HIF detection using wavelet transform, travelling wave and support vector machine

Bhongade, Sandeep; Golhani, Supriya

doi:10.1109/icepes.2016.7915922

Cited by 11 publications

(5 citation statements)

References 7 publications

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“…Commonly used intelligent classifiers in signal processing-based HIF detection techniques are probabilistic neural network (PNN) (Samantaray et al, 2008), artificial neural network (ANN) (Baqui et al, 2011), adaptive resonant theory (ART) neural network and Fuzzy ARTMAP (Nikoofekr et al, 2013), extreme learning machines (ELMs) (Reddy et al, 2013), genetic algorithm (GA) (Xie et al, 2013a), support vector machine (SVM) (Bhongade and Golhani, 2016), adaptive neuro-fuzzy inference system (ANFIS) , decision tree (DT) (Sekar and Mohanty, 2018), random forest (RF) (Sekar and Mohanty, 2020), convolution neural network (CNN) (Fan and Yin, 2019), and fuzzy logic control (FLC) (Suliman and Ghazal, 2019) explained in Section 4. These intelligent classifiers improved the efficiency, speed, and accuracy of signal processingbased procedures by detecting HIFs without the use of threshold settings.…”

Section: Hif Detectionmentioning

confidence: 99%

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: Hif Detectionmentioning

confidence: 99%

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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“…Some complex time-domain calculations are also used to detect arcing-HIF. On the basis of mathematical morphology techniques, waveform signals are matched and locally corrected using predefined structural elements for drawing time-domain features [24]. In [20], monocomponents of three-phase voltage are obtained by empirical mode decomposition and selected as arcing-HIF features.…”

Section: Time-domain Analysismentioning

confidence: 99%

“…Jamil et al [23] recommend the wavelet coefficients of voltage and current waveforms as fault feature. Approximate value [24], standard deviation [25] and distribution parameters of singular spectrum [26] of voltage and current are also defined as detection objects.…”

Section: Data Acquisitionmentioning

confidence: 99%

AI in arcing‐HIF detection: a brief review

Bai

2020

IET Smart Grid

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In the past few decades, the arcing-high-impedance fault (arcing-HIF) detection problems have become an important issue in the effectively grounded distribution network. Many solutions have been proposed to address this problem. The most attractive way is artificial intelligence (AI) method. The paper gives a comprehensive review of arcing-HIF detection in distribution network-based AI. First, characteristics and models of arcing-HIF are analysed, the arcing-HIF database construction method is also explained; this part is a foundation work for arcing-HIF detection. Next, arcing-HIF detection methods based AI are summarised in details including data acquisition, feature extraction and classifier selection. Then, a set of criteria are proposed to evaluate the reliability of arcing-HIF detection algorithm. Finally, the future trends and challenges to arcing-HIF detection are also fully accounted. This review can be a valuable guide for researchers who are interested in arcing-HIF detection-based AI.

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“…A combination of discrete wavelet transform and back propagation neural network has been used for the detection, classification and location estimation of HVAC transmission line faults (Saha et al, 2016). Different techniques such as wavelet transform, travelling wave and support vector machines has been used by the researchers for high impedance fault detection (Bhongade and Golhani, 2016). Probabilistic neural network based approach is described for fault classification of multi terminal series compensated transmission line (Raval and Pandya, 2016).…”

Section: Introductionmentioning

confidence: 99%

Wavelet transform based fault detector for protection of series capacitor compensated three phase transmission line

Kapoor¹

2018

Int. J. Eng. Sci. Tech

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This paper proposes wavelet transform based fault detector for the protection of series capacitor compensated three phase transmission line. Three phase current measured at the relay location of a transmission line is processed using wavelet transform with daubechies-4 mother wavelet. Numerous fault situations are studied to check the performance of the proposed fault detector. To assess the performance of proposed fault detector, various fault parameters such as fault inception time, fault resistance, fault location, fault type and ground resistance are varied. Simulation results exemplify the accurateness and effectiveness of the proposed fault detector.

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HIF detection using wavelet transform, travelling wave and support vector machine

Cited by 11 publications

References 7 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

AI in arcing‐HIF detection: a brief review

Wavelet transform based fault detector for protection of series capacitor compensated three phase transmission line

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