scholarly journals Analysis of High Impedance Faults Current Using Fourier, Wavelet and Stockwell Transforms

2020 ◽  
Author(s):  
Gabriela N. Lopes ◽  
Luiz H. P. C. Trondoli ◽  
José Carlos M. Vieira
Keyword(s):  
Distribution Systems ◽  
Test Field ◽  
Medium Voltage ◽  
Detection Techniques ◽  
Promising Tool ◽  
High Impedance ◽  
Random Behavior ◽  
Key Characteristics ◽  
High Impedance Faults ◽  
High Degree

Detection of high impedance faults (HIFs) in distribution systems is a challenging task, which has attracted the interest of the researchers for decades. The HIF current random behavior and its lowmagnitude cause difficulties for a reliable detection by traditional protection methods. Therefore, the hazards for grid devices, people and animals safety, associated with HIFs, motivate the research of new detection techniques. However, there is no fully efficient solution for this problem. In this context, this paper aimed to characterize HIFs by a set of real measurements considering different type of soils employing Fourier (FT), Wavelet (WT) and Stockwell Transforms (ST). The measurements were performed at the fault spot in a medium voltage test field specially built for this purpose. The idea is to highlight key characteristics of the HIF current waveforms pointed out by each of transform and assess which ones can be used as a promising tool for HIF detection. The results showed that the HIF current can be characterized by the interharmonic behavior during the fault, extracted by FT and by the high degree of energy variations at specific decomposition levels of WT and ST.

scholarly journals High-Impedance Faults in Power Distribution Systems: A Narrative of the Field's Developments

2020 ◽  
Author(s):  
Douglas Pinto Sampaio Gomes ◽  
Cagil Ozansoy
Keyword(s):  
Power Distribution ◽  
Distribution Systems ◽  
Historical Narrative ◽  
Power Distribution Systems ◽  
Knowledge Gaps ◽  
High Impedance ◽  
High Impedance Faults

High-impedance faults in power distribution systems is a lasting problem with decades of steady investigation. Due to the complexity of the problem, the field can also be challenging to navigate. Although there exist surveys of the field in the literature, it is not easy to find a comprehensive contextualization of how and when the field developments unfolded. This paper presents the historical narrative of the progress and developments based on the most cited papers since the inception of the field. The accounts are not limited to archaic and obsolete works. They are all contextualized from the seminal papers to contemporary methods and related technology. Quantitative figures on the survey of the methods and relevant knowledge gaps are also discussed at the closing of the paper.

scholarly journals WAMs Based Eigenvalue Space Model for High Impedance Fault Detection

2021 ◽  
Vol 11 (24) ◽  
pp. 12148
Author(s):  
Gian Paramo ◽  
Arturo S. Bretas
Keyword(s):  
Fault Detection ◽  
State Space ◽  
Distribution Systems ◽  
State Space Model ◽  
Real Life ◽  
Learning Tools ◽  
Space Model ◽  
High Impedance ◽  
High Impedance Fault ◽  
High Impedance Faults

High impedance faults present unique challenges for power system protection engineers. The first challenge is the detection of the fault, given the low current magnitudes. The second challenge is to locate the fault to allow corrective measures to be taken. Corrective actions are essential as they mitigate safety hazards and equipment damage. The problem of high impedance fault detection and location is not a new one, and despite the safety and reliability implications, relatively few efforts have been made to find a general solution. This work presents a hybrid data driven and analytical-based model for high impedance fault detection in distribution systems. The first step is to estimate a state space model of the power line being monitored. From the state space model, eigenvalues are calculated, and their dynamic behavior is used to develop zones of protection. These zones of protection are generated analytically using machine learning tools. High impedance faults are detected as they drive the eigenvalues outside of their zones. A metric called eigenvalue drift coefficient was formulated in this work to facilitate the generalization of this solution. The performance of this technique is evaluated through case studies based on the IEEE 5-Bus system modeled in Matlab. Test results are encouraging indicating potential for real-life applications.

scholarly journals Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3623
Author(s):  
Khushwant Rai ◽  
Farnam Hojatpanah ◽  
Firouz Badrkhani Ajaei ◽  
Katarina Grolinger
Keyword(s):  
Evaluation Studies ◽  
Operating Conditions ◽  
Training Data ◽  
Detection Techniques ◽  
High Impedance ◽  
Distribution Shape ◽  
Normal Behavior ◽  
Convolutional Autoencoder ◽  
High Impedance Fault ◽  
High Impedance Faults

High-impedance faults (HIF) are difficult to detect because of their low current amplitude and highly diverse characteristics. In recent years, machine learning (ML) has been gaining popularity in HIF detection because ML techniques learn patterns from data and successfully detect HIFs. However, as these methods are based on supervised learning, they fail to reliably detect any scenario, fault or non-fault, not present in the training data. Consequently, this paper takes advantage of unsupervised learning and proposes a convolutional autoencoder framework for HIF detection (CAE-HIFD). Contrary to the conventional autoencoders that learn from normal behavior, the convolutional autoencoder (CAE) in CAE-HIFD learns only from the HIF signals eliminating the need for presence of diverse non-HIF scenarios in the CAE training. CAE distinguishes HIFs from non-HIF operating conditions by employing cross-correlation. To discriminate HIFs from transient disturbances such as capacitor or load switching, CAE-HIFD uses kurtosis, a statistical measure of the probability distribution shape. The performance evaluation studies conducted using the IEEE 13-node test feeder indicate that the CAE-HIFD reliably detects HIFs, outperforms the state-of-the-art HIF detection techniques, and is robust against noise.

scholarly journals HIF Detection and Classification in Distribution Systems using Wavelet Transforms

2019 ◽  
Vol 8 (4) ◽  
pp. 10008-10013
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Distribution Systems ◽  
Fault Classification ◽  
Electrical Transmission ◽  
High Impedance ◽  
Fault Currents ◽  
Hand Off ◽  
High Impedance Faults ◽  
Highlight Extraction

High Impedance Faults (HIF) are generally occurs on distribution line. HIFs are, by and large, hard to recognize through traditional assurance, for example, distance or over current relays. This is primarily because of hand-off inhumanity toward the low level fault currents as well as constraints on other hand-off settings forced by HIFs. Regular assurance hand-off framework won't have the option to distinguish the HIFs and excursion the security transfer. HIFs on electrical transmission and dissemination systems include arcing as well as nonlinear attributes of flaw impedance which cause repeating example and contortion. Subsequently, the goal of most discovery plans is to recognize extraordinary highlights in examples of the voltages and current related with HIFs. Most traditional flaw discovery strategies for HIF for the most part include preparing data dependent on the component extraction of post HIF current and voltage. Wavelet transform is most appropriate for HIF location and for fault classification. This paper depicts another shortcoming location procedure which includes catching the present sign created in a framework under HIFs. The identification procedure depends on ascertaining the total entirety of the wavelet transform detail coefficients for one period. Wavelet transform is utilized for the disintegration of sign and highlight extraction.

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