Wind turbulence inputs for horizontal axis wind turbines

Wind turbine response characteristics in the presence of atmospheric turbulence was predicted using two major modeling steps. First, the important atmospheric sources for the force excitations felt by the wind turbine system were identified and characterized. Second, a dynamic model was developed which describes how these excitations are transmitted through the structure and power train. The first modeling step, that of quantifying the important excitations due to the atmospheric turbulence was established. The dynamic modeling of the second step was undertaken separately

Using conditional kernel density estimation for wind power density forecasting

Of the various renewable energy resources, wind power is widely recognized as one of the most promising. The management of wind farms and electricity systems can benefit greatly from the availability of estimates of the probability distribution of wind power generation. However, most research has focused on point forecasting of wind power. In this paper, we develop an approach to producing density forecasts for the wind power generated at individual wind farms. Our interest is in intraday data and prediction from 1 to 72 hours ahead. We model wind power in terms of wind speed and wind direction. In this framework, there are two key uncertainties. First, there is the inherent uncertainty in wind speed and direction, and we model this using a bivariate VARMA-GARCH (vector autoregressive moving average-generalized autoregressive conditional heteroscedastic) model, with a Student t distribution, in the Cartesian space of wind speed and direction. Second, there is the stochastic nature of the relationship of wind power to wind speed (described by the power curve), and to wind direction. We model this using conditional kernel density (CKD) estimation, which enables a nonparametric modeling of the conditional density of wind power. Using Monte Carlo simulation of the VARMA-GARCH model and CKD estimation, density forecasts of wind speed and direction are converted to wind power density forecasts. Our work is novel in several respects: previous wind power studies have not modeled a stochastic power curve; to accommodate time evolution in the power curve, we incorporate a time decay factor within the CKD method; and the CKD method is conditional on a density, rather than a single value. The new approach is evaluated using datasets from four Greek wind farms

Frequency and duration of low-wind-power events in Germany

In the transition to a renewable energy system, the occurrence of low-wind-power events receives increasing attention. We analyze the frequency and duration of such events for onshore wind power in Germany, based on 40 years of reanalysis data and open software. We find that low-wind-power events are less frequent in winter than in summer, but the maximum duration is distributed more evenly between months. While short events are frequent, very long events are much rarer. Every year, a period of around five consecutive days with an average wind capacity factor below 10% occurs, and every ten years a respective period of nearly eight days. These durations decrease if only winter months are considered. The longest event in the data lasts nearly ten days. We conclude that public concerns about low-wind-power events in winter may be overrated, but recommend that modeling studies consider multiple weather years to properly account for such events.Comment: This is an update version after peer revie

Short and long-term wind turbine power output prediction

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. 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. 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

Modeling and control of a variable speed variable pitch angle prototype wind turbine

This paper focuses on modeling, control and simulation of a 500 KW horizontal axis prototype wind turbine that is being developed in the context of the MILRES (National Wind Energy Systems) Project in Turkey. The prototype turbine is designed as variable speed variable pitch angle wind turbine due to its advantages in efficiency and the structure. Aerodynamic, mechanical and electrical subsystems along with pitch and torque controllers are designed in both Matlab/Simulink and S4WT simulation environments. The main control purpose is to generate a power curve that is close to the ideal power curve where the energy efficiency is maximized below the nominal wind speed of 11 m/s and the power is limited to the nominal value above the nominal wind speed. Turbsim is integrated with both environments to generate a realistic wind profile of Kaimal turbulence model. The performance analysis of the prototype turbine is done under the power production scenario in both environments. Start up, emergency stop, shut down and parked scenarios are also implemented in S4WT

A Practical Model and an Optimal Controller for Variable Speed Wind Turbine Permanent Magnet Synchronous Generator

The aim of this paper is the complete modeling and simulation of an optimal control system using practical setup parameters for a wind energy conversion system (WECS) through a direct driven permanent magnet synchronous generator (D-PMSG) feeding ac power to the utility grid. The generator is connected to the grid through a back-to-back PWM converter with a switching frequency of 10 KHz. A maximum power point tracking (MPPT) control is proposed to ensure the maximum power capture from wind turbine, and a PI controller designed for the wind turbine to generate optimum speed for the generator via an aerodynamic model. MATLAB/Simulink results demonstrate the accuracy of the developed control scheme

Wind Energy in Colombia: A Framework for Market Entry

The purpose of this report is to provide decision makers in Colombia (and by extension other countries or regions), who are considering the deployment or consolidation of wind power, with a set of options to promote its use. The options presented are the result of an analysis of the Colombian market; this analysis included simulations and modeling of the country’s power sector, and extensive consultations with operators, managers, and agents. More information on the analysis and simulations is presented in the appendixes. Wind was chosen to exemplify the range of renewable energy alternatives available to complement traditional power sector technologies on the basis of its technical maturity, its relatively low cost compared to other options, the country’s experience, and its wind power potential.wind energy, Colombia