Please use this identifier to cite or link to this item: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/1889
Title: A study on solar pv power generation influencing parameters using Captured data from faculty of engineering, university of jaffna solar Measuring station
Authors: Ahilan, K.
Valluvan, R.
Atputharajah, A.
Issue Date: 2018
Citation: Kanagasundaram, A. H. I. L. A. N., Valluvan, R. A. G. U. P. A. T. H. Y. R. A. J., & Atputharajah, A. R. U. L. A. M. P. A. L. A. M. (2018, January). A Study on Solar PV Power Generation Influencing Parameters Using Captured Data From Faculty Of Engineering, University Of Jaffna Solar Measuring Station. In International Conference On. Solar Energy Materials, Solar Cells & Solar Energy Applications.
Abstract: A number of parameters such as solar irradiance, temperature, wind speed, wind direction and soiling are influencing the solar energy harvesting. It is essential to develop deeper understanding of the factors influencing the solar energy production in a particular region and have reliable models to forecast energy production. For this research study, Killinochi district was chosen as it has a lot of potential for solar PV, and solar thermal compared to other districts. In this paper, initially it was analysed how the weather data and solar irradiance vary on a daily and yearly basis. Subsequently, the correlation between individual weather parameters, solar irradiance and silicon voltage are studied. Pearson correlation estimation was used for correlation studies. Based on correlation studies, it was found that the solar parameters are influencing the solar power generation. After that, temperature variations were modelled using ARIMA modelling and the model were used to forecast the next hour data. Similarly, the diffused horizontal irradiance (DHI) and global horizontal irradiance (GHI) data can be forecasted using ARIMA modelling, and the next hour data can be predicted. Future study will include modelling of correlation between solar irradiance and temperature or humidity using support vector regression methods; and DHI and GHI will be forecasted based on weather data. These prediction models are useful for power generation entities and households. The effects of soiling on PV modules which vary with soil type, location and weather patterns will also be considered.
URI: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/1889
Appears in Collections:Electrical & Electronic Engineering

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