摘要
简述了电力系统相角的测量意义,总结了测量的传统方法及其分类;对人工智能算法中的人工神经网络进行了研究,针对其实际测量中所需面对的频率变动、谐波掺杂、噪声干扰等问题,结合人工神经网络具有非线性映射、鲁棒容错的特性,提出了基于BP神经网络的相角测量方法。用准确的波形―相角数据对网络进行离线训练,再用于实际波形在线测量出对应相角。对其进行了仿真计算,仿真结果误差较小,证明了方法的可行性。
Described the power system phase angle measuring significance and classified the traditional method of the angle measurement. Studied the artificial neural networks in the Artificial intelligence algorithms. Facing several problems such as：frequency changes, harmonic doping, noise, combined with artificial neural network＇s nonlinear mapping, robust fault -tolerant features, a phase angle measurement based on BP neural network was proposed. Trained the network with exact wave - angle data, and then used the network to measure the phase angle correspond to the actual waveform. And carried out the simulation, the small error proved the feasibility of the method.
出处
《电气自动化》
2012年第5期86-87,93,共3页
Electrical Automation
关键词
相角测量
神经网络
工频变化
谐波
phase angle measurement
neural networks
frequency change
harmonic
