In order to improve the efficiency of transformer fault diagnosis and monitoring in power systems, and to realize fault diagnosis of unmanned remote adaptive transformer equipment, we present a method of multi-sensor and multi-direction optical image integrated monitoring in this paper. By monitoring and collecting transformer fault information combined with the changing characteristics of transformer temperature and electrical signals, we establish a transformer calculation model based on multi-level fault and multi-characteristic parameters. According to the characteristics of transformer faults, we use a deep belief network identification (DBNI) algorithm for the transformer and construct the training samples of the transformer diagnosis model using an optimum weight fusion algorithm. The experimental results show that the DBNI model can fully explore the characteristics of large samples, analyze multiple faults information, and extract the hidden features of fault samples. The DBNI model has higher fault diagnosis accuracy than a BP neural network and a single DBN without data fusion and SVM. The DBNI's fault diagnosis accuracy reaching 99.45%. The experimental results show that this model has good robustness of interference ability and can be used intuitively to carry out remote on-line unattended transformer fault diagnosis and information feedback.
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