Algorithm Based on Harmonic Wavelet Transform and Rule-Based Decision Tree for Detection and Classification of Transmission Line Faults

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.

This paper discusses fault detection and classification in the double circuit transmission line of a differential based scheme in STATCOM (Static synchronous Compensator) compensated line applying FDST (Fast Discrete S-Transform). Here, STATCOM is placed at middle point of one of the transmission line and the other line is uncompensated. The proposed scheme starts processing by extracting the faulty currents signal (A, B or C-phase) from both end (i.e, sending and receiving end) of the transmission line synchronously through GPS system. The current signals are passed through FDST to derive the Spectral Energy (SE) of current signal at both end of the transmission line. Then the Differential Energy (DE) is computed by using SE of current measured at sending end minus SE of current measured at receiving end of transmission line. The scheme is very effective and works accurately for fault detection and classification of the transmission line.

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.

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.

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.

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.

Application of neural networks in electrical power systems have demonstrated that this powerful tool can be employed as an alternative method for solving problems accurately and efficiently. In this paper, an artificial neural networks approach is presented for the detection, classification and isolation (location) of transmission line faults occurring in the system of transmission line. The main aim is to implement a complete scheme for distance protection of a transmission line system. In order to perform this goal, the distance protection task is subdivided into different neural network for fault detection, fault identification (classification) as well as fault location in different zones. 110 KV transmission line system is used to create training and testing data for the neural network by mat-lab program. The result show that the neural network is a powerful analysis tool for providing faults information that is more reliable and accurate than provided by the traditional relay.

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

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.

Faults in transmission lines cause instability of power system and result in degrading end users sophisticated equipment. Therefore, in case of fault and for the quick restoration of problematic phases, reliable and accurate fault detection and classification techniques are required to categorize the faults in a minimum time. In this work, 500 kV transmission line (Jamshoro‐New Karachi), Sindh, Pakistan has been modeled in MATLAB. The discrete wavelet transform (DWT) has been used to extract features from the transient current signal for different faults in 500 kV transmission line under various parameters such as fault location, fault inception angle, ground resistance and fault resistance and time series data has been obtained for fault classification. Moreover, the temporal convolutional neural network (TCN) is used for fault classification in 500 kV transmission network due to its robust framework. From simulation results, it is found that faults in 500 kV transmission line are classified with 99.9% accuracy. Furthermore, the simulation results of the TCN model compared to bidirectional long short‐term memory (BiLSTM) and Gated Recurrent Unit (GRU) and it has been found that TCN model is capable of classifying faults in 500 kV transmission line with high accuracy due to its ability to handle long receptive field size, less memory requirement and parallel processing due to dilated causal convolutions. Through this work, the meantime to repair of 500 kV transmission line can be reduced.

The advanced application of artificial intelligent techniques (AIT) was introduced recently in protection of transmission lines in electric power systems (EPS). Adaptive neuro-fuzzy inference system (ANFIS) is a promising tool among these AIT. In this proposed research, the application of ANFIS for distance relay protection for long transmission line, under different loading conditions, in electrical power systems (EPS) will be introduced and discussed. Based on multiple classified ANFIS and considering different loading conditions for long transmission lines in EPS, the suggested and proposed technique deals with fault detection, classification, and location in long transmission lines. All these tasks will be addressed in details in this article. It considers, firstly, detecting the fault occurrence in very short time and isolate the faulty section of the long transmission lines. Secondly to classify the fault type and deduce which of the three phases are exposed to the fault. Finally, locating the fault will be achieved easily even if the procedure here is completely different from short and medium transmission lines. The input training data of the ANFIS detection units are firstly derived from the fundamental values of the voltage and current measurements [using digital signal processing via discrete Fourier transform (DFT)]. These measurements were simulated considering different loading conditions.

Transmission lines are an imperative element of the modern power systems. Any faults in them can cause an undesirable interruption in power supply. Precise analysis of these faults is important in-order to ensure an incessant supply of power. For this purpose, fault detection and classification is needed to clear any such faults and re-establish the system to maintain its normal operation. This paper presents fuzzy logic system for this purpose. The proposed scheme is successfully able to detect and classify different symmetrical and unsymmetrical faults along with some peculiar cases related to High Impedance Faults (HIF) and evolving faults. The results obtained prove that this scheme performs better than several other existing techniques and also has a very small fault detection time. Western System Coordinating Council (WSCC) nine bus system has been opted for conducting the studies. All the simulations are performed using simulink and fuzzy logic toolboxes of MATLAB.

This article presents a fast, hardware efficient logic for fault detection and classification in transmission lines. The proposed logic employs only one-terminal current samples and is based on multi-resolution wavelet analysis. First-level high-frequency details of the modal currents in the range of 5 to 10 kHz are extracted. Depending on the amount of high-frequency components in the transformed current signals after processing, the faults are classified into ten types. An adaptive threshold value is chosen, rather than a fixed threshold in the case of faults involving the ground, to make the classification reliable and accurate. The validity of the proposed logic was exhaustively tested by simulating various types of faults on a system modeled in Alternative Transient Program (ATP)/Electromagnetic Transient Program (EMTP).The proposed logic was found to be acceptably reliable and accurate even in the presence of fault resistance and current transformer (CT) saturation.

This work presents a protection algorithmto diagnose the fault and its type, using alienation coefficients of current signals, on a parallel transmission system. Alienation Coefficients are computed by comparing samples of post-fault current signals for successive cycles, obtained for a quarter cycle period. This Alienation Coefficient is compared with the threshold to detect the faulty phase and consequent classification of type of fault. Thus.the fault detection time for this algorithm is only a quarter cycle period. The performance of the proposed scheme has been established by various case studies in which fault locations, incipient angles and fault impedances are varied.

A novel algorithm to fault detection and classification of transmission lines is proposed, by means of the wavelet singular entropy (WSE) technique. Moreover, the Euclidean Norm based on the WSE information of all phases is used to classify the faults. High frequency information plays an important role in the new “Transient Based Protection” (TBS) protection schemes, which use only the transient information of the phenomenon. Current signals are processed with the discrete wavelet transform (DWT), so that the produced energy by the wavelet coefficients is analyzed and a WSE threshold is also proposed. Fault simulation is carried out in DIgSILENT Power Factory, for phase to ground and three phase faults, for several cases of fault impedance conditions. Results shown that a time window of 5 ms is enough to identified a single-phase fault from a three-phase fault instead of the commonly 16.66 ms trip-time. The proposed algorithm shows satisfactory accuracy, and will be very useful in the TBS protection scheme for electrical power transmission systems.