Welcome To UTPedia

We would like to introduce you, the new knowledge repository product called UTPedia. The UTP Electronic and Digital Intellectual Asset. It stores digitized version of thesis, dissertation, final year project reports and past year examination questions.

Browse content of UTPedia using Year, Subject, Department and Author and Search for required document using Searching facilities included in UTPedia. UTPedia with full text are accessible for all registered users, whereas only the physical information and metadata can be retrieved by public users. UTPedia collaborating and connecting peoples with university’s intellectual works from anywhere.

Disclaimer - Universiti Teknologi PETRONAS shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.Best viewed using Mozilla Firefox 3 or IE 7 with resolution 1024 x 768.

Optimization of Maximum Power Point Tracking (MPPT) of Photovoltaic System using Artificial Intelligence (AI) Algorithms

Raal Mandour, Raal (2013) Optimization of Maximum Power Point Tracking (MPPT) of Photovoltaic System using Artificial Intelligence (AI) Algorithms. Universiti Teknologi Petronas. (Unpublished)

Download (1152Kb) | Preview


Although solar energy is one of the most available renewable energy resources, its usage is strongly influenced by environmental and technological aspects. Photovoltaic (PV) offers an environmentally friendly source of electricity, which is however still relatively costly today. In order to minimize the output power cost, the maximum power point tracking (MPPT) of the PV output for all sunshine conditions is a key factor to maximize the power output of a PV system for assigned conditions of radiation and temperature. The core of the MPPT is represented by the implemented algorithm devoted to find and maintain the operation near to the Maximum Power Point (MPP). In this paper, starting from the set of equations modeling a PV module, an innovative procedure to optimize the performance and efficiency of the MPPT algorithms is presented, simulated and verified. Artificial intelligence algorithms, specifically PSO and PSO combined with Incremental conductance algorithm, are used to achieve the stated goals. Studies on the conventional and intelligent algorithms are conducted and a comparison between their efficiencies and drawbacks is presented. Flowcharts are developed and simulation tools are identified. MATLAB simulations are shown and resulting graph and efficiencies under several irradiance values are specified along with comparison with the previously achieved performance of MPPT algorithms and different set of Data.

Item Type: Final Year Project
Academic Subject : Academic Department - Electrical And Electronics - Pervasisve Systems - Digital Electronics - Design
Divisions: Engineering > Electrical and Electronic
Depositing User: Users 2053 not found.
Date Deposited: 11 Jun 2013 08:59
Last Modified: 25 Jan 2017 09:39
URI: http://utpedia.utp.edu.my/id/eprint/6496

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...