Fault location and forewarning on transmission systems using travelling wave transients

University of Technology, Sydney. Faculty of Engineering.This thesis examines the main circuit modelling fundamentals and fault location techniques that may be applied to electricity transmission networks. Using a statistical comparison, it then investigates both impedance and travelling wave based fault location methods. This appears to be a novel comparison as no publications have been identified which draw conclusions on the accuracy of these fault location techniques. This work subsequently led TransGrid to install a new commercial travelling wave fault location system on the New South Wales 330kV transmission network. Following the commissioning of this system, there was an ongoing process to store data that was being observed by the travelling wave recorders. This data was later cross- referenced to determine the fault location, and the waveform interpreted to identify the source of the travelling wave transient. However, this analysis has revealed that the theoretical accuracy of this travelling wave system was not as good as previously expected from publication. The source of the degradation was tracked down and found to centre on the frequency response of the coupling transducers used by most conventional travelling wave recording hardware. These errors are not currently considered in publication but can result in several kilometres of uncertainty in a fault location calculation. Hence, it can be concluded that the use of conventional substation current transducers can introduce additional uncertainty into travelling wave fault location calculations. The source and nature of this uncertainty has subsequently led to the development of a novel unsynchronised fault location algorithm based on the continuous wavelet transform. This new technique also uses an assessment of waveform polarity to distinguish between signals generated by solid or incipient line faults. Several unusual events have also been observed which have led to a number of new developments in fault location and forewarning. These include specific requirements for impedance algorithms during unearthed inter-circuit faults on double circuit lines. Similarly, this thesis presents the development of a new method to forewarn of faults within oil impregnated current transformers. This has been based on the high frequency transients observed by the travelling wave system prior to the failure of a 330kV current transformer. This thesis also identifies significant potential for travelling wave techniques to forewarn of developing insulator faults on overhead circuits

Accurate location of high impedance and temporary faults in radial distribution networks using distributed travelling wave observers

This thesis addresses a novel method for fault location in radial distribution networks and provides a new vision for the optimal deployment of synchronised voltage travelling wave (TW) observers in distribution networks. The proposed method can locate high impedance and temporary faults. The delay effect of transformers is demonstrated by theory and laboratory tests. A new method to eliminate the transformer’s effect on the accuracy of the fault location algorithm is presented

Transient fault area location and fault classification for distribution systems based on wavelet transform and Adaptive Neuro-Fuzzy Inference System (ANFIS)

A novel method to locate the zone of transient faults and to classify the fault type in Power Distribution Systems using wavelet transforms and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) has been developed. It draws on advanced techniques of signal processing based on wavelet transforms, using data sampled from the main feeder current to extract important characteristics and dynamic features of the fault signal. In this method, algorithms designed for fault detection and classification based on features extracted from wavelet transforms were implemented. One of four different algorithms based on ANFIS, according to the type of fault, was then used to locate the fault zone. Studies and simulations in an EMTP-RV environment for the 25kV power distribution system of Canada were carried out by considering ten types of faults with different fault inception, fault resistance and fault locations. The simulation results showed high accuracy in classifying the type of fault and determining the fault area, so that the maximum observed error was less than 2%

Wide area protection and fault location : review and evaluation of PMU-based methods

Wide area protection (WAP) systems use multiple sources of information to improve trip times and reduce the complexity of protection settings. Therefore, such communications-enhanced schemes have the potential to replace conventional transmission system backup protection. Through review and assessment of the present state-of-the-art relating to WAP systems, this paper demonstrates how multiple synchrophasor data sources, and the associated communications systems, can be leveraged to enable new forms of supervisory protection. Two case studies are presented: a scalable WAP architecture for future decentralised power systems, and the validation a prototype WAP system, using the principle of distributed photonic sensing, highlighting how new tools can provide cost-effective solutions to emerging protection challenges

Fault Detection and Classification using Wavelet and ANN in DFIG and TCSC Connected Transmission Line

This paper presents fault detection and classification using Wavelet and ANN based methods in a DFIG-based series compensated system. The state-of-the art methods include Wavelet transform, Fourier transform, and Wavelet-neuro fuzzy methods-based system for fault detection and classification. However, the accuracy of these state-of-the-art methods diminishes during variable conditions such as changes in wind speed, high impedance faults, and the changes in the series compensation level. Specifically, in Wavelet transform based methods, the threshold values need to be adapted based on the variable field conditions. To solve this problem, this paper has proposed a Wavelet-ANN based fault detection method where Wavelet is used as an identifier and ANN is used as a classifier for detecting various fault cases. This methodology is also effective under SSR condition. The proposed methodology is evaluated on various fault and non-fault cases generated on an IEEE first benchmark model under varying compensation levels from 20% to 55%, impedance faults, and wind velocity from 6m/sec to 10m/sec using MATLAB/Simulink, OPALRT(OP4510) manufactured real-time digital simulator environment, Arduino board I/O ports communicating with external PC in which ANN model dumped, using Arduino support package of MATLAB. The preliminary results are compared with the state-of-the-art fault detection method, where the proposed method shows robust performance under varying field conditions

Impedance-compensated grid synchronisation for extending the stability range of weak grids with voltage source converters

This paper demonstrates how the range of stable power transfer in weak grids with voltage source converters (VSCs) can be extended by modifying the grid synchronisation mechanism of a conventional synchronous reference frame phase locked loop (PLL). By introducing an impedance-conditioning term in the PLL, the VSC control system can be virtually synchronised to a stronger point in the grid to counteract the instability effects caused by high grid impedance. To verify the effectiveness of the proposed approach, the maximum static power transfer capability and the small-signal stability range of a system with a VSC HVDC terminal connected to a weak grid are calculated from an analytical model with different levels of impedance-conditioning in the PLL. Such calculations are presented for two different configurations of the VSC control system, showing how both the static power transfer capability and the small-signal stability range can be significantly improved. The validity of the stability assessment is verified by time-domain simulations in the Matlab/Simulink environment.Peer ReviewedPostprint (published version

Centralised busbar differential and wavelet-based line protection system for multi- terminal direct current grids, with practical IEC-61869-compliant measurements

This paper presents a method for discriminative detection of DC faults on VSC-powered multi-terminal HVDC transmission systems using two fundamental guiding principles, namely instantaneous current-differential and travelling waves. The proposed algorithm utilises local voltage and current measurements from all transmission lines connected to a DC busbar, and current measurement from the DC side of the converter. The scheme operates at a sampling frequency of 96 kHz which conforms with IEC 61869-9. No long distance communication is involved while measurements and signal exchange within DC substations are enabled by the utilisation of IEC 61850. Performance is assessed firstly through detailed transient simulation, using verified models of modular multi-level converters, hybrid DC circuit breakers and inductive DC-line terminations. Furthermore, practical performance and feasibility of the scheme is evaluated through laboratory testing, using the real time Opal-RT hardware prototyping platform. Simulation and experimental results demonstrate that the proposed protection algorithm can effectively, and within a very short period of time (i.e. less than 1 ms), discriminate between busbar and line faults (internal faults), while remaining stable during external faults. Additionally, it has been demonstrated that IEC 61869-9 is suitable for enabling fast DC protection schemes incorporating travelling waves

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