A comparison of MLP, RNN and ESN in determining harmonic contributions from nonlinear loads

With the wide use of power electronics devices, harmonic currents are being injected into the power system, known as “harmonic pollution”. Although IEEE standards [1][2] have required the utilities and customers to limit the amount of harmonic current and voltage, the practical evaluation is complicated, as it is difficult to separate the contributions from the utilities and customers. A neural-network-based harmonic current prediction scheme was previously proposed by the authors to estimate the true harmonic current attributed to the nonlinearity of the load, instead of the distorted power supply. To test the feasibility of different types of neural networks in this application, this paper compares the performances and computational effort of three types of neural networks: Multilayer perceptron networks (MLP), simple recurrent network (RNN) and echo state network (ESN).