Solar Photovoltaic Maximum Power Point Tracking via Predictive Technique - ‘Ainaa Nur Batrisyia binti Abdul Rahman, - (Universiti Malaysia Sarawak, 2022)
The development of renewable energy is widely conducted as one of the initiatives to reduce carbon footprint. For this purpose, the utilization of solar energy is definitely part of it. It contributes almost none in carbon emission. Plus, this renewable source is highly targeted by the society that possess the awareness about the safety of our environment. Therefore, Solar Photovoltaic Maximum Power Point Tracking via Predictive Technique is introduced. The application of this system is not limited only for the household usage yet to the grid connected as well. Irradiance, temperature and the load resistance of the PV solar panel are the parameters that affect the output generation. The potential combination gained from P-V and I-V curves could be referred to develop the optimization system. Hence, from this information, the optimal output voltage, current and power can be delivered from the PV panel. Two elements that are used in this research: Maximum Power Point Tracking (MPPT) and Model Predictive Control (MPC). The first stage of this study is to develop the MPPT algorithm that are commonly divided into six types; Perturb and Observe (P&O), Incremental Conductance (INC), Fractional Open Circuit Voltage (FOVC), Fractional Short Circuit Current (FSCC), Artificial Neural (ANN) and Fuzzy Logic Control (FLC). The technique applied in this paper is Perturb and Observe with MPC that will boost the generated output. It is believed that this combination not only capable of increase the system efficiency but intensify the system performance to the desired level. Apart from that, DC-DC boost converter would be the intermediate in developing the algorithm to the system. The extraction of the maximum output power can be achieved through this mechanism. On the other hand, the algorithm would be diligently going through several alteration in order to achieve the stable coding command with varied environment factor. The robustness test is implemented to distinguish the characteristic of every possible characteristic that might affect the output. This can be classified in term of the rate of time responses during optimal power tracking, the solution to the non-linear properties of the PV arrays and the mitigation of the surge oscillation and slope convergence. In addition, benchmarking is done by comparing the proposed topology with modified P&O Fuzzy Logic Control.