New principle for busbar protection based on the Euclidean distance algorithm

Dong, Xingxing; Peng, Qian; Wu, Hao; Chang, Zhengwei; Yue, Yinggao; Zeng, Yi Fan

doi:10.1371/journal.pone.0219320

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…The calculation of ED is simple and intuitive. If two time‐series data are the same, ED is calculated to be zero, otherwise, there is a large value of ED [27]. In order to recognize the fault, the ED concept is applied to SFD signal with the following procedure:

\begin{equation}E{D_{SFD}}\left( i \right)\; = \sqrt {\mathop \sum \limits_{i = 1}^N {{\left( {SFD\left( i \right) - SFD\left( {i - {N_s}} \right)} \right)}^2}} \;\end{equation}

…”

Section: The Proposed Protection Frameworkmentioning

confidence: 99%

Protection framework for microgrids with inverter‐based DGs: A superimposed component and waveform similarity‐based fault detection and classification scheme

Jarrahi

Samet

Ghanbari

2022

IET Generation Trans & Dist

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Since the penetration level of the inverter‐based distributed generations (IBDGs) into microgrids (MGs) is increasing, the protection issues of such networks have become more challenging. The present work aims to design a protection framework for fault detection and classification in IBDG dominated MGs. In the proposed approach, the current waveforms measured from one end of the protected lines are processed using superimposed components. Then, the faults are identified using the Euclidean distance concept. A Pearson correlation coefficient‐based approach is also developed to characterize the faulty phases in the proposed fault classification unit. Furthermore, an auxiliary index is defined to discriminate load change conditions from the faults. In addition to being simple and efficient, the proposed technique is highly capable of functioning in different MG operating modes and configurations without changing its settings. Also, it demonstrates robust performance against IBDG impacts, noises, and stimulating fault scenarios. To assess the scheme proficiency, a modified IEEE 15‐bus system with highly penetrated IBDGs is simulated. The results confirm the quickness and high accuracy of the proposed technique. Moreover, the method is validated in an experimental laboratory test bench. Finally, the superiority of the method is confirmed by a comparative study with other recent methods.

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\begin{equation}E{D_{SFD}}\left( i \right)\; = \sqrt {\mathop \sum \limits_{i = 1}^N {{\left( {SFD\left( i \right) - SFD\left( {i - {N_s}} \right)} \right)}^2}} \;\end{equation}

…”

Section: The Proposed Protection Frameworkmentioning

confidence: 99%

Protection framework for microgrids with inverter‐based DGs: A superimposed component and waveform similarity‐based fault detection and classification scheme

Jarrahi

Samet

Ghanbari

2022

IET Generation Trans & Dist

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A hybrid intelligent busbar protection strategy using hyperbolic S‐transforms and extreme learning machines

Rahimnejad

Gil

Abdoos

et al. 2021

Engineering Reports

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In power systems, busbars connect important components such as generators, transmission lines, and loads. A typical fault occurrence on the busbar may result in the isolation of faulty sections from other normally operating parts of the system resulting from differential protection operation. Although the main busbars' protection scheme is differential protection, its operation is significantly affected by magnetic saturation of the current transformer (CT), particularly during external fault occurrence or energizing power transformers. Saturation of the CT may generate a spurious differential current and is the main reason for the differential scheme malfunctioning. Previous research presented different methods to modify and improve busbars' differential protection scheme. However, there has been lack of a comprehensive study to assess the efficiency of the busbar protection scheme regarding all involved, and influencing aspects including various fault types, energizing power transformer, (high) fault resistance, fault angle (changing from 0 • to 360 • ), and the angle of the sources. Thus, in this study, a hybrid intelligent busbar protection scheme is proposed and the effects of all these factors are investigated. The proposed strategy utilizes the hyperbolic S-transform as a signal processing technique to extract an efficient feature that is able to discriminate internal faults from other abnormal modes, that is, external faults and inrush current under CT saturation. To obtain this goal, a learning-based classification method known as extreme learning machines is used to classify the system conditions based on the selected features. The proposed protection scheme was found to have low sensitivity to CT saturation and noise and was able to accurately detect internal faults from half a cycle to one cycle of the power system depending on the fault resistance.

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An efficient hybrid methodology for detection of cancer-causing gene using CSC for micro array data

Sampathkumar

Rastogi

Arukonda

et al. 2020

J Ambient Intell Human Comput

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New principle for busbar protection based on the Euclidean distance algorithm

Cited by 3 publications

References 5 publications

Protection framework for microgrids with inverter‐based DGs: A superimposed component and waveform similarity‐based fault detection and classification scheme

Protection framework for microgrids with inverter‐based DGs: A superimposed component and waveform similarity‐based fault detection and classification scheme

A hybrid intelligent busbar protection strategy using hyperbolic S‐transforms and extreme learning machines

An efficient hybrid methodology for detection of cancer-causing gene using CSC for micro array data

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