Using high-frequency SCADA data for wind turbine performance monitoring: A sensitivity study

Gonzalez, Elena; Stephen, Bruce; Infield, David; Melero, Julio J.

doi:10.1016/j.renene.2018.07.068

Cited by 79 publications

(63 citation statements)

References 40 publications

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“…An interesting development is the use of the methods of this work for other control and monitoring issues related to wind turbine operation: for example, monitoring the effect of blade pitches re-alignment according to the technique proposed in [43], or monitoring the operation of the wind turbines [40]. Furthermore, a very promising direction of the studies about wind turbine power curve upgrades is the use of time-resolved data, having sampling time of the order of second: this kind of data have considerable potentiality for performance control and monitoring [44], but their time scale calls for more advanced time-series analysis [45].…”

Section: Resultsmentioning

confidence: 99%

Wind Turbine Power Curve Upgrades: Part II

Astolfi

Castellani

2019

Energies

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Wind turbine power upgrades have recently become a debated topic in wind energy research. Their assessment poses some challenges and calls for devoted techniques: some reasons are the stochastic nature of the wind and the multivariate dependency of wind turbine power. In this work, two test cases were studied. The former is the yaw management optimization on a 2 MW wind turbine; the latter is a comprehensive control upgrade (pitch, yaw, and cut-out) for 850 kW wind turbines. The upgrade impact was estimated by analyzing the difference between the post-upgrade power and a data-driven simulation of the power if the upgrade did not take place. Therefore, a reliable model for the pre-upgrade power of the wind turbines of interest was needed and, in this work, a principal component regression was employed. The yaw control optimization was shown to provide a 1.3% of production improvement and the control re-powering provided 2.5%. Another qualifying point was that, for the 850 kW wind turbine re-powering, the data quality was sufficient for an upgrade estimate based on power curve analysis and a good agreement with the model result was obtained. Summarizing, evidence of the profitability of wind turbine power upgrades was collected and data-driven methods were elaborated for power upgrade assessment and, in general, for wind turbine performance control and monitoring.

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Section: Resultsmentioning

confidence: 99%

Wind Turbine Power Curve Upgrades: Part II

Astolfi

Castellani

2019

Energies

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“…The two ways to do this are through filtering out abnormal periods using alarms and fault logs and by filtering out abnormal data using statistical methods. In [31], the authors built a historical fault database from cross referencing alarms and work order data, based on an extensive process described in [25]. This is then cross referenced with high frequency (i.e., 1 Hz) SCADA data and periods of abnormal operation filtered out.…”

Section: Nbm: Performance Monitoringmentioning

confidence: 99%

“…Example of normal (a) and abnormal performance (b) as indicated by the component-related SCADA alarm logs. Reproduced with permission from[31].…”

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confidence: 99%

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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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“…Basing on the above literature discussion, it can be fairly stated that many powerful techniques for the analysis of wind turbine vibration signatures are based on cyclo-stationarity. The downside is that this kind of analysis is particularly demanding as regards the data because, for example, the angular speed must be measured at high sampling rates, and this is not guaranteed even by using time-resolved operation data (as, for example, the ones analyzed in [22,23]): for this reason, most of the studies deal with numerical simulations [19] and laboratory test rig measurements [24]. On the other side, it should be noticed that industrial wind turbines are commonly equipped with commercial condition monitoring systems: these do not stock measurements continuously (they record when some trigger events occur) and stock them in treated form instead of raw.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements

et al. 2020

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Condition monitoring of gear-based mechanical systems in non-stationary operation conditions is in general very challenging. This issue is particularly important for wind energy technology because most of the modern wind turbines are geared and gearbox damages account for at least the 20% of their unavailability time. In this work, a new method for the diagnosis of drive-train bearings damages is proposed: the general idea is that vibrations are measured at the tower instead of at the gearbox. This implies that measurements can be performed without impacting the wind turbine operation. The test case considered in this work is a wind farm owned by the Renvico company, featuring six wind turbines with 2 MW of rated power each. A measurement campaign has been conducted in winter 2019 and vibration measurements have been acquired at five wind turbines in the farm. The rationale for this choice is that, when the measurements have been acquired, three wind turbines were healthy, one wind turbine had recently recovered from a planetary bearing fault, and one wind turbine was undergoing a high speed shaft bearing fault. The healthy wind turbines are selected as references and the damaged and recovered are selected as targets: vibration measurements are processed through a multivariate Novelty Detection algorithm in the feature space, with the objective of distinguishing the target wind turbines with respect to the reference ones. The application of this algorithm is justified by univariate statistical tests on the selected time-domain features and by a visual inspection of the data set via Principal Component Analysis. Finally, a novelty index based on the Mahalanobis distance is used to detect the anomalous conditions at the damaged wind turbine. The main result of the study is that the statistical novelty of the damaged wind turbine data set arises clearly, and this supports that the proposed measurement and processing methods are promising for wind turbine condition monitoring.

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Using high-frequency SCADA data for wind turbine performance monitoring: A sensitivity study

Cited by 79 publications

References 40 publications

Wind Turbine Power Curve Upgrades: Part II

Wind Turbine Power Curve Upgrades: Part II

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

Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements

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