Health Status Assessment for Wind Turbine with Recurrent Neural Networks

Sun, Zexian; Sun, Hexu

doi:10.1155/2018/6972481

Cited by 18 publications

(7 citation statements)

References 18 publications

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“…Sun et al [39] introduced an RNN based model for monitoring the health of the wind turbine. Due to the generation of individual faults, Supervisory Control and Data Acquisition (SCADA) variables of the wind turbine may change continuously.…”

Section: Development Of Deep Learning Based Rnn In Wind Energy Forecastingmentioning

confidence: 99%

Deep Learning Based Recurrent Neural Networks to Enhance the Performance of Wind Energy Forecasting: A Review

Paramasivan¹

2021

RIA

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In the modern era, deep learning is a powerful technique in the field of wind energy forecasting. The deep neural network effectively handles the seasonal variation and uncertainty characteristics of wind speed by proper structural design, objective function optimization, and feature learning. The present paper focuses on the critical analysis of wind energy forecasting using deep learning based Recurrent neural networks (RNN) models. It explores RNN and its variants, such as simple RNN, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional RNN models. The recurrent neural network processes the input time series data sequentially and captures well the temporal dependencies exist in the successive input data. This review investigates the RNN models of wind energy forecasting, the data sources utilized, and the performance achieved in terms of the error measures. The overall review shows that the deep learning based RNN improves the performance of wind energy forecasting compared to the conventional techniques.

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Section: Development Of Deep Learning Based Rnn In Wind Energy Forecastingmentioning

confidence: 99%

Deep Learning Based Recurrent Neural Networks to Enhance the Performance of Wind Energy Forecasting: A Review

Paramasivan¹

2021

RIA

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“…e traditional diagnosis of rotor fault causes is mainly based on the expert system [7], but the knowledge is difficult to obtain, and the portability is poor. A serious of running parameters, such as temperature and pressure, can accurately assess the operating status of equipment [8], but they are rarely used to build the intelligent diagnosis system of rotor fault causes. erefore, the intelligent algorithms can be used to diagnose rotor fault causes and realize the intelligent operation and maintenance depending on running parameters of a rotor.…”

Section: Introductionmentioning

confidence: 99%

Study on Intelligent Diagnosis of Rotor Fault Causes with the PSO-XGBoost Algorithm

Gu¹,

Wang

Qian

et al. 2021

Mathematical Problems in Engineering

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On basis of fault categories detection, the diagnosis of rotor fault causes is proposed, which has great contributions to the field of intelligent operation and maintenance. To improve the diagnostic accuracy and practical efficiency, a hybrid model based on the particle swarm optimization-extreme gradient boosting algorithm, namely, PSO-XGBoost is designed. XGBoost is used as a classifier to diagnose rotor fault causes, having good performance due to the second-order Taylor expansion and the explicit regularization term. PSO is used to automatically optimize the process of adjusting the XGBoost’s parameters, which overcomes the shortcomings when using the empirical method or the trial-and-error method to adjust parameters of the XGBoost model. The hybrid model combines the advantages of the two algorithms and can diagnose nine rotor fault causes accurately. Following diagnostic results, maintenance measures referring to the corresponding knowledge base are provided intelligently. Finally, the proposed PSO-XGBoost model is compared with five state-of-the-art intelligent classification methods. The experimental results demonstrate that the proposed method has higher diagnostic accuracy and practical efficiency in diagnosing rotor fault causes.

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“…More and more sensors are utilized in the wind turbine, which makes the variable parameters reflecting the status of each component of the wind turbine more abundant in the SCADA system, thus providing the possibility to assess the reliability of the power converter more comprehensively and accurately. As stated in [16], the health status of wind turbine is assessed by using recurrent neural networks, which is based on the SCADA data of subsystems, but the only a small number of SCADA variables are focused on, and the association levels with the failures of wind turbine are unclear. In this paper, SCADA variables with higher faults confidence levels are selected as multistate parameters to assess the reliability of wind power converter.…”

Section: Introductionmentioning

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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Health Status Assessment for Wind Turbine with Recurrent Neural Networks

Cited by 18 publications

References 18 publications

Deep Learning Based Recurrent Neural Networks to Enhance the Performance of Wind Energy Forecasting: A Review

Deep Learning Based Recurrent Neural Networks to Enhance the Performance of Wind Energy Forecasting: A Review

Study on Intelligent Diagnosis of Rotor Fault Causes with the PSO-XGBoost Algorithm

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

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