FDST approach statcom integrated double circuit transmission line

This paper describes the fault classification and detection of SVC (Static VAR Compensator) integrated differential relay based double circuit transmission line applying Discrete Wavelet Transform (DWT) approach. The SVC is placed at mid-point of circuit-I of double transmission line. The other circuit (circuit-II) remains uncompensated. The differential relay based protection scheme is used for the analysis of fault detection and classification. The process starts by extracting current signals (A, B or C-phase) from both sending and receiving end of the transmission line synchronously and processes through DWT to obtain the spectral energy (SE) at both the end of the line. The Differential Spectral Energy (DSE) of each phase current signals are obtained by SE of current measured at sending end minus SE of current measured at receiving end. This DSE value is used to register the fault pattern.

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 fast time–frequency transform based differential relaying scheme for UPFC based double-circuit transmission line

In this paper, an advanced scheme of fast discrete S-transform (FDST) based compensated line is proposed. This scheme is proposed for the detection and classification of the single circuit transmission line with reference to Unified power Flow controller (UPFC) Position, which is centrally located in the transmission line. The scheme starts processing by extracting each phase currents signals (A or B or C-phase) from both end (i.e, sending and receiving end) of the transmission line synchronously. It processes through FDST to derive the spectral energy (SE) of current signals. The Differential Spectral Energy (DSE) of current signals (i.e, spectral energy of current measured at sending end minus spectral energy of current measured at receiving end) of respective phases of transmission lines is used to register the fault pattern.

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

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.

Double circuit transmission line is very efficient in comparison with single line system because if a fault occurs in one transmission line, another line can deliver power to load. In this work, Fast Fourier Transform (FFT) based Phasor Measurement Unit (PMU) is used to detect and classify the different faults in a double circuit transmission line. For this purpose, at first current signal data have been captured with proper sampling frequency from one end of the double circuit transmission line and then FFT based parameter analysis is used to assess the fault conditions. Results using this method have come out very much optimistic.

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.

Improvement of synchronizing power and damping power by means of SSSC and STATCOM: A comparative study

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.

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.

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.

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 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.