Abstract
Abstract
Building electrical systems in modern buildings are increasingly playing a pivotal role in bringing people the convenience of life, at the same time, the building electrical system will inevitably fail. And the research of intelligent fault diagnosis algorithms for this field is still in its infancy. At this stage, the accuracy and reliability of fault monitoring and diagnosis of most building electrical systems are yet to be improved. Aiming at the current lack of effective diagnosis of faults in the building electrical system, this paper takes the fault situation of the electrical system in the building as the main object of research, and simulates the common building electrical faults through the comprehensive experimental platform of the building electrical, taking into account the problem of low diagnostic efficiency of the building electrical system. This paper puts forward a fault diagnostic algorithm based on the combination of compressed perception and the K-nearest neighbor algorithm, which is aimed at improving the diagnostic efficiency of building electrical system faults. The results show that the proposed fault diagnosis algorithm can not only improve the accuracy of fault classification but also shorten the time of fault classification, which greatly improves the fault diagnosis efficiency.