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Academic literature on the topic 'Series DC Arc Faults'

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Published: 8 June 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Journal articles on the topic "Series DC Arc Faults":

1

Wang, Lina, Ehtisham Lodhi, Pu Yang, Hongcheng Qiu, Waheed Ur Rehman, Zeeshan Lodhi, Tariku Sinshaw Tamir, and M. Adil Khan. "Adaptive Local Mean Decomposition and Multiscale-Fuzzy Entropy-Based Algorithms for the Detection of DC Series Arc Faults in PV Systems." Energies 15, no. 10 (May 15, 2022): 3608. http://dx.doi.org/10.3390/en15103608.

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DC series arc fault detection is essential for improving the productivity of photovoltaic (PV) stations. The DC series arc fault also poses severe fire hazards to the solar equipment and surrounding building. DC series arc faults must be detected early to provide reliable and safe power delivery while preventing fire hazards. However, it is challenging to detect DC series arc faults using conventional overcurrent and current differential methods because these faults produce only minor current variations. Furthermore, it is hard to define their characteristics for detection due to the randomness of DC arc faults and other arc-like transients. This paper focuses on investigating a novel method to extract arc characteristics for reliably detecting DC series arc faults in PV systems. This methodology first uses an adaptive local mean decomposition (ALMD) algorithm to decompose the current samples into production functions (PFs) representing information from different frequency bands, then selects the PFs that best characterize the arc fault, and then calculates its multiscale fuzzy entropies (MFEs). Eventually, MFE values are inputted to the trained SVM algorithm to identify the series arc fault accurately. Furthermore, the proposed technique is compared to the logistic regression algorithm and naive Bayes algorithm in terms of several metrics assessing algorithms’ validity for detecting arc faults in PV systems. Arc fault data acquired from a PV arc-generating experiment platform are utilized to authenticate the effectiveness and feasibility of the proposed method. The experimental results indicated that the proposed technique could efficiently classify the arc fault data and normal data and detect the DC series arc faults in less than 1 ms with an accuracy rate of 98.75%.
2

Omran, Alaa Hamza, Dalila Mat Said, Siti Maherah Hussin, and Sadiq H. Abdulhussain. "Photovoltaic system DC series arc fault: a case study." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 2 (November 1, 2022): 625. http://dx.doi.org/10.11591/ijeecs.v28.i2.pp625-635.

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<p>Photovoltaic (PV) systems are becoming increasingly popular; however, arc faults on the direct current (DC) side are becoming more widespread as a result of the effects of aging as well as the trend toward higher DC voltage levels, posing severe risk to human safety and system stability. The parallel arc faults present higher level of current as compared with the series arc faults, making it more difficult to spot the series arc. In this paper and for the aim of condition monitoring, the features of a DC series arc fault are analyzed by analysing the arc features, performing model’s simulation in PSCAD, and carrying out experimental studies. Various arc models are simulated and investigated; for low current arcs, the heuristic model is used where a set of parameters established. Moreover, the heuristic model’s simulated arc has been shown to be compatible with the experimental data. The features of arc noise in the electrode separation region and steady-arcing states with varied gap widths are investigated. It has been discovered that after an arc fault occurs, arc noise increases, notably in the frequency range below 50 kHz; where this property is useful for detecting DC series arc faults. Besides that, variations in air gap width are more sensitive to frequencies under 5 kHz.</p>
3

Dang, Hoang-Long, Sangshin Kwak, and Seungdeog Choi. "Advanced Learning Technique Based on Feature Differences of Moving Intervals for Detecting DC Series Arc Failures." Machines 12, no. 3 (February 28, 2024): 167. http://dx.doi.org/10.3390/machines12030167.

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DC microgrids are vital for integrating renewable energy sources into the grid, but they face the threat of DC arc faults, which can lead to malfunctions and fire hazards. Therefore, ensuring the secure and efficient operation of DC systems necessitates a comprehensive understanding of the characteristics of DC arc faults and the implementation of a reliable arc fault detection technique. Existing arc-fault detection methods often rely on time–frequency domain features and machine learning algorithms. In this study, we propose an advanced detection technique that utilizes a novel approach based on feature differences between moving intervals and advanced learning techniques (ALTs). The proposed method employs a unique approach by utilizing a time signal derived from power supply-side signals as a reference input. To operationalize the proposed method, a meticulous feature extraction process is employed on each dataset. Notably, the difference between features within distinct moving intervals is calculated, forming a set of differentials that encapsulate critical information about the evolving arc-fault conditions. These differentials are then channeled as inputs for advanced learning techniques, enhancing the model’s ability to discern intricate patterns indicative of DC arc faults. The results demonstrate the effectiveness and consistency of our approach across various scenarios, validating its potential to improve fault detection in DC systems.
4

Navalpakkam Ananthan, Sundaravaradan, Xianyong Feng, Charles Penney, Angelo Gattozzi, Robert Hebner, and Surya Santoso. "Voltage Differential Protection for Series Arc Fault Detection in Low-Voltage DC Systems." Inventions 6, no. 1 (December 31, 2020): 5. http://dx.doi.org/10.3390/inventions6010005.

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Series arc faults are challenging to detect in low-voltage dc (LVDC) distribution systems because, unlike other fault types, series arc faults result in only small changes in the current and voltage waveforms. Though there have been several approaches proposed to detect series arc faults, each approach has its requirements and limitations. A step change in the current and voltage waveforms at the arc inception is one of the characteristic signatures of these faults that can be extracted without requiring one to sample the waveforms at a very high frequency. This characteristic feature is utilized to present a novel approach based on voltage differential protection to detect series arc faults in LVDC systems. The proposed method is demonstrated using an embedded controller and experimental data that emulate a hardware-in-the-loop (HIL) test environment. The successful detection of series arc faults on two sets of series arc fault experimental data validated the approach. The results presented also illustrate the computational feasibility in implementing the approach in a real-time environment using an embedded controller. In addition, the paper discusses the robustness of the approach to load changes and loss of time synchronization between measurements at the two terminals of the line.
5

Guo, Feng, Shenghong Yao, Neng Zhang, and Yuchao He. "Detection and Location of Series DC Arc Fault in Photovoltaic System Based on VMD." Journal of Physics: Conference Series 2488, no. 1 (May 1, 2023): 012028. http://dx.doi.org/10.1088/1742-6596/2488/1/012028.

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Abstract A large number of connecting wires and electrical contact points exist in large photovoltaic power generation systems, which can easily cause the occurrence of an arc fault. Failure to detect and isolate faults in time will result in serious fire hazards. In the document, a new approach to fault arc monitoring and positioning built on variational mode decomposition is presented for a variety of series DC arc faults in photovoltaic systems. The time and frequency-domain properties of signals collected under different working conditions are obtained by the use of variational mode decomposition (VMD), and the detection of faults by matching changes in features under the same sub-mode. Finally, a 400KW photovoltaic system model is built by Matlab/Simulink for simulation verification. The outcome indicated that the technique is capable of accurately detecting and locating series DC arc faults.
6

Anggriawan, Dimas Okky, Epyk Sunarno, Epyk Sunarno, Eka Prasetyono, Eka Prasetyono, Suhariningsih Suhariningsih, Suhariningsih Suhariningsih, Muhammad Fauzi, and Muhammad Fauzi. "Implementation of Fast Fourier Transform and Artificial Neural Network in Series Arc Fault Identification and Protection System on DC Bus Microgrid." JTT (Jurnal Teknologi Terpadu) 11, no. 2 (October 28, 2023): 303–10. http://dx.doi.org/10.32487/jtt.v11i2.1869.

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A microgrid is a cluster of electrical sources and loads that are interconnected and synchronized. Microgrid operation is typically divided into two modes, isolated or connected to the grid with a single or standalone control system. In this context, it can enhance the reliability and quality of electricity supply for connected customers. When using a microgrid system, it is important to consider the risk of series arc faults. Series arc faults are sudden bursts of flames resulting from ionization of gas between two electrode gaps. These faults can occur due to manufacturing defects, installation Errors, aging, or corrosion on conductor rods, leading to imperfect connections. Detecting series arc faults in DC microgrid system operations can be challenging using standard protective devices. Failure in the protection system can pose risks of fire, electrical shock hazards, and power loss in the DC microgrid.Therefore, a device has been designed to detect series arc faults by utilizing the fast Fourier transform method and artificial neural network, which function to analyze DC signal and make decisions when faults occur by examining the average sum of current frequency values during normal and fault conditions. In this study, the average sum of current frequency values during normal conditions was found to range from 0.35437 to 0.36906 A, while during fault conditions, it ranged from 0.21450 to 0.22793 A, with an average protection identification time of 1087 ms and an ANN output accuracy of 99.98%.
7

Dang, Hoang-Long, Sangshin Kwak, and Seungdeog Choi. "DC Series Arc Fault Diagnosis Scheme Based on Hybrid Time and Frequency Features Using Artificial Learning Models." Machines 12, no. 2 (February 1, 2024): 102. http://dx.doi.org/10.3390/machines12020102.

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DC series arc faults pose a significant threat to the reliability of DC systems, particularly in DC generation units where aging components and high voltage levels contribute to their occurrence. Recognizing the severity of this issue, this study aimed to enhance DC arc fault detection by proposing an advanced recognition procedure. The methodology involves a sophisticated combination of current filtering using the Three-Sigma Rule in the time domain and the removal of switching noise in the frequency domain. To further enhance the diagnostic capabilities, the proposed method utilizes time and frequency signals generated from power supply-side signals as a reference input. The time–frequency features extracted from the filtered signals are then combined with artificial learning models. This fusion of advanced signal processing and machine learning techniques aims to capitalize on the strengths of both domains, providing a more comprehensive and effective means of detecting arc faults. The results of this detection process validate the effectiveness and consistency of the proposed DC arc failure identification schematic. This research contributes to the advancement of fault detection methodologies in DC systems, particularly by addressing the challenges associated with distinguishing arc-related distortions, ultimately enhancing the safety and dependability of DC electrical systems.
8

Dang, Hoang-Long, Sangshin Kwak, and Seungdeog Choi. "Empirical Filtering-Based Artificial Intelligence Learning Diagnosis of Series DC Arc Faults in Time Domains." Machines 11, no. 10 (October 17, 2023): 968. http://dx.doi.org/10.3390/machines11100968.

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Direct current (DC) networks play a pivotal role in the growing integration of renewable energy sources. However, the occurrence of DC arc faults can introduce disruptions and pose fire hazards within these networks. In order to ensure both safety and optimal functionality, it becomes imperative to comprehend the characteristics of DC arc faults and implement a dependable detection system. This paper introduces an innovative arc fault detection algorithm that leverages current filtering based on the empirical rule in conjunction with intelligent machine learning techniques. The core of this approach involves the sampling and subsequent filtration of current using the empirical rule. This filtering process effectively amplifies the distinctions between normal and arcing states, thereby enhancing the overall performance of the intelligent learning techniques integrated into the system. Furthermore, this proposed diagnosis scheme requires only the signal from the current sensor, which reduces the complexity of the diagnosis scheme. The results obtained from the detection process serve to affirm the effectiveness and reliability of the proposed DC arc fault diagnosis scheme.
9

Dang, Hoang-Long, Sangshin Kwak, and Seungdeog Choi. "Various Feature-Based Series Direct Current Arc Fault Detection Methods using Intelligence Learning Models and Diverse Domain Exclusion Techniques." Machines 12, no. 4 (April 3, 2024): 235. http://dx.doi.org/10.3390/machines12040235.

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The expansion of DC electrical distribution systems necessitates advancements in detecting and mitigating DC arc events, a significant contributor to fire accidents in low-voltage DC distribution systems. Detecting DC arc faults poses considerable challenges, making them a major safety concern in DC power lines. Conventional approaches mainly rely on arc current, which can vary during normal operation, potentially leading to false alarms. Moreover, these methods often require manual adjustment of detection thresholds for different systems, introducing the risk of malfunction. This study proposes an advanced arc fault recognition procedure that extracts and utilizes various key features for the DC arc detection. This work investigated new various features, which are the square average, the average, the median, the rms, the peak-to-peak, and the variance values, to find out which one can be the most effective features to detect the DC arc failure. The results of this detection process show good evidence for the effectiveness and reliability of the proposed malfunction detecting plan.
10

Telford, Rory David, Stuart Galloway, Bruce Stephen, and Ian Elders. "Diagnosis of Series DC Arc Faults—A Machine Learning Approach." IEEE Transactions on Industrial Informatics 13, no. 4 (August 2017): 1598–609. http://dx.doi.org/10.1109/tii.2016.2633335.

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Journal articles

Dissertations / Theses on the topic "Series DC Arc Faults":

1

Weerasekara, Madhawa. "DC arc faults in photovoltaic systems." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/130681/1/Madhawa_Weerasekara_Thesis.pdf.

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This research presents a detailed study of DC Arc faults in Photo-voltaic systems. A unique DC arc model is proposed and the use of Wavelet transforms and Mathematical Morphology to successfully detect DC arcs in a PV system is investigated. The proposed DC arc model is applicable for arc length changes caused by moving conductive parts. The study also presents a test setup to capture arc waveform for analysis.
2

Niassati, Nima. "Modeling of Series Arc Faults in a DC Power Network." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461358127.

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3

Vidales, Luna Benjamin. "Architecture de convertisseur intégrant une détection de défauts d'arcs électriques appliquée au sources d'énergie continues d'origine photovoltaïques." Electronic Thesis or Diss., Université de Lorraine, 2021. http://www.theses.fr/2021LORR0040.

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Détection de défaut d'arcs intégrée dans un convertisseur intelligent contrôlé par FPGA pour les panneaux photovoltaïques. La mise au point de convertisseur intelligents intégrant des dispositifs de protection est une thématique que cherche à développer l'Institut Technologique de Morelia (Mexique) avec laquelle nous collaborons sur ce projet. L'objectif plus spécifique de ce travail repose sur la détection de défauts d'arc électrique en se basant sur le contrôle intelligent des onduleurs utilisés dans la gestion de l'énergie produite par des panneaux photovoltaïques. Depuis plusieurs années, le développement croissant des panneaux solaires photovoltaïques comme source d’énergie s’est imposé et la sécurité de ces dispositifs liée à la détection de défauts d’arcs électriques est devenu un enjeu majeur. L'approche que nous proposons dans ce travail est le développement d'une stratégie novatrice pour la surveillance et la prédiction de défaillance du réseau électrique constitué de panneaux solaires en présence de défauts d’arcs. Actuellement, la majorité des systèmes de détection comprennent des modules détecteurs disposés dans le circuit électrique à protéger dont la robustesse est loin d'être optimale. L'approche que nous proposons consiste à développer un dispositif de surveillance et de détection de défaut directement intégré dans l'onduleur intelligent. Le contrôle optimal de l'onduleur intelligent assurera une détection fiable de défaut d'arc sans déclenchement intempestif. Le dispositif comprendra également un système de coupure. La méthode de détection que nous privilégions sera basée sur l'analyse du courant et de la tension de ligne. Les algorithmes seront basés sur une analyse temps/fréquence des signatures courant et de tension suivie par une logique pertinente de décision de telle manière à minimiser le taux de fausses détections.Le noyau du convertisseur intelligent est constitué par un FPGA. Le parallélisme des traitements de données assurera le respect des contraintes temps réel. Dans le cadre du projet de thèse, la mise en œuvre, le test des algorithmes de détection et l’implémentation optimale afin de respecter les contraintes temps réel dans le FPGA sera mené dans le cadre d’une cotutelle de thèse entre l’institut technologique de Morelia et l’Université de Lorraine
In this research work, the development of a multilevel inverter for PV applications is presented. The PV inverter, has two stages one DC/DC converter and one DC/AC inverter, and is capable of generating an AC multilevel output of nine levels, it's a transformerless inverter and uses a reduced number of components compared to other topologies. The conception of a novel DC/DC converter is capable of generating two isolated DC voltage levels needed to feed the DC/AC stage. This DC/DC stage is developed in two variants, buck and boost, the _rst to perform the reduction of voltage when the DC bus is too high, and second to increase the voltage when the DC bus is too low to perform interconnection with the grid through the DC/AC inverter. This is achieved thanks to the parallel functioning of the developed topology, which make use of moderated duty cycles, that reduces the stress in the passive and switching components, reducing potential losses. The validation of the PV inverter is performed in simulation and experimental scenarios. In the other hand, the response of the inverter facing an arc fault in the DC bus is studied by performing a series of tests where the fault is generated in strategic points of the DC side, this is possible thanks to the design and construction of an arc fault generator based in the specifications of the UL1699B norm. During the tests is observed that with the apparition of an arc fault, there is a lost in the half-wave symmetry of the AC multilevel output voltage waveform, generating even harmonics which aren't present during normal operation, only when an arc fault is present in the DC system. The monitoring of even harmonics set the direction for developing the detection technique. Since the magnitude of even harmonics in the inverter is very low, the total even harmonic distortion is employed as a base for the detection technique presented in this thesis. The effectiveness of this method is verified with a series of tests performed with different loads
4

Bauer, Eric Charles. "Series Dc Arc Characterization, Prevention & Detection inAircraft Systems." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu154411083086475.

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5

Moosavi, Anchehpoli Seyed Saeid. "Analysis and diagnosis of faults in the PMSM drivetrains for series hybrid electrical vehicles (SHEVs)." Thesis, Belfort-Montbéliard, 2013. http://www.theses.fr/2013BELF0224/document.

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L'intérêt pour les véhicules électriques ne cesse de croitre au sein de la société contemporaine compte tenu de ses nombreuses interrogations sur l’environnement et la dépendance énergétique. Dans ce travail de thèse, nous essayons d’améliorer l’acceptabtabilité sociétale du véhicule électrique en essayant de faire avancer la recherche sur le diagnostique des défauts d’une chaine de traction électrique. Les résultats escomptés devraient permettre à terme d’améliorer la fiabilité et la durabilité de ces systèmes.Nous commençons par une revue des problèmes des défauts déjà apparus dans les véhicules hybrides séries qui disposent de l’architecture la plus proche du véhicule électrique. Une étude approfondie sur le diagnostic des défauts d’un convertisseur de puissance statique (AC-DC) ainsi que celle du moteur synchrone à aimants permanents est menée. Quatre types de défauts majeurs ont été répertoriés concernant le moteur (court-circuit au stator, démagnétisation, excentricité du rotor et défaut des roulements). Au niveau du convertisseur, nous avons considéré le défaut d’ouverture des interrupteurs. Afin d’être dans les mêmes conditions d’utilisation réelle, nous avons effectué des tests expérimentaux à vitesse et charge variables. Ce travail est basé aussi bien sur l’expérimentation que sur la modélisation. Comme par exemple, la méthode des éléments finis pour l’étude de la démagnétisation de la machine. De même, l’essai en court-circuit du stator du moteur en présence d’un contrôle vectoriel.Afin de réaliser un diagnostic en ligne des défauts, nous avons développé un modèle basé sur les réseaux de neurones. L’apprentissage de ce réseau de neurone a été effectué sur la base des résultats expérimentaux et de simulations, que nous avons réalisées. Le réseau de neurones est capable d'assimiler beaucoup de données. Ceci nous permet de classifier les défauts en termes de sévérité et de les localiser. Il permet ainsi d'évaluer le degré de performance de la chaine de traction électrique en ligne en présence des défauts et nous renseigner ainsi sur l'état de santé du système. Ces résultats devraient aboutir à l’élaboration d’une stratégie de contrôle tolérant aux défauts auto-reconfigurable pour prendre en compte les modes dégradés permettant une continuité de service du véhicule ce qui améliorera sa disponibilité
The interest in the electric vehicles rose recently due both to environmental questions and to energetic dependence of the contemporary society. Accordingly, it is necessary to study and implement in these vehicle fault diagnosis systems which enable them to be more reliable and safe enhancing its sustainability. In this work after a review on problem of faults in the drivetrain of series hybrid electric vehicles (SHEV), a deep investigation on fault diagnosis of AC-DC power converter and permanent magnet synchronous motor (PMSM) have been done as two important parts of traction chains in SHEVs. In other major part of this work, four types of faults (stator winding inter turn short circuit, demagnetization, eccentricity ant bearing faults) of a PMSM have been studied. Inter turn short circuit of stator winding of PMSM in different speeds and loads has been considered to identify fault feature in all operation aspects, as it is expected by electric vehicle application. Experimental results aiming short circuits, bearing and eccentricity fault detection has been presented. Analytical and finite element method (FEM) aiming demagnetization fault investigation has been developed. The AC-DC converter switches are generally exposed to the possibility of outbreak open phase faults because of troubles of the switching devices. This work proposes a robust and efficient identification method for data acquisition selection aiming fault analysis and detection. Two new patterns under AC-DC converter failure are identified and presented. To achieve this goal, four different level of switches fault are considered on the basis of both simulation and experimental results. For accuracy needs of the identified pattern for SHEV application, several parameters have been considered namely: capacitor size changes, load and speed variations. On the basis of the developed fault sensitive models above, an ANN based fault detection, diagnosis strategy and the related algorithm have been developed to show the way of using the identified patterns in the supervision and the diagnosis of the PMSM drivetrain of SHEVs. ANN method have been used to develop three diagnosis based models for : the vector controlled PMSM under inter turn short circuit, the AC/DC power converter under an open phase fault and also the PMSM under unbalanced voltage caused by open phase DC/AC inverter. These models allow supervising the main components of the PMSM drivetrains used to propel the SHEV. The ANN advantages of ability to include a lot of data mad possible to classify the faults in terms of their type and severity. This allows estimating the performance degree of that drivetrains during faulty conditions through the parameter state of health (SOH). The latter can be used in a global control strategy of PMSM control in degraded mode in which the control is auto-adjusted when a defect occurs on the system. The goal is to ensure a continuity of service of the SHEV in faulty conditions to improve its reliability
6

Handy, Peter James. "The characterisation, modelling and detection of series arc faults in aircraft electrical power distribution systems featuring solid state power controllers (SSPCs)." Thesis, University of Warwick, 2016. http://wrap.warwick.ac.uk/80134/.

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Electrical power demand in aircraft has grown significantly over the last century, and this trend continues with the More Electric Aircraft (MEA) and All Electric Aircraft (AEA) concepts. Higher voltages such as 270VDC are required to deliver additional power to loads and to optimise aircraft mass. Increased voltages inflict more stress on the Electrical Wiring Interconnect System (EWIS) and increase the impact of series arc faults caused by wiring defects. Solid State Power Controllers (SSPCs) are used to provide fast protection in high voltage distribution systems. The aim of this work is the characterisation, modelling, simulation and detection of series arc faults in 28VDC and 270VDC electrical power distribution systems featuring SSPCs. The majority of passive detection schemes in the literature were designed based on empirical data rather than well characterised electric arc parameters, and thus nuisance trips are unavoidable. To address this series arc faults in 28VDC and 270VDC solid state power distribution systems were characterised using the SAE5692 "Loose terminal" method [8], and it was found that 270VDC arc faults cause a minimal ~5.6% reduction in loop current and load voltage compared with ~54% in 28VDC systems. SSPC output voltage transients caused by series arcs were found to be limited by the presence of SSPC snubbers. Increasing the system loop inductance was found to improve series arc stability resulting in fewer arc quench events. Increasing the capacitive load reduces arc stability and causes arcs to quench more readily thus simplifying detection. These results were later used to experimentally validate a novel series arc fault SPICE model based on the static Nottingham V-I model [9] and wider solid state electrical system model. The arc current and SSPC output voltage results were also used to create a prototype passive series arc fault detection system, which has been demonstrated to SAE5692 under laboratory conditions [8]. A novel multilayer PCB current sensor was developed and experimentally validated for this prototype. To further reduce nuisance trips an innovative active arc fault perturbation scheme was simulated and experimentally demonstrated using SSPC modulation to stimulate and detect arc quench. Another novel complementary series arc fault prevention / confirmation scheme was simulated and experimentally validated using SSPC leakage currents. To minimise nuisance trips due to manufacturing and installation errors a unique Built-In Test (BIT) scheme was also developed and experimentally validated using the SSPC to create artificial current and voltage stimuli.
7

Martel, Jean-Mary [Verfasser], Frank [Akademischer Betreuer] Berger, Peter [Gutachter] Schegner, and Michael [Gutachter] Anheuser. "Series arc faults in low-voltage AC electrical installations / Jean-Mary Martel ; Gutachter: Peter Schegner, Michael Anheuser ; Akademischer Betreuer: Frank Berger." Ilmenau : Universitätsbibliothek Ilmenau, 2018. http://d-nb.info/1152096966/34.

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8

Su, Jyun-Ming, and 蘇俊銘. "Detection of Series Arc Fault on DC Power System Circuit." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/c9hcjq.

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碩士
國立臺灣科技大學
電機工程系
104
More renewable energy power generation is installed and the proportion of DC power to power system is increasing. DC arc fault may present in DC power system. The arcing accompanied with high thermal and spark.It is easy to cause serious fires. Several overseas example of fires caused by DC arc fault at PV system were reported. USA and Taiwan have developed regulations that PV system shall include a DC arc fault circuit protection device.In this thesis, it implements an experiment platform to collect line current data of normal operation and series arc fault. Experiments include resistive load and inverter operating at different conditions. Then, the technique of digital signal processing is used to obtain the frequency-domain feature of experiment data. This study developes two detecting method. The first is spectrum energy detecting method, the second is artificial neural network(ANN) method. Detecting methods are implemented by using FPGA preliminarily. Two detecting methods are applied to experiment data and the results are compared to commercial PV AFD. In this thesis, the proposed methods can recognize normal operation and series arc fault effectively. If the detecting methods in this thesis can be practically used in the future, it could reduce the incidence of fire caused by arc fault.
9

Lai, De-Shin, and 賴德欣. "Design of a DC Series Arc Fault Detector for Photovoltaic Systems Protection." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/hy2889.

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碩士
國立臺灣科技大學
電機工程系
105
To deal with problems such as global energy shortage and climate changes caused by the greenhouse effect, distributed generation techniques on renewable power energies are under development by many countries. One of the most important renewable power energies is the PV systems. However, there are some safety issues about PV systems which need to be addressed. PV systems contain DC power systems. DC arc fault may present in DC power systems. In a DC series arc fault, the post-fault current is even smaller so this kind of fault couldn’t be isolated by conventional overcurrent protection devices. The arcing accompanied with high thermal. If arcing last long, it may cause a serious fire event. Thus, many countries have developed code about arcing fault protection. In this thesis, the current noise between 48.83 kHz ~ 93.99 kHz which contains a characteristic that post-fault magnitude of noise current is bigger than pre-fault. The current noise is converted by Fast Fourier Transform (FFT). Analyze the result of FFT and propose a series arc fault detecting method. According to this detecting method, a DC series arc fault detector for PV systems is implemented and an experiment platform is constructed by grid connected PV systems. The grid connected PV systems are constructed by Ploy Silicon panels connected serially. The proposed detector is tested in different kinds of condition and compared with two commercial detectors in experiment platform. According to the test result, the proposed detector and two commercial detectors can detect series arc fault with 100% accuracy but one of the commercial detectors have false actions when system current changed drastically by shading effect.
10

Chen, You-kun, and 陳又琨. "Application of Wavelet Transform to Series Arc Fault Detection for DC Power Systems." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/18130199412723981509.

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Abstract:
碩士
國立臺灣科技大學
電機工程系
102
Among any of energized conductors may cause arcing fault. No matter AC or DC systems. The arcing accompanied with the phenomenon of strong light, and high thermal. It is easy to cause serious fires. From viewpoint of the electrical, arc is like an unpredictable non-linear impedance. It lead to the difficulty of detection when the series arcing fault appears in the systems. DC arc is rarely discussed in the literature for the past years. However, the more renewable energy is generated, transmited, and consumed in the forming DC power systems, the more DC arc accidents presents. The major purpose of this study was to discuss the characteristics of DC series arc. In addition, there are three methods are proposed including the maximum differential, the bandpass filtering and the wavelet transform to detect the DC series arc. Finally, varifies switch arc and DC series arc have been generated by simulator to test the propose methods and commercial arc fault detector (AFD). The results show that both the propose methods and AFDs all could successfully detect DC series arc fault. However, the wavelet transform method presents the superiority over the others. It neither misoperation nor been interfere with the arc factors. It is concluded that this method is worth to promote and implement on the future product for DC arc protection.

Book chapters on the topic "Series DC Arc Faults":

1

Li, Zhihua, Zhiqun Ye, Chunhua Wu, and Wenxin Xu. "Modeling and Simulation Study of Photovoltaic DC Arc Faults." In Communications in Computer and Information Science, 137–46. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6364-0_14.

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2

Mahto, Rakeshkumar, and Reshma John. "Modeling of Photovoltaic Module." In Solar Cells [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.97082.

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Abstract:
A Photovoltaic (PV) cell is a device that converts sunlight or incident light into direct current (DC) based electricity. Among other forms of renewable energy, PV-based power sources are considered a cleaner form of energy generation. Due to lower prices and increased efficiency, they have become much more popular than any other renewable energy source. In a PV module, PV cells are connected in a series and parallel configuration, depending on the voltage and current rating, respectively. Hence, PV modules tend to have a fixed topology. However, in the case of partial shading, mismatching or failure of a single PV cell can lead to many anomalies in a PV module’s functioning. If proper attention is not given, it can lead to the forward biasing of healthy PV cells in the module, causing them to consume the electricity instead of producing it, hence reducing the PV module’s overall efficiency. Hence, to further the PV module research, it is essential to have an approximate way to model them. Doing so allows for understanding the design’s pros and cons before deploying the PV module-based power system in the field. In the last decade, many mathematical models for PV cell simulation and modeling techniques have been proposed. The most popular among all the techniques are diode based PV modeling. In this book chapter, the author will present a double diode based PV cell modeling. Later, the PV module modeling will be presented using these techniques that incorporate mismatch, partial shading, and open/short fault. The partial shading and mismatch are reduced by incorporating a bypass diode along with a group of four PV cells. The mathematical model for showing the effectiveness of bypass diode with PV cells in reducing partial shading effect will also be presented. Additionally, in recent times besides fixed topology of series–parallel, Total Cross-Tied (TCT), Bridge Link (BL), and Honey-Comb (H-C) have shown a better capability in dealing with partial shading and mismatch. The book chapter will also cover PV module modeling using TCT, BL, and H-C in detail.

Conference papers on the topic "Series DC Arc Faults":

1

Yao, Xiu, Luis Herrera, and Jin Wang. "Impact evaluation of series dc arc faults in dc microgrids." In 2015 IEEE Applied Power Electronics Conference and Exposition (APEC). IEEE, 2015. http://dx.doi.org/10.1109/apec.2015.7104771.

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Gajula, Kaushik, Vu Le, Xiu Yao, Shaofeng Zou, and Luis Herrera. "Quickest Detection of Series Arc Faults on DC Microgrids." In 2021 IEEE Energy Conversion Congress and Exposition (ECCE). IEEE, 2021. http://dx.doi.org/10.1109/ecce47101.2021.9595315.

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Kaya, Kerim, Okan Ozgonenel, and Ataberk Najafi. "Series DC Arc Fault Detection Method." In 2019 11th International Conference on Electrical and Electronics Engineering (ELECO). IEEE, 2019. http://dx.doi.org/10.23919/eleco47770.2019.8990461.

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Ananthan, Sundaravaradan Navalpakkam, Alvaro Furlani Bastos, Surya Santoso, Xianyong Feng, Charles Penney, Angelo Gattozzi, and Robert Hebner. "Signatures of Series Arc Faults to Aid Arc Detection in Low-Voltage DC Systems." In 2020 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2020. http://dx.doi.org/10.1109/pesgm41954.2020.9281618.

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5

Artale, Giovanni, Antonio Cataliotti, Valentina Cosentino, and Giuseppe Privitera. "Experimental characterization of series arc faults in AC and DC electrical circuits." In 2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2014. http://dx.doi.org/10.1109/i2mtc.2014.6860896.

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Çalikoğlu, Alperen, and Bunyamin Tamyurek. "Series DC Arc Fault in More Electric Aircraft." In 2023 IEEE Applied Power Electronics Conference and Exposition (APEC). IEEE, 2023. http://dx.doi.org/10.1109/apec43580.2023.10131503.

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Seeley, Danny, Mark Sumner, David W. P. Thomas, and Stephen Greedy. "DC Series Arc Fault Detection Using Fractal Theory." In 2023 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC). IEEE, 2023. http://dx.doi.org/10.1109/esars-itec57127.2023.10114909.

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Chen, Hai, Xiaoming Liu, Hongfei Shi, Meng Chen, and Jiayuan Zheng. "DC Series Arc Fault Diagnosis and Feature Extraction." In 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD). IEEE, 2023. http://dx.doi.org/10.1109/asemd59061.2023.10369057.

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9

Yun-Sik Oh, Gi-Hyeon Gwon, Chul-Hwan Kim, Doo-Ung Kim, Tack-Hyun Jung, Joon Han, and Keon-Woo Park. "A Scheme for Detecting DC Series Arc Faults in Low Voltage Distribution System." In 12th IET International Conference on Developments in Power System Protection (DPSP 2014). Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/cp.2014.0140.

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10

Chen, Shiying, Lingyu Zhu, Shengchang Ji, and Xiaojun Liu. "Detection of series DC arc fault using rogowski coil." In 2017 IEEE Conference on Electrical Insulation and Dielectric Phenomenon (CEIDP). IEEE, 2017. http://dx.doi.org/10.1109/ceidp.2017.8257633.

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