摘要
电压崩溃临近指标能够有效、快速地对电力系统电压安全进行评估。为此, 提出了一种电压崩溃临近指标的小波神经元网络模型。这个模型以非线性小波基为神经元函数, 通过优化伸缩因子和平移因子确定对应各神经元的小波基函数, 从而合成小波神经元网络, 达到全局最优拟和效果。经过训练的小波神经元网络能在线计算电压崩溃临近指标, 并且具有快速、准确等优点。文中对该模型与人工神经元网络模型进行了比较, 结果证明, 利用小波神经元网络模型进行电压崩溃临近指标预测比利用人工神经元网络模型具有更高的拟合精度, 计算速度更快。仿真结果表明, 该方法能有效地对电力系统电压崩溃做出早期预测, 是一种对系统电压安全进行快速、实时评估的有效工具。
Voltage collapse proximity indicator can be used to evaluate voltage stability effectively and quickly. This paper proposes a wavelet neural network model for estimating voltage collapse proximity indicator. The nervous cells function is the basis of nonlinear wavelets. A wavelet network is composed by the wavelet basis function and computed by an expansion and contraction factor and a translation factor to reach the global best approximation effect. By using the wavelet neural network after it has been trained, we can calculate on-line voltage collapse proximity indicator. It can be seen from the simulation results that this method is useful for early prediction of the voltage collapse phenomenon in power systems, and could be a fast, real-time tool for voltage security assessment.
出处
《现代电力》
2005年第2期12-15,共4页
Modern Electric Power
关键词
电力系统
电压安全评估
稳定控制
小波变换
神经元网络
power system
voltage security assessment
stability control
wavelet transform
neural network
