An Efficient Optimal Fractional Emotional Intelligent Controller for an AVR System in Power Systems

Journal of Artificial Intelligence and Data Mining

M. Moradi Zirkohi¹ 

[1] Department of Electrical Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran.;

关键词: Brain emotional learning based intelligent controller;    Cuckoo optimization algorithm;    fractional order PID;    Automatic Voltage Regulator;   

DOI  :  10.22044/jadm.2018.6797.1798

来源: DOAJ

【 摘 要 】

In this paper, a high-performance optimal fractional emotional intelligent controller for an Automatic Voltage Regulator (AVR) in power system using Cuckoo optimization algorithm (COA) is proposed. AVR is the main controller within the excitation system that preserves the terminal voltage of a synchronous generator at a specified level. The proposed control strategy is based on brain emotional learning, which is a self-tuning controller so-called brain emotional learning based intelligent controller (BELBIC) and is based on sensory inputs and emotional cues. The major contribution of the paper is that to use the merits of fractional order PID (FOPID) controllers, a FOPID controller is employed to formulate stimulant input (SI) signal. This is a distinct advantage over published papers in the literature that a PID controller used to generate SI. Furthermore, another remarkable feature of the proposed approach is that it is a model-free controller. The proposed control strategy can be a promising controller in terms of simplicity of design, ease of implementation and less time-consuming. In addition, in order to enhance the performance of the proposed controller, its parameters are tuned by COA. In order to design BELBIC controller for AVR system a multi-objective optimization problem including overshoot, settling time, rise time and steady-state error is formulated. Simulation studies confirm that the proposed controller compared to classical PID and FOPID controllers introduced in the literature shows superior performance regarding model uncertainties. Having applied the proposed controller, the rise time and settling time are improved 47% and 57%, respectively.

【 授权许可】

Unknown