Analysis of filling in missing data to provide maximum power extraction in Micro Grid

Abstract

An analysis of Micro Grid system performance requires both meteorological and electrical data for the assessment period. However, actual in-field data acquisition is rarely 100%, often resulting in a significant amount of incomplete datasets for performance assessment. These gaps, if not taken into account, may add noticeable bias in yield assessment and thus estimations of the lacking data need to be made. Approaches of back-filling the required data is given and validated here. This paper presents a strategy to back-fill data with good accuracy for both short and long term periods, while taking into account weather as well as system performance variations. Cases of data loss are identified. The first case is that of missing meteorological datasets, while electrical readings are available. This case is met in most small systems, either domestic or commercial, where installers reduce the cost by omitting the meteorological sensors. The second case is that of the electrical monitoring system being interrupted. The third case is a failure of both monitoring sub-systems, which could be due to communication or hardware failures. The last two cases are often met in the majority of solar farms. Two methods, by means of which it is possible to restore the missing data about the status or output current panel, are considered. The method of synthesis of meteorological data and also the method of restoration based on empirical orthogonal functions (EOFs), are considered. Method based on EOFs reconstructs missing data using empirical orthogonal functions, derived from the original data. While EOFs in a complete dataset would typically be calculated using singular value decomposition, the presence of missing data requires an iterative approach. The method allows for the estimation of missing values and full EOFs by first inserting mean values into the missing portions of the dataset and then calculating the EOFs. Because the resulting spatial EOFs and the time series of their magnitudes reconstruct the original data, a truncated version of the original dataset can be generated, using only as many EOFs as are deemed significant through validation. This provides an improved estimate of the missing information over simply inserting mean values, because the small-variance (i.e., noise) EOFs have been removed. Method of synthesis of meteorological data uses data collected from meteorological stations to estimate irradiance. Then module temperature is calculated from in-plane irradiance and ambient temperature using a simple linear thermal model. Using this information electrical performance is estimated. Two methods differ from each other, so detailed analysis and comparison of these methods are performed. The possibilities and requirements for their using are determined depending on the reasons of the loss of information in Micro Grid. The specific systems, that involve Micro Grid, in which methods can be used for providing maximum energy extraction are suggested. These two methods will be used in future research work based on applying the theory of fractals to the analysis of distributed generation systems of solar panels for maximum energy extraction.