时变工况下光伏阵列P-U曲线呈多个峰值特性,导致传统最大功率点跟踪（MPPT）控制算法极易陷入局部最优解且寻优速度慢,进而降低了光伏阵列的整体发电效率。为此,提出一种基于改进免疫算法与扰动观察法的双层MPPT控制策略。首先针对时变工况下P-U曲线的多个峰值特性,提出基于MPPT综合性能指标的改进免疫寻优算法,并将上层迭代完成的MPP点导入到下层进行扰动跟踪。仿真实验表明基于改进免疫与扰动观察法的光伏MPPT控制算法,寻优时间为同类算法的20%,稳态追踪精度比同类算法高0.26~0.84个百分点,异常值偏移量比同类算法低38.36%~60.89%,能有效提升时变工况下光伏阵列的最大功率跟踪效率和精度,且拥有稳态波动小等特点。
Abstract
The P-U curve of PV array shows multiple peak characteristics under time-varying conditions, which makes the traditional MPPT control algorithm easily fall into the local optimal solution, and the optimization speed is slow, which reduces the overall power generation efficiency of PV array. A two-layer MPPT control strategy based on improved immune algorithm and perturbation observation method is proposed. Firstly, according to the characteristics of multiple peaks of P-U curve under time-varying conditions, an improved immune optimization algorithm based on MPPT comprehensive performance index is proposed, and the MPP points iterated in the upper layer are imported to the lower layer for disturbance tracking. Simulation results show that the optimization time of the photovoltaic MPPT control algorithm based on improved immune and disturbance observation method is 20% of that of similar algorithms, the steady-state tracking accuracy is 0.26-0.84 percentage points higher than that of similar algorithms, and the outlier offset is 38.36%-60.89% lower than that of similar algorithms. It can effectively improve the maximum power tracking efficiency and accuracy of photovoltaic arrays under time-varying working conditions, and has the characteristics of small steady-state fluctuation.
关键词
光伏系统 /
最大功率跟踪 /
扰动观察法 /
改进免疫算法 /
Simulink仿真
Key words
photovoltaic system /
maximum power point tracking（MPPT） /
perturbation and observation（P&O） /
improved immune algorithm（IIA） /
simulink simulation
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