Application of Hybrid Differential Evolution–Grey Wolf Optimization Algorithm for Automatic Generation Control of a Multi-Source Interconnected Power System Using Optimal Fuzzy–PID Controller

This paper deals with an optimal hybrid fuzzy-Proportional Integral Derivative (fuzzy-PID) controller optimized by hybrid differential evolution–Grey Wolf optimization algorithm for automatic generation control of an interconnected multi-source power system. Here a two area system is considered; each area is provided with three types of sources namely a thermal unit with reheat turbine, a hydro unit and a gas unit. The dynamic performance of the system is analyzed under two cases: with AC tie-line and with AC-DC tie-line. The efficiency and effectiveness of the proposed controller is substantiated equally in the two cases. The sturdiness of the system is proved by varying the values of the system parameters. The supremacy of the recommended work is additionally ascertained by comparison with the recently published results like differential evolution optimized PID Controller and hybrid Local Unimodal Sampling-Teaching Learning based Optimization (LUS-TLBO) optimized fuzzy-PID controller. The dynamic performance of the system is observed in terms of settling time, peak overshoot and peak undershoot. Finally the analysis is extended by applying the proposed control technique in two different models namely (i) A three area unequal thermal system considering proper generation rate constraints (GRC) and (ii) A three area hydro-thermal system with mechanical hydro governor. These test results reveal the adaptability of the proposed method in multi-area interconnected power system.     

Design and Implementation of Adaptive Neuro–Fuzzy Inference System Based Control Algorithm for Distribution Static Compensator

This article focuses on the design and implementation of a distribution static compensator using an adaptive neuro–fuzzy inference system based controller. The distribution static compensator is controlled to provide power quality improvement, such as power factor correction, harmonics compensation, load balancing, and voltage regulation. Active and reactive power fundamental components of load currents are extracted using d-q theory. A distribution static compensator is realized using a voltage source converter. Both simulation and experimental results prove the effectiveness of the control algorithm under non-linear loads. The adaptive neuro–fuzzy inference system based controller works satisfactorily for power factor correction and harmonics reduction under balanced as well as unbalanced load conditions. Test results clearly depict the dynamics of the performance of the system under steady state as well as dynamics under load change and load unbalancing.