A novel traveling-wave-based method improved by unsupervised learning for fault location of power cables via sheath current monitoring

In order to improve the practice in maintenance of power cables, this paper proposes a novel traveling-wave-based fault location method improved by unsupervised learning. The improvement mainly lies in the identification of the arrival time of the traveling wave. The proposed approach consists of four steps: (1) The traveling wave associated with the sheath currents of the cables are grouped in a matrix; (2) the use of dimensionality reduction by t-SNE (t-distributed Stochastic Neighbor Embedding) to reconstruct the matrix features in a low dimension; (3) application of the DBSCAN (density-based spatial clustering of applications with noise) clustering to cluster the sample points by the closeness of the sample distribution; (4) the arrival time of the traveling wave can be identified by searching for the maximum slope point of the non-noise cluster with the fewest samples. Simulations and calculations have been carried out for both HV (high voltage) and MV (medium voltage) cables. Results indicate that the arrival time of the traveling wave can be identified for both HV cables and MV cables with/without noise, and the method is suitable with few random time errors of the recorded data. A lab-based experiment was carried out to validate the proposed method and helped to prove the effectiveness of the clustering and the fault location

A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

New Algorithms for Locating Faults in Series Capacitive Compensated Transmission Lines

The precise location of the fault in a series capacitive compensated transmission line (SCCTL) plays an integral part in limiting the maintenance time following its tripping due to the occurrence of a permanent fault. Since, an SCCTL acts as a huge corridor of power, its outage will result in huge monetary losses which are directly proportion to the time it remains out of service. In worst case scenario, the tripping of an SCCTL might lead to the cascaded tripping of the parallel transmission lines due to overloading. Therefore, the need for an accurate and robust fault location algorithm for the SCCTLs becomes critical. Consequently, the focus of this thesis is to develop new fault location algorithms for the SCCTLs. First of all, the concept of fault location in conventional transmission lines and its application to SCCTLs has been explained. The mathematical analysis of impedance-based fault location algorithms for SCCTLs which are the most widely used fault location algorithms for SCCTLs, is performed. The mathematical analysis enables a deeper look into the strengths and deficiencies of the existing algorithms. After the identification of the innate limitations of the existing fault location algorithms, three new impedance-based fault location algorithms have been proposed with the aim of maximum utilization of the available measurements to improve the accuracy of the fault location results in SCCTLs. The proposed impedance-based algorithms are then tested for various fault scenarios using simulations carried out in Matlab, and PSCAD. The comparative analysis of the proposed algorithms with the existing algorithms is also performed. The interest in traveling wave-based fault location algorithms has been renewed lately due to the availability of commercial relays capable of sampling in the range of 1 MHz. Therefore, the traveling wave theory which forms the basis of traveling wave-based fault location algorithms is discussed. The mathematical analysis of reflection, and transmission of the traveling waves from various points of discontinuity in an SCCTL has been performed which enables the understanding of the shortcomings of the existing fault location algorithms. Thereafter, a new single-ended traveling wave-based fault location algorithm has been proposed in this thesis. The performance of the proposed algorithm has been verified through the simulations carried out in PSCAD

Fault Location in Grid Connected Ungrounded PV Systems Using Wavelets

Solar photovoltaic (PV) power has become one of the major sources of renewable energy worldwide. This thesis develops a wavelet-based fault location method for ungrounded PV farms based on pattern recognition of the high frequency transients due to switching frequencies in the system and which does not need any separate devices for fault location. The solar PV farm used for the simulation studies consists of a large number of PV modules connected to grid-connected inverters through ungrounded DC cables. Manufacturers report that about 1% of installed PV panels fail annually. Detecting phase to ground faults in ungrounded underground DC cables is also difficult and time consuming. Therefore, identifying ground faults is a significant problem in ungrounded PV systems because such earth faults do not provide sufficient fault currents for their detection and location during system operation. If such ground faults are not cleared quickly, a subsequent ground fault on the healthy phase will create a complete short-circuit in the system, which will cause a fire hazard and arc-flashing. Locating such faults with commonly used fault locators requires costly external high frequency signal generators, transducers, relays, and communication devices as well as generally longer lead times to find the fault. This thesis work proposes a novel fault location scheme that overcomes the shortcomings of the currently available methods. In this research, high frequency noise patterns are used to identify the fault location in an ungrounded PV farm. This high frequency noise is generated due to the switching transients of converters combined with parasitic capacitance of PV panels and cables. The pattern recognition approach, using discrete wavelet transform (DWT) multi-resolution analysis (MRA) and artificial neural networks (ANN), is utilized to investigate the proposed method for ungrounded grid integrated PV systems. Detailed time domain electromagnetic simulations of PV systems are done in a real-time environment and the results are analyzed to verify the performance of the fault locator. The fault locator uses a wavelet transform-based digital signal processing technique, which uses the high frequency patterns of the mid-point voltage signal of the converters to analyze the ground fault location. The Daubechies 10 (db10) wavelet and scale 11 are chosen as the appropriate mother wavelet function and decomposition level according to the characteristics of the noise waveform to give the proposed method better performance. In this study, norm values of the measured waveform at different frequency bands give unique features at different fault locations and are used as the feature vectors for pattern recognition. Then, the three-layer feed-forward ANN classifier, which can automatically classify the fault locations according to the extracted features, is investigated. The neural network is trained with the Levenberg-Marquardt back-propagation learning algorithm. The proposed fault locating scheme is tested and verified for different types of faults, such as ground and line-line faults at PV modules and cables of the ungrounded PV system. These faults are simulated in a real-time environment with a digital simulator and the data is then analyzed with wavelets in MATLAB. The test results show that the proposed method achieves 99.177% and 97.851% of fault location accuracy for different faults in DC cables and PV modules, respectively. Finally, the effectiveness and feasibility of the designed fault locator in real field applications is tested under varying fault impedance, power outputs, temperature, PV parasitic elements, and switching frequencies of the converters. The results demonstrate the proposed approach has very accurate and robust performance even with noisy measurements and changes in operating conditions

Fault Location Algorithm for HVDC Transmission Based on Synchronized Fault Time

To determine the fault location of the dc line fault in an HVDC transmission system, a new algorithm based on the traveling wave method and learning based method is proposed in this paper. The relationship between the traveling wave time of arrival differences with fault location is presented.  The differences in traveling wave time of arrival measured at both ends of transmission line combined with associated fault locations form a fault pattern which is used to perform a simple calculation in order to determine the disturbance location. The fault current for different fault locations is simulated using the electromagnetic transient simulation software EMTDC/PSCAD. Performance of the proposed fault location algorithm is investigated using various fault location and resistance. The impact of data sampling rate also being investigated here. The simulation result shows that the proposed algorithm can reduce the sampling frequency and the number of train feature with the same accuracy