FUZZY LOGIC TECHNIQUE FOR CONGESTION LINE IDENTIFICATION IN POWER SYSTEM

Transcription

1 FUZZY LOGIC TECHNIQUE FOR CONGESTION LINE IDENTIFICATION IN POWER SYSTEM Mohd Ali N. Z, I. Musirin, H. Abdullah and S. I. Suliman Faculty of Electrical Engineering, Universiti Teknologi Mara Malaysia, Shah Alam, Selangor, Malaysia ABSTRACT Congestion problem is a significant issue in power system due to the increasing demand in this vicinity. Failure in properly managing the issue may lead to insecure power delivery to the consumer. Flexible Alternating Current Transmission (FACTs) can be a possible solution for the compensating purposes. This requires proper decision making so that proper sizing can be identified which in turns reducing monetary losses. This paper presents fuzzy logic technique for congested line identification as a decision tool. A pre-developed line voltage stability index, termed as fast voltage stability index (FVSI) is chosen as the incorporating instrument in this study. A sensitivity analysis equation is formulated termed as Fuzzy Congestion Index (FCI). Validation was conducted using the IEEE 30-Bus Reliable Test System (RTS). Results from the study revealed that the proposed technique managed to correctly identify the congested line. FCI was compared FVSI, indicating that the proposed technique revealed the suitability for identifying the congested line. This technique is also feasible for further implementation in larger and practical system. Keywords: congestion problem, FACT, fuzzy logic and congested line. INTRODUCTION Contingency and pattern of generation resulted from heavy flows tend to incur greater losses and has threaten stability and security. This ultimately makes certain generation patterns economically undesirable [1]. Electricity can be delivered to the load centres without violating operation limits such as voltage limit and thermal limit. In order to achieve secure and smooth delivery of electricity to the consumers, proper planning needs to be in place. Compensation process can be one of the possible choices to alleviate power disturbances and insecure power dispatch. On the other hand, load shedding and other schemes can be the other options especially when contingencies are experienced by the system. These days, the advancement of computational intelligence has been made use to alleviate power system problems especially when optimization process or decision making process are of urgency. Various techniques and schemes have been reported in these few decades. Various studies involving optimization techniques have been reported in [2-10]. These techniques apparently managed to identify the optimal sizing of the compensation devices, or even the optimal location in making sure that the compensation schemes are worth. In addition to optimization technique, utilization of index is also incorporated together so that other issues can be concurrently addressed. For instance, location optimization for the UPFC was conducted using voltage stability index and the voltage change index in [2]. In [1] line loss sensitivity indices, total system loss sensitivity indices, real power flow PI sensitivity indices were proposed for placement of TCSC and TCPAR. The optimal placement of TCSCs to minimise congestion cost based on the sensitivity factor approach was analysed in [3]. The extended voltage phasor approach was proposed in [4] for placement of FACTS controller from voltage stability viewpoint. In [5] contingency severity index was used to rank the branches to optimally locate the FACTS controllers for multiple contingencies. These studies indicated the significance of the computational intelligence applications in addressing congestion management. Other significant work is the implementation of sensitivity index for the reduction of total reactive power loss and the real power as reported in [6]. Optimal location of different types of FACTS controllers were attempted using different techniques such as Genetic Algorithm, Simulated Annealing, Tabu search, Particle swarm optimisation. [7-10]. This paper presents the application of fuzzy logic technique to solve congestion management problem. In this study, a new index termed as Fuzzy Congestion Index (FCI) is developed to indicate the level of congestion in the transmission line. With this implementation, the conventional power flow is no longer required for congestion management. The proposed technique is successfully tested on the IEEE 30 Bus RTS. A benchmark technique is used for comparative studies purposes; resulting that the proposed technique is feasible and worth in solving the congestion management issues. PROPOSED FUZZY LOGIC TECHNIQUE IN CONGESTED LINE IDENTIFICATION Fuzzy logic technique has been known as an artificial intelligence technique for decision making process. In this study, the congested line in power transmission system is identified through the fuzzy logic technique. In general, the general fundamental fuzzy logic process is depicted in Figure

2 Figure-1. Basic fuzzy logic process. General operators of fuzzy logic include fuzzification and defuzzification, which involve inference and rules development. Crisp inputs will be fed to this model, which ultimately produce crisp output. Fuzzy logic rules are initiated from the concept of Boolean logic, consisting the two possible states namely, True (1) and False (0). Nevertheless, the states do not exactly demonstrate accurate results due to the presence of fallacy. Consequently, a linguistic logic was proposed by Zadeh, which emulates the human brain. As shown in Figure-1, Fuzzifier is a block which converts the unfuzzy information (crisp) into the fuzzy form. On the other hand, fuzzy rules based on IF-THEN condition are developed. Membership functions can be derived which eventually produce the membership function output. Input of the fuzzy logic can come from the crisp carries. In fuzzification process, every input is mapped with the membership function, in order to produce fuzzy output. The next process is defuzzification. It is a reverse process of fuzzification; whereby results in the ordinary form are produced. Command Line can be used in order to build the fuzzy logic system. The behaviour of input and output can be represented by membership function. In this system, two inputs are required to be fed into the fuzzy system. The first input is the reactive loading, Q d. While the second input is the bus number of the system. It is obtained and linearly normalized within the range [1,30]. The output is called as the Fuzzy Congestion Index (FCI) where its range is from 0 and 1. FCI. Furthermore, FCI is the output of the fuzzy logic used to determine the suitable location to install TCSC on the proper transmission line. All the membership functions (MF) are in triangular shaped for reactive loading, Q d, bus number and FCI. Each of MF is assigned as S, SM, M, MH and H as shown in Figures-2, 3 and 4. The limitation of input 1 and input 2 are tabulated in Table-1. Figure-2. Membership function for reactive loading, Q d. Figure-3. Membership function for bus number. Figure-4. Membership function for FCI

3 Table-1. Limitation of input 1 and input 2. FUZZY CONGESTION INDEX (FCI) Fuzzy Congestion Index (FCI) is proposed in this study for the assessment of steady state voltage stability analysis of a transmission network. A pre-developed voltage index termed as Fast Voltage Stability Index (FVSI) is utilized to indicate the criticalness of voltage stability condition. Mathematical sensitivity equation is developed in order to evaluate the impact of variations in reactive and active loading conditions to FVSI and voltage in the system. The expression is given in Equation (1):- FCI FVSI P FVSI Qd Qd V V P (1) Two basic rules are utilized in this study, namely:- IF premise (antecedent) THEN conclusion (consequent) Since the fuzzy technique is used in determining the location of congested line, hence multiple antecedents have been created. The rules are tabulated in Table-2. From the table above, there are 25 rules of construction, some of the rules are as follows:- If is Reactive Loading is S and Bus Number is S then FCI is S If Reactive Loading is MH and Bus Number is SM then FCI is SM If Reactive Loading is H and Bus Number is M then FCI is M In electric power systems, the steady state voltage stability conditions are often analysed through PV diagrams for which the information can be obtained from the repeated power flow calculations. Initially, the load flow solution is executed to obtain the voltage and real and reactive power losses for every line in the system. From the power flow analysis, FVSI also can be calculated. In this study, power flow analysis takes a long time to reach the result. In order to improve the computational time of this conventional power flow program, fuzzy technique is proposed. The flowchart for the whole process in conventional power flow program are shown in Figure-5. All of the rules are written by using MATLAB. All inputs and outputs depend on the fuzzy rules which are given in the Table-2. Abbreviations used for the fuzzy sets are shown in Table-3. Table-2. Fuzzy decision to determine congested line. Table-3. Fuzzy input 1, input 2 and output. Figure-5. Flowchart for the whole process in conventional power flow programme

4 RESULTS AND DISCUSSION Table-4 tabulates the results of the study implemented on the IEEE 30-Bus RTS. Furthermore, instead of Fuzzy logic technique another method is used to validate the congested line. FVSI is utilized as a benchmarked tool for congested line determination. In the meantime, it is also utilized in the sensitivity analysis to develop the FCI calculations. From the table, each bus shows the different value of the loading condition. The reactive loading will increase until the FVSI value reaches the maximum point, i.e At this point, the voltage in the system will be collapsed. In this study, the second value before are taken as the result to determine the congested line. For instant, when the reactive loading at bus 3 is increased up to MVar, the FCI value is At the same time, the FVSI shows the safety point, with the corresponding voltage of p.u.. The congested line is determined at line 4 connecting bus 3 and bus 4. For the case of bus 12, the maximum loading increases to MVar. The index given by each technique is and respectively. While, the corresponding voltage level is p.u.. On the other hand, for the case of bus 15, the reactive loading is increased up to maximum value, 162.5MVar. At this point, the index given by each technique is and respectively. The voltage on this bus is p.u.. For bus 12 and bus 15, the congested lines are the same line, i.e. line 16, connecting bus 12 and bus 13. Apparently, the most suitable line to install FACTS device is the line connecting bus 12 and bus 13. The selection merely due to the judgement made both FCI and FVSI, when the congested line is the same. Table-4. Result fuzzy based on maximum limit of a voltage stability index result of congested line is chosen for TCSC installation. Results obtained from the study, implemented on IEEE 30-Bus RTS revealed that the proposed technique produced promising results. The proposed technique has the potential to reduce computational burden due to the absence of normal repetitive load flow. ACKNOWLEDGEMENTS The authors would like to acknowledge The Institute of Research Management and Innovation (IRMI) UiTM, Shah Alam and Ministry of Higher Education Malaysia (MOHE) for the financial support of this research. This research is supported by MOHE under the Fundamental Research Grant Scheme (FRGS) with project code: (File No: 600-RMI/FRGS 5/3 (109/2014)). REFERENCES [1] S. N. Singh and A.K. David. Congestion management by optimizing FACTS device location International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. [2] P.S. Venkataramu and T.Ananthapadmanabha Installation of unified power flow controller for voltage stability margin enhancement under line outage contingencies. Iranian Journal of Electrical and Computer Engineering. Vol. 5, No. 2. [3] Srinivasa Rao Pudi and S.C. Srivastava Optimal placement of TCSC based on sensitivity approach for congestion management. 15 th National Power System Conference. [4] Nikhlesh Kumar Sharma, Arindam Ghosh and Rajiv Kumar Varma A novel placement strategy for FACTS controllers. IEEE Transaction on Power Delivery. Vol. 18, No. 3, pp [5] S. Sutha and N. Kamaraj Optimal location of multi type FACTS devices for multiple contingencies using particle swarm optimization. International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering. Vol. 2, No. 10. [6] Seyed Abhas Taher and Hadi Besharat Transmission congestion management by determining optimal location of FACTS devices in deregulated power system. American Journal of Applied Sciences. Vol. 5, No. 3, pp CONCLUSIONS This paper has presented fuzzy logic technique for congestion line identification in power system. In this study, congested line is identified using fuzzy logic technique, taking fuzzy congestion index (FCI) as the output for decision making process. The FCI formulation was based on the sensitivity analysis involving FVSI. The [7] S. Gerbex, R. Cherkaoui and A. J. Germond Optimal location of FACTS devices to enhance power system security. IEEE Bolongo Power Tech Conference. 3:7. [8] K.Vijayakumar and R. P. Kumudinidevi A new method for optimal location of FACTS controllers 17542

5 using genetic algorithm. Journal of Theoretical and Applied Information Technology. Vol. 2, No. 10, pp [9] K.Kalaiselvi, V.Suresh Kumar and K. Chandrasekar Enhanced genetic algorithm for optimal electric power flow using TCSC and TCPS. Proceedings of the World Congress on Engineering. Vol 2. [10] M.Saravanan, S.Mary Raja Slochanal, P.Venkatesh, J. Prince Stephan Abraham Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability. Electric Power System Research, Vol. 77, No. 3-4, pp