Wind Turbine Pitch System Condition Monitoring and Fault Detection Based on Optimized Relevance Vector Machine Regression

Wei, Lu; Zheng, Qian; Zareipour, Hamidreza

doi:10.1109/tste.2019.2954834

Cited by 54 publications

(19 citation statements)

References 30 publications

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“…An essential aspect of digital twins is the coupling of the virtual replica with actual data. In [156], a condition monitoring and fault detection technique was presented for wind turbine pitch systems relying on SCADA data. In [157], a fault detection and diagnosis technique was presented for hydraulically actuated pitch systems using Kalman filters and an AI routine fed with local sensor data.…”

Section: Pitch and Yaw Systemsmentioning

confidence: 99%

“…In [160], a literature review was presented of fault diagnosis and prognosis techniques for wind turbine systems. These techniques can be applied on the virtual replica presented here to achieve a digital twin with condition monitoring and fault diagnosis as a use case, e.g., as shown in the aforementioned references [90,141,156,157].…”

Section: Digital Twin Architecturementioning

confidence: 99%

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Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load

et al. 2021

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The Industry 4.0 concept of a Digital Twin will bring many advantages for wind energy conversion systems, e.g., in condition monitoring, predictive maintenance and the optimisation of control or design parameters. A virtual replica is at the heart of a digital twin. To construct a virtual replica, appropriate modelling techniques must be selected for the turbine components. These models must be chosen with the intended use case of the digital twin in mind, finding a proper balance between the model fidelity and computational load. This review article presents an overview of the recent literature on modelling techniques for turbine aerodynamics, structure and drivetrain mechanics, the permanent magnet synchronous generator, the power electronic converter and the pitch and yaw systems. For each component, a balanced overview is given of models with varying model fidelity and computational load, ranging from simplified lumped parameter models to advanced numerical Finite Element Method (FEM)-based models. The results of the literature review are presented graphically to aid the reader in the model selection process. Based on this review, a high-level structure of a digital twin is proposed together with a virtual replica with a minimum computational load. The concept of a multi-level hierarchical virtual replica is presented.

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Section: Pitch and Yaw Systemsmentioning

confidence: 99%

Section: Digital Twin Architecturementioning

confidence: 99%

Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load

et al. 2021

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“…This paper could also quickly find the location of the fault by comparing component-related SCADA data before and after failure, along with other data from turbines close to the one examined. Wei et al [25] presented a technique using relevance vector machines to produce probability distributions of pitch motor power based on SCADA data inputs. Anomalous data were determined by confidence bands based on the healthy normal data.…”

Section: Pitch System Condition Monitoringmentioning

confidence: 99%

Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data

et al. 2021

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Wind turbine pitch system condition monitoring is an active area of research, and this paper investigates the use of the Isolation Forest Machine Learning model and Supervisory Control and Data Acquisition system data for this task. This paper examines two case studies, turbines with hydraulic or electric pitch systems, and uses an Isolation Forest to predict failure ahead of time. This novel technique compared several models per turbine, each trained on a different number of months of data. An anomaly proportion for three different time-series window lengths was compared, to observe trends and peaks before failure. The two cases were compared, and it was found that this technique could detect abnormal activity roughly 12 to 18 months before failure for both the hydraulic and electric pitch systems for all unhealthy turbines, and a trend upwards in anomalies could be found in the immediate run up to failure. These peaks in anomalous behaviour could indicate a future failure and this would allow for on-site maintenance to be scheduled. Therefore, this method could improve scheduling planned maintenance activity for pitch systems, regardless of the pitch system employed.

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“…Regression as one of the methods is widely used. For example, a supervised unmanned aerial vehicle (UAV) faults prediction method was presented based on the logistic regression and linear discriminant analysis (Yousefi et al , 2018), a flexible support vector regression was proposed to apply the practical fault detection of a high-frequency power supply (Yi et al , 2013), a normal behavior modeling method was designed using optimized relevance vector machine regression to predict wind turbine electric pitch system failures (Wei et al , 2020). However, most of these methods will reduce the fault detection performance when the data volume grows and the complex spatial-temporal correlation is involved in the data (Wang et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

Integrated fault detection for industrial process monitoring based on multi-dimensional Taylor network

Chen

et al. 2022

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Purpose This paper aims to provide a novel integrated fault detection method for industrial process monitoring. Design/methodology/approach A novel integrated fault detection method based on the combination of Mallat (MA) algorithm, weight-elimination (WE) algorithm, conjugate gradient (CG) algorithm and multi-dimensional Taylor network (MTN) dynamic model, namely, MA-WE-CG-MTN, is proposed in this paper. First, MA algorithm is taken as data pre-processing. Second, in virtue of approximation ability and low computation complexity owing to the simple structure of MTN, MTN dynamic models are constructed for each frequency band. Furthermore, the CG algorithm is used to discipline the model parameters and the outputs of MTN model of each frequency band are gained. Third, the authors introduce the WE algorithm to cut down the number of middle layer nodes of MTN, reducing the complexity of the network. Finally, the outputs of MTN model for each frequency band are superimposed to achieve outputs of MTN model, and fault detection is proceeded by the residual error generator based on the difference between the output of MTN model and the actual output. Findings The novel proposed method is used to perform fault detection for industrial process monitoring effectively, such as the Benchmark Simulation Model 1 wastewater treatment process. Originality/value The novel proposed method has generality and provides considerably improved performance and effectiveness, which is used to perform fault detection for industrial process monitoring. The proposed method has good robustness, low complexity and easy implementation.

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Wind Turbine Pitch System Condition Monitoring and Fault Detection Based on Optimized Relevance Vector Machine Regression

Cited by 54 publications

References 30 publications

Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load

Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load

Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data

Integrated fault detection for industrial process monitoring based on multi-dimensional Taylor network

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