Abstract
Series DC arc fault can cause fire hazards in the photovoltaic(PV) array. This paper proposes a time and time-frequency domain analysis method combining the loop current and the PV-side voltage for detecting the series DC arc fault. The fusion of the two different signals can enhance the anti-interference ability of the algorithm. In the time domain analysis, the conductance is put forward to represent the the circuit states. In comparison with some common feature characteristics including current and voltage, the changes of the conductance are more obvious when the arc occurs. In the time-frequency domain analysis, the Variational Mode Decomposition (VMD) is firstly adopted to extract the characteristic frequency band of the arc current signals. VMD can improve the quality of the frequency bands by conquering the modal aliasing and endpoints effects of some traditional modal decomposition algorithms. Then, shannon entropy of the corresponding frequency-band signals is calculated to reflect the variation of the signal complexity. Finally, the two optimal detection variable with rectangle window and the proper time window length are established to achieve the best identification results. In the experimental phase, the experimental results validate that the presented algorithm can not only detect the arc faults timely and accurately but also avoid the nuisance trips caused by the start and shutdown operation of grid-connected inverter with MPPT, the dynamic changes of load and shadow occlusion.
Highlights
During the last decades, the photovoltaic (PV) generation has become a prevailing trend
To identify the arc fault generated in the low solar irradiance, the paper [19] propounds a category based on a new signal denoising method of wavelet-singular value decomposition (SVD) to detect the arc fault at a low current level
The machine learning methods based on deep learning including Full Connected Neural (FCNN), Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN) are applied in series DC arc fault detection of photovoltaic array [24], [27]–[30]
Summary
The photovoltaic (PV) generation has become a prevailing trend. The maximum power tracking (MPPT) of the boost circuit will adjust the voltage and current to approach the normal condition as much as possible after the arc faults occur This function contributes to the nuisance tripping in this case. To identify the arc fault generated in the low solar irradiance, the paper [19] propounds a category based on a new signal denoising method of wavelet-singular value decomposition (SVD) to detect the arc fault at a low current level. The machine learning methods based on deep learning including Full Connected Neural (FCNN), Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN) are applied in series DC arc fault detection of photovoltaic array [24], [27]–[30]. To address the issues mentioned above, this paper presents an original time-frequency domain category based on VMD for detecting the series DC arc fault. K and the original signal is decomposed into K narrow-band IMF components
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