A Series Arc Fault Diagnosis Method in DC Distribution Systems Based on Multiscale Features and Random Forests

Yin, Zhendong; Wang, Li; Yang, Shanshui; Gao, Yang

doi:10.1109/jsen.2022.3228551

Cited by 9 publications

(10 citation statements)

References 38 publications

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“…DLA refers to the residual angle of the phase angle between the current vector and the voltage vector flowing through the dielectric under an alternating electric field [26], that is, 𝛿 = 90 • − arg(I ∕U ). From Figure 3, the zero-sequence current flowing through the non-faulty line i can be expressed as shown in Equation (12).…”

Section: Analysis Of the Impact Of Neutral Grounding Methods On Diele...mentioning

confidence: 99%

“…In Equation (12), ω represents the angular frequency of the voltage and current, and the DLA of the non-faulty line i is given by Equation (13).…”

Section: Analysis Of the Impact Of Neutral Grounding Methods On Diele...mentioning

confidence: 99%

“…In recent years, new methods such as extreme learning machines (ELM) [11], random forests (RF) [12], and fuzzy logic [13] have been widely applied in the field of fault diagnosis in distribution networks. However, these methods still require manual feature extraction, which is subjective, complex, and dependent on the research experience in the relevant field.…”

Section: Introductionmentioning

confidence: 99%

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Fault line selection algorithm for distribution networks based on AdapGL‐GIN network

Lu,

Hou,

2023

IET Generation Trans & Dist

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When a grounding fault occurs in the distribution network with distributed generation, the network topology becomes intricate, making it challenging to extract fault characteristics, resulting in a decrease in the precision of fault discrimination. To address this issue, a graph isomorphism network (GIN) approach based on the parameterized adaptive graph learning (AdapGL) module is proposed, transforming the distribution network fault selection problem into a graph classification task. First, the adjacency matrix of the distribution network's topology graph is initialized. This matrix, combined with feature vectors from the robust local mean decomposition energy entropy and transient dielectric loss angle of line, will be input into the GIN. Then, the AdapGL module is integrated into the GIN, dynamically learning and updating the one‐way relationships between actual network nodes to complete the graph classification task. Finally, a radial distribution network model (RDNM) and an improved IEEE 34 nodes model are established, and the fault selection results of the AdapGL‐GIN method are compared with those of other methods. The results indicate that the proposed method achieves higher accuracy than other methods, demonstrating significant practical importance in engineering applications.

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Section: Analysis Of the Impact Of Neutral Grounding Methods On Diele...mentioning

confidence: 99%

“…In Equation (12), ω represents the angular frequency of the voltage and current, and the DLA of the non-faulty line i is given by Equation (13).…”

Section: Analysis Of the Impact Of Neutral Grounding Methods On Diele...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fault line selection algorithm for distribution networks based on AdapGL‐GIN network

Lu,

Hou,

2023

IET Generation Trans & Dist

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“…Many efforts have been done to measure the current variation with time in the range from DC to kHz to detect the slow decrease of the arc current by means of a current shunt [20], a tunneling effect magnetic sensor [22], a lowfrequency current transformer [24], Hall effect probes [25] or commercial current transformers that can reach the range of MHz, [21], [26]. However, these devices do not comply with the constraints of sensitivity, or weight and size imposed by the aeronautic sector.…”

Section: Introductionmentioning

confidence: 99%

“…However, these devices do not comply with the constraints of sensitivity, or weight and size imposed by the aeronautic sector. Other types of sensors require a power supply [22], [24], [25], [27], [28] which could be a problem for this industrial application and should be avoided even when they have good sensitivity. Other authors, [29], use a Rogowski coil but their solution is hardly valid for aircraft systems because requires a shunt capacitor with the load on each monitored circuit and the sensitivity is well below than the proposed sensor in this paper.…”

Section: Introductionmentioning

confidence: 99%

High-Frequency Current Transformer Design to Detect Serial Arcs in More Electric Aircraft

Robles,

Martínez-Tarifa,

Barroso-de-María

et al. 2024

IEEE Trans. Transp. Electrific.

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Serial arcs in DC circuits is a well-known hazard in the field of photovoltaic systems and it is specially sensitive and critical in aerospace and aeronautic equipment. Unlike parallel arcs, the detection of this particular ionisation phenomenon cannot be done with the current available protection devices so the industry is interested in finding a solution. The first step is to choose a sensor capable of reading the physical consequences of an active arc and the appearance of high frequency current pulses derived from the arc has been proved to be a reliable method of detection. Inductive sensors are allegedly one of the most promising detectors in terms of sensitivity, cost and adaptability to the circuit wiring. For aircraft applications, size and weight are additional constraints which limit the use of certain heavy inductive sensors so an air-cored inductive sensor was proposed by the authors in a previous work. This paper is a step forward in the design of a solution using small and weightless toroidal ferrites to detect serial arcs. The geometric parameters, number of turns, cut-off frequency, bandwidth and sensitivity connected to a 50 Ω impedance of the proposed sensor are parameterised using a model based on a transformer equivalent circuit. The model is successfully validated with experimental measurements and a candidate is chosen according to the constraints imposed by the application in airborne electric systems. Finally, a complete set of measurements of arcs are conducted to test its performance compared with other two sensors. The aim is to quantify the sensitivity of the high frequency current pulses created by the arc activity when compared to the background noise.

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Diagnosis of DC series arc fault based on multiple wavelet transform and optimal fractional wavelet energy entropy

Yin,

Wang,

Qiu

et al. 2024

Review of Scientific Instruments

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In the DC distribution system, the propagation of arc noise can interfere with normal lines, and accurate and timely diagnosis of the location of series arc fault (SAF) is a challenging problem. In this article, a SAF diagnosis method is proposed from a system perspective, which can accurately identify the fault line. First, multiple wavelet transform is used to decompose the currents of different lines, and the fractional wavelet energy entropy is extracted to construct the feature vector. Then, random forest is employed to analyze the importance of features and to select the optimal features. Finally, a kernel extreme learning machine can fuse the features and output the diagnosis results. The offline experimental results indicate that the proposed method has a diagnosis accuracy of 99.82%, which is higher than those of nine comparison methods, and the effectiveness and advancement of the proposed method are verified. The online experimental results show that the proposed method can diagnose SAF within 110 ms, and the diagnosis speed is able to satisfy the requirements of UL1699B. Moreover, under transient conditions, the proposed method can effectively avoid false alarms and maintain stability.

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A Series Arc Fault Diagnosis Method in DC Distribution Systems Based on Multiscale Features and Random Forests

Cited by 9 publications

References 38 publications

Fault line selection algorithm for distribution networks based on AdapGL‐GIN network

Fault line selection algorithm for distribution networks based on AdapGL‐GIN network

High-Frequency Current Transformer Design to Detect Serial Arcs in More Electric Aircraft

Diagnosis of DC series arc fault based on multiple wavelet transform and optimal fractional wavelet energy entropy

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