基于变分与量子算法优化的建筑电气系统故障诊断方法研究

Research on Fault Diagnosis Method of Building Electrical System Based on Variational and Quantum Algorithm Optimization

随着智能建筑的发展,建筑电气系统的复杂性日益增加,故障类型多样、特征不明显,给故障诊断带来了巨大的挑战。本文提出了一种基于变分模态分解(VMD)与新型互维无量纲指标(MDI)相结合的方法,并通过量子遗传算法优化支持向量机(QGA-SVM),以提高故障诊断的准确性和效率。实验结果表明,该方法相较于传统的故障诊断方法,在特征提取与分类准确率方面表现更为优越,平均测试准确率达到了91.67%。

With the development of intelligent buildings,the complexity of building electrical systems is increasing,and the fault types are diverse and the characteristics are not obvious,which brings great challenges to fault diagnosis.In this paper,we propose a method based on the combination of Variational Mode Decomposition(VMD)and a novel Multidimensional Dimensionless Index(MDI),and optimize the support vector machine(QGA-SVM)through quantum genetic algorithm to improve the accuracy and efficiency of fault diagnosis.Experimental results show that compared with traditional fault diagnosis methods,the proposed method has superior performance in feature extraction and classification accuracy,and the average test accuracy reaches 91.67%.