Improving the efficiency, power quality, and cost-effectiveness of solar PV systems using intelligent techniques

Author Identifier

Aakash Hassan

Date of Award


Document Type



Edith Cowan University

Degree Name

Doctor of Philosophy


School of Engineering

First Supervisor

Octavian Bass

Second Supervisor

Mohammad Sherkat Masoum


Growing energy demand, depleting fossil fuels, and increasing environmental concerns lead to adaptation to clean and sustainable energy sources. Renewable energy sources are now believed to play a critical role in diminishing the deteriorating environment, supplying power to remote areas with no access to the grid, and overcoming the energy crisis by reducing the stress on existing power networks. Therefore, an upsurge in renewablesbased energy systems development has been observed during the previous few decades. In particular, solar PV technology has demonstrated extraordinary growth due to readily available solar energy, technological advancement, and a decline in costs. However, its low power conversion efficiency, intermittency, high capital cost, and low power quality are the major challenges in further uptake.

This research intends to enhance the overall performance of PV systems by providing novel solutions at all levels of a PV system hierarchy. The first level investigated is the solar energy to PV power conversion, where an efficient maximum power point tracking (MPPT) method is developed. Secondly, the dc to ac power conversion is explored, and an optimal PV system sizing approach with abidance to power quality constraints is developed. Finally, smart power management strategies are investigated to utilise the energy produced by solar PV efficiently, such that the minimum cost of energy can be achieved while considering various technical constraints. The methods involve Genetic Algorithm (GA) for finding the optimal parameters, mathematical models, MATLAB/Simulink simulations of solar PV system (including PV arrays, dc/dc converter with MPPT, batteries, dc/ac inverter, and electric load), and experimental testing of the developed MPPT method and power management strategies at the smart energy lab, Edith Cowan University. Highly dynamic weather and electricity consumption data encompassing multiple seasons are used to test the viability of the developed methods.

The results exhibit that the developed hybrid MPPT technique outperforms the conventional techniques by offering a tracking efficiency of above 99%, a tracking speed of less than 1s and almost zero steady-state oscillations under rapidly varying environmental conditions. Additionally, the developed MPPT technique can also track the global maximum power point during partial shading conditions. The analyses of power quality at the inverter’s terminal voltage and current waveforms revealed that solar PV capacity, battery size, and LC filter parameters are critical for the reliable operation of a solar PV system and may result in poor power quality leading to system failure if not selected properly. On the other hand, the optimal system parameters found through the developed methodology can design a solar PV system with minimum cost and conformance to international power quality standards. The comparison between the grid-connected and stand-alone solar PV system reveals that for the studied case, the grid-connected system is more economical than the stand-alone system but outputs higher life cycle emissions. It was also found that for grid tied PV systems, minimum cost of energy can be achieved at an optimal renewable to grid ratio. Additionally, applying a time varying tariff yields a slightly lower energy cost than the anytime flat tariff. A sensitivity analysis of the reliability index, i.e., loss of power supply probability (LPSP), demonstrates that for the stand-alone PV systems, there is an inverse relationship between LPSP and cost of energy. Contrarily, for grid-connected systems, the cost of energy does not vary significantly with the change in LPSP.



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