The proliferation of non-linear loads in recent times has a strong bearing on power quality due to the presence of harmonics in the power system. Harmonics lead to erroneous measurement and malfunctioning of protective relays, thus reducing the efficacy of transmission line protection. With the aim of improving the dependability of the protection scheme under varying non-linear loading condition, the study presents a hybrid support vector machine (SVM), artificial neural network (ANN) and Kalman filter based algorithm using voltage harmonics for the protection of three-phase transmission line. The post-fault voltage signals are processed by a Kalman filter to estimate the harmonic components, which serve as feature vectors for performing the fault detection and classification by SVM and zone identification as well as the location by ANN. The harmonic information discriminates faults from disturbances based on variations in the fundamental component, to improve the selectivity and accuracy. Considering the stochastic nature of fault occurrence in power systems, the efficacy of the proposed scheme is validated using Monte Carlo simulation. The experimental results confirm the superiority of using voltage harmonics for improving the dependability of transmission line protection.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-gtd.2016.1802

Protection scheme for power transmission lines based on SVM and ANN considering the presence of non-linear loads

When a fault occurs in transmission lines, first, it is detected, then classified, and finally, located by a distance protection. Having a fast fault detection approach helps saving the required time for the whole protection procedure. In this paper, a novel method for fault detection of transmission lines based on the summation of squared three-phase currents (SSC) and a moving average technique is proposed. The SSC is constant during a normal condition of the network, while it has considerable variation under a fault condition. Using a moving average concept, a criterion is defined, by which fault occurrence can be detected. The proposed fault detection method is evaluated in four different systems: a typical 5-bus system, a typical double-circuit transmission line, WSCC 9-bus system, and a laboratory small-scaled system. The results confirm a high degree of accuracy and speed of the proposed approach. Moreover, the performance of the method is compared with some other similar methods from different aspects.

Fast Current-Only Based Fault Detection Method in Transmission Line

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.

/pdf/real-time-sensing-and-fault-diagnosis-for-transmission-lines-3aiv06m5.pdf

Real-Time Sensing and Fault Diagnosis for Transmission Lines

This paper presents wavelet transform based fault detection scheme for the protection of series capacitor compensated double circuit transmission lines. Three phase current of both circuits measured at relay location are used for the detection of fault. Three phase current of both circuits are processed using Daubechies-4 mother wavelet up to level-1. Performance of wavelet transform is tested for inter-circuit faults, cross-country faults, faults at boundary locations, fault resistance variation, ground resistance variation, fault inception time variation and fault location variation. Accuracy of wavelet transform scheme is 100%.

Detection of Fault in Series Capacitor Compensated Double Circuit Transmission Line using Wavelet Transform

In order to remove noise and preserve the important features of a signal, a hybrid de-noising algorithm based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Permutation Entropy (PE), and Time-Frequency Peak Filtering (TFPF) is proposed. In view of the limitations of the conventional TFPF method regarding the fixed window length problem, CEEMDAN and PE are applied to compensate for this, so that the signal is balanced with respect to both noise suppression and signal fidelity. First, the Intrinsic Mode Functions (IMFs) of the original spectra are obtained using the CEEMDAN algorithm, and the PE value of each IMF is calculated to classify whether the IMF requires filtering, then, for different IMFs, we select different window lengths to filter them using TFPF; finally, the signal is reconstructed as the sum of the filtered and residual IMFs. The filtering results of a simulated and an actual gearbox vibration signal verify that the de-noising results of CEEMDAN-PE-TFPF outperforms other signal de-noising methods, and the proposed method can reveal fault characteristic information effectively.

A Hybrid De-Noising Algorithm for the Gear Transmission System Based on CEEMDAN-PE-TFPF

Low cost microcontroller based fault detector, classifier, zone identifier and locator for transmission lines using wavelet transform and artificial neural network: A hardware co-simulation approach

Raunak Kumar

Papers

Low cost microcontroller based fault detector, classifier, zone identifier and locator for transmission lines using wavelet transform and artificial neural network: A hardware co-simulation approach