Fault Detection and Classification Logic for Transmission Lines Using Multi-resolution Wavelet Analysis

This paper presents an algorithm based on harmonic wavelet transform (HWT) and rule-based decision tree for detection and classification of transmission line faults. The proposed algorithm has been implemented to provide voltage-based protection scheme to transmission line. Voltage recorded at both ends of transmission line has been decomposed using harmonic wavelet transform. Absolute values of output of harmonic wavelet transform based decomposition of voltage signal is taken as fault index and plotted for recognition of faults. Peak values of fault indexes are given as input to rule-based decision tree for classification of faults. The proposed voltage-based scheme proved to be effective in detection and classification of transmission line faults.

A scheme for detection, classification and location of transmission line faults, which combines the wavelet multi-resolution analysis approach and the differential equation approach, is presented. While detection and classification of faults is carried out by a wavelet analysis based algorithm, fault location is determined by a differential equation based algorithm. Using EMTP and MATLAB, studies have been carried out on two simulated power system models. The models are subjected to different types of faults while operating at different operating conditions and the performance of the proposed scheme is evaluated. The results of the simulation studies, which are presented, confirm the feasibility of the proposed scheme.

Deep learning techniques for transmission line fault classification – A comparative study

This paper presents an algorithm for detection and classification of different types of the power system faults occurring on the transmission line to provide the effective protection to the line against these faults. The proposed technique uses Stockwell transform based multi-resolution analysis of the current signals for detection of the faults and rule based decision tree for classification of faults in the power system network. Faults investigated in this research work include line to ground (LG) fault, double line (LL) fault, double line to ground (LLG) fault and three phase fault with the involvement of the ground (LLLG). Proposed study is performed in MATLAB/Simulink environment.

The artificial neural network is a powerful tool for the detection of the transmission line faults due to its ability to differentiate between various patterns. This paper deals with the application of artificial neural networks (ANNs) to the fault detection and classification in high voltage transmission lines for high speed protection which can be used in digital power system protection. The three phase currents of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. The ANN was trained and tested using various sets of field data, which was obtained from the simulation of faults at various fault scenarios (fault types, fault locations and fault resistance) of 230 kV, 193.2 km in length “Mansan-Shwesaryan, Mandalay Region, Myanmar” transmission line using a computer program based on MATLAB/Simulink. Simulation results confirm that the proposed method can efficiently be used for accurate fault classification on the transmission line.

Remote monitoring of transmission lines of a power system is significant for improved reliability and stability during fault conditions and protection system breakdowns. This paper proposes a smart backup monitoring system for detecting and classifying the type of transmission line fault occurred in a power grid. In contradiction to conventional methods, transmission line fault occurred at any locality within power grid can be identified and classified using measurements from phasor measurement unit (PMU) at one of the generator buses. This minimal requirement makes the proposed methodology ideal for providing backup protection. Spectral analysis of equivalent power factor angle (EPFA) variation has been adopted for detecting the occurrence of fault that occurred anywhere in the grid. Classification of the type of fault occurred is achieved from the spectral coefficients with the aid of artificial intelligence. The proposed system can considerably assist system protection center (SPC) in fault localization and to restore the line at the earliest. Effectiveness of proposed system has been validated using case studies conducted on standard power system networks.

This study presents an advanced technique provided with adaptive differential relay characteristics for avoiding current transformer (CT) saturation effects. The proposed technique uses the alienation and differential protection principles to make adaptive logic; if no CT saturation is detected, the differential principle alone is capable of tripping the busbar. If CT saturation is detected, both the alienation and differential principles must pickup in order to trip. The alienation coefficient is useful in CT saturation detection, evaluation of CT saturation degree and adapting the differential relay characteristics during saturation period of CTs to avoid false operation in the event of external faults. The differential current and alienation coefficient are calculated from two terminal currents of the busbar. The proposed protective scheme has several attractive features, including the simplicity of on‐line implementation, not affected by the remanent flux in the CT core, and it is independent of the CT parameters/characteristics and secondary burden. A real 19.57 kV busbar is simulated using Alternate Transients Program/Electro‐Magnetic Transient Program and MATLAB packages to examine the suggested scheme under different types of faults. Under normal operating conditions and external faults, the scheme remains stable and it operates only for the case of internal faults. Moreover, it is unaffected by CT saturation.

A new wavelet based fault detection, classification and location in transmission lines

A hybrid algorithm for phasor estimation is proposed that is immune to DC offset and current transformer (CT) saturation problems. The algorithm utilizes partial sum (PS)-based and multistage least-squares (MLS)-based methods before and after CT saturation is detected, respectively. The MLS-based method is initiated when the third difference of the secondary current detects the start point of the first saturation period. The determination of each saturation period is based on the sum of the secondary current from the start point of the first saturation period. A least-squares (LS) technique estimates the DC offset parameters from the single-cycle difference of the secondary current in the unsaturated periods. Removal of DC offset from the secondary current yields the sinusoidal waveform portion. Finally, the LS technique is used once again to estimate the phasor from the sinusoidal waveform portion. The performance of the algorithm was evaluated for a-g faults on a 345-kV 100-km overhead transmission line. The electromagnetic transient program was used to generate fault current signals for different fault angles and remanent fluxes. The performance evaluation shows that the proposed algorithm accurately estimates the phasor of a current signal regardless of DC offset and CT saturation. The paper concludes by describing the hardware implementation of the algorithm on a prototype unit based on a digital signal processor.

Transmission line protection is an important issue in power system engineering because 85-87% of power system faults are occurring in transmission lines. This paper presents a technique to detect and classify the different shunt faults on a transmission lines for quick and reliable operation of protection schemes. Discrimination among different types of faults on the transmission lines is achieved by application of evolutionary programming tools. PSCAD/EMTDC software is used to simulate different operating and fault conditions on high voltage transmission line, namely single phase to ground fault, line to line fault, double line to ground and three phase short circuit. The discrete wavelet transform (DWT) is applied for decomposition of fault transients, because of its ability to extract information from the transient signal, simultaneously both in time and frequency domain. The data sets which are obtained from the DWT are used for training and testing the SVM architecture. After extracting useful features from the measured signals, a decision of fault or no fault on any phase or multiple phases of a transmission line is carried out using three SVM classifiers. The ground detection task is carried out by a proposed ground index. Gaussian radial basis kernel function (RBF) has been used, and performances of classifiers have been evaluated based on fault classification accuracy. In order to determine the optimal parametric settings of an SVM classifier (such as the type of kernel function, its associated parameter, and the regularization parameter c), fivefold cross-validation has been applied to the training set. It is observed that an SVM with an RBF kernel provides better fault classification accuracy than that of an SVM with polynomial kernel. It has been found that the proposed scheme is very fast and accurate and it proved to be a robust classifier for digital distance protection.

In this paper it was analyzed the influence of a distributed renewable sources of medium voltage power network of the various fault regimes. The real electrical network considered is complex and, depending on the operating scheme, it can contain up to four renewable sources. This real three-phase electrical network was implemented in the “ATP - Alternative Transients Program” software for which the electrical parameters of the model have been transmitted by the power network operator's staffs. It is well-know that for the analysis of the transient regimes an important factor is the connected modes and parameters of the neutral grounding of the power network. For the studied 20 kV network, the neutral can operate with Petersen coil and also with low impedance resistor. Simulations have been made in the ATP model of the network in the event of the occurrence of various types of faults, near the 20kV bus-bars of the substation and at a further point in the grid, near one of the renewable sources.

Power transformer protection using an instantaneous-current-value negative sequence differential element

In the era of degrading energy sources, interconnection of conventional grid with Distributed Generations (DGs) will be the promising alternative. Besides this interconnection, the protection of the whole system is necessary to retain it's stability. This research work carries out the detection of transmission line faults for a DG interconnected system of Goa (a state in India) power transmission network, using wavelet Multi Resolution Analysis. For the purpose of fault discrimination, a moving window technique is selected. The single level decomposition of three phase currents of all Buses are obtained with Bior 1.5 mother wavelet. The calculated fault indices are compared with the pre-calculated threshold value. This provides sufficient information about the situation of fault in a transmission line. Fault detection and discrimination can be accomplished within half a cycle by using this protection technique, irrespective of fault resistance, ult distance and fault inception angle.

This article presents a new method for identification and classification of faults based on wavelet multiresolution analysis (MRA). Daubechies eight (D-8) wavelet transforms of the three phase currents on a transmission line are used. The peak absolute value, the mean of the peak absolute values, and summation of the third level output of MRA detail signals of current in each phase extracted from the original signals are used as the criterion for the analysis. The effects of fault distance, fault inception angle and fault impedance are examined. Extensive simulations are carried out to generate time domain input signal using EMTP (Microtran) on a 400 kV, 300 km long line and simulation results show that the proposed method is a simple, effective and robust method suitable for high impedance faults also.

The transmission line experiences the highest number of faults in a power system due to its high exposure and the high number of coincidences that might occur along it. This study presents an approach based on transmission line protection using wavelet transform with a bi-orthogonal wavelet 5.5 signal decomposition. The emphasis is on fault identification, and the proposed method studies the concentration of the coefficients and discriminates faulted phases from healthy phases. Up to Level 1, the signal is decomposed using a wavelet to capture the major features of the signal. A comprehensive assessment framework was used, and a 400 kV system was studied as an infinite bus coupled with a 300 km long transmission line. A contrivance for fault recognition is designed to determine anomalies in the transmission line between the faulted phases and their healthy counterparts with considerably higher detailed coefficients. Fault classification is then performed based on the choice of strict and carefully calibrated threshold values, in which faults are precisely identified and classified. A comparative analysis has been carried out with the relevant FFT, DFT and S-transform on the same MATALB platform. The proposed algorithm is also successfully validate for variation in system conditions like current inversion, CT saturation, non-linear effect of MOV and level of series compensation. An inclusive transmission line protection system is reliably and effectively cultivated through the custom of the planned protection procedure, in which it is shown to be highly capable of precisely sensing and categorizing faults. The replication outcomes demonstrate the effectiveness of the system in practical situations and demonstrate that it may be used in real power systems.