Research on the application of refinement hierarchical population algorithm in MPPT of photovoltaic arrays

Considering the slow convergence speed and unstable transmission capacity of photovoltaic systems in maximum power point tracking (MPPT), this paper proposes a MPPT control strategy based on the fine-layered population algorithm. This strategy improves the search efficiency of the algorithm by fine-layered sequence reconstruction of the sparrow search algorithm (SSA) and particle swarm optimization algorithm (PSO) populations. Firstly, a PSO population pause mechanism is introduced to avoid the premature convergence problem of PSO. Secondly, a fine-layered sequence is used to generate a uniformly distributed initial population, and the cooperative optimization of the sparrow group and the particle swarm is designed to improve the compatibility of the algorithm. The two main populations are decomposed, the number of individuals in the population is optimized, and the individual optimal fitness value of the population is updated. At the same time, the population position is reorganized in each layer to enable each layer to optimize independently, making the entire population easier to collaborate, increasing population diversity, and thereby improving search efficiency. Finally, strategies such as filling functions and sign matrices are introduced into the speed update position of the improved hierarchical population algorithm (PSSSA) to reduce the adverse effects of power fluctuations caused by boundary effects, improve the convergence and speed of the algorithm, and ensure that the optimal solution is obtained after each update. To verify the effectiveness of the PSSSA algorithm, a simulation model under the weather conditions of a certain laboratory in Northeast China was constructed using Matlab. The simulation results show that when it is applied to the maximum power tracking problem, in the case of rapid changes in external environmental illumination, it can accurately track the maximum power point within 0.2 seconds. Its tracking stability is significantly better than the Particle Swarm Optimization (PSO) algorithm, the Seagull Search Algorithm (SSA), and the Perturbation Observation (P&O) algorithm. The size of the maximum power point is approximately 64% larger than that of the PSO algorithm. At the same time, it can also ensure the stability and rapidity of the tracking effect.