Statistical Moments Approach in Grid-Connected Photovoltaic System Performance Evaluation

Solar energy is considered as one of the solution to the worldwide depletion of fossil fuel resources as well as the economic alternatives in protecting the atmosphere from the adverse consequences of global warming. Nevertheless solar power is often criticized because the output power generated is variable and virtually uncontrollable. Potential analysis on introduction of photovoltaic system at particular site however requires the knowledge of solar irradiance and photovoltaic power distributions. This paper will focus on the possibility in applying statistical moments approach in solar irradiance and photovoltaic power distribution evaluation. Applying the first to forth statistical moments, the density function approximation of the parameters from 5MW grid connected Photovoltaic system were evaluated using the Pearson system. This method is based on the relationship between the first four moments of the distribution where the probability distribution is estimated by equating their theoretical moments with the moments of empirical distributions. Application of various statistical moments has the advantage in estimating the potential of photovoltaic system in view of dynamic changes of skewness and kurtosis coefficients of solar power irradiance distributions.

A points estimation and series approximation method for uncertainty analysis

The objective of this article is to present an algorithm for moment evaluation and probability density function approximation of performance function for structural reliability analysis. In doing so, a point estimation method for probability moment of performance function is discussed at first. Based on the coherent relationship between the orthogonal polynomial and probability density function, formulas for point estimation are derived. Vector operators are defined to alleviate computational burden for computer programming. Then, by utilizing C-type Gram—Charlier series expansion method, a procedure for probability density function approximation of the performance function is studied. At last, the accuracy of the proposed method is demonstrated using three numerical examples.