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Fig. 6. Fuzzy reasoning representing the process from fuzzification to defuzzification. The main fuzzy reasoning blocks and the defuzzification process of the FLC used in this study are given in Fig. 6. The FLC used here is developed in Matlab/Simulink environment for multipurpose use as a control tool. With some simple modifications it can be used to control different systems. More detailed information about the Matlab/Simulink modeling of the FLC used here can be found in [19, 20]. The system dynamic performance is observed for three different controller structures, PI (Proportional + Integral), Fuzzy PI and Fuzzy controller. The simulation results are shown in Figs. 7-10 in this study.

Figure 6 Fuzzy reasoning representing the process from fuzzification to defuzzification. The main fuzzy reasoning blocks and the defuzzification process of the FLC used in this study are given in Fig. 6. The FLC used here is developed in Matlab/Simulink environment for multipurpose use as a control tool. With some simple modifications it can be used to control different systems. More detailed information about the Matlab/Simulink modeling of the FLC used here can be found in [19, 20]. The system dynamic performance is observed for three different controller structures, PI (Proportional + Integral), Fuzzy PI and Fuzzy controller. The simulation results are shown in Figs. 7-10 in this study.

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Fig. 2. Blok diagram of a four-area power system. Where, system matrix A, input matrix B, state matrix x, control matrix u and output matrix C [8, 11].
Fig. 2. Blok diagram of a four-area power system. Where, system matrix A, input matrix B, state matrix x, control matrix u and output matrix C [8, 11].
Fig. 4. Fuzzy inference system for Kp and K,. consists of three sections namely, fuzzifier, rule base, and defuzzifier as shown in Figs. 4, 5 for KP, KI and FLC, respectively. The error, e and change in error, de are inputs of FLC. Two input signals are converted to fuzzy numbers first in fuzzifier using five membership functions: Positive Big (PB), Positive Small (PS), Zero (ZZ), Negative Small (NS), Negative Big (NB), Small (S), Medium (M), Big (B), Very Big (VB) and Very Very Big (VVB). Triangular membership functions are used in this paper since it is easier to intercept membership degrees from a triangle. Then they are used in the rule table shown in Table I to determine the fuzzy number of the compensated output signal.
Fig. 4. Fuzzy inference system for Kp and K,. consists of three sections namely, fuzzifier, rule base, and defuzzifier as shown in Figs. 4, 5 for KP, KI and FLC, respectively. The error, e and change in error, de are inputs of FLC. Two input signals are converted to fuzzy numbers first in fuzzifier using five membership functions: Positive Big (PB), Positive Small (PS), Zero (ZZ), Negative Small (NS), Negative Big (NB), Small (S), Medium (M), Big (B), Very Big (VB) and Very Very Big (VVB). Triangular membership functions are used in this paper since it is easier to intercept membership degrees from a triangle. Then they are used in the rule table shown in Table I to determine the fuzzy number of the compensated output signal.
Fig. 5. Fuzzy inference system for FLC. Finally, resultant united fuzzy subsets representing the controller output are converted to the crisp values using the central of area (COA) defuzzifier scheme. The FLC parameters are chosen on the basis of a trial and error study of the control.
Fig. 5. Fuzzy inference system for FLC. Finally, resultant united fuzzy subsets representing the controller output are converted to the crisp values using the central of area (COA) defuzzifier scheme. The FLC parameters are chosen on the basis of a trial and error study of the control.
FUZZY RULES DECISION TABLE FOR FLC, FLC; AND FLC,
FUZZY RULES DECISION TABLE FOR FLC, FLC; AND FLC,
Fig. 7. Dynamic response of Area 1.
Fig. 7. Dynamic response of Area 1.
Fig. 8. Dynamic response of Area 2.
Fig. 8. Dynamic response of Area 2.
Fig. 10. Dynamic response of Area 4. Fig. 9. Dynamic response of Area 3. In this paper, a fuzzy logic PI controller is designed for load frequency control of four-area interconnected power systems. The system dynamic performances are observed via using different controllers.
Fig. 10. Dynamic response of Area 4. Fig. 9. Dynamic response of Area 3. In this paper, a fuzzy logic PI controller is designed for load frequency control of four-area interconnected power systems. The system dynamic performances are observed via using different controllers.
Related topics:
PID controller designPid ControllerFuzzy Logic ControllerLoad Frequency controlFuzzy logic controllersHybrid Fuzzy Pid ControllerPI ControllerFuzzy Logic Controller (FLC)Multi Area Power SystemFuzzy PID Controller
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