Transformer online monitoring and intelligent diagnosis system design

Abstract

Transformer is a pivotal equipment in the power system, and its safe and stable operation affects the entire power system. Therefore, the on-line monitoring and fault diagnosis of transformers are particularly important in practical applications. The traditional maintenance method is called regular maintenance. This maintenance method is to call a large number of inspection workers at regular intervals to carry out large-scale inspections, not only to collect various working data of the transformer, but also to observe its working status, and finally manually. Check whether there are hidden dangers in major machines, and judge whether the transformer can run safely and stably according to the data obtained by these large amounts of manpower and material resources. In order to improve the efficiency and accuracy of transformer fault diagnosis, and to carry out all-round real-time online monitoring and fault diagnosis of 220kV power transformers, considering the actual needs of electricity companies, this paper is based on the Qt development platform, C++, MySQL and other development environments. And developed a transformer online monitoring and intelligent diagnosis system, which mainly has the functions of providing basic information management, online monitoring, fault diagnosis, giving operation and maintenance suggestions, and providing users with visual comprehensive analysis. Tested with actual data, it is proved that the system can objectively reflect the operating state of the transformer and carry out real-time fault diagnosis. The system designed in this paper meets the needs of operators to understand the status of distribution transformers, and effectively solves the problem of untimely detection of distribution transformer faults. It can effectively improve the efficiency of detection, reduce the probability of major accidents, and reduce operation and maintenance costs., which saves financial and human resources, and is of great significance for improving economic efficiency and personal and equipment safety.