Fault diagnosis and early warning method and system for wind power plant

By using an edge-cloud collaborative architecture and a multimodal sensor array, combined with digital twin models and federated learning, the problem of multimodal data fusion in wind farm fault diagnosis systems has been solved, enabling high-sensitivity identification and accurate location of early faults, thereby improving the operational reliability and economy of wind farms.

CN121557053AActive Publication Date: 2026-02-24INNER MONGOLIA SIHUA NEW ENERGY DEVELOPMENT CO LTD +2
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Patent Information

Application Number
CN202511530499.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-24
Publication Date
2026-02-24
Estimated Expiration
2045-10-24

AI Technical Summary

Technical Problem

Existing fault diagnosis systems for wind farms rely on a single data source and lack multimodal data fusion, resulting in insensitivity to early, subtle fault characteristics, high false negative rates, poor model generalization ability, inability to achieve accurate location and collaborative operation and maintenance across the entire farm, and fragmented systems that are difficult to provide optimized operation and maintenance strategies.

Method used

It adopts a layered processing architecture with edge-cloud collaboration, collects data through a multimodal sensor array, and combines digital twin models and federated learning mechanisms to perform field-level spatiotemporal correlation analysis, realize fault identification, location and life prediction, generate collaborative operation and maintenance decisions, and form closed-loop management.

Benefits of technology

It significantly improves the accuracy of fault identification and location, reduces the false alarm rate, supports collaborative diagnosis of large-scale wind turbine clusters, improves operational reliability and economy, and promotes the operation and maintenance mode from passive response to proactive prediction and collaborative optimization.

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Abstract

The invention belongs to the technical field of wind power generation, and particularly relates to a fault diagnosis and early warning method and system for a wind power plant, and the method comprises the steps: collecting the state data of a plurality of physical fields through a plurality of types of sensor arrays disposed at key parts of a fan; performing preprocessing and feature extraction on the data to obtain high-dimensional feature data; driving the field-level digital twinborn model to perform real-time simulation based on the high-dimensional feature data, and determining an initial fault recognition result and severity by comparing a simulation predicted value with actual sensing data and combining with an unsupervised anomaly detection algorithm; a diagnosis result is calibrated through multi-modal high-dimensional feature data fusion; predicting the remaining useful life of the key component based on the fault severity and the evolution trend thereof, and generating a collaborative operation and maintenance decision by using a multi-objective optimization algorithm; and an operation and maintenance decision is automatically executed through a closed-loop feedback execution mechanism. According to the invention, the accuracy of fault diagnosis and early warning of the wind power plant is improved.
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