Modelling and Control of Wind Turbine (Published)
This work covers the modelling of wind turbines for power system studies. The operation of horizontal, variable speed wind turbines with pitch control was investigated. Complexities of various parts of a wind turbine model, such as aerodynamic conversion, drive train and generator representation were analyzed. The mathematical equations describing the dynamic behaviour of a wind energy system were successfully simulated in gPROMS. The wind turbine model was further tested upon step changes in the wind velocity as well as the blade pitch angle, confirming the need of power control. Using wind turbine model, a power control structure was generated, that takes into consideration the dynamical aspects of the wind turbine as well as constraints. An explicit parametric controller, a novel control method, was designed using MATLAB and the Parametric Optimization (POP) software. A simple explicit optimal control law was constructed that allows the on-line implementation via simple linear function evaluations. The controller was implemented using gO: MATLAB and the simulation results.
Citation: Abubakar M.N. (2022) Modelling and Control of Wind Turbine, International Research Journal of Pure and Applied Physics, Vol.9 No.1, pp.62-80
Design of a Renewable Energy Output Prediction System for 1000mw Solar-Wind Hybrid Power Plant (Published)
Problems associated with non-renewable energy sources such as fossil fuels make it necessary to move to cleaner renewable energy sources such as wind and solar. But the wind and sun are both intermittent sources of energy therefore accurate forecasts of wind and solar power are necessary to ensure the safety, stability and economy of utilizing these resources in large scale power generation. In this study, five meteorological parameters namely Temperature, Rainfall, Dew Point, Relative Humidity and Cloud Cover were collected for the year 2012 and used to predict wind and solar power output in Jos, Nigeria. The study used prediction algorithms such as Regression techniques and Artificial Neural Networks to predict the output of a 1000mW Solar-Wind Hybrid Power Plant over a period of one year. Individual prediction techniques were compared and Isotonic Regression was found to have the highest accuracy with errors of 40.5% in predicting solar power generation and 35.4% in predicting wind power generation. The relatively high levels of error are attributed to several limitations of the research work.