Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.505231
Title: Maximum power point tracking of PV system using ANFIS prediction and fuzzy logic tracking
Author: Aldobhani, Abdulaziz Mohamed Saeed
Awarding Body: De Montfort University
Current Institution: De Montfort University
Date of Award: 2008
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Abstract:
Operating faraway from maximum power point decreases the generated power from photovoltaic (PV) system. For optimum operation, it is necessary to continually track the maximum power point of the PV solar array. However with huge changes in external influences and the nonlinear relationship of electrical characteristics of PV panels it is a difficult problem to identify the maximum power point as a function of these influences. Many tracking control strategies have been proposed to track maximum power point such as perturb and observe, incremental conductance, parasitic capacitance, and neural networks. These proposed methods have some disadvantages such as high cost, difficulty, complexity and nonstability. This thesis presents a novel approach based on Adaptive NeuroFuzzy Inference System (ANFIS) to predict the maximum power point utilising the actual field data, which is performed in different environmental conditions. The short circuit current and open circuit voltage are used as inputs to PV panels instead of solar irradiation and cell junction temperature. The predicted $V_{max}$from ANFIS model is used as a reference voltage for fuzzy logic controller (FLC). The FLC is used to adjust the duty cycle of the electronic switch of two types of DC-DC converter. These DC-DC converters are used to interface between the load voltage and PV panels. The duty cycle of the electronic switch of the DC-DC converter is adjusted until the input voltage of the converter tracks the predicted $V_{max}$of the PV system. FLC rules and membership functions are designed to achieve the most promising performance at different environmental conditions, different load types and different rate of changes in the duty cycle of Buck-Boost and Buck converters. The membership functions and fuzzy rules of FLC are designed to balance between different required features such as quick tracking under different environmental conditions, high accuracy, stability and high efficiency.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.505231  DOI: Not available
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