Intelligent integrated maintenance for wind power generation

Pattison, David; Segovia-García, María del Carmen; Xie, Wenhao; Quail, Francis; Revie, Matthew; Whitfield, Robert Ian; Irvine, I.

doi:10.1002/we.1850

Cited by 31 publications

(31 citation statements)

References 23 publications

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“…Wind turbines have witnessed a rapid growth in rating over the last 40 years [1]. However, they are usually required to operate in harsh environments, particularly offshore [2,3]. According to previous research results, gearboxes of wind turbines contribute more than 20% of failures and account for around 12 days of lost operation per annum per turbine on average [4].…”

Section: Introductionmentioning

confidence: 99%

A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

et al. 2019

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Effective intelligent condition monitoring, as an effective technique to enhance the reliability of wind turbines and implement cost-effective maintenance, has been the object of extensive research and development to improve defect detection from supervisory control and data acquisition (SCADA) data, relying on perspective signal processing and statistical algorithms. The development of sophisticated machine learning now allows improvements in defect detection from historic data. This paper proposes a novel condition monitoring method for wind turbines based on Long Short-Term Memory (LSTM) algorithms. LSTM algorithms have the capability of capturing long-term dependencies hidden within a sequence of measurements, which can be exploited to increase the prediction accuracy. LSTM algorithms are therefore suitable for application in many diverse fields. The residual signal obtained by comparing the predicted values from a prediction model and the actual measurements from SCADA data can be used for condition monitoring. The effectiveness of the proposed method is validated in the case study. The proposed method can increase the economic benefits and reliability of wind farms.

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Section: Introductionmentioning

confidence: 99%

A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

et al. 2019

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“…Broader research of CMFD for a machine based on artificial intelligence was systematically introduced [82]. The intelligent O&M of WTs based on current signal was developed and applied to real wind farms [83]. For the fault diagnosis of WT bearing, support vector machine (SVM) can be used to predict its remaining useful life [84].…”

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines

Zhang

2019

Energies

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Wind power, as a renewable energy for coping with global climate change challenge, has achieved rapid development in recent years. The breakdown of wind turbines (WTs) not only leads to high repair expenses but also may threaten the stability of the whole power grid. How to reduce the operation and the maintenance (O&M) cost of wind farms is an obstacle to its further promotion and application. To provide reliable condition monitoring and fault diagnosis (CMFD) for WTs, this paper presents a comprehensive survey of the existing CMFD methods in the following three aspects: energy flow, information flow, and integrated O&M system. Energy flow mainly analyzes the characteristics of each component from the angle of energy conversion of WTs. Information flow is the carrier of fault and control information of WT. At the end of this paper, an integrated WT O&M system based on electrical signals is proposed.

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“…Pattison et al in [63] use DBN to represent the failure behaviour of wind turbines and update the conditional failure probabilities of DBN nodes with condition monitoring data estimated through a Kalman filter. Although dynamic dependencies between assets are not modelled, asset-specific information is used to update system-level failure probability calculations.…”

Section: Relevant Workmentioning

confidence: 99%

Improved Dynamic Dependability Assessment Through Integration With Prognostics

et al. 2017

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This version is available at https://strathprints.strath.ac.uk/60422/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Abstract-The use of average data for dependability assessments results in a outdated system-level dependability estimation which can lead to incorrect design decisions. With increasing availability of online data, there is room to improve traditional dependability assessment techniques. Namely, prognostics is an emerging field which provides asset-specific failure information which can be reused to improve the system level failure estimation. This paper presents a framework for prognosticsupdated dynamic dependability assessment. The dynamic behaviour comes from runtime updated information, asset interdependencies, and time-dependent system behaviour. A case study from the power generation industry is analysed and results confirm the validity of the approach for improved near real-time unavailability estimations.

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Intelligent integrated maintenance for wind power generation

Cited by 31 publications

References 23 publications

A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines

Improved Dynamic Dependability Assessment Through Integration With Prognostics

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