Fast Detection of Weak Arc Faults Based on Progressive Singular-Value-Decomposition and Empirical Analyses

Shen, Yu-Long; Xu, Zhihong

doi:10.1109/access.2022.3229586

Cited by 2 publications

(2 citation statements)

References 47 publications

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“…Y. -L. Shen et al [24] analyze signals through empirical wavelet transform, extracting frequency domain energy metrics of different frequency bands, and ultimately input the extracted frequency domain features into a fully connected neural network to achieve arc fault detection.…”

Section: Introductionmentioning

confidence: 99%

Explainability Approach-Based Series Arc Fault Detection Method for Photovoltaic Systems

Wang,

Zhou,

Paul

et al. 2024

IEEE Access

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Arc fault detection devices are mandatory worldwide for mitigating DC series arc faults in photovoltaic systems. However, they are prone to nuisance tripping. Artificial intelligence-based approaches can be a solution, but they are "black boxes" and challenging to modify. This paper proposes an explainability and attention-based method to investigate the intensive details of such an algorithm. The contributions of an arc feature to the proposed model can be visualized by the proposed interpretable methodology so that insensitive arc features can be removed to reduce the quantity of input data. Additionally, the structure of the proposed model can be optimized by cutting the redundant layers. Thus, an accuracy of 99.63% is achieved with only 48.48% of the parameters compared to the original model. Finally, the optimized model is implemented by a Cortex M7-based microprocessor with a runtime of only 7.8 ms, making it ready for industrial application.

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Section: Introductionmentioning

confidence: 99%

Explainability Approach-Based Series Arc Fault Detection Method for Photovoltaic Systems

Wang,

Zhou,

Paul

et al. 2024

IEEE Access

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“…Sparse coding is utilized in [18] to collect signal features, and a sparse representation and fully connected neural network (SRFCNN) is created for feature learning and classification, which can avoid the nonlinear load start-up miss-operation. The artificial intelligencebased arc fault detection methods outperform existing methods in terms of identification accuracy [19]. However, the neural network model has a complex structure and a high number of parameters, and it takes a lot of storage space and computer resources.…”

Section: Introductionmentioning

confidence: 99%

Arc_EffNet: A Novel Series Arc Fault Detection Method Based on Lightweight Neural Network

Ning,

Sheng,

Zhou

et al. 2023

Electronics

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Arc faults can cause a severe electric fire, especially series arc faults. Artificial intelligence (AI)-based arc fault detection methods can have a boasted detection accuracy. However, the complexity and large parameter quantity of the AI-based algorithm will hinder its real-time performance for detecting series arc faults. This paper proposes a lightweight arc fault detection method based on the EffNet module, which can make the algorithm less complex with the same detection accuracy level. An arc fault test platform was constructed to collect arc current data, covering eight types of loads required by the IEC 62606 standard. The raw arc current data are used directly as an input for the proposed algorithm, reducing the module’s complexity. According to features of arc current mainly represented in the time domain, the first and last convolution layers of the EffNet module can be improved. Additionally, the spatially separable convolution is well-tuned and trimmed to achieve a more lightweight and better-performance architecture for arc fault detection called Arc_EffNet. Remarkably, this model achieves an impressive arc detection accuracy of 99.75%. An arc fault detection prototype was built using the Raspberry Pi 4B to evaluate the real-time detecting performance of the proposed method. The experimental results show that the prototype takes a time of about 72 ms to respond to a series arc fault, which can fulfill the requirement of real-time detection for arc faults.

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Fast Detection of Weak Arc Faults Based on Progressive Singular-Value-Decomposition and Empirical Analyses

Cited by 2 publications

References 47 publications

Explainability Approach-Based Series Arc Fault Detection Method for Photovoltaic Systems

Explainability Approach-Based Series Arc Fault Detection Method for Photovoltaic Systems

Arc_EffNet: A Novel Series Arc Fault Detection Method Based on Lightweight Neural Network

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