Undersampled on-line ANN-EKF based estimation of harmonics/interharmonics in power systems

This paper describes a new approach to estimate harmonics/interharmonics of power system voltages and currents based on a hybrid artificial neural network (ANN) and extended Kalman filter (EKF) structure. A very low sampling rate is used to implement the proposed ANN-EKF structure with the modest hardware demands. The ANN structure is used for spectral estimation, consumes a very low processing power, and needs a few samples per cycle for real-time implementation. An EKF performs a secondary process on the ANN fundamental frequency phasor estimation to recover it from the errors due to the ANN internal setting. The presented ANN-EKF structure is a very simple and intelligent module to be used by utilities for statistical assessment of harmonics/interharmonics pollution. Besides, it can be a part of utility practices to control, compress, and process large volume of information flowing into data centers through sensors to be placed across the future intelligent power grids. The performance of the proposed method is evaluated by simulations in MATLAB-Simulink environment and confirmed by experimental results.