Main transformer intelligent partial discharge sensing system and method thereof

By integrating a variety of local discharge detection technologies and cloud computing technologies, the problem that a single monitoring means in the existing technology is easily disturbed by noise, and more accurate identification and fault positioning of local discharge of transformers is achieved, and the accuracy and stability of monitoring are improved.

CN119716410APending Publication Date: 2025-03-28STATE GRID CORPORATION OF CHINA +2
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Patent Information

Application Number
CN202411691122.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-25
Publication Date
2025-03-28

AI Technical Summary

Technical Problem

In the prior art, local discharge monitoring of transformers only relies on a single monitoring method and is susceptible to frequency band noise interference, affecting the accuracy and reliability of the evaluation results.

Method used

An integrated monitoring design is adopted with a variety of means, including three partial discharge detection technologies: ultra-high frequency, high frequency and ultrasonic. The electromagnetic wave signal and current signal are captured through frequency sensors and flow value sensors, and data processing and analysis are carried out in combination with cloud computing technology.

Benefits of technology

It realizes more accurate identification of local discharges, provides more comprehensive fault positioning and diagnostic information, improves the accuracy and stability of data acquisition, and ensures the timeliness and accuracy of monitoring work.

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Abstract

The invention provides a main transformer intelligent partial discharge sensing system and method, and the system comprises a monitoring device used for monitoring an electric appliance unit, and a cloud platform used for receiving and storing data. The method comprises the steps that a data set carrying a timestamp is packaged and then uploaded to a cloud platform, the cloud platform receives the data set and stores the data set to a server, the timestamp is read after the data set is received, and a data value of the data and an operation state of a current corresponding electric appliance unit are displayed at a local platform end according to the sequence of the timestamp; three partial discharge detection technologies of ultrahigh frequency, high frequency and ultrasonic wave are integrated, the type, position and severity of partial discharge can be identified more accurately by comprehensively analyzing the signal characteristics of the three different frequency bands, and more comprehensive information is provided for fault location and diagnosis of the transformer.
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