Wind turbine SCADA alarm analysis for improving reliability

Qiu, Yingning; Feng, Yuanjing; Tavner, Peter; Richardson, Paul; Erdös, Gábor; Chen, Bingdi

doi:10.1002/we.513

Cited by 110 publications

(103 citation statements)

References 9 publications

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“…A recent study from [17] has investigated the Key Performance Indicators (KPI) of alarms from 4 onshore, 30-40 WTs wind farm. The results show that an average alarm rate varying from 4-20 per 10 minutes and maximum alarm rate varying from 390-1,500 per 10 minutes.…”

Section: Scada Alarmsmentioning

confidence: 99%

“…These are very high figures from relatively small onshore wind farms and the alarm rate would need to be reduced to be interpretable by operators or maintainers. In 2011 [15] introduced timesequence and probability-based analysis method to analyse SCADA alarm data. These two methods have proved to be potential for rationalising and reducing alarm data providing fault detection, diagnosis and prognosis from the conditions generating the alarms.…”

Section: Scada Alarmsmentioning

confidence: 99%

“…Generally, the relation of a three layer ( ) feedforward ANN can be represented in vector notations [15]. If is a dimensional column vector presenting inputs and is a dimensional column vector representing the results of hidden layer, then:…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…A wind turbine's systematic performance can be monitored through a proper analysis of the information collected by the SCADA system which covers all the major WT sub-assemblies. Initial attempts to use SCADA alarms to detect wind turbine failure are made recently [15,17]. This paper focuses on using pattern recognition to analyse SCADA alarm and proves its feasibility on wind turbine fault diagnosis, concentrating particularly on a WT electric pitch system, which are known to be fault prone and which produce a significant number of SCADA alarms [17].…”

Section: Introductionmentioning

confidence: 99%

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Wind turbine SCADA alarm pattern recognition

Chen¹,

Qiu²,

Feng³

et al. 2011

IET Conference on Renewable Power Generation (RPG 2011)

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractCurrent wind turbine (WT) studies focus on improving their reliability and reducing the cost of energy, particularly when they are operated offshore. WT Supervisory Control and Data Acquisition (SCADA) systems contain alarm signals providing significant important information. Pattern recognition embodies a set of promising techniques for intelligently processing WT SCADA alarms. This paper presents the feasibility study of SCADA alarm processing and diagnosis method using an artificial neural network (ANN). The back-propagation network (BPN) algorithm was used to supervise a three layers network to identify a WT pitch system fault, known to be of high importance, from pitch system alarm. The trained ANN was then applied on another 4 WTs to find similar pitch system faults. Based on this study, we have found the general mapping capability of the ANN help to identify those most likely WT faults from SCADA alarm signals, but a wide range of representative alarm patterns are necessary for supervisory training.

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Section: Scada Alarmsmentioning

confidence: 99%

Section: Scada Alarmsmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wind turbine SCADA alarm pattern recognition

Chen¹,

Qiu²,

Feng³

et al. 2011

IET Conference on Renewable Power Generation (RPG 2011)

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“…Data from the four CCFs described in last Section will be extracted from signals. Alarm distribution & showers will also be extracted to validate the final result [18].…”

mentioning

confidence: 99%

Automated on‐line fault prognosis for wind turbine pitch systems using supervisory control and data acquisition

Chen¹,

Matthews²,

Tavner³

2015

IET Renewable Power Generation

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. (2015) 'Automated on-line fault prognosis for wind turbine pitch systems using supervisory control and data acquisition.', IET renewable power generation., 9 (5). pp. 503-513.Further information on publisher's website:http://dx.doi.org/10.1049/iet-rpg.2014.0181Publisher's copyright statement:This paper is a postprint of a paper submitted to and accepted for publication in IET Renewable Power Generation and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library.Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractCurrent wind turbine (WT) studies focus on improving their reliability and reducing the cost of energy, particularly when WTs are operated offshore. A SCADA system is a standard installation on larger WTs, monitoring all major WT sub-assemblies and providing important information. Ideally, a WT's health condition or state of the components can be deduced through rigorous analysis of SCADA data. Several programmes have been made for that purposes; however, the resulting cost savings are limited because of the data complexity and relatively low number of failures that can be easily detected in early stages. This paper proposes a new method for analysing WT SCADA data by using an a-priori knowledge-based ANFIS with the aim to achieve automated detection of significant pitch faults. The proposed approach has been applied to the pitch data of two different designs of 26 variable pitch, variable speed and 22 variable pitch, fixed speed WTs, with two different types of SCADA system, demonstrating the adaptability of the approach for application to variety of techniques. Results are evaluated using Confusion Matrix analysis and a comparison study of the two tests is addressed to draw conclusions.

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Artificial intelligence based prognostic maintenance of renewable energy systems: A review of techniques, challenges, and future research directions

Afridi

Ahmad

Hassan

2021

Intl J of Energy Research

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Summary Since the depletion of fossil fuels, the world has started to rely heavily on renewable sources of energy. With every passing year, our dependency on renewable sources of energy is increasing exponentially. As a result, complex and hybrid generation systems are being developed to meet the energy demands and ensure energy security in a country. The continual improvement in the technology and an effort toward the provision of uninterrupted power to the end‐users is strongly dependent on an effective and fault‐resilient Operation & Maintenance (O&M) system. Ingenious algorithms and techniques are hence been introduced aiming to minimize equipment and plant downtime. Efforts are being made to develop robust prognostic maintenance systems that can identify the faults before they occur. To this aim, complex Data Analytics and Artificial Intelligence (AI) algorithms are being used to increase the overall efficiency of these prognostic maintenance systems. This paper provides an overview of the predictive/prognostic maintenance frameworks reported in the literature. We pay a particular focus to the approaches, challenges, including data‐related issues, such as the availability of quality data and data auditing, feature engineering, interpretability, and security issues. Being a key aspect of ML‐based solutions, we also discuss some of the commonly used publicly available datasets in the domain. The paper also identifies the key future research directions to further enhance the prognostics maintenance procedures.

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Wind turbine SCADA alarm analysis for improving reliability

Cited by 110 publications

References 9 publications

Wind turbine SCADA alarm pattern recognition

Wind turbine SCADA alarm pattern recognition

Automated on‐line fault prognosis for wind turbine pitch systems using supervisory control and data acquisition

Artificial intelligence based prognostic maintenance of renewable energy systems: A review of techniques, challenges, and future research directions

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