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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.

The reliable storage of spring potential energy is a prerequisite for ensuring the correct closing and opening operations of a circuit breaker. A fault identification method for circuit breaker energy storage mechanism, combined with the current-vibration signal entropy weight characteristic and grey wolf optimization-support vector machine (GWO-SVM), is proposed by analyzing the energy conversion and transmission relationship between control loop, motor, transmission component, and spring. First, the current envelope is denoised in the modulus maxima wavelet domain, the time-domain feature is extracted via a Hilbert transform (HT), and the kurtosis is calculated. Second, the energy method is used to select K parameters, the objective function optimization method is used to select α for variational mode decomposition (VMD), the intrinsic mode function (IMF) is obtained by decomposing the vibration signal with VMD and the permutation entropy characteristics of IMFs are extracted. Third, the characteristic of current and vibration signals for classification are edited by the entropy weight method, and the corresponding weights are provided in accordance with the sample information amount and importance. Finally, the currentvibration entropy weight characteristics are constructed and sent to the SVM model for learning and training. The GWO algorithm is used to optimize the parameters of the SVM model based on the mixed kernel function, which reduces adverse effects from the model parameters' choice of circuit breaker fault diagnosis. The results show that the overall diagnostic accuracy for the experimental samples reaches 100% and has good generalization capability based on the current-vibration entropy weight characteristic and the GWO-SVM method. INDEX TERMS Current-vibration, entropy weight characteristic, grey wolf optimization-support vector machine (GWO-SVM), variational mode decomposition (VMD).

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According to the energy variation of the mechanical transmission in the process of circuit breaker operation which is characterized by acoustic and vibration signals, a new method of high Voltage circuit breaker mechanical fault diagnosis was proposed in this paper.