Most of the photovoltaic power stations are located in remote areas with complex terrain,which are affect-ed by the external environment and prone to various faults. The traditional PV array fault diagnosis methods have the prob-lems of low accuracy and low utilization of PV data. Aiming at the above problems,in this paper,we first improve the spar-row search algorithm (SSA) by introducing the Levy flight strategy and the dynamic adjustment strategy of the step factor to reduce the risk of the SSA algorithm falling into the local optimum and improve the optimization ability of the SSA algo-rithm. Then the improved levy adjustment sparrow search algorithm (LASSA) is used to optimize the key hyperparameters of the CatBoost model,and a photovoltaic array fault diagnosis model LASSA-based on CatBoost and using LASSA as the optimization strategy is proposed. CatBoost for accurate diagnosis of short-circuit,open-circuit,aging and shadow masking faults in PV arrays. The experimental results show that the fault diagnosis accuracy of the LASSA-CatBoost model is 99.7％,which is 3.6％ higher compared to the CatBoost model before optimization. Compared with the existing PV array fault diagnosis models,the LASSA-CatBoost model has higher accuracy and stability.
维普
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