Electric energy metering device fault diagnosis method and system based on data mining technology

Abstract

The invention relates to an electric energy metering device fault diagnosis method and system based on a data mining technology. The method comprises the following steps of 1, obtaining different fault state data of an electric energy metering device; step 2, processing fault data of the electric energy metering device, extracting key operation parameters and classifying metering fault types; 3, constructing an electric energy metering device fault diagnosis model based on an improved seagull optimization algorithm optimization support vector machine; 4, training the fault diagnosis model of the electric energy metering device; and 5, performing fault diagnosis by using the electric energy metering device fault diagnosis model. The built fault diagnosis model has high recognition precision and recognition efficiency for the operation state of the electric energy metering device, the fault diagnosis accuracy and stability of the electric energy metering device can be improved, and the fairness and justice of electric energy metering and the safe and stable operation of a power grid can be maintained.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of electric energy measuring devices, and in particular relates to a fault diagnosis method and system of electric energy measuring devices based on data mining technology. Background technique [0002] With the development of social economy, domestic electricity consumption increases, and power grid technology continues to develop, the construction of smart grid has become a necessary condition for social production and life. As a device for measuring and recording power generation, power consumption and power consumption, electric energy metering device is an important part of smart grid. The measurement accuracy and operation reliability of the electric energy metering device play an important role in ensuring the fairness and justice of electric energy measurement and the safe and stable operation of the power grid. Therefore, it is necessary to analyze the real-time operation data of...

AI Technical Summary

Problems solved by technology

However, with the increase of power consumption and the increase of data samples, the calculation complexity is too high. At the same time, the structure of the electric energy metering device is complex and there are many abnormal working states, which makes the model training time too long, which affects the real-time fault diagnosis of the electric energy metering device.

The internal parameter penalty factor C and the kernel function parameter g of the support vector machine directly determine the training efficiency and application effect of the support vector machine. The classification effect of the support vector machine model with randomly generated internal parameters is poor, and it cannot accurately diagnose the fault of the electric energy metering device

[0004] To sum up, the complex structure of the electric energy metering system, a large amount of power consumption data and various abnormal working conditions have brought difficulties to the fault diagnosis of the electric energy metering device. How to carry out accurate and efficient fault diagnosis on the electric energy metering device has become an urgent problem to be solved

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