丁惜瀛,翟晓寒,姚润宇,李闯,董鹤楠.基于径向基神经网络的UPQC并联侧谐波电流预测与补偿[J].电测与仪表,2021,58(8):139-145. dingxiying,zhaixiaohan,yaorunyu,lichuang,donghenan.Prediction and Compensation of Harmonic Current in Parallel Side of Unified Power Quality Controller Based on Radial Basis Neural Network[J].Electrical Measurement & Instrumentation,2021,58(8):139-145.
基于径向基神经网络的UPQC并联侧谐波电流预测与补偿
Prediction and Compensation of Harmonic Current in Parallel Side of Unified Power Quality Controller Based on Radial Basis Neural Network
The accuracy of harmonic current compensation on UPQC parallel side directly affects the quality of power supply. The traditional ip-iq method for extracting instruction current signals uses low-pass filters and coordinate transformation algorithms, which make the low-frequency harmonics unable to be filtered and there is a beat delay in compensation. Aiming at the problem of accuracy of UPQC instruction current signal extraction, a harmonic current extraction method based on RBF neural network and ip-iq method is proposed, which increases low frequency harmonic extraction, and the radial basis neural network is trained to fit the time-lag error in the ip-iq compensation process, generates harmonic current compensation module to compensate for the ip-iq method harmonic current extraction deviation. The current compensation effect of the traditional ip-iq method and the proposed instruction current extraction scheme is analyzed. After improvement, the harmonic content of the grid current decreases from 6.64% to 4.25% of the ip-iq method, which verifies the effectiveness of the proposed instruction current extraction strategy.
