A research group from Australia has developed a novel rule-based method for detecting and classifying underperformance in PV systems, using only inverter data from the alternating current (AC) side.
The method was validated using more than 1,000 PV systems across Australia, with more than 2,000 inverter monitors.
“Motivated by the need for reliable, low-cost underperformance detection in distributed PV systems, the proposed approach eliminates reliance on high-resolution direct current (DC) side measurements or complex sensor infrastructure,” said the research group. “This work addresses a critical gap in current performance monitoring practices, offering a robust, low-intervention solution for PV fleet operators seeking to improve reliability, fault response, and economic performance at scale.”
The novel method follows a five-step process.
Step one gathers and preprocesses all required data from the PV system. Inputs include AC power data at five-minute intervals, along with basic metadata such as system size, location, tilt, and azimuth.
Step two compares the expected generation under prevailing meteorological conditions with the measured output.
Step three applies daily if-then rules to detect underperformance. Major underperformance is flagged if actual generation falls below 60% of expected output for at least three consecutive non-cloudy days. Minor underperformance is recorded when generation is below 80% of expected for seven consecutive days. Additional algorithms identify systematic weekday–weekend variations that may indicate scheduled maintenance, as well as seasonal performance issues.
Step four uses algorithms based on five-minute data points to classify the type of underperformance. Specific routines detect generation tripping to zero, non-zero tripping, generation clipping, zero output, recurring underperformance, power flow anomalies, and excessive generation.
Step five produces a report summarizing flagged events, their severity, and the identified fault type.
Image: University of Technology Sydney, Solar Energy, CC BY 4.0
The proposed method was demonstrated in a case study that included 1,089 PV systems and 2,213 inverter monitors located in Australia, with five-minute resolution over the period from January 2021 to 14 September 2023.
The capacities ranged from small residential with less than 10 kW capacity to large installationsover 50 kW, spread across eight Australian states and territories.
To validate the system, the team uses a list of 807 manually labeled faults from 177 PV sites.
“The results of this dataset showed strong classification accuracy for major and minor underperformance (92% and 88%, respectively), with a lower accuracy for more ambiguous categories such as generation clipping (56%),” the group said. “These findings suggest opportunities to refine detection thresholds and better align algorithmic definitions with industry interpretations.”
They further noted that future work would focus on improving threshold tuning, reducing false positives, and incorporating complementary data sources, such as event logs, to enhance system robustness. They added that these approaches could ultimately support the development of low-intervention, integrated monitoring systems capable of ensuring sustained performance and safety across a range of PV installations.
Their findings appeared in “A robust rule-based method for detecting and classifying underperformance in photovoltaic systems using inverter data,” published in Solar Energy. Scientists from Australia’s University of Technology Sydney, energy resources management company Diagno Energy, and the University of New South Wales have contributed to the study.
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