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In the process of analyzing the surface cleanliness of photovoltaic modules, it is easy to be affected by nonlinear signals, resulting in inaccurate recognition results. To address this issue, a nonlinear autoregressive identification technique for surface cleanliness of photovoltaic modules has been proposed. Analyze the impact of dirt and hot spot effects on the power generation of photovoltaic modules, and obtain nonlinear signals of the surface time-history response of photovoltaic modules. Simulate the time domain nonlinear uncleanness problem on the surface of photovoltaic modules, analyze nonlinear signal units, extract corresponding nonlinear features from the time history response, and reduce the interference of environmental uncertainties. Filter the time history response linear signal through a linear function, calculate the probability conditional variance, and determine the nonlinear autoregressive identification index. The identification structure of nonlinear autoregressive dirt and hot spot effect is constructed, dirt is identified by nonlinear autoregressive I-V curve, and hot spot effect is identified by nonlinear autoregressive Loss function. From the experimental results, it can be seen that the I-V characteristics of dirt identified using the studied technology show that when the voltage is 0, the short-circuit current is 0.41A, and when the current is 0, the open-circuit voltage is 19.5V; The identified I-V characteristics of hot spot effect show that the maximum short-circuit current corresponding to curves 1, 2 and 3 are 4.0A, 4.0A and 2.0A respectively, and the maximum open circuit voltage is 100V, 80V and 55V respectively, which is consistent with the actual data and has accurate identification effect.