A new teaching learning artificial bee colony based maximum power point tracking approach for assessing various parameters of photovoltaic system under different atmospheric conditions
In recent years, the greatest obstacle in the universe has been environmental contamination and shortage of energy due to the rapid utilization of conventional fossil fuels. It is evident that it has a serious impact on weather conditions, water resources, and people's livelihood. Hence, many c...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2024
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/42483/1/ir.A%20new%20teaching%20learning%20artificial%20bee%20colony%20based%20maximum%20power%20point%20tracking%20approach.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | In recent years, the greatest obstacle in the universe has been environmental contamination and shortage of energy due to the rapid utilization of conventional fossil fuels. It is evident that it has a serious impact on weather conditions, water resources, and people's livelihood. Hence, many countries are trying to implement renewable/sustainable energy sources to preserve the environment. For the electrical industry, renewable energy sources (RES‘s) perform a prominent role in fulfilling the energy shortage and satisfying consumers without any blackouts. However, the difficult task in the electrical system is to balance both power production and desired load demand without any voltage, current and frequency changes. Another difficulty in the electrical system with the integration of RES‘s is to minimize the entire cost, including initial cost, operational cost, replacement cost, and maintenance cost. Besides, the performance of the Renewable Energy (RE)-based system has to be enriched with regard to settling time, accuracy, speed, and efficiency. Hence, to optimize the cost of integrating RES‘s through newly developed maximum power point tracking (MPPT) based optimization method such as grasshopper optimization algorithm (GOA) has been introduced. The cost of integration of PV/WT/Battery is $408540. In addition, the Levelized Cost of Energy (LCE) factor is 0.502 at 210 kWh/day. Besides enhancing the RE-based performance in terms of settling time, accuracy, speed, and efficiency through hybrid gray wolf optimization with differential evolution (GWO-DE). As per simulation outcome the GWO-DE extracts maximum peak power (MPP) of 674.6W in 0.06 sec with 99.88% efficiency. In addition, to enhance the performance teaching learning-based artificial bee colony (TLABC) method has been used at distinct weather conditions. In order to ascertain the effectiveness of the proposed methods, the proposed methods have been compared with other studied methods. As per the simulation outcomes, the TLABC method shows a better response in terms of average tracking time, convergence speed of 0.5175 sec, 9 iterations, and efficiency of 99.99% under various atmospheric circumstances. Further, the proposed system has been developed in the PV laboratory and validated with simulation results. |
|---|