Rank-Sum-Weight Method Based Systematic Determination of Weights for Controller Tuning for Automatic Generation Control

Abstract

The design and performance evaluation of a grey wolf optimizer (GWO) aided rank-sum-weight method based proportional-integral-derivative regulator with derivative filter (PIDn) for automatic generation control (AGC) of two-area interconnected power systems are presented in this research. The derivative gain filter is used to lessen the impacts of noise in the input signal. Sub-objectives based on integral of time multiplied square error (ITSE) of frequency deviations, tie-line power deviation, and area-control errors (ACEs) are used to formulate the objective function for adjusting regulator settings. A single overall objective function is formed by combining these sub-objectives. ITSEs of two areas, ITSEs of tie-line power deviation, and ITSEs of ACEs of two areas comprise up the overall objective function. In the control design, the weights in the overall objective function are used to evaluate relative significance of each sub-objective. In contrast to previous techniques, where weights are either considered equal by ignoring the relative relevance of sub-objectives or selected randomly, the weights in this article are obtained using the rank-sum-weight method systematically. Using the GWO algorithm, the overall objective function is minimized. For six different circumstances including different load disturbances in interconnected areas, the effectiveness of the proposed GWO aided rank-sum-weight method based controller is examined. The performance of the GWO-tuned controller is also compared to those of other controllers tuned using the differential evolution, elephant herding optimization, Nelder-Mead simplex, membrane computing, and Luus-Jaakola algorithms. Time domain specifications are tabulated for each of the six circumstances. The findings are also plotted to demonstrate the frequency and tie line power fluctuations. A statistical analysis is also performed in order to assess the overall efficacy of the suggested controller.