This paper proposes a novel Stability-Based Artificial Intelligence Method for predicting the optimum parameters of the proportional-integral-derivative controller in an automatic voltage regulator system. To implement the stability-based artificial intelligence method, first, parameters which are of great importance for the control of the system are applied to the system randomly, data are collected, and then artificial intelligence studies are carried out. The suggested approach has been applied to the system and compared with other control methods in the literature, namely the improved Kidney Inspired algorithm, Jaya algorithm, Tree Seed algorithm, Water Wave Optimization, and Biography-Based Optimization to test the robustness of the new method. The numerical results indicate that the proposed method significantly outperforms all other methods.

No abstract

In power systems, the constant frequency, constant voltage, and the power output are desired and determine the quality of the generated electrical energy. Therefore, frequency control is crucial in power systems. The parameters of conventional controllers used in power generation plants are determined according to the system's characteristics at the stage of installation, they cannot adapt to the changing system dynamics as the lifespan of power plants increases. Thus, studies on the automatic adaptation of controller parameters to the continuously changing system dynamics are needed. In this study, conventional PI and PID controllers applied to the power system for frequency control of a hydroelectric power plant were examined comparatively with Fuzzy Gain Scheduled PI (FGPI) controller and Adaptive Neuro-Fuzzy Inference Systembased PI (ANFIS-PI) and PID (ANFIS-PID) controllers in the simulation environment. The obtained results demonstrated that Adaptive Neuro-Fuzzy Inference System-based controllers were quite successful compared to the others.

No abstract