Control of the Automatic Voltage Regulator System with a Novel Stability-based Artificial Intelligence Method

Abstract

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.

Design of a Novel PID Controller Based on Machine Learning Algorithm for a Micro-Thermoelectric Cooler of the Polymerase Chain Reaction Device

“…We see that one of the roots of 𝛿 𝑖 (𝑧) is 𝑧 0 = 0. Other roots are difficult to find, because (28) needs to be solved analytically. In this case, we can plot (28) and examine the result graphically.…”

“…Other roots are difficult to find, because (28) needs to be solved analytically. In this case, we can plot (28) and examine the result graphically. The plot 𝑘𝑘 𝑝 + cos(𝑧)/sin(𝑧) is intersected by the 𝑇 𝐿 𝑧 line.…”

“…On the other hand, a new goal function and modified Aquila optimizer algorithm were developed to construct a PIDD 2 controlled automatic voltage regulator system effectively [27]. Tabakh and Tiryaki proposed an artificial intelligence method for predicting the optimum parameters of proportional-integral-derivative controller in an automatic voltage regulator system [28]. Snásel et al proposed an integrated optimization technique for power system stabilization that combines the weighted mean of vectors optimizer with chaotic-orthogonal-based learning and basic Gaussian strategies for a single-machine infinite-bus system in the power system stabilizer model [29].…”