MPPT Control Method for Photovoltaic System Based on Particle Swarm Optimization and Bacterial Foraging Algorithm

Abstract

The P-V output feature of photovoltaic (PV) array presents multi-wave peaks under non-uniform illumination, so the traditional algorithm can not overcome the shortcomings of the local optimal value. In this paper, an optimization algorithm based on particle swarm and bacteria foraging is proposed, which is applied to the maximum power point tracking (MPPT) of PV arrays. The algorithm introduces the tendency operation to find the optimal solution in the local range. The replication operation is introduced to avoid the blind randomness of population update, and the convergence speed of the algorithm is accelerated. The migration operation is introduced to avoid the algorithm falling into the local optimal solution. The output power characteristics of PV array under occlusion are analyzed, and the MPPT control method experiment is carried out using bacterial foraging algorithm (BFA). Experimental results show that the algorithm can get rid of the constraint of local optimal value, quickly find the global maximum power point, and the control precision is high. It provides a new implementation method for PV array MPPT.

Residual current detection method based on improved VMD-BPNN

“…They can be combined with different requirements and targeted to provide specific solutions to different problems, and in this paper, the bacterial foraging particle swarm algorithm is chosen to solve the problem of adaptive parameter selection for VMD. In this paper, the basic idea of particle swarm algorithm is introduced into the bacterial foraging algorithm to construct a hybrid bacterial foraging optimization algorithm, which has good search speed and accuracy, can effectively make up for the defects of slow BFO operation and low accuracy of PSO operation, avoid the problem of local convergence, and is suitable for solving the optimization of complex functions [ 21 ]. Bacterial Foraging Optimization-Particle Swarm Optimization (BFO-PSO) has the advantages of fast global search and high operational accuracy compared to similar population intelligence algorithms such as Fruit Fly Optimization Algorithm (FOA) [ 22 ] and Ant Colony Optimization (ACO) [ 23 ], so this paper proposes the bacterial particle swarm optimization algorithm for the combination of parameters of VMD [ k , ɑ] for simultaneous optimization search.…”

Analysis About Modules and Strings Mismatch Loss in the Photovoltaic Plant

“…Therefore, the equivalent model of constant voltage source can be established by using the method of linear source analysis. For a string of individual module, the maximum power should work when the load is consistent with the internal resistance [8], which can be expressed by the following formula:…”

Research on Photovoltaic MPPT Under Complex Conditions Based on an Advanced Great Wall Construction Algorithm Method

“…However, the overall control is complex and poses challenges in implementation [11] . Inspired by the Particle Swarm Optimization algorithm, numerous intelligent algorithms have been applied to photovoltaic MPPT, such as the Sea Cucumber Algorithm (SSA) [12,13] , the Slime Mould Algorithm (SMA) [14] , the Bacterial Foraging Optimization Algorithm [15,16] , the Cuckoo Search Algorithm (CSA) [17,18] . Researchers have devised enhancement strategies based on these methodologies.…”