Taxonomy of high impedance fault detection algorithm

High Impedance Fault detection in a solar photovoltaic (PV) and wind generator integrated power system is described in this paper using discrete wavelet transform and a neural network with radial basis function (NNRBF). For this paper, the integration of solar photovoltaic and wind systems was modelled in a MATLAB/Simulink environment to create an IEEE 13-bus system. Microgrids (MG’s) are mostly powered by renewable energy. Uncertainty about renewables has shifted attention to ensuring a steady supply and long-term viability. It has been addressed in the paper whether or not a small-scale distant end source connection may be made at the terminal of a radial distribution feeder. Some typical power system problems compromise the reliability of the grid’s power supply. To solve this problem, this study suggests a criterion algorithm based on the neural network with radial basis function (NNRBF), and a defect detection method based on the discrete wavelet transform (DWT). The MATLAB/Simulink model of the system is then used to produce fault and travelling wave signals. The db4 wavelet is used to deconstruct the travelling wave signals into detail and approximate signals, which are then combined with the data from the two-terminal travelling wave localization approach for fault detection. After that, the optimal maximum coefficients of the wavelets are extracted and fed into the proposed radial basis function neural network (NNRBF). The results show that both the criterion algorithm and the fault detection algorithm are reliable in their assessments of whether or not faults exist in the power system, and that neither algorithm is particularly sensitive to variations in fault type, fault detection, fault initial angle, or transition resistance. After that, the optimal maximum coefficients of the wavelets are extracted and fed into the proposed radial basis function neural network (NNRBF). Overhead distribution system faults are simulated in Matlab/Simulink, and the technique is rigorously validated across a wide range of system situations. It has been shown through simulations that the proposed method can be relied upon to successfully and dependably protect high impedance fault (Hi-Z).

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“…Electrical circuit schematic that depicts the PMSG, rectifier, and boost converter (Mishra and Panigrahi, 2019).…”

Section: Figurementioning

confidence: 99%

Fault detection in a distribution network using a combination of a discrete wavelet transform and a neural Network’s radial basis function algorithm to detect high-impedance faults

Gogula

Edward²

2023

Front. Energy Res.

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“…Variable resistors vary the fault resistance, and a voltage supply with random values mimics HIF unpredictability. Some of the other HIF models are, two-time varying series resistors with different characteristics (Nam et al, 2001), Kizilcay's model (Zhang et al, 2016;Mishra and Panigrahi, 2019) and Matthews arc model (Gammon and Matthews, 2001).…”

Section: Modeling Of Hifmentioning

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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“…As a consequence, HIFs require robust algorithms able to distinguish them from other fault types. One effective way to extract the most relevant characteristics of HIFs is commonly based on multiresolution approaches [19], [27]. In general, multiresolution analysis facilitates feature extraction because it provides information in time and frequency at different resolution levels.…”

Section: Classification Approach For High Impedance Faults a Feature ...mentioning

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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Taxonomy of high impedance fault detection algorithm

Cited by 42 publications

References 107 publications

Fault detection in a distribution network using a combination of a discrete wavelet transform and a neural Network’s radial basis function algorithm to detect high-impedance faults

Fault detection in a distribution network using a combination of a discrete wavelet transform and a neural Network’s radial basis function algorithm to detect high-impedance faults

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

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

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