Communication anomaly detection method and system for wind turbine variable pitch control system

By performing time-sequential processing and adaptive decomposition of the CAN message stream of the wind turbine pitch control system, and combining it with the isolated forest model for anomaly scoring, the problem of insufficient accuracy and robustness of communication anomaly detection in the existing technology is solved, and early fault identification and predictive maintenance of the communication system are realized.

CN122372465APending Publication Date: 2026-07-10BEIJING HUANENG XINRUI CONTROL TECH +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
BEIJING HUANENG XINRUI CONTROL TECH
Filing Date
2026-03-24
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing methods for detecting communication anomalies in wind turbine pitch control systems cannot effectively predict communication degradation trends, resulting in insufficient accuracy and robustness in communication anomaly detection. This makes it impossible to identify potential faults in a timely manner, increasing system complexity and maintenance costs.

Method used

By performing time-series processing on the original CAN message stream, extracting the inter-frame arrival time sequence, and combining it with SCADA data to determine the operating conditions in real time, the system performs time-series decomposition that is adaptive to the operating conditions. It then uses a pre-trained isolated forest model to perform anomaly scoring and cumulative evaluation, and outputs the health status of the communication system.

Benefits of technology

It significantly improves the accuracy and robustness of communication anomaly detection in wind turbine pitch control systems, enabling early identification of potential faults, reducing false alarms, and enhancing the system's predictive maintenance capabilities.

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Abstract

This disclosure provides a method and system for detecting communication anomalies in a wind turbine pitch control system. First, the original CAN message stream is processed into a time series, extracting multiple inter-frame arrival time sequences. Then, the wind turbine's operating condition is determined in real-time using SCADA data, generating an operating condition time series. Further, an adaptive time series decomposition is used to combine the inter-frame arrival time sequences and the operating condition time series. Before decomposition, a logarithmic transformation is performed on the data, effectively addressing the inherent shortcomings of traditional additive model time series decomposition in handling heteroscedasticity, thus obtaining multiple variance-stable residual sequences. Finally, a pre-trained isolated forest model is used to score these residual sequences for anomalies, and the cumulative evaluation of anomaly scores combined with state judgment outputs the health status of the wind turbine pitch control system. This approach significantly improves the accuracy and robustness of communication anomaly detection in wind turbine pitch control systems.
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