Modeling of soiled PV module with neural networks and regression using particle size composition

• A soiled photovoltaic module is modeled using regression and neural networks.
• Experimentally obtained data is used for the modeling.
• Particle size composition of the soil is used as a quantifying parameter.
• Influence of particle size composition on the losses is studied from the models.

Particle size composition of the soil accumulated on a photovoltaic module influences its power output. It is therefore crucial to understand, quantify and model this soiling phenomenon with respect to particle size composition for predicting soiling losses. Five different soil samples from Shekhawati region in India are collected and relative percentage of standard particle sizes which are 2.36 mm, 1.18 mm, 600 μm, 300 μm, 150 μm, 75 μm and less than 75 μm are determined from sieve analysis. In order to understand and quantify the soiling effect, regression model is developed and to predict the power loss at various levels of irradiances, neural networks model is developed from the obtained experimental data. These models were compared and validated for the power output obtained at wide range of irradiance levels. It was concluded that regression can be used to analyze and quantify the particle size influence on the soiling losses of a PV module while neural networks are efficient in predicting the power output of a soiled panel. It was also observed that influence of 75 μm and lesser size particles is predominant on the power output at low irradiance levels (300–500 W/m2) while it is the 150 μm particle size that impact the power output at higher levels of irradiance (1000–1200 W/m2).

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