Fault detection method of high voltage circuit breaker based on dbn-ga neural network

Abstract

The high-voltage circuit breaker fault detection method based on the DBN-GA neural network disclosed by the present invention specifically follows the following process: the current data monitored by the online monitoring system is used as an input variable; then, the fault is constructed using a deep learning algorithm based on a deep belief neural network Type prediction model, determine the restricted Boltzmann machine model, denoted as RBM, extract a part of the current data samples to build the model and train it; after training the restricted Boltzmann machine, the entire deep belief neural network The model is trained and learned; finally, all the data is input into the trained fault type prediction model, and the input opening and closing coil current data is processed by the fault type prediction model to complete the fault detection of the high voltage circuit breaker. The method disclosed by the invention can more accurately and effectively judge the fault type of the circuit breaker while making up for the deficiency of the artificial neural network detection, and thus can efficiently repair it.

Description

technical field [0001] The invention belongs to the technical field of detection methods for high-voltage circuit breakers, and relates to a fault detection method for high-voltage circuit breakers based on a DBN-GA neural network. Background technique [0002] High-voltage circuit breakers are the most important control and protection devices in power systems, which are related to the reliability and safety of power transmission, power distribution and power consumption. High voltage circuit breakers are capable of multiple operations under system fault and non-fault conditions. The circuit breaker can also close, carry and break the normal current of the operating circuit, and can also close, carry and break the specified overload current within the specified time. High-voltage circuit breakers generally use electromagnets as the first control element to operate, and most of the operating mechanisms are DC electromagnets. When the current passes through the coil, magneti...

AI Technical Summary

Problems solved by technology

It can not only identify features, classify data, but also use it to generate data, but because its learning process is too simple, there may be incomplete training defects in the training process; therefore, using genetic algorithm (GA) to optimize deep belief neural network The network can solve this problem and update its weight until it is within the set error range to improve the accuracy of diagnosis. This method is well applied in circuit breaker fault diagnosis

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