System and method for online monitoring of mutual inductor based on deep network

Abstract

The invention discloses a system for online monitoring of a mutual inductor based on a deep network. The system comprises a mutual inductor monitoring assembly, a mutual inductor performance evaluation assembly, a mutual inductor standing book maintenance assembly and a monitoring equipment management assembly. The mutual inductor monitoring assembly carries out real-time monitoring and alarm on various functions of a mutual inductor and mutual inductor monitoring equipment. the mutual inductor performance evaluation assembly determines a deep neural network model for mutual inductor metering performance evaluation based on a deep learning algorithm according to pre-collected mutual inductor parameters, outputs a performance evaluation result of the mutual inductor by using the deep neural network model, displays the performance evaluation result of the mutual inductor, and analyzes the trend of the performance evaluation result of the mutual inductor. The mutual inductor standing book maintenance assembly is used for maintaining the standing book of the mutual inductor. The monitoring equipment management assembly manages the standing book information of a plant station, the standing book information of the mutual inductor, the standing book information of the monitoring equipment and the system of the detection equipment.

Description

technical field [0001] This application relates to the field of online monitoring technology, in particular to a system and method for online monitoring of transformers based on a deep network. Background technique [0002] As an integral part of the electric energy metering device, the transformer is in a high-risk state in the quality management of the whole life cycle. Not only is the basic information of power transformers incomplete or even wrong, but also due to technical bottlenecks, the measurement performance evaluation and prediction of power transformers in operation have become blank, and the measurement status of power transformers has become a blind spot for electric energy metering devices. Due to the ownership of transformer assets and the lack of relevant supervision, transformers that have exceeded their operating life are still responsible for the work of gateway measurement, which brings huge hidden dangers to the fairness of trade settlement of electric ...

AI Technical Summary

Problems solved by technology

Not only is the basic information of the power transformer incomplete or even wrong, but also due to technical bottlenecks, the evaluation and prediction of the measurement performance of the power transformer in operation has become blank, and the measurement status of the power transformer has become a blind area of ​​the electric energy measurement device.

Due to the ownership of transformer assets and the lack of relevant supervision, transformers that have exceeded their operating life are still responsible for the work of gateway measurement, which brings huge hidden dangers to the fairness of trade settlement of electric energy and seriously restricts the standardized management of electric energy metering devices. Become a point of risk management and control

[0003] In view of the technical bottlenecks in the above-mentioned prior art, the measurement performance evaluation and prediction of power transformers in operation have become blank, and the measurement status of power transformers has become a blind spot for electric energy measurement devices.

Due to the ownership of transformer assets and the lack of related supervision, transformers that have exceeded their operating life are still responsible for the work of gateway measurement, which brings huge hidden dangers to the fairness of electric energy trade settlement and seriously restricts the standardized management of electric energy metering devices. It has become a technical problem of risk management and control, and no effective solution has been proposed so far

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