  Automatic fault detection (AFD) is a key technology to optimize the Operation
and Maintenance of photovoltaic (PV) systems portfolios. A very common approach
to detect faults in PV systems is based on the comparison between measured and
simulated performance. Although this approach has been explored by many
authors, due to the lack a common basis for evaluating their performance, it is
still unclear what are the influencing aspects in the design of AFD algorithms.
In this study, a series of AFD algorithms have been tested under real operating
conditions, using monitoring data collected over 58 months on 80 rooftop-type
PV systems installed in Germany. The results shown that this type of AFD
algorithm have the potential to detect up to 82.8% of the energy losses with
specificity above 90%. In general, the higher the simulation accuracy, the
higher the specificity. The use of less accurate simulations can increase
sensitivity at the cost of decreasing specificity. Analyzing the measurements
individually makes the algorithm less sensitive to the simulation accuracy. The
use of machine learning clustering algorithm for the statistical analysis
showed exceptional ability to prevent false alerts, even in cases where the
modeling accuracy is not high. If a slightly higher level of false alerts can
be tolerated, the analysis of daily PR using a Shewhart chart provides the high
sensitivity with an exceptionally simple solution with no need for more complex
algorithms for modeling or clustering.
