A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines

Wu, Yueqi; Ma, Xiandong

doi:10.1016/j.renene.2021.09.067

Cited by 52 publications

(22 citation statements)

References 44 publications

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“…The results indicate that the proposed models are much better than the baseline LSTM models [21]. Another study by Wu et al [22] proposed a hybrid model based on LSTM and a statistical toolkit named Kullback-Leibler divergence (KLD) that accurately evaluates the performance of the turbine and diagnose the faults [22]. This proposed model is tested on the two faulty wind turbines for fault detection and identification purposes.…”

Section: Related Workmentioning

confidence: 97%

Energy Load Forecasting Using a Dual-Stage Attention-Based Recurrent Neural Network

Özcan

Çatal

Kasif

2021

Sensors

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Providing a stable, low-price, and safe supply of energy to end-users is a challenging task. The energy service providers are affected by several events such as weather, volatility, and special events. As such, the prediction of these events and having a time window for taking preventive measures are crucial for service providers. Electrical load forecasting can be modeled as a time series prediction problem. One solution is to capture spatial correlations, spatial-temporal relations, and time-dependency of such temporal networks in the time series. Previously, different machine learning methods have been used for time series prediction tasks; however, there is still a need for new research to improve the performance of short-term load forecasting models. In this article, we propose a novel deep learning model to predict electric load consumption using Dual-Stage Attention-Based Recurrent Neural Networks in which the attention mechanism is used in both encoder and decoder stages. The encoder attention layer identifies important features from the input vector, whereas the decoder attention layer is used to overcome the limitations of using a fixed context vector and provides a much longer memory capacity. The proposed model improves the performance for short-term load forecasting (STLF) in terms of the Mean Absolute Error (MAE) and Root Mean Squared Errors (RMSE) scores. To evaluate the predictive performance of the proposed model, the UCI household electric power consumption (HEPC) dataset has been used during the experiments. Experimental results demonstrate that the proposed approach outperforms the previously adopted techniques.

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Section: Related Workmentioning

confidence: 97%

Energy Load Forecasting Using a Dual-Stage Attention-Based Recurrent Neural Network

Özcan

Çatal

Kasif

2021

Sensors

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“…Due to its sustainable and clean characteristics, the wind energy sector has attracted continuous attention and has significantly grown during the past two decades [2]. For example, Global Wind Energy Council reported that the global wind energy sector had shown year-to-year growth of 19% in 2019 [3]. The wind turbine (WT) is utilized in order to transfer the wind energy first to mechanical and then the electrical energy [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…WTs are subjected to harsh environmental conditions such as volatile (i.e., complex alternating) loads, extreme temperature differences, and frequent impact, and therefore, are prone to failure [6]. According to statistics, the annual O&M cost of a WT may reach approximately 3% of its original cost [3]. Currently, how to minimize the wind farm O&M costs with the early and accurate prediction of WT faults stands as an urgent problem to be solved for the wind energy community.…”

Section: Introductionmentioning

confidence: 99%

Fault Classification of Wind Turbine Gearbox Bearings Based on Convolutional Neural Networks

Karpat¹,

Kalay

Dirik

et al. 2022

tjes

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Gearbox bearings are critical elements of wind power generation systems. Their stable operation supports the power generation, thus reducing the downtime and improving the economic efficiency of wind farms. With the wide availability of sensors, data-driven methods have started to be utilized instead of physical-based methods for condition monitoring of wind energy infrastructures. Deep learning provides significant advantages to achieving this end due to its ability to extract and select representative features without expert knowledge. The present study proposed an intelligent method based on one-dimensional convolutional neural networks (1D-CNN) to extract useful features from the vibration signals and classify different bearing faults. The performance of the proposed 1D-CNN model was evaluated employing the Case Western Reserve University dataset. As a result, the proposed method achieved an average prediction accuracy of 99.56%. The findings confirmed that the method has good stability and potentially be used to reduce operation and maintenance costs.

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“…Mao et al [ 22 ] proposed a semirandom subspace method with a bidirectional gate recurrent unit (a modified RNN algorithm) to take full advantage of fusion features for bearing fault diagnosis. Wu and Ma [ 23 ] proposed an improved RNN method for wind turbine fault diagnosis based on long short-term memory and Kullback–Leibler divergence. The abovementioned deep learning-based research approaches produced good fault diagnostic conclusions.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Dilated Convolutional Neural Network and Its Application in Rotating Machinery Fault Diagnosis

Cai

Wang

Yao

et al. 2022

Computational Intelligence and Neuroscience

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Fault diagnosis of rotating machinery is an attractive yet challenging task. This paper presents a novel intelligent fault diagnosis scheme for rotating machinery based on ensemble dilated convolutional neural networks. The novel fault diagnosis framework employs a model training strategy based on early stopping optimization to ensemble several one-dimensional dilated convolutional neural networks (1D-DCNNs). By varying the dilation rate of the 1D-DCNN, different receptive fields can be obtained to extract different vibration signal features. The early stopping strategy is used as a model update threshold to prevent overfitting and save computational resources. Ensemble learning uses a weighted mechanism to combine the outputs of multiple 1D-DCNN subclassifiers with different dilation rates to obtain the final fault diagnosis. The proposed method outperforms existing state-of-the-art classical machine learning and deep learning methods in simulation studies and diagnostic experiments, demonstrating that it can thoroughly mine fault features in vibration signals. The classification results further show that the EDCNN model can effectively and accurately identify multiple faults and outperform existing fault detection techniques.

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A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines

Cited by 52 publications

References 44 publications

Energy Load Forecasting Using a Dual-Stage Attention-Based Recurrent Neural Network

Energy Load Forecasting Using a Dual-Stage Attention-Based Recurrent Neural Network

Fault Classification of Wind Turbine Gearbox Bearings Based on Convolutional Neural Networks

Ensemble Dilated Convolutional Neural Network and Its Application in Rotating Machinery Fault Diagnosis

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