Ground and phase fault classification for half-wavelength transmission lines relaying purposes

Bertoletti, Augusto Zanin; Cardoso, J.R.; Marchesan, Gustavo; Prado, Josue Campos do

doi:10.1016/j.ijepes.2023.108966

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…When predicting the service life, we should try our best to exclude the influence of external interference factors and human factors in order to obtain more accurate and reliable results. The service life of the relay protection device can guide the development, design, processing, creation and maintenance of the device [8][9]. Due to the rapid upgrading of relay protection devices and the variety of products, the service life of relay protection devices can not be well determined.…”

Section: Service Life Prediction Structure Of Relay Protection Devicementioning

confidence: 99%

Service life prediction method of relay protection device considering acceleration state and operation characteristics

Jiao,

Liu,

Wen

et al. 2024

International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023)

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Section: Service Life Prediction Structure Of Relay Protection Devicementioning

confidence: 99%

Service life prediction method of relay protection device considering acceleration state and operation characteristics

Jiao,

Liu,

Wen

et al. 2024

International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023)

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“…Classification loss and regression loss belong to the loss types of the two. The expression formula is as follows: The probability that there is no defect in the anchor is i c , the weight is λ [8][9].The expression formula of the two loss functions is as follows:…”

Section: Design Of Digital Imaging Software For Transmission Line Def...mentioning

confidence: 99%

Design of x-ray digital imaging software for transmission line flaw detection robot

Li,

Zhang,

et al. 2024

Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023)

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Real time and stability are crucial requirements in transmission line inspection. Software can quickly process image data and output results while maintaining stability to cope with various complex environments and operating conditions, which is highly challenging. Therefore, this article designs a new X-ray digital imaging software for transmission line inspection robots. The imaging system for robot X-ray defect detection software is composed of a BJI-1X light generator and a Raines digital imager. According to this imaging principle, internal image information of defects is collected using X-ray digital imaging instruments, and defect categories are obtained using image processing software based on its hierarchical recognition model. Using Faster RCNN algorithm to construct defect area localization model, improve digital imaging accuracy, and ultimately output digital imaging results. The experimental results show that when the balance factor is set to 0.30 and the adjustment factor is set to 1.5, the software in this paper has the highest accuracy in digital imaging of defects. The software in this article can accurately locate the defect area, obtain its location boundary box, and has high clarity. It also has excellent digital imaging performance in determining the type of digital imaging defect.

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“…The methods proposed in References [19][20][21][22][23][24] do not require the identification of the traveling wave head, and their measurement time scales are large enough. Therefore, we classify this method as a failure analysis method.…”

Section: Status Of the Studymentioning

confidence: 99%

“…Therefore, we classify this method as a failure analysis method. Among them, References [19,20] built transmission equations to identify the fault location, based on different frequency components of voltage and current, while References [21,22] identified the fault location based on the same frequency (IF) component of both the voltage and current. Reference [23] obtained the voltage expression of the fault point from both ends and acquired the fault distance based on the equality of the calculation results.…”

Section: Status Of the Studymentioning

confidence: 99%

“…Existing fault analysis methods in References [14][15][16][17][18][19][20][21][22][23][24][25] cannot be applied to both hybrid lines and feeders with branches in distribution networks and have significant limitations. Distribution network fault characteristics are complex, and there are differences between various distribution network fault characteristics.…”

Section: Status Of the Studymentioning

confidence: 99%

See 1 more Smart Citation

Application of a Centroid Frequency-Based Back Propagation Neural Network Fault Location Method for a Distribution Network Considering Renewable Energy Assessment

Zhao,

Lu,

Chen

et al. 2024

Electronics

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The distribution network is a crucial component of the power system as it directly connects to users and serves the purpose of distributing power and balancing the load. With the integration of new energy sources through distributed generation (DG), the distribution network has undergone a transformation from a single power radial network into a complex multi-source network. Consequently, traditional fault location methods have proven inadequate in this new network structure. Therefore, the focus of this paper is to investigate fault location techniques specifically tailored for DG integration into distribution networks. This paper analyzes how fault conditions impact the characteristics of single-phase grounding faults and extracts spectral feature quantities to describe differences in zero-sequence currents under various fault distances. This paper also proposes a fault location method based on centroid frequency and a BPNN (back propagation neural network). The method uses centroid frequency to describe the features of zero-sequence currents; to simulate the mapping relationship between fault conditions and spectral features, BPNN is employed. The mapping relationship differs for different lines and distribution networks. When a line faults, the spectral features are calculated, along with the transition resistance and fault closing angle. The corresponding mapping relationship is then called upon to complete distance measurements. This location method can be applied to various types of distribution lines and fault conditions with high accuracy. Even with insufficient training samples, sample expansion can ensure accuracy in fault distance measurement. We built a distribution network consisting of four feeders with different types and lengths of each line on Simulink and verified the effectiveness of the proposed method by setting different fault conditions. The results suggest that the method has a clear advantage over other frequency domain-based approaches, especially for hybrid lines and feeder lines with branches in distribution networks. Additionally, the method achieves a measurement accuracy within a range of 100 m.

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Ground and phase fault classification for half-wavelength transmission lines relaying purposes

Cited by 5 publications

References 15 publications

Service life prediction method of relay protection device considering acceleration state and operation characteristics

Service life prediction method of relay protection device considering acceleration state and operation characteristics

Design of x-ray digital imaging software for transmission line flaw detection robot

Application of a Centroid Frequency-Based Back Propagation Neural Network Fault Location Method for a Distribution Network Considering Renewable Energy Assessment

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