The application of fault diagnosis techniques and monitoring methods in building electrical systems – based on ELM algorithm

IF 0.6 Q4 ENGINEERING, MECHANICAL
Guanghui Liu
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引用次数: 0

Abstract

The reliability of modern building electrical systems are receiving increasing attention as they become more intelligent and complex. As the majority of building electrical systems use neutral point grounding, earth faults or short circuits can get worse over time and damage both the distribution system and the electrical equipment. To this end, the corresponding three phases and four categories, namely three-phase voltage, three-phase current after fault, three-phase voltage distortion rate, three-phase current distortion rate, a total of 12 dimensional fault feature vectors and 10 fault simulation types, were summarised and extracted in conjunction with the actual operating conditions of the system. Using traditional fault identification ideas and neural network algorithm as reference, a 12-dimensional fault feature vector is used as the model input to construct a building electrical fault diagnosis and detection model based on ELM algorithm. Results showed that the ELM-based model’s classification accuracy for this experimental sample was 97.56 %, its AUC was 0.92, and its RMSE was 0.3521. These figures were higher than the classification accuracy and performance of the BP algorithm and GA-BP algorithm fault diagnosis models, and they also demonstrate better robustness and generalizability. The model also has a 97.27 % correct rate in fault discrimination, while the computation time is only 0.201 s, and its fault identification and diagnosis speed is faster than other algorithmic models. At the same time, this research model has a good fault monitoring accuracy of up to 98.6 % for building electrical systems. The research can provide a more sensitive, accurate and rapid fault monitoring method for the current building electrical system. It also improves the reliability of the building electrical system in a complex environment and achieves better protection of the system. This has a certain significance for the development of the building electrical industry.
基于ELM算法的故障诊断技术和监测方法在建筑电气系统中的应用
随着现代建筑电气系统的智能化和复杂化,其可靠性受到越来越多的关注。由于大多数建筑电气系统使用中性点接地,随着时间的推移,接地故障或短路会变得更严重,并损坏配电系统和电气设备。为此,结合系统实际运行情况,总结并提取出相应的三相电压、故障后三相电流、三相电压畸变率、三相电流畸变率等三相四大类,共12个维故障特征向量和10种故障仿真类型。在借鉴传统故障识别思想和神经网络算法的基础上,以12维故障特征向量作为模型输入,构建了基于ELM算法的建筑电气故障诊断检测模型。结果表明,基于elm的模型对该实验样本的分类准确率为97.56%,AUC为0.92,RMSE为0.3521。这些数据均高于BP算法和GA-BP算法故障诊断模型的分类精度和性能,并表现出更好的鲁棒性和泛化性。该模型的故障识别正确率为97.27%,计算时间仅为0.201 s,故障识别和诊断速度快于其他算法模型。同时,该模型对建筑电气系统的故障监测准确率高达98.6%。该研究可为当前建筑电气系统提供一种更加灵敏、准确、快速的故障监测方法。提高了建筑电气系统在复杂环境下的可靠性,实现了对系统更好的保护。这对建筑电气行业的发展具有一定的意义。
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来源期刊
Journal of Measurements in Engineering
Journal of Measurements in Engineering ENGINEERING, MECHANICAL-
CiteScore
2.00
自引率
6.20%
发文量
16
审稿时长
16 weeks
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