Open-Circuit Fault Diagnosis of Wind Power Converter Using Variational Mode Decomposition, Trend Feature Analysis and Deep Belief Network

Zhang, Jingxuan; Sun, Hexu; Sun, Zexian; Dong, Yan; Dong, Weichao

doi:10.20944/preprints202002.0392.v1

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…Initially, the DFIG equipped with partial scale converter became attractive due to the controllability of active and reactive power. However, with the development of technology, the PMSG equipped with full scale converter has more obvious advantages in low-voltage ride-through capability, higher efficiency and power density [19], which is the main development trend of the wind turbine market in the future. Fig.…”

Section: The Proposed Strategymentioning

confidence: 99%

Reliability Assessment of Wind Power Converter Considering SCADA Multistate Parameters Prediction Using FP-Growth, WPT, K-Means and LSTM Network

Zhang

Sun

et al. 2020

IEEE Access

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In order to cooperate the wind farm operators with grasping the operation status of wind power converter, a novel reliability assessment strategy is proposed based on supervisory control and data acquisition (SCADA) multistate parameters prediction of permanent magnet synchronous generator (PMSG) wind turbine. The strategy considers ''off-line training, on-line matching and assessment''. The operation reliability of wind power converter is obtained via the analysis and weight computing of confidence level, prediction value and actual value of SCADA multistate parameters. In the ''off-line training'' part, first, the FP-Growth association algorithm is employed to analyze the confidence levels of SCADA variables to the faults of wind power converter. The variables with high confidence level are defined as SCADA multistate parameters. Afterwards, wavelet packet transform (WPT) and K-means algorithm are employed to decompose, reconstruct, normalize and cluster the time series (off-line data) of multistate parameters under normal operation of wind turbine, to improve the generalization capability of long short term memory (LSTM) prediction model. In the part of ''on-line matching and assessment'', the actual value time series (on-line data) of multistate parameters are decomposed and reconstructed by WPT. Each normalized sample is matched to the closest cluster centroid, which is generated in the part of ''off-line training''. Then the corresponding LSTM model is conducted to predict based on the each sample. The final prediction value is sum of prediction results of entire samples in clusters. Finally, the reliability of wind power converter is assessed by the proposed strategy. The effectiveness of proposed assessment strategy is verified by the experimental results on a PMSG wind power converter. INDEX TERMS Reliability assessment, wind power converter, association rules algorithm, deep learning, recurrent neural network.

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Section: The Proposed Strategymentioning

confidence: 99%

Reliability Assessment of Wind Power Converter Considering SCADA Multistate Parameters Prediction Using FP-Growth, WPT, K-Means and LSTM Network

Zhang

Sun

et al. 2020

IEEE Access

Self Cite

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A Layering Linear Discriminant Analysis-Based Fault Diagnosis Method for Grid-Connected Inverter

Jin

Wang

Zhou

et al. 2022

JMSE

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Grid-connected inverters are the core equipment for connecting marine energy power generation systems to the public electric utility. The variation of current sensor fault severity will make fault samples multimodal. However, linear discriminant analysis assumes that the same fault is independent and identically distributed. To solve this problem, this paper proposes a layering linear discriminant analysis method based on traditional linear discriminant analysis. The proposed method divides the historical fault data based on the sensor fault severity layer-by-layer until the distribution of the same fault category in each subset is very close. Linear discriminant analysis is used to analyze historical fault data in each subgroup, and the kappa coefficient is applied as the basis for ending the training process. A BP neural network is employed to estimate the fault severity during the testing process, and the fault diagnosis sub-model is selected. The proposed method enables the accurate diagnosis of faults with different distributions in the same category and provides an accurate estimate of the sensor’s fault severity degree. The estimated value of the sensor’s fault degree can provide critical information for the maintenance of the equipment and can be used to correct the sensor’s output.

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Fault Diagnosis of High-Speed Brushless Permanent-Magnet DC Motor Based on Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

Liu

Zhao³

et al. 2021

Symmetry

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With the development of reliability theory, people realized that “absolutely reliable” machines could not be made. With its incomparable advantages, the high-speed permanent-magnet brushless DC motor is usually used in the symmetrical structure of high-speed operation working systems, which at present are widely used in aerospace and other fields. The structure of the manufacturing process involves a strict processing, but in the process of work failure could still occur. No matter what field the high-speed permanent magnet brushless DC motor is applied to, it is very important to identify states and run fault diagnosis, which is of great significance to maintain the reliability of the motor and its working system. In this study, the fault diagnosis method of a high-speed permanent-magnet brushless DC motor is studied, and a combination model of modified gray wolf optimization algorithm (MGWO) and support vector machine (SVM) have been proposed for the motor fault diagnosis research. Based on the traditional gray wolf optimization (GWO) algorithm, the optimization performance of the algorithm is improved by initializing the population through a tent map and introducing a sine wave dynamic adaptive factor. Then the modified algorithm is used to optimize the internal parameters of SVM to improve the diagnostic accuracy of the model. Through the signal acquisition test, the current signals under different fault states and faultless states were collected, and the current signal data set required for the experiment is obtained. The experimental result showed that, compared with GWO or sailfish optimization (SFO) optimized SVM models, Extreme learning machine and Back Propagation neural network classical classification models, the fault diagnosis accuracy of the proposed model is the highest, proving the excellent classification performance and good robustness of the MGWO-SVM model.

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Open-Circuit Fault Diagnosis of Wind Power Converter Using Variational Mode Decomposition, Trend Feature Analysis and Deep Belief Network

Cited by 3 publications

References 22 publications

Reliability Assessment of Wind Power Converter Considering SCADA Multistate Parameters Prediction Using FP-Growth, WPT, K-Means and LSTM Network

Reliability Assessment of Wind Power Converter Considering SCADA Multistate Parameters Prediction Using FP-Growth, WPT, K-Means and LSTM Network

A Layering Linear Discriminant Analysis-Based Fault Diagnosis Method for Grid-Connected Inverter

Fault Diagnosis of High-Speed Brushless Permanent-Magnet DC Motor Based on Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

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