Condition Monitoring of Wind Power Converters Using Heat Flux Sensor

Baygildina, Elvira; Smirnova, Liudmila; Juntunen, Raimo; Murashko, Kirill; Mityakov, A. V.; Kuisma, Mikko; Pyrhönen, Olli; Peltoniemi, Pasi; Hynynen, Katja; Mityakov, V. Yu.; Сапожников, С. З.

doi:10.15866/iree.v11i3.8404

Cited by 3 publications

(2 citation statements)

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“…In [33], a hybrid model is established covering inverter and permanent magnet synchronous generators to diagnose inverter open circuit fault by analyzing the residual signal of the generator stator current through the model. In [34], the use of heat flux sensors to monitor the condition of a wind power converter, mainly for the failure and aging of electronic power devices, i.e., insulated-gate bipolar transistors (IGBTs) in the converter, where models are developed for the implementation of condition monitoring is proposed. In [35], a generic mathematical model of a two-level converter with open-switch faults is described.…”

Section: Model-based Methodsmentioning

confidence: 99%

Deep Learning Method for Fault Detection of Wind Turbine Converter

et al. 2021

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The converter is an important component in wind turbine power drive-train systems, and usually, it has a higher failure rate. Therefore, detecting the potential faults for prediction of its failure has become indispensable for condition-based maintenance and operation of wind turbines. This paper presents an approach to wind turbine converter fault detection using convolutional neural network models which are developed by using wind turbine Supervisory Control and Data Acquisition (SCADA) system data. The approach starts with the selection of fault indicator variables, and then the fault indicator variables data are extracted from a wind turbine SCADA system. Using the data, radar charts are generated, and the convolutional neural network models are applied to feature extraction from the radar charts and characteristic analysis of the feature for fault detection. Based on the analysis of the Octave Convolution (OctConv) network structure, an improved AOctConv (Attention Octave Convolution) structure is proposed in this paper, and it is applied to the ResNet50 backbone network (named as AOC–ResNet50). It is found that the algorithm based on AOC–ResNet50 overcomes the issues of information asymmetry caused by the asymmetry of the sampling method and the damage to the original features in the high and low frequency domains by the OctConv structure. Finally, the AOC–ResNet50 network is employed for fault detection of the wind turbine converter using 10 min SCADA system data. It is verified that the fault detection accuracy using the AOC–ResNet50 network is up to 98.0%, which is higher than the fault detection accuracy using the ResNet50 and Oct–ResNet50 networks. Therefore, the effectiveness of the AOC–ResNet50 network model in wind turbine converter fault detection is identified. The novelty of this paper lies in a novel AOC–ResNet50 network proposed and its effectiveness in wind turbine fault detection. This was verified through a comparative study on wind turbine power converter fault detection with other competitive convolutional neural network models for deep learning.

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Section: Model-based Methodsmentioning

confidence: 99%

Deep Learning Method for Fault Detection of Wind Turbine Converter

et al. 2021

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“…This method can detect multiple open circuit faults and ensure that they are immune from false alarms. Baygildina et al [14] proposed the use of heat flux sensors to monitor the state of power converters, mainly for the failure and aging of power electronics, i.e., IGBTs in the converters. Wu and Liu [15] developed a fault diagnosis approach for detecting the fault that causes the pitch angle change in the wind turbine pitch actuator.…”

Section: %mentioning

confidence: 99%

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

et al. 2020

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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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