FAULT DETECTION FOR SMALL-SCALE PHOTOVOLTAIC POWER INSTALLATIONS : A Case Study of a Residential Solar Power System

• Main Author: Brüls, Maxim
• Format: Others
• Language: English
• Published: Högskolan Dalarna, Mikrodataanalys 2020
• Subjects: Random Forest; Regression; Solar Power; Photovoltaic Module; Fault Detection; Renewable Energy; Econometrics; Supervised Learning; Computer and Information Sciences; Data- och informationsvetenskap
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:du-35965

Fault detection for residential photovoltaic power systems is an often-ignored problem. This thesis introduces a novel method for detecting power losses due to faults in solar panel performance. Five years of data from a residential system in Dalarna, Sweden, was applied on a random forest regression to estimate power production. Estimated power was compared to true power to assess the performance of the power generating systems. By identifying trends in the difference and estimated power production, faults can be identified. The model is sufficiently competent to identify consistent energy losses of 10% or greater of the expected power output, while requiring only minimal modifications to existing power generating systems.