Frequency-Domain Characteristics of Series DC Arcs in Photovoltaic Systems with Voltage-Source Inverters

Kim, Jaechang; Kwak, Sangshin

doi:10.3390/app10228042

Cited by 13 publications

(8 citation statements)

References 21 publications

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“…The load current at the time is saved using an oscilloscope, and frequency analysis is performed using a personal computer. When the arc is generated in the inverter load, the frequency within the 5 kHz to 40 kHz band is observed to increase irrespective of the shape of frequency fluctuations [30]. Therefore, we analyzed the characteristics of frequencies within the 5 kHz to 40 kHz band.…”

Section: Proposed Arc Detection Methods Using Ama a Frequency Characteristics During The Arcmentioning

confidence: 99%

“…The centralized type of frequency fluctuations is caused by the inverter with a constant switching frequency. Also, the spread type of frequency fluctuations is generated when the inverter with non-constant switching frequency is connected [30] or inverters with constant switching frequency are connected in parallel. Therefore, since there can be various inverters as the load of the PV system, an algorithm that can effectively detect the DC series arc regardless of the type of frequency fluctuations is required.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, moving averages of a specific frequency band are used to develop the DC series arc detection technique that is effective for both centralized and spread type frequency fluctuations. In the proposed technique, ADI for the DC series arc detection is derived using which is the average of the 5 kHz to 40 kHz frequency band that increases characteristically when the DC series arc occurs [30]. The proposed technique uses and which are the moving averages of with different intervals to derive the proposed ADI.…”

Section: Introductionmentioning

confidence: 99%

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DC Series Arc Detection Algorithm Based on Adaptive Moving Average Technique

2021

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This paper proposes a DC series arc detection algorithm in a photovoltaic (PV) system using an adaptive moving average (AMA). The proposed algorithm uses two moving averages of which is the average of 5 kHz to 40 kHz frequency band. One is which is the moving average highly affected by recent . The other is which is the moving average heavily affected by past . There is a little difference between and before arcing because is approximately constant. However, this difference increases when the arc occurs because slowly follows . This difference is used as an arc detection indicator (ADI) in this study. Additionally, AMA is proposed to avoid nuisance tripping in the normal transient state. The proposed method determines the arc occurrence using the relative magnitudes of the two moving averages. Therefore, it is less affected by the shape of the frequency fluctuations caused by the load inverter. Hence, the proposed algorithm is effective in the centralized and spread-type of frequency fluctuations. These results were verified through an arc detection test and nuisance tripping test using arc experimental data and MATLAB.

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Section: Proposed Arc Detection Methods Using Ama a Frequency Characteristics During The Arcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DC Series Arc Detection Algorithm Based on Adaptive Moving Average Technique

2021

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“…The current amplitude was the arc current magnitude before arcing. In the case of inverter loads, the arc current before and after arcing was the inverter's input current [35]. As shown in Figure 1, a DC voltage was supplied to the load.…”

Section: Series DC Arc Characteristicsmentioning

confidence: 99%

Series DC Arc Fault Detection Using Machine Learning Algorithms

Dang

Kim

Kwak³

et al. 2021

IEEE Access

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The wide variety of arc faults induced by different load types renders residential series arc fault detection complicated and challenging. Series dc arc faults could cause fire accidents and adversely affect power systems if not promptly detected. However, in practical power systems, they are difficult to detect because of a low arc current, absence of a zero-crossing period, and various abnormal behavior based on different types of power loads and controllers. In particular, conventional protection fuses may not be activated when they occur. Undetected arc faults could cause false operation of power systems and potentially lead to damage to property and human casualties. Therefore, it is imperative to develop a detection system for series arc faults in DC systems for the reliable and efficient operation of such systems. In this study, several typical loads, especially nonlinear and complex loads such as power electronic loads, were chosen and analyzed, and five time-domain parameters of the current-average value, median value, variance value, RMS value, and distance of the maximum and minimum values-were chosen for arc fault detection. Various machine learning algorithms were used for arc fault detection and their detection accuracies were compared.

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“…The nonlinear load specifications are illustrated in Table 1. The inverters (three-and single-phases) were assembled by using several transistor modules (insulated gate bipolar transistor) [23]. The switching rate was varied when the model predictive control (MPC) was adopted.…”

Section: Arc Current Characteristics and Feature Extractionsmentioning

confidence: 99%

Different Domains Based Machine and Deep Learning Diagnosis for DC Series Arc Failure

Dang

Kwak²,

Choi

2021

IEEE Access

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Series arc faults are becoming more dangerous in DC systems. Without detecting in time and separation correctly, these fault events can cause electrical fires or explosions, creating a massive threat to people's safety and properties. This paper presents an analysis and comparison of DC series arc fault detection using various artificial intelligence (AI) algorithms in DC systems. The combinations of six feature parameters in both time and frequency domains with various AI techniques are recommended to detect DC series arc fault effectively. The performance and effectiveness of different combinations between feature parameters and learning techniques are summarized and discussed. Finally, practical challenges are identified, and suitable combinations of feature parameters and learning techniques are recommended for different operation conditions.

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Frequency-Domain Characteristics of Series DC Arcs in Photovoltaic Systems with Voltage-Source Inverters

Cited by 13 publications

References 21 publications

DC Series Arc Detection Algorithm Based on Adaptive Moving Average Technique

DC Series Arc Detection Algorithm Based on Adaptive Moving Average Technique

Series DC Arc Fault Detection Using Machine Learning Algorithms

Different Domains Based Machine and Deep Learning Diagnosis for DC Series Arc Failure

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