Hybrid adaptive control for variable-speed variable-pitch wind energy systems using general regression neural network
Abstract. This paper presents a novel hybrid adaptive control approach for the VS-VP half-direct driven WECS by combining pitch control with variable generator torque regulation in different operating regions. A general regression neural network (GRNN) is employed to derive the reference commands of generator torque and pitch angle from the real-time signals of generator power and speed. Furthermore, a fast and effective nonlinear PID pitch controller is presented to track the reference command of pitch angle in the full load region. The proposed GRNN based hybrid adaptive control strategies have been developed and validated using simulations. This study shows that the proposed method is much faster, more accurate and effective than conventional linear control approach.
Abstract This work proposes a multi-period and spatially explicit framework conceived to drive strategic policies on biofuels. A Mixed Integer Linear Programming (MILP) model is proposed as quantitative tool to optimise the oncoming transition towards more sustainable infrastructures. This paper addresses the design of bioethanol supply chains where both corn grain and stover are considered as suitable biomass. A Mixed Integer Linear Program is proposed to optimise the system financial performance and to comply with EU environmental regulation by taking into account a wide number of technological options. Bioethanol production in Northern Italy is chosen as a demonstrative case study.