基于半监督聚类的通信缺陷研判知识库构建及迭代技术

Communication Fault Diagnosis Knowledge Base Construction and Iteration Based on Semi-supervised Clustering

电力通信网是电力系统不可缺少的重要组成部分,是电网调度自动化和生产管理现代化的基础,是确保电网安全、经济、稳定运行的重要技术手段。传统的通信缺陷研判工作依赖人工经验,难以满足日益庞大、复杂的通信网络安全生产需求。而基于规则引擎或神经网络等方法在生产环境应用中逐渐遇到瓶颈,样本较少难以训练,且作为黑盒较难在生产环境中独立使用。针对上述问题,本文提出基于改进马尔科夫聚类的告警聚类算法和基于序列相似性计算与OPTICS聚类的缺陷研判算法,以适应当前缺陷数据小样本场景,在上述算法结果基础上,利用少量的缺陷单标签构建缺陷研判知识库及其迭代学习机制,通过实际生产积累的数据进行验证,结果表明相关算法及其知识库在应对实际生产问题时效果良好。

Power communication network is an indispensable and important part of power system,it’s the basis of power grid dis⁃patching automation and production management modernization,and an important technical means to ensure the safe,economic and stable operation of power grid.The diagnosis of communication faults still depends on manual experience,which is difficult to meet the safety production needs of increasingly large and complex communication network.Methods based on rule engine or neural network gradually encounter bottlenecks in the application of production environment.It is difficult to train due to less samples,or work independently in production environment as a black box.To solve the above problems,this paper proposes an alarm clustering algorithm based on improved Markov-clustering and a fault diagnosis algorithm based on sequence similarity and OPTICS clustering,adapt to the current small sample scenario of fault data.On the basis of the above algorithm results,the fault diagnosis knowledge base and its iterative learning mechanism are constructed by using a small number of labels.It is verified by the data accumulated in actual production.The results show that the relevant algorithms and knowledge base have a good effect in dealing with actual production problems.