Australian researchers have developed multi-stage algorithms to remotely detect and accurately diagnose underperforming solar panels in residential and commercial PV systems.
Researchers from the University of New South Wales (UNSW) and the University of Technology Sydney have developed algorithms that they claim can automatically detect a range of common solar panel underperformance issues, including wiring errors, degradation and shading.
Fiacre Rougieux, senior lecturer from the UNSW School of Photovoltaic and Renewable Energy Engineering, said the technology can also identify clipping, tripping and export limits and has the potential to revolutionize PV system fault diagnosis.
“This is a game changer for Australian residential and commercial system operators,” he said. “By analyzing inverter data and maximum power every five minutes, this algorithm can accurately diagnose underperforming issues, enabling early intervention and maximizing energy production.”
Rougieux said the researchers, working together as part of a NSW Smart Sensing Network project, used sensors and different types of analytical approaches to develop a two-pronged approach to diagnosing substandard solar panel performance, which is estimated to cost AUD 7 billion ($4.6 billion) costs. worldwide in avoidable losses.
“We have created a high-level diagnosis using only AC data, which can detect broad categories of problems such as zero generation and shutdown,” he said. “The advantage of this approach is that this diagnosis is completely technology agnostic and can work with any brand of inverter and maximum power point tracker.”
With many inverter brands providing rich AC and DC information, Rougieux said the team has also developed a more detailed algorithm using both AC and DC data, which can provide more actionable insights for asset owners through more specific faults such as shading and string problems to detect and classify.
“This type of diagnosis requires both statistical rule-based methods, supported by machine learning approaches for cases that cannot be solved by conventional rules-based methods,” he said.
The technology is now fully integrated into a commercial production platform, which is being used by project industry partner Global Sustainable Energy Solutions to monitor more than 100 MW of solar energy.
UTS team leader Ibrahim Ibrahim said the technology, which can be deployed on more than 1,200 PV systems, has enabled proactive measures that maximize energy production and increase system reliability.
“By significantly reducing avoidable losses, which number in the billions worldwide, such technologies provide significant cost savings for photovoltaic system owners,” he said.
Rougieux said the software could replace the need for expensive contractors to figure out on-site why a solar system is underperforming.
“We had a municipality that had an underperforming system for five months,” he said. “That contractor had concluded an operation and maintenance contract, but this major problem went unnoticed for months. Our algorithms picked it up almost immediately. The big surprise for us was the staggering number of systems where an operations and maintenance contractor completely missed the underperformance we discovered.”
The research team is now working on improving the algorithm so that it can diagnose a wider range of problems, such as shading, pollution and detailed grid-side faults.
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