Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring

Astolfi, Davide; Castellani, Francesco; Lombardi, Andrea; Terzi, Ludovico

doi:10.3390/en14041105

Cited by 31 publications

(32 citation statements)

References 60 publications

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“…As regards the data pre-processing, in general it should be noted that the operation of wind turbines in industrial farms is affected by curtailments dictated by grid requirements and it is therefore necessary to filter the data appropriately. This was done as follows (similarly to [22]):…”

Section: Test Cases and Data Setsmentioning

confidence: 99%

SCADA Data Analysis Methods for Diagnosis of Electrical Faults to Wind Turbine Generators

2021

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The electric generator is estimated to be among the top three contributors to the failure rates and downtime of wind turbines. For this reason, in the general context of increasing interest towards effective wind turbine condition monitoring techniques, fault diagnosis of electric generators is particularly important. The objective of this study is contributing to the techniques for wind turbine generator fault diagnosis through a supervisory control and data acquisition (SCADA) analysis method. The work is organized as a real-world test-case discussion, involving electric damage to the generator of a Vestas V52 wind turbine sited in southern Italy. SCADA data before and after the generator damage have been analyzed for the target wind turbine and for reference healthy wind turbines from the same site. By doing this, it has been possible to formulate a normal behavior model, based on principal component analysis and support vector regression, for the power and for the voltages and currents of the wind turbine. It is shown that the incipience of the fault can be individuated as a change in the behavior of the residuals between model estimates and measurements. This phenomenon was clearly visible approximately two weeks before the fault. Considering the fast evolution of electrical damage, this result is promising as regards the perspectives of exploiting SCADA data for individuating electric damage with an advance that can be useful for applications in wind energy practice.

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Section: Test Cases and Data Setsmentioning

confidence: 99%

SCADA Data Analysis Methods for Diagnosis of Electrical Faults to Wind Turbine Generators

2021

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Section: Multivariate Wind Turbine Power Curve Modelsmentioning

confidence: 99%

“…In [17], three test cases of practical interest are analyzed: Senvion MM92, Vestas V90 and Vestas V117 wind turbines, sited in southern Italy and owned by the ENGIE Italia company. The peculiarity of [17] is that a vast set of possible covariates is included and the most appropriate for the regression are individuated through a sequential features selection algorithm employing a Support Vector Regression with Gaussian kernel. In general, the result is that the set of selected covariates is larger than the standard in the literature (order of 10) and the selection depends on the technology of the wind turbines: for the Senvion MM92, the pitch control is electric and the most important covariates are those related to the rotor speed control; for the Vestas wind turbines, the pitch control is hydraulic and the most important covariates are related to the pitch control.…”

Section: Multivariate Wind Turbine Power Curve Modelsmentioning

confidence: 99%

“…Furthermore, the main innovative aspects of the test case analysis proposed in Section 3 regard the exploitation of the SCADA data sets at disposal. Similarly to [17] and differently with respect to the standard in the literature (see Section 2), the minimum, maximum and standard deviation of the main measurements are included as possible covariates, in addition to the average values. It is shown in Section 3 that, in light of this approach, the error metrics diminish considerably and it is possible to explore a further innovative direction, which is the formulation of a model excluding the most important input variable (the wind speed).…”

Section: Introductionmentioning

confidence: 99%

“…The regression problem is likely complicated by increasing rotor size. In [17], the same kind of method is tested on three real-world wind turbines (Senvion MM92, Vestas V90 and Vestas V117) and the highest error (normalized to the rated) occurs for the Vestas V117 wind turbine, which is 3.45 MW against 2 MW of the other test cases.…”

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Perspectives on SCADA Data Analysis Methods for Multivariate Wind Turbine Power Curve Modeling

Astolfi

2021

Machines

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Wind turbines are rotating machines which are subjected to non-stationary conditions and their power depends non-trivially on ambient conditions and working parameters. Therefore, monitoring the performance of wind turbines is a complicated task because it is critical to construct normal behavior models for the theoretical power which should be extracted. The power curve is the relation between the wind speed and the power and it is widely used to monitor wind turbine performance. Nowadays, it is commonly accepted that a reliable model for the power curve should be customized on the wind turbine and on the site of interest: this has boosted the use of SCADA for data-driven approaches to wind turbine power curve and has therefore stimulated the use of artificial intelligence and applied statistics methods. In this regard, a promising line of research regards multivariate approaches to the wind turbine power curve: these are based on incorporating additional environmental information or working parameters as input variables for the data-driven model, whose output is the produced power. The rationale for a multivariate approach to wind turbine power curve is the potential decrease of the error metrics of the regression: this allows monitoring the performance of the target wind turbine more precisely. On these grounds, in this manuscript, the state-of-the-art is discussed as regards multivariate SCADA data analysis methods for wind turbine power curve modeling and some promising research perspectives are indicated.

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Detecting bearing failures in wind energy parks: A main bearing early damage detection method using SCADA data and a convolutional autoencoder

Tutivén

Encalada-Dávila

Vidal

et al. 2023

Energy Science & Engineering

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Wind energy maintenance and operation costs can total millions of dollars each year in an average industrial‐size wind park. Therefore, moving from preventive and corrective maintenance to predictive maintenance is imperative in the wind energy sector. This paper contributes to this challenge by providing a main bearing early damage detection technique that exclusively uses standard supervisory control and data acquisition (SCADA) data (10‐min average) and a convolutional autoencoder with the following contributions. (i) Entirely semisupervised (not requiring the labeling of data through work order logs and avoiding the problem of data imbalance between classes) based only on healthy data, thus expanding its range of application (even when the failure of interest has never occurred in the park before). (ii) Validated using real‐world SCADA data and shown to be resistant to seasonality, and operational and environmental conditions. (iii) Reliable predictions with minimum false alarms thanks to specially designed fault prognosis indicators based on the image mean square error metric. (vi) The early warning is achieved months in advance, thus providing adequate time for plant operators to plan properly. (v) The main use of exogenous variables in the model (variables that are not affected by other variables, e.g., wind speed, wind turbulence, and ambient temperature) guarantees the detection of damage directly related only to the low‐speed shaft temperature (the only nonexogenous variable used by the stated model). (vi) Finally, the proposed strategy is validated in a wind park made up of 12 wind turbines.

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Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring

Cited by 31 publications

References 60 publications

SCADA Data Analysis Methods for Diagnosis of Electrical Faults to Wind Turbine Generators

SCADA Data Analysis Methods for Diagnosis of Electrical Faults to Wind Turbine Generators

Perspectives on SCADA Data Analysis Methods for Multivariate Wind Turbine Power Curve Modeling

Detecting bearing failures in wind energy parks: A main bearing early damage detection method using SCADA data and a convolutional autoencoder

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