The objectives of the automatic generation control (AGC) are attained via secondary control mechanism with optimal controller gains. The best solutions of these gains are identified by population search-based algorithms with the help of performance metrices. The controller design issues of AGC treat these performance metrics as a cost function. This paper introduces a new performance measure implemented with dominated samples information extracted from the frequency and tie line power changes. This new integral square error (NISE) is incorporated in fitness function and tested on isolated and multi area interconnected power system scenarios with satin bowerbird optimizer to tune the controller gains. Additionally, integral-tilt proportional-fractional order derivative controller is proposed in this paper and comparisons are made with popular controllers to show the merits in terms of AGC specifications. Optimal gain parameters of all controllers are achieved with existing integral square error and the proposed NISE cost functions and extensive simulations are carried out to illustrate the merits of the work. Studies are also expanded to nonlinear systems in order to evaluate the adaptability, performance improvement, and effectiveness of the suggested cost function, controller, and tuning method.
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