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In this paper we propose the design and economic evaluation of the water pumping systems for rice cultivation using solar energy, gasoline fuel and compare both systems. The design of the water and gasoline engine pumping system were evaluated. The gasoline fuel cost used in rice cultivation in an area of 1.6 acres. Under same conditions of water pumping system is replaced by the photovoltaic system which is composed of a solar panel, a converter and an electric motor pump which is compose of a direct current (DC) motor or an alternating current (AC) motor with an inverter. In addition, the battery is installed to increase the efficiency and productivity of rice cultivation. In order to verify, the simulation and economic evaluation of the storage energy battery system with batteries and without batteries are carried out. Finally the cost of four solar pumping systems was evaluated and compared with that of the gasoline pump. The results showed that the solar pumping system can be used to replace the gasoline water pumping system and DC solar pump has a payback less than 10 years. The systems that can payback the fastest is the DC solar pumping system without batteries storage system. The system the can payback the slowest is AC solar pumping system with batteries storage system. However, VAC motor pump of 220 V can be more easily maintained than the motor pump of 24 VDC and batteries back up system can supply a more stable power to the pump system.

This paper proposes a model predictive control of photovoltaic grid-connected inverter based on system identification. The single phase inverter is experimented and its model is determined by using System identification approach with Hammerstein-Wiener model. The derived nonlinear voltage model has accuracy more around 97.34% and it is transformed to the state space model by linearization. A simulation of model based controller uses the discrete time model of inverter to predict the behavior of the output voltage for each possible switching state every sampling time. Then cost function is applied as a criterion for selecting the most suitable switching state for the next sampling interval. The model output is compared with the reference voltage sine wave and the error is feedback to the optimizer. Simulation results shown that the proposed control scheme can achieve the output target with 97% of accuracy.Index Terms-Model predictive control, system identification, hammersteinwiener model, grid connected inverter.

In this paper, modeling of one type of grid connected single phase inverter commercially available in Thailand is carried out. An inverter of a grid-connected photovoltaic system has been tested and its model determined. The inverter operates in six steady state conditions and its modeling is done using nonlinear system identification approach with Hammerstein Wiener Model. The models with no cross validation and cross validation data of six steady state conditions have been experimented. The results comparison on modeling by using cross validation and no cross validation data has been analyzed by model order, model accuracies and model error. The average percentage accuracy of system identification with cross validation data and no cross validation is 81.89 and 63.83 respectively.