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Jay Johnson – One of the best experts on this subject based on the ideXlab platform.
High fidelity “replay” Arc Fault detection testbed2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC), 2016Co-Authors: Hezi Zhu, Robert S. Balog, Zhan Wang, Stephen Mcconnell, Phillip C. Hatton, Jay JohnsonAbstract:
Arc Faults in photovoltaic power systems pose safety concerns ranging from localized damage to the equipment, electric shock hazard for humans, and fire that spreads to buildings and beyond the PV systems. Thus, robust and effective detection of initial Arcs before they become sustained Arc Faults is imperative. However, high frequency noise caused by the switching of DC/DC power optimizers or DC/AC inverters can mask the signals produced by an Arc, making it difficult or impossible to reliably identify if an Arc is occurring. This paper discusses a testbed that was developed for the purpose of testing Arc Fault detectors in a laboratory environment using precise-reproduction, or replay, of pre-recorded Arc signals. The testbed is capable of replaying both the Arc signature and the noise from the power electronic circuits at proper amplitude to represent real-world conditions. The testbed is characterized and validated by frequency analysis across the range of frequencies typically associated with an Arc Fault. Fast Fourier Transform (FFT) analysis of reproduced Arc signals further justifies the effectiveness of the testbed and a certified Arc Fault detector (AFD) is tested using reproduced Arc signal. Utilization of such a testbed will facilitate the study of reliable detection algorithms.
Arc–Fault unwanted tripping survey with UL 1699B-listed products2015 IEEE 42nd Photovoltaic Specialist Conference (PVSC), 2015Co-Authors: Jay Johnson, Kenneth M. Armijo, Modi Avrutsky, Daniel Eizips, Sergey KondrashovAbstract:
Since adoption of the 2011 National Electrical Code®, many photovoltaic (PV) direct current (DC) Arc–Fault circuit interrupters (AFCIs) and Arc–Fault detectors (AFDs) have been introduced into the PV market. To meet the Code requirements, these products must be listed to Underwriters Laboratories (UL) 1699B Outline of Investigation. The UL 1699B test sequence was designed to ensure basic Arc–Fault detection capabilities with resistance to unwanted tripping; however, field experiences with AFCI/AFD devices have shown mixed results. In this investigation, independent laboratory tests were performed with UL-listed, UL-recognized, and prototype AFCI/AFDs to reveal any limitations with state-of-the-art Arc–Fault detection products. By running AFCIs and stand-alone AFDs through realistic tests beyond the UL 1699B requirements, many products were found to be sensitive to unwanted tripping or were ineffective at detecting harmful Arc–Fault events. Based on these findings, additional experiments are encouraged for inclusion in the AFCI/AFD design process and the certification standard to improve products entering the market.
parametric study of pv Arc Fault generation methods and analysis of conducted dc spectrumPhotovoltaic Specialists Conference, 2014Co-Authors: Jay Johnson, Kenneth M. ArmijoAbstract:
Many photovoltaic (PV) direct current (DC) Arc–Fault detectors use the frequency content of the PV system to detect Arcs. The spectral content is influenced by the duration and power of the Arc, surrounding insulation material geometry and chemistry, and electrode geometry. A parametric analysis was conducted in order to inform the Underwriters Laboratories (UL) 1699B (“Photovoltaic DC Arc–Fault Circuit Protection”) Standards Technical Panel (STP) of improvements to Arc–Fault generation methods in the certification standard. These recommendations are designed to reduce the complexity of the experimental setup, improve testing repeatability, and quantify the uncertainty of the Arc–Fault radio frequency (RF) noise generated by different PV Arcs in the field. In this investigation, we (a) discuss the differences in establishing and sustaining Arc–Faults for a number of different test configurations and (b) compare the variability in Arc–Fault spectral content for each respective test, and analyze the evolution of the RF signature over the duration of the Fault; with the ultimate goal of determining the most repeatable, ‘worst case’ tests for adoption by UL.
Nicolas Britsch – One of the best experts on this subject based on the ideXlab platform.
Multi criteria series Arc Fault detection based on supervised feature selectionInternational Journal of Electrical Power and Energy Systems, 2019Co-Authors: Hien Duc Vu, Edwin Calderon, Patrick Schweitzer, Serge Weber, Nicolas BritschAbstract:
As a field of reseArch Arc Fault detection in domestic appliances has existed for a long time and many detection algorithms have been published, patterned or implemented on commercial products. None of them, however, guarantees perfect discrimination and all are susceptible to false negatives or false positives (i.e. indicating the absence of Arcing Fault, when in reality it is present, or recognizing normal functioning as an Arcing condition). This phenomenon can be explained by the fact that all methods have been based on some features of Arc Fault which can be shared with load and network conditions such as noisy loads, the plugging-in or unplugging of appliances, the change of functioning mode of an appliance on a network and so on. A solution for limiting this phenomenon is multi Arc–Fault feature recognition. This reseArch presents a method for finding and combining Arc Fault features in order to obtain better performance than using a single Arc Fault feature. The choice of Arc–Fault features and the algorithm for combining them are based on machine learning techniques. The method proposed here can be used for different network conditions and loads. The effectiveness of this method has been verified by a number of experimental tests including not only the requirements of standard Arc Fault detection, but also the most difficult situations such as multiple loads masking and transient loads.
Kai Yang – One of the best experts on this subject based on the ideXlab platform.
A novel Arc Fault detector for early detection of electrical firesSensors (Switzerland), 2016Co-Authors: Kai Yang, Rencheng Zhang, Jianhong Yang, Canhua Liu, Shouhong Chen, Fujiang ZhangAbstract:
Arc Faults can produce very high temperatures and can easily ignite combustible materials; thus, they represent one of the most important causes of electrical fires. The application of Arc Fault detection, as an emerging early fire detection technology, is required by the National Electrical Code to reduce the occurrence of electrical fires. However, the concealment, randomness and diversity of Arc Faults make them difficult to detect. To improve the accuracy of Arc Fault detection, a novel Arc Fault detector (AFD) is developed in this study. First, an experimental Arc Fault platform is built to study electrical fires. A high-frequency transducer and a current transducer are used to measure typical load signals of Arc Faults and normal states. After the common features of these signals are studied, high-frequency energy and current variations are extracted as an input eigenvector for use by an Arc Fault detection algorithm. Then, the detection algorithm based on a weighted least squares support vector machine is designed and successfully applied in a microprocessor. Finally, an AFD is developed. The test results show that the AFD can detect Arc Faults in a timely manner and interrupt the circuit power supply before electrical fires can occur. The AFD is not influenced by cross talk or transient processes, and the detection accuracy is very high. Hence, the AFD can be installed in low-voltage circuits to monitor circuit states in real-time to facilitate the early detection of electrical fires.
Series Arc Fault detection algorithm based on autoregressive bispectrum analysisAlgorithms, 2015Co-Authors: Kai Yang, Fujiang Zhang, Rencheng Zhang, Jianhong Yang, Shouhong Chen, Xingbin ZhangAbstract:
Arc Fault is one of the most critical reasons for electrical fires. Due to the diversity, randomness and concealment of Arc Faults in low-voltage circuits, it is difficult for general methods to protect all loads from series Arc Faults. From the analysis of many series Arc Faults, a large number of high frequency signals generated in circuits are found. These signals are easily affected by Gaussian noise which is difficult to be eliminated as a result of frequency aliasing. Thus, a novel detection algorithm is developed to accurately detect series Arc Faults in this paper. Initially, an autoregressive model of the mixed high frequency signals is modelled. Then, autoregressive bispectrum analysis is introduced to analyze common series Arc Fault features. The phase information of Arc Fault signal is preserved using this method. The influence of Gaussian noise is restrained effectively. Afterwards, several features including characteristic frequency, fluctuation of phase angles, diffused distribution and incremental numbers of bispectrum peaks are extracted for recognizing Arc Faults. Finally, least squares support vector machine is used to accurately identify series Arc Faults from the load states based on these frequency features of bispectrum. The validity of the algorithm is experimentally verified obtaining Arc Fault detection rate above 97%.
ReseArch on Series Arc Fault Characteristics Based on BSS and Harmonic AnalysisProceedings of the International Conference on Logistics Engineering Management and Computer Science, 2015Co-Authors: Zheng Wang, Kai Yang, Fengming Huang, Qiuhong Chen, Shouhong ChenAbstract:
Arc Fault is one of the most important reasons of electrical fires. The common characteristics of series Arc Faults are always submerged in load currents which are acquired as mixed signals. It is difficult to extract useful Arc Fault characteristics through conventional approaches. Hence, novel approaches based on blind source separation and harmonic analysis are developed for characteristic reseArch of series Arc Faults. At first, a low-voltage Arc Fault experimental platform was set up to generate Arc Faults of different loads, and a large number of load currents were acquired to analyze Fault characteristics. When series Arc Faults occurred in circuits, currents would distort a little. Then, in order to obtain current minor changes, blind source separation (BSS) was used to separate distortions from observed mixed signals. Finally, common characteristics of series Arc Faults were found. The amplitudes of odd harmonic components in Arc Fault currents change rapidly. The total odd harmonic ratio fluctuates tempestuously with inconstancy but increases a lot. These harmonic characteristics provide very important bases for Arc Fault detection.