Convolutional neural network power system intelligent fault detection method and system based on Spearman level correlation

Abstract

The invention provides a convolutional neural network power system intelligent fault detection method and system based on Spearman level correlation, and the method comprises the steps: setting a phasor measurement unit at a regional network node, and carrying out the measurement of data; performing Spearman correlation analysis on the acquired data, and proposing an image generation method basedon an analysis result; establishing an equivalent fault network, verifying the relation between the fault characteristics and the Spearman level correlation, and demonstrating the feasibility of the method; taking the generated image as an initial convolutional layer, and establishing a convolutional neural network architecture based on Spearman level correlation; and verifying the rationality andsuperiority of the method based on PSCAD / EMTDC according to the established architecture. A plurality of types of electric quantity data are comprehensively used for fault diagnosis, the position ofa fault in the power system can be quickly and accurately identified through the convolutional neural network, the problems that the power system has volatility and the traditional detection method isinaccurate due to addition of a distributed power supply and the like are solved, and the robustness and the self-adaptability of the power system are higher.

Description

Technical field [0001] The invention relates to the technical field of power system fault detection, in particular to a power system fault detection system and method based on Spearman level correlation convolutional neural network. Background technique [0002] The development of the power grid and the progress of society have put forward higher and higher requirements for the operation of the power grid, and it is particularly important to strengthen the detection and processing of power grid faults. Fault detection has always been an important and non-negligible task in the power system. It can improve the reliability of the power system and reduce the losses caused by faults. The fault and abnormal handling of the distribution network is the primary task of the operation of the distribution network. To ensure the economic efficiency of the distribution network is an important task for the operation of the distribution network. Today’s power system topology and coupling inter...

AI Technical Summary

Problems solved by technology

Today's power system topology and coupling interference are becoming more and more complex, and fault detection in power systems is becoming more and more difficult. Traditional methods suitable for single fault types or simple systems are difficult to achieve satisfactory results, and deep learning methods such as convolutional neural networks It shows great potential in feature extraction and image recognition, and has important research value and practical significance for the detection and identification of power system faults.

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