Electrical equipment infrared image real-time detection and diagnosis method based on lightweight deep learning

Abstract

The invention provides an electrical equipment infrared image real-time detection and diagnosis method based on lightweight deep learning. The electrical equipment infrared image real-time detection and diagnosis method comprises the following steps: S1, acquiring an infrared image of electrical equipment of a transformer substation through an infrared thermal imager; s2, preprocessing the obtained infrared image through an algorithm to form a data set for training; s3, performing target label processing on the obtained normal and fault data sets of the electrical equipment; s4, randomly distributing the processed data set into a training set and a test set; s5, constructing an infrared image real-time detection and diagnosis model of the improved lightweight single-shot multi-box detector; s6, performing parameter adjustment and training of the model by using the divided training set; and S7, carrying out automatic detection and diagnosis on the trained detection and diagnosis model by using the divided test set so as to prove the effectiveness of the detection and diagnosis model. Through the above steps, real-time detection and diagnosis of infrared images of various electrical devices (especially effective schemes can be deployed in limited environments such as embedded devices) are realized.

Description

technical field [0001] The invention relates to the field of safety monitoring of the operating state of electrical equipment, in particular to a method for real-time detection and diagnosis of infrared images of electrical equipment based on lightweight deep learning. Background technique [0002] To meet the growing demand for sustainable energy, larger and more complex power systems are required. Power systems require continuous inspection and preventive maintenance to ensure their normal, trouble-free operation. Among them, the detection of substations is very important, because electrical problems in substations will not only cause power outages in the power system, local economic losses, and may even cause casualties. Therefore, real-time and effective detection of substation is very important to ensure its safe and long-term operation. [0003] Thermal imaging has become a widely accepted condition monitoring technique because of its many advantages over other types...

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Problems solved by technology

[0005] Although the above method has achieved good results in terms of detection accuracy, it has not conducted a good trade-off study on the size of the model, detection speed and detection accuracy, and has not realized the monitoring of the operating status of electrical equipment in a restricted environment. Effective judgment, so the present invention implements a real-time detection and diagnosis method that can be deployed in a constrained environment after a trade-off study

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