With the active promotion of green, low-carbon, and intelligent strategies in the energy sector, the application of battery systems such as electric vehicles and energy storage stations is becoming increasingly widespread globally. However, it has also resulted in a higher frequency of DC electrical safety incidents. DC arc faults caused by mechanical collisions, loose connections, and insulation damage, among other things, have become one of the leading causes of battery system safety accidents. Currently, there is a lack of in-depth and comprehensive research on arc faults specifically in battery systems. To ensure the safe operation of batteries and other system components, battery systems must have fast, effective, and reliable protection measures. This review comprehensively reviews DC arc fault detection, early warning strategies, and protection technologies in battery systems. Methods used for fault detection are discussed and compared in detail. Timely and accurate warning strategies are summarized, and protective measures against arc-causing disasters are outlined. Some perspectives on future research are presented. This review is of great significance in guiding future improvements in addressing arc fault issues in battery systems. 

A comprehensive review of DC arc faults and their mechanisms, detection, early warning strategies, and protection in battery systems

Photovoltaic systems provide electrical power with reduced emissions at competitive costs compared to legacy systems. A low or medium voltage dc distribution system is usually used for solar integration. In dc systems, parallel and series arc faults are a safety concern. Thus, reliable and timely detection and mitigation of arc faults are critical. DC arc detection methods typically use time or frequency spectrum variations of the circuit current or voltage to differentiate the arcing event from other system events. Since practical systems include power electronics and maximum-power-point tracking, any detection scheme must perform robustly in the electrical environment that these components establish in the dc power system. A capacitor placed in parallel with the main system is an effective sensor for series arc fault detection and localization applicable in this complex electrical environment. This article shows that the analysis of the amplitude, polarity, and spectrum characteristics of the capacitor current and voltage resulting from perturbations caused by the arc provides an effective method to identify and localize faults. The detection accuracy of the proposed approach is 98.3% and the localization accuracy rate is 100% for the correctly detected faults.

Development of a Fault Detection and Localization Algorithm for Photovoltaic Systems

Arc fault detection devices are mandatory worldwide for mitigating DC series arc faults in photovoltaic systems. However, they are prone to nuisance tripping. Artificial intelligence-based approaches can be a solution, but they are “black boxes” and challenging to modify. This paper proposes an explainability and attention-based method to investigate the intensive details of such an algorithm. The contributions of an arc feature to the proposed model can be visualized by the proposed interpretable methodology so that insensitive arc features can be removed to reduce the quantity of input data. Additionally, the structure of the proposed model can be optimized by cutting the redundant layers. Thus, an accuracy of 99.63% is achieved with only 48.48% of the parameters compared to the original model. Finally, the optimized model is implemented by a Cortex M7-based microprocessor with a runtime of only 7.8 ms, making it ready for industrial application.

Explainability Approach-Based Series Arc Fault Detection Method for Photovoltaic Systems

The aeronautical industry imposes high security and redundancy in all its embedded systems (electrical, hydraulic and mechanical). Therefore, the increase in voltage levels expected in More Electric Aircraft or propulsion applications pushes studies to consider more carefully the phenomenon of arc faults. This failure can be more frequent and dangerous at high voltage and high current levels. Currently, there is no robust and reliable embedded system capable of detecting, with high accuracy, arcs on aircraft. To recognize the series DC arc faults from the rest of the nominal behavior of the system, this article implements a linear classifier founded on the extraction of relevant features from the conventional current signals. The feature ranking is performed through the orthogonalization Gram-Schmidt procedure and the feature selection through a wrapper approach. The used database contains current signal measurements of series DC arc faults and nominal behaviors (simple and complex). Due to the high requirements in terms of performance and real time implementation, the impact of the sampling frequency (SF) of the current sensor signals is studied.

Impact of the Sampling Frequency on the Detection of Series DC Arc Faults in an Aeronautical Environment Using Machine Learning Techniques

Arc duration and erosion characteristics of cables are closely related to electrical fire hazards. In this study, the arc duration and arc erosion characteristics of copper and aluminum core cables were explored. The main erosion modes of copper core cable with mica insulation (HC0) and aluminum core cable with polyimide (PI) insulation (HA0) were determined. Results show that several large molten droplets with diameters of 5 ± 0.5 mm would drip down from the molten pool on the cable end during arcing of HC0, but the molten droplets are adhered to the molten pool and do not drip down during arcing of HA0. We concluded that the differences in melting energy and oxide film between copper and aluminum are the main reasons for this phenomenon. Besides, the molten pool can be formed on the cable end as the insulation is heatproof, which plays a vital role in prolonging the arc duration of cables. The arc duration of cables with heatproof insulation is longer than those with common insulation. This means that the cables with heatproof insulation have higher fire hazards from the perspective of arc duration. To further understand the influence of molten pools on arc duration, an arc duration model was developed based on energy balance, which is beneficial in determining the arc duration of cables with heatproof insulation in electrical fire accident investigation.

Study on arc duration and erosion characteristics of cable electrodes in AC series circuits

In the new era of increasingly electric aircraft, the need for reliable and safe electrical systems is more important than ever. In addition, the wide-scale adoption of dc distribution is considered a key enabling technology for more efficient aircraft operation. In this context, arc fault detection devices (AFDDs) have become a topic of interest for the aviation industry with ongoing research to characterize the impact and adequately protect against severe dc series arc faults. Although dc arc faults have been widely investigated for utility applications (such as solar photovoltaic systems), direct adoption of current practices for validating arc detection devices is not straightforward due to the distinct aircraft operating environment. This article provides a first-of-its-kind landscaping exercise of published series arc fault testing based on factors associated with aircraft applications that have the potential to influence the arc characteristics. In addition, an appraisal and associated gap analysis of published arc test platforms is undertaken in order to assess their suitability to support in-depth testing of the impact and mitigation of series arcs within future aircraft dc electrical systems and identify future testing needs, in particular, to better facilitate a comprehensive performance validation of new AFDDs.

Review of DC Series Arc Fault Testing Methods and Capability Assessment of Test Platforms for More-Electric Aircraft

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Review of DC Series Arc Fault Testing Methods and Capability Assessment of Test Platforms for More-Electric Aircraft