BITS Faculty Publications
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Item Modeling of soiled photovoltaic modules with neural networks using particle size composition of soil(IEEE, 2015) Kumar, RajneeshThe performance of PV systems is said to be affected due to soiling predominantly in dry and arid regions. It is therefore necessary to develop methods for estimating the losses that occur due to soiling. For development of this model the particle size composition of the soil is taken as the quantifying parameter. Particle size composition was determined from Sieve Analysis. A series of experiments were conducted on PV panel by artificially soiling with five different soils taken from Shekhawati region of Rajasthan in India. A neural network based modelling of a soiled PV module using particle size composition is proposed. The experimental data obtained is then used to train and develop a neural network which is the approximate model of a soiled solar PV panel using which the power losses of a soiled panel can be predicted.Item Modeling of soiled PV module with neural networks and regression using particle size composition(Elsevier, 2016-01) Kumar, RajneeshParticle 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).Item Characterization of power losses of a soiled PV panel in Shekhawati region of India(Elsevier, 2016-06) Kumar, RajneeshThis paper introduces a model that quantifies the relationship between power output, incident irradiance and soil particle size composition of soiled photovoltaic panels. Soil samples used in artificial soiling experiments were collected from Shekhawati region in India and their relative percentage of standard particle sizes is determined from sieve analysis. A non-linear relationship between irradiance and power is obtained using regression analysis showing the effect of particle size composition present on the panel. Further, the tilt angle for maximum power extraction is determined for each soiled panel and the deviation from the optimum tilt angle of a clean panel is observed. It is concluded that, when the soil present on the panel is rich in the particles with diameter (75 μm and below), the deviation from the tilt angle of a clean panel is 4°, however if the soil contains higher composition of both 150 μm and 300 μm particle sizes the deviation is 8°.