Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5450702 | Solar Energy | 2017 | 13 Pages |
Abstract
In this paper a new method is developed for automatically detecting outliers or faults in the solar energy production of identical sets (sister arrays) of photovoltaic (PV) solar panels. The method involves a two-stage unsupervised approach. In the first stage, “in control” energy production data are created by using outlier detection methods and functional principal component analysis in order to remove global and local outliers from the data set. In the second stage, control charts for the “in control” data are constructed using both a parametric method and three non-parametric methods. The control charts can be used to detect outliers or faults in the production data in real-time or at the end of the day. As an illustration, the method is applied to analysis of the real energy production data of six sets of “identical” PV solar panels over a period of three years. Tests indicate that the proposed method is able to successfully detect a reduction in efficiency in one of the solar panel sets by up to 5%. Control charts based on parametric and non-parametric methods both show good performance results.
Related Topics
Physical Sciences and Engineering
Energy
Renewable Energy, Sustainability and the Environment
Authors
FermÃn Mallor, Teresa León, Liesje De Boeck, Stefan Van Gulck, Michel Meulders, Bart Van der Meerssche,