Adaptive Wind Generation Modeling by Fuzzy Clustering of Experimental Data

De, Fabrizio; Vaccaro, Alfredo; Villacci, D.

doi:10.3390/electronics7040047

Cited by 14 publications

(12 citation statements)

References 23 publications

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“…Two years of data have been provided for this study, courtesy of the ENGIE Italia company. The outliers [34] have been removed using a procedure similar to [17]. The average wind speed-blade pitch curve is computed and, for each wind speed measurement, an absolute deviation of the measured blade pitch with respect to the average higher than 2 • is used to discriminate anomalous behavior.…”

Section: Discussion and Perspectivesmentioning

confidence: 99%

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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Section: Discussion and Perspectivesmentioning

confidence: 99%

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

Astolfi

2021

Machines

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“…where T rate and T F are the rated running time and fault outage time of the metering equipment, respectively. Because there are a large number of smart meters and their types are different [18], the data of smart meters derived from the electricity information collection system cannot be applied to the operation quality assessment directly. Hence, the data clustering method is used to explore the typical characteristics of the operation quality of each supplier's smart meters, so as to achieve a comprehensive assessment.…”

Section: Index Mining Of Operation Quality Assessment Of Smart Metersmentioning

confidence: 99%

“…In addition, non-quality factors such as environmental noise and electromagnetic interference may also lead to problems such as bad data and null data. Therefore, using data analysis technology to cluster the collected raw data first will help improve the data quality of smart meters [17,18]. Data analysis technology has gradually been applied to the field of power systems, which mainly includes data processing and data analysis algorithms.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Quality Assessment Algorithm for Smart Meters

Liu

Ye²,

Lin

et al. 2019

Energies

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With the improvement of operation monitoring and data acquisition levels of smart meters, mining data associated with smart meters becomes possible. Besides, precisely assessing the operation quality of smart meters plays an important role in purchasing metering equipment and improving the economic benefits of power utilities. First, seven indexes for assessing operation quality of smart meters are defined based on the metering data and the Gaussian mixture model (GMM) clustering algorithm is applied to extract the typical index data from the massive data of smart meters. Then, the combination optimization model of index’s weight is presented with the subject experience of experts and object difference of data considered; and the comprehensive assessment algorithm based on the revised technique for order preference by similarity to an ideal solution (TOPSIS) is proposed to evaluate the operation quality of smart meters. Finally, the proposed data-driven assessment algorithm is illustrated by the actual metering data from Zhejiang Ningbo power supply company of China and practical application is briefly introduced. The results show that the proposed algorithm is effective for assessing the operation quality of smart meters and could be helpful for energy measurement and asset management.

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“…The rationale is that a wind turbine gets derated by forcing it to pitch anomalously. This method is practical, but for an in-depth analysis of outliers devoted methods could be employed [24].…”

mentioning

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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Adaptive Wind Generation Modeling by Fuzzy Clustering of Experimental Data

Cited by 14 publications

References 23 publications

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

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

Comprehensive Quality Assessment Algorithm for Smart Meters

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

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