针对光伏模组积灰与阴影特性识别问题，详细分析了积灰和阴影的光伏特性曲线差异，揭示了阴影光伏曲线的拐点时变特性。提出由特性曲线的拐点数量及电流电压特性条件共同构成训练模型的输入特征量，基于CatBoost算法训练积灰和阴影识别模型。最后，利用光伏模组实测数据对CatBoost算法、ID3和GA-BP算法训练出的识别模型进行性能分析和对比测试，结果表明基于CatBoost训练出的识别模型输入量区分性强、诊断精度高，极具工程应用价值。

Aiming at the problem of identifying the characteristics of ash accumulation and shadow of photovoltaic modules, the difference of photovoltaic characteristic curves of ash accumulation and shadow was analyzed in detail, and the time-varying characteristics of the inflection point of the shadow photovoltaic curve were revealed. The number of inflection points of the characteristic curve and the current and voltage characteristic conditions were proposed to form the input feature quantity of the training model, and the dust accumulation and shadow recognition model was trained based on the CatBoost algorithm. Finally, the performance analysis and comparative test of the recognition model trained by CatBoost algorithm, ID3 and GA-BP algorithm were carried out by using the measured data of photovoltaic modules, and the results show that the recognition model trained based on CatBoost has strong discrimination and high diagnostic accuracy, which is of great engineering application value.