High Impedance Faults Detection and Classification in Renewable Energy-Based Distribution Networks Using Time-Varying Kalman Filtering Technique

Mumtaz, Faisal; Asif, Muhammad; Khan, Haseeb Hassan; Abbas, Shahrukh; Imran, Kashif; Haider, Muhammad Usman; Yousif, Muhammad; Ullah, Asad

doi:10.3390/engproc2022020034

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…The test system had 9-line sections, while switches S-1 and S-2 were provided for the topology changing purpose. The detailed parameterization of the CIGRE benchmark test system is acquired from [14].…”

Section: A Cigre Benchmark Test Bedmentioning

confidence: 99%

“…The presented scheme was very robust and capable of detecting and localizing different faults under various operating conditions. HIF was detected and classified in microgrids using Kalman filtering [14]. In [15] the authors utilized the wavelet spectrum of energy to identify, classify & localize various faults in AC microgrids during the OGM of operation.…”

Section: Introductionmentioning

confidence: 99%

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Hardware Supported Fault Detection and Localization Method for AC Microgrids Using Mathematical Morphology with State Observer Algorithm

Mumtaz,

Imran,

Rehman

et al. 2024

IEEE Access

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Microgrids are the future version of advanced distribution networks due to the fast growth of renewable energy resources near consumers' side. The microgrids are operated in on-grid mode (OGM) with the utility grid, and isolation mode (IM) without the utility grid. This dual operational mode causes protection and control challenges in the microgrids. This research paper suggests an advanced hardware-supported fault detection, phase identification & localization method for AC microgrids. The scheme deploys a Discrete Kalman Filter (DKF) for state estimation of voltage and current signals. Then, a Mathematical Morphology (MM) is engaged for generating a novel fault detection/classification index named segregated energy signature (SES) from estimated voltage and current signals. The system is faulty if the SES is higher than a predefined threshold setting, while phase identification is achieved by default because of the per-phase implementation of DKF&MM. Moreover, the directional features of the cumulative energy signature (CES) are also computed from MM-based non-fundamental current and voltage to localize the faulty section. The established scheme is tested on the CIGRE microgrid benchmark test bed on Matlab-Simulink software. In addition, the suggested method is also examined on the dSPACE MicroLab testing hardware setup in the Smart grid lab. The result illustrates that the proposed scheme successfully detects, classifies, and localizes the low impedance fault (LIF) as well as high impedance fault (HIF) in both operational modes and topological structures with 96.6% accuracy. INDEX TERMSDistributed generation; Discrete Kalman filter; Fault detection; Hardware in the loop; Microgrids protection; Mathematical morphology.

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Section: A Cigre Benchmark Test Bedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hardware Supported Fault Detection and Localization Method for AC Microgrids Using Mathematical Morphology with State Observer Algorithm

Mumtaz,

Imran,

Rehman

et al. 2024

IEEE Access

Self Cite

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Active High-Impedance Fault Detection Method for Resonant Grounding Distribution Networks

Yao,

Liu,

Chen

et al. 2024

IEEE Access

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Aimed at the high-impedance fault detection problem, this paper proposes a fault-feature enhancement method for actively detecting high-impedance faults. The essential characteristic of this method is that the fault features are actively enhanced without affecting the normal terminal voltage. The method consists of two stages, i.e., the feature-enhancement stage and the fault-detection stage. The terminal voltage difference between the fault and normal state is analyzed in the first stage. Then, the feasibility of enhancing terminal voltage difference by the auxiliary signal is proved, and the selection criteria of the auxiliary signal is determined based on IEEE standards. In the second stage, the local-scale energy entropy ratios are applied to reconstruct the measurements. Then, high-impedance faults are detected by discriminating reconstructed data statistical characteristics differences before and during the fault. The simulation results show that the proposed method can accurately identify high-impedance faults with 75 kΩ transition impedance in an environment with a signal-to-noise ratio of 15 dB and reliably detect the faults with 75 kΩ transition impedance on buses and feeders at different positions. The method is robust to noise, transition impedance, and switching events.

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Two-Stage Hybrid-Filtering Based Fault Detection & Classification method for Active Distribution Networks

Mumtaz

Khan

Haider

et al. 2022

2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)

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High Impedance Faults Detection and Classification in Renewable Energy-Based Distribution Networks Using Time-Varying Kalman Filtering Technique

Cited by 6 publications

References 14 publications

Hardware Supported Fault Detection and Localization Method for AC Microgrids Using Mathematical Morphology with State Observer Algorithm

Hardware Supported Fault Detection and Localization Method for AC Microgrids Using Mathematical Morphology with State Observer Algorithm

Active High-Impedance Fault Detection Method for Resonant Grounding Distribution Networks

Two-Stage Hybrid-Filtering Based Fault Detection & Classification method for Active Distribution Networks

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