Evaluating Meteorological Effects on Wind Turbine Performance: Anomaly Detection and Energy Loss Quantification

The Power curve of wind turbine ignores the dynamic behavior of the wind and represents the wind turbine power as a function of the average wind speed. Power curve is not an adequate tool for estimating the power of variable speed wind turbines. Because, in addition to average wind speed, the output power of these turbines is highly dependent on the dynamic of the wind. In this paper, by introducing a new curve named controllers power curve, a method is presented to modify power curve of variable speed wind turbines according to the wind dynamics. The controllers' power curve is a 3-D curve that represent the amount of electrical power that generated because of controllers' performance taking to account the wind characteristics and reactions of controllers to instantaneous wind speed variations. In proposed method, the EPF of the wind represents the wind dynamics. Also the controllers' performance is simulated using a new factor named controllability factor. Simulation studies showed the effectiveness of the proposed method based on controllers' power curve.

A comprehensive approach to wind turbine power curve modeling: Addressing outliers and enhancing accuracy

Spatiotemporal variation in air density and associated effects on the wind-induced response of high-rise buildings

The instantaneous wind speed variations are ignored in the wind turbine power curves. Hence, the output power of the wind turbines becomes a function of only the average wind speed. In the variable-speed wind turbines, the output power is a function of wind speed as well as the wind dynamics because in order to extract the maximum amount of electrical energy, the control system in these turbines will adjust the rotor speed and blades' pitch angle according to the instantaneous wind speed. Since the wind dynamics is not taken into account in the power curve, the power generated by the controllers is overlooked and therefore the turbine output power is not represented accurately by the power curve. In this paper, a new method is proposed using a new index called wind turbine controllability factor in order to make it possible to calculate precisely the output power of wind turbines based on the wind dynamics. In this method, the energy pattern factor is used as an indicator of the amount of instantaneous wind speed variations and power curve is modified corresponding to the values of the energy pattern factor.

Based upon the analysis of power curves of 66 wind turbines in megawatt class, a mathematical model of the power curve has been developed, which, contrary to the already existing models, allows determination of the power curves for a wind turbine merely by using its two main parameters: wind-turbine generator nameplate capacity and rotor diameter. It has been proved that the mathematical model of the power curve for a wind turbine of specified unit size presented as a spline can be approximated to the power curve of wind turbine of another unit size by introducing respective scaling factors along Х and Y axes. From the results of the investigation a linear dependence was revealed between scaling factors and respective values of WT generator nameplate capacity, rotor diameter, and respective equations of linear regression were obtained. A statistical analysis based on the results of modeling of the power curves for 66 wind turbines with power ranging from 2.0 to 3.6 MW and rotor diameter from 100 to 140 m showed their high average coefficient of determination R2 = 0.995, and a standard deviation of simulation absolute error is broadly congruent with an error of power curve experimental determination. The developed mathematical model of power curve enables prompt and reliable determining the power curve for a wind turbine of specified unit size, if there is no free access to it, or for the wind turbines being designed.

The major wind turbine performance assessment methodology established in the field of wind energy is observing and comparing wind turbine power curves (WTPC) of multiple wind turbines. In this paper, adjustments to this method are proposed to improve overall accuracy of the assessment. While preprocessing the supervisory control and data acquisition (SCADA) data points, density peak clustering (DPC) filters out invalid points such as maintenance and peak-power limitation for a more accurate WTPC. This paper introduces a new variable, performance index "S", to evaluate both overall and segmented wind turbine performance without the influence of wind distribution. Further researches are also discussed in this paper.

This paper proposes a novel machine parameter independent control algorithm that enables wind turbines to quickly and effectively extract maximum power from the wind turbine under changing atmospheric conditions. The energy captured by a wind turbine varies due to variations in wind velocity, air density (influenced by temperature and humidity), turbine aerodynamics, and generator dynamics. Without power electronics and power extraction control techniques, wind systems have low power extraction efficiency. Given that the power from the wind turbine is dependent on generator rotor speed, the proposed algorithm indirectly uses the tip speed ratio (TSR) to assist in the maximum power point search (MPPS). The slope (or indirect TSR) assisted MPPS provides historic system state information and preempts misguided control decisions due to deceptive power readings caused by atmospheric changes. In this paper, the wind energy system, algorithm design framework, performance, and real-time wind speed data is presented.

To identify the influence of turbulence and vertical wind profile in wind turbine performance, wind speed measurements at different heights have been performed. Measurements have been developed using a cup anemometer and a LIDAR equipment, specifically a pulsed wave one. The wind profile has been recorded to study the effect of the atmospheric conditions over the energy generated by a wind turbine located close to the LIDAR system. The changes in the power production of the wind turbine are relevant.

This paper presents a computational fluid dynamics (CFD) analysis and blade element momentum (BEM) analysis of horizontal small-scale wind turbines under various parameters. Random airfoils such as the NACA 0012, NACA 0018, NACA 4412, S1016, S1210, S1223, and SC20402 are chosen, and the glide ratio is analysed. A Reynolds number of 100,000 is considered because of the small wind turbine design. The foil with the highest glide ratio is selected. Various notations are used to represent the chord and the twist angle of the blade, and the blade with the best chord and twist angle is chosen. The lift, drag, and power coefficients and power curves are analysed via the BEM. The power curves are evaluated for different air densities, and a higher value of power is obtained. Finally, via CFD, the SST K-{omega } model for turbulence is analysed for various angles of attack via Ansys Fluent for different air densities. The results are satisfactory compared with those of the CFD and BEM analyses. The Weibull distribution is analysed to understand the required frequencies for various wind speeds so that the installation of wind turbines can be made convenient in preferred areas with suitable wind properties.

Day-ahead wind power forecasting based on the clustering of equivalent power curves

Wind turbine power curve modeling based on interval extreme probability density for the integration of renewable energies and electric vehicles

In the wind energy industry, it is of great importance to develop models that accurately forecast the power output of a wind turbine, as such predictions are used for wind farm location assessment or power pricing and bidding, monitoring, and preventive maintenance. As a first step, and following the guidelines of the existing literature, we use the supervisory control and data acquisition (SCADA) data to model the wind turbine power curve (WTPC). We explore various parametric and non-parametric approaches for the modeling of the WTPC, such as parametric logistic functions, and non-parametric piecewise linear, polynomial, or cubic spline interpolation functions. We demonstrate that all aforementioned classes of models are rich enough (with respect to their relative complexity) to accurately model the WTPC, as their mean squared error (MSE) is close to the MSE lower bound calculated from the historical data. However, all aforementioned models, when it comes to forecasting, seem to have an intrinsic limitation, due to their inability to capture the inherent auto-correlation of the data. To avoid this conundrum, we show that adding a properly scaled ARMA modeling layer increases short-term prediction performance, while keeping the long-term prediction capability of the model. We further enhance the accuracy of our proposed model, by incorporating additional environmental factors that affect the power output, such as the ambient temperature and the wind direction.

This paper presents the setup of an open control Research Wind Turbine and its validation in Hardware-in-the-Loop operation on a System Test Bench. The Wind Turbine is a modified NEG Micon NM80/2750 with a rated power of 2.75 MW and all components updated to the state of the art. Its original automation is replaced by an open control based on a Programmable Logic Control by Siemens. Both the control and the operation management are implemented in MATLAB Simulink and integrated by automatic code generation which allows the efficient validation of innovative control methods in experiment after model based design in simulation. Also, the simulation models of the original Wind Turbine and the Turbine on the Test Bench are introduced. The Wind Turbine is validated on a System Test Bench in Hardware-in-the-Loop operation offering a realistic test earlier in the development process. A System Test Bench enables the efficient and reproducible investigation of the behavior of an entire Wind Turbine drive train only without the big components rotor and tower. Though, due to the missing rotor inertia, the drive train behavior differs strongly from the original one on the field. The presented Hardware-in-the-Loop system consists of the models of the wind as well as the rotor and the Inertia Emulation, which reproduces the real dynamic. It is also implemented in Simulink and realized on a dSPACE computer. The Research Wind Turbine is validated with turbulent wind in the whole operating range matching the expected behavior in simulation well. This is also the first Hardware-in-the-Loop operation of a Wind Turbine on a System Test Bench at this power level.

Variations in air density from January 1972 to April 1975 at heights near 200 km

During the operation of wind turbines, there are a lot of abnormal data collected from wind power data, which cannot be used to truly evaluate the performance and operation state of wind turbines. According to the distribution characteristics of these abnormal data, an outlier delete method using Bayesian change point-quartile combined algorithm is proposed. Firstly, to identify and delete the abnormal data below wind power curve, based on the Bayesian theorem, change point position is identified. Finally, the quartile method is utilized to delete the upper sparse outliers. The proposed method is applied to 10-min field monitoring wind power data, root mean square error (RMSE), program running time and the rejection rate of field wind turbine data are used to measure model accuracy, the results show that compared with the least squares (LS) change point method, the method proposed in this paper is feasible and effective.