ANN及FFT技术用于电力系统预想事故分析

本文开发了一种基于人工神经元网络和快速付里叶变换技术进行预想事故快速选的方法。利用快速解耦潮流计算的迭代一次法分别构造了反映预想事故严重程度的有功性能指标ＰＩｐ和无功性能指标ＰＩｖ，同是还构造了一个多层感知型神经元网络并用ＢＰ算法加以训练。

APPLICATION OF ANN AND FFT TECHNIQUES TO CONTINGENCY ANALYSIS

A new approach based on artificial neural network (ANN) combined with fast Fourier transform (FFT) techniques is developed for single-line contingency screening in steady-state security analysis. The results obtained from 1P-1Q iteration of the fast decoupled load flow calculation are adopted to construct two kinds of performance indices PIp (active performance index) and PIv (reactive performance index) which reflect the severe degrees of certain contingencies. A multi-layered ANN is trained to calculate the performance indices using error back propagation algorithm. FFT for inputs of the ANN is used to improve and speed up the training procedure. The effectiveness of the proposed method is demonstrated by contingency screening on IEEE test systems.The calculating accuracy,high capturing rate and analysis speed for contingency screening are obtained using the proposed method.