Integrated power generation equipment health state supervision system

By designing an integrated power generation equipment health status supervision system and using multi-source data access and machine learning models for fault diagnosis, the problems of inconsistent supervision of power generation equipment and difficulty in fault identification in the existing technology are solved, and efficient and accurate equipment health status management is achieved.

CN120067837AActive Publication Date: 2025-05-30TIANJIN GUONENG JINNENG BINHAI THERMAL POWER CO LTD
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Patent Information

Application Number
CN202510537369.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-27
Publication Date
2025-05-30
Estimated Expiration
2045-04-27

AI Technical Summary

Technical Problem

It is difficult for the existing technology to achieve unified supervision of power generation equipment and high-precision fault identification, resulting in high operation and maintenance costs, difficult equipment fault prediction, and information islands.

Method used

Design an integrated power generation equipment health status supervision system, and collect and analyze data from asynchronous motors, battery online monitoring systems and other equipment through multi-source data access modules, wireless communication modules, data storage modules, fault diagnosis and evaluation modules, early warning modules and visual human-computer interaction modules, and use parent convolutional neural networks and child machine learning models for fault prediction and diagnosis.

Benefits of technology

It realizes the integration and centralized management of multi-source data of power generation equipment, improves the accuracy and efficiency of fault identification, reduces operation and maintenance costs, and avoids information island problems.

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Abstract

The invention provides an integrated power generation equipment health state supervision system. Comprising the following steps: acquiring operation parameters and environment parameter data of an asynchronous motor precision point inspection diagnosis device, a TLI-X8 battery online monitoring system, a collecting ring intelligent data acquisition system, a transformer oil dissolved gas and micro water online monitoring system, a generator partial discharge online monitoring device and a wireless temperature measurement system; and training prediction is performed to obtain fault prediction data, so that accurate fault diagnosis can be realized on the basis of extracting original data features by deep learning. According to the application, an asynchronous motor precision point inspection diagnosis device, a transformer oil gas analysis system, a partial discharge detection system, a wireless temperature measurement system, a battery online monitoring system and the like are organically combined, and power generation equipment faults are predicted through the mother convolutional neural network model and the sub-machine learning model, so that early fault discrimination is realized; and the accuracy and efficiency of equipment fault identification are greatly improved.
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