Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data

Zhao, Yingying; Li, Dongsheng; Dong, Ao; Kang, Dahai; Lv, Qin; Shang, Li

doi:10.3390/en10081210

Cited by 110 publications

(85 citation statements)

References 30 publications

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“…For example, if a single point is classified as looking unhealthy, but the next three points are not, should a maintenance investigation take place? Using a "sliding window" metric, whereby the portion of samples predicted as unhealthy in each window is measured, can give a confidence score for this and help build a system that estimates RUL, as demonstrated in [29].…”

Section: Classification-based Approachesmentioning

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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Section: Classification-based Approachesmentioning

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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“…The SCADA data used in this work comes from 173 wind turbines in a wind farm located in Ningxia, China. In this work, the SCADA data contains 37 parameters for each wind turbine with a one-minute interval, which can be divided into four categories, including the condition parameters, the health parameters, the performance parameters, and the controlling parameters [41]. We use two types of wind turbines with rated powers of 1500 kW and 2000 kW as the research object, and correspondingly establish two normal behavior models to fit the gearbox oil temperature under normal working conditions of turbines.…”

Section: Data Descriptionmentioning

confidence: 99%

Gearbox Fault Prediction of Wind Turbines Based on a Stacking Model and Change-Point Detection

Yuan

Sun

Ma³

2019

Energies

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The fault diagnosis and prediction technology of wind turbines are of great significance for increasing the power generation and reducing the downtime of wind turbines. However, most of the current fault detection approaches are realized by setting a single alarm threshold. Considering the complicated working conditions of wind farms, such methods are prone to ignore the fault, send out a false alarm, or leave insufficient troubleshooting time. In this work, we propose a gearbox fault prediction approach of wind turbines based on the supervisory control and data acquisition (SCADA) data. A stacking model composed of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBOOST) was constructed as the normal behavior model to describe the normal conditions of the wind turbines. We used the Mahalanobis distance (MD) instead of the residual to measure the deviation of the current state from the normal conditions of the turbines. By inputting the MD series into the proposed change-point detection algorithm, we can obtain the change point at which the fault symptom begins to appear, and thus achieving the fault prediction of the gearbox. The proposed approach is validated on the historical data of 5 wind turbines in a wind farm, which proves its effectiveness to detect the fault in advance.

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“…The scope of the research here presented abides in OPEX reduction; more specifically, this paper is intended to reduce the Operations and Maintenance (O&M) costs. How to reduce the O&M costs has become an ongoing challenge for WFs; the figures associated with these costs are notorious [4,5] and may rise by up to 32% and 12-30% for offshore and onshore WFs, respectively [6,7]. However, aiming at lowering the LCoE by reducing O&M costs is a two-fold challenge since it also entails minimizing the lost energy production [8] for the entire lifecycle, which oscillates around 20 years perspective [9].…”

Section: Background and Introductionmentioning

confidence: 99%

“…In [19] the objectives of a RAM database can be seen and in [10] different sources of WT data are analyzed. The challenges faced when building such database with quality data have been addressed by the integration of data coming from different sources [4,10,19,20]. Nonetheless, to translate the data into information and exploit its inherent value, it is essential a correct assessment of the failure process and therefore the selection of the proper time-to-failure model, which will enable optimization of the maintenance plan [21].…”

Section: Background and Introductionmentioning

confidence: 99%

Framework for Managing Maintenance of Wind Farms Based on a Clustering Approach and Dynamic Opportunistic Maintenance

et al. 2019

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Energies 2019, 12, 2036 2 of 17 account for up to one quarter of the proportion of the LCoE, with 80% of this expenses directly attributed to maintenance [10][11][12].In view of the maintenance role in the LCoE and thus in the profitability of WFs, it is important to consider models for optimization of O&M plans and decisions [2]. The evolution of maintenance models and methodologies have kept pace with the constant technological evolution of Wind Turbines (WTs) [3]. According to [3], the goal of all the approaches and methodologies is determining the most adequate maintenance plan, the management of the resources, and the aspects related to Reliability, Availability, and Maintainability (RAM) of the WTs.Within such context, the WF operators are bound to develop new techniques and decision-support tools for optimal maintenance strategies, if they strive to maximize the profitability of the investment [8]. Accordingly, maintenance management discipline acquires a highly significant position since it provides a comprehensive perspective for the management of WTs, allowing for optimal maintenance strategies which reduce maintenance costs while maximizing availability [3,5].The decision-making process in the asset management field has been divided into strategic (long-term), tactic (medium-term), and operational (short-term) to achieve excellence in maintenance [13][14][15]. It is the purpose of the research in this paper to address the different aspects of the maintenance decision-making process. This is done by answering the research question of whether it is possible to achieve maintenance excellence for the lifecycle of WFs by a maintenance strategy which considers the different behaviors of the WTs and integrated business-related objectives.Aiming at providing an answer for the aforementioned research question, a technical framework for managing maintenance is proposed in the paper. The framework is a comprehensive proposal that considers different aspects regarding the maintenance of a WF. Within the possibilities offered by the current trend for big data, certain key aspects to create a failure database are integrated in the framework, which enables a clustering approach based on the failure behaviors of the different failure modes of each WT. This approach supports an opportunistic maintenance policy with dynamic thresholds that regard not only to the reliability of the assets but also business considerations. Besides, the framework also incorporates the strategic view through a Life-Cycle Cost (LCC) perspective integrated by means of a multi-objective optimization and supported by simulation techniques that provide valuable information to find an attractive trade-off between cost and performance.

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Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data

Cited by 110 publications

References 30 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

Gearbox Fault Prediction of Wind Turbines Based on a Stacking Model and Change-Point Detection

Framework for Managing Maintenance of Wind Farms Based on a Clustering Approach and Dynamic Opportunistic Maintenance

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