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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Maamar Bettayeb
University of Sharjah
Computer Science & IT Engineering

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Estimating one-diode-PV model using autonomous groups particle swarm optimization Mohammad AlShabi; Chaouki Ghenai; Maamar Bettayeb; Fahad Faraz Ahmad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp166-174

Abstract

In this paper, the one-diode model of a photovoltaic PV solar cell (PVSC) is estimated for an experimental characteristic curves data by using a recently proposed version of the Particle Swarm Optimization (PSO) algorithm, which is known as the Autonomous Groups Particles Swarm Optimization (PSOAG). This meta-heuristic algorithm is used to identify the model of the PVSC. The PSOAG divides the particles into groups and then, uses different functions to tune the social and cognitive parameters of these groups. This is done to show the individuals’ diversity inside the swarm. Although, these individuals do their duties as part of the society, they are not similar in terms of intelligence and ability. By using these groups, the performance of the PSO is improved in terms of convergence rate and escaping the local minima/maxima. Six versions of PSOAG algorithms were developed in this work. Therefore, nine versions of PSOAG, including these six algorithms and three newly developed PSOAG reported previously, will be used in this research to cover more social’s behaviors. The results are compared to the original PSO and other versions of PSO like conventional and Asymmetric Time-varying Accelerated Coefficient PSOs, and the improved PSO. The result shows that the proposed methods improve the performance by up to 14% in terms of root mean squared error and maximum absolute error, and by up to 20% in term of convergence rate, when these were compared to the best results obtained from the other algorithms.
Estimating PV models using multi-group salp swarm algorithm Mohammad Al-Shabi; Chaouki Ghenai; Maamar Bettayeb; Fahad Faraz Ahmad; Mamdouh El Haj Assad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i2.pp398-406

Abstract

In this paper, a multi-group salp swarm algorithm (MGSSA) is presented for estimating the photovoltaic (PV) solar cell models. The SSA is a metaheuristic technique that mimics the social behavior of the salp. The salps work in a group that follow a certain leader. The leader approaches the food source and the rest follows it, hence resulting in slow convergence of SSA toward the solution. For several groups, the searching mechanism is going to be improved. In this work, a recently developed algorithm based on several salp groups is implemented to estimate the single-, double-, triple-, Quadruple-, and Quintuple-diode models of a PV solar cell. Six versions of MGSSA algorithms are developed with different chain numbers; one, two, four, six, eight and half number of the salps. The results are compared to the regular particle swarm optimization (PSO) and some of its newly developed forms. The results show that MGSSA has a faster convergence rate, and shorter settling time than SSA. Similar to the inspired actual salp chain, the leader is the most important member in the chain; the rest has less significant effect on the algorithm. Therefore, it is highly recommended to increase the number of leaders and reduce the chain length. Increasing the number of leaders (number of groups) can reduce the root mean squared error (RMSE) and maximum absolute error (MAE) by 50% of its value.
Co-Authors Chaouki Ghenai Chaouki Ghenai Fahad Faraz Ahmad Mamdouh El Haj Assad Mohammad Al-Shabi Mohammad AlShabi