is used to obtain the economic generation of generators present in the GENCO. SVM model is developed to obtain the ATC and the economic generation. The approach is applied to IEEE 24 bus RTS and IEEE 118- bus systems. From the results it is seen that the method is effective. The seventh Chapter deals with the enhancement of ATC with series and shunt compensation. The selection of type of compensation is carried out using RPF. The degree of series and shunt compensation required

24 24 to meet the desired ATC is obtained from SVM model. The test results on IEEE 24-bus RTS system are presented. In the concluding eighth Chapter the significant contributions of this thesis are presented. The scope for further work in the ATC estimation and enhancement is discussed.

Re-Dispatching Generation to Increase Power System Security Margin and Support Low Voltage Bus

Re-Dispatching Generation to Increase Power System Security Margin and Support Low Voltage Bus 496 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL 15, NO 2, MAY 2000 Re-Dispatching Generation to Increase Power System Security Margin and Support Low Voltage Bus Ronghai Wang, Student Member, IEEE, and Robert

More information

International Journal of Scientific & Engineering Research, Volume 6, Issue 1, January ISSN

International Journal of Scientific & Engineering Research, Volume 6, Issue 1, January ISSN International Journal of Scientific & Engineering Research, Volume 6, Issue 1, January-2015 851 APPLICATION OF ARTIFICIAL NEURAL NETWORKS (ANNs) IN REACTIVE POWER OPTIMIZATION Kamel A. Shoush, Member,

More information

Optimal Proxy-Limited Lines for Representing Voltage Constraints in a DC Optimal Powerflow

Optimal Proxy-Limited Lines for Representing Voltage Constraints in a DC Optimal Powerflow Optimal Proxy-Limited Lines for Representing Voltage Constraints in a DC Optimal Powerflow by Michael Schlindwein A thesis submitted in fulfillment of the requirements for the degree of Master of Science

More information

Analysis of Power System Stability by Using Optimally Located SVC and STATCOM

Analysis of Power System Stability by Using Optimally Located SVC and STATCOM Master Thesis on Analysis of Power System Stability by Using Optimally Located SVC and STATCOM XR EE ES 2009:010 Thesis Examiner: Thesis Supervisor: Submitted by: Mehrdad Ghandhari Hector Latorre / Jai

More information

Voltage Stability assessment by SVC Device Via CPF

Voltage Stability assessment by SVC Device Via CPF ICEN 2 International Conference on Electrical Networks. Sidi Bel-Abbès, September 28 & 29,2 Voltage Stability assessment by SVC Device Via CPF O. L. BEKI *, M.K. FELLAH** and M. F. BENKHOIS *** * ICEPS

More information

A Deterministic Dynamic Programming Approach for Optimization Problem with Quadratic Objective Function and Linear Constraints

A Deterministic Dynamic Programming Approach for Optimization Problem with Quadratic Objective Function and Linear Constraints A Deterministic Dynamic Programming Approach for Optimization Problem with Quadratic Objective Function and Linear Constraints S. Kavitha, Nirmala P. Ratchagar International Science Index, Mathematical

More information

Aggregation of Buses for a Network Reduction HyungSeon Oh, Member, IEEE

Aggregation of Buses for a Network Reduction HyungSeon Oh, Member, IEEE IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 27, NO. 2, MAY 2012 705 Aggregation of Buses a Network Reduction HyungSeon Oh, Member, IEEE Abstract A simple but precise model would improve the computation efficiency

More information

PowerWorld Tutorial. Yen-Yu Lee The University of Texas at Austin Jan 18, Updated December 26, 2012, by Ross Baldick

PowerWorld Tutorial. Yen-Yu Lee The University of Texas at Austin Jan 18, Updated December 26, 2012, by Ross Baldick PowerWorld Tutorial Yen-Yu Lee The University of Texas at Austin Jan 18, 2010 Updated December 26, 2012, by Ross Baldick 1 Introduction PowerWorld is one of the most popular power system simulation tools.

More information

A Neuro-Fuzzy Application to Power System

A Neuro-Fuzzy Application to Power System 2009 International Conference on Machine Learning and Computing IPCSIT vol.3 (2011) (2011) IACSIT Press, Singapore A Neuro-Fuzzy Application to Power System Ahmed M. A. Haidar 1, Azah Mohamed 2, Norazila

More information

9. Lecture Neural Networks

9. Lecture Neural Networks Soft Control (AT 3, RMA) 9. Lecture Neural Networks Application in Automation Engineering Outline of the lecture 1. Introduction to Soft Control: definition and limitations, basics of "smart" systems 2.

More information

Neural Network Weight Selection Using Genetic Algorithms

Neural Network Weight Selection Using Genetic Algorithms Neural Network Weight Selection Using Genetic Algorithms David Montana presented by: Carl Fink, Hongyi Chen, Jack Cheng, Xinglong Li, Bruce Lin, Chongjie Zhang April 12, 2005 1 Neural Networks Neural networks

More information

Stability Assessment of Electric Power Systems using Growing Neural Gas and Self-Organizing Maps

Stability Assessment of Electric Power Systems using Growing Neural Gas and Self-Organizing Maps Stability Assessment of Electric Power Systems using Growing Gas and Self-Organizing Maps Christian Rehtanz, Carsten Leder University of Dortmund, 44221 Dortmund, Germany Abstract. Liberalized competitive

More information

Using a genetic algorithm for editing k-nearest neighbor classifiers

Using a genetic algorithm for editing k-nearest neighbor classifiers Using a genetic algorithm for editing k-nearest neighbor classifiers R. Gil-Pita 1 and X. Yao 23 1 Teoría de la Señal y Comunicaciones, Universidad de Alcalá, Madrid (SPAIN) 2 Computer Sciences Department,

More information

Evaluation of Model-Based Condition Monitoring Systems in Industrial Application Cases

Evaluation of Model-Based Condition Monitoring Systems in Industrial Application Cases Evaluation of Model-Based Condition Monitoring Systems in Industrial Application Cases S. Windmann 1, J. Eickmeyer 1, F. Jungbluth 1, J. Badinger 2, and O. Niggemann 1,2 1 Fraunhofer Application Center

More information

Louis Fourrier Fabien Gaie Thomas Rolf

Louis Fourrier Fabien Gaie Thomas Rolf CS 229 Stay Alert! The Ford Challenge Louis Fourrier Fabien Gaie Thomas Rolf Louis Fourrier Fabien Gaie Thomas Rolf 1. Problem description a. Goal Our final project is a recent Kaggle competition submitted

More information

Classification. Vladimir Curic. Centre for Image Analysis Swedish University of Agricultural Sciences Uppsala University

Classification. Vladimir Curic. Centre for Image Analysis Swedish University of Agricultural Sciences Uppsala University Classification Vladimir Curic Centre for Image Analysis Swedish University of Agricultural Sciences Uppsala University Outline An overview on classification Basics of classification How to choose appropriate

More information

Introduction: Model Relationships Network Model Overview Example Commercial Model Overview Component Hierarchy & Definitions Example of Structure

Introduction: Model Relationships Network Model Overview Example Commercial Model Overview Component Hierarchy & Definitions Example of Structure 1 2 3 Introduction: Model Relationships Network Model Overview Example Commercial Model Overview Component Hierarchy & Definitions Example of Structure MISO Functions Served by the Network Model by the

More information

3 Feature Selection & Feature Extraction

3 Feature Selection & Feature Extraction 3 Feature Selection & Feature Extraction Overview: 3.1 Introduction 3.2 Feature Extraction 3.3 Feature Selection 3.3.1 Max-Dependency, Max-Relevance, Min-Redundancy 3.3.2 Relevance Filter 3.3.3 Redundancy

More information

Anomaly Detection on Data Streams with High Dimensional Data Environment

Anomaly Detection on Data Streams with High Dimensional Data Environment Anomaly Detection on Data Streams with High Dimensional Data Environment Mr. D. Gokul Prasath 1, Dr. R. Sivaraj, M.E, Ph.D., 2 Department of CSE, Velalar College of Engineering & Technology, Erode 1 Assistant

More information

THE preceding chapters were all devoted to the analysis of images and signals which

THE preceding chapters were all devoted to the analysis of images and signals which Chapter 5 Segmentation of Color, Texture, and Orientation Images THE preceding chapters were all devoted to the analysis of images and signals which take values in IR. It is often necessary, however, to

More information

CHAPTER 3 SURFACE ROUGHNESS

CHAPTER 3 SURFACE ROUGHNESS 38 CHAPTER 3 SURFACE ROUGHNESS 3.1 SURFACE ROUGHNESS AND ITS IMPORTANCE The evaluation of surface roughness of machined parts using a direct contact method has limited flexibility in handling the different

More information

EMS / DMS. DISTRIBUTION MANAGEMENT SYSTEM- Functional Description

EMS / DMS. DISTRIBUTION MANAGEMENT SYSTEM- Functional Description EMS / DMS DISTRIBUTION MANAGEMENT SYSTEM- Content 1. INTRODUCTION... 4 2. MODES OF INTERACTION WITH THE SCADA SYSTEM... 5 2.1 Simulation Mode... 5 2.2 State Estimation Mode (See functional description

More information

Efficient Tuning of SVM Hyperparameters Using Radius/Margin Bound and Iterative Algorithms

Efficient Tuning of SVM Hyperparameters Using Radius/Margin Bound and Iterative Algorithms IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 13, NO. 5, SEPTEMBER 2002 1225 Efficient Tuning of SVM Hyperparameters Using Radius/Margin Bound and Iterative Algorithms S. Sathiya Keerthi Abstract This paper

More information

Analytical model A structure and process for analyzing a dataset. For example, a decision tree is a model for the classification of a dataset.

Analytical model A structure and process for analyzing a dataset. For example, a decision tree is a model for the classification of a dataset. Glossary of data mining terms: Accuracy Accuracy is an important factor in assessing the success of data mining. When applied to data, accuracy refers to the rate of correct values in the data. When applied

More information

An Initial Seed Selection Algorithm for K-means Clustering of Georeferenced Data to Improve

An Initial Seed Selection Algorithm for K-means Clustering of Georeferenced Data to Improve An Initial Seed Selection Algorithm for K-means Clustering of Georeferenced Data to Improve Replicability of Cluster Assignments for Mapping Application Fouad Khan Central European University-Environmental

More information

LMP Step Pattern Detection based on Real-Time Data

LMP Step Pattern Detection based on Real-Time Data LMP Step Pattern Detection based on Real-Time Data Haoyu Yuan, Fangxing Li, Yanli Wei Department of Electrical Engineering and Computer Science The University of Tennessee, Knoxville Knoxville, TN 37996

More information

A Neural Network for Real-Time Signal Processing

A Neural Network for Real-Time Signal Processing 248 MalkofT A Neural Network for Real-Time Signal Processing Donald B. Malkoff General Electric / Advanced Technology Laboratories Moorestown Corporate Center Building 145-2, Route 38 Moorestown, NJ 08057

More information

Content based Image Retrievals for Brain Related Diseases

Content based Image Retrievals for Brain Related Diseases Content based Image Retrievals for Brain Related Diseases T.V. Madhusudhana Rao Department of CSE, T.P.I.S.T., Bobbili, Andhra Pradesh, INDIA S. Pallam Setty Department of CS&SE, Andhra University, Visakhapatnam,

More information

ADVANCED IMAGE PROCESSING METHODS FOR ULTRASONIC NDE RESEARCH C. H. Chen, University of Massachusetts Dartmouth, N.

ADVANCED IMAGE PROCESSING METHODS FOR ULTRASONIC NDE RESEARCH C. H. Chen, University of Massachusetts Dartmouth, N. ADVANCED IMAGE PROCESSING METHODS FOR ULTRASONIC NDE RESEARCH C. H. Chen, University of Massachusetts Dartmouth, N. Dartmouth, MA USA Abstract: The significant progress in ultrasonic NDE systems has now

More information

ADVANCED ANALYTICS USING SAS ENTERPRISE MINER RENS FEENSTRA

ADVANCED ANALYTICS USING SAS ENTERPRISE MINER RENS FEENSTRA INSIGHTS@SAS: ADVANCED ANALYTICS USING SAS ENTERPRISE MINER RENS FEENSTRA AGENDA 09.00 09.15 Intro 09.15 10.30 Analytics using SAS Enterprise Guide Ellen Lokollo 10.45 12.00 Advanced Analytics using SAS

More information

Interconnection and Transmission

Interconnection and Transmission Interconnection and Transmission Gary L. Brown, P.E. Director of Energy Development Gary@ZGlobal.biz 916-985-9461 Kevin Coffee Energy Management Committee Meeting Irvine, California August 26, 2015 Interconnection

More information

Machine Learning. Unsupervised Learning. Manfred Huber

Machine Learning. Unsupervised Learning. Manfred Huber Machine Learning Unsupervised Learning Manfred Huber 2015 1 Unsupervised Learning In supervised learning the training data provides desired target output for learning In unsupervised learning the training

More information

Face Tracking. Synonyms. Definition. Main Body Text. Amit K. Roy-Chowdhury and Yilei Xu. Facial Motion Estimation

Face Tracking. Synonyms. Definition. Main Body Text. Amit K. Roy-Chowdhury and Yilei Xu. Facial Motion Estimation Face Tracking Amit K. Roy-Chowdhury and Yilei Xu Department of Electrical Engineering, University of California, Riverside, CA 92521, USA {amitrc,yxu}@ee.ucr.edu Synonyms Facial Motion Estimation Definition

More information

Hybrid Feature Selection for Modeling Intrusion Detection Systems

Hybrid Feature Selection for Modeling Intrusion Detection Systems Hybrid Feature Selection for Modeling Intrusion Detection Systems Srilatha Chebrolu, Ajith Abraham and Johnson P Thomas Department of Computer Science, Oklahoma State University, USA ajith.abraham@ieee.org,

More information

Performance Assurance in Virtualized Data Centers

Performance Assurance in Virtualized Data Centers Autonomic Provisioning with Self-Adaptive Neural Fuzzy Control for End-to-end Delay Guarantee Palden Lama Xiaobo Zhou Department of Computer Science University of Colorado at Colorado Springs Performance

More information

Generation, Transmission, and End User Facilities

Generation, Transmission, and End User Facilities Procedures for Interconnection of Generation, Transmission, and End User To the Grand River Dam Authority Transmission System Table of Contents GRDA/SPP Interaction... 3 Standards... 3 Generation... 3

More information

Transient Stability Improvement of Long Transmission Line System by Using SVC

Transient Stability Improvement of Long Transmission Line System by Using SVC Transient Stability Improvement of Long Transmission Line System by Using SVC Dr.Tarlochan Kaur 1 and Sandeep Kakran 2 1 Associate Professor, EED, PEC University of Technology, Chandigarh, India 2 Assistant

More information

10703 Deep Reinforcement Learning and Control

10703 Deep Reinforcement Learning and Control 10703 Deep Reinforcement Learning and Control Russ Salakhutdinov Machine Learning Department rsalakhu@cs.cmu.edu Policy Gradient I Used Materials Disclaimer: Much of the material and slides for this lecture

More information

Target Tracking in Wireless Sensor Network

Target Tracking in Wireless Sensor Network International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 6 (2014), pp. 643-648 International Research Publications House http://www. irphouse.com Target Tracking in

More information

MLR Institute of Technology

MLR Institute of Technology Course Name : Engineering Optimization Course Code : 56021 Class : III Year Branch : Aeronautical Engineering Year : 2014-15 Course Faculty : Mr Vamsi Krishna Chowduru, Assistant Professor Course Objective

More information

Object and Action Detection from a Single Example

Object and Action Detection from a Single Example Object and Action Detection from a Single Example Peyman Milanfar* EE Department University of California, Santa Cruz *Joint work with Hae Jong Seo AFOSR Program Review, June 4-5, 29 Take a look at this:

More information

DYNAMIC SITE LAYOUT PLANNING USING MTPE PRINCIPLE FROM PHYSICS

DYNAMIC SITE LAYOUT PLANNING USING MTPE PRINCIPLE FROM PHYSICS DYNAMIC SITE LAYOUT PLANNING USING MTPE PRINCIPLE FROM PHYSICS Mohsen Andayesh* and Farnaz Sadeghpour Department of Civil Engineering, University of Calgary, Calgary, Canada * Corresponding author (m.andayesh@ucalgary.ca)

More information

Transmission Management in the Deregulated Environment

Transmission Management in the Deregulated Environment Transmission Management in the Deregulated Environment RICHARD D CHRISTIE, MEMBER, IEEE, BRUCE F WOLLENBERG, FELLOW, IEEE, AND IVAR WANGENSTEEN Invited Paper Three very different methods of accomplishing

More information

Machine Learning Classifiers and Boosting

Machine Learning Classifiers and Boosting Machine Learning Classifiers and Boosting Reading Ch 18.6-18.12, 20.1-20.3.2 Outline Different types of learning problems Different types of learning algorithms Supervised learning Decision trees Naïve

More information

Supervised vs unsupervised clustering

Supervised vs unsupervised clustering Classification Supervised vs unsupervised clustering Cluster analysis: Classes are not known a- priori. Classification: Classes are defined a-priori Sometimes called supervised clustering Extract useful

More information

Comparative analysis of data mining methods for predicting credit default probabilities in a retail bank portfolio

Comparative analysis of data mining methods for predicting credit default probabilities in a retail bank portfolio Comparative analysis of data mining methods for predicting credit default probabilities in a retail bank portfolio Adela Ioana Tudor, Adela Bâra, Simona Vasilica Oprea Department of Economic Informatics

More information

Sensor Tasking and Control

Sensor Tasking and Control Sensor Tasking and Control Outline Task-Driven Sensing Roles of Sensor Nodes and Utilities Information-Based Sensor Tasking Joint Routing and Information Aggregation Summary Introduction To efficiently

More information

Reddit Recommendation System Daniel Poon, Yu Wu, David (Qifan) Zhang CS229, Stanford University December 11 th, 2011

Reddit Recommendation System Daniel Poon, Yu Wu, David (Qifan) Zhang CS229, Stanford University December 11 th, 2011 Reddit Recommendation System Daniel Poon, Yu Wu, David (Qifan) Zhang CS229, Stanford University December 11 th, 2011 1. Introduction Reddit is one of the most popular online social news websites with millions

More information

CASE STUDY : Transient Stability Simulation Package

CASE STUDY : Transient Stability Simulation Package CASE STUDY : Transient Stability Simulation Package CLIENT NAME : A major T&D solutions provider in the world END CUSTOMER : A public T&D utility in one of the SAARC nations PROJECT TITLE : Customized

More information

GEORGE J. ANDERS, Ph.D., P.Eng., Fellow IEEE

GEORGE J. ANDERS, Ph.D., P.Eng., Fellow IEEE GEORGE J. ANDERS, Ph.D., P.Eng., Fellow IEEE George Anders is a president of Anders Consulting. Between 1975 and 2012 he has been employed by Ontario Hydro and its successor companies in Toronto, Canada.

More information

Real-Time Model-Free Detection of Low-Quality Synchrophasor Data

Real-Time Model-Free Detection of Low-Quality Synchrophasor Data Real-Time Model-Free Detection of Low-Quality Synchrophasor Data Meng Wu and Le Xie Department of Electrical and Computer Engineering Texas A&M University College Station, TX NASPI Work Group meeting March

More information

Analysis and optimization methods of graph based meta-models for data flow simulation

Analysis and optimization methods of graph based meta-models for data flow simulation Rochester Institute of Technology RIT Scholar Works Theses Thesis/Dissertation Collections 8-1-2010 Analysis and optimization methods of graph based meta-models for data flow simulation Jeffrey Harrison

More information

EMS-LECTURE 2: WORKING OF EMS

EMS-LECTURE 2: WORKING OF EMS EMS-LECTURE 2: WORKING OF EMS Introduction: Energy Management systems consists of several applications programs which are used by the operator in a control centre for effective decision making in the operation

More information

An Efficient Clustering Method for k-anonymization

An Efficient Clustering Method for k-anonymization An Efficient Clustering Method for -Anonymization Jun-Lin Lin Department of Information Management Yuan Ze University Chung-Li, Taiwan jun@saturn.yzu.edu.tw Meng-Cheng Wei Department of Information Management

More information

Parallel Approach for Implementing Data Mining Algorithms

Parallel Approach for Implementing Data Mining Algorithms TITLE OF THE THESIS Parallel Approach for Implementing Data Mining Algorithms A RESEARCH PROPOSAL SUBMITTED TO THE SHRI RAMDEOBABA COLLEGE OF ENGINEERING AND MANAGEMENT, FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

More information

Face Recognition Based on LDA and Improved Pairwise-Constrained Multiple Metric Learning Method

Face Recognition Based on LDA and Improved Pairwise-Constrained Multiple Metric Learning Method Journal of Information Hiding and Multimedia Signal Processing c 2016 ISSN 2073-4212 Ubiquitous International Volume 7, Number 5, September 2016 Face Recognition ased on LDA and Improved Pairwise-Constrained

More information

One category of visual tracking. Computer Science SURJ. Michael Fischer

One category of visual tracking. Computer Science SURJ. Michael Fischer Computer Science Visual tracking is used in a wide range of applications such as robotics, industrial auto-control systems, traffic monitoring, and manufacturing. This paper describes a new algorithm for

More information

Distribution Static Var Compensators and Static Synchronous Compensators for Suppressing Voltage Fluctuation

Distribution Static Var Compensators and Static Synchronous Compensators for Suppressing Voltage Fluctuation Distribution Static Var Compensators and Static Synchronous Compensators for Suppressing Voltage Fluctuation KOJIMA, Takehiko * ISOTANI, Hitoshi * YAMADA, Makoto * A B S T R A C T The rapidly expanding

More information

Electrical Metrology Applications of LabVIEW Software

Electrical Metrology Applications of LabVIEW Software Journal of Software Engineering and Applications, 2013, 6, 113-120 http://dx.doi.org/10.4236/jsea.2013.63015 Published Online March 2013 (http://www.scirp.org/journal/jsea) 113 Hala M. Abdel Mageed, Ali

More information

Energy Solutions for Buildings

Energy Solutions for Buildings Energy Solutions for Buildings Making energy safe, reliable, efficient, productive and green Make the most of your energy SM 1 Solutions to enable and sustain energy savings Technology is crucial to make

More information

Review on Methods of Selecting Number of Hidden Nodes in Artificial Neural Network

Review on Methods of Selecting Number of Hidden Nodes in Artificial Neural Network Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 11, November 2014,

More information

Simulation and Analysis of Static Var Compensator with Matlab

Simulation and Analysis of Static Var Compensator with Matlab The International Journal Of Engineering And Science (IJES) Volume 4 Issue 12 Pages PP -07-11 2015 ISSN (e): 2319 1813 ISSN (p): 2319 1805 Simulation and Analysis of Static Var Compensator with Matlab

More information

In the Name of God. Lecture 17: ANFIS Adaptive Network-Based Fuzzy Inference System

In the Name of God. Lecture 17: ANFIS Adaptive Network-Based Fuzzy Inference System In the Name of God Lecture 17: ANFIS Adaptive Network-Based Fuzzy Inference System Outline ANFIS Architecture Hybrid Learning Algorithm Learning Methods that Cross-Fertilize ANFIS and RBFN ANFIS as a universal

More information

Quality Assessment of Power Dispatching Data Based on Improved Cloud Model

Quality Assessment of Power Dispatching Data Based on Improved Cloud Model Quality Assessment of Power Dispatching Based on Improved Cloud Model Zhaoyang Qu, Shaohua Zhou *. School of Information Engineering, Northeast Electric Power University, Jilin, China Abstract. This paper

More information

Lecture #11: The Perceptron

Lecture #11: The Perceptron Lecture #11: The Perceptron Mat Kallada STAT2450 - Introduction to Data Mining Outline for Today Welcome back! Assignment 3 The Perceptron Learning Method Perceptron Learning Rule Assignment 3 Will be

More information

PV211: Introduction to Information Retrieval

PV211: Introduction to Information Retrieval PV211: Introduction to Information Retrieval http://www.fi.muni.cz/~sojka/pv211 IIR 15-1: Support Vector Machines Handout version Petr Sojka, Hinrich Schütze et al. Faculty of Informatics, Masaryk University,

More information

A New Approach to Ant Colony to Load Balancing in Cloud Computing Environment

A New Approach to Ant Colony to Load Balancing in Cloud Computing Environment A New Approach to Ant Colony to Load Balancing in Cloud Computing Environment Hamid Mehdi Department of Computer Engineering, Andimeshk Branch, Islamic Azad University, Andimeshk, Iran Hamidmehdi@gmail.com

More information

ECS289: Scalable Machine Learning

ECS289: Scalable Machine Learning ECS289: Scalable Machine Learning Cho-Jui Hsieh UC Davis Sept 22, 2016 Course Information Website: http://www.stat.ucdavis.edu/~chohsieh/teaching/ ECS289G_Fall2016/main.html My office: Mathematical Sciences

More information

Using Decision Boundary to Analyze Classifiers

Using Decision Boundary to Analyze Classifiers Using Decision Boundary to Analyze Classifiers Zhiyong Yan Congfu Xu College of Computer Science, Zhejiang University, Hangzhou, China yanzhiyong@zju.edu.cn Abstract In this paper we propose to use decision

More information

The Affinity Effects of Parallelized Libraries in Concurrent Environments. Abstract

The Affinity Effects of Parallelized Libraries in Concurrent Environments. Abstract The Affinity Effects of Parallelized Libraries in Concurrent Environments FABIO LICHT, BRUNO SCHULZE, LUIS E. BONA, AND ANTONIO R. MURY 1 Federal University of Parana (UFPR) licht@lncc.br Abstract The

More information

An evolutionary annealing-simplex algorithm for global optimisation of water resource systems

An evolutionary annealing-simplex algorithm for global optimisation of water resource systems FIFTH INTERNATIONAL CONFERENCE ON HYDROINFORMATICS 1-5 July 2002, Cardiff, UK C05 - Evolutionary algorithms in hydroinformatics An evolutionary annealing-simplex algorithm for global optimisation of water

More information

Using Machine Learning to Optimize Storage Systems

Using Machine Learning to Optimize Storage Systems Using Machine Learning to Optimize Storage Systems Dr. Kiran Gunnam 1 Outline 1. Overview 2. Building Flash Models using Logistic Regression. 3. Storage Object classification 4. Storage Allocation recommendation

More information

You ve already read basics of simulation now I will be taking up method of simulation, that is Random Number Generation

You ve already read basics of simulation now I will be taking up method of simulation, that is Random Number Generation Unit 5 SIMULATION THEORY Lesson 39 Learning objective: To learn random number generation. Methods of simulation. Monte Carlo method of simulation You ve already read basics of simulation now I will be

More information

2. REVIEW OF LITERATURE

2. REVIEW OF LITERATURE 2. REVIEW OF LITERATURE Early attempts to perform control allocation for aircraft with redundant control effectors involved various control mixing schemes and ad hoc solutions. As mentioned in the introduction,

More information

Attentional Based Multiple-Object Tracking

Attentional Based Multiple-Object Tracking Attentional Based Multiple-Object Tracking Mark Calafut Stanford University mcalafut@stanford.edu Abstract This paper investigates the attentional based tracking framework of Bazzani et al. (2011) and

More information

Application of Clustering Techniques to Energy Data to Enhance Analysts Productivity

Application of Clustering Techniques to Energy Data to Enhance Analysts Productivity Application of Clustering Techniques to Energy Data to Enhance Analysts Productivity Wendy Foslien, Honeywell Labs Valerie Guralnik, Honeywell Labs Steve Harp, Honeywell Labs William Koran, Honeywell Atrium

More information

Online Pose Classification and Walking Speed Estimation using Handheld Devices

Online Pose Classification and Walking Speed Estimation using Handheld Devices Online Pose Classification and Walking Speed Estimation using Handheld Devices Jun-geun Park MIT CSAIL Joint work with: Ami Patel (MIT EECS), Jonathan Ledlie (Nokia Research), Dorothy Curtis (MIT CSAIL),

More information

USING IMAGES PATTERN RECOGNITION AND NEURAL NETWORKS FOR COATING QUALITY ASSESSMENT Image processing for quality assessment

USING IMAGES PATTERN RECOGNITION AND NEURAL NETWORKS FOR COATING QUALITY ASSESSMENT Image processing for quality assessment USING IMAGES PATTERN RECOGNITION AND NEURAL NETWORKS FOR COATING QUALITY ASSESSMENT Image processing for quality assessment L.-M. CHANG and Y.A. ABDELRAZIG School of Civil Engineering, Purdue University,

More information

OBJECT-CENTERED INTERACTIVE MULTI-DIMENSIONAL SCALING: ASK THE EXPERT

OBJECT-CENTERED INTERACTIVE MULTI-DIMENSIONAL SCALING: ASK THE EXPERT OBJECT-CENTERED INTERACTIVE MULTI-DIMENSIONAL SCALING: ASK THE EXPERT Joost Broekens Tim Cocx Walter A. Kosters Leiden Institute of Advanced Computer Science Leiden University, The Netherlands Email: {broekens,

More information

Hybrid Models Using Unsupervised Clustering for Prediction of Customer Churn

Hybrid Models Using Unsupervised Clustering for Prediction of Customer Churn Hybrid Models Using Unsupervised Clustering for Prediction of Customer Churn Indranil Bose and Xi Chen Abstract In this paper, we use two-stage hybrid models consisting of unsupervised clustering techniques

More information

Accelerometer Gesture Recognition

Accelerometer Gesture Recognition Accelerometer Gesture Recognition Michael Xie xie@cs.stanford.edu David Pan napdivad@stanford.edu December 12, 2014 Abstract Our goal is to make gesture-based input for smartphones and smartwatches accurate

More information

Frequently Asked Questions Real-Time Revenue Sufficiency Guarantee

Frequently Asked Questions Real-Time Revenue Sufficiency Guarantee Frequently Asked Questions Revenue Sufficiency Guarantee What is Revenue Sufficiency Guarantee (RSG)? Midwest ISO has the responsibility to ensure that adequate capacity is available and committed to meet

More information

Mining Web Data. Lijun Zhang

Mining Web Data. Lijun Zhang Mining Web Data Lijun Zhang zlj@nju.edu.cn http://cs.nju.edu.cn/zlj Outline Introduction Web Crawling and Resource Discovery Search Engine Indexing and Query Processing Ranking Algorithms Recommender Systems

More information

CHAPTER 4 STOCK PRICE PREDICTION USING MODIFIED K-NEAREST NEIGHBOR (MKNN) ALGORITHM

CHAPTER 4 STOCK PRICE PREDICTION USING MODIFIED K-NEAREST NEIGHBOR (MKNN) ALGORITHM CHAPTER 4 STOCK PRICE PREDICTION USING MODIFIED K-NEAREST NEIGHBOR (MKNN) ALGORITHM 4.1 Introduction Nowadays money investment in stock market gains major attention because of its dynamic nature. So the

More information

Clustering. Mihaela van der Schaar. January 27, Department of Engineering Science University of Oxford

Clustering. Mihaela van der Schaar. January 27, Department of Engineering Science University of Oxford Department of Engineering Science University of Oxford January 27, 2017 Many datasets consist of multiple heterogeneous subsets. Cluster analysis: Given an unlabelled data, want algorithms that automatically

More information

CPFLOW for Power Tracer and Voltage Monitoring

CPFLOW for Power Tracer and Voltage Monitoring PSERC CPFLOW for Power Tracer and Voltage Monitoring Voltage Collapse Monitor Final Project Report Power Systems Engineering Research Center A National Science Foundation Industry/University Cooperative

More information

Reliable Nonlinear Parameter Estimation Using Interval Analysis: Error-in-Variable Approach

Reliable Nonlinear Parameter Estimation Using Interval Analysis: Error-in-Variable Approach Reliable Nonlinear Parameter Estimation Using Interval Analysis: Error-in-Variable Approach Chao-Yang Gau and Mark A. Stadtherr Λ Department of Chemical Engineering University of Notre Dame Notre Dame,

More information

Image Interpolation using Collaborative Filtering

Image Interpolation using Collaborative Filtering Image Interpolation using Collaborative Filtering 1,2 Qiang Guo, 1,2,3 Caiming Zhang *1 School of Computer Science and Technology, Shandong Economic University, Jinan, 250014, China, qguo2010@gmail.com

More information

Particle Swarm Optimization

Particle Swarm Optimization Particle Swarm Optimization Gonçalo Pereira INESC-ID and Instituto Superior Técnico Porto Salvo, Portugal gpereira@gaips.inesc-id.pt April 15, 2011 1 What is it? Particle Swarm Optimization is an algorithm

More information

Application of overvoltage protection to the Peruvian Power System

Application of overvoltage protection to the Peruvian Power System Application of overvoltage protection to the Peruvian Power System Presented to Western Protective Relay Conference 2008 Spokane, Washington, USA Prepared by Yofre Jacome, and Francisco Torres, COES SINAC

More information

Future Grid Initiative Technology Challenges in Designing the Future Grid to Enable Sustainable Energy Systems

Future Grid Initiative Technology Challenges in Designing the Future Grid to Enable Sustainable Energy Systems Future Grid Initiative Technology Challenges in Designing the Future Grid to Enable Sustainable Energy Systems Vijay Vittal Director, Power Systems Engineering Research Center Ira A. Fulton Chair Professor,

More information

Acknowledgements. Prof. Dan Negrut Prof. Darryl Thelen Prof. Michael Zinn. SBEL Colleagues: Hammad Mazar, Toby Heyn, Manoj Kumar

Acknowledgements. Prof. Dan Negrut Prof. Darryl Thelen Prof. Michael Zinn. SBEL Colleagues: Hammad Mazar, Toby Heyn, Manoj Kumar Philipp Hahn Acknowledgements Prof. Dan Negrut Prof. Darryl Thelen Prof. Michael Zinn SBEL Colleagues: Hammad Mazar, Toby Heyn, Manoj Kumar 2 Outline Motivation Lumped Mass Model Model properties Simulation

More information

CHAPTER 4 SEGMENTATION

CHAPTER 4 SEGMENTATION 69 CHAPTER 4 SEGMENTATION 4.1 INTRODUCTION One of the most efficient methods for breast cancer early detection is mammography. A new method for detection and classification of micro calcifications is presented.

More information

LS-OPT : New Developments and Outlook

LS-OPT : New Developments and Outlook 13 th International LS-DYNA Users Conference Session: Optimization LS-OPT : New Developments and Outlook Nielen Stander and Anirban Basudhar Livermore Software Technology Corporation Livermore, CA 94588

More information

30 th AUG KERI (J. YOON )

<PSI Scenarios & Arrangements in NEA Countries> 30 th AUG KERI (J. YOON ) 30 th AUG. 2017 KERI (J. YOON jyyoon@keri.re.kr ) Background NEA power system have many complementary characteristics and difficulties, which

More information

AIIA shot boundary detection at TRECVID 2006

AIIA shot boundary detection at TRECVID 2006 AIIA shot boundary detection at TRECVID 6 Z. Černeková, N. Nikolaidis and I. Pitas Artificial Intelligence and Information Analysis Laboratory Department of Informatics Aristotle University of Thessaloniki

More information

Ant Colony Based Load Flow Optimisation Using Matlab

Ant Colony Based Load Flow Optimisation Using Matlab Ant Colony Based Load Flow Optimisation Using Matlab 1 Kapil Upamanyu, 2 Keshav Bansal, 3 Miteshwar Singh Department of Electrical Engineering Delhi Technological University, Shahbad Daulatpur, Main Bawana

More information

Fixture Layout Optimization Using Element Strain Energy and Genetic Algorithm

Fixture Layout Optimization Using Element Strain Energy and Genetic Algorithm Fixture Layout Optimization Using Element Strain Energy and Genetic Algorithm Zeshan Ahmad, Matteo Zoppi, Rezia Molfino Abstract The stiffness of the workpiece is very important to reduce the errors in

More information

Multivariate Data Analysis and Machine Learning in High Energy Physics (V)

Multivariate Data Analysis and Machine Learning in High Energy Physics (V) Multivariate Data Analysis and Machine Learning in High Energy Physics (V) Helge Voss (MPI K, Heidelberg) Graduierten-Kolleg, Freiburg, 11.5-15.5, 2009 Outline last lecture Rule Fitting Support Vector

More information

Facial Expression Classification with Random Filters Feature Extraction

Facial Expression Classification with Random Filters Feature Extraction Facial Expression Classification with Random Filters Feature Extraction Mengye Ren Facial Monkey mren@cs.toronto.edu Zhi Hao Luo It s Me lzh@cs.toronto.edu I. ABSTRACT In our work, we attempted to tackle

More information