High-impedance fault identification and location by using mode decomposition integrated adaptive multi-kernel extreme learning machine technique for distributed generator-based microgrid

Sarangi, Swetalina; Sahu, Binod Kumar; Rout, Pravat Kumar

doi:10.1007/s00202-022-01658-6

Cited by 8 publications

(2 citation statements)

References 53 publications

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“…The authors in [120] proposed a hybrid clustering algorithm based on the KNN algorithm and K-means algorithm to provide reduced computational complexity in fault scenarios and aim to enable real-time data processing for fault localization. The authors in [121] proposed a noise-based decomposition technique called "ensembled empirical mode decomposition (EEMD) technique" and "adaptive multi-kernel extreme learning machine" (AMKELM) for high-impedance faults (HIFs) location in MGs. The EEMD technique is used to decompose the fault current and extract the current amplitude signal to calculate the differential energy profile.…”

Section: Data Feature Extraction For Fault Localization and Intellige...mentioning

confidence: 99%

Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends

et al. 2023

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Thanks to smart grids, more intelligent devices may now be integrated into the electric grid, which increases the robustness and resilience of the system. The integration of distributed energy resources is expected to require extensive use of communication systems as well as a variety of interconnected technologies for monitoring, protection, and control. The fault location and diagnosis are essential for the security and well-coordinated operation of these systems since there is also greater risk and different paths for a fault or contingency in the system. Considering smart distribution systems, microgrids, and smart automation substations, a full investigation of fault location in SGs over the distribution domain is still not enough, and this study proposes to analyze the fault location issues and common types of power failures in most of their physical components and communication infrastructure. In addition, we explore several fault location techniques in the smart grid’s distribution sector as well as fault location methods recommended to improve resilience, which will aid readers in choosing methods for their own research. Finally, conclusions are given after discussing the trends in fault location and detection techniques.

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Section: Data Feature Extraction For Fault Localization and Intellige...mentioning

confidence: 99%

Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends

et al. 2023

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“…In the last three decades, several methods have been presented for the detection of HIF. These methods can be generally classified into frequency domain algorithms [6], time domain algorithms [7], the combined frequency and time domain (hybrid) algorithms [8], and expert systems [9].…”

Section: Introductionmentioning

confidence: 99%

A Simple Approach to Detect High Impedance Fault Using Morphological Gradient Edge Detector

Salehi,

Zolfaghari,

Maritz

2024

IEEE Access

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Detection of high impedance faults (HIF) is one of the biggest challenges in power distribution networks. HIF usually occurs when conductors in the distribution network are broken and accidently come into contact the ground or a tree branch. The current of this fault is close to the load current level and cannot be detected by overcurrent relays. Also, some regular system phenomena such as capacitor switching, load switching, and inrush current and saturation phenomena in current transformer (CT) represent some features which may overlap the components of HIF; making HIF detection schemes more complex. In this paper, a new method for HIF detection is presented which is able to distinguish any type of HIF from regular system phenomena. To achieve this, the scheme of morphological gradient edge detection (MGED) is used to process voltage signals. The MGED extracts two main features from the processed signals: first, the edges or changes in the signal are elicited and then, these features are extracted after two cycles from the onset of the fault. Then, based on these features, a high impedance fault detection index (HIFDI) is introduced for distinguishing and classifying HIF from other regular system phenomena. The simulation results for different types of HIF fault in a sample 20 kV distribution feeder and IEEE 34-bus distribution test system using EMTP confirm the fast and accurate performance of the proposed method.INDEX TERMS High impedance fault, mathematical morphology, distribution network.

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Fault Location Estimation Using Ensemble Averaging Decomposition and Hybrid Meta-Heuristic Optimized Kernel-Based ELM Technique for DG-Integrated Microgrid

Sarangi,

Sahu,

Rout

2023

Smart Grids and Energy

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High-impedance fault identification and location by using mode decomposition integrated adaptive multi-kernel extreme learning machine technique for distributed generator-based microgrid

Cited by 8 publications

References 53 publications

Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends

Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends

A Simple Approach to Detect High Impedance Fault Using Morphological Gradient Edge Detector

Fault Location Estimation Using Ensemble Averaging Decomposition and Hybrid Meta-Heuristic Optimized Kernel-Based ELM Technique for DG-Integrated Microgrid

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