Arc Fault Detection in Photovoltaic Systems

Arc faults in photovoltaic (PV) systems occur when unintended electrical discharges form a conductive plasma path across a gap in the circuit, generating high temperatures that can ignite surrounding material. In PV arrays, series DC arc faults are especially challenging because the absence of an alternating zero crossing means conventional overcurrent protection is often ineffective. The concealment, randomness and rapid intermittency of arc faults demand detection strategies that combine physical insight into volt–ampere characteristics with advanced signal processing and machine learning. Recent progress spans time and frequency domain analyses, high-frequency coupling sensors and convolutional and recurrent neural networks, each seeking to distinguish fault signatures from normal operating transients in diverse load conditions.

Effective arc fault detection is crucial for mitigating fire risks in both rooftop and large-scale solar installations. Early detection systems integrate with DC–DC converters and grid-connected inverters, offering real-time monitoring and fast disconnection before thermal damage occurs. As photovoltaic capacity expands globally, robust fault detection not only safeguards assets and lives but also underpins the reliability and regulatory compliance of emerging distributed energy resources. The field continues to evolve through interdisciplinary collaboration between power electronics, computational intelligence and sensor innovation.

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Arc Fault Detection in Photovoltaic Systems publication trend

The graph below shows the total number of articles in arc fault detection in photovoltaic systems across all publications each year (not limited to Nature Index journals).

Technical terms

Arc fault: An unintended plasma discharge bridging a circuit gap, producing high temperatures and posing a fire hazard.

DC series arc fault: An arc fault occurring in the series branch of a direct-current circuit, often undetected by conventional protection.

Variational Mode Decomposition: An adaptive signal decomposition technique that separates a signal into intrinsic modes via constrained optimisation.

Shannon entropy: A quantitative measure of information content or complexity in a signal, reflecting the unpredictability of its components.

Small-signal modelling: Linearisation of a nonlinear system around an operating point to analyse dynamic responses to small perturbations.

References

1. Application of the Variational Mode Decomposition-Based Time and Time–Frequency Domain Analysis on Series DC Arc Fault Detection of Photovoltaic Arrays. IEEE Access (2019).
2. A Review for Solar Panel Fire Accident Prevention in Large-Scale PV Applications. IEEE Access (2020).
3. A DC Series Arc Fault Detection Method Using Line Current and Supply Voltage. IEEE Access (2020).
4. DC Series Arc Fault Detection Algorithm for Distributed Energy Resources Using Arc Fault Impedance Modeling. IEEE Access (2020).