Advanced diagnostics for PV systems

Australia's rapid shift toward renewable energy has placed rooftop solar photovoltaic (PV) systems at the centre of the clean energy transition.

Yet for commercial and industrial operators, the full potential of these systems is frequently undermined by faults, degradation, and grid disturbances that existing monitoring tools are too coarse to detect or diagnose with precision.

Without granular, actionable insights, operators face reactive maintenance cycles, unnecessary downtime, and reduced energy yield, ultimately weakening confidence in solar investment.

The UTS Institute for Sustainable Futures (ISF) is a co-lead on this RACE for 2030 project, working alongside academic lead UNSW and industry partner Diagno to develop next-generation diagnostic capabilities for PV systems. The research applies data analytics and machine learning to two interconnected diagnostic challenges.

The first focuses on transforming raw inverter error codes into meaningful, traceable fault diagnoses. Rather than simply flagging that something has gone wrong, the research enriches automated fault detection with contextual, device-level information, enabling operators to pinpoint the nature and location of a fault and take targeted corrective action.

This shift from reactive troubleshooting to evidence-based maintenance has direct implications for system uptime, maintenance costs, and the longevity of solar assets.

The second challenge involves accurately distinguishing between faults originating within the PV system itself versus those caused by the broader electricity network. Grid-side disturbances are frequently misattributed, leading to unnecessary site visits and wasted resources.

By drawing on telemetry from peer assets, the research develops a more reliable method for isolating whether an event stems from a network provider incident, a site-specific issue, or a local grid compliance deviation.

Together, these capabilities form a core part of a broader diagnostic platform that moves well beyond the high-level insights offered by conventional monitoring tools.

ISF's contribution helps position Australia at the frontier of intelligent solar asset management, advancing both the reliability and scalability of the nation's clean energy infrastructure.