A new intelligent fault diagnosis and prognosis method for wind turbine doubly-fed induction generator

Condition Monitoring and fault-prognosis approaches are typical methods to reduce the energy production cost and Wind Turbine downtime. In this paper, a new CM combinatory system and fault prognosis are proposed based on an adaptive threshold, feature-level fusion, and new degradation indicator and the CM operation is based on a new index Symptom of Degeneration crossing of an adaptive threshold. Also, a new adaptive threshold is proposed based on the fuzzy rules and WT operation point. Fault prognosis is conducted with the Least-Squares Support-Vector Machine method, and Particle Swarm Optimization is employed for the optimum selecting of the wavelet Kernel function and the SVM parameters. The proposed technique is compared with other methods and the simulation results illustrate the PSO-LS-SVM superiorities. The effectiveness of the proposed prognostic structure is evaluated using a WT test-rig prototype. The experimental results demonstrate that the Condition-Based Maintenance is improved by the proposed structure and the RUL is predicted before serious damage occurrences.

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Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data

Energies ◽

10.3390/en12173396 ◽

2019 ◽

Vol 12 (17) ◽

pp. 3396 ◽

Cited By ~ 2

Author(s):

Mingzhu Tang ◽

Wei Chen ◽

Qi Zhao ◽

Huawei Wu ◽

Wen Long ◽

...

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Support Vector Regression ◽

Confidence Intervals ◽

Wind Turbines ◽

Large Scale ◽

Support Vector ◽

Effective Response ◽

Support Vector Regression Model ◽

Doubly Fed

Fault diagnosis and forecasting contribute significantly to the reduction of operating and maintenance associated costs, as well as to improve the resilience of wind turbine systems. Different from the existing fault diagnosis approaches using monitored vibration and acoustic data from the auxiliary equipment, this research presents a novel fault diagnosis and forecasting approach underpinned by a support vector regression model using data obtained by the supervisory control and data acquisition system (SCADA) of wind turbines (WT). To operate, the extraction of fault diagnosis features is conducted by measuring SCADA parameters. After that, confidence intervals are set up to guide the fault diagnosis implemented by the support vector regression (SVR) model. With the employment of confidence intervals as the performance indicators, an SVR-based fault detecting approach is then developed. Based on the WT SCADA data and the SVR model, a fault diagnosis strategy for large-scale doubly-fed wind turbine systems is investigated. A case study including a one-year monitoring SCADA data collected from a wind farm in Southern China is employed to validate the proposed methodology and demonstrate how it works. Results indicate that the proposed strategy can support the troubleshooting of wind turbine systems with high precision and effective response.

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An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples

Renewable Energy ◽

10.1016/j.renene.2019.06.103 ◽

2020 ◽

Vol 145 ◽

pp. 642-650 ◽

Cited By ~ 17

Author(s):

Jinjiang Wang ◽

Yuanyuan Liang ◽

Yinghao Zheng ◽

Robert X. Gao ◽

Fengli Zhang

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Predictive Maintenance ◽

Diagnosis And Prognosis ◽

Turbine Bearing ◽

Fault Diagnosis And Prognosis

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Support vector data description for fusion of multiple health indicators for enhancing gearbox fault diagnosis and prognosis

Measurement Science and Technology ◽

10.1088/0957-0233/22/2/025102 ◽

2010 ◽

Vol 22 (2) ◽

pp. 025102 ◽

Cited By ~ 57

Author(s):

Dong Wang ◽

Peter W Tse ◽

Wei Guo ◽

Qiang Miao

Keyword(s):

Fault Diagnosis ◽

Health Indicators ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Data Description ◽

Diagnosis And Prognosis ◽

Fault Diagnosis And Prognosis ◽

Multiple Health

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Research on Fault Detection for Three Types of Wind Turbine Subsystems Using Machine Learning

Energies ◽

10.3390/en13020460 ◽

2020 ◽

Vol 13 (2) ◽

pp. 460 ◽

Cited By ~ 1

Author(s):

Zuojun Liu ◽

Cheng Xiao ◽

Tieling Zhang ◽

Xu Zhang

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Fault Detection ◽

Convolutional Neural Network ◽

Wind Turbine ◽

Cost Effective ◽

Support Vector ◽

Detection Accuracy ◽

Machine Method ◽

Scada System

In wind power generation, one aim of wind turbine control is to maintain it in a safe operational status while achieving cost-effective operation. The purpose of this paper is to investigate new techniques for wind turbine fault detection based on supervisory control and data acquisition (SCADA) system data in order to avoid unscheduled shutdowns. The proposed method starts with analyzing and determining the fault indicators corresponding to a failure mode. Three main system failures including generator failure, converter failure and pitch system failure are studied. First, the indicators data corresponding to each of the three key failures are extracted from the SCADA system, and the radar charts are generated. Secondly, the convolutional neural network with ResNet50 as the backbone network is selected, and the fault model is trained using the radar charts to detect the fault and calculate the detection evaluation indices. Thirdly, the support vector machine classifier is trained using the support vector machine method to achieve fault detection. In order to show the effectiveness of the proposed radar chart-based methods, support vector regression analysis is also employed to build the fault detection model. By analyzing and comparing the fault detection accuracy among these three methods, it is found that the fault detection accuracy by the models developed using the convolutional neural network is obviously higher than the other two methods applied given the same data condition. Therefore, the newly proposed method for wind turbine fault detection is proved to be more effective.

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Support Vector Machine for Machine Fault Diagnosis and Prognosis

Journal of System Design and Dynamics ◽

10.1299/jsdd.2.12 ◽

2008 ◽

Vol 2 (1) ◽

pp. 12-23 ◽

Cited By ~ 20

Author(s):

Bo-Suk YANG ◽

Achmad WIDODO

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Support Vector ◽

Diagnosis And Prognosis ◽

Machine Fault Diagnosis ◽

Machine Fault ◽

Fault Diagnosis And Prognosis

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Wind Turbine Gearbox Anomaly Detection based on Adaptive Threshold and Twin Support Vector Machines

IEEE Transactions on Energy Conversion ◽

10.1109/tec.2021.3075897 ◽

2021 ◽

pp. 1-1

Author(s):

Harsh S Dhiman ◽

Dipankar Deb ◽

S. M. Muyeen ◽

Innocent Kamwa

Keyword(s):

Support Vector Machines ◽

Anomaly Detection ◽

Wind Turbine ◽

Adaptive Threshold ◽

Support Vector ◽

Wind Turbine Gearbox ◽

Twin Support Vector Machines ◽

Vector Machines

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SCADA Data-Driven Wind Turbine Main Bearing Fault Prognosis Based on One-Class Support Vector Machines

Renewable Energy and Power Quality Journal ◽

10.24084/repqj19.290 ◽

2021 ◽

Vol 19 ◽

pp. 338-343

Author(s):

A. Insuasty ◽

◽

C. Tutivén ◽

Y. Vidal

Keyword(s):

Wind Turbine ◽

Data Driven ◽

Support Vector ◽

Main Bearing ◽

Bearing Fault ◽

Data Contamination ◽

Vector Machines ◽

Unsupervised Approach ◽

Fault Prognosis ◽

And Performance

This work proposes a fault prognosis methodology to predict the main bearing fault several months in advance and let turbine operators plan ahead. Reducing downtime is of paramount importance in wind energy industry to address its energy loss impact. The main advantages of the proposed methodology are the following ones. It is an unsupervised approach, thus it does not require faulty data to be trained; ii) it is based only on exogenous data and one representative temperature close to the subsystem to diagnose, thus avoiding data contamination; iii) it accomplishes the prognosis (various months in advance) of the main bearing fault; and iv) the validity and performance of the established methodology is demonstrated on a real underproduction wind turbine.

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