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In order to improve the accuracy of temperature prediction of switch gear equipment, a method for temperature prediction of switch gear equipment based on LSTM recurrent neural network is proposed. Firstly, obtain the relevant data set of the power switch gear equipment, and analyze and select the characteristic variables and data preprocessing of the original data set. Secondly, the processed data set is input to the LSTM recurrent neural network for training, and an LSTM temperature prediction model is obtained. Finally, taking the bus equipment of 6 kV switchgear as an example, and a comparison experiment of device temperature prediction is performed with various prediction algorithms. The experimental results show that: By comparing with the traditional neural network and classic recurrent neural network (RNN) temperature prediction model, the method proposed in this paper has higher accuracy for the temperature prediction of the equipment in the switchgear. It provides an effective way for active predictive maintenance. [ABSTRACT FROM AUTHOR]

Temperature is an important indicator to measure the health status of power switchgear equipment. Accurate temperature prediction of equipment in the switchgear can effectively grasp its operating status in advance. The traditional BP neural network can realize temperature prediction, but it is easy to fall into local minimum values during the training process，which affects the accuracy of temperature prediction This paper proposes a PSO-op- timized BP neural network to predict the temperature of equipment. Firstly?the power opera¬tion data set is preprocessed. Before the network training, the weight and threshold of the neural network are optimized by PSO to obtain the PSO-BP prediction model. Finally?the es¬tablished prediction model is applied to the temperature prediction for the switchgear equip- ment. The experimental results show that，compared with the traditional neural network temperature prediction method，the method proposed in this paper can effectively predict the temperature for equipment in the switchgear. This method provides an effective way for grid management from traditional preventive maintenance to active predictive. [ABSTRACT FROM AUTHOR]

Partial discharge accelerates insulation aging and leads to insulation failure of high voltage equipment． PD test is an important basis for condition evaluation of switchgear． It is usually combined with transient earth voltage and ultrasonic detection methods to detect partial discharge of switchgear，which is vulnerable to external signal interference and difficult to locate the fault． Therefore，based on the formation mechanism of transient earth voltage，this paper analyzes the characteristics of transient earth voltage and proposes a long － term monitoring method for suspicious HV switchgear． The antenna is used to shield the external interference signal，and the switchgear partial discharge signal is identified from complex signal sources，Finally the fault location of partial discharge can be located by the method of matrix probe． The fault location is found and located in the actual working case，which can verify the method is accurate and feasible． [ABSTRACT FROM AUTHOR]