Systematic method for the design of a full-scale fuzzy PID controller for SVC to control power system stability

Lo, K.L.; Sadegh, Mahmoud Oukati

doi:10.1049/ip-gtd:20030125

Cited by 37 publications

(20 citation statements)

References 8 publications

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“…In binary coding part of chromosome, mutation only performs 1-bit flip, i.e., the bit value changes from '0' to '1' or from '1' to '0'. If the mutation takes place in the real coded part, we use the nonlinear mutation (Lo and Sadegh, 2003;Zhi-Hua Cui et al, 2003). The nonlinear mutation operator is defined as follows:…”

Section: : Evaluation Of Fitness Valuementioning

confidence: 99%

Optimal control of a CSTR process

Soukkou

Khellaf

Leulmi

et al. 2008

Braz. J. Chem. Eng.

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-Designing an effective criterion and learning algorithm for find the best structure is a major problem in the control design process. In this paper, the fuzzy optimal control methodology is applied to the design of the feedback loops of an Exothermic Continuous Stirred Tank Reactor system. The objective of design process is to find an optimal structure/gains of the Robust and Optimal Takagi Sugeno Fuzzy Controller (ROFLC). The control signal thus obtained will minimize a performance index, which is a function of the tracking/regulating errors, the quantity of the energy of the control signal applied to the system, and the number of fuzzy rules. The genetic learning is proposed for constructing the ROFLC. The chromosome genes are arranged into two parts, the binary-coded part contains the control genes and the real-coded part contains the genes parameters representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. The performances of the ROFLC are compared to these found by the traditional PD controller with Genetic Optimization (PD_GO). Simulations demonstrate that the proposed ROFLC and PD_GO has successfully met the design specifications.

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Section: : Evaluation Of Fitness Valuementioning

confidence: 99%

Optimal control of a CSTR process

Soukkou

Khellaf

Leulmi

et al. 2008

Braz. J. Chem. Eng.

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“…the bit value changes from '0' to '1' or from '1' to '0'. If the mutation takes place in the real-coded part, we use the nonlinear mutation [22,23]. Figure 4 shows the mutation mechanisms.…”

Section: Hybrid Learningmentioning

confidence: 99%

How to optimize the TS‐fuzzy knowledge base to achieve desired performances: Accuracy and robustness

Soukkou¹,

Leulmi²,

Khellaf³

2007

Optim Control Appl Methods

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SUMMARYDesigning an effective criterion/learning to find the best rule and optimal structure is a major problem in the design process of fuzzy neural controller. In this paper, we introduce a new robust model of Takagi Sugeno fuzzy logic controller. A hybrid learning algorithm, called hybrid approach to fuzzy supervised learning (HAFSL), which combines the genetic algorithm (GA) and gradient descent technique (GD) is proposed for constructing an efficient and robust fuzzy neural network controller (FNNC). Two phases of design and learning process are presented in this work. A GA is used for finding near optimal structure/parameters of the FNNC that minimizes the number of rules (initialization procedure). The second stage of learning algorithm uses the backpropagation algorithm based on GD method to fine tune the consequent parameters of the controller. The genes of chromosome are arranged into two parts, the first part contains the control genes (the certainty factors) and the second part contains the parameters genes that representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. The performances of the HAFSL are compared to these found by the traditional PI with genetic optimization (GA-PI). Simulations demonstrate that the proposed HAFSL and GA-PI algorithms have good generalization capabilities and robustness on the water bath temperature control system.

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“…Also, a combination of simple, arithmetic and heuristic crossover as the crossover function, and the uniform mutation, non-uniform mutation, Decentralised coordination of facts multi-non-uniform mutation and boundary mutation as mutation operators were adopted. The details of these operators are given in Lo and Sadegh (2003).…”

Section: Gas Learning Theorymentioning

confidence: 99%

Decentralised coordination of facts devices for power system stability enhancement using intelligent programming

Sadegh

2005

COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

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This paper seeks to propose a systematic method to design multi fuzzy FACTS based stabilizers in a multi-machine power system. Conventional FACTS based stabilizers are decentralized controllers that adopt local measurements and operate in closed loop. To improve overall system dynamic performance, a coordinating application of FACTS based stabilizer is essential. Although, numerous researches have indicated the effectiveness and superiority of fuzzy logic controllers in comparison with the conventional linear controllers in power system application but researchers have not adequately investigated coordination of multi fuzzy controllers in multi-machine power systems to provide optimal performance. Genetic algorithm is used to determine optimum values of controllers' parameters. The search space of the optimisation procedure is decreased to a smaller one, design and computation time can be reduced significantly and the design process becomes more systematic

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Systematic method for the design of a full-scale fuzzy PID controller for SVC to control power system stability

Cited by 37 publications

References 8 publications

Optimal control of a CSTR process

Optimal control of a CSTR process

How to optimize the TS‐fuzzy knowledge base to achieve desired performances: Accuracy and robustness

Decentralised coordination of facts devices for power system stability enhancement using intelligent programming

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