01 Dec 2020
TL;DR: An adaptive opposition approach is suggested to select whether or not to use an opposition-based learning (OBL) method for optimization algorithms, based on adding an adaptive updating mechanism to enable the original algorithm to obtain more accurate results with more complex problems.
Abstract: The greater the demand for energy, the more important it is to improve and develop permanent energy sources, because of their advantages over non-renewable energy sources. With the development of artificial intelligence algorithms and the presence of so many data, the evolution of simulation models has increased. In this research, an improvement to one recent optimization algorithm called the artificial hummingbird algorithm (AHA) is proposed. An adaptive opposition approach is suggested to select whether or not to use an opposition-based learning (OBL) method. This improvement is developed based on adding an adaptive updating mechanism to enable the original algorithm to obtain more accurate results with more complex problems, and is called the adaptive opposition artificial hummingbird algorithm (AOAHA). The proposed AOAHA was tested on 23 benchmark functions and compared with the original algorithm and other recent optimization algorithms such as supply–demand-based optimization (SDO), wild horse optimizer (WHO), and tunicate swarm algorithm (TSA). The proposed algorithm was applied to obtain accurate models for solar cell systems, which are the basis of solar power plants, in order to increase their efficiency, thus increasing the efficiency of the whole system. The experiments were carried out on two important models—the static and dynamic models—so that the proposed model would be more representative of real systems. Two applications for static models have been proposed: In the first application, the AOAHA satisfies the best root-mean-square values (0.0009825181). In the second application, the performance of the AOAHA is satisfied in all variable irradiance for the system. The results were evaluated in more than one way, taking into account the comparison with other modern and powerful optimization techniques. Improvement showed its potential through its satisfactory results in the tests that were applied to it.
TL;DR: In this paper, a developed version of eagle strategy GBO with chaotic (ESCGBO) is proposed to enhance the original GBO performance and its search efficiency in solving difficult optimization problems such as this.
Abstract: The global trend towards renewable energy sources, especially solar energy, has had a significant impact on the development of scientific research to manufacture high-performance solar cells. The issue of creating a model that simulates a solar module and extracting its parameter is essential in designing an improved and high performance photovoltaic system. However, the nonlinear nature of the photovoltaic cell increases the challenge in creating this model. The application of optimization algorithms to solve this issue is increased and developed rapidly. In this paper, a developed version of eagle strategy GBO with chaotic (ESCGBO) is proposed to enhance the original GBO performance and its search efficiency in solving difficult optimization problems such as this. In the literature, different PV models are presented, including static and dynamic PV models. Firstly, in order to evaluate the effectiveness of the proposed ESCGBO algorithm, it is executed on the 23 benchmark functions and the obtained results using the proposed algorithm are compared with that obtained using three well-known algorithms, including the original GBO algorithm, the equilibrium optimizer (EO) algorithm, and wild horse optimizer (WHO) algorithm. Furthermore, both of original GBO and developed ESCGBO are applied to estimate the parameters of single and double diode as static models, and integral and fractional models as examples for dynamic models. The results in all applications are evaluated and compared with different recent algorithms. The results analysis confirmed the efficiency, accuracy, and robustness of the proposed algorithm compared with the original one or the recent optimization algorithms.
TL;DR: In this paper , the musical chairs algorithm (MCA) is introduced to estimate the PV cell parameters faster and more accurately than many metaheuristic algorithms, and the results obtained from using 10 optimization algorithms showed that the error associated with MCA is 20% of the average error of the other optimization algorithms.
TL;DR: Zhang et al. as mentioned in this paper proposed an improved algorithm, i.e., Elite Learning Adaptive Differential Evolution (ELADE), which combines four strategies, including the parameters adaptive strategy, elite learning strategy, chaotic last-place elimination strategy, and population size reduction strategy, to boost the exploitation process of differential evolution to effectively balance the ability to avoid local optimum and accelerate convergence speed.
••01 Jan 2022
TL;DR: An overview of the most popular types applied for PV parameter estimation in the literature can be found in this article , where the optimization of these parameters is very critical for the efficiency of the overall PV system.
Abstract: Solar photovoltaic (PV) systems are utilized in different parts of the world to generate electrical energy. There are various parameters to generate electricity for a PV system. The optimization of these parameters is very critical for the efficiency of the overall PV system. Therefore, this chapter provides an introduction about optimization algorithms and an overview of the most popular types applied for PV parameter estimation in the literature.