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A Lightweight, Transferable, and Self-Adaptive Framework for Intelligent DC Arc-Fault Detection in Photovoltaic Systems

Xiaoke Yang, Long Gao, Haoyu He, Hanyuan Hang, Qi Liu, Shuai Zhao, Qiantu Tuo, Rui Li

Abstract

Arc-fault circuit interrupters (AFCIs) are essential for mitigating fire hazards in residential photovoltaic (PV) systems, yet achieving reliable DC arc-fault detection under real-world conditions remains challenging. Spectral interference from inverter switching, hardware heterogeneity, operating-condition drift, and environmental noise collectively compromise conventional AFCI solutions. This paper proposes a lightweight, transferable, and self-adaptive learning-driven framework (LD-framework) for intelligent DC arc-fault detection. At the device level, LD-Spec learns compact spectral representations enabling efficient on-device inference and near-perfect arc discrimination. Across heterogeneous inverter platforms, LD-Align performs cross-hardware representation alignment to ensure robust detection despite hardware-induced distribution shifts. To address long-term evolution, LD-Adapt introduces a cloud-edge collaborative self-adaptive updating mechanism that detects unseen operating regimes and performs controlled model evolution. Extensive experiments involving over 53,000 labeled samples demonstrate near-perfect detection, achieving 0.9999 accuracy and 0.9996 F1-score. Across diverse nuisance-trip-prone conditions, including inverter start-up, grid transitions, load switching, and harmonic disturbances, the method achieves a 0% false-trip rate. Cross-hardware transfer shows reliable adaptation using only 0.5%-1% labeled target data while preserving source performance. Field adaptation experiments demonstrate recovery of detection precision from 21% to 95% under previously unseen conditions. These results indicate that the LD-framework enables a scalable, deployment-oriented AFCI solution maintaining highly reliable detection across heterogeneous devices and long-term operation.

A Lightweight, Transferable, and Self-Adaptive Framework for Intelligent DC Arc-Fault Detection in Photovoltaic Systems

Abstract

Arc-fault circuit interrupters (AFCIs) are essential for mitigating fire hazards in residential photovoltaic (PV) systems, yet achieving reliable DC arc-fault detection under real-world conditions remains challenging. Spectral interference from inverter switching, hardware heterogeneity, operating-condition drift, and environmental noise collectively compromise conventional AFCI solutions. This paper proposes a lightweight, transferable, and self-adaptive learning-driven framework (LD-framework) for intelligent DC arc-fault detection. At the device level, LD-Spec learns compact spectral representations enabling efficient on-device inference and near-perfect arc discrimination. Across heterogeneous inverter platforms, LD-Align performs cross-hardware representation alignment to ensure robust detection despite hardware-induced distribution shifts. To address long-term evolution, LD-Adapt introduces a cloud-edge collaborative self-adaptive updating mechanism that detects unseen operating regimes and performs controlled model evolution. Extensive experiments involving over 53,000 labeled samples demonstrate near-perfect detection, achieving 0.9999 accuracy and 0.9996 F1-score. Across diverse nuisance-trip-prone conditions, including inverter start-up, grid transitions, load switching, and harmonic disturbances, the method achieves a 0% false-trip rate. Cross-hardware transfer shows reliable adaptation using only 0.5%-1% labeled target data while preserving source performance. Field adaptation experiments demonstrate recovery of detection precision from 21% to 95% under previously unseen conditions. These results indicate that the LD-framework enables a scalable, deployment-oriented AFCI solution maintaining highly reliable detection across heterogeneous devices and long-term operation.

Paper Structure

This paper contains 39 sections, 9 equations, 13 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Description and Challenges
  3. System Architecture
  4. DC Arc-Fault Physics and Signal Characteristics
  5. Challenges of Reliable Arc-Fault Detection
  6. Challenge 1: Intra-System Variability
  7. Challenge 2: Cross-Hardware Heterogeneity
  8. Challenge 3: Real-World Deployment Drift
  9. The LD-Framework
  10. LD-Spec: Frequency-Domain Arc Detection
  11. Feature Engineering
  12. LD-Spec Architecture
  13. Dual-Threshold Decision Mechanism
  14. LD-Align: Cross-Hardware Transfer
  15. Feature Construction
  16. ...and 24 more sections

Figures (13)

  • Figure 1: Overview of the PV-BESS system architecture.
  • Figure 2: Spectrum of PV currents during normal and arc-fault operation. Arc faults exhibit elevated broadband spectral magnitude.
  • Figure 3: Time-domain waveforms and spectrograms of PV current under no-load steady operation and typical DC arc-fault events.
  • Figure 4: Spectrum of PV currents from two inverter platforms exhibiting distinct switching-frequency characteristics.
  • Figure 5: Spectrum of PV currents from a laboratory PV source and a real solar panel under identical current conditions.
  • ...and 8 more figures