Improved sizing of a damped passive filter for harmonic attenuation using a teaching-learning-based optimization algorithm for an industrial site

Abstract

Based on measurements of current harmonics that adversely affect power quality in an industrial setting, and to ensure compliance with permissible levels according to IEEE 519, this paper aims to compare the effectiveness of conventional and optimized sizing of damped passive filter components to reduce total harmonic distortion (THD) and improve the power factor (PF). The optimization of passive filter element values relies on a powerful metaheuristic algorithm called teaching-learning-based optimization (TLBO). Simulation results show that the THD of current and voltage reach values of 17.34% and 8.61%, respectively, and decrease to very low values after implementation of the damped passive filter in the optimal case using the TLBO algorithm. The results also demonstrated a proportional relationship between the harmonic currents in the load and the overall increase in losses in the transformer. Optimizing the damped passive filter reduced harmonic distortion, improved the PF, and limited energy losses, thus confirming the superior ability of this algorithm to identify a very high efficiency solution.