Detection of High impedance Fault in Distribution Network Using Fuzzy Logic Control

Suliman, Mohammed Y.; Ghazal, Mohammed

doi:10.1109/iceccpce46549.2019.203756

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…Another advantage of pq theory is its simplicity in calculations, which includes an exception to the separation requirement between the alternated value and mean value in the calculated 265 components of power [23] for algebraic calculations. The "Clarke Transformation" is used by the pq theory to change a reference frame system of abc coordinates to α-β-0 coordinates [24].…”

Section: Powers Measuringmentioning

confidence: 99%

Active and reactive energy storage STATCOM distribution system power management

Albanna,

Al-Karakchi,

Al-Rifaie

2024

IJPEDS

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One of the critical factors influencing the overall development of modern power systems is the control of active and reactive power flows in distribution power systems. The static synchronous compensator (STATCOM) with storage energy is a powerful device that can control active and reactive power flow in a distribution system. A simulation model of power management using STATCOM with energy storage is presented in this paper. A fuzzy logic controller is proposed to manage the powers. The simulation results demonstrate STATCOM's ability to manage the active and reactive power flow in a controlled distribution line, and thus the powers regulated between feeders, by utilizing storage energy.

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Section: Powers Measuringmentioning

confidence: 99%

Active and reactive energy storage STATCOM distribution system power management

Albanna,

Al-Karakchi,

Al-Rifaie

2024

IJPEDS

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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%

“…The method gives a classification efficiency of 97.69%. FFT extracts features from the signal, and fuzzy logic classifies HIF and non-HIF events (Suliman and Ghazal, 2019). The detection is performed by analyzing the third and fifth harmonics of magnitude and phase angle.…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

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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“…FT is continuous over time; however, discrete Fourier transform (DFT) is commonly used in computational applications and was implemented in [76] for HIF detection. Another form known as fast Fourier transform (FFT) was applied in [77]. However, for HIF diagnosis application, FT can only represent the features in the frequency domain.…”

Section: Feature Extractionmentioning

confidence: 99%

High-Impedance Fault Diagnosis: A Review

Aljohani

Habiballah

2020

Energies

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High-impedance faults (HIFs) represent one of the biggest challenges in power distribution networks. An HIF occurs when an electrical conductor unintentionally comes into contact with a highly resistive medium, resulting in a fault current lower than 75 amperes in medium-voltage circuits. Under such condition, the fault current is relatively close in value to the normal drawn ampere from the load, resulting in a condition of blindness towards HIFs by conventional overcurrent relays. This paper intends to review the literature related to the HIF phenomenon including models and characteristics. In this work, detection, classification, and location methodologies are reviewed. In addition, diagnosis techniques are categorized, evaluated, and compared with one another. Finally, disadvantages of current approaches and a look ahead to the future of fault diagnosis are discussed.

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Detection of High impedance Fault in Distribution Network Using Fuzzy Logic Control

Cited by 6 publications

References 12 publications

Active and reactive energy storage STATCOM distribution system power management

Active and reactive energy storage STATCOM distribution system power management

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

High-Impedance Fault Diagnosis: A Review

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