A Resilient Protection Scheme for Common Shunt Fault and High Impedance Fault in Distribution Lines Using Wavelet Transform

Bhatnagar, Maanvi; Yadav, Anamika; Swetapadma, Aleena

doi:10.1109/jsyst.2022.3172982

Cited by 20 publications

(3 citation statements)

References 28 publications

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“…Several kinds of mother wavelets may be used to extract characteristics from the current signal. After several calculations using several wavelet families, it was determined that the Daubechies family's "db1" wavelet can accurately identify fault in the current signal [24]. The original current signals are divided into several frequency levels using wavelet algorithms.…”

Section: ) Discrete Wavelet Transform and Anfismentioning

confidence: 99%

Advanced Fault Detection, Classification, and Localization in Transmission Lines: A Comparative Study of ANFIS, Neural Networks, and Hybrid Methods

Kanwal,

Jiriwibhakorn

2024

IEEE Access

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Electric systems are getting more complex with time, and primitive protection methods such as traveling wave and impedance-based methods face limitations and shortcomings. This paper incorporates and presents the applications of an adaptive neuro-fuzzy inference system and compares it with a back propagation neural network, self-organizing map, and hybrid method of discrete wavelet with adaptive neuro-fuzzy inference system for fault detections, classification, and localization in transmission lines. These methods, in comparison with primitive methods, could be capable of detecting, identifying, and predicting the location of the faults more accurately. The IEEE 9-bus system is utilized to obtain data from one end of the transmission line to develop an ANFIS-based model. This system is simulated in MATLAB/Simulink for different fault cases at various locations. The three-phase voltage and current at one end of IEEE 9-bus number seven are taken for training. Three ANFIS models are developed for fault detection, classification, and localization and compared with other models. For verification of the models, mean square error, mean absolute error, and regression analysis have been computed and compared for all the models. All four techniques have performed well for fault classification, detection, and location. However, the percentage error for the ANFIS-based fault model is less compared to backpropagation, self-organizing map, and discrete wavelet transform with ANFIS. Therefore, the proposed ANFIS models can be implemented for deploying in real-time-based protection systems.

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Section: ) Discrete Wavelet Transform and Anfismentioning

confidence: 99%

Advanced Fault Detection, Classification, and Localization in Transmission Lines: A Comparative Study of ANFIS, Neural Networks, and Hybrid Methods

Kanwal,

Jiriwibhakorn

2024

IEEE Access

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“…Therefore, the protective devices must include a digital logic to avoid false operations. In this case, a delay of 2 cycles should be included in the digital logic to confirm that a HIF occurred, while other works report times between 1 and 5 cycles of the fundamental frequency [33].…”

Section: B Detection Based On the Ht Coefficientsmentioning

confidence: 99%

Hermite Transform Based Algorithm for Detection and Classification of High Impedance Faults

et al. 2022

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This work proposes a new algorithm to classify high impedance faults (HIFs) in distribution systems. HIFs can be represented by small current magnitudes with non-linear variations, which complicate their detection in distribution grids. The proposed method uses the Hermite transform (HT) as a signal processing technique that offers several advantages, one of the more important being the ability to analyze the signal at multiple resolution levels and at different frequency bands thanks to their filter functions based on Gaussian derivatives. The Hermite coefficients are used to extract signal features that allow efficient identification of the transient behaviour related to HIFs and other typical faults. In this sense, a multichannel approach based on the Hermite transform is proposed to classify different types of faults and HIFs. This analysis is carried out in a distribution network considering photovoltaic (PV) systems considering three different classifiers, which are also used to compare our results with the discrete wavelet transform (DWT) as signal decomposition model; the comparison suggests that our proposal presents better performance discriminating HIFs from typical faults.

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“…Consequently, numerous theories and research endeavors have emerged in this field, each employing distinct methodologies and theories [1][2][3][4]. In engineering and other fields, different theories have been employed for diagnosis, such as wavelet analysis [5][6][7], while others have utilized deep learning [8][9][10][11], and neural networks [12][13][14][15], among others. The outcomes have exhibited variations in terms of accuracy and analysis time.…”

Section: Introductionmentioning

confidence: 99%

Transmission Line Fault Classification Based on the Combination of Scaled Wavelet Scalograms and CNNs Using a One-Side Sensor for Data Collection

Altaie,

Abderrahim,

Alkhazraji

2024

Sensors

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This research focuses on leveraging wavelet transform for fault classification within electrical power transmission networks. This study meticulously examines the influence of various parameters, such as fault resistance, fault inception angle, fault location, and other essential components, on the accuracy of fault classification. We endeavor to explore the interplay between classification accuracy and the input data while assessing the efficacy of combining wavelet analysis with deep learning methodologies. The data, sourced from network recorders, including phase currents and voltages, undergo a scaled continuous wavelet transform (S-CWT) to generate scalogram images. These images are subsequently utilized as inputs for pretrained deep learning models. The experiments encompass various fault scenarios, spanning distinct fault types, locations, times, and resistance values. A remarkable feature of the proposed work is the attainment of 100% classification accuracy, obviating the need for additional algorithmic enhancements. The foundation of this achievement is the deliberate selection of the right input. The decision to employ an identical number of samples as the number of scales for the CWT emerges as a pivotal factor. This approach underpins the high accuracy and renders supplementary algorithms superfluous. Furthermore, this research underscores the versatility of this approach, showcasing its effectiveness across diverse networks and scenarios. Wavelet transform, after rigorous experimentation, emerges as a reliable tool for capturing transient fault characteristics with an optimal balance between time and frequency resolutions.

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A Resilient Protection Scheme for Common Shunt Fault and High Impedance Fault in Distribution Lines Using Wavelet Transform

Cited by 20 publications

References 28 publications

Advanced Fault Detection, Classification, and Localization in Transmission Lines: A Comparative Study of ANFIS, Neural Networks, and Hybrid Methods

Advanced Fault Detection, Classification, and Localization in Transmission Lines: A Comparative Study of ANFIS, Neural Networks, and Hybrid Methods

Hermite Transform Based Algorithm for Detection and Classification of High Impedance Faults

Transmission Line Fault Classification Based on the Combination of Scaled Wavelet Scalograms and CNNs Using a One-Side Sensor for Data Collection

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