Power System Harmonics Estimation using Hybrid Archimedes Optimization Algorithm-based Least Square Method

Estimating harmonics of a power system with different optimization techniques has emerged as a potential field of research in recent times. The amount of necessary information in an unknown signal, polluted with noise can be effectively determined by utilizing stochastic optimization techniques. In this context, this study proposes a hybridized algorithm termed as Archimedes optimization algorithm-based least square (AOA-LS) technique for estimation of harmonics of a power system. The proposed optimization algorithm contributes in predicting the phases of the harmonic signal and conventional least-square (LS) method determines the amplitudes. The simulation was carried out for a voltage wave obtained from a standard testing module's load bus terminal under two noisy conditions: Uniform noise and Gaussian noise. Furthermore, for each noisy situation, the signal-to-noise ratios (SNR) are set to 0 dB, 10 dB, 20 dB, and 40 dB, respectively. For the purpose of comparative analysis, performance of the proposed AOA-LS scheme is evaluated and compared with three of the other techniques known as Firefly algorithm-based LS (FA-LS), Particle swarm optimization with passive congregation based LS (PSOPC-LS), and Artificial bee colony based LS (ABC-LS). According to the findings, the proposed algorithm surpasses all the algorithms in terms of estimation accuracy and computational time.