基于ANN模型的在线电压稳定裕度评估

文章将ANN网络模型引入电压稳定裕度的评估体系中,采用实时的相量测量单元(PMU)获得了电压幅值和相位角,使电压稳定裕度就可以及时地被表示出来。进一步采用必要的的控制手段来控制电力系统可以有效的提高速率。最后在英格兰39节点测试系统上进行了验证。证明了ANN网络模型可以很好地分析正常状态系统和故障状态系统,人工神经网络模型在计算电力系统电压稳定裕度具有可行性。

Online evaluating of Voltage Stability Margin Using an Artificial Neural Network

In this paper, an Artificial Neural Network (ANN)based method is developed for quickly estimating the long-term voltage stability margin. The investigation presented in the paper showed that node voltage magnitudes and the phase angles are the best predictors of voltage stability margin. If the voltage magnitudes and phase angles are obtained in real-time from phasor measurement units (PMUs) using the proposed method, the voltage stability margin can be estimated in real time and used for initiating stability control actions. Finally, a suboptimal approach to determine the best locations for PMUs is presented. Feasibility is proved in the calculation of the power system voltage stability margin using ANN model.