Multicharacteristics Arc Model and Autocorrelation-Algorithm Based Arc Fault Detector for DC Microgrid

Miao, Wenchao; Wang, Zhuofan; Wang, Fei; Lam, Kwok-Tai; Pong, Philip W. T.

doi:10.1109/tie.2022.3186351

Cited by 15 publications

(5 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Arc light, arc sound, and electromagnetic radiation Similarity of the steady burning arc spectrum [16] Fourth-order Hilbert curve fractal antenna [17] Volt-ampere, current sag, and power spectral of arc faults [18] Reception of electromagnetic radiation signals with comparable characteristic frequencies [19] Planar localization method requiring two detection points [20]…”

Section: Detection Methods Key Techniques Referencementioning

confidence: 99%

“…The test results demonstrate that the characteristic frequency of electromagnetic radiation signals can be utilized as a detection parameter for DC arc faults in PV systems, which have higher frequencies and longer pulse intervals compared to switch operations. In [18], a noninvasive arc fault detector based on magnetic-field sensing and autocorrelation algorithm is developed for DC microgrids. A multicharacteristics arc model is established based on the volt-ampere, current sag, and power spectral characteristics of arc faults.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Arc Detection of Photovoltaic DC Faults Based on Mathematical Morphology

Song,

Lu,

et al. 2024

Machines

View full text Add to dashboard Cite

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.

show abstract

Section: Detection Methods Key Techniques Referencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Arc Detection of Photovoltaic DC Faults Based on Mathematical Morphology

Song,

Lu,

et al. 2024

Machines

View full text Add to dashboard Cite

show abstract

“…However, due to the problems in the setting of detection devices and the randomness of arc occurrence, such detection methods are often limited and inefficient. The other kind of methods mainly focus on the internal characteristics of arc occurrence, such as the time-frequency domain characteristics of fault current and voltage [10][11][12][13]. This kind of method has high detection accuracy, wide monitoring range, and fast response speed, so it has become the mainstream identification method in power grid fault detection.…”

Section: Introductionmentioning

confidence: 99%

Arc Grounding Fault Identification Using Integrated Characteristics in the Power Grid

Liu,

Zhang,

Dai

et al. 2024

ENERGY

View full text Add to dashboard Cite

Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points. The existing arc fault identification technology only uses the fault line signal characteristics to set the identification index, which leads to detection failure when the arc zero-off characteristic is short. To solve this problem, this paper presents an arc fault identification method by utilizing integrated signal characteristics of both the fault line and sound lines. Firstly, the waveform characteristics of the fault line and sound lines under an arc grounding fault are studied. After that, the convex hull, gradient product, and correlation coefficient index are used as the basic characteristic parameters to establish fault identification criteria. Then, the logistic regression algorithm is employed to deal with the reference samples, establish the machine discrimination model, and realize the discrimination of fault types. Finally, simulation test results and experimental results verify the accuracy of the proposed method. The comparison analysis shows that the proposed method has higher recognition accuracy, especially when the arc dissipation power is smaller than 2 × 10 3 W, the zero-off period is not obvious. In conclusion, the proposed method expands the arc fault identification theory.

show abstract

“…Similarly, R. Jiang demonstrated a series arc fault detection method based on a highfrequency RLC arc model and 1D convolutional neural network (1DCNN) to avoid the effect of unknown loads, such as current features vary with loads [15]. W. Miao established a multi-characteristics arc model for simulation research of arc faults based on the volt-ampere and power spectral characteristics [16].…”

Section: Introductionmentioning

confidence: 99%

Dynamic climbing behavior and self-extinction mechanism of DC arc in a gradually changed air gap

Guan,

Yang,

Yin

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

DC arc behavior is normally a complicated and rapid time-dependent evolution process. To investigate the climbing behavior, dynamic properties, as well as the self-extinction mechanism, an experimental platform in collaboration with a pair of Jacob’s ladder electrodes was established. The overall evolution process including arc generation, arc combustion, and arc self-extinction was well accomplished. The dynamic climbing behavior and dynamic emission spectrum were captured. The dynamic volt-ampere characteristic, excitation temperature and electron density were all analyzed during the overall evolution process. Furthermore, the self-extinction mechanism under the function of Jacob’s ladder electrodes was explored. The experimental observations show that a two-stage climbing behavior is performed in succession. The dynamic resistance is fluctuated significantly in purple arc stage, while it is stable in yellow arc stage. The excitation temperature is positively correlated with arc current, but not strictly linear. The co-action results of two dynamic features make it more favorable for quick separation from the external ability of power supply. As a result, the arc is transitioned from self-sustaining state to non-self-sustaining state. This paper is regarded as a preliminary research aiming to provide some references for dynamic properties and self-extinction mechanism of the climbing arc in arcing horn.

show abstract

Multicharacteristics Arc Model and Autocorrelation-Algorithm Based Arc Fault Detector for DC Microgrid

Cited by 15 publications

References 32 publications

Arc Detection of Photovoltaic DC Faults Based on Mathematical Morphology

Arc Detection of Photovoltaic DC Faults Based on Mathematical Morphology

Arc Grounding Fault Identification Using Integrated Characteristics in the Power Grid

Dynamic climbing behavior and self-extinction mechanism of DC arc in a gradually changed air gap

Contact Info

Product

Resources

About