System and method for acquiring and processing dry data of transformer

Through the multi-source data acquisition and deep learning model combined with multi-objective evolution algorithm and Kalman filtering, the precise endpoint prediction and energy consumption evaluation of the transformer drying process are achieved, and the problems of insufficient flexibility and high energy consumption in the existing technology are solved, and efficient energy-saving and safe drying control are achieved.

CN120579167AActive Publication Date: 2025-09-02JIANGSU WEILAN DIGITAL INTELLIGENCE TECH CO LTD
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Patent Information

Application Number
CN202511079451.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-04
Publication Date
2025-09-02
Estimated Expiration
2045-08-04

AI Technical Summary

Technical Problem

The drying treatment of existing transformers relies on manual judgment of fixed parameters, and lacks flexibility. The high-temperature heating and vacuum exhaust modes cause high energy consumption and cannot meet modern production needs.

Method used

A multi-source acquisition and processing module is used to collect five-dimensional dynamic data in real time, and a dry end point prediction model is constructed through attention recognition unit, LSTM and fully connected unit, and real-time correction feedback is performed by combining multi-objective evolution algorithm and Kalman filtering to achieve dynamic drying control.

Benefits of technology

It realizes accurate end point prediction and energy consumption evaluation of the transformer drying process, ensures high efficiency and energy saving and safety, adapts to complex industrial environments, and provides high-fidelity end point prediction and energy consumption evaluation.

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Abstract

The invention discloses an acquisition and processing system and method for dry data of a transformer, and relates to the technical field of data processing. Comprising the following steps: step 1, multi-source data real-time acquisition and edge preprocessing; 2, constructing a drying end point prediction model; 3, constructing a composite objective function, and solving the composite objective function by adopting a multi-objective evolutionary algorithm; 4, performing real-time correction and feedback; according to the method, key parameters in the drying treatment process are monitored in real time, an accurate drying end point prediction model is automatically constructed, heating and air exhaust curves are dynamically optimized according to the real-time working condition and the production strategy, and traditional manual blind judgment and static presetting are replaced; and meanwhile, the operation energy consumption is remarkably reduced, the optimal balance of the energy consumption and the production efficiency is realized through a multi-objective evolutionary algorithm and an online Kalman filtering correction closed loop mechanism, the drying quality and the equipment safety are improved, the energy cost is greatly saved, and the dual requirements of modern intelligent manufacturing for flexibility, energy conservation and consumption reduction are met.
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