Coordinated parameter design of power system stabilizers and static synchronous compensator using gradual hybrid differential evaluation

Wang, Sheng-Kuan

doi:10.1016/j.ijepes.2016.02.016

Cited by 22 publications

(8 citation statements)

References 20 publications

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“…In this subsection, the optimal setting for the synchronous machine controllers is considered just based on the stability index (SI) that has been used in many research works and the GA design procedure is applied for only the SI term of the objective function in Equation 11. The optimal redesign parameters have been listed in Table .…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Evaluation of effectiveness of second-order modal interaction in design procedure of synchronous machines controllers

Rahmatian

Seyedtabaii

Maghouli

2018

Int Trans Electr Energ Syst

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Summary Conventional power system stabilizer (PSS) design may fail in preserving under stress system stability; therefore, PSS redesign using a linearized system stability index (SI) is often prescribed. In this paper, an efficient and more robust algorithm with new ideas is proposed where a twin of SI and a new nonlinearity index form the design objective, a nonlinear participation factor identifies the most involved generators which then both exciters and PSSs of them are tuned. The new design while reduces the adverse effect of modal interaction, significantly improves the dynamic stability with respect to SI design. The method is applied to 2 test systems, and it is shown that the SINI design secures stability in cases that SI design fails. The efficiency and validity of the method are confirmed by comparing the results using both DIgSILENT and MATLAB software.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Evaluation of effectiveness of second-order modal interaction in design procedure of synchronous machines controllers

Rahmatian

Seyedtabaii

Maghouli

2018

Int Trans Electr Energ Syst

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“…Then, each vector in the initial population are recombined with the mutant vector to generate a trial population, Pu,g, with Np trial vector, ui,g. This process can be captured through mathematical equation as described in ( 13) and ( 14) [20].…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

Design feedback controller of six pulse three phase rectifier based on differential evolution algorithm

Setiadi

Swandaru

Nugroho

2021

IJEECS

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<span id="docs-internal-guid-b9128c38-7fff-a778-f2e1-7959fee15cb3"><span>The application of power electronics devices is increasing significantly in this past few year. This condition could happen due to the development of semiconductor technology. The application of power electronics is varied from power plant application to vehicles application. One of the power electronics devices that becoming popular is rectifier. Rectifier can be used to transform AC electricity to DC. This paper presents fully controlled six pulse three phase rectifier which designed to stabilize the DC voltage output on a certain loads variation. PID controller is used as the close loop controller system of the rectifier. To get optimal coordination differential evolution algorithm is used to tune the parameter of PID controller. simulation is carried out in MATLAB/SIMULINK environment. From the simulation results, it is found that the performance of six pulse three phase rectifier can be enhanced significantly by using PID controller based on differential evolution algorithm.</span></span>

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“…Certainly, genetic algorithms have been one the most used heuristics to solve the tuning of the PSS. However, there are diverse optimization techniques such as the Tabu search [11], the Firefly algorithm [12,13], the Seeker Optimization algorithm (SOA) [14], the Simulated Annealing algorithm (SA) [15], the Artificial Bee Colony (ABC) [16], the Genetic Programming algorithm [17], the Collective Decision Optimization algorithm (CDO) [18], the Cuckoo search algorithm [19], or the Gradual Hybrid Differential algorithm (GHDE) [20].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Particle Swarm Optimization (PSO) Algorithm for Tuning of Power System Stabilizers in Multimachine Electric Power Systems

et al. 2020

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The application of artificial intelligence-based techniques has covered a wide range of applications related to electric power systems (EPS). Particularly, a metaheuristic technique known as Particle Swarm Optimization (PSO) has been chosen for the tuning of parameters for Power System Stabilizers (PSS) with success for relatively small systems. This article proposes a tuning methodology for PSSs based on the use of PSO that works for systems with ten or even more machines. Our new methodology was implemented using the source language of the commercial simulation software DigSilent PowerFactory. Therefore, it can be translated into current practice directly. Our methodology was applied to different test systems showing the effectiveness and potential of the proposed technique.

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Coordinated parameter design of power system stabilizers and static synchronous compensator using gradual hybrid differential evaluation

Cited by 22 publications

References 20 publications

Evaluation of effectiveness of second-order modal interaction in design procedure of synchronous machines controllers

Evaluation of effectiveness of second-order modal interaction in design procedure of synchronous machines controllers

Design feedback controller of six pulse three phase rectifier based on differential evolution algorithm

Implementation of Particle Swarm Optimization (PSO) Algorithm for Tuning of Power System Stabilizers in Multimachine Electric Power Systems

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