Evolutionary Algorithms, namely Genetic Algorithm (GA), Particle Swarms Optimization (PSO) and Differential Evolution (DE) are used to solve optimization problems. As in photovoltaics (PV), the I-V characteristic of PV cells is non-linear, which requires a resolution method. So according to the three methods cited (GA, PSO, and DE), what is the best method for solving this non-linear problem according to the strengths and weaknesses of each ?.
Dear Rachid,
The optimization problem in the pv system where it is required to locate the point of maximum power point is characterized by a relativity broad maximum not a sharp maximum so it will be not very critical for the resolution issue. I think the speed of convergence to the global maximum is important and distinguishing for such a problem. Also the oscillations around the steady sate solution is an other performance parameters of the optimization method.
Best wishes
Hi, Rachid Herbazi.
To resolve, optimize and predict the non-linearity of solar PV cell, there are various optimization techniques available for optimal performance, accuracy and convergence speed. For Evolutionary Algorithms (EA), the Genetic Algorithm (GA) is faster than classical methods but it tends to stuck at local minima. The improved GA requires higher computational resources and different parameters are required to tune.
In contrast, Differential Evolution (DE) is fast and accurate without any employment of probability distribution. However, its population can be stagnant in some sub-optimal values. Last but not least, Particle Swarm Optimization (PSO) exhibits the highest performance by considering different best positions to update the population. However, it tends to converge pre-maturely and can be trapped at local minima just like GA.
Back to your question, the choice of these EA methods is really depend on your choice, application and design requirement. For maximum performance, PSO is recommended due to its maturity compared to GA. DE is better than GA in term of the accuracy and computational time while GA is faster than classical methods.
I hope these information helps you. For more information, you may also read these papers:
[1] Article A review on meta-heuristics methods for estimating parameter...
[2] www.researchgate.net/publication/324242353
[3] www.researchgate.net/publication/269106576
[4] https://ieeexplore.ieee.org/document/8273010
Hi
Please check the following work. It describes the process in details. I believe it will help you.
https://www.researchgate.net/publication/283226108_Experimental_analysis_of_solar_PV_characteristics_under_standard_condition
Regards
Dear Kah Yung Yap and Md Sazal Miah,
Thank you very much for these details which are very useful.
Dear Rachid,
The optimization problem in the pv system where it is required to locate the point of maximum power point is characterized by a relativity broad maximum not a sharp maximum so it will be not very critical for the resolution issue. I think the speed of convergence to the global maximum is important and distinguishing for such a problem. Also the oscillations around the steady sate solution is an other performance parameters of the optimization method.
Best wishes
Dear Abdelhalim abdelnaby Zekry,
Thank you for your answer and information sharing. You are absolutely right.
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