文献类型：会议论文

英文题名：A neural network model for power system inter-harmonics estimation

作者：Xiao, Xiu-Chun[1,2]; Jiang, Xiao-Hua[2]; Xie, Shi-Yi[1]; Lu, Xiao-Min[2]; Zhang, Yu-Nong[2]

机构：[1] College of Information, Guangdong Ocean University, Zhanjiang, 524025, China; [2] School of Information Science and Technology, Sun Yat-sen University, Guangzhou, 510275, China

会议论文集：Proceedings 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010

会议日期：23 September 2010 through 26 September 2010

会议地点：Changsha

语种：英文

外文关键词：Nonlinear programming - Gaussian noise (electronic) - Harmonic analysis

外文摘要：A neural network model is proposed to estimate the parameters of inter-harmonics. As to this specific neural network, an adaptive learning algorithm based on improved Levenberg-Marquardt algorithm is derived. Because of the complete match between the neural network and the inter-harmonic model, the presented algorithm can effectively improve the precision in the process of inter-harmonics estimation and meanwhile, accelerate its convergence. Simulation results have testified its performance with a variety of generated inter-harmonics. If noise is not taken into consideration, the introduced algorithm can accurately measure the power system inter-harmonics. Furthermore, when the signal is polluted with Gaussian noise, our method can still maintain relatively high level of accuracy. ? 2010 IEEE.