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

• Published in: Measurement science & technology Vol. 35; no. 5; p. 55110
• Main Authors: Li, Zhangling, Wang, Qi, Xiong, Jianbin, Cen, Jian, Dai, Qingyun, Liang, Qiong, Lu, Tiantian
• Format: Journal Article
• Language: English
• Published: 01.05.2024
• ISSN: 0957-0233; 1361-6501; 1361-6501
• DOI: 10.1088/1361-6501/ad2255

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.