A NEW METHOD FOR NON-INTEGER HARMONICS MEASUREMENT BASED ON FFT ALGORITHM AND NEURAL NETWORK

By using an artificial neural network (ANN) model, high measurement accuracy of integer harmonics can be obtained. The invalidation of an ANN model being used in non-integer harmonics analysis is pointed out. In order to detect non-integer harmonics precisely, an improved ANN model is presented in this paper. By combining the windowed fast Fourier transform (FFT) algorithm with the improved ANN model, we provide a new method for measurement of non-integer harmonics. The following approach is adopted. Firstly, the Hanning-windowed FFT algorithm processes the sampled signal. By this time, the number of harmonics and the orders of harmonics are obtained. Secondly, choose the number of neural nodes according to the number of harmonics. Thirdly, choose the initial values of orders of harmonics according to the result obtained from the Hanning-windowed FFT algorithm. Finally, by using the improved ANN model, non-integer harmonics can be detected precisely. The simulation results show that close non-integer harmonics can be separated from a signal with higher accuracy by using the method presented in the paper. It provides a reliable basis for managing harmonics.