A Novel Hybrid Harmonic Estimation Technique Based on Recursive Least Square and Artificial Protozoa Optimizer

Harmonic estimation in renewable energy integrated power systems is crucial for ensuring power quality and system reliability. This paper presents a novel hybrid harmonic estimation technique that combines the Recursive Least Squares (RLS) method with an Artificial Protozoa Optimizer (APO). The proposed method leverages the adaptive filtering capabilities of RLS and the robust optimization properties of APO to accurately estimate harmonic components under varying operating conditions. The APO mimics the foraging behaviour and adaptive movement of protozoa, enhancing the convergence speed and estimation accuracy of the hybrid technique. Simulation results demonstrate the effectiveness of the proposed method in dealing with the non-linarites and uncertainties inherent in renewable energy sources, outperforming traditional estimation methods. The hybrid approach shows significant improvements in terms of accuracy, convergence time, and robustness, making it a promising solution for real-time harmonic monitoring in modern power systems.