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

In this paper, a high-performance optimal fractional emotional intelligent controller is proposed for an Automatic Voltage Regulator (AVR) in a power system using the Cuckoo optimization algorithm (COA). 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 upon brain emotional learning, which is a self-tuning controller so-called brain emotional learning-based intelligent controller (BELBIC), and is based on the sensory inputs and emotional cues. The major contribution of this paper is the use of the merits of the fractional order PID (FOPID) controllers; an FOPID controller is employed to formulate the stimulant input (SI) signal. This is a distinct advantage over the papers published in the literature, in which a PID controller has been reported to be used to generate the SI signal. 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-consumption. In addition, in order to enhance the performance of the proposed controller, its parameters are tuned by COA. COA is a novel advanced optimization algorithm proved to have a high efficiency. In order to design a BELBIC controller for an AVR system, a multi-objective optimization problem including overshoot, settling time, rise time, and steady-state error is formulated. The simulation studies confirm that the proposed controller, compared to the classical PID and FOPID controllers introduced in the literature, shows a superior performance regarding the model uncertainties. Having applied the proposed controller, the rise time and the settling time were found to be improved by 47% and 57%, respectively.