Parameter Identification Of Photovoltaic System Based On Meta-heuristic Algorithm

The modern world has become inseparable from fossil fuels.However,a series of environmental problems,such as global warming,air pollution,and species extinction,have led to the realization that the overuse of fossil fuels may be the root cause.In order to continue to meet people’s energy needs while protecting the environment,photovoltaic system that convert solar energy into electricity has received a lot of attention.Designing accurate mathematical model is an important tool to study and optimize the performance of photovoltaic system.Among them,the single-diode model and the double-diode model are the two most widely used models in practical applications.Since both models are implicitly transcendental equations,proposing a method that can accurately extract the model parameters has become a hot research topic.The specific work is as follows:(1)The improved heap-based optimizer is proposed for the parameter identification of photovoltaic system.The heap-based optimizer is a new meta-heuristic algorithm that uses the heap data structure to map agents at the human enterprise hierarchy level.To further enhance the capability of the algorithm,the following improvements are made: remove the renewal mechanism of employee self-contribution,modify the wheel from the original three parts to two parts,and revise the update mechanism of communication between colleagues.The results comparing with different meta-heuristic algorithms such as heap-based optimizer,crow search algorithm and spherical evolution algorithm show that the improved heapbased optimizer has the performance of fast convergence speed and high convergence accuracy,and can achieve effective parameter identification of photovoltaic system,and also shows stronger optimization stability under the special conditions of different temperature or irradiance.(2)The hybrid optimization algorithm is proposed for the parameter identification of photovoltaic system.The particle swarm optimization has advantages in exploitation,while the cuckoo search algorithm stands out in exploration.Therefore,based on the particle swarm algorithm,the hybrid algorithm is proposed and applied in the parameter identification by introducing the random reselection of parasitic nests strategy of the cuckoo search algorithm.The results show that the hybrid algorithm has higher convergence accuracy and better identification results than some metaheuristic algorithms such as particle swarm optimization and cuckoo search algorithm,and also shows stronger optimization stability under the special conditions of different temperature or irradiance.(3)The improved shuffling frog-leaping algorithm is proposed for the parameter identification of photovoltaic system.The memory evolution mechanism and shuffle strategy guarantee the shuffling frog-leaping algorithm to solve nonlinear and multimodal problems.The delayed dynamic step mechanism is proposed to enhance the identifying ability of shuffling frog-leaping algorithm.The results compared with some metaheuristic algorithms such as shuffling frog-leaping algorithm and improved JAYA algorithm show that the improved shuffling frog-leaping algorithm has faster convergence speed and higher convergence accuracy,and also shows stronger optimization stability under the special conditions of different temperature or irradiance.The results of the comparison between the three proposed algorithms show that the improved heap-based optimizer has the fastest convergence speed,the hybrid algorithm has the highest convergence accuracy,and the improved shuffling frog-leaping algorithm converges its performance close to the respective strengths of the other two algorithms at the cost of increasing the complexity.Each of the three algorithms has its own characteristics and can be used to achieve more effective parameter identification of photovoltaic system for different specific requirements.