基于多维能力和知识图谱-多层感知机的变压器运行状态画像构建方法

Construction Method for Transformer Operating State Portrait Based on Multi-dimensional Capability and Knowledge Graph-multilayer Perceptron

利用大数据和画像技术对电力变压器运行状态进行准确评价有利于保障电力系统的安全稳定运行。针对电力变压器运行状态传统评价方法存在的评价维度过于单一、主观性较强等不足,提出了一种基于多维能力和知识图谱-多层感知机的变压器运行状态画像构建方法。首先,构建了由绝缘水平、负载能力、抗短路能力、能效等级和调压能力五个能力构成的变压器运行状态画像体系;然后,融合知识图谱(knowledge graph,KG)与多层感知机(multilayer perceptron,MLP),建立了一种变压器运行状态画像分析模型;最后,基于某地区1368台110kV变压器的实际运行数据,开展了变压器运行状态画像的实例分析,并与随机森林(random forest,RF)和支持向量机(support vector machine,SVM)方法的画像分析结果进行对比。研究结果表明,所提方法对变压器运行状态画像的准确率达到96.35%,优于RF算法(准确率89%)和SVM算法(准确率77%),为电力变压器的运行状态评价提供了一种新思路。

Accurate evaluation for operating state of power transformer using big data and portrait technology is beneficial to ensure the safe and stable operation of power system.Aiming to the shortcomings of traditional condition evaluation methods,such as too single evaluation dimension and strong subjectivity,a construction method for transformer operating state portrait based on multi-dimensional capability and knowledge graph-multilayer perceptron(KG-MLP)is proposed.Firstly,the portrait system of transformer operation state is constructed,which is composed of insulation level,load capacity,anti-short circuit ability,energy efficiency level and voltage regulation capacity.Then,by combining the KG and MLP,a portrait analysis model of transformer operating state is built.Finally,according to the actual operation data of 1368110kV transformers in a certain area,an example analysis of transformer operation state portrait is carried out.Also,the results are compared with those of Random Forest(RF)and Support Vector Machine(SVM)methods.The results show that the proposed method can accurately construct the transformer running state portrait with an accuracy of 96.35%,which is superior to RF algorithm(accuracy of 89%)and SVM algorithm(accuracy of 77%),providing a new way of evaluation for power transformer operating state.