A Single-Terminal Fault Location Method for HVDC Transmission Lines Based on a Hybrid Deep Network

Wang, Lei; He, Yigang; Li, Lie

doi:10.3390/electronics10030255

Cited by 12 publications

(5 citation statements)

References 36 publications

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“…Compared with the EMD algorithm, the VMD algorithm detected more accurately under noisy conditions. In order to improve the fault location accuracy, Wang et al [24] introduced the VMD feature quantity into a kind of fault location model based on a deep hybrid Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network, which effectively improved the learning effect of the model.…”

Section: Introductionmentioning

confidence: 99%

A Two-Terminal Directional Protection Method for HVDC Transmission Lines of Current Fault Component Based on Improved VMD-Hilbert Transform

Liu,

Han,

et al. 2023

Energies

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The traveling wave protection of high voltage direct current (HVDC) transmission lines is susceptible to the influence of transition resistance. As a backup protection, current differential protection has absolute selectivity, but usually requires an increase in delay to avoid misoperation caused by distributed capacitance on the line, resulting in a longer action time. Based on this, a two-terminal directional protection method for HVDC transmission lines is proposed based on Sparrow Search Algorithm (SSA)-Variational Mode Decomposition (VMD) and Hilbert phase difference. On the basis of analyzing the directional characteristics of the current fault component at both ends of the rectifier and inverter sides under different faults, SSA is first used to optimize the parameters of VMD. The residual components representing the direction of the current fault component at both ends are extracted through VMD, and then the Hilbert phase difference of the residual components at both ends is calculated to identify faults inside and outside the line area. In addition, fault pole selection can be achieved based on the ratio of the sum of multi-band Hilbert energy of single-terminal voltage fault components at the positive and negative poles. Simulation experiments have shown that the proposed protection scheme can quickly and effectively identify fault and has good tolerance to transition resistance and noise interference.

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Section: Introductionmentioning

confidence: 99%

A Two-Terminal Directional Protection Method for HVDC Transmission Lines of Current Fault Component Based on Improved VMD-Hilbert Transform

Liu,

Han,

et al. 2023

Energies

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“…Luo et al (2019) introduced the automatic encoder into the deep learning network model to realize the fault identification and analysis of radial distribution network; (Sun et al, 2021) proposed an adaptive long short memory network regression model to realize the state detection and fault identification of power transmission network by establishing the corresponding relationship between similar time factors and long and short-term memory network (LSTM). Based on the high-voltage direct current high voltage direct current system (HVDC), Wang, He and Li (2021) optimized convolutional neural network (CNN) and LSTM network models to realize fault identification and judgment of transmission lines; Rai, Londhe and Raj (2020) focused on the scene of active distribution network, and used CNN to build a fault identification model to support its safe operation. Based on the convolutional neural network (CNN), Zhang et al (2022) constructed a network structure fully suitable for power grid fault diagnosis, and took the minimum cross entropy as the goal to mine the deep fault features to achieve the fault diagnosis analysis of AC/DC transmission system.…”

Section: Introductionmentioning

confidence: 99%

Fault Analysis Method of Active Distribution Network Under Cloud Edge Architecture

Dong

Sha

Song

et al. 2023

International Journal of Information Technologies and Systems Approach

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Efficient fault treatment of active distribution network is an important guarantee to ensure the steady-state reliability of the system. In order to improve the accuracy of distribution network fault identification and analysis, a fault processing method based on deep learning is proposed in this paper. This method collects massive heterogeneous data sets using patrol robot to realize real-time perception and accurate acquisition of distribution network status. Relying on the processing mode of distribution network cloud edge collaboration, the principal component analysis method is used at the edge to effectively remove redundant data, providing a complete and reliable data support for the deep network model. Meanwhile, the attention mechanism is added to the cloud to improve the depth confidence network, further realizing the extraction of useful feature information for complex data sets and avoiding the interference of irrelevant information on the recognition results. The simulation experiment is based on the actual active distribution network model. The experimental results show that the fault identification accuracy of the proposed method can reach 0.9255, indicating an excellent distribution network fault identification and analysis ability to support safe operation of active distribution network.

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“…The maintenance position is determined by the flexible manipulator. The application of the flexible robot for automatic maintenance of the heating line can not only improve the maintenance accuracy, but also reduce the maintenance delay [17][18][19]. Therefore, in order to improve the effectiveness of automatic maintenance of heating defects in high-voltage transmission lines, this paper uses a flexible maintenance robot, compensates the positioning error through a highly adaptive maintenance platform, adjusts the movement state of the maintenance arm, and designs an effective automatic maintenance method of heating defects in high-voltage transmission lines.…”

Section: Introductionmentioning

confidence: 99%

Research on automatic maintenance method of flexible maintenance robot for heating defects of high voltage transmission lines

Hai

Chen

et al. 2023

Second International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2023)

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The conventional automatic maintenance method for heating defects of high voltage transmission lines mainly uses the aggregate spatiotemporal correlation analysis method to obtain the location characteristics of heating defects, which is vulnerable to the change of defect attributes, leading to the high deviation of automatic maintenance positioning distance. Therefore, it is necessary to design a new automatic maintenance method for heating defects of high voltage transmission lines using flexible maintenance robots. That is to say, a flexible robot maintenance platform for thermal defects of highvoltage transmission lines is constructed, which compensates the positioning error of the automatic maintenance of thermal defects of the flexible maintenance robot, optimizes the movement state of the joint of the automatic maintenance arm for thermal defects of the flexible maintenance robot, and thus realizes the automatic maintenance of thermal defects of high-voltage transmission lines. The experimental results show that under the same maintenance environment, the automatic maintenance positioning distance of the automatic maintenance method for heating defects of high-voltage transmission lines based on the flexible maintenance robot designed in this paper is consistent with the actual distance for heating defects, and there is no automatic maintenance positioning deviation, which proves that the designed automatic maintenance method has good maintenance effect, reliability, and certain application value. It has made a certain contribution to improving the operation reliability of high-voltage transmission lines.

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A Single-Terminal Fault Location Method for HVDC Transmission Lines Based on a Hybrid Deep Network

Cited by 12 publications

References 36 publications

A Two-Terminal Directional Protection Method for HVDC Transmission Lines of Current Fault Component Based on Improved VMD-Hilbert Transform

A Two-Terminal Directional Protection Method for HVDC Transmission Lines of Current Fault Component Based on Improved VMD-Hilbert Transform

Fault Analysis Method of Active Distribution Network Under Cloud Edge Architecture

Research on automatic maintenance method of flexible maintenance robot for heating defects of high voltage transmission lines

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