A building electrical system fault diagnosis method based on random forest optimized by improved sparrow search algorithm

Addressing the problems of manual dependence and low accuracy of traditional building electrical system fault diagnosis, this paper proposes a novel method, which is based on random forest (RF) optimized by improved sparrow search algorithm (ISSA-RF). Firstly, the method utilizes a fault collection platform to acquire raw signals of various faults. Secondly, the features of these signals are extracted by time-domain and frequency-domain analysis. Furthermore, principal component analysis is employed to reduce the dimensionality of the extracted features. Finally, the reduced features are input into ISSA-RF for classification. In ISSA-RF, the ISSA is used to optimize the parameters of the RF. The parameters for ISSA optimization are n_estimators and min_samples_leaf. In this case, the accuracy of the proposed method can reach 98.61% through validation experiment. In addition, the proposed method also exhibits superior performance compared with traditional fault classification algorithms and the latest building electrical fault diagnosis algorithms.