Wind Turbine Operation Curves Modelling Techniques

Wind turbines are machines operating in non-stationary conditions and the power of a wind turbine depends non-trivially on environmental conditions and working parameters. For these reasons, wind turbine power monitoring is a complex task which is typically addressed through data-driven methods for constructing a normal behavior model. On these grounds, this study is devoted the analysis of meaningful operation curves, which are rotor speed-power, generator speed-power and blade pitch-power. A key point is that these curves are analyzed in the appropriate operation region of the wind turbines: the rotor and generator curves are considered for moderate wind speed, when the blade pitch is fixed and the rotational speed varies (Region 2); the blade pitch curve is considered for higher wind speed, when the rotational speed is rated (Region 2 12). The selected curves are studied through a multivariate Support Vector Regression with Gaussian Kernel on the Supervisory Control And Data Acquisition (SCADA) data of two wind farms sited in Italy, featuring in total 15 2 MW wind turbines. An innovative aspect of the selected models is that minimum, maximum and standard deviation of the independent variables of interest are fed as input to the models, in addition to the typically employed average values: using the additional covariates proposed in this work, the error metrics decrease of order of one third, with respect to what would be obtained by employing as regressors only the average values of the independent variables. In general it results that, for all the considered curves, the prediction of the power is characterized by error metrics which are competitive with the state of the art in the literature for multivariate wind turbine power curve analysis: in particular, for one test case, a mean absolute percentage error of order of 2.5% is achieved. Furthermore, the approach presented in this study provides a superior capability of interpreting wind turbine performance in terms of the behavior of the main sub-components and eliminates as much as possible the dependence on nacelle anemometer data, whose use is critical because of issues related to the sites complexity.

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Estimation of the Performance Aging of the Vestas V52 Wind Turbine through Comparative Test Case Analysis

Energies ◽

10.3390/en14040915 ◽

2021 ◽

Vol 14 (4) ◽

pp. 915 ◽

Cited By ~ 1

Author(s):

Davide Astolfi ◽

Raymond Byrne ◽

Francesco Castellani

Keyword(s):

Data Analysis ◽

Wind Speed ◽

Wind Turbine ◽

Wind Turbines ◽

Rotational Speed ◽

General Idea ◽

Gaussian Kernel ◽

Test Cases ◽

Power Curve ◽

The Given

It is a common sense expectation that the efficiency of wind turbines should decline with age, similarly to what happens with most technical systems. Due to the complexity of this kind of machine and the environmental conditions to which it is subjected, it is far from obvious how to reliably estimate the impact of aging. In this work, the aging of five Vestas V52 wind turbines is analyzed. The test cases belong to two different sites: one is at the Dundalk Institute of Technology in Ireland, and four are sited in an industrial wind farm in a mountainous area in Italy. Innovative data analysis techniques are employed: the general idea consists of considering appropriate operation curves depending on the working control region of the wind turbines. When the wind turbine operates at fixed pitch and variable rotational speed, the generator speed-power curve is studied; for higher wind speed, when the rotational speed has saturated and the blade pitch is variable, the blade pitch-power curve is considered. The operation curves of interest are studied through the binning method and through a support vector regression with a Gaussian kernel. The wind turbine test cases are analyzed vertically (each in its own history) and horizontally, by comparing the behavior at the two sites for the given wind turbine age. The main result of this study is that an evident effect of aging is the worsening of generator efficiency: progressively, less power is extracted for the given generator rotational speed. Nevertheless, this effect is observed to be lower for the wind turbines in Italy (order of −1.5% at 12 years of age with respect to seven years of age) with respect to the Dundalk wind turbine, which shows a sharp decline at 12 years of age (−8.8%). One wind turbine sited in Italy underwent a generator replacement in 2018: through the use of the same kind of data analysis methods, it was possible to observe that an average performance recovery of the order of 2% occurs after the component replacement. It also arises that for all the test cases, a slight aging effect is visible for higher wind speed, which can likely be interpreted as due to declining gearbox efficiency. In general, it is confirmed that the aging of wind turbines is strongly dependent on the history of each machine, and it is likely confirmed that the technology development mitigates the effect of aging.

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

Energies ◽

10.3390/en14041105 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1105 ◽

Cited By ~ 2

Author(s):

Davide Astolfi ◽

Francesco Castellani ◽

Andrea Lombardi ◽

Ludovico Terzi

Keyword(s):

Standard Deviation ◽

Wind Turbine ◽

Wind Turbines ◽

Curve Analysis ◽

Gaussian Kernel ◽

Support Vector ◽

Features Selection ◽

Power Curve ◽

Working Parameters ◽

Extracted Power

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.

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Wind Turbine Multivariate Power Modeling Techniques for Control and Monitoring Purposes

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4048490 ◽

2020 ◽

Vol 143 (3) ◽

Author(s):

Davide Astolfi ◽

Francesco Castellani ◽

Francesco Natili

Keyword(s):

Wind Turbine ◽

Principal Component ◽

Data Driven ◽

Gaussian Kernel ◽

Support Vector ◽

Rotor Speed ◽

Power Modeling ◽

Ambient Conditions ◽

Power Monitoring ◽

Turbine Performance

Abstract Wind turbine performance monitoring is a complex task because the power has a multivariate dependence on ambient conditions and working parameters. Furthermore, wind turbine nacelle anemometers are placed behind the rotor span and the control system estimates the upwind flow through a nacelle transfer function: this introduces a data quality issue. This study is devoted to the analysis of data-driven techniques for wind turbine performance control and monitoring: operation data of six 850 kW wind turbines sited in Italy have been employed. The objective of this study is an assessment of several easily implementable techniques and input variables selections for data-driven models whose target is the power of a wind turbine. Three model types are selected: one is linear (Principal Component Regression) and two are nonlinear (Support Vector Regression with Gaussian Kernel and Feedforward Artificial Neural Network). The models' validation provides meaningful indications: the linear model in general has lower performance because it cannot reproduce properly the nonlinear pitch behavior when approaching rated power. Therefore, it is concluded that a nonlinear model should be employed and the achieved mean absolute error is of the order of 1.3% of the rated power. Furthermore, the errors are kept at the order of 2% of the rated power for the models whose input is the rotor speed instead that wind speed: this observation supports that, in case it is needed because of nacelle anemometer biases, the power monitoring can be acceptably implemented using the rotor speed.

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SHAPING OF AN AUTONOMOUS POWER SYSTEM FOR GUARANTEED POWER SUPPLY WITH THE PREDOMINANCE WIND ENERGY

Alternative Energy and Ecology (ISJAEE) ◽

10.15518/isjaee.2018.22-24.028-050 ◽

2018 ◽

pp. 28-50

Author(s):

S. G. Ignatiev ◽

S. V. Kiseleva

Keyword(s):

Energy Storage ◽

Wind Speed ◽

Wind Energy ◽

Wind Turbine ◽

Wind Turbines ◽

Energy Demand ◽

Diesel Generator ◽

Average Wind Speed ◽

Wind Speeds ◽

Average Wind

Optimization of the autonomous wind-diesel plants composition and of their power for guaranteed energy supply, despite the long history of research, the diversity of approaches and methods, is an urgent problem. In this paper, a detailed analysis of the wind energy characteristics is proposed to shape an autonomous power system for a guaranteed power supply with predominance wind energy. The analysis was carried out on the basis of wind speed measurements in the south of the European part of Russia during 8 months at different heights with a discreteness of 10 minutes. As a result, we have obtained a sequence of average daily wind speeds and the sequences constructed by arbitrary variations in the distribution of average daily wind speeds in this interval. These sequences have been used to calculate energy balances in systems (wind turbines + diesel generator + consumer with constant and limited daily energy demand) and (wind turbines + diesel generator + consumer with constant and limited daily energy demand + energy storage). In order to maximize the use of wind energy, the wind turbine integrally for the period in question is assumed to produce the required amount of energy. For the generality of consideration, we have introduced the relative values of the required energy, relative energy produced by the wind turbine and the diesel generator and relative storage capacity by normalizing them to the swept area of the wind wheel. The paper shows the effect of the average wind speed over the period on the energy characteristics of the system (wind turbine + diesel generator + consumer). It was found that the wind turbine energy produced, wind turbine energy used by the consumer, fuel consumption, and fuel economy depend (close to cubic dependence) upon the specified average wind speed. It was found that, for the same system with a limited amount of required energy and high average wind speed over the period, the wind turbines with lower generator power and smaller wind wheel radius use wind energy more efficiently than the wind turbines with higher generator power and larger wind wheel radius at less average wind speed. For the system (wind turbine + diesel generator + energy storage + consumer) with increasing average speed for a given amount of energy required, which in general is covered by the energy production of wind turbines for the period, the maximum size capacity of the storage device decreases. With decreasing the energy storage capacity, the influence of the random nature of the change in wind speed decreases, and at some values of the relative capacity, it can be neglected.

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Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

Entropy ◽

10.3390/e23080975 ◽

2021 ◽

Vol 23 (8) ◽

pp. 975

Author(s):

Yancai Xiao ◽

Jinyu Xue ◽

Mengdi Li ◽

Wei Yang

Keyword(s):

Machine Learning ◽

Wavelet Transform ◽

Fault Diagnosis ◽

Wind Turbine ◽

Wind Turbines ◽

Gear Tooth ◽

Support Vector ◽

Grey Wolf Optimizer ◽

Empirical Wavelet Transform ◽

Low Pass

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.

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Numerical Simulation and Wind Tunnel Investigation on Static Characteristics of VAWT Rotor Starter with Lift-Drag Combined Structure

Energies ◽

10.3390/en14196167 ◽

2021 ◽

Vol 14 (19) ◽

pp. 6167

Author(s):

Fang Feng ◽

Guoqiang Tong ◽

Yunfei Ma ◽

Yan Li

Keyword(s):

Numerical Simulation ◽

Wind Speed ◽

Wind Tunnel ◽

Wind Turbine ◽

Wind Turbines ◽

Speed Ratio ◽

Static Characteristics ◽

Torque Coefficient ◽

Starting Characteristics ◽

Static Torque

In order to get rid of the impact of the global financial crisis and actively respond to global climate change, it has become a common choice for global economic development to develop clean energy such as wind energy, improve energy efficiency and reduce greenhouse gas emissions. With the advantages of simple structure, unnecessary facing the wind direction, and unique appearance, the vertical axis wind turbine (VAWT) attracts extensive attention in the field of small and medium wind turbines. The lift-type VAWT exhibits outstanding aerodynamic characteristics at a high tip speed ratio, while the starting characteristics are generally undesirable at a low wind speed; thus, how to improve the starting characteristics of the lift-type VAWT has always been an important issue. In this paper, a lift-drag combined starter (LDCS) suitable for lift-type VAWT was proposed to optimize the starting characteristics of lift-type VAWT. With semi-elliptical drag blades and lift blades equipped on the middle and rear part outside the starter, the structure is characterized by lift-drag combination, weakening the adverse effect of the starter with semi-elliptical drag blades alone on the output performance of the original lift-type VAWT and improving the characteristics of the lift-drag combined VAWT. The static characteristic is one of the important starting characteristics of the wind turbine. The rapid development of computational fluid dynamics has laid a solid material foundation for VAWT. Thus the static characteristics of the LDCS with different numbers of blades were investigated by conducting numerical simulation and wind tunnel tests. The results demonstrated that the static torque coefficient of LDCS increased significantly with the increased incoming wind speed. The average value of the static torque coefficient also increased significantly. This study can provide guidelines for the research of lift-drag combined wind turbines.

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Numerical Simulation of Dynamics of a Spar Type Floating Wind Turbine and Comparison With Laboratory Measurements

Volume 6: Ocean Space Utilization; Ocean Renewable Energy ◽

10.1115/omae2016-54149 ◽

2016 ◽

Cited By ~ 1

Author(s):

Hyunseong Min ◽

Cheng Peng ◽

Fei Duan ◽

Zhiqiang Hu ◽

Jun Zhang

Keyword(s):

Numerical Simulation ◽

Wind Speed ◽

Numerical Simulations ◽

Wind Turbine ◽

Wind Turbines ◽

Deep Water ◽

Offshore Wind ◽

Wind Loads ◽

Floating Offshore Wind Turbines ◽

The Cost

Wind turbines are popular for harnessing wind energy. Floating offshore wind turbines (FOWT) installed in relatively deep water may have advantages over their on-land or shallow-water cousins because winds over deep water are usually steadier and stronger. As the size of wind turbines becomes larger and larger for reducing the cost per kilowatt, it could bring installation and operation risks in the deep water due to the lack of track records. Thus, together with laboratory tests, numerical simulations of dynamics of FOWT are desirable to reduce the probability of failure. In this study, COUPLE-FAST was initially employed for the numerical simulations of the OC3-HYWIND, a spar type platform equipped with the 5-MW baseline wind turbine proposed by National Renewable Energy Laboratory (NREL). The model tests were conducted at the Deepwater Offshore Basin in Shanghai Jiao Tong University (SJTU) with a 1:50 Froude scaling [1]. In comparison of the simulation using COUPLE-FAST with the corresponding measurements, it was found that the predicted motions were in general significantly smaller than the related measurements. The main reason is that the wind loads predicted by FAST were well below the related measurements. Large discrepancies are expected because the prototype and laboratory wind loads do not follow Froude number similarity although the wind speed was increased (or decreased) in the tests such that the mean surge wind force matched that predicted by FAST at the nominal wind speed (Froude similarity) in the cases of a land wind turbine [1]. Therefore, an alternative numerical simulation was made by directly inputting the measured wind loads to COUPLE instead of the ones predicted by FAST. The related simulated results are much improved and in satisfactory agreement with the measurements.

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Importance of Shear in Site Assessment of Wind Turbine Fatigue Loads

Journal of Solar Energy Engineering ◽

10.1115/1.4039748 ◽

2018 ◽

Vol 140 (4) ◽

Cited By ~ 3

Author(s):

René M. M. Slot ◽

Lasse Svenningsen ◽

John D. Sørensen ◽

Morten L. Thøgersen

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Wind Turbines ◽

Structural Reliability ◽

Turbine Blades ◽

Structural Response ◽

Fatigue Loading ◽

Worst Case ◽

Shear Model ◽

Wind Climate

Wind turbines are subjected to fatigue loading during their entire lifetime due to the fluctuating excitation from the wind. To predict the fatigue damage, the design standard IEC 61400-1 describes how to parametrize an on-site specific wind climate using the wind speed, turbulence, wind shear, air density, and flow inclination. In this framework, shear is currently modeled by its mean value, accounting for neither its natural variance nor its wind speed dependence. This very simple model may lead to inaccurate fatigue assessment of wind turbine components, whose structural response is nonlinear with shear. Here we show how this is the case for flapwise bending of blades, where the current shear model leads to inaccurate and in worst case nonconservative fatigue assessments. Based on an optimization study, we suggest modeling shear as a wind speed dependent 60% quantile. Using measurements from almost one hundred sites, we document that the suggested model leads to accurate and consistent fatigue assessments of wind turbine blades, without compromising other main components such as the tower and the shaft. The proposed shear model is intended as a replacement to the mean shear, and should be used alongside the current IEC models for the remaining climate parameters. Given the large number of investigated sites, a basis for evaluating the uncertainty related to using a simplified statistical wind climate is provided. This can be used in further research when assessing the structural reliability of wind turbines by a probabilistic or semiprobabilistic approach.

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Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data

Energies ◽

10.3390/en12173396 ◽

2019 ◽

Vol 12 (17) ◽

pp. 3396 ◽

Cited By ~ 2

Author(s):

Mingzhu Tang ◽

Wei Chen ◽

Qi Zhao ◽

Huawei Wu ◽

Wen Long ◽

...

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Support Vector Regression ◽

Confidence Intervals ◽

Wind Turbines ◽

Large Scale ◽

Support Vector ◽

Effective Response ◽

Support Vector Regression Model ◽

Doubly Fed

Fault diagnosis and forecasting contribute significantly to the reduction of operating and maintenance associated costs, as well as to improve the resilience of wind turbine systems. Different from the existing fault diagnosis approaches using monitored vibration and acoustic data from the auxiliary equipment, this research presents a novel fault diagnosis and forecasting approach underpinned by a support vector regression model using data obtained by the supervisory control and data acquisition system (SCADA) of wind turbines (WT). To operate, the extraction of fault diagnosis features is conducted by measuring SCADA parameters. After that, confidence intervals are set up to guide the fault diagnosis implemented by the support vector regression (SVR) model. With the employment of confidence intervals as the performance indicators, an SVR-based fault detecting approach is then developed. Based on the WT SCADA data and the SVR model, a fault diagnosis strategy for large-scale doubly-fed wind turbine systems is investigated. A case study including a one-year monitoring SCADA data collected from a wind farm in Southern China is employed to validate the proposed methodology and demonstrate how it works. Results indicate that the proposed strategy can support the troubleshooting of wind turbine systems with high precision and effective response.

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EWSE and Uncertainty and Disturbance Estimator Based Pitch Angle Control for Wind Turbine Systems Operating in Above-Rated Wind Speed Region

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4045561 ◽

2019 ◽

Vol 142 (3) ◽

Author(s):

Xuguo Jiao ◽

Qinmin Yang ◽

Bo Fan ◽

Qi Chen ◽

Yong Sun ◽

...

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Pitch Angle ◽

Support Vector ◽

Control Scheme ◽

High System ◽

Uncertainty And Disturbance Estimator ◽

Systematic Solution ◽

The Stability ◽

Disturbance Estimator

Abstract As wind energy becomes a larger part of the world's energy portfolio, the control of wind turbines is still confronted with challenges including wind speed randomness and high system uncertainties. In this study, a novel pitch angle controller based on effective wind speed estimation (EWSE) and uncertainty and disturbance estimator (UDE) is proposed for wind turbine systems (WTS) operating in above-rated wind speed region. The controller task is to maintain the WTS's generator power and rotor speed at their prescribed references, without measuring the wind speed information and accurate system model. This attempt also aims to bring a systematic solution to deal with different system characteristics over wide working range, including extreme and dynamic environmental conditions. First, support vector machine (SVR) based EWSE model is developed to estimate the effective wind speed in an online manner. Second, by integrating an UDE and EWSE model into the controller, highly turbulent and unpredictable dynamics introduced by wind speed and internal uncertainties is compensated. Rigid theoretical analysis guarantees the stability of the overall system. Finally, the performance of the novel pitch control scheme is testified via the professional Garrad Hassan (GH) bladed simulation platform with various working scenarios. The results reveal that the proposed approach achieves better performance in contrast to traditional L1 adaptive and proportional-integral (PI) pitch angle controllers.

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