High Impedance Faults Detection in EHV Transmission Lines Using the Wavelet Transforms

Eldin, E.S.T.; Ibrahim, Doaa Khalil; Aboul-Zahab, Essam M.; Saleh, Saber M.

doi:10.1109/pes.2007.385458

Cited by 10 publications

(5 citation statements)

References 9 publications

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“…The method is independent of load variations and unbalanced conditions. The algorithm is fast, accurate, and simple; it can be added to an existing digital relay microprocessor (Eldin et al, 2007).…”

Section: Jmarty Model Test Systemmentioning

confidence: 99%

“…The scheme utilizes the ripple frequencies and sub-and superharmonics usually associated with the HIF phenomenon. Eldin et al (2007) introduced two methods for detecting HIFs in extra-highvoltage transmission lines. Both approaches investigated the origins and dynamics of HIF-related arcing.…”

Section: Miscellaneous Hif Detection Schemesmentioning

confidence: 99%

“…Eldin et al (2007) considered the JMARTY model with Egyptian transmission line parameters, such as a transmission line length of 125 km and a resistive load of 600 MW, as shown in Figure 9A. DWT's feature extraction technique with classifier moving window pattern recognition is used in HIF detection in extra-high voltage transmission.…”

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confidence: 99%

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Application of signal processing techniques and intelligent classifiers for high-impedance fault detection in ensuring the reliable operation of power distribution systems

Subathra

George

et al. 2023

Front. Energy Res.

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High-impedance fault (HIF) is always a threat and the biggest challenge in the power transmission and distribution system (PTDS). For a PTDS to operate effectively, HIF diagnosis is essential. However, given the HIF’s nature and the involved complexity, detection, identification, and fault location are difficult. This will be even more complicated in conventional PTDSs as they are inefficient and highly vulnerable. Given the importance and urgent need for HIF diagnosis in PTDS, this study reviews state-of-the-art HIF phenomenon and detection techniques and proposes the use of “various signal processing techniques for fault feature extraction” and “ different classifiers for identifying HIF.” First, HIF current/voltage signals are analyzed using signal processing techniques, which include the discrete wavelet transform (DWT), pattern recognition, Kalman filtering, TT transform, mathematical morphology (MM), S transform (ST), fast Fourier transform (FFT), principal component analysis (PCA), linear discriminant analysis (LDA), and wavelet transforms, such as dual-tree, maximum overlap discrete wavelet transform (MODWT), and lifting wavelet transform (LWT). Second, the various HIF and non-HIF faults are classified using intelligent classifiers. The intelligent classifiers include artificial neural networks (ANNs), probabilistic neural networks (PNNs), genetic algorithms (GAs), fuzzy logic, adaptive neuro-fuzzy interface system, support vector machine (SVM), extreme learning machine (ELM), adaptive resonance theory, random forests (RFs), decision trees (DTs), and convolution neural networks (CNNs). In addition to the comparative discussion of various classifier techniques, their evaluation criterion and performance are prioritized. Third, this review also studied different test systems, such as radial distribution network, mesh distribution network, IEEE 4 node, IEEE 13 node feeder, IEEE 34 node feeder, IEEE 39 node feeder, IEEE 123 node feeder, Palash feeder, and test microgrid systems, to assess the pertinence of various HIF detection schemes and the behavior along with methods to locate the HIF. Overall, we believe this review would serve as a comprehensive compendium of advanced techniques for HIF diagnosis in different test systems.

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Section: Jmarty Model Test Systemmentioning

confidence: 99%

Section: Miscellaneous Hif Detection Schemesmentioning

confidence: 99%

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Application of signal processing techniques and intelligent classifiers for high-impedance fault detection in ensuring the reliable operation of power distribution systems

Subathra

George

et al. 2023

Front. Energy Res.

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“…For detection, classification and estimation of faults location ANN is used and for this purpose the three phase voltages and currents of local terminal are decomposed with Bior4.4 mother wavelet. El Sayed Tag Eldin et al [17] proposed discrete wavelet transformed technique for high impedance faults detection in EHV transmission line. The authors used ATP-EMTP simulation for testing the proposed scheme to present voltage signals of a one pre-fault cycle and other cycle after fault occurred and the results have various advantages over traditional HIF (High impedance fault) detection techniques.…”

Section: Wavelet Transformationmentioning

confidence: 99%

Intelligent techniques for fault diagnosis in transmission lines — An overview

Singh

Vishwakarma

2015

2015 International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE)

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This paper presents survey and review of research and development in the field of faults detection, classification and their location that occurs in the transmission network. Transmission lines are integral part of the power system network and its main aim is to transmit the generated power to the consumer with least interruption. With an ever-increasing demand of electric power day by day because of increasing industrialization and urbanization of life style, fast and accurate fault analysis is very essential for better performance and minimal interruptions in power system. Fast detection and clearing of faults in transmission lines is very important for maintaining the normal operation of power system network. This paper presents a comprehensive review of various algorithms that have been developed and used over the last few years for the detection and classification of faults in transmission lines and mainly concentrate on following algorithm and their implementation for fault analysis such as, wavelet transform (WT), discrete wavelet transform (DWT), multi-resolution analysis (MRA), Wavelet energy entropy, artificial neural network (ANN), wavelet-neuro-fuzzy approach and support vector machine (SVM). Keywords-Wavelet transform (WT), Discrete wavelet transform (DWT), Artificial neural network (ANN), Adaptive neuro-fuzzy inference system (ANFIS), Support vector machine (SVM).I.

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“…The identification approach based on frequency domain involved 3 rd harmonic current (Sharaf & Wang, 2003), statistical pattern recognition approach (Sedighi et al, 2005), energy technique (Aucoin & Russell, 1982), randomness technique (Russell & Chinchali, 1989), half cycle asymmetry (Kwon et al, 1991) and amplitude ratio technique. c) Wavelet transform: Wavelet transform can be employed to examine the transient phenomena of HIFs signals in both the time and frequency domains (Aucoin & Jones, 1996;Eldin et al, 2007;Saleh, 2009;Shaaban, 2010;Aucoin, 1987). d) Adaptive Neuro Fuzzy Inference System (ANFIS) mixed with some digital signal processing techniques (Jang, 1993).…”

Section: Introductionmentioning

confidence: 99%

An Artificial Intelligence Based Approach for High Impedance Faults Analysis in Distribution Networks

Aziz¹,

Hassan

El-Zahab

2012

International Journal of System Dynamics Applications

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This paper presents a new approach for high impedance faults analysis (detection, classification and location) in distribution networks using Adaptive Neuro Fuzzy Inference System. The proposed scheme was trained by data from simulation of a distribution system under various faults conditions and tested for different system conditions. Details of the design process and the results of performance using the proposed method are discussed. The results show the proposed technique effectiveness in detecting, classifying, and locating high impedance faults. The 3 rd harmonics, magnitude and angle, for the 3 phase currents give superior results for fault detection as well as for fault location in High Impedance faults. The fundamental components magnitude and angle for the 3 phase currents give superior results for classification phase of High Impedance faults over other types of data inputs.

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High Impedance Faults Detection in EHV Transmission Lines Using the Wavelet Transforms

Cited by 10 publications

References 9 publications

Application of signal processing techniques and intelligent classifiers for high-impedance fault detection in ensuring the reliable operation of power distribution systems

Application of signal processing techniques and intelligent classifiers for high-impedance fault detection in ensuring the reliable operation of power distribution systems

Intelligent techniques for fault diagnosis in transmission lines — An overview

An Artificial Intelligence Based Approach for High Impedance Faults Analysis in Distribution Networks

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High Impedance Faults Detection in EHV Transmission Lines Using the Wavelet Transforms

Cited by 10 publications

References 9 publications

Application of signal processing techniques and intelligent classifiers for high-impedance fault detection in ensuring the reliable operation of power distribution systems

Application of signal processing techniques and intelligent classifiers for high-impedance fault detection in ensuring the reliable operation of power distribution systems

Intelligent techniques for fault diagnosis in transmission lines &#x2014; An overview

An Artificial Intelligence Based Approach for High Impedance Faults Analysis in Distribution Networks

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Intelligent techniques for fault diagnosis in transmission lines — An overview