基于故障传播模型与监督学习的电力通信网络故障定位

针对电力通信网络中的故障定位问题,分析了一种网络设备或链路故障引发的大范围连通片故障告警情形,提出一种基于故障传播模型和监督分类学习方法的故障定位算法。首先使用改进的故障传播模型求得初步定位结果,用最少的故障数目解释当前告警;然后通过故障源-故障告警向量分解将故障定位问题转化为监督分类问题,定位告警区域内部故障;最后加入猜测的故障设备和故障链路完善定位结果以提高定位准确率。模拟结果表明提出的故障定位算法的故障检测率达到84%~95%,具有较高的故障定位可靠性。

Fault localization for electric power communication network based on fault propagation model and supervised learning

To solve the fault localization problem in electric power communication network, the large-scale connected area fault alarms caused by device or link faults were investigated, and a fault localization algorithm based on fault propagation model and supervised learning method was proposed. First, an improved fault propagation model was used to obtain an initial result with the minimum faults. Then the fault localization problem was transformed into a supervised classification problem by fault alarm vector decomposition to localize the faults within the fault warning areas. Finally, conjectural fault devices and links were added to improve the location results of previous two steps and increase the accuracy. The simulation results show that accuracy of fault localization of the proposed algorithm reaches 84%-95%, which achieves high reliability in fault location.