函数型连接神经网络偶发事件分类器设计

偶发事件分析是电力系统安全分析和可靠性计算中的关键问题,需要大量的计算时间.为了有效地减少大电力系统开断模拟的计算工作量,提出一种用于识别偶发事件的函数型连接神经网络(FLNN)分类模型和算法.为获取事故后的系统状态信息,用相对于故障前系统状态的变化量反映事件的行为特征,设计了一组行为指标(PI),构造出神经网络分类器.讨论了各种行为指标及其组合对分类器的不同影响,指出支路功率行为指标是较好的行为指标.通过对IEEE-RTS24试验系统进行的分类计算表明,该分类器不仅网络结构和学习算法简单,而且提高了计算速度和精度.

Design on Contingency Classifier of Functioned Link Neural Network

Contingency analysis is thekey computational issue in power system steady state security analysis and reliability calculations. This task requires a large amount of CPU time. In order to reduce effectively requirements of computationin outagesimulation ofbulk power system,a functioned link neural network (FLNN) classified model and algorithm employed to identify contingencies is presented. For the sake of gaining post-accident information of system states, a group of performance index (PI) is designed according to the performance characters relative to the changes of base case.Moreover, a neural network classifieris constructed. A varietyof the effects of PI and combinations of PI on the proposed classifieris discussed. That branch flow performance indices are better than the others is explanted. The resultsof classification by applied the FLNNclassifierto the IEEE-RTS24 show that it not only make network and algorithmsimpler, but also improvethe speed and accurate of contingency analysis.