A hybrid recursive least square pso based algorithm for harmonic estimation

The presence of harmonics shapes the performance of a power system. Hence harmonic estimation of paramount importance while considering a power system network. Harmonics is an important parameter for power system control and enhance power system relaying, power quality monitoring, operation and control of electrical equipments. The increase in nonlinear load and time varying device causes periodic distortion of voltage and current waveforms which is not desirable electrical network. Due to this nonlinear load or device, the voltage and current waveform contains sinusoidal component other than the fundamental frequency which is known as the harmonics. Some existing techniques of harmonics estimation are Least Square (LS), Least Mean Square (LMS),Recursive Least Square (RLS), Kalman Filtering (KF), Soft Computing Techniques such as Artificial neural networks (ANN),Least square algorithm, Recursive least square algorithm, Genetic algorithm(GA) ,Particle swarm optimization(PSO) ,Ant colony optimization, Bacterial foraging optimization(BFO), Gravitational search algorithm, Cooker search algorithm ,Water drop algorithm, Bat algorithm etc. Though LMS algorithm has low computational complexity and good tracking ability ,but it provides poor estimation performance due to its poor convergence rate as the adaptation step-size is fixed. In case of RLS suitable initial choice of covariance matrix and gain leading to faster convergence. The thesis also proposed a hybrid recurvive least square pso based algorithm for power system harmonics estimation. In this thesis, the proposed hybrid approaches topower system harmonics estimation first optimize the unknown parametersof the regressor of the input power system signal using Particle swarm optimization and then RLS are applied for achieving faster convergence in estimating harmonics of distorted signal