Classification of Faults in Power Transmission Lines using Fuzzy-Logic Technique

Prasad, Avagaddi; Edwar, J. Belwin; Roy, Chiranjiv; Divyansh, G.; Kumar, Abhay

doi:10.17485/ijst/2015/v8i1/77065

Cited by 17 publications

(10 citation statements)

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“…Simulation studies are carried out in power systems computer-aided design software in combination with electromagnetic transients including direct current on 400 kV, 300 km transmission line models designed for different types of single phaseto-ground faults (Dileep and Raghunath, 2014). Prasad et al (2015) have implemented a new approach using 2 fuzzy rule systems, whereas Prasad and Belwin Edward (2016) implemented a new method using currents at 1 end of the overhead line with the help of DWT (Prasad and Belwin, 2016). Muhammad (2016) located different faults in high voltage transmission lines in the Kurdistan power system, which was proposed and carried out in order to determine which artificial neural network (ANN) fault locator structure delivered the best performance (Aree, 2016).…”

Section: Introductionmentioning

confidence: 99%

Modeling Features of a Single Phase-to-Earth Fault in a Medium Voltage Overhead Transmission Line

Saeed

Sheikhyounis

2020

UKH J SCI ENG

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The modeling and calculation of a single phase-to-earth fault of 6 to 35 kV have specific features when compared with circuits with higher nominal voltages. In this paper, a mathematical analysis and modeling of a 3-phase overhead transmission line with distributed parameters consisting of several nominal T-shaped, 3-phase links with concentrated parameters replaced by 1 nominal T-shaped link were carried out. Further analysis showed that not accounting for the distributed nature of the line parameters did not cause significant errors in the assessment of the maximum overvoltage in the arc suppression in single phase-to-earth faults, and that sufficient accuracy insures the representation of the line by only 1 nominal T-shaped, 3-phase link. Such a modeling technique makes it impossible to identify the location of single-phase faults, which is the property of higher harmonic amplification of individual frequencies. Chain equivalent schemas with constant parameters are valid for a single frequency, thereby providing an opportunity to study the nature of the wave process by the discrete selection of parameters. Next in the mathematical representation, we consider the overhead transmission lines as lines with distributed parameters.

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Section: Introductionmentioning

confidence: 99%

Modeling Features of a Single Phase-to-Earth Fault in a Medium Voltage Overhead Transmission Line

Saeed

Sheikhyounis

2020

UKH J SCI ENG

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“…Most commonly, Fuzzy Logic is used for control systems such as car control system, aerospace vehicle control systems, and kitchen appliances [16]. The reason for which the research is using Fuzzy Logic is that, it is easy to be understand for its language and system, and also provide the most precise results [12]. Fuzzy Logic can give a precise and smoother result as it is using range in between "true or false" to provide multiple results for each different range.…”

Section: Introductionmentioning

confidence: 99%

Voltage & Current Magnitude Pattern Recognization by Using Fuzzy Logic Toolbox for Fault Types Classification

Awalin¹,

Fatini²,

Abdullah³

et al. 2018

IJEECS

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<p>This research introduces the appropriate input pattern of Fuzzy Logic design for fault type classification of Single Line to Ground Fault at distribution network. The proposed design is solely using Fuzzy Logic as the research technique with input data from PSCAD simulation. PSCAD software simulate the circuit configuration for fault disturbance at the distribution network. The research technique was applied with multiples input values of voltage and current that extracted from the PSCAD simulation. This research testifies the output result by using different fault resistance values; 0.01Ω, 10Ω, 30Ω, 50Ω and 70Ω. Voltage sag and current swell of phase a, b and c that were obtained from the PSCAD simulation have been used as the input variables for Fuzzy Logic design. The acquired results that represented in average accuracy shown that voltage sag and current swell can draw a satisfying accuracy in classifying the fault type.</p>

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“…Fuzzy Logic can give a precise and smoother result as it is using range in between 'true or false' to provide multiple results for each different range. Hence, Fuzzy Logic is one of the suitable methods to classify the type of faults for this research [12].…”

Section: Introductionmentioning

confidence: 99%

Fault Types Classification Accuracy Considering Four Different Methods

Awalin¹,

Naidu²,

Mokhlis³

et al. 2018

Preprint

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This research introduces four different tools designed for fault type classifications at distribution network. The proposed designs are using Artificial Neural Network, Fuzzy Logic, conventional method and Support Vector Machine as the research techniques with input data obtained from PSCAD simulation. The circuit configuration for fault disturbance at the distribution network was simulated by PSCAD simulation program. The research techniques were applied with multiples input values of voltage and current that extracted from the PSCAD simulation. This research testifies the output result by using different fault resistance values; 0.01Ω, 10Ω, 30Ω, 50Ω and 70Ω. Voltage sag and current swell of phase a, b and c that were obtained from the PSCAD simulation have been used as the input variables for the four different research tools design. The acquired results that represented in average accuracy (%) shows that voltage sag and current swell can draw a satisfying accuracy in classifying the fault type.

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Classification of Faults in Power Transmission Lines using Fuzzy-Logic Technique

Cited by 17 publications

References 0 publications

Modeling Features of a Single Phase-to-Earth Fault in a Medium Voltage Overhead Transmission Line

Modeling Features of a Single Phase-to-Earth Fault in a Medium Voltage Overhead Transmission Line

Voltage & Current Magnitude Pattern Recognization by Using Fuzzy Logic Toolbox for Fault Types Classification

Fault Types Classification Accuracy Considering Four Different Methods

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