Transmission Line Fault Detection and Identification in an Interconnected Power Network using Phasor Measurement Units

Khan, Abdul Qayyum; Ullah, Qudrat; Sarwar, Muhammad; Gul, Sufi Tabassum; Iqbal, Naeem

doi:10.1016/j.ifacol.2018.09.558

Cited by 24 publications

(8 citation statements)

References 10 publications

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“…Among all available methods and algorithm, the MATLAB/Simulink model developed is based on Discrete Fourier Transform (DFT). Fundamental ideas on the application and significance of DFT for the creation of PMU are cited by the authors in [6], [7]. In conclusion, DFT method has the best harmonic suppression among the available methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation & simulation of fault locating algorithm and localization for linear transmission lines on PMU based two terminal system.

Ghimire¹,

Lamsal²,

Gaire³

et al. 2022

int.jour.sci.res.mana.

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The demand of electricity is increasing rapidly. Among all, electricity is most reliable and sustainable source. With the growth in demand, power system will be more complex and sophisticated. Transmission lines are backbone for supplying electricity to every consumer. Transmission line are mostly prone to faults. So, clearing the fault of transmission in least possible time is most important to reduce the damage occurred due to fault. The protection of the transmission line could benefit tremendously from the capacity to locate and identify the issue. The Phasor Measurement Unit is used in this work to offer a contemporary method of fault localization and detection. The PMU transmission line and fault have been modelled using MATLAB/Simulink. The detection of fault uses discrete wavelet transform, PMU uses discrete Fourier transform and the localization of fault is done using Universal algorithm. In general, this paper provides the concept of PMU and its application in fault detection and locating the fault point in transmission as well as distribution line. The approach taken in this paper has been tested for different line length, source impedance and different types of faults.

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Section: Literature Reviewmentioning

confidence: 99%

Implementation & simulation of fault locating algorithm and localization for linear transmission lines on PMU based two terminal system.

Ghimire¹,

Lamsal²,

Gaire³

et al. 2022

int.jour.sci.res.mana.

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show abstract

“…An SVM-based technique for fault identification and location estimation problems is investigated in [44] using wavelet packet transform (WPT), which has some disadvantages such as noise sensitivity and inefficiency in terms of memory and time for large datasets of TLs. Fault detection and identification of TLs based on power spectral density index, NN, k-Nearest Neighbor, Naive Bayes, and SVM are studied in [6],and the resultant performance is highly sensitive to noises.…”

Section: Literature Reviewmentioning

confidence: 99%

“…These techniques can handle the problem of fault detection and prediction of the abnormalities occurring in the system. They include different schemes such as wavelet transform [4], Fourier-Taylor transformation [5], and power spectral density index [6].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Sensing and Fault Diagnosis for Transmission Lines

Shakiba¹,

Shojaee²,

Azizi³

et al. 2022

IJNDI

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Article Real-Time Sensing and Fault Diagnosis for Transmission Lines Fatemeh Mohammadi Shakiba 1, Milad Shojaee 1, S. Mohsen Azizi 1,2, and Mengchu Zhou 1,* 1 Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark 07102, NJ, USA. 2 The school of Applied Engineering and Technology, New Jersey Institute of Technology, Newark 07102, NJ, USA. * Correspondence: mengchu.zhou@njit.edu Received: 12 October 2022 Accepted: 8 November 2022 Published: 22 December 2022 Abstract: Protection of high voltage transmission lines is one of the crucial problems in the power system engineering. Accurate and timely detection and identification of transmission line short circuit faults can considerably improve and simplify their recovery process and hence save the costs associated with the downtime of a power system. Hence, it is essential that a robust and reliable fault diagnosis system completes its operation within an acceptable time window after fault occurrence in the presence of uncertainties and disturbances in the system. The significant costs of mistakenly detected or undetected faults based on the conventional techniques motivate us to present a robust detection and identification system by using the convolutional neural networks. The robustness of this technique is analyzed for the variations of the phase difference between two connected buses, fault resistance, source inductance fluctuations, fault inception angle, local bus voltage fluctuations, and measurement noises. The time delay analysis is also conducted to indicate that the presented technique is able to detect, identify, and estimate the location of faults before tripping relays and circuit breakers disconnect a faulty region.

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“…Researchers [12] indicated similar objectives for using evolutionary coding software (PSCAD/EMTDC) to simulate several operational and faulty situations for the high voltage transmission network. A combined method was proposed to recognise and identify faults in the transmission system comprising an interconnected network [13]. This work proposed to use phasor measurement units (PMUs) to determine positive sequence current and voltage characteristics.…”

Section: Introductionmentioning

confidence: 99%

Faults detection, location, and classification of the elements in the power system using intelligent algorithm

et al. 2023

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This study proposes an intelligent protection relay design that uses artificial neural networks to secure electrical parts in power infrastructure from different faults. Electrical transformer and transmission lines are protected using intelligent differential and distance relay, respectively. Faults are categorized, and their locations are pinpointed using three-phase current values and zero-current characteristics to differentiate between non-earth and ground faults. The optimal aspects of the artificial neural network were chosen for optimal results with the least possible error. Levenberg-Marquardt was established as the ideal training technique for the suggested system comprising the differential relay. Levenberg-Marquardt was the optimal training technique for the proposed framework consisting of the differential relay. Fault detection and categorization were performed using 20 and 50 hidden layers, and the corresponding error rates were 9.9873e-3 and 1.1953e-29. In the context of fault detection by the distance relay, the hidden layer neuron counts were 400, 250, and 300 for fault detection, categorization, and location; training error rates were 7.8761e-2, 1.2063e-6, and 1.1616e-26, respectively.

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Transmission Line Fault Detection and Identification in an Interconnected Power Network using Phasor Measurement Units

Cited by 24 publications

References 10 publications

Implementation & simulation of fault locating algorithm and localization for linear transmission lines on PMU based two terminal system.

Implementation & simulation of fault locating algorithm and localization for linear transmission lines on PMU based two terminal system.

Real-Time Sensing and Fault Diagnosis for Transmission Lines

Faults detection, location, and classification of the elements in the power system using intelligent algorithm

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