Faulted feeder identification in active grounded networks

Hagh, Mehrdad Tarafdar; Rezaei, Hossein; Daneshvar, Mohammadreza

doi:10.1049/iet-gtd.2018.6996

Cited by 14 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, the faulty feeder detection methods can be divided into two categories according to the origin of signals, which are the active detection [6] and the passive detection. Owing to the richness and stability of transient fault signal features, the detection methods employed in RG systems are mainly the passive detection based on transient signals.…”

Section: A Previous Related Workmentioning

confidence: 99%

Fault Detection Based on the Generalized S-Transform With a Variable Factor for Resonant Grounding Distribution Networks

et al. 2020

View full text Add to dashboard Cite

Fault detection in resonant grounding (RG) distribution networks remains a challenge due to weak fault signals, extremely complex fault conditions, and unstable intermittent arc grounding faults. This paper addresses this issue by applying generalized S-transform (GST) with a variable factor to conduct denoising of transient zero-sequence currents based on threshold filtering followed by time-frequency distribution filtering in sequence. Meanwhile, this paper proposes a comprehensive multi-criteria faulty feeder detection method based on the transient zero-sequence current polarity (criterion 1), the energy relative entropy (criterion 2), and the total transient current energy (criterion 3). Here, criteria 2 and 3 are based on the time-frequency representation of the GST. The performances of the proposed denoising and faulty feeder detection methods are evaluated under single line to ground faults based on simulations conducted using a modeled 10 kV RG networks with overhead and cable mixed lines in addition to reasonably sophisticated permanent and intermittent arc discharge models to ensure that the simulations faithfully represent actual complex working conditions. Compared with existing method, simulation experiments and field test show that the method proposed in this paper provide a better denoising effect with stronger self-adaptability, higher detection accuracy, and a faster calculation speed. INDEX TERMS Fault detection, resonant grounding distribution networks, generalized S-transform, variable factor, denoising method, intermittent arc grounding fault, multi-criteria.

show abstract

Section: A Previous Related Workmentioning

confidence: 99%

Fault Detection Based on the Generalized S-Transform With a Variable Factor for Resonant Grounding Distribution Networks

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The fault equivalent circuit is shown in Fig. 5b, since AC power distribution systems require supply reliability [26][27][28], usually, adopt high-impedance-grounding, the fault current on the DC line is small:…”

Section: Bipolar Short-circuit Faultmentioning

confidence: 99%

“…Δt ≥ Δt set_D (26) where U set_H represents the overvoltage threshold of BCP, U set_L represents the undervoltage threshold of the BCP, Δt set_D represents the delay time of the BCP.…”

Section: Setting and Configuration Of Bcpmentioning

confidence: 99%

See 1 more Smart Citation

Protection schemes and settings of DC distribution systems

Jia

Chen

Zhao

et al. 2020

IET gener. transm. distrib.

View full text Add to dashboard Cite

Flexible DC distribution system has become the research and development trend of the future distribution network with its unique advantages such as high efficiency and flexibility. However, the DC fault identification and protection is challenging due to the fact that the short-circuit current provided by converters is limited and last for a very short period, how to identify and isolate faults reliably using limited fault transient information becomes a serious problem faced by system protection. Based on the analysis of the fault characteristics of three typical faults in the DC distribution system, the setting principle and the calculation methods are proposed, and the reasonable range of protection setting boundaries is derived. According to the requirements of protections, the parameters of the actual DC distribution system are used for setting calculations, and a complete set of DC line protection schemes is designed in this study. Finally, the performance of the proposed protection scheme is verified in PSCAD/EMTDC simulation platform, the built simulation model has been successfully applied to the analysis and calculation in the commissioning of the DC distribution system.

show abstract

“…For example, in case of single-phase grounding fault, the line voltage remains symmetrical, but the phase voltage of non-fault phase will increase by three times. At this time, there is a risk of insulation breakdown of power equipment, which may cause two-point or multi-point grounding short-circuit fault, leading to further development of the fault (Liang, et al, 2020;Hagh, et al, 2019;Miguel, 2022). Therefore, the distribution network fault must be identified in time and handled quickly.…”

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

View full text Add to dashboard Cite

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.

show abstract

Faulted feeder identification in active grounded networks

Cited by 14 publications

References 29 publications

Fault Detection Based on the Generalized S-Transform With a Variable Factor for Resonant Grounding Distribution Networks

Fault Detection Based on the Generalized S-Transform With a Variable Factor for Resonant Grounding Distribution Networks

Protection schemes and settings of DC distribution systems

Fault Analysis Method of Active Distribution Network Under Cloud Edge Architecture

Contact Info

Product

Resources

About