The optimization of photovoltaic (PV) systems relies on the development of an accurate model of the parameter values for the solar/PV generating units This work proposes a modified artificial jellyfish search optimizer (MJSO) with a novel premature convergence strategy (PCS) to define effectively the unknown parameters of PV systems The PCS works on preserving the diversity among the members of the population while accelerating the convergence toward the best solution based on two motions: (i) moving the current solution between two particles selected randomly from the population, and (ii) searching for better solutions between the best-so-far one and a random one from the population To confirm its efficacy, the proposed method is validated on three different PV technologies and is being compared with some of the latest competitive computational frameworks The numerical simulations and results confirm the dominance of the proposed algorithm in terms of the accuracy of the final results and convergence rate In addition, to assess the performance of the proposed approach under different operation conditions for the solar cells, two additional PV modules (multi-crystalline and thin-film) are investigated, and the demonstrated scenarios highlight the utility of the proposed MJSO-based methodology

https://www.mdpi.com/1996-1073/14/7/1867/pdf

An Improved Artificial Jellyfish Search Optimizer for Parameter Identification of Photovoltaic Models

Orthogonal learning-based Gray Wolf Optimizer for identifying the uncertain parameters of various photovoltaic models

On the problem formulation for parameter extraction of the photovoltaic model: Novel integration of hybrid evolutionary algorithm and Levenberg Marquardt based on adaptive damping parameter formula

Parameter identification of photovoltaic cell model based on improved grasshopper optimization algorithm

Using Metaheuristics Algorithms (MHAs) to Optimize Water Supply Operation in Reservoirs: a Review

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...

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

This paper proposes a multi-objective lion swarm optimization based on multi-agent (MOMALSO) for solving the increasingly complex multi-objective optimization problem in engineering practice. First, the Multi-agent system is introduced into the lion swarm optimization (LSO) algorithm. The optimization mechanism of LSO and the information exchange between the agents are integrated to enhance the local search and global search ability of the algorithm, and the self-learning operation can accelerate the approximate Pareto front obtained by the algorithm near to real front. Besides, the external archive is introduced for extending the LSO into a multi-objective algorithm. Finally, the simulations compared with other three algorithms are performed, and the results show that MOMALSO has significant advantages in both convergence and coverage, which verifies the superiority and effectiveness of the algorithm in multi-objective optimization.

A multi-objective lion swarm optimization based on multi-agent

Distributed Fault-Tolerant Secondary Control For Dc Microgrids Against False Data Injection Attacks

In PV power generation systems, flexible power point tracking control needs to be introduced to regulate the output power on the PV side for meeting the grid connection requirements. However, traditional FPPT control methods tend to cause certain oscillations in the system. This paper proposes an FPPT method based on the adaptive lion swarm optimization algorithm (ALSO). First, an ALSO algorithm is proposed, which adds adaptive scaling parameters to the lion swarm optimization algorithm, so that the number of adult lions can be adjusted adaptively with increasing iterations to coordinate the local exploitation and global search abilities of the algorithm. Then the ALSO is used to selectively optimize the operation of different intervals so as to achieve the FPPT control on the left or right side of the maximum power point. The test experiments for the ALSO algorithm show that the ALSO algorithm has strong local exploitation and global search abilities, which is better than the compared algorithms. Through the simulations on the PV grid-connected system, the validity and superiority of the ALSO-based method over the traditional FPPT algorithm in terms of dynamic and steady-state performance are verified under different operating conditions. 

/pdf/a-flexible-power-point-tracking-algorithm-based-on-adaptive-1eruyrxm.pdf

Zong-kui Xie

Papers

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

A multi-objective lion swarm optimization based on multi-agent

Distributed Fault-Tolerant Secondary Control For Dc Microgrids Against False Data Injection Attacks

A flexible power point tracking algorithm based on adaptive lion swarm optimization for photovoltaic system