Fault Detection and Classification for Wide Area Backup Protection of Power Transmission Lines Using Weighted Extreme Learning Machine

Harish, Ani; Prince, A.; Jayan, M. V.

doi:10.1109/access.2022.3196769

Cited by 12 publications

(2 citation statements)

References 46 publications

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“…All of these factors may result in significant errors during the machine learning training stage [58]. As a result, a preprocessing stage is necessary in which the collected data is subjected to activities such as labeling [59], data normalization, outlier removal, and missing data treatment [58]. Further, in some applications, preprocessing may include resampling, data compression, and data splitting [60].…”

Section: ) Data Preprocessingmentioning

confidence: 99%

Machine Learning Methods for Fault Diagnosis in AC Microgrids: A Systematic Review

Zaben,

Worku,

Hassan

et al. 2024

IEEE Access

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AC microgrids are becoming increasingly important for providing reliable and sustainable power to communities. However, the evolution of distribution systems into microgrids has changed the way they respond to faults and hence their protection requirements. Faults in microgrids could hinder operation stability and damage the system components. The types, locations, and resistances of faults, as well as microgrid operation modes, distributed generation penetration levels, load changes, and system topologies, all affect how the microgrid responds to faults. In order to offer quick restoration and to protect the microgrid components, fault detection and classification are therefore essential for microgrids. In this direction, unconventional methods such as artificial intelligence have been increasing in popularity over the last years. Pattern recognition is a methodology that machine learning as an approach to artificial intelligence is concerned with. The combination of protection with machine learning may be motivating in order to achieve the goal of intelligent operation in the smart grid. In this paper, fault detection, classification and location methods are reviewed for microgrid application. Different methods applied for both fault location and fault Machine Learning Supervised Unsupervised Reinforcement Learning Output (continuous) Output (discrete) Semi-supervised Regression Classification Dimension Reduction Association Rules Clustering Unlabeled Data Combined data (Labeled + Unlabeled)

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Section: ) Data Preprocessingmentioning

confidence: 99%

Machine Learning Methods for Fault Diagnosis in AC Microgrids: A Systematic Review

Zaben,

Worku,

Hassan

et al. 2024

IEEE Access

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show abstract

“…The vibration signal of diesel engine is unsteady and nonlinear. Because there are many vibration excitation sources of diesel engine, many noises are often mixed in the signal [17][18]. There are three main steps for the state detection of the cylinder.…”

Section: Analysis Of Diesel Engine Vibration Characteristicsmentioning

confidence: 99%

Working State of Diesel Engine Cylinder Based on Continuous Wavelet Transform Algorithm

2022

KME

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In reality, discrete Fourier transform is a very important signal processing method. It is widely used in analysis and control systems. When the diesel engine starts, the excitation signal in the cylinder will reflect the basic principle of wavelet transform technology. In order to monitor the working state of diesel engine in real time, the continuous wavelet transform algorithm is studied in this paper. In this paper, we mainly use the fault detection experiment, use the continuous wavelet transform algorithm to process the data, and study the working state of the cylinder. The experimental results show that in most cases, the diesel engine cylinder is not working fully. The test accuracy of multi state fixed point signal is 85.28%, and the test accuracy of mobile acquisition signal is 87.41%.

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Line protection in presence of high penetration of wind energy: a review on possible solutions

Prasad,

Biswal,

Ray

2024

Electr Eng

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Fault Detection and Classification for Wide Area Backup Protection of Power Transmission Lines Using Weighted Extreme Learning Machine

Cited by 12 publications

References 46 publications

Machine Learning Methods for Fault Diagnosis in AC Microgrids: A Systematic Review

Machine Learning Methods for Fault Diagnosis in AC Microgrids: A Systematic Review

Working State of Diesel Engine Cylinder Based on Continuous Wavelet Transform Algorithm

Line protection in presence of high penetration of wind energy: a review on possible solutions

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