Load frequency control of large scale power system using quasi-oppositional grey wolf optimization algorithm

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• 作者：Dipayan Guha^a ; ^{guha.dipayan@yahoo.com" class="auth_mail" title="E-mail the corresponding author} ; Provas Kumar Roy^b ; Subrata Banerjee^c
• 关键词：Load frequency control ; Grey wolf optimization ; Oppositional based learning ; Quasi-oppositional based learning ; Sensitivity analysis
• 刊名：Engineering Science and Technology,an International Journal
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：19
• 期：4
• 页码：1693-1713
• 全文大小：1568 K

文摘

This article presents a newly developed, novel and efficient optimization technique called quasi-oppositional grey wolf optimization algorithm (QOGWO) for the first time to solve load frequency control problem (LFC) of a power system. Grey wolf optimization (GWO) is a recently developed meta-heuristic optimization technique based on the effect of leadership hierarchy and hunting mechanism of wolves in nature. Two widely employed test systems; viz. two-area hydro-thermal and four-area hydro-thermal power plant, are considered to establish the effectiveness of the proposed QOGWO algorithm. Optimal proportional-integral-derivative controller (PID) is designed for each area separately using proposed algorithm employing integral time absolute error (ITAE) based fitness function. The validity of proposed QOGWO method is tangibly verified by comparing its simulation results with those of GWO and other approaches available in the literature. Time domain simulation results confirm the potentiality and efficacy of the proposed QOGWO method over other intelligent methods like fuzzy logic, artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) controller. Finally, sensitivity analysis is performed to show the robustness of the designed controller under different uncertainty conditions.