An Innovative Method to Evaluate the Real Performance of Wind Turbines With Respect to the Manufacturer Power Curve: Case Study from Mauritania

Amato, Angela; Heiba, Bamba; Spertino, Filippo; Malgaroli, Gabriele; Ciocia, Alessandro; Yahya, Ahmed Med; Mahmoud, Abdel Kader

doi:10.1109/eeeic/icpseurope51590.2021.9584790

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…A few differences are worth noting for appreciating the peculiarities of the test cases as regards the wind speed measurement, which is known to be a critical point [ 38 , 39 ]. In Test Case 1, more recent wind turbines are used, which are controlled by AI-based algorithms developed by the manufacturer.…”

Section: Case Studymentioning

confidence: 99%

Condition Monitoring of Wind Turbine Systems by Explainable Artificial Intelligence Techniques

Astolfi

Vaccaro

2023

Sensors

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The performance evaluation of wind turbines operating in real-world environments typically relies on analyzing the power curve, which shows the relationship between wind speed and power output. However, conventional univariate models that consider only wind speed as an input variable often fail to fully explain the observed performance of wind turbines, as power output depends on multiple variables, including working parameters and ambient conditions. To overcome this limitation, the use of multivariate power curves that consider multiple input variables needs to be explored. Therefore, this study advocates for the application of explainable artificial intelligence (XAI) methods in constructing data-driven power curve models that incorporate multiple input variables for condition monitoring purposes. The proposed workflow aims to establish a reproducible method for identifying the most appropriate input variables from a more comprehensive set than is usually considered in the literature. Initially, a sequential feature selection approach is employed to minimize the root-mean-square error between measurements and model estimates. Subsequently, Shapley coefficients are computed for the selected input variables to estimate their contribution towards explaining the average error. Two real-world data sets, representing wind turbines with different technologies, are discussed to illustrate the application of the proposed method. The experimental results of this study validate the effectiveness of the proposed methodology in detecting hidden anomalies. The methodology successfully identifies a new set of highly explanatory variables linked to the mechanical or electrical control of the rotor and blade pitch, which have not been previously explored in the literature. These findings highlight the novel insights provided by the methodology in uncovering crucial variables that significantly contribute to anomaly detection.

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Section: Case Studymentioning

confidence: 99%

Condition Monitoring of Wind Turbine Systems by Explainable Artificial Intelligence Techniques

Astolfi

Vaccaro

2023

Sensors

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“…This means that in case the wind turbine is situated in a harsh environment [5] or is operating incorrectly (for example, if it is subjected to systematic yaw error [6,7]), the interaction between the wind field and the wind turbine rotor is different with respect to standard conditions and typically unpredictable. Furthermore, it is not rare that anemometers of industrial wind turbines are affected by bias [8][9][10] and therefore do not measure the wind speed correctly.…”

Section: Introductionmentioning

confidence: 99%

Advanced Methods for Wind Turbine Performance Analysis Based on SCADA Data and CFD Simulations

et al. 2023

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Deep comprehension of wind farm performance is a complicated task due to the multivariate dependence of wind turbine power on environmental variables and working parameters and to the intrinsic limitations in the quality of SCADA-collected measurements. Given this, the objective of this study is to propose an integrated approach based on SCADA data and Computational Fluid Dynamics simulations, which is aimed at wind farm performance analysis. The selected test case is a wind farm situated in southern Italy, where two wind turbines had an apparent underperformance. The concept of a space–time comparison at the wind farm level is leveraged by analyzing the operation curves of the wind turbines and by comparing the simulated average wind field against the measured one, where each wind turbine is treated like a virtual meteorological mast. The employed formulation for the CFD simulations is Reynolds-Average Navier–Stokes (RANS). In this work, it is shown that, based on the above approach, it has been possible to identify an anemometer bias at a wind turbine, which has subsequently been fixed. The results of this work affirm that a deep comprehension of wind farm performance requires a non-trivial space–time comparison, of which CFD simulations can be a fundamental part.

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“…For example, in [3], it was reported that in 2020, 28% of wind turbines installed in Europe were older than 15 years of age, with peaks in the order of 50% in Spain, Germany, and Denmark. This matter of fact motivates a recent deeper attention to the analysis and the interpretation of wind turbine performance, which is in general complicated because there are issues related with quantity and quality of nacelle anemometer data [4,5]. It is generally expected that the performance of whatever technical system declines with age [6,7] and the growing amount of aged operating wind turbines poses the issue of wind turbine performance analysis with a major urgency.…”

Section: Introductionmentioning

confidence: 99%

Discussion of Wind Turbine Performance Based on SCADA Data and Multiple Test Case Analysis

Astolfi

Pandit

Terzi³

et al. 2022

Energies

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This work is devoted to the formulation of innovative SCADA-based methods for wind turbine performance analysis and interpretation. The work is organized as an academia–industry collaboration: three test cases are analyzed, two with hydraulic pitch control (Vestas V90 and V100) and one with electric pitch control (Senvion MM92). The investigation is based on the method of bins, on a polynomial regression applied to operation curves that have never been analyzed in detail in the literature before, and on correlation and causality analysis. A key point is the analysis of measurement channels related to the blade pitch control and to the rotor: pitch manifold pressure, pitch piston traveled distance and tower vibrations for the hydraulic pitch wind turbines, and blade pitch current for the electric pitch wind turbines. The main result of this study is that cases of noticeable under-performance are observed for the hydraulic pitch wind turbines, which are associated with pitch pressure decrease in time for one case and to suspected rotor unbalance for another case. On the other way round, the behavior of the rotational speed and blade pitch curves is homogeneous and stable for the wind turbines electrically controlled. Summarizing, the evidence collected in this work identifies the hydraulic pitch as a sensible component of the wind turbine that should be monitored cautiously because it is likely associated with performance decline with age.

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An Innovative Method to Evaluate the Real Performance of Wind Turbines With Respect to the Manufacturer Power Curve: Case Study from Mauritania

Cited by 3 publications

References 10 publications

Condition Monitoring of Wind Turbine Systems by Explainable Artificial Intelligence Techniques

Condition Monitoring of Wind Turbine Systems by Explainable Artificial Intelligence Techniques

Advanced Methods for Wind Turbine Performance Analysis Based on SCADA Data and CFD Simulations

Discussion of Wind Turbine Performance Based on SCADA Data and Multiple Test Case Analysis

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