الفهرس | Only 14 pages are availabe for public view |
Abstract As electricity consumption continues to increase and fossil fuel prices rise, it is crucial to shift towards renewable energy sources to combat extinction risk and global climate change. PV-based power plants, in particular, are becoming more prominent due to their affordability, abundance, eco-friendliness, and lack of moving components. However, the non-linear characteristics of PV energy present a challenge that necessitates Maximum Power Point Tracking (MPPT) to achieve optimal power output. This study proposes a new MPPT control method for a gridconnected PV system that utilizes the Arithmetic Optimization Algorithm (AOA). The adopted MPPT algorithm is variable-step Incremental Conductance (IC) as it is the most popular and efficient compared with numerous conventional MPPT algorithms. The step size of IC is obtained by the Proportional Integral (PI) controller. The PI regulator gains are obtained utilizing the contemporary AOA. To perform this study, a 100 kW PV system linked to the utility is established and evaluated employing MATLAB/SIMULINK. The optimization method pursues minimizing four standard error indicators without being biased towards a specified index, thereby more precisely conveying the result of the suggested methodology. To validate the findings of the proposed procedure, the obtained results while employing AOA-based IC-MPPT controllers are contrasted with the Grey Wolf Optimization (GWO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Modified Incremental Conductance (MIC) based controllers. Considering five different weather conditions patterns: step irradiance with constant temperature, ramp irradiance and temperature, the later one is various irradiance with a constant temperature, the fourth one is realistic irradiance and temperature, and the last one is variable irradiance with variable temperature.The results reveal that AOA has decreased the rise time by 61, 3, 4.5, and 26.9% compared to the MIC, GWO, GA, and PSO in extracting MPPT of the proposed system, respectively. Moreover, the settling time has a reduction of 94, 84.7, 86.6, and 79.3%, respectively, against the MIC, GWO, GA, and PSO in the extraction of the MPPT in step condition. Moreover, the AOA and GWO assist with enhancing dynamic response to other scenarios over GA and PSO. After that, the three PI controllers of MPPT, DC link voltage, and inverter current are tuned using AOA. The results of AOA-based-PI regulators are compared with those of GWO and conventional algorithms to prove the effectiveness of the recommended control strategy. The results clarified that the performance of the AOA-based-MPPT controller achieved a reduction in the settling time by 14.1% and 76.9%, respectively, contrasted to the GWO and conventional methodologies to track the MPP. Besides, the AOA-based MPPT controller slightly decreased the overshot percentage by 3% and 1.4%, correspondingly compared to the GWO and conventional approaches. Further, the AOA-based DC link voltage controller contributes to a reduction in overshot percentages of 11.3% and 2.7%, respectively, compared to the GWO and conventional algorithms, while the GWO settles slightly faster than AOA by 1.3%. However, AOA has a decrease in the settling time of 87.9% compared to the conventional technique. Additionally, the AOAbased inverter current regulator improves the performance of the grid reactive power and reduces the settling time by 43.1% and 85.5%, correspondingly, compared to the GWO and conventional techniques. Finally, this work addresses the major limitation of PV-based power plants that only provide active power by proposing a control circuit that allows the PV inverter to inject or absorb reactive power into the grid. |