Wind turbine SCADA alarm pattern recognition

Chen, B.; Qiu, Yingning; Feng, Yuanjing; Tavner, Peter; Song, Wei

doi:10.1049/cp.2011.0164

Cited by 28 publications

(20 citation statements)

References 10 publications

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“…The set of features from the SCADA data for the classification stage was based on the features selected in [9,25,40], with some additions. This amounted to the following features:…”

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confidence: 99%

A Robust Prescriptive Framework and Performance Metric for Diagnosing and Predicting Wind Turbine Faults Based on SCADA and Alarms Data with Case Study

et al. 2018

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Using 10-minute wind turbine supervisory control and data acquisition (SCADA) system data to predict faults can be an attractive way of working toward a predictive maintenance strategy without needing to invest in extra hardware. Classification methods have been shown to be effective in this regard, but there have been some common issues in their application within the literature. To use these data-driven methods effectively, historical SCADA data must be accurately labelled with the periods when turbines were down due to faults, as well as with the reason for the fault. This can be manually achieved using maintenance logs, but can be highly tedious and time-consuming due to the often unstructured format in which this information is stored. Alarm systems can also help, but the sheer volume of alarms and false positives generated complicate efforts. Furthermore, a way to implement and evaluate the field deployed system beyond simple classification metrics is needed. In this work, we present a prescribed and reproducible framework for: (i) automatically identifying periods of faulty operation using rules applied to the turbine alarm system; (ii) using this information to perform classification which avoids some of the common pitfalls observed in literature; and (iii) generating alerts based on a sliding window metric to evaluate the performance of the system in a real-world scenario. The framework was applied to a dataset from an operating wind farm and the results show that the system can automatically and accurately label historical stoppages from the alarms data. For fault prediction, classification scores are quite low, with precision of 0.16 and recall of 0.49, but it is envisaged that this can be greatly improved with more training data. Nonetheless, the sliding window metric compensates for the low raw classification scores and shows that 71% of faults can be predicted with an average of 30 h notice, with false alarms being active for 122 h of the year. By adjusting some of the parameters of the fault prediction alerts, the duration of false alarms can be drastically reduced to 2 h, but this also reduces the number of predicted faults to 8%.

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“…The set of features from the SCADA data for the classification stage was based on the features selected in [9,25,40], with some additions. This amounted to the following features:…”

mentioning

confidence: 99%

A Robust Prescriptive Framework and Performance Metric for Diagnosing and Predicting Wind Turbine Faults Based on SCADA and Alarms Data with Case Study

et al. 2018

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“…SIMAP [15], the Venn diagram [18], Alarm Pattern Recognition [19], data-driven [27] and normal behaviour models [28] approaches were found to be similar to the proposed approach, however this new approach has shown the following advantages:…”

Section: Discussionmentioning

confidence: 96%

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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“…In [49], the authors aimed to reduce the burden of analysis of alarms on operators through the use of pattern recognition techniques based on ANNs. Alarm sequences that occurred during or leading up to fault-related shut-downs were identified.…”

Section: Enhancing Turbine Alarmsmentioning

confidence: 99%

Issues with Data Quality for Wind Turbine Condition Monitoring and Reliability Analyses

et al. 2019

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In order to remain competitive, wind turbines must be reliable machines with efficient and effective maintenance strategies. However, thus far, wind turbine reliability information has been closely guarded by the original equipment manufacturers (OEMs), and turbine reliability studies often rely on data that are not always in a usable or consistent format. In addition, issues with turbine maintenance logs and alarm system data can make it hard to identify historical periods of faulty operation. This means that building new and effective data-driven condition monitoring techniques and methods can be challenging, especially those that rely on supervisory control and data acquisition (SCADA) system data. Such data are rarely standardised, resulting in challenges for researchers in contextualising these data. This work aims to summarise some of the issues seen in previous studies, highlighting the common problems seen by researchers working in the areas of condition monitoring and reliability analysis. Standards and policy initiatives that aim to alleviate some of these problems are given, and a summary of their recommendations is presented. The main finding from this work is that industry would benefit hugely from unified standards for turbine taxonomies, alarm codes, SCADA operational data and maintenance and fault reporting.

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Wind turbine SCADA alarm pattern recognition

Cited by 28 publications

References 10 publications

A Robust Prescriptive Framework and Performance Metric for Diagnosing and Predicting Wind Turbine Faults Based on SCADA and Alarms Data with Case Study

A Robust Prescriptive Framework and Performance Metric for Diagnosing and Predicting Wind Turbine Faults Based on SCADA and Alarms Data with Case Study

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

Issues with Data Quality for Wind Turbine Condition Monitoring and Reliability Analyses

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