Unsupervised Fault Detection and Analysis for Large Photovoltaic Systems Using Drones and Machine Vision

Abstract

One of the most important sources of clean energy in the future is expected to be solar energy which is considered a real time source. Research efforts to optimize solar energy utilization are mainly concentrated on the components of solar energy systems. Photovoltaic (PV) modules are considered the main components of solar energy systems and PVs’ operations typically occur without any supervisory mechanisms, which means many external and/or internal obstacles can occur and hinder a system’s efficiency. To avoid these problems, the paper presents a system to address and detect the faults in a PV system by providing a safer and more time efficient inspection system in real time. In this paper, we proposing a real time inspection and fault detection system for PV modules. The system has two cameras, a thermal and a Charge-Coupled Device CCD. They are mounted on a drone and they used to capture the scene of the PV modules simultaneously while the drone is flying over the solar garden. A mobile PV system has been constructed primarily to validate our real time proposed system and for the proposed method in the Digital Image and Signal Processing Laboratory (DISPLAY) at Western Michigan University (WMU). Defects have been detected accurately in the PV modules using the proposed real time system. As a result, the proposed drone mounted system is capable of analyzing thermal and CCD videos in order to detect different faults in PV systems, and give location information in terms of panel location by longitude and latitude.

Clustering-Based Self-Imputation of Unlabeled Fault Data in a Fleet of Photovoltaic Generation Systems

“…To evaluate the accuracy of imputation performance, normalized root mean square error (NRMSE) is adopted since the PV capacities in the fleet are different. In this study, RMSE was normalized by mean as described in Equations (14) and (15) Low NRMSE implies valuable imputation performance where abnormal pattern is restored similar to the real profile. The daily abnormal pattern for a corresponding site is then substituted by the reconstructed normal pattern.…”

“…PV faults occur because of various reasons at different locations, such as a module, string, or any other spot related to the PV systems. Visual and thermal methods employed in PV fault detection can detect superficial problems, such as browning, soiling or snow, discoloration, delamination, and hot spots, using auxiliary measurements [14]. However, this requires expensive and complicated equipment [15].…”

Economic Analysis of Grid-Connected PV System Regulations: A Hungarian Case Study

“…As one of the consequences, many cities started to include solar energy programs in their urban planning all around the world in order to support sustainable development and environmental protection. The reasons are truly understandable; the energy from the Sun, which is the base of most natural processes, is available to everyone and it is a sustainable, plentiful and clean resource [2][3][4][5][6][7][8][9][10][11]. It can be stated that the annual solar energy potential on the surface of the Earth is thousands of times greater than the current global energy demand.…”

Changes of Photovoltaic Performance as a Function of Positioning Relative to the Focus Points of a Concentrator PV Module: Case Study

“…Many cities around the world have started to develop their solar energy programs in their urban planning to promote sustainable development and environmental protection. Energy from the Sun, which is available for all mankind, is the basis of most natural processes and it is a sustainable, plentiful, and clean resource [2][3][4][5][6][7][8][9][10][11]. The annual solar energy potential arriving on the Earth's surface is approximately 8000 times greater than the current energy demand of the world.…”