Fault Diagnosis of Pitch System Based On Data Statistics

Wind turbines (WTs) generally comprise several complex and interconnected systems, such as hub, converter, gearbox, generator, yaw system, pitch system, hydraulic system control system,integration control system, and auxiliary system. Moreover, fault diagnosis plays an important role in ensuring WT safety. In the past decades, machine learning (ML) has showed a powerful capability in fault detection and diagnosis of WTs, thereby remarkably reducing equipment downtime and minimizing financial losses. This study provides a comprehensive review of recent studies on ML methods and techniques for WT fault diagnosis. These studies are classified as supervised, unsupervised, and semi-supervised learning methods. Existing state-of-the-art methods are analyzed and characteristics are discussed. Perspectives on challenges and further directions are also provided.

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Effective fault diagnosis method for the pitch system, the drive train, and the generator with converter in a wind turbine system

Electrical Engineering ◽

10.1007/s00202-021-01446-8 ◽

2022 ◽

Author(s):

Abdelmoumen Saci ◽

Lakhmissi Cherroun ◽

Ahmed Hafaifa ◽

Omar Mansour

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Drive Train ◽

Wind Turbine System ◽

Diagnosis Method ◽

Pitch System

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Method for Fault Diagnosis of PMSM Current Sensor in Fan Pitch System

2019 IEEE Sustainable Power and Energy Conference (iSPEC) ◽

10.1109/ispec48194.2019.8975380 ◽

2019 ◽

Author(s):

Jin Li ◽

Chaobo Chen ◽

Chang Xu ◽

Xinyu Sha ◽

Yue Wang ◽

...

Keyword(s):

Fault Diagnosis ◽

Current Sensor ◽

Pitch System

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Pitch fault diagnosis of wind turbines in multiple operational states using supervisory control and data acquisition data

Wind Engineering ◽

10.1177/0309524x18791407 ◽

2018 ◽

Vol 43 (5) ◽

pp. 443-458 ◽

Cited By ~ 1

Author(s):

Lu Wei ◽

Zheng Qian ◽

Cong Yang ◽

Yan Pei

Keyword(s):

Fault Diagnosis ◽

Data Acquisition ◽

Wind Turbines ◽

Supervisory Control ◽

Domain Knowledge ◽

Gaussian Mixture ◽

Abnormal Behavior ◽

Diagnosis Method ◽

Pitch System ◽

Relationship Of

Supervisory control and data acquisition data including comprehensive signal information have been widely applied to fault diagnosis. However, because of the complex operational condition of wind turbines, supervisory control and data acquisition data become complicated and abstract to study. This article proposes a pitch fault diagnosis method of wind turbines in multiple operational states using supervisory control and data acquisition data. According to the performance of characteristic parameters in nine operational states of wind turbines, Gaussian mixture model clustering and the analysis of normal performance curves are applied to model the relationship of pitch angle, rotor speed, and wind speed. Four cases have been studied to demonstrate the feasibility of the proposed method. The advantages of the proposed approach are as follows: (1) simplifying the analysis of supervisory control and data acquisition data through dividing the data into nine parts; (2) detecting pitch faults earlier than supervisory control and data acquisition monitoring system; (3) visualizing the abnormal behavior of the pitch system; and (4) improving the interpretability of the method with the incorporation of domain knowledge.

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Error Analysis for Correlation Algorithm Based Servo Valve Frequency Characteristic Measurement

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.128-129.1438 ◽

2011 ◽

Vol 128-129 ◽

pp. 1438-1442

Author(s):

Hua Yao Chang ◽

Jun Zheng Wang ◽

Jiang Bo Zhao ◽

Shou Kun Wang

Keyword(s):

Fault Diagnosis ◽

Fault Tree ◽

Back Propagation ◽

Graph Model ◽

Bond Graph ◽

Effect Analysis ◽

Variable Pitch ◽

Cause And Effect ◽

Cause And Effect Analysis ◽

Pitch System

A fault diagnosis based on bond graph model is proposed for hydraulic variable pitch system. Because the knowledge representation of bond graph model can provide information of cause and effect between components, a bond graph model of hydraulic variable pitch system is given above rated wind speed. A fault tree, using cause and effect analysis by back propagation, is developed. Qualitative value of parameters is assigned and the fault source is detected by analyzing boundary parameters of fault tree. Comparing with quantitative fault diagnosis based on model, the bond graph fault diagnosis is more flexible and has good completeness.

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