Intelligent Fault Detection in Solar Panels
Standing
Graduate (PhD)
Type of Proposal
Visual Presentation (Poster, Installation, Demonstration)
Faculty
Faculty of Engineering
Proposal
In this paper, we study recent researches in intelligent fault detection and control systems of solar panels. Solar panels used in buildings are prone to varies faults. These faults can be both electrical and environmental. Electrical faults are mainly due to mismatch in design or simple electrical failure of a cell while, environmental faults are often caused by partial shading and extreme weather conditions. Electrical faults can be categorized into component failure, sustained system isolation, brief system isolation, inverters shutdown, shading, maximum power point tracking unit failure and spot heating. Mentioned faults can greatly impact the efficiency of solar panels. To overcome this issue , faults have to be detected and then compensated. Varies methods of fault detection are proposed by different studies. Simplest method of fault detection involves monitoring the output of panels to look for abnormalities during different hours of operation. Such methods while cheap, lack the ability to provide the type and source of faults. To detect the type of faults, varies studies offer different methods such as ideal cell model based efficiency comparison using artificial intelligence, sensor based monitoring systems and process history based approach are used. While monitoring systems based on efficiency are the cheapest, sensor based monitoring systems are the most accurate. In this study, we propose a new method to increase the efficiency and accuracy of the system even further by mixing all three methods and designing a sensor based intelligent control system. Our proposed system will monitor the output of panels periodically to track the output power for changes. We implemented a sensor based system with a central PLC controller to monitor possible failures in maximum power point tracking units as well as other physical components. Our proposed method results in faster more distinguished failure detection.
Grand Challenges
Sustainable Industry
Intelligent Fault Detection in Solar Panels
In this paper, we study recent researches in intelligent fault detection and control systems of solar panels. Solar panels used in buildings are prone to varies faults. These faults can be both electrical and environmental. Electrical faults are mainly due to mismatch in design or simple electrical failure of a cell while, environmental faults are often caused by partial shading and extreme weather conditions. Electrical faults can be categorized into component failure, sustained system isolation, brief system isolation, inverters shutdown, shading, maximum power point tracking unit failure and spot heating. Mentioned faults can greatly impact the efficiency of solar panels. To overcome this issue , faults have to be detected and then compensated. Varies methods of fault detection are proposed by different studies. Simplest method of fault detection involves monitoring the output of panels to look for abnormalities during different hours of operation. Such methods while cheap, lack the ability to provide the type and source of faults. To detect the type of faults, varies studies offer different methods such as ideal cell model based efficiency comparison using artificial intelligence, sensor based monitoring systems and process history based approach are used. While monitoring systems based on efficiency are the cheapest, sensor based monitoring systems are the most accurate. In this study, we propose a new method to increase the efficiency and accuracy of the system even further by mixing all three methods and designing a sensor based intelligent control system. Our proposed system will monitor the output of panels periodically to track the output power for changes. We implemented a sensor based system with a central PLC controller to monitor possible failures in maximum power point tracking units as well as other physical components. Our proposed method results in faster more distinguished failure detection.