Fault diagnosis of direct-drive wind turbine based on support vector machine

An, Xueli; Jiang, Dongxiang; Li, S H; Chen, J

doi:10.1088/1742-6596/305/1/012030

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…The solution of the In Ref. [30], a fault diagnosis method for a direct-drive wind turbine based on support vector machine (SVM) and feature selection was presented. Five direct-drive wind turbine experiments were carried out, the features of which were analyzed.…”

Section: A Support Vector Machine (Svm)mentioning

confidence: 99%

Fault Diagnosis Methods Based on Machine Learning and its Applications for Wind Turbines: A Review

et al. 2021

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With the increase in the installed capacity of wind power systems, the fault diagnosis and condition monitoring of wind turbines (WT) has attracted increasing attention. In recent years, machine learning (ML) has played a crucial role as an emerging technology for fault diagnosis in wind power systems has played a crucial role. Even though ML methods have shown great potential in dealing with the issues related to the fault diagnosis of WT, there are still some challenges encountered in many aspects. In this paper, typical fault diagnosis methods based on ML methods for wind power systems are thoroughly reviewed in terms of both theoretical fundamentals and industrial applications, including traditional machine learning (TML), artificial neural networks (ANN), deep learning (DL) and transfer learning (TL), in the development line of ML technologies. The advantages and disadvantages of various methods are analyzed and discussed. Meanwhile, a distribution diagram is provided for the discussions of ML methods applied for WT fault diagnosis, and the existing challenges on the applications for fault diagnosis based on ML for wind power generation systems are presented. Moreover, some prospects for future research directions are provided. INDEX TERMSwind turbines, machine learning, fault diagnosis, review Nomenclature AE autoencoder AI artificial intelligence ANN artificial neural networks ART adaptive resonance theory BPNN back propagation neural network CA clustering algorithm CNN convolutional neural network DAE denoising autoencoder DBN deep belief network FCM fuzzy C-means clustering HMM hidden Markov model LSSVM least squares support vector machine LSTM long short-term memory network ML machine learning RBFNN radial basis function neural network RF random forest RLM extreme learning machine RNN recurrent neural network RVM relevance vector machine SAE stacked autoencoder SCADA supervisory control and data acquisition SDAE stacked denoising autoencoders 23 SOM self-organizing map 24 SVM support vector machine 25 t-SNE t-distributed logistic neighbor embedding 26 TL transfer learning 27 TML traditional machine learning 28 VMD variational mode decomposition 29 WT wind turbines 30 I. INTRODUCTION 31 With the increasing consumption of fossil fuels and the 32 gradual deterioration of environmental problems, there is an 33 urgent need to find a clean and renewable energy source. 34 Wind energy is irreplaceable in energy structures owing 35 to its rapid growth. Wind power accounts for 20% of the 36 world's total electricity, and WT are receiving increasing 37 attention as the core components of wind power generators [1], 38 [2]. Usually, wind power generators are installed in remote 39 areas or offshore areas where traffic is inconvenient, and 40 the gearbox is generally installed in a sky above tens or 41even hundreds of meters from the ground. In addition, the 42 blades are often subjected to complex alternating impact loads

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Section: A Support Vector Machine (Svm)mentioning

confidence: 99%

Fault Diagnosis Methods Based on Machine Learning and its Applications for Wind Turbines: A Review

et al. 2021

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“…It expresses the ratio between the object number of matching rules and the object number of condition part in the matching rules. According to formula (5), compute the belief level for each rule of the node { } . Add the rules ,the belief level of which are greater than 0.75,to the rule set of the node.…”

Section: Suppose That the Decision Rulementioning

confidence: 99%

“…Literature [4] proposes a wavelet neural network based fault diagnosis method for converter with the use of improved algorithm of variable weight and alter learning coefficient. Literature [5],which takes comprehensive consideration of wind speed, rotational speed,time domain and frequency domain feature parameters of vibration in horizontal direction and vertical direction, and many other multi-source information, proposes a vector machine support(SVM)-based fault diagnosis method for direct-drive wind turbine generator. Literature [6] introduces a method for fast data classification and the draft boundary of the fault and non-fault by adopting single-layer feed-forward neural network.…”

Section: Introductionmentioning

confidence: 99%

Application of Multiple Level Rules Set in Fault Diagnosis of the Wind Power Generation System

2012

AMR

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For the incompletion problem of sensors’ collected data in fault diagnosis of the wind power system, this article puts forward a kind of multiple level rules set based on rough set. First, let the sensors’ collected data go through Fourier transform and extract its feature attributes as well as discrete them. Establish the decision table of fault diagnosis according to attribute values. Then set out from the decision table to establish a multiple level set of nodes with diverse reduced levels and deduce the rules of each node, which has a corresponding belief level. When in reasoning and decision-making of the new data using the multiple level rules set, match the information of the new data with the rule of its corresponding node. Finally, achieve the fault diagnosis of wind power generation system by choosing comprehensive evaluation algorithm. The result of the diagnosis example shows the reliability and accuracy of this method in the diagnosis of fault types for wind power generation system.

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“…Support vector machine (SVM) falls under the category of machine learning; this learning is a type of classification technique that could classify objects or data to its respective class [77]. It can be used in many applications, such as classifying faults diagnosis in wind turbine (WT) systems [78][79][80][81][82][83][84][85].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Technique Using Support Vector Machine in Wind Turbine Fault Diagnosis

Shahrulhisham

Chong²,

Yaw³

et al. 2022

J. Phys.: Conf. Ser.

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Wind energies are one of the most used resources worldwide and favours the economy by not emitting harmful gases that could lead to global warming. It is a cost-efficient method and environmentally friendly. Hence, explains the popularity of wind energy production over the years. Unfortunately, a minor fault could be contagious by affecting the nearby components, then a more complicated problem might arise, which may be costly. Thus, this article conducted a machine learning technique, support vector machine (SVM) to monitor the health of the wind turbine system by classifying the class of healthy data and faulty data. Some SVM types were experimented with, including Linear, Quadratic, Cubic, Fine Gaussian, Medium Gaussian, and Coarse Gaussian. Then these models were trained under different validation schemes that are cross-validation, holdout validation, and re-substitution validation as an approach to evaluate the performance of each model. In the end, Cubic SVM is proven to outperformed other models under the provision of 10-fold cross-validation with an accuracy of 98.25%. The result showed that Cubic SVM has the best performance while Linear SVM has the least accuracy among other models. Hence choosing the default value is preferred as the final product to diagnose the fault in wind turbine systems.

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Fault diagnosis of direct-drive wind turbine based on support vector machine

Cited by 5 publications

References 8 publications

Fault Diagnosis Methods Based on Machine Learning and its Applications for Wind Turbines: A Review

Fault Diagnosis Methods Based on Machine Learning and its Applications for Wind Turbines: A Review

Application of Multiple Level Rules Set in Fault Diagnosis of the Wind Power Generation System

Application of Machine Learning Technique Using Support Vector Machine in Wind Turbine Fault Diagnosis

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