High Impedance Fault Detection based on Mathematical Morphology for Radial Distribution Network

Panigrahi, Rasmi Ranjan; Mishra, Manohar; Rajan, Abhishek; Mohapatra, Sangram Keshori

doi:10.1109/aespc44649.2018.9033309

Cited by 5 publications

(8 citation statements)

References 21 publications

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“…IEEE PSRC distribution of radials 11 KV (Panigrahi et al, 2018) MM employs morphology gradient, and the statistical features are obtained from dilation and erosion Identify HIF occurrence in 0.08 s Radial 13.8 kV distribution feeder (Wali et al, 2018) FFT extracts features, which are then subjected to power spectrum analysis…”

Section: Test System With Ratings Methodology Used Remarksmentioning

confidence: 99%

“…It is tested in six different situations of three broken and three unbroken conductors. The method is implemented along with a single-phase energy meter capable of detecting the presence of HIFs, voltage sag-swells, capacitor/load switching (Panigrahi et al, 2018;Prasad et al, 2022), transformer and feeder energization (Biswal et al, 2022), power electronic loads, arc furnace loads, and distributed generators (DG). The method gives satisfactory results in HIF detection.…”

Section: Hif Detectionmentioning

confidence: 99%

“…Mathematical morphology (MM) is a signal processing technique applied to issues in the power system illustrated in the literature (Sekar and Mohanty, 2018;Panigrahi et al, 2018). MM uses simple arithmetic operations, such as set theory and integral geometry, and due to its simple calculations, the processing time is less.…”

Section: Time-domain Analysismentioning

confidence: 99%

“…With 2,000 cases in two-bus systems, the accuracy obtained is 92.5%, whereas the eight-bus system showed 90.19%. Panigrahi et al (2018) used the IEEE Power System Relaying Committee Working Group at medium voltage levels. A simple 11-kV radial distribution feeder with eight nodes is shown in Figure 4F.…”

Section: Radial Distribution Networkmentioning

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: Test System With Ratings Methodology Used Remarksmentioning

confidence: 99%

Section: Hif Detectionmentioning

confidence: 99%

Section: Time-domain Analysismentioning

confidence: 99%

Section: Radial Distribution Networkmentioning

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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“…In this section, the performance of the proposed method is compared with other existing methods [35] and [45] based on mathematical morphology. In [45], the voltage signal is processed by morphology and then the energy index is used for diagnosis.…”

Section: Comparisonmentioning

confidence: 99%

A Simple Approach to Detect High Impedance Fault Using Morphological Gradient Edge Detector

Salehi,

Zolfaghari,

Maritz

2024

IEEE Access

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Detection of high impedance faults (HIF) is one of the biggest challenges in power distribution networks. HIF usually occurs when conductors in the distribution network are broken and accidently come into contact the ground or a tree branch. The current of this fault is close to the load current level and cannot be detected by overcurrent relays. Also, some regular system phenomena such as capacitor switching, load switching, and inrush current and saturation phenomena in current transformer (CT) represent some features which may overlap the components of HIF; making HIF detection schemes more complex. In this paper, a new method for HIF detection is presented which is able to distinguish any type of HIF from regular system phenomena. To achieve this, the scheme of morphological gradient edge detection (MGED) is used to process voltage signals. The MGED extracts two main features from the processed signals: first, the edges or changes in the signal are elicited and then, these features are extracted after two cycles from the onset of the fault. Then, based on these features, a high impedance fault detection index (HIFDI) is introduced for distinguishing and classifying HIF from other regular system phenomena. The simulation results for different types of HIF fault in a sample 20 kV distribution feeder and IEEE 34-bus distribution test system using EMTP confirm the fast and accurate performance of the proposed method.INDEX TERMS High impedance fault, mathematical morphology, distribution network.

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A Review on High-Impedance Ground Fault Detection Techniques in Distribution Networks

Gadanayak

2020

Lecture Notes in Networks and Systems

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High Impedance Fault Detection based on Mathematical Morphology for Radial Distribution Network

Cited by 5 publications

References 21 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

A Simple Approach to Detect High Impedance Fault Using Morphological Gradient Edge Detector

A Review on High-Impedance Ground Fault Detection Techniques in Distribution Networks

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