基于复合神经网络的电力系统暂态稳定评估和裕度预测

Power System Transient Stability Assessment and Stability Margin Prediction Based on Compound Neural Network

提出一种基于复合神经网络的暂态稳定评估与故障临界切除时间(CCT)裕度预测新方法,它将概率神经网络(PNN)和径向基函数(RBF)网络组合使用,充分利用两者各自的优点,以提高暂态稳定评估能力和CCT裕度预测能力。该方法首先利用PNN进行暂态事故场景分类,分类时充分考虑了相邻故障样本类型重叠的影响;进一步采用RBF网络对分类结果进行裕度预测;最后,通过自检和校正以提高预测精度。利用New England 39节点系统,通过与反向传播(BP)神经网络、RBF神经网络等方法的比较,证明了本文方法的优越性。

A method based on compound artificial neural network （ANN） to predict critical clearing time （CCT） margin and to do transient stability assessment is derived in this paper. It consists of probabilistic neural network （PNN） and radial basic function （RBF） network to integrate their advantages. PNN network is first used to classify sampling data and it can consider the overlapping influence in adjacent samples in the classification. Then RBF network is used to forecast the CCT margin based on the classification results. A self-checking procedure and an amending procedure are added to improve the prediction accuracy. New England 39 bus system is used to do the validation. Numerical studies reveal that the proposed method is better than the traditional BP and RBF methods not only on predictive accuracy but also on calculation speed. So the given method is helpful to power system transient stability assessment.