An improved lion swarm optimization for parameters identification of photovoltaic cell models

2019 ◽  
Vol 42 (6) ◽  
pp. 1191-1203
Author(s):  
Zhong-qiang Wu ◽  
Zong-kui Xie ◽  
Chong-yang Liu
Keyword(s):  
Parameter Identification ◽  
Cell Model ◽  
Chaotic Map ◽  
Photovoltaic Cell ◽  
Standard Test ◽  
Local Optimum ◽  
Intelligent Algorithm ◽  
Swarm Optimization ◽  
Test Function ◽  
Adaptive Parameter

In this paper, a parameter identification method of photovoltaic cell model based on improved lion swarm optimization is presented. Lion swarm optimization is a novel intelligent algorithm proposed in recent years, but it has problems such as local optimum and slow convergence. To overcome such limitations, we can combine the tent chaotic map, adaptive parameter and chaotic search strategy to further improve the search ability of the algorithm and avoid trapping in local optimum. The simulation of standard test function shows that the performance of improved lion swarm algorithm is superior to the other six algorithms. Then the algorithm is applied to the parameter identification of photovoltaic cells under two kinds of models and different irradiance, the simulation results verify the superiority and effectiveness of the improved lion swarm optimization in the application of photovoltaic cell parameter identification.

scholarly journals Parameter Identification of Photovoltaic Cell Model Based on Enhanced Particle Swarm Optimization

2021 ◽  
Vol 13 (2) ◽  
pp. 840
Author(s):  
Rongjie Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Identification ◽  
Mean Squared Error ◽  
Cell Model ◽  
Particle Swarm ◽  
Photovoltaic Cell ◽  
Estimation Accuracy ◽  
Swarm Optimization ◽  
Minimum Mean Squared Error ◽  
Enhanced Particle Swarm Optimization

Photovoltaic (PV) cell (PVC) modeling predicts the behavior of PVCs in various real-world environmental settings and their resultant current–voltage and power–voltage characteristics. Focusing on PVC parameter identification, this study presents an enhanced particle swarm optimization (EPSO) algorithmto accurately and efficiently extract optimal PVC parameters. Specifically, the EPSO algorithm optimizes the minimum mean squared error between measured and estimated data and, on this basis, extractsthe parameters of the single-, double-, and triple-diode models and the PV module. To examine its effectiveness, the proposed EPSO algorithm is compared with other swarm optimization algorithms. The effectiveness of the proposed EPSO algorithm is validated through simulation. In addition, the proposed EPSO algorithm also exhibits advantages such as an excellent optimization performance, a high parameter estimation accuracy, and a low computational complexity.

scholarly journals Improved Adaptive Holonic Particle Swarm Optimization

2019 ◽  
Vol 2019 ◽  
pp. 1-22 ◽  
Author(s):  
Hao Li ◽  
Hongbin Jin ◽  
Hanzhong Wang ◽  
Yanyan Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Standard Test ◽  
Local Optimum ◽  
Swarm Optimization ◽  
State Switching ◽  
Grouping Strategy ◽  
Number Of Iterations

For the first time , the Holonic Particle Swarm Optimization (HPSO ) algorithm applies multiagent theory about the improvement in the PSO algorithm and achieved good results. In order to further improve the performance of the algorithm, this paper proposes an improved Adaptive Holonic Particle Swarm Optimization (AHPSO) algorithm. Firstly, a brief review of the HPSO algorithm is carried out, and the HPSO algorithm can be further studied in three aspects: grouping strategy, iteration number setting, and state switching discrimination. The HPSO algorithm uses an approximately uniform grouping strategy that is the simplest but does not consider the connections between particles. And if the particles with larger or smaller differences are grouped together in different search stages, the search efficiency will be improved. Therefore, this paper proposes a grouping strategy based on information entropy and system clustering and combines two grouping strategies with corresponding search methods. The performance of the HPSO algorithm depends on the setting of the number of iterations. If it is too small, it is difficult to search for the optimal and it wastes so many computing resources. Therefore, this paper constructs an adaptive termination condition that causes the particles to terminate spontaneously after convergence. The HPSO algorithm only performs a conversion from extensive search to exact search and still has the potential to fall into local optimum. This paper proposes a state switching condition to improve the probability that the algorithm jumps out of the local optimum. Finally, AHPSO and HPSO are compared by using 22 groups of standard test functions. AHPSO is faster in convergence than HPSO, and the number of iterations of AHPSO convergence is employed in HPSO. At this point, there exists a large gap between HPSO and the optimal solution, i.e., AHPSO can have better algorithm efficiency without setting the number of iterations.

An Improved Particle Swarm Optimization Algorithm with Invasive Weed

2012 ◽  
Vol 621 ◽  
pp. 356-359 ◽  
Author(s):  
Huan Zhao ◽  
Jiang Long Yu ◽  
Arash Tahmasebi ◽  
Pei Hong Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Local Optimum ◽  
Exploration And Exploitation ◽  
Invasive Weed ◽  
Swarm Optimization ◽  
Test Function ◽  
Stochastic Optimization Algorithm

This paper presents a hybrid algorithm based on invasive weed optimization (IWO) and particle swarm optimization (PSO), named IW-PSO. IWO is a relatively novel numerical stochastic optimization algorithm. By incorporating the reproduction and spatial dispersal of IWO into the traditional PSO, exploration and exploitation of the PSO can be enhanced and well balanced to achieve better performance. In a set of 15 test function problem, the parameters of IW-PSO were analyzed and selected, and the computational results show that IW-PSO can effectively obtain higher quality solutions so as to avoid being trapped in local optimum, comparing with PSO and IWO.

Parameter identification of the photovoltaic cell model with a hybrid Jaya-NM algorithm

Optik ◽  
2018 ◽  
Vol 171 ◽  
pp. 200-203 ◽  
Author(s):  
Xiong Luo ◽  
Longpeng Cao ◽  
Long Wang ◽  
Zihan Zhao ◽  
Chao Huang
Keyword(s):  
Parameter Identification ◽  
Cell Model ◽  
Photovoltaic Cell

scholarly journals Chicken Swarm Optimization Based on Elite Opposition-Based Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
Author(s):  
Chiwen Qu ◽  
Shi’an Zhao ◽  
Yanming Fu ◽  
Wei He
Keyword(s):  
Optimization Problems ◽  
Local Development ◽  
Random Search ◽  
Standard Test ◽  
Population Diversity ◽  
Local Optimum ◽  
Basic Algorithm ◽  
Swarm Optimization ◽  
Opposition Based Learning ◽  
Chicken Swarm Optimization

Chicken swarm optimization is a new intelligent bionic algorithm, simulating the chicken swarm searching for food in nature. Basic algorithm is likely to fall into a local optimum and has a slow convergence rate. Aiming at these deficiencies, an improved chicken swarm optimization algorithm based on elite opposition-based learning is proposed. In cock swarm, random search based on adaptive t distribution is adopted to replace that based on Gaussian distribution so as to balance the global exploitation ability and local development ability of the algorithm. In hen swarm, elite opposition-based learning is introduced to promote the population diversity. Dimension-by-dimension greedy search mode is used to do local search for individual of optimal chicken swarm in order to improve optimization precision. According to the test results of 18 standard test functions and 2 engineering structure optimization problems, this algorithm has better effect on optimization precision and speed compared with basic chicken algorithm and other intelligent optimization algorithms.

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