自适应免疫粒子群算法在光伏MPPT中的应用

Application of adaptive immune particle swarm optimization in photovoltaic MPPT

光伏阵列在局部遮阴条件下,其P-U特性曲线呈多峰特性,传统的最大功率点跟踪(MPPT)算法容易陷入局部最优,而无法追踪到最大功率点。粒子群(PSO)算法适用于复杂多极值的寻优问题,因而在多峰值MPPT中得到广泛应用。针对粒子群算法寻优过程中易早熟收敛至局部最优、迭代后期收敛速度慢以及精度低等问题,提出了一种自适应免疫粒子群算法。该算法对惯性权重和学习因子进行自适应调整,并且与免疫算法相结合。仿真结果表明:该算法在静态局部遮阴以及动态局部遮阴条件下,均能追踪到最大功率点,并且收敛速度更快,精度更高,稳定性更好。

The P-U characteristic curve of photovoltaic array shows multi-peak characteristics under local shading.The traditional MPPT algorithm is easy to fall into local optimal,but cannot track the maximum power point.Particle swarm optimization(PSO)algorithm is suitable for complex multi-extremum optimization problem,so it is widely used in multi-peak MPPT.In order to solve the prob‐lems of precocious convergence to local optimal,slow convergence in late iteration and low precision in particle swarm optimization,an adaptive immune particle swarm optimization algorithm was pro‐posed in this paper.The algorithm adaptively adjusted inertia weight and learning factor,and was combined with immune algorithm.The simulation results show that the algorithm can track the maximum power point under both static and dynamic local shading conditions,and the convergence speed is faster,the accuracy is higher,and the stability is better.