Abstract
In complex power systems, when power equipment fails, multiple concurrent failures usually occur instead of a single failure. Concurrent failures are so common and hidden in complex systems that diagnosis requires not only analysis of failure characteristics, but also correlation between failures. Therefore, in this paper, a concurrent fault diagnosis method is proposed for power equipment based on graph neural networks and knowledge graphs. First, an electrical equipment failure knowledge map is created based on operational and maintenance records to emphasize the relevance of the failed equipment or component. Next, a lightweight graph neural network model is built to detect concurrent faults in the graph data. Finally, a city’s transformer concurrent fault is taken as an example for simulation and validation. Simulation results show that the accuracy and acquisition rate of graph neural network mining in Knowledge Graph is superior to traditional algorithms such as convolutional neural networks, which can achieve the effectiveness and robustness of concurrent fault mining.
Highlights
At the end of 2016, China had built the world’s largest power grid and achieved a huge amount of construction (Liu et al, 2020)
There is a lot of research on fault diagnosis methods, which can be divided into two main types
To further investigate the correlation between multiple faults, this paper selects the Graph Convolutional Neural Network (GCN), with strong topological feature expression ability as the basis, proposes a method for transformer concurrent fault diagnosis based on graph neural network and knowledge graph
Summary
At the end of 2016, China had built the world’s largest power grid and achieved a huge amount of construction (Liu et al, 2020). Due to the large and complex system, the power grid is more likely and more severe than a simple system (Wang et al, 2019a). Multiple faults will occur at the same time (Wang et al, 2019b). This type of faults is called concurrent faults (Qin et al, 2018) and is known as compound faults or multiple faults. Concurrent faults in different scenarios are completely different, and the characteristics of the faults are extremely complex and difficult to diagnose (Ma et al, 2018). Research on how to diagnose concurrent transformer faults is crucial for the operation and maintenance of transmission and transmission equipment, and for the safe and reliable transmission of power systems
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
