Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance 2013
DOI: 10.4018/978-1-4666-2086-5.ch012
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ANN-Based Self-Tuning Frequency Control Design for an Isolated Microgrid

Abstract: The increasing need for electrical energy, limited fossil fuel reserves, and the increasing concerns with environmental issues call for fast development in the area of distributed generations (DGs) and renewable energy sources (RESs). A Microgrid (MG) as one of the newest concepts in the power systems consists of several DGs and RESs that provides electrical and heat power for local loads. Increasing in number of MGs and nonlinearity/complexity due to entry of MGs to the power systems, classical and nonflexibl… Show more

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Cited by 17 publications
(8 citation statements)
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“…To meet the operational challenges in MG frequency control, several authors proposed various intelligent techniques to optimize the PID controller parameters according to MG operating conditions. In, [19][20][21][22][23][24][25][26][27][28] the authors proposed various swarm-intelligence methods for LFC of MG. Das et al 19 proposed genetic algorithm (GA) based PID controller, Srinivasarathnam et al 20 proposed grey wolf optimization (GWO) based PID controller, El-Fergany and El-Hameed 21 proposed social spider optimization (SSO) based PID controller, Shankar and Mukherjee 22 proposed harmony search algorithm based PID controller, Ray and Mohanty 23 proposed firefly algorithm based PID controller, and Shankar et al 24 proposed fruit fly algorithm-based PID controller for LFC of MG. However, most of the performance of the swarm-intelligence techniques relies on their algorithm-specific parameters, and improper selection of these parameters may lead the solution toward the local minima.…”
Section: Related Work and Key Gapsmentioning
confidence: 99%
See 3 more Smart Citations
“…To meet the operational challenges in MG frequency control, several authors proposed various intelligent techniques to optimize the PID controller parameters according to MG operating conditions. In, [19][20][21][22][23][24][25][26][27][28] the authors proposed various swarm-intelligence methods for LFC of MG. Das et al 19 proposed genetic algorithm (GA) based PID controller, Srinivasarathnam et al 20 proposed grey wolf optimization (GWO) based PID controller, El-Fergany and El-Hameed 21 proposed social spider optimization (SSO) based PID controller, Shankar and Mukherjee 22 proposed harmony search algorithm based PID controller, Ray and Mohanty 23 proposed firefly algorithm based PID controller, and Shankar et al 24 proposed fruit fly algorithm-based PID controller for LFC of MG. However, most of the performance of the swarm-intelligence techniques relies on their algorithm-specific parameters, and improper selection of these parameters may lead the solution toward the local minima.…”
Section: Related Work and Key Gapsmentioning
confidence: 99%
“…25 For further improvements in frequency control, several authors proposed neural network-based schemes in MG frequency control. 26,27 Bevrani et al 26 proposed a generalized artificial neural network (ANN) based model for LFC of an MG. Safari et al 27 proposed PSO optimized ANN controller for LFC of an MG. However, a general limitation with these techniques is that it operates the system as a black box and analyzes it functionally.…”
Section: Related Work and Key Gapsmentioning
confidence: 99%
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“…In (Bevrani, 2009) about the system frequency and different control methods, some information have been comprehensively presented. In the context of intelligent system frequency control several works have been so far reported as (H. Bevrani, Habibi, & Shokoohi, 2013). In this study, to find an optimal performance for the PI controller, Artificial Neural Networks (ANNs) were used as a supervisor unit to online tuning of the controller parameters.…”
Section: Literature Reviewmentioning
confidence: 99%