a b s t r a c tThis paper investigates the application of model free control (MFC) strategy to optimally control the oscillation of single body heaving waves energy converter (WEC). The aim of the proposed controller is to maximize the electrical energy output of the device. The MFC is an off-line fixed structure control approach that combines between simplicity and insensitivity to system uncertainties and/or operational conditions. The proposed controller is based on a classical linear compensator, which is designed and tuned using only the well-known dynamics of the WEC. Therefore, any extra unknown or partially known dynamics are taken care of using the ultra-local model (ULM). Simulation results show that the MFC performance is superior to that of the base linear compensator, in terms of reference tracking capability and robustness towards uncertainties. Also other well-established control strategies are used to further validate the proposed controller.
The availability of short-term forecast weather model for a particular country or region is essential for operation planning of energy systems. This paper presents the first step by a group of researchers at UAE University to establish a weather model for the UAE using the weather data for at least 10 years and employing various models such as classical empirical models, artificial neural network (ANN) models, and time-series regression models with autoregressive integrated moving-average (ARIMA). This work uses time-series regression with ARIMA modeling to establish a model for the mean daily and monthly global solar radiation (GSR) for the city of Al-Ain, United Arab Emirates. Time-series analysis of solar radiation has shown to yield accurate average long-term prediction performance of solar radiation in Al-Ain. The model was built using data for 10 years (1995–2004) and was validated using data of three years (2005–2007), yielding deterministic coefficients (R2) of 92.6% and 99.98% for mean daily and monthly GSR data, respectively. The low corresponding values of mean bias error (MBE), mean absolute bias error (MABE), mean absolute percentage error (MAPE), and root-mean-square error (RMSE) confirm the adequacy of the obtained model for long-term prediction of GSR data in Al-Ain, UAE.
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