CHAPTER 3 INTELLIGENT FUZZY LOGIC CONTROLLER

Transcription

1 38 CHAPTER 3 INTELLIGENT FUZZY LOGIC CONTROLLER 3.1 INTRODUCTION The lack of intelligence, learning and adaptation capability in the control methods discussed in general control scheme, revealed the need for continuous expert intervention for the control of non-linear systems. During the past few years we have witnessed a rapid growth in the number and variety of applications of fuzzy logic and neural networks, ranging from consumer electronics and industrial process control to decision support systems and financial trading. Neuro-fuzzy modelling, together with the new driving force from stochastic, gradient-free optimisation techniques such as genetic algorithms and stimulated annealing, forms the constituents of so called soft computing, which is aimed at solving real world decision making, modelling and control problems (Jang et al. 2005). These problems are usually imprecisely defined and require human intervention. Thus, Neurofuzzy and soft computing, with their ability to incorporate human knowledge are likely to play increasingly important roles in the conception and design of hybrid intelligent systems. Fuzzy control system is a real time expert system that enhances conventional system design with engineering expertise. Fuzzy logic controllers are knowledge based controllers usually derived from a knowledge acquisition process or automatically synthesized from self organising control architectures.

2 39 This chapter is organised as follows. Section 2 describes the background of Fuzzy set theory and Section 3 describes the applications of Fuzzy sets to power systems. Section 4 describes the design and structure of fuzzy logic controller for isolated wind-micro hydro-diesel hybrid power system. Section 5 demonstrates the simulation results. Section 6 shows the analysis and performance comparison of the hybrid system with Fuzzy Logic Controller and conventional PIC. Summary and discussion of the chapter is given in section BACKGROUND OF FUZZY SET THEORY In recent years, fuzzy system applications have received increasing attentions in various areas of power system such as operation, planning, control and management. The concept of Fuzzy Logic was conceived by Zadeh (1965), a professor at the University of California at Berkley and presented as a way of processing data by allowing partial set membership rather than crisp set membership or non-membership. Fuzzy set theory provides a methodology that allows modelling of the systems that are too complex or not well defined by mathematical formulation. Fuzzy Logic Controllers based on fuzzy set theory are used to represent the experience and knowledge of a human operator in terms of linguistic variables that are called fuzzy rules. Since an experienced human operator adjust the system inputs to get a desired output by just looking at the system output without any knowledge on the system s dynamics and interior parameter variations, the implementation of linguistic fuzzy rules based on the procedures done by human operators does not also require a mathematical model of the system. Therefore a Fuzzy Logic Controller (FLC) becomes non-linear and adaptive in nature having a robust performance under parameter variations with the ability to get desired control actions for complex, uncertain and non-linear systems without the requirement of their mathematical models and parameter estimation.

3 40 Fuzzy logic based controllers provide a mathematical foundation for approximate reasoning, which has been proven to be very successful in a variety of applications (Maiers and Sherif 1985). 3.3 APPLICATION OF FUZZY SETS TO POWER SYSTEMS Increasing interest has been seen in applying fuzzy set theory to power system problems from the number of publications on this topic in recent years. Power system components have physical and operational limits which are usually described as hard inequality constraints in mathematical formulations. The fuzzy set approach inherently incorporates soft constraints and thus simplifies implementation of such considerations (Momoh et al. 1995). In the area of power system control, the optimal control theory is often applied in research to design controllers for the enhancement of power system stability and other purposes (Ross 2004). Recently developed fuzzy logic based controllers show promise for robust performance and adaptive schemes. The ability of the power systems to withstand disturbances depends on dynamic performance of the LFC and AVR control loops. The control objectives are dependent on the operating state of the power system. The conventional control strategy for the LFC problem is to take the integral of the control error as the control signal. The Proportional Integral (PI) Control approach is successful in achieving zero steady-state error in the frequency of the system, but exhibits relatively poor dynamic performance as evidenced by large overshoot, settling time and transient oscillations. Also the control design is normally based on a parameter model of the system derived by a linearization process and for particular operating point. Hence, fixed gain controllers designed for particular load and regulation may no longer be suitable for all operating conditions (Mathur and Manjunath 2007). Many investigations in the area of LFC of an isolated power system have been reported in the literature. Different control strategies like adaptive, optimal,

4 41 PID controllers have been applied to improve the performance characteristics and reduce transient oscillations (Zeynelgil 2002). The main aim of such control strategies is to extend the margins of the regions of stability and therefore to satisfy increasingly complex control requirements in power system. In recent years, modern intelligent methods such as Fuzzy Logic (FL), Artificial Neural Network (ANN) and Genetic Algorithm have gained increasing interest for applications in LFC of power system (Vinod Kumar 1998). Karnavas and Papadopoulos (2002) proposed an intelligent load frequency controller to regulate the power output and system frequency and found that the controllers exhibit satisfactory dynamic performance and overcome the drawbacks associated with conventional techniques. It is to be appreciated that (Nanda and Mangala 2004) have studied the conventional integral and fuzzy logic controller in an interconnected hydro-thermal system and proposed a set of fuzzy rules for improving the dynamic performance of the system. The design of fuzzy controller is one of the largest application areas of the fuzzy set theory, where fuzzy logic is described as computing with words rather than numbers, fuzzy control is described as control with sentences rather than equations. Instead of describing the control strategy in terms of differential equations, control is expressed as set of linguistic rules (Engelbrecht 2002). In my work, application of Fuzzy Logic Controller for LFC and BPC of an isolated wind-micro hydro-diesel hybrid power system is considered for implementation and FLC is designed with fuzzy rules for control of frequency and generated power. It has better adaptability towards changes in load and regulation than the conventional controllers thereby providing improved performance with respect to settling time and oscillations.

5 FUZZY LOGIC CONTROLLER FOR AN ISOLATED WIND- MICRO HYDRO-DIESEL HYBRID POWER SYSTEM With the increasing demand for high precision autonomous and intelligent controllers with wide operating conditions, conventional PID control approaches are unable to adequately deal with system complexity, non-linearity and with uncertainty. Intelligent control is a new emerging discipline that is designed to deal with real-time problems. Rather being model based, intelligent controller is experience based and it is the amalgam of the disciplines of Artificial Intelligence, Systems theory and Operations research. For practical implementation, intelligent controllers must demonstrate rapid learning convergence, be temporarily stable, and be robust to parameter changes and internal & external disturbances. Conventional PID Controllers generally do not work well for nonlinear systems, higher order and time-delayed linear systems, complex and vague systems that have no precise mathematical models. Conventionally, the system is modelled analytically by a set of differential equations from which the control parameters are adjusted to satisfy the controller specification. Hence, to overcome this drawback, alternate methods using improved dynamic models or adaptive and intelligent controllers are required (Mathur 2006). A fuzzy logic based design can resolve the weakness of conventional approaches cited above. In the Fuzzy Logic Controller (FLC), the adjustments on the control parameters are handled by a fuzzy rule based expert system. The use of fuzzy logic control is motivated by the need to deal with high complex and performance robustness problems. It is well known that fuzzy logic is much closer to human decision making than traditional logical systems. Fuzzy control provides a design paradigm such that a controller can be designed for

6 43 complex, ill-defined process without knowledge of quantitative data regarding the input-output relations Hybrid System Configuration with FLC Here, the conventional PI Controllers are replaced with Fuzzy Logic Controllers for LFC and BPC of an isolated wind-micro hydro-diesel hybrid power system and the block diagram of the system model is shown in Figure 3.1. In the wind turbine generating unit, the FLC is designed as a supplementary controller for the pitch control. This controller detects the deviation of the wind power generation ( P GW ) as an input signal, so that the wind power generation can be maintained constant. For the diesel generating unit, the FLC is designed to improve the performance of the governor. It uses a system frequency deviation ( Fs) of the power system as a feedback input, so that it can offset the mismatch between the generation and load demand. Figure 3.1 Block diagram of wind-micro hydro-diesel hybrid power system with FLC

7 44 The performance of the proposed FLC is analyzed for an unpredicted load changes. The efficiency of the proposed method is validated and compared against conventional PI controller used by Bhatti et al. (1997b) for various load disturbances Design and Structure of Fuzzy Logic Controller The backbone of the FLC is embodied in a set of fuzzy rules, not an elaborated set of equations. The success of the Fuzzy Logic Controller is mainly due to their ability to cope with knowledge represented in a linguistic form instead of representation in the conventional mathematical framework. The main advantage is being their ability to incorporate experience, intuition and heuristics into the system instead of relying on mathematical models. The design of Fuzzy Logic Controller for LFC and BPC of an isolated wind-micro hydro-diesel hybrid power system is demonstrated in this section. The main objective in the controller design is to develop an intelligent FLC to take care of the responses of the system output. The basic structure of FLC is shown in Figure 3.2. Figure 3.2 Basic structure of Fuzzy Logic Controller

8 45 The Fuzzy Logic Controller consists of three sections: Fuzzifier, Rule base fuzzy inference and De-fuzzifier. The fuzzifier block Fuzzification plays important role in dealing with uncertain information, which might be objective or subjective in nature. The fuzzification block in the Fuzzy Logic Controller represents the process of making crisp quantity into fuzzy. In fact, the fuzzifier converts the crisp input to a linguistic variable using the membership functions stored in the fuzzy knowledge base. Fuzziness in a fuzzy set is characterized by the membership functions. Using suitable membership functions, the ranges of input and output variables are assigned with linguistic variables. These variables transform the numerical values of the input of the fuzzy controller to fuzzy quantities. These linguistic variables specify the quality of the control. Triangular, trapezoid and Gaussian are more common membership functions to use in fuzzy control systems. Rule base fuzzy inference block The heart of the fuzzy system is a Knowledge rule base which consists of information storage for linguistic variables definition (database) and fuzzy rules (control base). The concepts associated with the database are used to characterize fuzzy control rules and a fuzzy data manipulation in Fuzzy Logic Controller. A lookup table is made based on discrete universes that define the output of a controller for all possible combination of the input signals. The heuristic rules of the knowledge base are used to determine the fuzzy controller action. The inference mechanism determines how

9 46 the fuzzy logic operations are performed, and together with the knowledge base determine the output of each fuzzy IF THEN rules. De-fuzzifier block The purpose of De-fuzzification is to convert the output fuzzy variable to a crisp value, so that it can be used for control purpose. It is employed because crisp control action is required in practical applications. Fuzzy Logic Controller (FLC) can be easily implemented in power system for LFC (Cam and Kocaarshan 2005, Juang and Lu 2006, Cam 2007, Anand and Ebenezer Jeyakumar 2009, Soundarrajan and Sumathi 2009). The LFC problem considered here is composed of the sudden load perturbations or a change in wind input power which continually disturb the normal operation of a power system. Hence, the deviations of frequency must be controlled by the Fuzzy Logic Controllers. The structure of Fuzzy logic control system for LFC model is shown in Figure 3.3. Figure 3.3 Structure of Fuzzy logic control system for LFC of hybrid system Consistent with the LFC design, the first step in designing the FLC is to choose the correct input signals to the LFC of the hybrid system. The

10 47 error signal and its derivative are usually chosen as inputs to the fuzzy controller. These two signals are then used as rule antecedent (IF part) in the formation of rule base, and the control output is used to represent the contents of the rule consequent (THEN part) in the formation of rule base. Generally, a controller design based on fuzzy logic for a dynamic system involves the following steps. 1. Understanding of the system dynamic behaviour and characteristics. Define the states and input/output control variables and their variation ranges. 2. Identify appropriate fuzzy sets and membership functions. 3. Create the degree of fuzzy membership function for each input/output variable and then complete fuzzification. 4. Define a suitable inference engine. Construct the fuzzy rule base, to relate the input and output variables. Decide how the action will be executed by assigning strengths to the rules. 5. Decide the de-fuzzification method. Combine the rules and defuzzify the output for converting into crisp vales. FLC is implemented using simulink, an interactive environment in MATLAB for modelling, analyzing and simulating the dynamic systems. The fuzzy file created in the fuzzy tool box is linked to the model through the FLC block included in the central loop. The Mamdani fuzzy inference system is used for the proposed controller. The design steps are followed in sequence by creating the Fuzzy Inference System (FIS) editor shown in Figure 3.4. The inputs and outputs are selected from the editor window in popup menu.

11 48 Figure 3.4 FIS Editor of FLC for LFC and BPC of the hybrid system The FIS editor displays the detailed information about input/output, file name, FIS type (Mamdani model), fuzzification and de-fuzzification etc. The input and output variable of the system are labelled and the membership function is edited by double clicking the input variable icon. The rules are added by selecting Edit rules in the Edit command drop down menu. Fuzzification: The fuzzy based control system in this work is designed to control the frequency and generated power of the hybrid power system. The controller uses two input variables and one output control variable. The error signal (i.e.) the difference between the reference set value and the actual value is termed as the first input variable. The change in error (i.e.) error with unit delay is assigned as another input variable. The inputs of the fuzzy logic controller designed on diesel side of the system for LFC are as follows.

12 49 Input 1: error E = ( Fs) Input 2: change in error E = ( Fs) The fuzzy output variable is the change in control signal ( Pc). This control signal acts as input to the governor of diesel side for LFC of the proposed hybrid system. The inputs of Fuzzy Logic Controller designed in the wind side for Blade Pitch Controller (BPC) are as follows. Input 1: error E = ( P GW ) Input 2: change in error E = ( P GW ) The input and output variables in the proposed controller are represented as a set of seven linguistic variables namely, NL - Negative Large NM - Negative Medium NS - Negative Small Z - Zero PS - Positive Small PM - Positive Medium PL - Positive Large The precise numerical values obtained by measurements are converted to membership values of the various linguistic variables. The degree to which a fuzzy number belongs to a set is known as Membership Function (MF). In LFC, for the first input variable E, MF range is from to 0.1 and for the second input variable E, MF range is from -1 to 1. For the output variable Pc, range is from -1.5 to 1.5. Degree of

13 50 membership function plays an important role in designing a Fuzzy Logic Controller. Here, the FLC designed for LFC and BPC consists of seven membership functions (two trapezoidal and five triangular MFs) for the two inputs and one output variable as shown in Figure 3.5. Figure 3.5 Membership functions of input and output variable of FLC. The MF is symmetrical in shape and overlaps the adjacent MF by 50%. Overlapping in MF is important because it allows for a good interpolation of input values i.e., the entire input space is accommodated. Knowledge base: The knowledge base consists of a database of the LFC model. It provides all the necessary definitions for the fuzzification process such as membership functions, fuzzy set representation of the input-output variables and the mapping functions between the physical and fuzzy domain. The rule base should cover all the possible combinations of input value. Rule conditions are joined by using minimum intersection operator so that the resulting membership function for a rule is given by µ( Pc) = min(µ (E), µ ( E)) (3.1)

14 51 The rules of FLC give the controller its intelligence, since the rules are developed based on the expert knowledge obtained from the experienced operator. In FLC design, the desired effect is to keep the output frequency and generated power of the hybrid system at its rated value under various load disturbances and variations of wind inputs. From this desired objective, the rules are derived for every combination of input state variables in order to obtain the desired output variable. As the number of rules increases, the computational efficiency and robustness of the system will also be improved. Fuzzy rule bases are developed using a conjunctive relationship of the antecedents in the rules. This is termed as an Intersection Rule Configuration (IRC) by Combs and Andrews (1998) because the inference process maps the intersection of antecedent fuzzy sets to output consequent fuzzy sets. IRC is a general exhaustive search of solutions that utilizes every possible combination of rules in determining an outcome. The combinational explosion in rules are given by R = l n (3.2) where R = Number of rules l = Number of linguistic labels for each input variable n = Number of input variables. In the present work, the value of l is 7 and n is 2, hence the number of rules R for LFC and for BPC of the hybrid system are 49. Each rule is mentioned in the Table 3.1.

15 52 Table 3.1 Rule base of Fuzzy Logic Controller for LFC and BPC E/ E NL NM NS Z PS PM PL NL PL PL PL PM PM PS Z NM PL PM PM PM PS Z PS NS PM PM PS PS Z NS NM Z PL PM PS Z NS NM NL PS PM PS Z NS NS NM NM PM PS Z NS NM NM NM NL PL Z NS NM NM NL NL NL In IRC approach, the intersection of the input values that is related to the output is achieved with an AND operation which are of IF-THEN type. The basic function of the rule base is to represent in a structured way, the control policy of an experienced human operator in the form of a set of production rules such as IF (process state) THEN (control output) The control rules are shown in Table 3.1, where every cell shows the output membership function of a control rule with two input membership function. For example, consider the fifth row and second column, which means: IF error ( Fs) is PS and change in error ( Fs) is NM, THEN control output ( Pc) is PS. The above IF THEN rule is fuzzy description of the control logic representing the human expert s qualitative knowledge. Since the controller selected from the fuzzy tool box is with rule viewer, the effect of control

16 53 signal for change in rules can be viewed through the Graphical User Interface (GUI) as shown in Figure 3.6. Figure 3.6 Fuzzy Rule viewer of FLC The first two columns of plots (yellow plots) show the membership functions referenced by antecedent, or the IF part of each rule. The third column of plot (blue plots) shows the membership functions referenced by the consequent, or the THEN part of each rule. The rule viewer is a MATLAB technical computing environment based display of the FIS. It is used as diagnostic test to find which rules are active and how individual membership function shapes are influencing the results. The rule viewer is road map for the whole fuzzy inference process. From the fuzzy rule viewer screen shot in Figure 3.6, it is inferred that, at this particular instance, IF ( Fs) is and ( Fs) is -0.1 THEN the control output signal Pc generated to initiate the turbine governor action is The rule viewer also shows the overall

17 54 result. After rules are evaluated, crisp values are produced by de-fuzzification of the corresponding membership function. De-Fuzzification: The reverse of fuzzificztion is called de-fuzzification. The mathematical procedure of converting fuzzy values into crisp vales is known as de-fuzzification. According to the real world requirements, the linguistic variables have to be transformed to crisp output. De-fuzzification plays a great role in a fuzzy logic based control system design, since it converts fuzzy sets into a numeric value without looking any information. Different de-fuzzification methods exist to accomplish the task and naturally there exist trade-offs to each method. The selection of the right strategy depends on the application and the type of MF used. Centre of gravity method is the best well known De-fuzzification method, because of its computational speed and accuracy in real-time control. Centre of gravity method is used in this research work for De-fuzzification. It obtains the centre of area occupied by the fuzzy set. It is given by the equation: X = µ ). µ ) (3.3) Since the final output is a combination of recommended actions of many rules, the controller is more robust to accommodate the changes in power system parameters. 3.5 SIMULATION RESULTS The Load Frequency Control and Blade Pitch Control of an isolated hybrid power system with Fuzzy Logic Controller and conventional PI Controller are simulated using simulink package version 6.3 available in MATLAB 7.1. Simulink, developed by Mathworks, is a commercial tool for modelling, simulating and analyzing multi-domain dynamic systems. Its primary interface is a graphical block diagramming tool and a customizable

18 55 set of block of libraries. Simulink is widely used in control theory and digital signal processing for multi-domain simulation and model based design. MATLAB is a software package for high performance numerical computation and visualization. It provides an interactive environment with hundreds of reliable and accurate built in mathematical functions. The tool boxes in MATLAB are specialized collections of M-files (MATLAB language programs) built specifically for solving particular classes of problems. The fuzzy logic tool box contains comprehensive identification tools for automatic control, signal processing, system pattern recognition, time series prediction, data mining and financial applications. Procedure for simulation of Fuzzy Logic Controller for LFC and BPC of an isolated hybrid power system: 1. Open the simulink model from MATLAB. 2. Set the parameters start time, stop time, minimum and maximum time step sizes. 3. Draw the simulink model of the isolated hybrid power system shown in Figure 3.7 by dragging transfer function blocks, summer blocks, PID controller block, fuzzy logic tool box etc. from simulink library. 4. Draw the Fuzzy Logic Controller block for LFC and BPC on diesel side and wind side respectively. 5. Create the Fuzzy Inference System (f4.fis) file for the fuzzy logic control block. 6. Export the created f4.fis file to the Fuzzy Logic Controller in the workspace. 7. Connect all the blocks with straight line connectors as per transfer function simulink model of the system.

19 56 8. The output data files are linked to the.m file of the system for storing all the time responses for change in frequency, change in wind power, change in diesel power and change in hydro power of the considered system. 9. Apply step load change on the isolated hybrid power system. 10. Simulate the model, by setting the simulation parameters like start and stop time, type of solver, step sizes and tolerances. 11. Store the output parameters in the corresponding output data files by running the.m file and view the results. Fuzzy Logic Controller is designed and implemented for maintaining the system frequency and generated power within the specified limits of an isolated wind-micro hydro-diesel hybrid power system. Simulations are performed using the parameters given in Appendix 1, with the designed FLC for LFC and BPC to the simulink model of the hybrid power system shown in Figure 3.7. Simulations are also performed for the same system with conventional PIC mentioned in the previous chapter for comparison. Simulations are carried out for 1% and 2% step increase in load power ( PL= 0.01 p.u. and 0.02 p.u.) at t = 0s. All the performance criteria such as settling time overshoot and steady state values are considered to get minimized for change in frequency, change in wind power, change in diesel power, and change in hydro power during various load disturbances and wind input disturbances to get the optimum performance of the wind-micro hydro-diesel hybrid power system. The responses of change in frequency of the system, change in wind power generation, change in diesel power generation and change in hydro power generation for 0.01p.u step load change are shown in Figures 3.8, 3.9, 3.10 and 3.11 respectively for the proposed FLC and conventional PIC.

20 57 Figure 3.7 Simulink model of wind-micro hydro-diesel hybrid system with FLC for LFC and BPC

21 58 3 x PIC FLC Time in secs. Figure 3.8 Frequency deviation of the hybrid system with FLC and conventional PIC for a step load change of 1% 5 x PIC FLC Time in secs. Figure 3.9 Change in wind power generation of the hybrid system with FLC and conventional PIC for a step load change of 1%

22 PIC FLC Time in secs. Figure 3.10 Change in diesel power generation of the hybrid system with FLC and conventional PIC for a step load change of 1% change in hydro power(delpgh) in p.u.kw 10 x PIC FLC Time in secs. Figure 3.11 Change in hydro power generation of the hybrid system with FLC and conventional PIC for a step load change of 1%

23 60 The settling time in seconds for all the responses of the hybrid system of FLC and conventional PIC for various load disturbances are tabulated in Table 3.2. Table 3.2 Settling time in seconds for deviations in frequency, wind, diesel and hydro power generation for various step load disturbances Load change (p.u.) Change in frequency Change in wind power change in diesel power change in hydro power FLC PIC FLC PIC FLC PIC FLC PIC ANALYSIS AND PERFORMANCE COMPARISON An investigation on the dynamic responses of change in frequency and change in generated power has been carried out using FLC for LFC and BPC of an isolated wind-micro hydro-diesel hybrid power system and compared with conventional PIC for different load changes and wind input variations. From Figures 3.8 to 3.11, the responses of FLC for the hybrid system show that the proposed controller is successful in stabilizing the frequency of the system. Figure 3.12 represents the performance comparison of the proposed FLC and conventional PIC for step load change of 1% in terms of settling time.

24 61 Settling time in seconds Frequency deviation (Hz) Change in wind power (p.u.kw) Change in diesel power (p.u.kw) Change in hydro power (p.u.kw) FLC PIC Figure 3.12 Comparison of dynamic responses of FLC and conventional PIC for 1% step load disturbance in terms of settling time (seconds) Figures 3.13, 3.14, 3.15 and 3.16 show performance comparison between the proposed FLC and conventional PIC in terms of settling time against various load changes for frequency deviation, change in wind power generation, diesel power generation and hydro power generation respectively. Settling time in seconds % 2% Load disturbance in percentage FLC PIC Figure 3.13 Comparison of change in frequency response in terms of settling time (seconds) between FLC and conventional PIC against various load changes

25 62 Settling time in seconds FLC PIC 0 1% 2% Load disturbance in percentage Figure 3.14 Comparison of change in wind power response in terms of settling time (seconds) between FLC and conventional PIC against various load changes Settling time in seconds % 2% Load disturbance in percentage FLC PIC Figure 3.15 Comparison of change in diesel power response in terms of settling time (seconds) between FLC and conventional PIC against various load changes

26 Settling time in seconds % 2% FLC PIC Load disturbance in percentage Figure 3.16 Comparison of change in hydro power response in terms of settling time (seconds) between FLC and conventional PIC against various load changes It is observed that the overshoot and settling time of proposed FLC is lower than those of the conventional PI Controller. 3.7 SUMMARY In this chapter, Fuzzy Logic Controller is proposed and designed for LFC and BPC of an isolated wind-micro hydro-diesel hybrid power system. Intelligent control schemes and application of fuzzy logic in power system are reviewed. With the brief introduction about the fuzzy logic control, FLC is designed with rule base for the proposed work. The designed FLC is implemented for LFC and BPC of the proposed isolated hybrid system. The simulink model of the system with FLC is developed in simulink package version 6.3 available in MATLAB 7.1. The model is simulated for different load changes and the results are compared with conventional PI Controller developed by Bhatti et al. (1997b). From the simulation results of LFC, it is

27 64 clear that the proposed FLC is effective and provides significant improvement in system performance. Fuzzy control offers simple but robust solutions that cover a wide range of system parameters and adjust it-self for major disturbances. Still to improve the dynamic performance of the hybrid for various step load disturbances and wind input disturbances, the work is extended in further chapters.