11.2021.34.49

Detection and Prediction of Faults in Photovoltaic Solar Panel Using Regression Analysis

Author(s):

Asfandyar Khalid, Naveed Ullah*, Asim Ahmad Riaz*, Muhammad Zeeshan Zahir, Zuhaib Ali Khan

Affiliation(s):

Department of Mechanical Engineering, University of Engineering & Technology, Peshawar 25000, Pakistan.

Corresponding Author Email: naveedullah@uetpeshawar.edu.pk; engr.asim@uetpeshawar.edu.pk

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Our world has never faced a far graver threat than it does today, the energy crisis. Low lying rural areas have been identified as mostly suffered areas due to non-availability of power. The energy production from solar photovoltaic system has gained attention world-wide as it a clean and renewable source of energy. The power output of solar panel mainly depends on solar irradiance and temperature. But just like many other power generation sources PV systems does come with its fair share of short comings too that reduces the performance and even causes serious safety concerns. This paper covers major faults like open circuit, short circuit and partial shading. These faults are induced in the PV strings and then output characteristics such as open circuit voltage, short circuit current, current and voltage at maximum power point (MPP) are measured using multi-meter and SP lite 2 sensor. The output characteristics are compared to the healthy strings and based on the difference in current (I) and voltage (v) output at maximum power point (MPP) of each fault induced system, faults are detected. The datasets collected for detection is then analyzed in Minitab software using regression analysis and predictive model is generated for each fault. Based on the amount of solar irradiation, surface temperature, ambient temperature, and voltage, current for each fault is predicted. Machine learning methodologies are then used to validate fault detection and prediction. Random Forest classifier is used for fault classification while linear regression is used for current prediction.