An artificial neural network-based solution to locate the multilocation faults in double circuit series capacitor compensated transmission lines

Swetapadma, Aleena; Yadav, Anamika

doi:10.1002/etep.2517

Cited by 34 publications

(14 citation statements)

References 25 publications

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“…The majority of studies have been validated on shunt faults, while only a few have addressed the issues related to the multi-location (two-location) faults in TCSCTLs. The popular computational approaches used in the development of multi-location fault models include neural networks [10,11], fuzzy rule-based systems (FRS) [12,13], and support vector machines [14]. Among these methods, FRS is proven to be the most suitable method, followed by support vector machines and neural network techniques.…”

Section: Related Researchmentioning

confidence: 99%

Fuzzy Rule-Based Fault Location Technique for Thyristor-Controlled Series-Compensated Transmission Lines

Kumar¹,

Ramesha²,

Jagadha³

et al. 2021

IJFIS

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Estimating the distance of a transmission line with a flexible alternating current transmission system including a thyristor-controlled series compensator is a challenging task. The distance estimation technique based on a fuzzy rule-based system (FRS) in a thyristor-controlled seriescompensated transmission line with multi-location faults is investigated in this study. The Haar wavelet current coefficients of the relaying bus are utilized as inputs to accomplish the distance estimation task. The FRS is illustrated through the Mamdani system in the LabVIEW software. The efficacy of the FRS is studied considering the effects of variation with respect to fault parameters. The main characteristic FRS is that it does not involve any two-end communication links because it employs relay terminal measurements only.

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Section: Related Researchmentioning

confidence: 99%

Fuzzy Rule-Based Fault Location Technique for Thyristor-Controlled Series-Compensated Transmission Lines

Kumar¹,

Ramesha²,

Jagadha³

et al. 2021

IJFIS

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“…In the last decade, a vast number of research work have been reported on different aspects related to various faults in the area of underground power distribution system likely analysis of faults [25][26][27][28][29][30], adaptive fault [31][32][33], SC, OC and ground faults [34][35][36][37], detection [38,39], classification [40] and fault location [41][42][43][44][45][46], sustainable protection [47,48], noise monitoring [49], and for thermal test [50].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic-SUGPDS Model for Faults Detection and Isolation of Underground Power Cable Based on Detection and Isolation Algorithm and Smart Sensors

Rajpoot

Pandey²,

Rajpoot³

et al. 2021

J. Electr. Eng. Technol.

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This paper proposes a SUGPDS model based on Detection and Isolation algorithm and smart sensors, namely micro phasor measurement unit, smart sensing and switching device, phasor data concentrator, and ZigBee technology, etc. for the identification, classification, and isolation of the various fault occurs in the underground power cable in the distribution system. The proposed SUGPDS is a quick and smart tool in supervising, managing, and controlling various faults and issues and maintaining the reliability, stability, and uninterrupted flow of electricity. First, the SUGPDS model is analyzed using a distributed parameter approach. Then, the proper arrangement of the system required for the implantation of SUGPDS is demonstrated using figures. The Phasor data concentrator plays an essential role in developing the detection and classification report for identification and classification. Finally, smart sensing and switching device installed at a different location isolated the faulty phase from a healthy network. This approach helps to decrease power consumption. Hence, SUGPDS has super abilities compared to the underground power distribution system. The effectiveness of the proposed method and model is demonstrated via figures and tables. Keywords Fault detection and isolation • Micro phasor measurement unit (µPMU) • Underground power cable • Phasor data concentrator (PDC) • Smart sensing and switching device (SSSD) • Smart underground power distribution system (SUGPDS) • Short-circuit fault • Open-circuit fault • Ground fault

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“…These days, the ANN approach [9][10][11] and fuzzy logic approach [12][13][14] also known as artificial intelligent (AI) techniques, have led to an improved output in terms of system robustness and a highly accurate network. Typically, the AI methods required feature extraction to extract important feature information for diagnostic purposes.…”

Section: Introductionmentioning

confidence: 99%

Fault classification on transmission line using LSTM network

Omar

Osman

Ibrahim

et al. 2020

IJEECS

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Deep Learning has ignited great international attention in modern artificial intelligence techniques. The method has been widely applied in many power system applications and produced promising results. A few attempts have been made to classify fault on transmission lines using various deep learning methods. However, a type of deep learning called Long Short-Term Memory (LSTM) has not been reported in literature. Therefore, this paper presents fault classification on transmission line using LSTM network as a tool to classify different types of faults. In this study, a transmission line model with 400 kV and 100 km distance was modelled. Fault free and 10 types of fault signals are generated from the transmission line model. Fault signals are pre-processed by extracting post-fault current signals. Then, these signals are fed as input to the LSTM network and trained to classify 10 types of faults. The white Gaussian noise of level 20 dB and 30 dB signal to noise ratio (SNR) is also added to the fault current signals to evaluate the immunity of the proposed model. Simulation results show promising classification accuracy of 100%, 99.77% and 99.55% for ideal, 30 dB and 20 dB noise respectively. Results has been compared to four different methods which can be seen that the LSTM leading with the highest classification accuracy. In line with the purpose of the LSTM functions, it can be concluded that the method has a capability to classify fault signals with high accuracy.

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An artificial neural network-based solution to locate the multilocation faults in double circuit series capacitor compensated transmission lines

Cited by 34 publications

References 25 publications

Fuzzy Rule-Based Fault Location Technique for Thyristor-Controlled Series-Compensated Transmission Lines

Fuzzy Rule-Based Fault Location Technique for Thyristor-Controlled Series-Compensated Transmission Lines

A Dynamic-SUGPDS Model for Faults Detection and Isolation of Underground Power Cable Based on Detection and Isolation Algorithm and Smart Sensors

Fault classification on transmission line using LSTM network

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