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. (C) 2017 Elsevier Ltd. All rights reserved.