Identification of harmonic loads using fast fourier transform and radial basis Function Neural Network

Harmonic distortion is one of power quality problems in power system which it could affect equipment malfunction and many other problems. Therefore harmonic distortion load identification becomes important concern in the power system. In this paper, proposes a Radial Basis Function (RBF) Neural Network to identification of harmonic load. A low cost microcontroller gathers harmonic load data with Fast Fourier Transform (FFT) method as analysis of current waveform in the loads. RBF is implemented with several of the training and test data from combination of 15 different types of load to validate the accuracy and efficiency of the proposed algorithm. The results demonstrate that the proposed method has high accuracy to determine loads based on their harmonic signature.