Antlion optimizer-ANFIS load frequency control for multi-interconnected plants comprising photovoltaic and wind turbine

被引：98

作者：

Fathy, Ahmed ^{[1
,2
]}

Kassem, Ahmed M. ^{[3
]}

机构：

[1] Jouf Univ, Fac Engn, Dept Elect Engn, Al Jouf, Saudi Arabia

[2] Zagazig Univ, Fac Engn, Elect Power & Machine Dept, Zagazig, Egypt

[3] Sohag Univ, Dept Elect Engn, Fac Engn, Sohag, Egypt

来源：

ISA TRANSACTIONS | 2019年 / 87卷

关键词：

Load frequency control; Renewable energy sources; Antlion optimizer; AUTOMATIC-GENERATION CONTROL; POWER-SYSTEM; ALGORITHM; MANAGEMENT; VOLTAGE; DESIGN; MPPT; LFC;

D O I：

10.1016/j.isatra.2018.11.035

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes optimal load frequency control (LFC) designed by Adaptive Neuro Fuzzy Inference System (ANFIS) trained via antlion optimizer (ALO) for multi-interconnected system comprising renewable energy sources (RESs). Two systems are modeled and investigated: the first one has two plants of grid connected photovoltaic (PV) system with maximum power point tracker (MPFT) and thermal plant while the second comprises four plants of thermal, wind turbine and grid connected PV systems. ALO is employed to get the optimal gains of Proportional-Integral (PI) controller such that the integral time absolute error (ITAE) of frequency and tie line power deviations is minimized. The input and output of the optimized PI controller are used to train the ANFIS-LFC with Gaussian surface membership functions. Different load disturbances are studied and the results are compared with other reported approaches. The obtained results confirmed the accuracy and reliability of the proposed approach in designing LFC for multi-interconnected power systems. (C) 2018 ISA. Published by Elsevier Ltd. All rights reserved.

引用

页码：282 / 296

页数：15

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