A heuristic approach of estimation and prediction of short-circuit current of a photovoltaic cell by Kalman filter

Exploiting non-renewable energy to fulfill the huge demand of human race is an ever increasing burden on the atmosphere of the earth. To lessen this burden several renewable energy sources are used - one of which is photovoltaic energy. The source of solar /photovoltaic energy is the radiation of the sun which is abundant in nature. A solar cell produces electric energy when sunlight is incident on it. Photon (produced due to interaction between light and matter) hits the p-n junction of a solar cell (made up of photosensitive material) produces electrons in abundance, which may be routed through for proper functioning of the load. The amount of current produced by a solar cell/panel depends on various factors due to which the state variables of a cell keep changing. The various state variables in question manifest different behaviour as time and place changes and empirical formulae of a solar cell does not suffice. In absence of such empirical formula, one has to rely on estimation in order to predict the behaviour of a solar cell. In this paper the estimation of state of a solar cell has been done by a Kalman filter, which is a linear quadratic estimator. Though the behavior of a PV cell is overall non-linear, but the EKF has been avoided due to its complexity and Kalman Filter has been adopted instead. Suitable assumptions are made regarding linearity of the system and gaussianity of noise, inherent to the Kalman filter and a piecewise linear model has been used to estimate and predict the short-circuit current of a PV cell. A comparative study with running average has also been made to prove that Kalman filter is a better option as an estimator than its first generation counterpart.