Effect of Time History on Normal Behaviour Modelling Using SCADA Data to Predict Wind Turbine Failures

McKinnon, Conor; Turnbull, A.; Koukoura, Sofia; Carroll, J. Douglas; McDonald, Alasdair

doi:10.3390/en13184745

Cited by 21 publications

(10 citation statements)

References 25 publications

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“…Consequently, the training and test datasets were split in such a way that each set had one year of data. This approach ensures that the detected anomalies are not due to seasonality [ 37 ], and the model can cope with various operating and environmental conditions. Therefore, the available SCADA data were divided as follows: data corresponding to 2017 (47,232 samples) were used for training, and data from 2018 (43,920 samples) were used for testing.…”

Section: Fault Prognosis Methodologymentioning

confidence: 99%

Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data

Encalada-Dávila

Puruncajas

Tutivén

et al. 2021

Sensors

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As stated by the European Academy of Wind Energy (EAWE), the wind industry has identified main bearing failures as a critical issue in terms of increasing wind turbine reliability and availability. This is owing to major repairs with high replacement costs and long downtime periods associated with main bearing failures. Thus, the main bearing fault prognosis has become an economically relevant topic and is a technical challenge. In this work, a data-based methodology for fault prognosis is presented. The main contributions of this work are as follows: (i) Prognosis is achieved by using only supervisory control and data acquisition (SCADA) data, which is already available in all industrial-sized wind turbines; thus, no extra sensors that are designed for a specific purpose need to be installed. (ii) The proposed method only requires healthy data to be collected; thus, it can be applied to any wind farm even when no faulty data has been recorded. (iii) The proposed algorithm works under different and varying operating and environmental conditions. (iv) The validity and performance of the established methodology is demonstrated on a real underproduction wind farm consisting of 12 wind turbines. The obtained results show that advanced prognostic systems based solely on SCADA data can predict failures several months prior to their occurrence and allow wind turbine operators to plan their operations.

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Section: Fault Prognosis Methodologymentioning

confidence: 99%

Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data

Encalada-Dávila

Puruncajas

Tutivén

et al. 2021

Sensors

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“…Similarly, in Gao and Hong (2021) the systematic yaw error of a wind turbine is diagnosed through the individuation of under-performance based on the data-driven analysis of the wind turbine power curve. In McKinnon et al (2020), the target of the model is the gearbox temperature and the input variables include a mix of environmental measurements (wind speed), operation variables (generator speed and power) and temperatures collected at other meaningful subcomponents (generator temperature, bearing temperature, nacelle temperature). A similar procedure is applied in Encalada-Dávila et al (2021), while in Vidal et al (2018) the target of the model is the temperature of the component of interest and the input variables are working parameters and do not include temperatures of other components.…”

Section: Different Types Of Data and Feature Selectionmentioning

confidence: 99%

SCADA data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trends

et al. 2022

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This paper reviews the recent advancement made in data-driven technologies based on SCADA data for improving wind turbines’ operation and maintenance activities (e.g. condition monitoring, decision support, critical components failure detections) and the challenges associated with them. Machine learning techniques applied to wind turbines’ operation and maintenance (O&M) are reviewed. The data sources, feature engineering and model selection (classification, regression) and validation are all used to categorise these data-driven models. Our findings suggest that (a) most models use 10-minute mean SCADA data, though the use of high-resolution data has shown greater advantages as compared to 10-minute mean value but comes with high computational challenges. (b) Most of SCADA data are confidential and not available in the public domain which slows down technological advancements. (c) These datasets are used for both, the classification and regression of wind turbines but are used in classification extensively. And, (d) most commonly used data-driven models are neural networks, support vector machines, probabilistic models and decision trees and each of these models has its own merits and demerits. We conclude the paper by discussing the potential areas where SCADA data-based data-driven methodologies could be used in future wind energy research.

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“…These costs can easily overcome approximately the 20% of the total energy generation cost for wind power plants (WPPs) [4]. When a WT has a lot downtime, in efficiency it means that productivity decreases while the operation and maintenance (O&M) costs increase [5], [6]. Condition monitoring systems (CMSs) appear as a solution for this problem, since with this strategy it is possible to know the current state of a component and provide an indication of fault or non-fault [7].…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Multi-Fault Detection based on SCADA Data via an AutoEncoder

Encalada-Dávila¹,

Tutivén²,

Puruncajas³

et al. 2024

RE&PQJ

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Nowadays, wind turbine fault detection strategies are settled as a meaningful pipeline to achieve required levels of efficiency, availability, and reliability, considering there is an increasing installation of this kind of machinery, both in onshore and offshore configuration. In this work, it has been applied a strategy that makes use of SCADA data with an increased sampling rate. The employed wind turbine in this study is based on an advanced benchmark, established by the National Renewable Energy Laboratory (NREL) of USA. Different types of faults on several actuators and sensed by certain installed sensors have been studied. The proposed strategy is based on a normality model by means of an autoencoder. As of this, faulty data are used for testing from which prediction errors were computed to detect if those raise a fault alert according to a defined metric which establishes a threshold on which a wind turbine works securely. The obtained results determine that the proposed strategy is successful since the model detects the considered three types of faults. Finally, even when prediction errors are small, the model is able to detect the faults without problems.

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Effect of Time History on Normal Behaviour Modelling Using SCADA Data to Predict Wind Turbine Failures

Cited by 21 publications

References 25 publications

Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data

Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data

SCADA data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trends

Wind Turbine Multi-Fault Detection based on SCADA Data via an AutoEncoder

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