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Figure 14: variation of Af, ( with SMES and SPSS) © 2005 - 2009 JATIT. All rights reserved.

Figure 14 variation of Af, ( with SMES and SPSS) © 2005 - 2009 JATIT. All rights reserved.

Related Figures (15)

Fig.1.General diagram of a two-area power system
Fig.1.General diagram of a two-area power system
Fig-2. Detailed block diagram of the single- area interconnected between the 7-th and j-th areas. Detailed block Where biis the frequency bias coefficient of i-th
Fig-2. Detailed block diagram of the single- area interconnected between the 7-th and j-th areas. Detailed block Where biis the frequency bias coefficient of i-th
A Two area interconnected system shown in figure 4. is utilized to describe the concept of decentralized direct tie-line power flow control by SMES and SSPSs supposed, for decentralized control, a SMES is located in one area, while SSPS is located on the tie-line between both areas for power flow control. When the sudden load fluctuations or some disturbance occur in any area, the active power output of SMES, is controlled to suppress the frequency deviation of its located area while the SSPS is utilized to modulate the tie-line power flow directly in order to diminish the frequency deviation of another area. The block diagram of linear zed system model of the two area interconnected system with SMES and SSPS is illustrated in fig 5.BothSMES & SSPS are represented by first order lag controls. The decentralized control strategy of SMES &SSPS is stated As follows.
A Two area interconnected system shown in figure 4. is utilized to describe the concept of decentralized direct tie-line power flow control by SMES and SSPSs supposed, for decentralized control, a SMES is located in one area, while SSPS is located on the tie-line between both areas for power flow control. When the sudden load fluctuations or some disturbance occur in any area, the active power output of SMES, is controlled to suppress the frequency deviation of its located area while the SSPS is utilized to modulate the tie-line power flow directly in order to diminish the frequency deviation of another area. The block diagram of linear zed system model of the two area interconnected system with SMES and SSPS is illustrated in fig 5.BothSMES & SSPS are represented by first order lag controls. The decentralized control strategy of SMES &SSPS is stated As follows.
Fig.5: Linear zed System Model of Two Area Interconnected System with SMES &SSPS PSO is a population-based optimization method first proposed by Eberhart and Colleagues [24, 25]. Some of the attractive features of PSO ease of implementation and the fact that information is required. It can be used include the no gradient to solve a wide array of different optimization problems. Like evolutionary algorithms, PSO technique conducts search using a _ population § of corresponding to individuals. Eac represents a candidate solution to the particles, h particle problem at hand. In a PSO system, particles change their positions by flying around in a multid imensional search space until computational limitations are exceeded.
Fig.5: Linear zed System Model of Two Area Interconnected System with SMES &SSPS PSO is a population-based optimization method first proposed by Eberhart and Colleagues [24, 25]. Some of the attractive features of PSO ease of implementation and the fact that information is required. It can be used include the no gradient to solve a wide array of different optimization problems. Like evolutionary algorithms, PSO technique conducts search using a _ population § of corresponding to individuals. Eac represents a candidate solution to the particles, h particle problem at hand. In a PSO system, particles change their positions by flying around in a multid imensional search space until computational limitations are exceeded.
Fig 7. Basic structure of a fuzzy logic system ‘uzzy logic is derived from fuzzy set theory where nfinite number of memberships is allowed. The legree of each member ship for each element is ndicated by a number in the interval [0 1], [24]. Nhile traditional controllers are linear, fuzzy logic ontrollers are nonlinear. Moreover, fuzzy logic ontrollers do not need a controlled plant model, nd not sensitive to plant parameter variations. By ising fuzzy logic the human experience can be used n the design of the controller. Fuzzy logic ontrollers are rule-based controllers. The rules of he system are written in natural language and ranslated into fuzzy logic.
Fig 7. Basic structure of a fuzzy logic system ‘uzzy logic is derived from fuzzy set theory where nfinite number of memberships is allowed. The legree of each member ship for each element is ndicated by a number in the interval [0 1], [24]. Nhile traditional controllers are linear, fuzzy logic ontrollers are nonlinear. Moreover, fuzzy logic ontrollers do not need a controlled plant model, nd not sensitive to plant parameter variations. By ising fuzzy logic the human experience can be used n the design of the controller. Fuzzy logic ontrollers are rule-based controllers. The rules of he system are written in natural language and ranslated into fuzzy logic.
6 SIMULATION RESULTS The fuzzy logic controllers are designed for the computer simulation. First, fuzzy logic controller is based on the expert experience, as described in the above section. Second, the fuzzy logic controller is based on the APSO algorithms only to find optimal range of the membership functions (F the LC with APSO algorithms). Last, the optimal fuzzy controller is based on the APSO algorithms so as to search the optimal range of the members functions, the optimal shape of the members hip hip functions and the optimal fuzzy inference rules (FLC with APSO algorithms). After the evolution process, the optimal values of Ke and Kde in F LC with APSO algorithms are calculated as 0.001 and 0.005, respectively. The best chromosomes in F with APSO algorithms are pursued as: LC
6 SIMULATION RESULTS The fuzzy logic controllers are designed for the computer simulation. First, fuzzy logic controller is based on the expert experience, as described in the above section. Second, the fuzzy logic controller is based on the APSO algorithms only to find optimal range of the membership functions (F the LC with APSO algorithms). Last, the optimal fuzzy controller is based on the APSO algorithms so as to search the optimal range of the members functions, the optimal shape of the members hip hip functions and the optimal fuzzy inference rules (FLC with APSO algorithms). After the evolution process, the optimal values of Ke and Kde in F LC with APSO algorithms are calculated as 0.001 and 0.005, respectively. The best chromosomes in F with APSO algorithms are pursued as: LC
nis number of rules. In this study 49 rules are used. The set of | fuzzy rules are given in Table 1. The centroid method is used in Defuzzification process.
nis number of rules. In this study 49 rules are used. The set of | fuzzy rules are given in Table 1. The centroid method is used in Defuzzification process.
Figure 10 : variation of Af, ( with SMES anc SPSS) Figure 9 : variation of Af, ( with SMES and SPSS)
Figure 10 : variation of Af, ( with SMES anc SPSS) Figure 9 : variation of Af, ( with SMES and SPSS)
n the present work, the intelligent controller based on Fuzzy Logic-APSO Algorithms is in agreement with the step reference of two area system. In the fuzzy logic LFC , the optimization of membership functions and rules was required, its significance being shown in the minimal over/undershoot time of speed response. Therefore the membership functions are adjusted in optimal values so as to give a steady state error in frequency deviation value equal zero. The computer MATLAB simulation demonstrate that the fuzzy controller associated to the APSO algorithms approach became very strong, giving very good results and possessing good robustness.
n the present work, the intelligent controller based on Fuzzy Logic-APSO Algorithms is in agreement with the step reference of two area system. In the fuzzy logic LFC , the optimization of membership functions and rules was required, its significance being shown in the minimal over/undershoot time of speed response. Therefore the membership functions are adjusted in optimal values so as to give a steady state error in frequency deviation value equal zero. The computer MATLAB simulation demonstrate that the fuzzy controller associated to the APSO algorithms approach became very strong, giving very good results and possessing good robustness.
Figure 13: variation of Af, ( with Integral control) and fuzzy control
Figure 13: variation of Af, ( with Integral control) and fuzzy control
Related topics:
Automatic Generation ControlIntelligent fuzzy control
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