【摘要】 实际工程中，光伏阵列在随机变化的环境中会出现局部遮光的情况，从而导致光伏阵列的功率-电压特性曲线会呈现多峰值状态，传统的最大功率点跟踪(maximum power point tracking, MPPT)算法易陷入局部最优解，追踪速度和精准度无法得到满足。针对这一问题，提出一种基于布谷鸟搜索算法(cuckoo search algorithm, CS)和电导增量法(conductivity increment method, CI)结合的光伏MPPT算法，在算法前期利用布谷鸟搜索算法将大步长和小步长交替使用使得全局搜索能力增强，找到全局最大功率点所处区域附近；在后期，采用步长小、控制精度高的CI进行局部寻优，快速准确地锁定到最大功率点。在MATLAB/Simulink中搭建仿真模型，并与原始布谷鸟搜索算法和粒子群优化(particle swam optimization, PSO)算法进行比较。仿真结果表明，将CS与CI结合的算法使得收敛速度更快，精度更高，稳定状态时功率曲线的波动更小。更多还原

【Abstract】 In actual engineering, the photovoltaic array will appear local shading in a randomly changing environment, resulting in the power-voltage characteristics curve of the photovoltaic array showing multiple peak states, and the traditional maximum power point tracking(MPPT) algorithm is easy to fall into the local optimal solution, and the tracking speed and accuracy cannot be satisfied. Aiming at this problem, a photovoltaic maximum power point tracking algorithm based on the combination of cuckoo search algorithm(CS) and conductivity increment method(CI) is proposed, and the cuckoo algorithm is used alternately in the early stage of the algorithm to make the global search ability strong, and find the area near the global maximum power point; In the later stage, the CI with small step size and high control accuracy is used to locally optimize and quickly and accurately lock to the maximum power point. The simulation models are constructed in MATLAB/Simulink and compared them with the original cuckoo algorithm and particle swarm optimization(PSO) algorithm. The simulation results show that the algorithm combining the CS with the CI makes the convergence speed faster, the accuracy is higher, and the fluctuation of the power curve is smaller in the steady state.更多还原