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Auteur : Grafiati
Publié le 8 juin 2024

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1

Wang, Lina, Ehtisham Lodhi, Pu Yang, Hongcheng Qiu, Waheed Ur Rehman, Zeeshan Lodhi, Tariku Sinshaw Tamir et 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 (15 mai 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%.
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Omran, Alaa Hamza, Dalila Mat Said, Siti Maherah Hussin et Sadiq H. Abdulhussain. « Photovoltaic system DC series arc fault : a case study ». Indonesian Journal of Electrical Engineering and Computer Science 28, no 2 (1 novembre 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>
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Dang, Hoang-Long, Sangshin Kwak et Seungdeog Choi. « Advanced Learning Technique Based on Feature Differences of Moving Intervals for Detecting DC Series Arc Failures ». Machines 12, no 3 (28 février 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.
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Navalpakkam Ananthan, Sundaravaradan, Xianyong Feng, Charles Penney, Angelo Gattozzi, Robert Hebner et Surya Santoso. « Voltage Differential Protection for Series Arc Fault Detection in Low-Voltage DC Systems ». Inventions 6, no 1 (31 décembre 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.
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Guo, Feng, Shenghong Yao, Neng Zhang et Yuchao He. « Detection and Location of Series DC Arc Fault in Photovoltaic System Based on VMD ». Journal of Physics : Conference Series 2488, no 1 (1 mai 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.
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Anggriawan, Dimas Okky, Epyk Sunarno, Epyk Sunarno, Eka Prasetyono, Eka Prasetyono, Suhariningsih Suhariningsih, Suhariningsih Suhariningsih, Muhammad Fauzi et 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 (28 octobre 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%.
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Dang, Hoang-Long, Sangshin Kwak et Seungdeog Choi. « DC Series Arc Fault Diagnosis Scheme Based on Hybrid Time and Frequency Features Using Artificial Learning Models ». Machines 12, no 2 (1 février 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.
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Dang, Hoang-Long, Sangshin Kwak et Seungdeog Choi. « Empirical Filtering-Based Artificial Intelligence Learning Diagnosis of Series DC Arc Faults in Time Domains ». Machines 11, no 10 (17 octobre 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.
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Dang, Hoang-Long, Sangshin Kwak et 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 (3 avril 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.
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Telford, Rory David, Stuart Galloway, Bruce Stephen et Ian Elders. « Diagnosis of Series DC Arc Faults—A Machine Learning Approach ». IEEE Transactions on Industrial Informatics 13, no 4 (août 2017) : 1598–609. http://dx.doi.org/10.1109/tii.2016.2633335.

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Wang, Yao, Cuiyan Bai, Xiaopeng Qian, Wanting Liu, Chen Zhu et Leijiao Ge. « A DC Series Arc Fault Detection Method Based on a Lightweight Convolutional Neural Network Used in Photovoltaic System ». Energies 15, no 8 (14 avril 2022) : 2877. http://dx.doi.org/10.3390/en15082877.

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Although photovoltaic (PV) systems play an essential role in distributed generation systems, they also suffer from serious safety concerns due to DC series arc faults. This paper proposes a lightweight convolutional neural network-based method for detecting DC series arc fault in PV systems to solve this issue. An experimental platform according to UL1699B is built, and current data ranging from 3 A to 25 A is collected. Moreover, test conditions, including PV inverter startup and irradiance mutation, are also considered to evaluate the robustness of the proposed method. Before fault detection, the current data is preprocessed with power spectrum estimation. The lightweight convolutional neural network has a lower computational burden for its fewer parameters, which can be ready for embedded microprocessor-based edge applications. Compared to similar lightweight convolutional network models such as Efficientnet-B0, B2, and B3, the Efficientnet-B1 model shows the highest accuracy of 96.16% for arc fault detection. Furthermore, an attention mechanism is combined with the Efficientnet-B1 to make the algorithm more focused on arc features, which can help the algorithm reduce unnecessary computation. The test results show that the detection accuracy of the proposed method can be up to 98.81% under all test conditions, which is higher than that of general networks.
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Yuelong Gu, Chunyang Gong, Hui Chen, Jun Zhang et Zhixin Wang. « Series DC Arc Characteristic and Diagnosis Strategy for Distributed PV Power Generation ». Electrotehnica, Electronica, Automatica 70, no 4 (15 novembre 2022) : 1–10. http://dx.doi.org/10.46904/eea.22.70.4.1108001.

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In order to realize the nation’s goal of achieving carbon peaking before year 2030 and the carbon neutrality by year 2060, the installed capacity of distributed PV power generation system has increased year by year in China, which increases the probability of DC arc fault. The features of DC arc may be very weak under some conditions. When the current of DC arc is large and the voltage level of photovoltaic array is high, DC arc will be very stable, and it is hard to be detected. Besides, if there are many high-power devices around DC arc detector, the output signal of current sensor will be seriously interfered, and it will be more difficult to distinguish between normal state and arc fault state. DC arc can be divided into unstable combustion stage and stable combustion stage. When in stable combustion stage, DC arc is also hard to be detected. Fortunately, the features of DC arc are obvious when in unstable combustion stage and the value of current spectrum energy will significantly increase. When the spectrum energy exceeds the set threshold in five consecutive time windows, it is preliminarily determined that there is a suspected arc. If the spectrum energy still exceeds the set threshold within five consecutive time windows, it is finally determined that there is an arc fault, otherwise, short PV array for further diagnosis. If there is an arc fault, the fluctuation characteristics of DC arc will be enhanced and the loop current will go fluctuating intensely again, then spectrum energy calculated will exceed the set threshold and an arc fault alarm can be sent out. Taking into consideration the different features of DC arc’s unstable combustion stage and stable combustion stage, a Series DC arc diagnosis method based on current spectrum energy is proposed, which amplifies the fluctuation characteristics of arc current and keeps arc current fluctuating strongly for a long time by shorting the PV array. The spectrum energy is calculated through FFT analysis of each PV string’s current, compared to the calculated spectrum energy with the set threshold, and then decided whether DC arc fault exists. The results to an experimental platform verify the effectiveness of the arc detection method, which has advantages of low calculation cost and high accuracy.
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Li, Xinran, Chenyun Pan, Dongmei Luo et Yaojie Sun. « Series DC Arc Simulation of Photovoltaic System Based on Habedank Model ». Energies 13, no 6 (18 mars 2020) : 1416. http://dx.doi.org/10.3390/en13061416.

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Despite the rapid development of photovoltaic (PV) industry, direct current (DC) fault arc remains a major threat to the safety of PV system and personnel. While extensive research on DC fault arc has been conducted, little attention has been paid to the long-time interactions between the PV system and DC arc. In this paper, a simulation system with an arc model and PV system model is built to overcome the inconvenience of the fault-arc experiments and understand the mechanism of these interactions. For this purpose, the characteristics of the series DC arc in a small grid-connected PV system are first investigated under uniform irradiance. Then, by comparing with different arc models, the Habedank model is selected to simulate the fault arc and a method to determine its parameters under DC arc condition is proposed. The trends of simulated arc waveforms are consistent with the measured data, whose fitting degree in adjusted R-squared is between 0.946 and 0.956. Finally, a phenomenon observed during the experiment, that the negative perturbation of the maximum power point tracking (MPPT) algorithm can reduce the arc current, is explained by the proposed model.
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Uriarte, Fabian M., Angelo L. Gattozzi, John D. Herbst, Hunter B. Estes, Thomas J. Hotz, Alexis Kwasinski et Robert E. Hebner. « A DC Arc Model for Series Faults in Low Voltage Microgrids ». IEEE Transactions on Smart Grid 3, no 4 (décembre 2012) : 2063–70. http://dx.doi.org/10.1109/tsg.2012.2201757.

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Song, Lei, Chunguang Lu, Chen Li, Yongjin Xu, Jiangming Zhang, Lin Liu, Wei Liu et Xianbo Wang. « Arc Detection of Photovoltaic DC Faults Based on Mathematical Morphology ». Machines 12, no 2 (14 février 2024) : 134. http://dx.doi.org/10.3390/machines12020134.

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With the rapid growth of the photovoltaic industry, fire incidents in photovoltaic systems are becoming increasingly concerning as they pose a serious threat to their normal operation. Research findings indicate that direct current (DC) fault arcs are the primary cause of these fires. DC arcs are characterized by high temperature, intense heat, and short duration, and they lack zero crossing or periodicity features. Detecting DC fault arcs in intricate photovoltaic systems is challenging. Hence, researching DC fault arcs in photovoltaic systems is of crucial significance. This paper discusses the application of mathematical morphology for detecting DC fault arcs. The system utilizes a multi-stage mathematical morphology filter, and experimental results have shown its effective extraction of fault arc features. Subsequently, we propose a method for detecting DC fault arcs in photovoltaic systems using a cyclic neural network, which is well-suited for time series processing tasks. By combining multiple features extracted from experiments, we trained the neural network and achieved high accuracy. This experiment demonstrates that our recurrent neural network (RNN) based scheme for DC fault arc recognition has significant reference value and implications for future research. The ROC curve on the test set approaches 1 from the initial state, and the accuracy on the test set remains at 98.24%, indicating the strong robustness of the proposed model.
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Luping, Chen, Wang Peng et Xu Liangjun. « Novel detection method for DC series arc faults by using morphological filtering ». Journal of China Universities of Posts and Telecommunications 22, no 5 (octobre 2015) : 84–91. http://dx.doi.org/10.1016/s1005-8885(15)60685-9.

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Pang, Ruiwen, et Wenfang Ding. « Series Arc Fault Characteristics and Detection Method of a Photovoltaic System ». Energies 16, no 24 (12 décembre 2023) : 8016. http://dx.doi.org/10.3390/en16248016.

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The DC arc is the main cause of fire in photovoltaic (PV) systems. This is due to the fact that the DC arc has no zero-crossing point and is prone to stable combustion. Failure to detect it in a timely manner can seriously endanger the PV system. This study analyzes the influences of the series arc and the maximum power point tracking (MPPT) algorithm on the PV output characteristics based on the PV equivalent circuit module. The PV voltage and current variation characteristics are obtained when the series arc occurs. The findings indicate that the input voltage of the converter remains unchanged due to the MPPT algorithm before and after the series arc occurs. Furthermore, the PV faulty string output current will drastically decrease when the series arc fault occurs. On this basis, a series arc detection method based on the current change is proposed, suppressing the combustion of the series arc by increasing the target voltage of the MPPT algorithm. The experimental results show that the proposed method can effectively detect and extinguish the series arc in the PV system within 0.6 s. Compared to the other methods, the proposed method can be integrated into the PV system without additional hardware.
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Yao, Xiu, Vu Le et Inhwan Lee. « Unknown Input Observer-Based Series DC Arc Fault Detection in DC Microgrids ». IEEE Transactions on Power Electronics 37, no 4 (avril 2022) : 4708–18. http://dx.doi.org/10.1109/tpel.2021.3128642.

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K. Bachache, Nasseer. « Detector of DC Series Arc Fault in a Large Photovoltaic System using Discrete Wavelet Transform ». Bilad Alrafidain Journal for Engineering Science and Technology 1, no 1 (14 août 2022) : 1–5. http://dx.doi.org/10.56990/bajest/2022.010101.

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In this paper, the frequency analysis methods can detect the fire accident of the solar arc we propose Discrete Wavelet Transform (DWT). The DWT can express time-axis information even in the frequency domain. In case of DWT algorithm, it was implemented based on Arduino linked with MATLAM. The DC arc accident has been implemented using standard using Rogowski coil. This coil is used to simulate the current sensor for large-capacity PV system application. The constructing and simulated DC series arc generation circuit was verified under actual arc generation conditions. In addition, the performance of the accident detector manufactured was studied The experiment results and its simulation demonstrate, the proposed method is more efficient than others.
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Sun, Liling, Han Wu et Xiangdong Lu. « Study on the Feature Space Detection Method of DC Arc Fault for Photovoltaic system ». E3S Web of Conferences 256 (2021) : 01015. http://dx.doi.org/10.1051/e3sconf/202125601015.

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An arc fault on the DC side of the photovoltaic system is a potential safety hazard and is difficult to detect due to the complexity of photovoltaic systems. The detection method of series arc fault in photovoltaic systems is investigated here. The DC arc fault test platform for a photovoltaic system is established to collect the current signal under normal and fault conditions. In this study, the time domain characteristics, frequency domain characteristics, and time-frequency domain characteristics are compared by analysing the current data from the photovoltaic system in before and after fault states: corresponding feature vectors are used to construct the arc fault feature space of the system, and according to the position of the current signal in the feature space the fault is detected, so as to realise effective arc fault feature information. Then the method of establishing the arc fault feature space is introduced and key parameters of the feature space are determined. Finally, the anti-interference ability of arc fault feature space detection is verified. The results showed that the detection method is both feasible and accurate.
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Chae, Suyong, Jinju Park et Seaseung Oh. « Series DC Arc Fault Detection Algorithm for DC Microgrids Using Relative Magnitude Comparison ». IEEE Journal of Emerging and Selected Topics in Power Electronics 4, no 4 (décembre 2016) : 1270–78. http://dx.doi.org/10.1109/jestpe.2016.2592186.

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Park, Hwa-Pyeong, et Suyong Chae. « DC Series Arc Fault Detection Algorithm for Distributed Energy Resources Using Arc Fault Impedance Modeling ». IEEE Access 8 (2020) : 179039–46. http://dx.doi.org/10.1109/access.2020.3027869.

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Xiong, Qing, Xianyong Feng, Angelo L. Gattozzi, Xiaojun Liu, Linzi Zheng, Lingyu Zhu, Shengchang Ji et Robert E. Hebner. « Series Arc Fault Detection and Localization in DC Distribution System ». IEEE Transactions on Instrumentation and Measurement 69, no 1 (janvier 2020) : 122–34. http://dx.doi.org/10.1109/tim.2019.2890892.

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Le, Vu, Xiu Yao, Chad Miller et Bang-Hung Tsao. « Series DC Arc Fault Detection Based on Ensemble Machine Learning ». IEEE Transactions on Power Electronics 35, no 8 (août 2020) : 7826–39. http://dx.doi.org/10.1109/tpel.2020.2969561.

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Artale, Giovanni, Giuseppe Caravello, Antonio Cataliotti, Valentina Cosentino, Dario Di Cara, Salvatore Guaiana, Nicola Panzavecchia et Giovanni Tinè. « Characterization of DC series arc faults in PV systems based on current low frequency spectral analysis ». Measurement 182 (septembre 2021) : 109770. http://dx.doi.org/10.1016/j.measurement.2021.109770.

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Yao, Xiu, Luis Herrera, Shengchang Ji, Ke Zou et Jin Wang. « Characteristic Study and Time-Domain Discrete- Wavelet-Transform Based Hybrid Detection of Series DC Arc Faults ». IEEE Transactions on Power Electronics 29, no 6 (juin 2014) : 3103–15. http://dx.doi.org/10.1109/tpel.2013.2273292.

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Luna, Benjamin Vidales, José Luis Monroy-Morales, Manuel Madrigal Martínez, Domingo Torres-Lucio, Serge Weber et Patrick Schweitzer. « Analysis of Internal Signal Perturbations in DC/DC and DC/AC Converters under Arc Fault ». Energies 14, no 11 (22 mai 2021) : 3005. http://dx.doi.org/10.3390/en14113005.

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The constant increase in electrical energy consumption has led to a growth of photovoltaic installations (PV) along with the corresponding power converters for proper operation. Power electronics converters represent a challenge to maintain the system’s performance and safety; one such problem is series DC Arc Fault (AF). DC AFs lead to fire risk, damaging the main bus and the loads when not detected and interrupted in time. Therefore, research about DC AFs in power electronics converters must be carried out to predict the behavior and help avoid damage to the system. In this work, an innovative hybrid multilevel inverter for PV applications is used to explore the effect of series DC AFs in the converters’ internal signals, with the aims of setting the bases for the development of a detection system for power electronics. Both stages of conversion (DC/DC and DC/AC) are presented. In addition, the placement of the MPPT converter was considered for the tests. The AF experimental tests were performed with a generator based on the UL1699B specifications. The measurements of signals were performed in strategic points of the DC side, and changes and how to exploit them are discussed. This study contributes to a better understanding of the DC AF phenomenon and provides new insights for the development of new PV system protections.
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S. R. Purohit, Sunilkumar M. Hattaraki, Soumya P.Hampangoudra, Rashmi Nimbaragi et Savita Mattihal Shweta Bagali. « Arduino - Uno Based Underground Cable Fault Detection System (AUCFDS) ». World Journal of Advanced Research and Reviews 18, no 2 (30 mai 2023) : 288–92. http://dx.doi.org/10.30574/wjarr.2023.18.2.0810.

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Today, it is very challenging to manually find underground cable faults like wear and tear or rodents, because it costs more and takes more time. Finding the fault sources is also very difficult because the entire line must be dug to check for cable line faults. Our suggested system utilises an Arduino- microcontroller to help locate the fault's exact location. The standard concept of Ohm's law is applied in this system. The location of the shorted cable line determines the current flow when a low DC voltage is applied to underground cable lines at the feeder end through a series resistor. The system makes use of a rectified power supply and an Arduino- microcontroller board. The Arduino - microcontroller board is interfaced with a combination of resistors and current sensing circuits. the cable's length is represented by digital data that is sent from the internal ADC device to the microcontroller and displayed on the LCD Display.
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Kanemaru, Makoto, Kentaro Kokura, Mitsugi Mori, Takashi Shindoi et Masato Yamamoto. « Identification Technique of DC Series Arc-fault Strings in Photovoltaic Systems ». IEEJ Transactions on Power and Energy 139, no 1 (1 janvier 2019) : 39–45. http://dx.doi.org/10.1541/ieejpes.139.39.

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Ma, Tao, Ersheng Tian, Zhenxing Liu, Shuxin Liu, Tianhong Guo, Taowei Wang et Long Fu. « Detection of DC Series Arc Fault Based on VMD and ELM ». Journal of Physics : Conference Series 1486 (avril 2020) : 062037. http://dx.doi.org/10.1088/1742-6596/1486/6/062037.

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Kanemaru, Makoto, Kentaro Kokura, Mitsugi Mori, Takashi Shindoi et Masato Yamamoto. « Identification technique of DC series arc‐fault strings in photovoltaic systems ». Electrical Engineering in Japan 207, no 2 (avril 2019) : 12–19. http://dx.doi.org/10.1002/eej.23204.

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Zhao, Shuangle, Guodong You, Xiaoxin Hou, Xiating Xu, Yiduo Zhang et Hui An. « A Spatial Location Method for DC Series Arc Faults Based on RSSI and Bayesian Regularization Neural Network ». IEEE Sensors Journal 21, no 24 (15 décembre 2021) : 27868–77. http://dx.doi.org/10.1109/jsen.2021.3126058.

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33

Qader, Mohammed Redha, Hedaia Al-Asooly et Isa Salman Qamber. « Influence of System Parameters on Fuse Protection Use in Regenerative DC Drives ». Energies 2, no 2 (16 juin 2009) : 411–26. http://dx.doi.org/10.3390/en20200411.

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Current limiting fuses are widely used to protect the thyristors in DC drive systems. One very important problem is the choice of the correct voltage rating for fuses protecting regenerative DC drives, where many types of fault may occur, which makes fuse protection difficult. In the event of a commutation failure while regenerating, the fuses need to interrupt the loop supplied by the AC and DC voltages acting in series, which is the most difficult case for protection by fuses. In this paper a detailed study of the complete interruption process has been investigated by modeling of arcing process of the fuse protection against the regenerative circuit internal commutation fault. The effect of varying the motor time constant, supply impedance, number of fuses used to clear the fault and DC machine rating on the total transient response is studied. The model of a 200 A fuse is employed in this study. Fuses in series with both the semiconductor devices (F1) and fuses in AC lines (F2) are considered. Comparison was made between arc energy produced for fuses protecting the regenerative circuit if failure occurs, with the arc energy produced in a standard AC test in order to investigate the required voltage rating for the fuse.
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34

Dirhamsyah, Dirhamsyah, Diana Alia et Dimas Okky Anggriawan. « Hardware implementation of series DC arc fault protection using fast Fourier transform ». TELKOMNIKA (Telecommunication Computing Electronics and Control) 19, no 5 (1 octobre 2021) : 1679. http://dx.doi.org/10.12928/telkomnika.v19i5.20521.

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Dirhamsyah, Dirhamsyah, Diana Alia et Dimas Okky Anggriawan. « Hardware implementation of series DC arc fault protection using fast Fourier transform ». TELKOMNIKA (Telecommunication Computing Electronics and Control) 19, no 5 (1 octobre 2021) : 1679. http://dx.doi.org/10.12928/telkomnika.v19i5.20521.

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Xiong, Qing, Shengchang Ji, Xiaojun Liu, Xining Li, Lingyu Zhu, Xianyong Feng, Angelo L. Gattozzi et Robert E. Hebner. « Electromagnetic Radiation Characteristics of Series DC Arc Fault and Its Determining Factors ». IEEE Transactions on Plasma Science 46, no 11 (novembre 2018) : 4028–36. http://dx.doi.org/10.1109/tps.2018.2864605.

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37

Gu, Jyh-Cherng, De-Shin Lai, Jing-Min Wang, Jiang-Jun Huang et Ming-Ta Yang. « Design of a DC Series Arc Fault Detector for Photovoltaic System Protection ». IEEE Transactions on Industry Applications 55, no 3 (mai 2019) : 2464–71. http://dx.doi.org/10.1109/tia.2019.2894992.

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Loulijat, Azeddine, Mouncef El marghichi et Sarah Abboud. « Secures and maintains non-linear control of DFIG-wind turbine by implementing the appropriate protection configuration against overcurrent in the rotor circuit under grid fault ». E3S Web of Conferences 469 (2023) : 00016. http://dx.doi.org/10.1051/e3sconf/202346900016.

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One significant disadvantage of the doubly fed induction generator (DFIG) is its high vulnerability to grid faults, which can be attributed to the direct linkage of its stator windings to the electrical network. The electromagnetic pairing of the stator and rotor in the DFIG implies that a voltage dip causing excessive stator current results in high currents in the sensitive back-to-back inverters and an overcharge in the DC-link capacitor. A comparative study of two protection configurations with a sliding mode non-linear control (SMC) for the rotor transient current for the better operation of the DFIG under network faults is presented in this document. One conventional configuration consists of a crowbar with DC-chopper and another is changed by adding to the crowbar an RL series device known as an appropriate protection configuration (APC). Both are placed within the rotor windings and ride-side converter (RSC) to achieve secures and maintains SMC of DFIG. The comparison of the results reached with the MATLAB/ SIMULINK application is evidenced by the success of these two configuration reduce the high rotor current and DC-link voltage. Additionally, the conventional configuration, in conjunction with the APC, diminishes the current RSC to levels below 0.2kA and exactly 2.25kA, while also being capable of absorbing up to 2.52kA and 1.3kA in the event of a grid fault. So the main difference is that with the APC, the decoupling of the RSC to the rotor in the presence of a fault can be averted, thereby assuring the control of all stator power via the RSC converte.
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Lu, Qiwei, Zeyu Ye, Mengmeng Su, Yasong Li, Yuce Sun et Hanqing Huang. « A DC Series Arc Fault Detection Method Using Line Current and Supply Voltage ». IEEE Access 8 (2020) : 10134–46. http://dx.doi.org/10.1109/access.2019.2963500.

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Park, Hwa-Pyeong, Mina Kim, Jee-Hoon Jung et Suyong Chae. « Series DC Arc Fault Detection Method for PV Systems Employing Differential Power Processing Structure ». IEEE Transactions on Power Electronics 36, no 9 (septembre 2021) : 9787–95. http://dx.doi.org/10.1109/tpel.2021.3061968.

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Lu, Shibo, Tharmakulasingam Sirojan, B. T. Phung, Daming Zhang et Eliathamby Ambikairajah. « DA-DCGAN : An Effective Methodology for DC Series Arc Fault Diagnosis in Photovoltaic Systems ». IEEE Access 7 (2019) : 45831–40. http://dx.doi.org/10.1109/access.2019.2909267.

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Barroso-de-María, Gabriel, Guillermo Robles, Juan Manuel Martínez-Tarifa et Alexander Cuadrado. « Modelling Inductive Sensors for Arc Fault Detection in Aviation ». Sensors 24, no 8 (20 avril 2024) : 2639. http://dx.doi.org/10.3390/s24082639.

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Modern aircraft are being equipped with high-voltage and direct current (HVDC) architectures to address the increase in electrical power. Unfortunately, the rise of voltage in low pressure environments brings about a problem with unexpected ionisation phenomena such as arcing. Series arcs in HVDC cannot be detected with conventional means, and finding methods to avoid the potentially catastrophic hazards of these events becomes critical to assure further development of more electric and all electric aviation. Inductive sensors are one of the most promising detectors in terms of sensitivity, cost, weight and adaptability to the circuit wiring in aircraft electric systems. In particular, the solutions based on the detection of the high-frequency (HF) pulses created by the arc have been found to be good candidates in practical applications. This paper proposes a method for designing series arc fault inductive sensors able to capture the aforementioned HF pulses. The methodology relies on modelling the parameters of the sensor based on the physics that intervenes in the HF pulses interaction with the sensor itself. To this end, a comparative analysis with different topologies is carried out. For every approach, the key parameters influencing the HF pulses detection are studied theoretically, modelled with a finite elements method and tested in the laboratory in terms of frequency response. The final validation tests were conducted using the prototypes in real cases of detection of DC series arcs.
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Xing, Lu, Yinghong Wen, Shi Xiao, Dan Zhang et Jinbao Zhang. « A Deep Learning Approach for Series DC Arc Fault Diagnosing and Real-Time Circuit Behavior Predicting ». IEEE Transactions on Electromagnetic Compatibility 64, no 2 (avril 2022) : 569–79. http://dx.doi.org/10.1109/temc.2021.3131670.

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Zhao, Shuangle, Yao Wang, Feng Niu, Chen Zhu, Youxin Xu et Kui Li. « A Series DC Arc Fault Detection Method Based on Steady Pattern of High-Frequency Electromagnetic Radiation ». IEEE Transactions on Plasma Science 47, no 9 (septembre 2019) : 4370–77. http://dx.doi.org/10.1109/tps.2019.2932747.

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Xia, Kun, Sheng He, Yuan Tan, Quan Jiang, Jingjun Xu et Wei Yu. « Wavelet packet and support vector machine analysis of series DC ARC fault detection in photovoltaic system ». IEEJ Transactions on Electrical and Electronic Engineering 14, no 2 (4 octobre 2018) : 192–200. http://dx.doi.org/10.1002/tee.22797.

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Li, Xin, Haoqi Wang, Panfeng Guo, Wei Xiong et Jianan Huang. « Series Dc arc fault detection and location in wind-solar-storage hybrid system based on variational mode decomposition ». Electric Power Systems Research 209 (août 2022) : 107991. http://dx.doi.org/10.1016/j.epsr.2022.107991.

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Oh, Yun-Sik, Joon Han, Gi-Hyeon Gwon, Doo-Ung Kim et Chul-Hwan Kim. « Development of Fault Detector for Series Arc Fault in Low Voltage DC Distribution System using Wavelet Singular Value Decomposition and State Diagram ». Journal of Electrical Engineering and Technology 10, no 3 (1 mai 2015) : 766–76. http://dx.doi.org/10.5370/jeet.2015.10.3.766.

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Liu, Shengyang, Lei Dong, Xiaozhong Liao, Xiaodong Cao, Xiaoxiao Wang et Bo Wang. « Application of the Variational Mode Decomposition-Based Time and Time–Frequency Domain Analysis on Series DC Arc Fault Detection of Photovoltaic Arrays ». IEEE Access 7 (2019) : 126177–90. http://dx.doi.org/10.1109/access.2019.2938979.

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Ahmed, Mohamed Adel, Tarek Kandil et Emad M. Ahmed. « Enhancing Doubly Fed Induction Generator Low-Voltage Ride-through Capability Using Dynamic Voltage Restorer with Adaptive Noise Cancellation Technique ». Sustainability 14, no 2 (12 janvier 2022) : 859. http://dx.doi.org/10.3390/su14020859.

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Some of the major challenges facing micro-grids (MGs) during their connection with the utility grid are maintaining power system stability and reliability. One term that is frequently discussed in literature is the low-voltage ride-through (LVRT) capability, as it is required by the utility grid to maintain its proper operation and system stability. Furthermore, due to their inherent advantages, doubly fed induction generators (DFIGs) have been widely installed on many wind farms. However, grid voltage dips and distortion have a negative impact on the operation of the DFIG. A dynamic voltage restorer (DVR) is a commonly used device that can enhance the LVRT capability of DFIG compared to shunt capacitors and static synchronous compensator (STATCOM). DVR implements a series compensation during fault conditions by injecting the proper voltage at the point of common coupling (PCC) in order to preserve stable terminal voltage. In this paper, we propose a DVR control method based on the adaptive noise cancelation (ANC) technique to compensate for both voltage variation and harmonic mitigation at DFIG terminals. Additionally, we propose an online control of the DC side voltage of the DVR using pulse width modulation (PWM) rectifier to reduce both the size of the storage element and the solid-state switches of the DVR, aiming to reduce its overall cost. A thorough analysis of the operation and response of the proposed DVR is performed using MATLAB/SIMULINK under different operating conditions of the grid. The simulation results verify the superiority and robustness of the proposed technique to enhance the LVRT capability of the DFIG during system transients and faults.
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Yang, Kai, Ren Cheng Zhang, Jian Hong Yang et Xiao Mei Wu. « Research on Low-Voltage Series Arc Fault Detection Based on Higher-Order Cumulants ». Advanced Materials Research 889-890 (février 2014) : 741–44. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.741.

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Arc faults are one of the main reasons of electrical fires. For the difficulty of series arc fault detection, very few of techniques have successfully protected loads from arc faults in low-voltage circuits, especially in China. When series arc faults occur, shoulders will appear in load currents. The shoulder widths of non-arc faults such as normal load arcs are stable, while those of arc faults are variable because the appearance of arc faults is erratic. Therefore, a novel detection method based on shoulder characteristics was proposed. To better capture shoulder widths, original currents were firstly converted into pulses. Then, the pulse widths were used to detect arcs and the fourth-order cumulants of their differential were used to distinguish arc faults from normal operations. Finally, an arc fault detection device (AFDD) prototype was developed for test. The results show this prototype can discriminate arc faults effectively from normal operations.
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