Fuzzy logic power system stabilizer based on genetically optimized adaptive network

Hariri, A.; Malik, O.P.

doi:10.1016/s0165-0114(98)00200-0

Cited by 11 publications

(2 citation statements)

References 9 publications

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“…Mahmoud Najafi was at the Control and Instrument Department of National Iranian Oil Company (IOOC ) and now is the ICT manager at IOOC (e-mail: mnajafi@iooc.co.ir ) Artificial neural networks have been successfully applied to the LFC problem with rather promising results [9,10]. Moreover, fuzzy logic control techniques for the LFC problem are mostly based on fuzzy gain scheduling of proportional integral (PI) controller parameters [11][12][13]. Recently, applications of fuzzy logic theory to the engineering issues have drawn tremendous attention from researchers [14].…”

Section: Introductionmentioning

confidence: 99%

Simulated Annealing Optimization Method on Decentralized Fuzzy Controller of Large Scale Power Systems

Najafi¹

2012

IJCEE

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Abstract-The centralized control of large scale systems is usually infeasible due to their high dimensionality, nonlinearity, coupling, time delay and etc. In order to overcome the mentioned difficulties, this paper presents an application of decentralized fuzzy logic controller (FLC) which is optimum by Simulated Annealing (SA) algorithm on two interconnected power area for the load frequency control as a case study of large scale systems. The simulation study is undertaken, based on MATLAB / SIMULINK to demonstrate and confirm the effectiveness of proposed method.Index Terms-Fuzzy logic control; simulated annealing optimization method, large scale systems; load frequency control.

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Section: Introductionmentioning

confidence: 99%

Simulated Annealing Optimization Method on Decentralized Fuzzy Controller of Large Scale Power Systems

Najafi¹

2012

IJCEE

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“…The fuzzy logic has been applied to many power system operational problems, including control applications using the selfadaptive and rule based approaches [15][16][17]. Some methods use fuzzy logic for expert system applications, such as load forecasting [18], fault diagnosis [19], and high voltage equipment insulation evaluation [20].…”

Section: Introductionmentioning

confidence: 99%

The uniform fuzzy index of power system security

Halilčević

Gubina

2009

Int Trans Elec Energy Syst

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SUMMARYThe paper presents a new approach to assessment of the power system security. Using fuzzy membership functions of power system elements, it continuously quantifies the security state of a power system, as opposed to conventional methods which use off-line screening for the most vulnerable system states. It introduces global fuzzy security index (GFSI) of the power system using system's variables characterized by fuzzy sets of a trapezoidal form. GFSI is calculated by multiplying the security membership grade (SMG) values of the power system's segments that exhibit the lowest SMG values. The power system security levels are traced continually through time using real data of power flow and state estimation calculations as well as measurements of variables inaccessible by the load flow analysis. The GFSI enables the system operator to identify the insecure state and then continuously monitor the distance of the power system's actual state and the insecure one. Application of the algorithm is illustrated using the data of Bosnian power system.

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Excitation Control in Power Systems

Malik

2017

Wiley Encyclopedia of Electrical and Electronics Engineering

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Excitation system, an integral part of the synchronous generator, performs control and protection functions that include control of voltage and reactive power. Power system stability can also be enhanced by supplying additional signal through the excitation system. This function is performed by the power system stabilizer (PSS) that generates a control signal introduced as a supplementary input into the excitation system. Due to the nonlinear characteristics, wide operating conditions, and unpredictability of perturbations in a power system, the conventional fixed parameterPSSgenerally cannot maintain the same quality of performance under all operating conditions. To improve power system performance and stability, various approaches have been proposed. An overview of the development and successful implementation of various approaches, including adaptive PSSs based on analytical and artificial intelligence techniques, is provided.

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Fuzzy logic power system stabilizer based on genetically optimized adaptive network

Cited by 11 publications

References 9 publications

Simulated Annealing Optimization Method on Decentralized Fuzzy Controller of Large Scale Power Systems

Simulated Annealing Optimization Method on Decentralized Fuzzy Controller of Large Scale Power Systems

The uniform fuzzy index of power system security

Excitation Control in Power Systems

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