Series Arc Fault Identification for Photovoltaic System Based on Time-Domain and Time-Frequency-Domain Analysis

Chen, Silei; Li, Xingwen; Xiong, Jiayu

doi:10.1109/jphotov.2017.2694421

Cited by 84 publications

(44 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of its higher possibility of occurrence than parallel arc [10], series arc was chosen to study here. The experiment was conducted at a small grid-connected PV station.…”

Section: Methodsmentioning

confidence: 99%

Series DC Arc Simulation of Photovoltaic System Based on Habedank Model

Pan

Luo³

et al. 2020

Energies

View full text Add to dashboard Cite

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.

show abstract

“…Because of its higher possibility of occurrence than parallel arc [10], series arc was chosen to study here. The experiment was conducted at a small grid-connected PV station.…”

Section: Methodsmentioning

confidence: 99%

Series DC Arc Simulation of Photovoltaic System Based on Habedank Model

Pan

Luo³

et al. 2020

Energies

View full text Add to dashboard Cite

show abstract

“…Similar problem is observed in Tekpeti et al, 6 where maximum fault current profiles under solar irradiation fluctuation are analyzed. Methods based on frequency-domain analysis, like FFT 8,9 ,STFFT 10,11 , and WT 12 methods, came to prominence after it was recognized that SEA causes superposition of pink noise to a PV current signal. In Lu et al, 7 the authors provided a detailed review of SEA detection methods, and classified them as Fast Fourier Transformation (FFT), short time fast Fourier transformation (STFFT), wavelets transformation (WT), artificial intelligence (AI), statistical, and other methods.…”

Section: Introductionmentioning

confidence: 99%

“…In Lu et al, 7 the authors provided a detailed review of SEA detection methods, and classified them as Fast Fourier Transformation (FFT), short time fast Fourier transformation (STFFT), wavelets transformation (WT), artificial intelligence (AI), statistical, and other methods. Methods based on frequency-domain analysis, like FFT 8,9 ,STFFT 10,11 , and WT 12 methods, came to prominence after it was recognized that SEA causes superposition of pink noise to a PV current signal. 8,9 Pink noise, or 1/f noise, is characterized by a spectral density that is inversely proportional to the frequency.…”

Section: Introductionmentioning

confidence: 99%

Series arc fault detection in photovoltaic system by small‐signal impedance and noise monitoring

Georgijević

Stojić

Radaković

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary This paper presents a new method for the fast detection of series electrical arc (SEA) fault in a photovoltaic (PV) system, which relies on a power converter output switching waveforms as the excitation signal used to identify PV small‐signal impedance at switching frequency. In addition to the small‐signal impedance value, the proposed method extracts the high frequency noise power value caused by SEA from the PV current and uses it to indicate the presence of SEA. Both theoretical analysis and measurements performed on a commercial PV system show that SEA causes a step‐like increase in small‐signal reactance. SEA was detected in less than 10 ms during tests of the proposed algorithm. Moreover, various tests showed that there was no false tripping during transients in normal operation. Finally, the proposed algorithm was implemented and tested with a commercially available digital signal processor.

show abstract

“…Some of the time domain-based techniques have utilized current direction for detection of fault. [18][19][20] In Chen et al, 21 it was tried to detect series arc fault with an interesting method based on current unstable fluctuations in time domain and extra arc noise amplitude in time-frequency domain. Time domain-based methods are sensitive to the switching instrument noises.…”

mentioning

confidence: 99%

“…17 Frequency domain decomposition functions such as short time Fourier transform (STFT) and frequency-time domain tools such as wavelet are the common tools to detect series DC arc faults. [18][19][20] In Chen et al, 21 it was tried to detect series arc fault with an interesting method based on current unstable fluctuations in time domain and extra arc noise amplitude in time-frequency domain. However, it was noted that the method may have maloperation when abrupt current change occurs in heavy load condition due to load growth and MPPT impact.…”

mentioning

confidence: 99%

Kalman filter–based approach for detection of series arc fault in photovoltaic systems

Ahmadi

Samet

Ghanbari

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary Series arc fault is one of the main failures of photovoltaic (PV) systems that should be detected as soon as possible after occurrence. Electrical shock hazard and fire in PV systems are the main consequences of delayed series arc fault detection. In this paper, an approach based on analyzing high‐frequency components of voltage signal derived by Kalman filter is presented to detect series arc fault occurrence. A criterion is defined using energy variations of the estimated harmonics for this purpose. The performance of the proposed method is evaluated by plenty of simulations and experiments in different conditions. These cases include different arc lengths, electrodes movements, temporary shadings, and different types of noise. The results confirm that the proposed technique is efficient from speed response, reliability, and accuracy points of view.

show abstract

Series Arc Fault Identification for Photovoltaic System Based on Time-Domain and Time-Frequency-Domain Analysis

Cited by 84 publications

References 38 publications

Series DC Arc Simulation of Photovoltaic System Based on Habedank Model

Series DC Arc Simulation of Photovoltaic System Based on Habedank Model

Series arc fault detection in photovoltaic system by small‐signal impedance and noise monitoring

Kalman filter–based approach for detection of series arc fault in photovoltaic systems

Contact Info

Product

Resources

About