A Fault Location Identification Technique for Active Distribution System

Ganivada, Phanindra Kumar; Jena, Premalata

doi:10.1109/tii.2021.3103543

Cited by 24 publications

(3 citation statements)

References 27 publications

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“…Literature 21 , compared to literature 20 , is independent of fault type, but it requires communication synchronization. The methods proposed in literatures 22,23 are applicable to DG-integrated distribution networks, but do not consider the effects of neutral grounding method, transition resistance and communication desynchronization. The specific method comparison results are shown in Table 5.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Combining Eqs. ( 19), (22), and (25), it can be obtained that the phase of ΔI M1 is behind the phase of I Mpre , whereas the phase of ΔI M2 is ahead of the phase of I Mpre . Therefore, the phase angle variation of the positive sequence fault component at both ends of the Region 1 can be approximated as follows: www.nature.com/scientificreports/ From above, when a fault occurs in a downstream region with DG access, the positive sequence fault current phase upstream of the fault point will be ahead of the normal operation phase.…”

Section: Fault Features Of Distribution Network With Dgmentioning

confidence: 94%

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Current spectral norm and phase variation based fault region identification for active distribution network

Chen,

Li,

Zeng

et al. 2024

Sci Rep

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The paper presents a fault region identification method for the active distribution network (ADN) with limited PMU. First, PMU configuration and region division strategies are proposed based on the network topology. Next, the difference in three-phase current variations between the normal and fault regions of the ADN is analyzed. A multi-dimensional state monitoring matrix is built using the measured current data. The spectral norm ratio coefficient is constructed based on the 2-norm to lower the complexity of the multi-dimensional state monitoring matrix and quantify the difference in state changes before and after the fault occurs in each region. Then, the fault region is identified by combining each region’s spectral norm ratio coefficient and the change of the current phase. Finally, an IEEE 33-node simulation model is created to simulate faults under different working conditions. According to the simulation results, the suggested approach is less impacted by fault type, neutral point grounding mode, and transition resistance. Furthermore, even if the communication does not match the rigorous synchronization requirements, the proposed method can still complete the fault identification of the distribution network correctly and has high robustness.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Fault Features Of Distribution Network With Dgmentioning

confidence: 94%

Current spectral norm and phase variation based fault region identification for active distribution network

Chen,

Li,

Zeng

et al. 2024

Sci Rep

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“…The use of neural networks in fault detection is a recent study area that has advanced quickly over the past ten years compared to the error detection method. The expert approach involves reasoning to resolve the issue; however, it is time-consuming and contains numerous regulations [15]. Although Petri nets can parallel data processing, simultaneous processing becomes a routine task, thus increasing the complexity [3].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks-Based Fault Diagnosis Model for Distribution Network

Chen

et al. 2023

DGAEJ

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With many branch lines in radiant distribution networks, diagnosing faults in a distribution network is very difficult. It is of great significance to identify different types of faults quickly and accurately for the stable operation of the power grid. This research presents a fault identification model for a distribution network based on artificial neural networks. The principal component analysis first extracts features from transitory data in a distribution network. The resulting low-dimensional data is subsequently used to update the artificial neural network model. The artificial neural network may also identify the type of fault. The proposed model’s fault detection accuracy is improved over the traditional approach by examining distribution network fault data during the simulation test.

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