# DC Series Arc Fault Detection with LCL-Type Boost Converter

Mina Kim, Hwa-Pyeong Park

► To cite this version:

Mina Kim, Hwa-Pyeong Park. DC Series Arc Fault Detection with LCL-Type Boost Converter. The 26th European Conference on Power Electronics and Applications, GDR SEEDS France & EPE Association, Mar 2025, Paris, France. 10.34746/epe2025-0067 . hal-05081821

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Submitted on 23 May 2025

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# DC Series Arc Fault Detection with LCL-Type Boost Converter

Mina Kim¹ and Hwa-Pyeong Park²

1. Hanbat National University / 125, Dongseo-daero, Yuseong-gu, Daejeon

2. Korea Energy Institute of Technology / KENTECH, 21 Kentech-gil, Naju-si, Jeollanam-do, 58330, Republic of Korea

E-Mail: minakim@hanbat.ac.kr

## Acknowledgements

This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2024-00336435)

## Keywords

«DC Arc Fault», «Fault Diagnosis», «Module Level Power Electronics», «DC/DC Converter». «DC Power Conversion», «LCL Filter», «Photovoltaic (PV)».

## Abstract

The damaged and loose connector can induce the DC series arc fault condition. Previously, the frequency domain analysis using PV current, such as Fourier transform and wavelet transform, was widely employed for the series arc fault detection. However, performance of arc fault detection is not consistent according to system configurations. This paper investigates the DC series arc fault capability using the frequency spectrum analysis according to the impedance of power converter. From the characteristics of arc fault condition, the boost converter using LCL-type filter can clarify the arc fault condition in the frequency domain analysis. The experimental results using 800-W prototype converter can verify the frequency spectrum change at the arc fault condition with the proposed converter with LCL-type filter.

## Introduction

The power generation utilizing renewable energy sources has become a major trend in achieving carbon neutrality. With the increase in the number of PV systems, several standards have been invented with increasing interest in their safety and reliability from electrical problems [1]-[4]. As one of major fault, the DC series arc fault occurs by loose connectors, damaged cables, and cracked solder joints. The National Electrical Code (NEC) requires the installation of an arc

<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /><figcaption>Fig. 1. Designed PV systems: (a) Structure of DC optimizer with arc fault condition, (b) Scheme of boost converter with LCL filter.</figcaption></figure>

fault detection device (AFDD) for PV systems over 80 V [5]. Also, Underwriters Laboratories (UL) invented the UL 1699 B standards to guarantee the performance of AFDDs [6]. Therefore, the DC series arc fault detection is necessary to guarantee the safety of PV systems.

Various DC series arc fault detection algorithms have been introduced for PV systems. A DC series arc fault detection based on a time domain analysis was evaluated using voltage and current information [7], [8]. These methods have a simple implementation with the characteristics of the arc fault resistance. However, it is difficult to distinguish the arc fault condition and normal operation with small current magnitude variation. In [9]-[11], frequency domain analyses were used with the measured PV current to determine the arc fault characteristics. It considers the chaotic
variation of arc impedance, which can distinguish between the normal and fault conditions. However, the AC magnitude variation depends on the impedance design of converter and inverter, which determines the precision of arc fault detection. In [12]-[15], hybrid arc fault detection methods employing time and frequency domain analyses were introduced to improve the detection precision. It increases the available parameters with multi-domain analyses, which can increase the series arc fault characteristics to classify the arc and normal conditions. In [12] and [13], statistical analyses in the time and frequency domains can distinguish the normal and arc fault condition. However, it passively measures the PV current from the designed power systems, which is difficult to achieve the consistent fault detection performance. In [16]-[18], arc fault detection methods using artificial intelligence are developed using normal and fault data. It can achieve high fault detection precision with several arc fault and normal characteristics according to the machine learning (ML) and neural network (NN) models. AFDDs use a micro-processor to achieve cost-effectiveness, which is difficult to implement the online ML and NN. Therefore, a predefined model is normally applied to the micro-processor. It reduces the precision of arc fault detection, since the arc fault characteristics are different according to the structure of the PV systems, PV inverters and converters, and line length. In [9]-[18], the signal processing methods were introduced to detect the DC series arc fault condition. The fault signals are measured with the current transformer and Rogowski coil, which is decoupled with the power converter. However, the magnitude of arc fault signal is different according to the power stage design of converter and inverter. Therefore, the fault detection precision of AFDD depends on the design of PV systems.

The various series arc fault detection methods based on the hardware design and its modulation were introduced in [19] and [20]. In [19], the duty cycle modulation for the buck converter was introduced to detect and extinguish the arc fault condition. It utilizes the resistance characteristics of series arc fault condition, which classify the normal and fault condition in the power level. However, the frequent its operation reduces the power generation of PV systems. In [20], the input filter design was analyzed to detect and extinguish the series arc fault condition. It maximizes the arc resistance with small input current for the arc fault transient duration.

However, it is only applied for the fault transient duration. The series arc fault detection in the power level has the reduction in power generation and limited fault detection capability.

The partial shading of PV panels degrades the performance of power generation [21]-[24]. The module level power electronics (MLPEs) are used to generate the maximum power of each PV panel with overcoming the partial shading, as shown in Fig. 1 (a) [25]-[28]. However, the number of MLPE increase the possibility of DC series arc fault condition between the PV panel and MLPE. The conventional AFDDs using signal processing methods, such as time and frequency domain analyses, have different performance according to the system configuration. In this paper, the boost converter using the LCL-type filter is proposed to achieve consistent arc fault detection capability through the desired impedance design of converter, which can classify the fault condition with the frequency spectrum. The experimental results using 800-W prototype converter can verify the signal magnitude change using the fast Fourier transform to clarify the series arc fault condition.

## Limited Arc Fault Detection Capability

The conventional DC series arc fault detection algorithm uses the frequency domain analyses, such as Fourier transform and wavelet transform. It utilizes the AC component variation of PV current for the wide frequency spectrum according to the characteristics of chaotic arc impedance change. However, the magnitude of arc fault signal is not consistent according to the power converters and inverters. Fig. 2 (a) and (b) shows the impedance of conventional boost converter. where $L_b$ is the boost inductor, $R_{eq}$ is the equivalent load resistor, $R_s$, $L_s$, $R_l$, and $L_l$ are the line resistors and inductors of PV panel and converter, respectively. Fig. 2 (c) shows the impedance of conventional topology according to the operating frequencies. In DC and low frequencies, the load resistance is dominant. As increase of operating frequency, the $L_b$ is dominant. The AC current magnitude can be derived with considerations of series arc fault resistance, as follows:

$$i_{ac,c}(\omega) = V_{in} / z_{in,c}(\omega) \quad (1)$$

$$z_{in,c}(\omega) \approx r_{arc} + j\omega L_b + R_{eq} \quad (2)$$
<figure><img src="image_4.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Power Flow</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>(c)</figcaption></figure>

<caption>Fig. 2. Characteristics of conventional boost converter: (a) Topology Structure, (b) Equivalent impedance model, (c) Impedance according to operating frequencies.</caption>

where $r_{arc}$ is the arc resistance, $V_{in}$ is the PV voltage, and $\omega$ is the angular frequency. The high inductance of $L_b$ reduces the AC current magnitude according to the arc fault as increase of operating frequency, which increases the difficulty of arc fault detection.

Fig. 3 shows the experimental results of frequency spectrum analyses using Fourier transform according to $L_b$ values. The experimental specifications, such as input voltage range, load current, and switching frequency. Fig. 3 (a) shows the frequency spectrum at the normal condition, which uses 110 µH of $L_b$. It has only auxiliary power noise, because the frequency domain analysis measures from 0 to 100 kHz range. Fig. 3 (b) and (c) show the series arc fault condition

<figure><img src="image_7.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Time (200ms/Div)</figcaption></figure>

<caption>(c)</caption>

<caption>Fig. 3. Experimental results using conventional boost converter according to $L_b$ values: (a) Normal condition with 110 µH, (b) Arc fault condition with 110 µH, (c) Arc fault condition with 1 mH.</caption>

with 110 µH and 1 mH of $L_b$, respectively. The digital controller can distinguish the arc fault
<figure><img src="image_10.png" /><figcaption>Fig. 4. Characteristics of proposed topology: (a) Impedance model between PV panel and converter, (b) Impedance according to operating frequencies, (c) Input current according to frequencies.</figcaption></figure>

condition with frequency spectrum analyses at the small Lb condition. However, the high Lb is difficult to classify the arc fault and normal conditions, since it has only small magnitude increase below 3 kHz range. Therefore, the power conversion of MLPE is tightly coupled with the series arc fault detection capability.

## Impedance Analysis of Proposed Converter

This paper proposes the boost converter using the LCL-type filter to decouple the power conversion and arc fault detection. Fig. 1 (b) shows the circuit diagram of proposed converter.

<figure><img src="image_11.png" /><figcaption>Fig. 5. Experimental setups with prototype converter.</figcaption></figure>

where Lb1, Lb2, and Cf are two boost inductors and filter capacitor. Fig. 4 (a) is the impedance model of proposed boost converter. From the LCL-type filter, the AC current magnitude of PV current can be derived with the considerations of DC series arc resistance, as follows:

$$i_{ac,p}(\omega) = V_{in} / z_{in,p}(\omega) \quad (3)$$

$$z_{in,p}(\omega) \approx R_{arc} + j\omega L_{b1} + (j\omega L_{b2} + R_{eq}) \parallel (1 / j\omega C_f) \quad (4)$$

From (4), Fig. 4 (b) shows the impedance according to the operating frequencies. The $R_{eq}$ is dominant at the DC and low frequencies. The LCL-type filter induces small impedance at the designed resonant frequency ($f_{res}$). In addition, the $L_{b1}$ is significant at the high frequency range over $f_{res}$. From (4), with neglecting $R_{eq}$, the $z_{in,p}$ can be described as follows:

$$z_{in,p}(\omega) = \frac{-j\omega^3 L_{b1} L_{b2} C_f + j\omega(L_{b1} + L_{b2})}{-\omega^2 L_{b2} C_f + 1} \quad (5)$$

From (5), the $f_{res}$ can be derived as follows:

$$f_{res} = \sqrt{(L_{b1} + L_{b2}) / (C_f L_{b1} L_{b2})} \quad (6)$$

From (6), the $f_{res}$ determines the desired frequency range for the DC series arc fault detection. From (4) and (6), the $z_{in,p}$ at $f_{res}$ can be derived as follows:

$$z_{in,p}(\omega_{res}) \approx L_{b1}^2 R_{eq} / L_{b2}^2 = L_n^2 R_{eq} \quad (7)$$

where $L_n$ is the $L_{b1}/L_{b2}$. From (7), the $L_n$ and load resistance determines the impedance magnitude at the designed resonant frequency for the DC series arc fault detection. Fig. 4 (c) shows the $i_{in}$ using unity AC voltage according to the $L_n$ ratio. The small $L_n$ ratio reduces the input impedance at $f_{res}$, which increases the magnitude of fault signal, vice versa.

## Experimental Results

Fig. 5 shows the experimental setup to verify the frequency spectrum change of arc fault condition and DC/DC power conversion. The DC/DC converter is designed for two PV panels
<figure><img src="image_12.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>(c)</figcaption></figure>

Fig. 6. Experimental results with Case 1: (a) Normal operation with frequency spectrum, (b) Arc fault operation with frequency spectrum, (c) Fault detection verification with FFT.

(LG, LG420QAK-A6). A series arc fault
generator is designed with the UL 1699B
standards. This experiment designs two setups for
verify the fault signal measuring. When L_n is
0.227, Fig. 6 (a) shows the steady state operation
at the normal condition. It has zero arc voltage
and small input current ripple. Also, its frequency
spectrum is measured as a reference data to detect
the fault condition. Fig. 6 (b) shows the series arc
fault operation, which has high magnitude in

frequency spectrum using input current of MLPE.
The fault condition can be distinguished with the
magnitude difference between the reference and
arc fault condition at the designed frequency. The
magnitude difference of input current is 36.8
dBmV according to the arc fault condition. It can
achieve the arc fault detection capability using the
frequency domain analyses. The FFT of input
current is designed with 110 kHz sampling
frequency and 1024 FFT size. The FFT
magnitude change compares for 10 times to
distinguish the normal and fault conditions, which
requires 93 ms time duration. Fig. 6 (c) shows the
arc fault detection performance. It can detect the
fault condition within 103 ms.

Conclusion

The series arc fault detection using the
frequency spectrum of PV current is tightly
coupled with the power stage design of converter.
This paper proposes the boost converter
employing the LCL-type filter to achieve the
series arc fault detection capability with proper
impedance design. The impedance with LCL-type
filter is analyzed for the arc fault detection. The
experimental results can verify the performance
of fault detection capability using the proposed
converter.

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