Fault classification and fault area detection in GUPFC-compensated double-circuit transmission lines based on the analysis of active and reactive powers measured by PMUs

AbstractThis paper presents a phasor-distance based faulty phase detection and fault classification technique for parallel transmission lines. Detection and classification of faulty phase(s) have been carried out by deriving indices from the change in phasor values of current with a distance of one cycle. The derived indices have zero values during normal operating conditions whereas the index corresponding to the faulty phase exceeds the pre-defined threshold in case of occurrence of a fault. A separate ground detection algorithm has been utilized for the identification of involvement of ground in a faulty situation. The performance of the proposed technique has been evaluated for intra-circuit, inter-circuit and simultaneous faults with wide variations in system and fault conditions. The suggested technique has been evaluated for over 23,000 diversified simulated fault cases as well as 14 recorded real fault events. The performance of the proposed technique remains consistent under Current Transformer (CT) saturation as well as different amount and direction of power flow. Moreover, suitability to different power system network has also been studied. Also, faults having fault current less than pre-fault conditions have been detected accurately. The results obtained suggest that it is able to detect faulty phases as well as classify faults within quarter-cycle from the inception of fault with impeccable accuracy. Besides, as modern digital relays have been already equipped with phasor computation facility, phasor-based technique can be easily incorporated with relative ease. At last, a comparative evaluation suggests its superiority in terms of fault classification accuracy, fault detection time, diversify fault scenarios and computational requirement among other existing techniques.

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Classification and direction discrimination of faults in transmission lines using 1D convolutional neural networks

International Journal of Power Electronics and Drive Systems (IJPEDS) ◽

10.11591/ijpeds.v12.i3.pp1928-1939 ◽

2021 ◽

Vol 12 (3) ◽

pp. 1928

Author(s):

Ahmed Thamer Radhi ◽

Wael Hussein Zayer ◽

Adel Manaa Dakhil

Keyword(s):

Neural Network ◽

Neural Networks ◽

Feature Extraction ◽

Transmission Line ◽

Convolutional Neural Networks ◽

Transmission Lines ◽

Fault Classification ◽

Line System ◽

Direction Discrimination ◽

Conventional Methods

<span lang="EN-US">This paper presents a fast and accurate fault detection, classification and direction discrimination algorithm of transmission lines using one-dimensional convolutional neural networks (1D-CNNs) that have ingrained adaptive model to avoid the feature extraction difficulties and fault classification into one learning algorithm. A proposed algorithm is directly usable with raw data and this deletes the need of a discrete feature extraction method resulting in more effective protective system. The proposed approach based on the three-phase voltages and currents signals of one end at the relay location in the transmission line system are taken as input to the proposed 1D-CNN algorithm. A 132kV power transmission line is simulated by Matlab simulink to prepare the training and testing data for the proposed 1D- CNN algorithm. The testing accuracy of the proposed algorithm is compared with other two conventional methods which are neural network and fuzzy neural network. The results of test explain that the new proposed detection system is efficient and fast for classifying and direction discrimination of fault in transmission line with high accuracy as compared with other conventional methods under various conditions of faults.</span>

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A rate-of-change-of-current based fault classification technique for thyristor-controlled series-compensated transmission lines

International Journal of Emerging Electric Power Systems ◽

10.1515/ijeeps-2021-0031 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Nishant H. Kothari ◽

Bhavesh R. Bhalja ◽

Vivek Pandya ◽

Pushkar Tripathi

Keyword(s):

Transmission Lines ◽

Noise Signal ◽

Current Transformer ◽

Rate Of Change ◽

Fault Classification ◽

Support Vector ◽

Svm Classifier ◽

Classification Technique ◽

Suggested Technique ◽

Zero Sequence

Abstract This paper presents a new fault classification technique for Thyristor-Controlled Series-Compensated (TCSC) transmission lines using Support Vector Machine (SVM). The proposed technique is based on the utilization of post-fault magnitude of Rate-of-Change-of-Current (ROCC). Fault classification has been carried out by giving ROCC of three-phases and zero sequence current as inputs to SVM classifier. The performance of SVM as a binary-class, and multi-class classifier has been evaluated for the proposed feature. The validity of the suggested technique has been tested by modeling a TCSC based 400 kV, 300 km long transmission line using PSCAD/EMTDC software package. Based on the above model, a large number of diversified fault cases (41,220 cases) have been generated by varying fault and system parameters. The effect of window length, current transformer (CT) saturation, noise-signal, and sampling frequency have also been studied. It has been found that the proposed technique provides an accuracy of 99.98% for 37,620 test cases. Moreover, the performance of the suggested technique has also been found to be consistent upon evaluating in a 12-bus power system model consisting of a 365 kV, 60 Hz, 300 km long TCSC line. Comparative evaluation of the proposed SVM based technique with other recent techniques clearly indicates its superiority in terms of fault classification accuracy.

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