A Hybrid SCA-Teaching Learning Optimized Adaptive Interval Type-2 Fuzzy PID Controlled HSAPF with PQ Enrichment

This article presents a hybrid sine cosine algorithm with a teaching learning-based optimization (hSCATLBO) method to optimize the parameters of an Interval Type-2 Fuzzy Proportional Integral Derivative (IT2FPID) controlled Hybrid Shunt Active Power Filter (HSAPF) for reactive power and harmonic compensation. In this study, the optimal parameters for the IT2FPID controller are determined using a novel hSCATLBO method, focusing on Integral Time Absolute Error (ITAE). Unlike traditional (id-iq) or (p-q) techniques, the compensation strategy for HSAPF, only requires the current from the source side. The proposed system has been tested under extreme nonlinear load conditions, and various parameters such as Reactive Power (Q), Input Power Factor (IPF), and Total Harmonic Distortion (THD) have been assessed to liken the performance of the anticipated controller. The proposed hSCATLBO-IT2FPID-HSAPF has been experimentally verified using dSPACE. The comparative analysis reveals that the hSCATLBO-optimized IT2FPID-HSAPF provides superior harmonic compensation when compared with hSCATLBO-optimized Type-1 Fuzzy Proportional Integral Derivative (T1FPID) controller-based HSAPF under different operating conditions.