A fuzzy logic based power system stabilizer with learning ability

Hariri, A.; Malik, O.P.

doi:10.1109/60.556370

Cited by 76 publications

(24 citation statements)

References 7 publications

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“…In last decade, Fuzzy Logic Controllers (FLCs) and Artificial Neural Network Controllers (ANNCs) being used as power system stabilizers, have been developed and tested [11]- [18]. Unlike other classical control methods, FLCs and ANNCs are model-free controllers; i.e they do not required an exact mathematical model of the controlled system.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Power System Stabilizer Using ANFIS and Genetic Algorithms

Fraile-Ardanuy

Zufiria²

2005

Computational Intelligence and Bioinspired Systems

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Abstract. This paper presents an adaptive Power System Stabilizer (PSS) using an Adaptive Network Based Fuzzy Inference System (AN-FIS) and Genetic Algorithms (GAs). Firstly, genetic algorithms are used to tune a conventional PSS on a wide range of operating conditions and then, the relationship between these operating points and the PSS parameters is learned by the ANFIS. The ANFIS optimally selectes the classical PSS parameters based on machine loading conditions. The proposed stabilizer has been tested by performing nonlinear simulations using a synchronous machine-infinite bus model. The results show the robustness and the capability of the stabilizer to enhance system damping over a wide range of operating conditions and system parameter variations.

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Section: Introductionmentioning

confidence: 99%

Adaptive Power System Stabilizer Using ANFIS and Genetic Algorithms

Fraile-Ardanuy

Zufiria²

2005

Computational Intelligence and Bioinspired Systems

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“…In [5], the work provided an analysis of the performance of PSS under various system conditions and operating loads, and presented two approaches for the tuning of PSS parameters and investigated how PSS tuning and the dynamic performances of the system were affected by some factors. A fuzzy logic-based PSS with learning ability was proposed in [6], where the PSS employed a multilayer adaptive neural network, which was trained directly from the input and the output of the generating unit. The algorithm which combined the positive features of artificial neural networks and fuzzy logic control schemes provided good damping of power systems over a wide range of operating conditions and improved the dynamic performance of the system.…”

Section: Introductionmentioning

confidence: 99%

Using Bacterial Foraging Algorithm to Design Optimal Power System Stabilizer and Comparisons with Genetic Algorithm and Particle Swarm Optimization

Alomoush¹

2017

IJPER

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Abstract. The Bacterial Foraging (BF) is a global optimization algorithm that imitates the foraging behavior of Escherichia coli (E. coli) bacteria. Foraging habit of individuals and groups of E. coli bacteria for nutrients is modeled as a distributed optimization process. This paper applies this powerful algorithm to design an optimal controller for a single-machine-infinite-bus (SMIB) system equipped with a power system stabilizer (PSS). The main target of the optimal design is to improve the dynamic and steady-state responses when the system is subjected to sever typical disturbances. The BF-based design of the PSS will be also compared with the designs obtained by the two commonly used global optimizers; the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Simulation results presented in the paper indicate robustness and superiority of the proposed BF algorithm to solve the design problem, where the optimal BF-based controller can significantly stabilize the system and efficiently dampen oscillations under disturbances. Results also disclose that BF has the capability to offer a much better-quality global solution with better convergence characteristics than GA and PSO. Results show that the BF-based controller has better impact on system performance in terms of damping the electromechanical oscillations of the rotor angle, rotational speed, electromagnetic torque, and terminal voltage under various disturbance conditions and its damping speed is considerably faster. The significant improvement associated with the BFbased PSS is due to the fact that the BF algorithm leads to a better global solution of the optimization problem under study.

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“…Jang [6] has presented the theory of Neuro-Fuzzy systems (NFSs). Hariri and Malik [9] have presented a FLPSS with learning ability. They have arbitrarily chosen seven bell-shaped linguistic variables for each of the two inputs without exploring the possibility of achieving the desired performance with a smaller number of linguistic variables.…”

Section: Introductionmentioning

confidence: 99%

“…Recently Neuro-Fuzzy controllers, also referred to as adaptive network fuzzy inference system (ANFIS) based controllers [6,7,9], have been proposed to over come the above problems. Jang [6] has presented the theory of Neuro-Fuzzy systems (NFSs).…”

Section: Introductionmentioning

confidence: 99%