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Mathematical modelling and hybrid ACO-PSO technique for PV performance improvement

Ali Mahmood, Humada (2016) Mathematical modelling and hybrid ACO-PSO technique for PV performance improvement. PhD thesis, Universiti Malaysia Pahang.

Mathematical modelling and hybrid ACO-PSO technique for PV performance improvement.pdf

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Photovoltaic (PV) solar energy systems have been playing an important role in the field of energy generation for the last few decades. For such systems to attain the maximum efficiency and reliability in power generation, certain factors should be considered to improve the extracted power. For the purpose, this thesis is focused on some of the most important issues, assisting to improve the extracted power status. One of the most important issue for each PV system is the modelling of PV cells and the modules; the accuracy of these models is the main target in building any PV system. Therefore, this study is focused on developing an accurate and reliable PV model, based on five main parameters; the photocurrent, Iph, the reverse diode saturation current, Io, the ideality factor of diode, n, the series resistance, Rs, and the shunt resistance, Rsh. Performance of these five solar cell parameters (Iph, Io, n, RS, Rsh) and their effect on both the Current–Voltage (I–V) and Power–Voltage (P–V) characteristic curves, were tested and compared with other models, respectively. Firstly, the photocurrent, Iph effect was studied; the results showed that the increase in the Iph leads to an increase in the maximum power point (MPP) in a prominent way. In addition to this increment in MPP, an increase in the values of Isc and Voc were also observed. With an increase in Io, a regular increasing mode was observed in MPP, the Isc and Voc values in a similar manner. The value of changing n, showed no effect on Isc and Voc values, but increasing n values lead to a decrease in MPP values in the P–V characteristic curve. The increasing Rs values exhibited a decrease in the value of MPP, while not affecting the the Isc and Voc values, in a smiliar pattern with increasing n values. Finally, the effect of Rsh value was also tested; showed a barely noticeable effect on MPP, Isc , and Voc values. Secondly, a hybrid Ant Colony Optimisation-Particle Swarm Optimisation (ACO-PSO) algorithm was proposed to optimally determine the MPPT parameters. To improve the overall performance of the maximum power point (MPPT) system, the efforts of oscillation filtering and noise suppression were taken in this design, as well as the time response and the settling time. The proposed method is employed to track the global MPP under different shadow conditions, based on three different irradiation levels to test the ability and accuracy of the proposed method. The results of tracking MPP by the proposed MPPT technique showed that the improved method tracked the MPP for all the tested cases with a reasonable accuracy and in a very short convergence time as compared to the P&O method. Thirdly, to develop a new configuration incorporates ACO-PSO and PID to improve the steady state condition after tracking the MPP. The improved PID controller had contributed in attaining the steady state condition and assuring that there is no oscillation around the MPP. In the comparison with the P&O method, it still has a notable oscillation around the MPP, which results in decreasing the efficiency of the extracted power from the PV system. Moreover, in this study, two 5 kWp PV plants from two different PV technologies (mono-crystalline silicon (c-Si) and copper–indium–diselenide (CIS)) were used to validate the PV model performance based on energy generation, energy efficiency, and the performance ratio. Also, two different models from the literature were used to validate the PV model performance. For all of the validation factors, the energy generated, energy efficiency, and performance ratio of the proposed model showed that it is approximately fitting the real results for both of the CIS and c-Si plants with high level of accuracy than the compared models.

Item Type: Thesis (PhD)
Additional Information: Thesis (Doctor of Philosophy in Electrical Engineering) -- Universiti Malaysia Pahang – 2016
Uncontrolled Keywords: PV performance; Mathematical modelling; Ant Colony Optimisation-Particle Swarm Optimisation
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Electrical & Electronic Engineering
Depositing User: Ms. Nurezzatul Akmal Salleh
Date Deposited: 26 Jan 2017 00:58
Last Modified: 29 Nov 2021 04:59
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