Abstract
Nowadays the demand of power supply reliability has been strongly increased as the development within power industry grows rapidly. Nevertheless such large demand requires substantial power grid to sustain. Therefore power equipment’s running and testing data which contains vast information underpins online monitoring and fault diagnosis to finally achieve state maintenance. In this paper, an intelligent fault diagnosis model for power equipment based on case-based reasoning (IFDCBR) will be proposed. The model intends to discover the potential rules of equipment fault by data mining. The intelligent model constructs a condition case base of equipment by analyzing the following four categories of data: online recording data, history data, basic test data, and environmental data. SVM regression analysis was also applied in mining the case base so as to further establish the equipment condition fingerprint. The running data of equipment can be diagnosed by such condition fingerprint to detect whether there is a fault or not. Finally, this paper verifies the intelligent model and three-ratio method based on a set of practical data. The resulting research demonstrates that this intelligent model is more effective and accurate in fault diagnosis.
Highlights
The increasing scale of power system and the growing amount of power equipment have accelerated the integration of power grid
We propose an intelligent fault diagnosis model for power equipment based on case-based reasoning which will satisfy the new requirements of power grid
We establish equipment fingerprinting by training the cases based on SVM regression theory in order to diagnose the status of power equipment
Summary
The increasing scale of power system and the growing amount of power equipment have accelerated the integration of power grid. The subject as to ensure the safety of power equipment, to observe potential fault in time, to reduce power system accident effectively, and to improve power supply quality and reliability, has become the most urgent problem in relation to power system. CBM can ensure the safety of power grid, minimize the resources of maintenance of power equipment, reduce the operating cost of power enterprise, and improve the benefits of enterprise and society. CBM assesses the health status of equipment base on online condition information and attribute information of power equipment comprehensively. In order to ensure the safety and reliability of the power equipment, the equipment with unperfected running status requires timely maintenance. We propose an intelligent fault diagnosis model for power equipment based on case-based reasoning which will satisfy the new requirements of power grid. We summarize the paper and put forward the potential challenges
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