Infrared imaging detection is an important method detecting the high-voltage(HV) power equipments running state. It is a kinds of non-contact on-line measurement that can determine the HV power equipment running state, find the fault position and predictive its future state. In the process of the infrared images analyzing, the computer captures remote equipments images, calculates images' moment invariants as characteristic vector of recognition, recognizes power equipments by support vector machine (SVM). The system further analyzes images and find if images has convex hull, intensive stochastic noise, or false edges, sequently make a conclusion whether the running state of equipments is in order. Replace of scouting by people themselves, the use of image recognition in power system can timely find troubles and potential troubles of power equipments.
In order to avoid the deficiencies of conventional high voltage circuit breaker mechanical properties detection methods, a new algorithm based on image block matching with diamond search strategy is presented in this paper. The motion of auxiliary mark on the pull rod or shaft is firstly recorded by a high-speed and high-definition digital camera when the circuit breaker is open or close. Then the motion trajectory is acquired through diamond image block matching method. The mechanical parameters, such as travel and open and close velocity, are calculated according to the travel-time curve of the circuit breaker. Finally, evaluation model is constructed taking mechanical parameters characteristic values as inputs of ELM. Comparing to the existing techniques, our method is a noncontact measurement based on computer vision. It is easy and convenient for practical application since it need not any electrical and mechanical connection to the breaker. Another advantage of our method is that it can obtain the line and angle displacement simultaneously. The experiment results on the circuit breaker of 220 kv show that our method is effective for breaker mechanical properties detection.
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