Fault line detection method based on the improved SVD de‐noising and ideal clustering curve for distribution networks

Wei, Xiangxiang; Wen, Bin; Yang, Dechang; Wang, Bin; Wang, Xiaowei; Yang, Xuefei; Gao, Jie

doi:10.1049/iet-smt.2017.0163

Cited by 14 publications

(3 citation statements)

References 18 publications

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“…We summarize a few recent works that utilize SVD. Wei et al [29] use SVD to denoise Transient Zero Sequence Currents (TZSC) measurements for detecting fault lines using the euclidean distance between ideal clustering curves. Choqueuse et al [30] used SVD to determine if a system is unbalanced and to estimate angular frequencies from 3-phase signals.…”

Section: Related Workmentioning

confidence: 99%

Denoising with Singular Value Decomposition for Phase Identification in Power Distribution Systems

Zaragoza¹,

Rao²

2022

Preprint

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Phase identification is the problem of determining what phase(s) that a load is connected to in a power distribution<br>system. However, real world sensor measurements used for phase identification have some level of noise that can hamper the ability to identify phase connections using data driven methods. Knowing the phase connections is important to keep the distribution system balanced so that parts of the system aren’t overloaded which can lead to inefficient operations, accelerated component degradation, and system destruction at worst. We use Singular Value Decomposition (SVD) with the optimal Singular Value Hard Threshold (SVHT) as part of a feature engineering pipeline to denoise data matrices of voltage magnitude measurements. This approach results in a reduction in frobenius error and an increase in average phase identification accuracy over a year of time series data. K-medoids clustering is used on the denoised voltage magnitude measurements to perform phase identification.<br>

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Section: Related Workmentioning

confidence: 99%

Denoising with Singular Value Decomposition for Phase Identification in Power Distribution Systems

Zaragoza¹,

Rao²

2022

Preprint

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“…Data show that most faults occurred in distribution network, of which 80% are the single‐phase‐to‐earth fault [18–20]. Besides, the integration of DGs poses challenges for distribution network due to the possible variations in fault analysis through power electronic converters [21].…”

Section: Advantages Of Proposed Strategymentioning

confidence: 99%

Current‐limiting strategy based on PR controller and active power filter for droop controlled microgrid

Gao

Wang

Yang³

2019

J. eng.

Self Cite

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“…For different types of signals and noises, finding the best denoising method has always been one of the main problems in signal processing. According to the characteristics of different signals and noises, many basic denoising methods have been proposed, such as Fourier transform, wavelet transform [1], [2], singular value decomposition (SVD) [3], [4], empirical mode decomposition (EMD) [5], [6], blind source separation [7], sparse decomposition [8], stochastic resonance [9], and etc. These basic denoising methods have their own limitations, and cannot get ideal signal denoising result generally.…”

Section: Introductionmentioning

confidence: 99%

Signal Denoising Based on Duffing Oscillators System

Luo¹,

Cui

2020

IEEE Access

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Signal denoising is an important aspect of signal processing. It is a meaningful direction to solve this problem by using chaotic oscillator. As a kind of chaotic oscillator, Duffing oscillator is often used in the field of periodic signal or pulse signal detection, but it has not been used in signal denoising. In this paper, a new Duffing oscillators system for signal denoising is proposed. The system utilizes the coupling between linear and nonlinear restoring forces of the oscillators to achieve complete synchronization of the system. Because the external signal will cause a large deviation of the oscillator's trajectory, while the Gauss white noise will only produce a small deviation. Based on this phenomenon, signal denoising can be realized. The simulation results show that, compared with the existing technology, our method is effective for the denoising of complex irregular signal, and can recover the mutation points of signal well. This method has less computational complexity and is easy to implement parallel computation. It can be used in real-time signal processing.

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Fault line detection method based on the improved SVD de‐noising and ideal clustering curve for distribution networks

Cited by 14 publications

References 18 publications

Denoising with Singular Value Decomposition for Phase Identification in Power Distribution Systems

Denoising with Singular Value Decomposition for Phase Identification in Power Distribution Systems

Current‐limiting strategy based on PR controller and active power filter for droop controlled microgrid

Signal Denoising Based on Duffing Oscillators System

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