Maximizing Efficiency through Transformer Parameter Estimation Using the Corona Herd Immunity Algorithm: An Experimental Approach

Abstract

This paper enhances transformer efficiency by accurately estimating equivalent circuit parameters using two nature-inspired optimization algorithms: Corona Herd Immunity Optimization (CHIO) and the Human Felicity Algorithm (HFA). Accurate parameter estimation is essential for improving transformer performance and enabling condition-based monitoring. CHIO draws inspiration from the concept of herd immunity, particularly as applied during the COVID-19 pandemic, while HFA is modeled on the human drive for happiness and well-being. The proposed methods are tested on three transformer types: a 1 kVA, 240/100V power transformer; a 15 kVA, 2400/240V distribution transformer; and a 66 kVA, 415/415V isolation transformer. Experimental validation is conducted at Neo Teletronix Pvt. Ltd., where power and isolation transformers are analyzed. Optimizing these parameters improves operational reliability, reduces energy losses, and extends transformer lifespan—key benefits for grid integration and industrial power systems. The optimized parameters also enhance predictive maintenance and fault detection, minimizing downtime. Results obtained using CHIO are benchmarked against experimental data and compared with established algorithms like Particle Swarm Optimization (PSO) and the Gravitational Search Algorithm (GSA), showing that CHIO consistently delivers superior performance in transformer efficiency optimization.