Prediction of Energy Gains From Jordanian Wind Stations Using Artificial Neural Network (Published)
System and environmental parameters affecting the output of the wind farm system at different stations in Jordan have been computationally investigated, using artificial neural network (ANN). For the several variables identified, statistical analysis was employed to indicate their relative significance to the targeted output, with the aid of the Pearson’s correlation coefficients. ANN shows proficiency in the prediction of the original experimental data for all the stations and turbines. In the simulation, the energy gain increases with the increase in the system and environmental parameters. However, there appears to be a phenomenon of threshold value in the output parameter, which limits the impacts of change in the input parameters on the eventual response of the output. It can be deduced that there is a minimum energy gain value below which increase in any of the system/environmental parameters will not have positive impact on the energy output. Findings show that the turbine characteristics, like rotor diameter and hub height, have more significant impact on the energy gain than the environmental factor like wind speed. The uniqueness of this work is that it predicts the important output of the wind farm system based on the logical arrangement of detailed parameters that are found in all operational units of the system in order to elicit desired effects.
Keywords: Artificial Neural Network, Energy Gains, Rotor Diameter., Wind Farm, Wind Turbine
Effect of Wind Turbine Generators on the Small Signal Stability of Power Systems (Published)
Wind energy provides a viable and environmentally friendly option. It can add security to national energy system with decreasing global reserves of fossil fuels. So for increasing wind power penetration, the wind farms (WFs) will be directly influencing the power system. Recent technologies related to wind energy are mostly equipped with doubly fed induction generators (DFIG) because of its many advantages. This paper establishes simulation model with the IEEE 9 and IEEE 14 bus systems considering replacement of the synchronous generator in these systems one by one by wind turbines based DFIG at the same MVA ratings and changing the location of wind resources. The PSAT simulation environment based on Matlab software is used to analyze the small signal stability of the grid interconnection of the different types of wind turbines. The results show that wind power integration can have a positive or negative impact on small signal stability of the power system depending on the location of the wind farm.
Keywords: Doubly Fed Induction Generators, PSAT, Power System Small Signal Stability, Wind Energy, Wind Farm
