High-impedance fault modeling and classification in power distribution networks

Carvalho, José Genilson Sousa; Almeida, Aryfrance Rocha; Ferreira, Danton Diego; Santos, Bartolomeu Ferreira dos; Vasconcelos, Luis Henrique Pereira; Sobreira, Danilo de Oliveira

doi:10.1016/j.epsr.2021.107676

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…In subsequent research [39], the latter mentioned model was corrected by adding one (or two) variable resistors in series with DC voltage sources to model the random behavior of effective impedance and as a result of the randomness of the fault current. In other methods [20], differential equations have been used to model the behavior and characteristics of HIF.…”

Section: B Hif Modelmentioning

confidence: 99%

“…In the paradigm of hybrid methods, discrete wavelet transforms [15]- [17] has typically been proposed by researchers. Algorithms based on expert systems also use techniques such as Kalman filters [18], and training-based methods such as decision trees [19], artificial neural networks [20], [21], fuzzy neural methods and genetic schemes [22].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: B Hif Modelmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Due to the need to interface the non-linear conductanceresistance model with the standard models, in this work, the model is implemented with a non-linear resistance by using a block of a controlled current source through Matlab-Simulink software. Other HIF models can be found in [18].…”

Section: B Modelling Of High Impedance Faultsmentioning

confidence: 99%

“…In a different approach, the EMD-based method proposed in [16] uses voltage signals to identify the predominant harmonic components, and the classification is performed by applying an artificial neural network (ANN). In the same context, in [18] a multilayer perceptron (MLP) is proposed. In this case the classification method is based on higher-order statistics (HOS), which is also combined with Fisher's discrimination ratio to extract specific patterns associated with the HIFs.…”

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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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“…In 1949, the Power Systems Relaying Committee Working Group concluded that it was impossible at that time to develop techniques for detecting HIF [9]. However, especially in the last four decades, many researchers have been working to develop techniques that can detect, classify, and/or locate this type of fault [3], [6], [10], [11]. Despite the several studies presented in the literature, this research problem still lacks a definitive solution that presents high reliability and security in terms of detection and differentiation between the occurrence of HIF and other events common to distribution systems, such as switching loads or capacitors bank [6], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks: Modeling and Comparison to Detect High Impedance Faults

Diefenthäler,

Sausen,

De Campos

et al. 2023

IEEE Access

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High impedance faults constitute one of the biggest challenges in electrical power systems. In overhead distribution systems, faults are caused by tree branches that touch the electrical grid or by the rupture of energized conductors on low conductivity soils. They are also known as low-current faults, which are not detected by conventional protection systems, compromising the quality of the power supply and causing hazardous risks to the electrical system. This paper aims to address the problem of high impedance faults detection using Artificial Neural Networks: two Multi Layer Perceptron networks, being one Neural Pattern Recognition and another Neural Fitting, and a Convolutional Neural Network. The neural networks are trained and analyzed in scenarios based on a medium-voltage distribution grid model, located in the Basque Country, Spain. The network topologies are implemented, repeatedly trained considering multiple architectures, and validated in other scenarios with different location, time, and duration of the fault using the Matlab software. After, the criteria of accuracy, reliability, security, safety and sensitivity are evaluated. At last, a comparative analysis between them is carried out, and from the results obtained, a superior performance of the Convolutional Neural Network in compared to the Multi Layer Perceptron networks is observed.

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High-impedance fault modeling and classification in power distribution networks

Cited by 11 publications

References 14 publications

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

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

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

Artificial Neural Networks: Modeling and Comparison to Detect High Impedance Faults

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