Design Of Intelligent And Hybrid Based MPPT Controller For Photovoltaic Water Pumping System
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Date
2018-12
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Addis Ababa University
Abstract
Maximum power point tracking plays an important role for Photovoltaic power pumping systems because it optimize the power output from a Pholovoltaic system for a given set of conditions. This thesis presents a maximum power point tracker using artificial intelligence and hybrid methods for a standalone Photovoltaic water pumping system. This work focused on designing of different intelligence control methods to get maximum amount of power for Dolo Ado Woreda refugee camp appropriate power for the required amount of demand to pump water from the ground to 600 residential house hold needs. In this work scaling and sizing the whole components of the standalone photovoltaic water pumping system, such as, Photovoltaic panel, direct converter, inverter, and low pass filter is applied to generate a 29.04kW power by using a boost converter as a supporter for maximum power point traking algorithm by adjusting the duty cycle of the boost converter to maximize the output to the inverter which is feeding an alternative 17.5kW current load would achieved with three proposed intelligent controllers.
After sized and designed the proposed system components, each System elements are individually modelled in MATLAB/SIMULINK and then connected to assess performance under different environmental conditions. First, each technique is compared with the direct connection matched system. The results show that the direct connection PV system response oscillates far from the tracking point and the three proposed controller method dynamics responses are around the maximum power point under different level of temperature and irradiation. The performance of the three proposed controllers for photovoltaic water pumping system is evaluated through simulation studies and compared. The simulation results show that the efficiency of the photovoltaic water pumping system with the fuzzy logic, artificial neural network and artificial neuro fuzzy inference system controller is 97.68%, 99.32% and 99.88% respectively, whereas the same without any controller is found to be 88.06.
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Keywords
Maximum Power Point Traking (MPPT), Artificial intelligent (AI), Hybrid, neural network (NN), Fuzzy logic (FL), Photovoltaic (PV)