Fault detection, classification and localization in HV power transmission lines using ANN

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.

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.

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.

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

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.

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.

In this article, a new full-scheme distance protection for a series-compensated transmission line is proposed. The new combination of hyperbolic S-transform and learning machines is applied for fault detection, classification, and location, which are the three main aspects of distance relays. The hyperbolic S-transform is used for extracting useful features from the current and voltage signals sample of the power system from one terminal. The extracted features are employed for distance protection using support vector classification and support vector regression methods. The proposed method is evaluated by the different series capacitor location in the transmission line, where a metal oxide varistor protects the series capacitor against over-voltages. Extensive simulation experiments show that the proposed protection scheme provides accurate tripping logic with good accuracy of fault location. Also, the non-tripping decision for external faults that occurred on adjacent uncompensated transmission line is embedded in the proposed scheme. The various operating conditions of the series-compensated transmission line are simulated in the PSCAD/EMTDC (Winnipeg, Manitoba, Canada) environment. The obtained results of support vector classification and support vector regression confirm that the new extracted features from the time-frequency hyperbolic S-transform-matrix are very effective and reliable.

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.

Fault detection, classification, and location in transmission lines compensated with Unified Interphase Power Controller (UIPC) based on random forest algorithm

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 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.