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

Abstract

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.

Data-Driven Predictive Maintenance of Wind Turbine Based on SCADA Data

“…These metrics are viz: coefficient of determination(R-Squared), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The corresponding (17) to (20) shows the calculation formulas for the relevant metrics:…”

“…When considering sub-components to monitor, decisions should be based on failure rates and downtime per failure. Priority show is given to components that are more prone to failure and have extended lead times for replacement [17]. Data based on a survey of failure of wind turbine subsystems from two wind farms in China showed that 68% total downtime was caused by generator, converter, and pitch systems [18].…”

Deep Learning Method for Fault Detection of Wind Turbine Converter

“…It is widely applied to the fault detection/diagnosis of the converter. The typically applied techniques include neural networks, expert systems, support vector machine (SVM), fuzzy logic and cluster analysis [45][46][47][48][49].…”

“…The three-phase output current signal is selected as the research object and is processed by the wavelet transform to reduce the signal noise. In [49], fault detection was conducted for three wind turbine subsystems, including a pitch system, generator and converter by developing SVR, SVM and convolutional neural network (CNN) models using SCADA system data. It is verified that the CNN model's performance is superior to SVR and SVM models.…”

A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks

“…The success of such an approach aimed at identifying failures is determined by the accuracy of the model developed. Several tools, such as K-nearest Neighbors [29,33], clustering algorithms [34], Support Vector Machines [35][36][37][38], both static and dynamic neural networks [39][40][41][42][43][44][45][46][47][48][49], and even deep learning approaches [50,51], have been proven very effective in modeling the relations between the parameters of a wind turbine.…”