Early Warning of Distribution Transformer Based on BP Neural Network Considering the Influence of Extreme Weather

When extreme weather occurs, it may cause grid equipment failures and regional power outages. It can even give rise to a large-scale power outage, which affect people's normal life seriously and bring about irreparable losses. In order to avoid losses and ensure reliability of users' power consumption, researchers have carried out research from two aspects: before and after the occurrence of extreme weather. After the occurrence of extreme weather, the research analysis the cause of key equipments' failure and the emergency restoration has been relatively mature, which already have various methods. However, it is relatively weak for the researches about warning before the occurrence of extreme weather. This paper makes research on disaster prevention measures before the occurrence of extreme weather, and proposes early warning of distribution transformer power supply failure based on BP neural network considering the influence of extreme weather. The method proposed in this paper is easier to promote and apply from the perspective of the distribution network. With BP neural network, taking into account extreme weather and other nonlinear factors, it can more accurately realize the distribution transformer fault warning. Based on a distribution transformer data set, compare the distribution transformer failure warning results with the results of the Logistic regression algorithm and the support vector machine algorithm. It is shown that the BP neural network algorithm results are optimal.