Series arc is prone to cause fire accidents, but its occurrences induced by different load types and connections make the detection challengeable. This paper proposes a series arc fault detection and location algorithm for multi-load circuit topology, especially for branch arc faults and nonlinear power loads. Several typical loads of paralleling connected are considered to measure the current changes caused by the arcing phenomenon at different positions. Different aspects of arc features are extracted by time-domain, frequency-domain, and wavelet packet energy analysis. A feature selection method based on random forest (RF) is adopted to determine the specific feature sets according to the reduction of Gini impurity. The integrated top ten features with a high correlation to the arc were selected for different combinations of loads and are input into the deep neural network (DNN) for calculating and training. Eventually, a comprehensive arc detection model with the function of detection and location determination for different load types is obtained. It proves that the proposed RF-DNN-based arc detection algorithm can identify and protect the series arc faults in multi-load scenarios efficiently and accurately to meet the practical needs.

Series Arc Fault Detection Based on Random Forest and Deep Neural Network

Arc fault detection is important technology to guarantee the safety of power systems and is therefore essential for producing practical power systems for real-world applications. However, fuses and arc fault detection devices (AFDD) struggle to detect series arc faults in DC systems, because the series arc fault induces small current variation between the normal and abnormal conditions. In addition, switching noise from the grid-connected inverter makes detecting arc fault conditions even more difficult. This paper proposes arc fault detection algorithm based on the relative comparison of current variability in terms of frequency spectrum and time series. The operational principle of the proposed algorithm is analyzed to detect the arc fault condition. In addition, the investigation of arc fault impedance using the small-signal modeling can obtain the resonant frequency of arc fault condition at low frequency range. From the impedance model, the frequency analysis range can be designed to avoid the switching noise of inverter. The performance of proposed arc fault detection algorithm is verified with a 3.8 kW grid-connected PV system and arc fault generator.

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DC Series Arc Fault Detection Algorithm for Distributed Energy Resources Using Arc Fault Impedance Modeling

This article presents a new method for effective detection of ac series arc fault (AF) (SAF) and extraction of SAF characteristics in residential buildings, which addresses the challenges with conventional current detection methods in discriminating arcing and nonarcing current due to their similarity. Different from the traditional method, in the proposed method, the differential magnetic flux is coupled to obtain high-frequency signals by putting the live line and the neutral line through the current transformer, which can effectively solve the problem of SAF features disappearing in the trunk-line current. However, similar to the traditional method, the effectiveness of the proposed coupling method could also be compromised when being used in cases with dimmer load and load starting process. This is found to be caused by the presence of high-amplitude pulse phenomenon in the nonarcing signals in these scenarios, which are incorrectly detected as arcing signals in other loads. To address this issue, a short-observation-window singular value decomposition and reconstruction algorithm (SOW-SVDR) is used to enhance the capability to identify SAFs by the coupling method. The proposed method has been implemented and validated according to the UL1699 standard with different types of loads connected to the system and also tested under their starting processes. The experimental results show that the proposed approach is more effective in detecting AFs compared with existing methods.

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A Coupling Method for Identifying Arc Faults Based on Short-Observation-Window SVDR

A systematic review on DC-microgrid protection and grounding techniques: Issues, challenges and future perspective

The circular Rogowski coil with a rectangular section is the most common and basic measuring unit of current. However, in some situations, a noncircular Rogowski coil is more suitable because of the limited space to be embedded in and the demand for decreasing the size of the Rogowski coil. Circular Rogowski coil is usually wound uniformly to guarantee the positional accuracy and anti-interference ability considering the very good symmetry. However, for noncircular Rogowski coil, the turns arrangement is usually designed based on experience due to the lack of theoretic direction. This article first proposed a method of turns arrangement of the noncircular Rogowski coil with a rectangular section, which the positional accuracy and anti-interference ability can be achieved at the same time. By establishing the equivalent relation between mutual inductance and electric potential, the turns density of the noncircular Rogowski coil can be acquired by solving the charge density on its equivalent electrodes. The turns density is proven to be unique. For a given turn number, the turns arrangement of the noncircular Rogowski coil can be obtained by discretizing the turns density. As an example of the method, a square Rogowski coil, which can be used in chip current measurement in Press Pack insulated gate bipolar transistors (IGBTs), is particularly designed and contrasted with the traditional square Rogowski coil with turns equidistantly arranged to verify the great positional accuracy and anti-interference ability. The results of the calculation and experiment show that this method is very effective.

Method of Turns Arrangement of Noncircular Rogowski Coil With Rectangular Section

Series arc faults can cause fire hazards in dc distribution systems. During a series arc fault, the line impedance usually increases rapidly and the arc current includes high-frequency components. To capture the arc-induced high-frequency signals, parallel capacitors are added to the circuit. The characteristics of the currents through these capacitors permit fault detection and localization. This paper determined that a Rogowski coil could be constructed with adequate bandwidth to cover the relevant frequency band of 1 kHz-1 MHz, found that the amplitude of the pulse and the difference in the integrated fast Fourier transform of the current signal are well correlated with the series fault, described experiments to demonstrate that, over the range tested, the two parameters are robust with respect to the pressure of the atmosphere in which the arc forms, the electrode material, the speed at which the electrodes separate to initiate the arc, the switch operation, the load-type and the load change, and demonstrated that within the well-known limits of traveling wave localization, such localization can be used based on the capacitor current pulse information after it has been validated by the spectrum analysis as a fault and not a spurious signal. The proposed approach was demonstrated in two low-voltage dc cable circuits.

Series Arc Fault Detection and Localization in DC Distribution System

In modular multilevel converters (MMCs) of HVDC system, the metallized film capacitors (MFCs) suffer from complex electrical stresses. The aging of MFC is related to the accumulation of self-healing processes, which threatens the safety and reliability of the power system. In this study, a test platform was built for applying DC voltage as well as DC superimposed AC voltage to MFCs. On this platform, the ultrasonic method and ultra high frequency (UHF) method were investigated so as to detect the self-healing discharge signals of operating MFCs. The differences of ultrasonic signals and UHF signals were compared between DC voltage and DC superimposed AC voltage, and the feasibility of MFC monitoring methods based on the detection of ultrasonic and UHF signals was demonstrated.

Research on Self-Healing Discharge Detection Method of Metallized Film Capacitor Based on Ultrasonic and Ultra High Frequency Signals

The modular multilevel converter (MMC) has been rapidly developed and widely applied since it was proposed in 2001, especial in high voltage direct current transmission (HVDC), the MMC has been the primary choice. The reliability of submodule capacitors is a major concern since many practitioners consider it the most vulnerable component in power electronic devices. Therefore, it’s meaningful to put forward an on-line capacitor condition monitoring method to keep its safety operation. This paper presents an on-line monitoring method for capacitance and ESR values of submodule capacitors. In this method, the voltage and current equations are constructed by using MMCs operation characteristics and capacitor equivalent model to obtain the state parameters. Among them, the Cotes rule is used to simplify the calculation and Kalman filter is used to reduce the error. Simulation results verify the feasibility of this method. It shows that the capacitance calculation error is less than 1%, and the ESR calculation error is less than 4.2%.

An On-line Capacitance and ESR Monitoring Method of Submodule Capacitors for Nearest Level Modulation Based Modular Multilevel Converters

Metallized film capacitors (MFCs) are reliable because of the self-healing feature and are widely used in the sub-module of the modular multilevel converter (MMC-SM). To reflect the practical working condition of MMC-SM, the self-healing characteristics of MFC in MMC-SM under DC and AC superimposed voltage with harmonics were studied in this paper. A film level experimental platform was built, in which the specimens were double-layer metallized film elements. A parallel superimposing circuit was adopted, where the DC voltage and the AC voltage containing harmonics were applied with independent sources. The self-healing breakdown voltages were recorded and analyzed by the Weibull distribution. It was found that the harmonic components have a significant effect on the self-healing characteristics of MFC. As the harmonic component of the applied voltage increases, the self-healing breakdown voltage decreases. Besides, the shapes of self-healing areas are distinct in different regions of double-layer films, which are highly related to the local film square resistances of the breakdown spots.

Linzi Zheng

Papers

Series Arc Fault Detection and Localization in DC Distribution System

Research on Self-Healing Discharge Detection Method of Metallized Film Capacitor Based on Ultrasonic and Ultra High Frequency Signals

An On-line Capacitance and ESR Monitoring Method of Submodule Capacitors for Nearest Level Modulation Based Modular Multilevel Converters

Self-healing Characteristics of Metallized Film Capacitor under DC and AC Superimposed Voltage with Harmonics