CHAPTER 5 FREQUENCY STABILIZATION USING SUPERVISORY EXPERT FUZZY CONTROLLER

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1 85 CAPTER 5 FREQUENCY STABILIZATION USING SUPERVISORY EXPERT FUZZY CONTROLLER 5. INTRODUCTION The simulation studies presented in the earlier chapter are obviously proved that the design of a classical fuzzy logic controller is found that it is easier than the integral controller, but its performance is not up to the expected level, considering the stability and robustness of the systems. With a view to deal with such limitations the online tuning approach is discussed in this chapter. Basically there are two different tuning approaches for achieving the optimal parameters of the fuzzy logic controller i.e. offline and online tuning. The online tuning is achieved by adding with an expert supervisory fuzzy system to a controller (Abd 2003, Lin 2006). The expert supervisory fuzzy system will continuously examine the power system condition and modify the direct fuzzy controller parameters according to some specified evaluation criterion. The online tuning method will enhance the controller performance in terms of stability and robustness (Mudi 999, Pal 2008). The systematic design procedure of a supervisory expert fuzzy logic control scheme and its implementation are discussed in this chapter. The validation of the control scheme is made using MATLAB simulation. 5.2 SUPERVISORY EXPERT FUZZY CONTROLLER The structure of the proposed supervisory expert fuzzy control (SEFC) scheme is shown in Figure 5.. The control structure consist of a

2 86 Supervisory Fuzzy Controller (SFC), which provides a mechanism to the main goal of the system and a Direct Fuzzy Controller (DFC), which delivers the solutions to particular situations. A standard Mamdani type rule base fuzzy system has been selected for both the types. In the proposed SEFC structure, the Area Control Error (ACE) and change in system input are selected as decision making input parameters of supervisory expert system. The outputs of the supervisory expert system will modify the input scaling factor of the direct fuzzy controller based on the system condition (Kanagaraj 2009). These online scaling factor modifications, namely K and K 2 values respectively, improve the controller performance and reduce the involvement of human operation. The purpose of using change in system input and error as decision making parameters is that the disturbances to the interconnected power system or any abnormal output of the controller can be identified instantly by supervisory expert system either to modify the controller parameters or to move the system operation to safe mode. Figure 5. Structure of the Supervisory Expert Fuzzy Control Scheme

3 SUPERVISORY EXPERT FUZZY CONTROLLER BAESD FREQUENCY STABILIZATION IN A PARALLEL AC-DC INTERCONNECTED POWER SYSTEM 5.3. Membership functions and Rule Base Symmetrical triangular shape membership functions with 50% overlap are selected for the present control scheme. The triangular shape membership functions divide each input and output universe of discourse into adjustment interval with specific linguistic value. The range of universe of discourse of the input and output are predetermined based on the power system operating level. The SEFC is associated with the inputs Area control error (ACE) and the power plant input (U). Similarly the Direct fuzzy logic controller is associated with the inputs E and E. The input and output of the SEFC is assigned with various fuzzy membership functions. The three triangular membership functions are used for the inputs and outputs. The area control error input membership functions are represented by N (negative), Z (zero) and P (positive). The input and output membership functions are represented by L (low), M (medium) and (high).the input and the output membership functions of the SEFC are shown in Figures 5.2 and 5.3.

4 88 N Z P Membership value 0-0 ACE (a) Area Control Error (ACE) L M Membership value U (b) Power Plant Input U Figure 5.2 SEFC Input membership functions

5 89 L M Membership value K (a) Scaling Factor (K ) L M Membership value K 2 (b)scaling Factor Change in Error (K 2 ) Figure 5.3 SEFC Output membership functions

6 90 For the direct fuzzy controller the membership functions of the inputs E and E are Negative Large (NL), Negative Medium (NM), Negative Small (NS), Zero (ZE), Positive Small (PS), Positive Medium (PM) and Positive Large (PL) and the output membership functions are denoted as Zero (ZE), Very Small (VS), Small (S), Medium (M), Large (L), Very Large (VL) and Very Very Large (VVL). The fuzzy membership functions for inputs and outputs are shown in Figures 5.4 and 5.5. Membership value NL NM NS ZE PS PM PL E (a) Error (E) Membership value 0 NL NM NS ZE PS PM PL E (b) Change in Error ( E) Figure 5.4 Input membership functions of direct fuzzy controller

7 9 Membership value ZE VS S M L V L VVL U Controlled output (U) Figure 5.5 Output membership functions of direct fuzzy controller The intersection minimum operation method has been employed in both the controllers to perform the fuzzy implication operation, which is generally expressed for the two input fuzzy system as A (x ) A (x ) A (x ), A (x ) i i 2 i i 2 min (5.) where A i ( x ) and A i ( x 2 ) are the membership values of input fuzzy sets x and x 2 respectively. The fuzzy rule- base reflects the collected expert Knowledge about how a particular control problem must be treated. The expert knowledge is represented in the form of IF-TEN rules for decision making process, the input fuzzy sets engaged in the IF part of the rule and output fuzzy set engaged in the TEN part of the rule. The rule-base of SEFC is developed with ACE and u as the premise and the gain multiplying factors K and K 2 are consequent of the fuzzy control rules. The structure of the fuzzy rules of SEFC is expressed as, IF ACE is N and U is L TEN, K is L and K 2 is. (5.2)

8 92 Using the fuzzy subsets of ACE and U, totally 8 linguistic fuzzy rules have been developed, as shown in Table 5.. Some of the common criteria which are considered during the development of fuzzy rules of SEFC are: (i) To reduce undershoot and to reduce the settling time the controller gain has to be set a small value when error is high. This may be a possible rule like, IF ACE is P and U is, TEN K is and K 2 is L. (ii) The controller gain should be almost constant and minimum value, if ACE and U are close to zero. For example, the rule IF ACE is Z and U is M, TEN K is M and K 2 is M. (iii) To improve the controller performance under load disturbance, gain around the steady condition is made sufficiently large. The rule is such as, IF ACE is N and U is L, TEN K is L and K 2 is. Similarly the fuzzy rules of direct fuzzy controller are developed using the underlying knowledge about interconnected area system frequency. In the rule structure, the E and E are the premise and controlled output U is the consequence. Thus the 49 rules have been used for the direct fuzzy controller which relates the fuzzy subsets of each inputs and output. The structure of the fuzzy control rules in direct fuzzy controller is expressed as, IF E is PM and E is NS, TEN U is L (5.3) Table 5.2. The linguistic fuzzy rules of direct fuzzy controller are shown in

9 93 Table 5. Linguistic fuzzy rules of the Supervisory Expert Fuzzy Logic Controller Input Output ACE U K K 2 N Z P L M L M L M L L M M M M L L L L Table 5.2 Linguistic fuzzy rules of the Direct Fuzzy Logic Controller E U E NL NM NS ZE PS PM PL NL ZE ZE ZE ZE VS S M NM ZE ZE ZE ZE S M L NS ZE ZE VS VS M L VL ZE ZE VS S S L VL VL PS VS S M M VL VVL VVL PM S M L L VVL VVL VVL PL M L VL VL VVL VVL VVL Figure 5.6 shows the surface view of the supervisory expert fuzzy system, which gives a three dimensional view of the system it is possible to encounter the troubles in diplaying the three area interconnected AC-DC power systems. The supervisory expert fuzzy logic rule viewer for the given typical values of both inputs and output is shown in Figure 5.7.

10 94 Figure 5.6 Three dimensional surface view of the supervisory expert fuzzy logic control rules Figure 5.7 Supervisory expert fuzzy logic rule viewers The primary feedback loop of the on-line mechanism consists of a rule-base direct fuzzy controller whose output is directly applied to power plant area to achieve the desired output. owever the input and the input scaling factor of this controller are modified based on the current status of the system by using SEFC. The SEFC system is designed with a separate rulebase fuzzy controller which is connected in the secondary feedback loop. The supervisory expert system will continuously evaluate the controller performance and produce the appropriate multiplying factor for the primary

11 95 feedback loop controller inputs. The multiplying factors K and K 2 are determined based on controller performance at each sampling interval by the rule-base supervisory expert system. Multiplying factors of the supervisory expert system modifies the input magnitude of the direct fuzzy controller. This SEFC method will enhance the performance of the direct fuzzy controller in terms of stability and robustness Defuzzification Method A defuzzification method converts the fuzzy output from the inference mechanism to a real world crisp value. There are various types of defuzzification. owever, the centre- average defuzzification method is most frequently used to calculate the crisp output from the fuzzy input and is expressed as, U c (c) (5.4) n CRISP i i i n i (c) i where U Crisp is the output of the fuzzy controller, c i denotes the centre of the membership function of the consequent of i th rule, denotes the membership value for the rule s premise and n represents the total number of fuzzy rules Results and Discussions ere, a three area interconnected AC-DC reheat thermal power system has been considered for the system study. It is shown in Figure 5.8. The simulation tests were carried out to compare system dynamic response under similar conditions of operation of the power system.

12 96 Load Disturbance -K- / ace ace u 0.08s+ 0.3s+ 5s+ 0s+ Area- 20s+ Scope sfp FLC Scope2 To Workspace In In2 Out s VDC ace u ace FLC2 0.08s+ 0.3s+ 5s+ 0s+ Area s+ Scope -K- /2.4 Load Disturbance sfp2 To Workspace -K- -K s -K- ace u ace FLC / s+ Area-3 0.3s+ Load Disturbance 5s+ 0s+ Area s+ Scope3 sfp3 To Workspace2 Figure 5.8 Modelling of three area interconnected AC-DC reheat thermal power systems using SEFC For the system study, Integral control, fuzzy logic control and supervisory expert fuzzy logic control schemes have been applied for the three area interconnected AC-DC power systems. The system is simulated for a step load disturbance of 0% (0. p.u. MW) occurring in area-.due to this, change in dynamics response of the system has been observed. Figures 5.9, 5.0 and 5. indicate the frequency deviations of areas, 2 and 3 for a step load disturbance in area-. From Figure 5.9 one can infer that in Integral control, there is an overshoot and the frequency stabilization occurs only after 0 seconds. In fuzzy logic control, overshoot is eliminated and the frequency stabilization occurs after 7 seconds. Whereas in SEFC there is no overshoot and the frequency stabilization occurs within 2.5 seconds.

13 97 Figure 5.0 shows the frequency deviation in area-2 for a 0% disturbance in area-. One can observe the same result except that the frequency stabilization takes place in.4 seconds for Integral control, in 7.6 seconds for fuzzy logic control and in 4.5 seconds for SEFC. Similarly from Figure 5., which shows the frequency deviation in area-3 for a 0% disturbance in area-, one can observe the frequency stabilization as.2 seconds for integral control, 6.5 seconds for fuzzy logic control and 2.5 seconds for SEFC. Figure 5.9 Frequency deviations in area- for a 0% Disturbance in Area- Also the same study is done in the system s response for a step-load disturbance of 30 %( 0.3 p.u.mw) occurring in area- and the frequency deviations of areas, 2 and 3 are shown in Figures 5.2, 5.3 and 5.4 respectively. From the comparison, one can observe that the proposed SEFC instantly responds to the step load disturbance and makes the system stable within a short time.

14 98 Figure 5.0 Frequency deviations in area-2 for a 0% Disturbance in Area- Figure 5. Frequency deviations in area-3 for a 0% Disturbance in Area-

15 99 Figure 5.2 Frequency deviations in area- for a 30% Disturbance in Area- Figure 5.3 Frequency deviations in area-2 for a 30% Disturbance in Area-

16 00 Figure 5.4 Frequency deviations in area-3 for a 30% Disturbance in Area- The Area Control Error (ACE) for a 0% step load disturbance in area- is shown in Figures 5.5, 5.6 and 5.7. Figure 5.5 ACE deviations in area- (0% Disturbance in Area-)

17 0 From the comparison, one can observe that the proposed SEFC instantly responds to the step load disturbance and attain the steady state in a stipulated time than the integral controller and fuzzy logic controller. Figure 5.6 ACE deviations in area-2 (0% Disturbance in Area-) Figure 5.7 ACE deviations in area-3 (0% Disturbance in Area-)

18 02 Similarly, the Area Control Error (ACE) for a 30% step load disturbance in area-is shown in Figures 5.8, 5.9 and From the comparison, one can observe that the Supervisory Expert Fuzzy Controller instantaneously responds to the step load disturbance and reduces the system error within a short time than the Integral controller and Fuzzy Logic controller. Figure 5.8 ACE deviations in area- (30% Disturbance in Area-) Figure 5.9 ACE deviations in area-2 (30% Disturbance in Area-)

19 03 Figure 5.20 ACE deviations in area-3 (30% Disturbance in Area-) The effectiveness of the proposed control has been compared by using error criteria such as Integral Square Error (ISE), Integral Absolute Error (IAE) and settling time. The formulae of the ISE and IAE are given as, 2 ACE dt ISE (5.5) 0 IAE ACE dt (5.6) 0 where ACE is Area Control Error The performance by numerical comparison for a step load disturbance of 0% and 30% in aera- are presented in Table 5.3. From this table, one can observe that the ISE and IAE in SEFC are less compared to the integral control and fuzzy logic control. Also the settling time in SEFC is faster than the integral control and fuzzy logic control.

20 04 Table 5.3 Numerical Comparison for different Load Disturbances ISE IAE Settling Time T s (S) Types of Control 0% Disturbance 30% Disturbance 0% Disturbance 30% Disturbance 0% Disturbance 30% Disturbance Integral Control Fuzzy Control Supervisory Fuzzy Control Practically load on the power systems are continuously varied from time to time. In order to test the robustness of the proposed controller, different step load disturbances have been applied for different time periods as shown in Figure 5.2. It is seen from the Figure 5.2 that in supervisory AC-DC, at the time of 0% load disturbance, the frequency stabilization occurs within 2.5 seconds. At 6 seconds, another 0% of load disturbance is applied. Frequency stabilization occurs within 3 seconds. At the time of 3 seconds the load disturbance is increased from 20% to 40%. During this condition, the frequency stabilization occurs within 3.2 seconds. Similarly, at the same system, if the load disturbance is reduced by 50% at a time of 46.5 seconds, the frequency stabilization occurs within 4.8 seconds. Even though the load has been suddenly reduced, the proposed SEFC reacts perfectly. The results of different load disturbances show that the robustness of the proposed SEFC technique is better than the integral and direct fuzzy logic control techniques.

21 05 Figure 5.2 Performance Comparison of Frequency deviation in area- for a different load disturbance in area- 5.4 SUMMARY i. The results proved that the proposed SEFC maintains the system frequency without any steady state error unlike integral and fuzzy logic controller. ii. The Supervisory Expert Fuzzy Logic controller instantly responds and reduces the Area Control Error (ACE) within a short time than the Integral controller and Fuzzy Logic controller. iii. The proposed SEFC instantaneously responds to the step load disturbance and makes the system stable within a short time. iv. The results of different load disturbances show that the robustness of the proposed SEFC technique is better than the integral and fuzzy logic control techniques.