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Wind Turbine Monitoring through Operation Data Analysis

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A3: Wind, Wave and Tidal Energy".

Deadline for manuscript submissions: closed (15 December 2021) | Viewed by 19056

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Special Issue Editors

Dr. Davide Astolfi

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Guest Editor

Department of Engineering, University of Perugia, 06125 Perugia, Italy
Interests: wind turbines; condition monitoring; fault diagnosis; non-stationary machinery; control and monitoring; vibrations; applied statistics; numerical modelling; mechanical systems dynamics
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Dr. Francesco Castellani

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Guest Editor

Department of Engineering, University of Perugia, Via G. Duranti 93, IT06125 Perugia, Italy
Interests: wind turbine; vibrations; aeroelasticity; fault diagnosis; wakes; SCADA; applied aerodynamics; mechanical system dynamics
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Special Issue Information

Dear Colleagues,

In recent years, the widespread development of sensory, SCADA control, and data transmission systems has endowed wind energy scholars and practitioners with large amounts of information to be reduced through data mining into knowledge about the operation of wind turbine technology.

Operation data analysis has therefore become a keystone for wind turbine control and monitoring; nevertheless, due to the nonstationary conditions to which wind turbines are subjected, innovative statistical and computational methods are required for producing reliable results with practical impact on wind farm operation and management and, eventually, on the cost of energy.

Several methods have been developed in recent years for wind turbine monitoring: power curve or, in general, operational curve analysis and modeling; multivariate regressions for the data-driven modeling of wind turbine power, taking into account its joint dependence on ambient conditions and working parameters; and subcomponent temperature analysis and modeling for fault diagnosis. Furthermore, the analysis of operation data has demonstrated promising potential for the detection of sensory faults and control system biases (for example, the systematic zero-point shift of the yaw angle).

On these grounds, it can be stated that the literature about operation data analysis for wind turbine monitoring is productive and stimulating; therefore, the present Special Issue aims to collect innovative research contributions, possibly supported by real-world test case analysis.

Topics of interest include, but are not limited to:

Wind turbine power curve analysis;
Data mining methods for wind turbine yaw and/or pitch behavior analysis;
Statistical, artificial intelligence, and deep learning data analysis methods for wind turbine performance monitoring;
Wind turbine fault diagnosis through operation data analysis;
Operation assessment of wind turbine optimization technology;
Validation of CFD simulations, wake models, and engineering models against real-world operation data;
Time-resolved operation data analysis.

Dr. Davide Astolfi
Prof. Francesco Castellani
Guest Editors

Manuscript Submission Information

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Keywords

wind turbines
operation data analysis
performance control
condition monitoring
fault diagnosis

Published Papers (7 papers)

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Editorial

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4 pages, 163 KiB

Open AccessEditorial

Editorial on the Special Issue “Wind Turbine Monitoring through Operation Data Analysis”

by Davide Astolfi and Francesco Castellani

Energies 2022, 15(10), 3664; https://0-doi-org.brum.beds.ac.uk/10.3390/en15103664 - 17 May 2022

Cited by 2 | Viewed by 1181

Abstract

Horizontal axis wind turbines likely constitute the most promising renewable energy technology worldwide and their exploitation has been recently accelerating due to energy transition policies [...] Full article

(This article belongs to the Special Issue Wind Turbine Monitoring through Operation Data Analysis)

Research

Jump to: Editorial

18 pages, 2926 KiB

Open AccessArticle

SCADA Data-Based Working Condition Classification for Condition Assessment of Wind Turbine Main Transmission System

by Huanguo Chen, Chao Xie, Juchuan Dai, Enjie Cen and Jianmin Li

Energies 2021, 14(21), 7043; https://0-doi-org.brum.beds.ac.uk/10.3390/en14217043 - 28 Oct 2021

Cited by 7 | Viewed by 2005

Abstract

Due to the complex and variable conditions under which wind turbines operate, existing working condition classification methods are inadequate for condition assessment of the main transmission system. Because working conditions are too few after classification, it cannot effectively describe the complex and variable working conditions of wind turbine. This can lead to high false-alarm rates in the condition monitoring, which affect normal operations. This paper proposes a working condition classification method for the main transmission system of wind turbines based on supervisory control and data acquisition (SCADA) data. Firstly, correlation analysis of SCADA data acquired by wind farm is used to select the parameters relevant to the main transmission system. Secondly, according to the wind turbine control principle, the working conditions are initially divided into four phases: shutdown, start-up, maximum wind energy tracking, and constant speed. The k-means clustering algorithm is used to subdivide the maximum wind energy-tracking phase and constant speed phase, which account for a larger proportion of the working conditions, to achieve better classification. Finally, a case study is used to demonstrate the calculation of alarm thresholds and alarm rates for each working condition. The results are compared with the direct use of k-means clustering for working condition classification. It is concluded that the proposed method can significantly reduce the false-alarm rate of the vibration detection process. Full article

(This article belongs to the Special Issue Wind Turbine Monitoring through Operation Data Analysis)

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30 pages, 19108 KiB

Open AccessArticle

Advanced Discretisation and Visualisation Methods for Performance Profiling of Wind Turbines

by Michiel Dhont, Elena Tsiporkova and Veselka Boeva

Energies 2021, 14(19), 6216; https://0-doi-org.brum.beds.ac.uk/10.3390/en14196216 - 29 Sep 2021

Cited by 4 | Viewed by 1454

Abstract

Wind turbines are typically organised as a fleet in a wind park, subject to similar, but varying, environmental conditions. This makes it possible to assess and benchmark a turbine’s output performance by comparing it to the other assets in the fleet. However, such a comparison cannot be performed straightforwardly on time series production data since the performance of a wind turbine is affected by a diverse set of factors (e.g., weather conditions). All these factors also produce a continuous stream of data, which, if discretised in an appropriate fashion, might allow us to uncover relevant insights into the turbine’s operations and behaviour. In this paper, we exploit the outcome of two inherently different discretisation approaches by statistical and visual analytics. As the first discretisation method, a complex layered integration approach is used. The DNA-like outcome allows us to apply advanced visual analytics, facilitating insightful operating mode monitoring. The second discretisation approach is applying a novel circular binning approach, capitalising on the circular nature of the angular variables. The resulting bins are then used to construct circular power maps and extract prototypical profiles via non-negative matrix factorisation, enabling us to detect anomalies and perform production forecasts. Full article

(This article belongs to the Special Issue Wind Turbine Monitoring through Operation Data Analysis)

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12 pages, 4440 KiB

Open AccessArticle

Analysis of Wind Turbine Aging through Operation Data Calibrated by LiDAR Measurement

by Hyun-Goo Kim and Jin-Young Kim

Energies 2021, 14(8), 2319; https://0-doi-org.brum.beds.ac.uk/10.3390/en14082319 - 20 Apr 2021

Cited by 15 | Viewed by 2118

Abstract

This study analyzed the performance decline of wind turbine with age using the SCADA (Supervisory Control And Data Acquisition) data and the short-term in situ LiDAR (Light Detection and Ranging) measurements taken at the Shinan wind farm located on the coast of Bigeumdo Island in the southwestern sea of South Korea. Existing methods have generally attempted to estimate performance aging through long-term trend analysis of a normalized capacity factor in which wind speed variability is calibrated. However, this study proposes a new method using SCADA data for wind farms whose total operation period is short (less than a decade). That is, the trend of power output deficit between predicted and actual power generation was analyzed in order to estimate performance aging, wherein a theoretically predicted level of power generation was calculated by substituting a free stream wind speed projecting to a wind turbine into its power curve. To calibrate a distorted wind speed measurement in a nacelle anemometer caused by the wake effect resulting from the rotation of wind-turbine blades and the shape of the nacelle, the free stream wind speed was measured using LiDAR remote sensing as the reference data; and the nacelle transfer function, which converts nacelle wind speed into free stream wind speed, was derived. A four-year analysis of the Shinan wind farm showed that the rate of performance aging of the wind turbines was estimated to be −0.52%p/year. Full article

(This article belongs to the Special Issue Wind Turbine Monitoring through Operation Data Analysis)

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18 pages, 6341 KiB

Open AccessArticle

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

by Davide Astolfi, Francesco Castellani, Andrea Lombardi and Ludovico Terzi

Energies 2021, 14(4), 1105; https://0-doi-org.brum.beds.ac.uk/10.3390/en14041105 - 19 Feb 2021

Cited by 27 | Viewed by 3394

Abstract

Due to the stochastic nature of the source, wind turbines operate under non-stationary conditions and the extracted power depends non-trivially on ambient conditions and working parameters. It is therefore difficult to establish a normal behavior model for monitoring the performance of a wind turbine and the most employed approach is to be driven by data. The power curve of a wind turbine is the relation between the wind intensity and the extracted power and is widely employed for monitoring wind turbine performance. On the grounds of the above considerations, a recent trend regarding wind turbine power curve analysis consists of the incorporation of the main working parameters (as, for example, the rotor speed or the blade pitch) as input variables of a multivariate regression whose target is the power. In this study, a method for multivariate wind turbine power curve analysis is proposed: it is based on sequential features selection, which employs Support Vector Regression with Gaussian Kernel. One of the most innovative aspects of this study is that the set of possible covariates includes also minimum, maximum and standard deviation of the most important environmental and operational variables. Three test cases of practical interest are contemplated: a Senvion MM92, a Vestas V90 and a Vestas V117 wind turbines owned by the ENGIE Italia company. It is shown that the selection of the covariates depends remarkably on the wind turbine model and this aspect should therefore be taken in consideration in order to customize the data-driven monitoring of the power curve. The obtained error metrics are competitive and in general lower with respect to the state of the art in the literature. Furthermore, minimum, maximum and standard deviation of the main environmental and operation variables are abundantly selected by the feature selection algorithm: this result indicates that the richness of the measurement channels contained in wind turbine Supervisory Control And Data Acquisition (SCADA) data sets should be exploited for monitoring the performance as reliably as possible. Full article

(This article belongs to the Special Issue Wind Turbine Monitoring through Operation Data Analysis)

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21 pages, 3048 KiB

Open AccessEditor’s ChoiceArticle

Analysis of Wind Turbine Aging through Operation Curves

by Davide Astolfi, Raymond Byrne and Francesco Castellani

Energies 2020, 13(21), 5623; https://0-doi-org.brum.beds.ac.uk/10.3390/en13215623 - 27 Oct 2020

Cited by 23 | Viewed by 3123

Abstract

The worsening with age of technical systems performance is a matter of fact which is particularly timely to analyze for horizontal-axis wind turbines because they constitute a mature technology. On these grounds, the present study deals with the assessment of wind turbine performance decline with age. The selected test case is a Vestas V52 wind turbine, installed in 2005 at the Dundalk Institute of Technology campus in Ireland. Operation data from 2008 to 2019 have been used for this study. The general idea is analyzing the appropriate operation curves for each working region of the wind turbine: in Region 2 (wind speed between 5 and 9 m/s), the generator speed–power curve is studied, because the wind turbine operates at fixed pitch. In Region 2

\frac{1}{2}

(wind speed between 9 and 13 m/s), the generator speed is rated and the pitch control is relevant: therefore, the pitch angle–power curve is analyzed. Using a support vector regression for the operation curves of interest, it is observed that in Region 2, a progressive degradation occurs as regards the power extracted for given generator speed, and after ten years (from 2008 to 2018), the average production has diminished of the order of 8%. In Region 2

\frac{1}{2}

, the performance decline with age is less regular and, after ten years of operation, the performance has diminished averagely of the 1.3%. The gearbox of the test case wind turbine was substituted with a brand new one at the end of 2018, and it results that the performance in Region 2

\frac{1}{2}

has considerably improved after the gearbox replacement (+3% in 2019 with respect to 2018, +1.7% with respect to 2008), while in Region 2, an improvement is observed (+1.9% in 2019 with respect to 2018) which does not compensate the ten-year period decline (−6.5% in 2019 with respect to 2008). Therefore, the lesson is that for the test case wind turbine, the generator aging impacts remarkably on the power production in Region 2, while in Region 2

\frac{1}{2}

, the impact of the gearbox aging dominates over the generator aging; for this reason, wind turbine refurbishment or component replacement should be carefully considered on the grounds of the wind intensity distribution onsite. Full article

(This article belongs to the Special Issue Wind Turbine Monitoring through Operation Data Analysis)

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17 pages, 7595 KiB

Open AccessArticle

Wind Turbine Systematic Yaw Error: Operation Data Analysis Techniques for Detecting It and Assessing Its Performance Impact

by Davide Astolfi, Francesco Castellani, Matteo Becchetti, Andrea Lombardi and Ludovico Terzi

Energies 2020, 13(9), 2351; https://0-doi-org.brum.beds.ac.uk/10.3390/en13092351 - 08 May 2020

Cited by 31 | Viewed by 4319

Abstract

The widespread availability of wind turbine operation data has considerably boosted the research and the applications for wind turbine monitoring. It is well established that a systematic misalignment of the wind turbine nacelle with respect to the wind direction has a remarkable impact in terms of down-performance, because the extracted power is in first approximation proportional to the cosine cube of the yaw angle. Nevertheless, due to the fact that in the wind farm practice the wind field facing the rotor is estimated through anemometers placed behind the rotor, it is challenging to robustly detect systematic yaw errors without the use of additional upwind sensory systems. Nevertheless, this objective is valuable because it involves the use of data that are available to wind farm practitioners at zero cost. On these grounds, the present work is a two-steps test case discussion. At first, a new method for systematic yaw error detection through operation data analysis is presented and is applied for individuating a misaligned multi-MW wind turbine. After the yaw error correction on the test case wind turbine, operation data of the whole wind farm are employed for an innovative assessment method of the performance improvement at the target wind turbine. The other wind turbines in the farm are employed as references and their operation data are used as input for a multivariate Kernel regression whose target is the power of the wind turbine of interest. Training the model with pre-correction data and validating on post-correction data, it is estimated that a systematic yaw error of

4^{\circ}

affects the performance up to the order of the 1.5% of the Annual Energy Production. Full article

(This article belongs to the Special Issue Wind Turbine Monitoring through Operation Data Analysis)

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