针对神经网络最大功率追踪（maximum power point tracking, MPPT）算法由于缺乏实用性改进而难以应用于工程实践的问题，提出一种改进轻量级神经网络MPPT算法。其通过知识蒸馏算法减小神经网络复杂度及内存占用率，得到轻量级神经网络模型，并结合优化变步长扰动观察法来修正模型精度问题所带来的预测误差；算法前期通过轻量级模型预测最大功率点电压范围并快速追踪进入范围，后期通过优化扰动观察法不断更新范围上下限，最终使上下限收敛至最大功率点。之后在MATALB/Simulink中建立仿真模型，最后搭建实物模型并进行对比实验。结果表明：所提算法追踪效率更高，纹波电压抑制更稳定，并且在嵌入式设备中资源占用率更小。

To address limitations in the engineering application of neural network based maximum power point tracking(MPPT) algorithms, an improved lightweight neural network MPPT algorithm was proposed. The complexity and memory usage of the neural network were reduced through a knowledge distillation compression algorithm, and a lightweight model was obtained. The inherent theoretical error of model predictions was corrected using an optimized variable step-size perturb and observe method. In the initial stage, the neural network predicted the voltage range of the maximum power point. In the later stage, disturbance observation progressively refined this range until it converged at the maximum power point. A simulation model was developed in MATLAB/Simulink, and a physical model was constructed for comparative experiments. Results indicate that the proposed algorithm achieves higher tracking efficiency, improved ripple voltage suppression, and lower resource consumption rate in embedded devices.