A Multiscale Spatio-Temporal Convolutional Deep Belief Network for Sensor Fault Detection of Wind Turbine

Wang, Hong; Wang, Hongbin; Jiang, Guoqian; Wang, Yueling; Ren, Shuang

doi:10.3390/s20123580

Cited by 13 publications

(9 citation statements)

References 42 publications

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“…When wind turbines are operated for long periods of time, problems in communication due to foreign objects or slip ring failures may occur [58][59][60].…”

Section: (B) Abnormal Hub Detection Datamentioning

confidence: 99%

Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research

Peng

Shangguan

et al. 2023

Sustainability

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Power generation from wind farms is growing rapidly around the world. In the past decade, wind energy has played an important role in contributing to sustainable development. However, wind turbines are extremely susceptible to component damage under complex environments and over long-term operational cycles, which directly affects their maintenance, reliability, and operating costs. It is crucial to realize efficient early warning of wind turbine failure to avoid equipment breakdown, to prolong the service life of wind turbines, and to maximize the revenue and efficiency of wind power projects. For this purpose, wind turbines are used as the research object. Firstly, this paper outlines the main components and failure mechanisms of wind turbines and analyzes the causes of equipment failure. Secondly, a brief analysis of the cost of wind power projects based on equipment failure is presented. Thirdly, the current key technologies for intelligent operation and maintenance (O&M) in the wind power industry are discussed, and the key research on decision support systems, fault diagnosis models, and life-cycle costs is presented. Finally, current challenges and future development directions are summarized.

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“…When wind turbines are operated for long periods of time, problems in communication due to foreign objects or slip ring failures may occur [58][59][60].…”

Section: (B) Abnormal Hub Detection Datamentioning

confidence: 99%

Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research

Peng

Shangguan

et al. 2023

Sustainability

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“…Wang H, Wang H, Jiang G, et al proposed an adaptive handwriting recognition method, first building a writer-independent LDA classifier and then building a sample library using different writing style fonts of multiple handwriters. Finally, the LDA classifier is trained [17].…”

Section: Related Workmentioning

confidence: 99%

Application of Improved Deep Belief Network Model in 3D Art Design

2022

Mathematical Problems in Engineering

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In recent years, driven by the high-speed computing performance of computers and massive data on the Internet, deep nervine networks with highly abstract feature extraction and classification capabilities have been widely used in 3D art design and other fields, and a large number of breakthrough results have emerged. 3D art design is a research hotspot in the field of computer vision, which has broad application prospects and practical application value. Aiming at the problems of slow convergence and long training time of traditional deep belief network in the process of data feature expression, this paper proposes an unsupervised learning algorithm, namely adaptive deep belief network, and applies it to 3D art design. Its linear correction unit has good sparsity, which can improve the network performance well. The deep belief network DBN is formed by stacking the restricted Boltzmann machine RBM. The recognition research of 3D art design by optimizing the wavelet deep belief network can effectively improve the recognition rate and recognition speed of handwritten character recognition and achieve good results.

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“…The global installations of wind turbines (WTs) have experienced a rapid growth over recent decades, 1–4 but wind power inevitably has encountered challenges, such as faults. Owing to the unexpected faults within WTs, the operation and maintenance (O&M) fees can be costly, adding up to the levelized cost of energy (LCOE) 5 .…”

Section: Introductionmentioning

confidence: 99%

Wind turbine condition monitoring based on three fitted performance curves

Zhang,

Robinson,

Basu

2024

Wind Energy

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Based on SCADA data, this study aims at fitting three performance curves (PCs), power curve, pitch angle curve, and rotor speed curve, to accurately describe the normal behaviour of a wind turbine (WT) for performance monitoring and identification of anomalous signals. The fitting accuracy can be undesirably affected by erroneous SCADA data. Hence, outliers generated from raw SCADA data should be removed to mitigate the prediction inaccuracy, so various outlier detection (OD) approaches are compared in terms of area under the curve (AUC) and mean average precision (mAP). Among them, a novel unsupervised SVM‐KNN model, integrated by support vector machine (SVM) and k nearest neighbour (KNN), is the optimum detector for PC refinements. Based on the refined data by the SVM‐KNN detector, several common nonparametric regressors have largely improved their prediction accuracies on pitch angle and rotor speed curves from roughly 86% and 90.6%, respectively, (raw data) to both 99% (refined data). Noticeably, under the SVM‐KNN refinement, the errors have been reduced by roughly five times and 10 times for pitch angle and rotor speed predictions, respectively. Ultimately, bootstrapped prediction interval is applied to conduct the uncertainty analysis of the optimal predictive regression model, reinforcing the performance monitoring and anomaly detection.

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A Multiscale Spatio-Temporal Convolutional Deep Belief Network for Sensor Fault Detection of Wind Turbine

Cited by 13 publications

References 42 publications

Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research

Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research

Application of Improved Deep Belief Network Model in 3D Art Design

Wind turbine condition monitoring based on three fitted performance curves

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