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Register a new userEnergy management strategy for an optimum control of a standalone photovoltaic-batteries water pumping system for agriculture applications
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Pumping water using multiple energy sources is the ideal solution for supplying of potable water in isolated or arid areas. In this paper, an effective control and energy management strategy for a stand-alone photovoltaic-batteries water pumping system for agriculture applications is presented. The system is composed of photovoltaic solar panels as primary energy sources, and Lead-Acid batteries as seconder energy sources to supply the brushless DC motor and the centrifugal pump. The energy management strategy uses an intelligent algorithm to satisfy the energy demanded by the motor, also to maintain the state-of-charge of the battery between safe margins in order to eliminate the full discharge and the destruction of the batteries. Drift is a major problem in photovoltaic systems; this phenomenon occurs when the solar irradiation changes rapidly. Classical MPPT algorithms do not solve this problem, for this reason a modified P&O has been implemented, the obtained results shown the efficiency of the algorithm compared to the conventional P&O. Computer simulation results confirm the effectiveness of the proposed energy management algorithm under random meteorological conditions.
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Optimization of materials performance for specific applications often requires balancing multiple aspects of materials functionality. Even for the cases where generative physical model of material behavior is known and reliable, this often requires search over multidimensional parameter space to identify low-dimensional manifold corresponding to required Pareto front. Here we introduce the multi-objective Bayesian Optimization (MOBO) workflow for the ferroelectric/anti-ferroelectric performance optimization for memory and energy storage applications based on the numerical solution of the Ginzburg-Landau equation with electrochemical or semiconducting boundary conditions. MOBO is a low computational cost optimization tool for expensive multi-objective functions, where we update posterior surrogate Gaussian process models from prior evaluations, and then select future evaluations from maximizing an acquisition function. Using the parameters for a prototype bulk antiferroelectric (PbZrO3), we first develop a physics-driven decision tree of target functions from the loop structures. We further develop a physics-driven MOBO architecture to explore multidimensional parameter space and build Pareto-frontiers by maximizing two target functions jointly: energy storage and loss. This approach allows for rapid initial materials and device parameter selection for a given application and can be further expanded towards the active experiment setting. The associated notebooks provide both the tutorial on MOBO and allow to reproduce the reported analyses and apply them to other systems (https://github.com/arpanbiswas52/MOBO_AFI_Supplements).
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