Wind Farms Production: Control and Prediction

Wind energy resources, unlike dispatchable central station generation, produce power dependable on external irregular source and that is the incident wind speed which does not always blow when electricity is needed. This results in the variability, unpredictability, and uncertainty of wind resources. Therefore, the integration of wind facilities to utility electrical grid presents a major challenge to power system operator. Such integration has significant impact on the optimum power flow, transmission congestion, power quality issues, system stability, load dispatch, and economic analysis. Due to the irregular nature of wind power production, accurate prediction represents the major challenge to power system operators. Therefore, in this thesis two novel models are proposed for wind speed and wind power prediction. One proposed model is dedicated to short-term prediction (one-hour ahead) and the other involves medium term prediction (one-day ahead). The accuracy of the proposed models is revealed by comparing their results with the corresponding values of a reference prediction model referred to as the persistent model. Utility grid operation is not only impacted by the uncertainty of the future production of wind farms, but also by the variability of their current production and how the active and reactive power exchange with the grid is controlled. To address this particular task, a control technique for wind turbines, driven by doubly-fed induction generators (DFIGs), is developed to regulate the terminal voltage by equally sharing the generated/absorbed reactive power between the rotor-side and the grid-side converters. To highlight the impact of the new developed technique in reducing the power loss in the generator set, an economic analysis is carried out. Moreover, a new aggregated model for wind farms is proposed that accounts for the irregularity of the incident wind distribution throughout the farm layout. Specifically, this model includes the wake effect and the time delay of the incident wind speed of the different turbines on the farm, and to simulate the fluctuation in the generated power more accurately and more closer to real-time operation. Recently, wind farms with considerable output power ratings have been installed. Their integrating into the utility grid will substantially affect the electricity markets. This thesis investigates the possible impact of wind power variability, wind farm control strategy, wind energy penetration level, wind farm location, and wind power prediction accuracy on the total generation costs and close to real time electricity market prices. These issues are addressed by developing a single auction market model for determining the real-time electricity market prices

Wind speed prediction based on univariate ARIMA and OLS on the Colombian Caribbean Coast

Greater incorporation of wind energy into power systems has necessitated the development of accurate and reliable techniques for wind speed forecasting. However, although there are multiple studies, none are set up for the Colombia Caribbean coast. This is a disadvantage because the potential of wind resources in this region is greater than the hydroelectric potential of the whole country, but all this potential has yet to be developed. In this paper, based on time series, Autoregressive Integrated Moving Average (ARIMA), and Multiple Regression with Ordinary Least Squares (OLS) in the study, two models are proposed and their performance for wind speed prediction is compared. The data were collected in the meteorological station located in the experimental farm of the Atlantic University, in Barranquilla, Colombia, and variables analyzed included wind speed, wind direction, temperature, relative humidity, solar radiation, and pressure. The results of the two approaches indicated that among all the involved models, the ARIMA model has the best predicting performance. Also, it is essential to highlight that through this work, decision-makers would explore the local wind potential, allowing for the possibility of predicting future wind speed, and thus giving them the ability to plan the production and the interaction of other sources of energy

Predicting the energy output of wind farms based on weather data: important variables and their correlation

Pre-print available at: http://arxiv.org/abs/1109.1922Wind energy plays an increasing role in the supply of energy world wide. The energy output of a wind farm is highly dependent on the weather conditions present at its site. If the output can be predicted more accurately, energy suppliers can coordinate the collaborative production of different energy sources more efficiently to avoid costly overproduction. In this paper, we take a computer science perspective on energy prediction based on weather data and analyze the important parameters as well as their correlation on the energy output. To deal with the interaction of the different parameters, we use symbolic regression based on the genetic programming tool DataModeler. Our studies are carried out on publicly available weather and energy data for a wind farm in Australia. We report on the correlation of the different variables for the energy output. The model obtained for energy prediction gives a very reliable prediction of the energy output for newly supplied weather data. © 2012 Elsevier Ltd.Ekaterina Vladislavleva, Tobias Friedrich, Frank Neumann, Markus Wagne

Lifetime prediction of turbine blades using global precipitation products from satellites

The growing size of wind turbines leads to extremely high tip speeds when the blades are rotating. The blades are prone to leading edge erosion when raindrops hit the blades at such high speeds, and blade damage will eventually affect the power production until repair or replacement of the blade is performed. Since these actions come with a high cost, it is relevant to estimate the blade lifetime for a given wind farm site prior to wind farm construction. Modeling tools for blade lifetime prediction require input time series of rainfall intensities and wind speeds in addition to a turbine-specific tip speed curve. In this paper, we investigate the suitability of satellite-based precipitation data from the Global Precipitation Measurement (GPM) mission in the context of blade lifetime prediction. We first evaluate satellite-based rainfall intensities from the Integrated Multi-Satellite Retrievals for GPM (IMERG) final product against in situ observations at 18 weather stations located in Germany, Denmark, and Portugal. We then use the satellite and in situ rainfall intensities as input to a model for blade lifetime prediction, together with the wind speeds measured at the stations. We find that blade lifetimes estimated with rainfall intensities from satellites and in situ observations are in good agreement despite the very different nature of the observation methods and the fact that IMERG products have a 30 min temporal resolution, whereas in situ stations deliver 10 min accumulated rainfall intensities. Our results indicate that the wind speed has a large impact on the estimated blade lifetimes. Inland stations show significantly longer blade lifetimes than coastal stations, which are more exposed to high mean wind speeds. One station located in mountainous terrain shows large differences between rainfall intensities and blade lifetimes based on satellite and in situ observations. IMERG rainfall products are known to have a limited accuracy in mountainous terrain. Our analyses also confirm that IMERG overestimates light rainfall and underestimates heavy rainfall. Given that networks of in situ stations have large gaps over the oceans, there is a potential for utilizing rainfall products from satellites to estimate and map blade lifetimes. This is useful as more wind power is installed offshore including floating installations very far from the coast.</p

Evaluation of an operating MOD-OA 200 kW wind turbine blade

Operating loads and structural damage were monitored during operation of the MOD-OA electric generating system. The turbine was damaged locally between stations 48 and 125 after 2.8 million rotations. Loads due to degraded yaw stiffness and fretting at rib station 48 were identified as primary to this distress. The repaired blades operated an additional 4.8 million rotations without problems