基于神经逻辑网络冗余纠错和FNN组合的配网高容错性故障定位

创造性提出了基于多值神经逻辑网络 (MNLN)冗余纠错和前馈神经网络 (FNN)组合的配网故障定位原理和实现方法。根据配网SCADA系统汇集的信息具有冗余特征 ,及配网拓扑结构的关联性可获得各馈线终端单元 (FTU)信息之间的冗余关系 ,提出了基于MNLN原理的信息冗余纠错的模型及逻辑推理规则。经过纠错处理的、无畸变的信息即形成故障定位FNN模型的输入矢量集。文中所提出原理和方法对配电网具有广泛的通用性 ,具高容错性能和重要实用价值

Fault Section Diagnosis with High Fault- Tolerance Performance for Distribution Networks Based on the Combination of Neural Logic Network Redundant Error Correct and FNN

The principle and realization method of fault secti on diagnosis for distribution networks, based on the combination of multiple-valu ed neural logic network (MNLN) redundant error correct and feedfoward neural net works (FNN) are creatively presented. According to the redundant character of in formation which is collected by SCADA system of distribution networks and redund ant relation among information of Feeder Terminal Units (FTUs) which can acquire d from the correlativity of topology structure of distribution networks. The mod el and inference rules of information for redundant error correct based on the t heory of MNLN are proposed. Therefore, the error-corrected disposed information without distortion form the input vector of FNN model to be used as fault secti on diagnosis. In this paper, the proposed principle and method have widely gener ality for distribution networks, have high fault-tolerance performance and impo rtant practical worth.