Data Mining Methods for Failure Classification on PV-Modules Monitored under Field-Conditions

The focus of this work is to demonstrate the advantage of collecting modul based PV-power system monitoring data with modern communication technology and the intelligent data analysis algorithms of computer science. Prediction and recognition of different faults in large PV-arrays are very important for the effective operation of PV-plants. Different PV-Systems are monitored with a large amount of data collected. The monitoring data is combined with weather data from free internet sources. The challenge in this work is using intelligent data analysis methods of data mining technology for a fault detection of different failure types in real PV-plants. The focus is on using neural network classifier for failure types of partial shading, defect bypass diodes, hailstorm damage and mechanical damage.