Seeker optimization algorithm for load-tracking performance of an autonomous power system

Abstract This paper focuses on the application of a novel heuristic population-based seeker optimization algorithm (SOA) for the optimization of the different tunable parameters of an autonomous power system model. In the SOA, the act of human searching capability and understanding are exploited for the purpose of optimization. In this algorithm, the search direction is based on empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple Fuzzy rule. The SOA yields off-line, nominal system parameters. This paper also presents the design and the performance analysis of a Sugeno Fuzzy logic (SFL) controller for the studied model which tracks the deviation of terminal voltage in real-time for any sort of input perturbation. Genetic algorithm is taken for the sake of comparison. Time-domain simulation of the investigated power system model reveals that the proposed SOA–SFL yields on-line, off-nominal controller parameters, resulting in on-line terminal voltage response.