Fault location on the high voltage series compensated power transmission networks

Nowadays power transmission networks are capable of delivering contracted power from any supplier to any consumer over a large geographic area under market control, and thus transmission lines are incorporated with FACTs series compensated devices to increase the power transfer capability with improvement to system integrity. Conventional fault location methods developed in the past many years are not suitable for FACTs transmission networks. The obvious reason is that FACTs devices in transmission networks introduce non-linearity in the system and hence linear fault detection methods are no longer valid. Therefore, it is still a matter of research to investigate developing new fault detection techniques to cater for modern transmission network configurations and solve implementation issues maintaining required accuracy. This PhD research work is based on developing an accurate and robust new fault location algorithm for series compensated high voltage transmission lines, considering many issues such as transmission line models, configurations with series compensation features. Building on the existing knowledge, a new algorithm has been developed for the estimation of fault location using the time domain approach. In this algorithm, instantaneous fault signals from the transmission line ends are measured and applied to the algorithm to calculate the distance to fault. The new algorithm was tested on two port transmission line model developed using EMTP/ATP software and measured fault data from the simulations are exported to the MATLAB space to run the algorithm. Broad range of faults has been simulated considering various fault cases to test the algorithm and statistical results obtained. It was observed that the accuracy of location of fault on series compensated transmission line using this algorithm is in the range from 99.7 % to 99.9% in 90% of fault cases. In addition, this algorithm was further improved considering many practical issues related to modern series compensated transmission lines (with TCSC var compensators) achieving similar accuracies in the estimation of fault location

Data-driven Protection of Transformers, Phase Angle Regulators, and Transmission Lines in Interconnected Power Systems

This dissertation highlights the growing interest in and adoption of machine learning approaches for fault detection in modern electric power grids. Once a fault has occurred, it must be identified quickly and a variety of preventative steps must be taken to remove or insulate it. As a result, detecting, locating, and classifying faults early and accurately can improve safety and dependability while reducing downtime and hardware damage. Machine learning-based solutions and tools to carry out effective data processing and analysis to aid power system operations and decision-making are becoming preeminent with better system condition awareness and data availability. Power transformers, Phase Shift Transformers or Phase Angle Regulators, and transmission lines are critical components in power systems, and ensuring their safety is a primary issue. Differential relays are commonly employed to protect transformers, whereas distance relays are utilized to protect transmission lines. Magnetizing inrush, overexcitation, and current transformer saturation make transformer protection a challenge. Furthermore, non-standard phase shift, series core saturation, low turn-to-turn, and turn-to-ground fault currents are non-traditional problems associated with Phase Angle Regulators. Faults during symmetrical power swings and unstable power swings may cause mal-operation of distance relays, and unintentional and uncontrolled islanding. The distance relays also mal-operate for transmission lines connected to type-3 wind farms. The conventional protection techniques would no longer be adequate to address the above-mentioned challenges due to their limitations in handling and analyzing the massive amount of data, limited generalizability of conventional models, incapability to model non-linear systems, etc. These limitations of conventional differential and distance protection methods bring forward the motivation of using machine learning techniques in addressing various protection challenges. The power transformers and Phase Angle Regulators are modeled to simulate and analyze the transients accurately. Appropriate time and frequency domain features are selected using different selection algorithms to train the machine learning algorithms. The boosting algorithms outperformed the other classifiers for detection of faults with balanced accuracies of above 99% and computational time of about one and a half cycles. The case studies on transmission lines show that the developed methods distinguish power swings and faults, and determine the correct fault zone. The proposed data-driven protection algorithms can work together with conventional differential and distance relays and offer supervisory control over their operation and thus improve the dependability and security of protection systems

Fault management in networks incorporating Superconducting Cables (SCs) using Artificial Intelligence (AI) techniques.

With the increasing penetration of renewable energy sources, the immense growth in energy demand and the ageing of existing system infrastructure, future power systems have started to face reliability and resiliency challenges. To mitigate these issues, the need for bulk power corridors which enable the effective sharing of the available power capacity, between countries and from remote renewable energy sources, is rendered imperative. In this context, the deployment of multi-layer Superconducting Cables (SCs) with High Temperature Superconducting (HTS) tapes have been considered as a promising solution towards the modernisation of power systems. As opposed to conventional copper cables, SCs are characterised by a plethora of technically-attractive features such as compact structure, higher current-carrying capability, lower losses, higher power transfer at lower operating voltages and over longer distances, and reduced environmental impact. The performance of SCs is mainly determined by the structure of the cable and the electro-magneto-thermal properties of the HTS tapes, accounting for the critical current, critical temperature and critical magnetic field. Particularly, during steady state conditions, HTS tapes operate in superconducting mode, providing tangible benefits to power system operation such as a current-flowing path with approximately zero resistance. However, under certain transient conditions (e.g., electric faults), when the fault current flowing through HTS tapes reaches values higher than the critical current, HTS tapes start to quench. The quenching phenomenon is accompanied by a rapid increase in the equivalent resistance and temperature of SCs, the generation of Joule heating and the subsequent reduction in fault current magnitudes. Consequently, the transition of SCs from superconducting state to resistive state, during transient conditions, introduces many variables in the fault management of such cable technologies. Therefore, in order to exploit the technological advantages offered by SC applications, accommodate their wide-scale deployment within future energy grids, and accelerate their commercialisation, the detailed evaluation of their transient response and the consequent development of reliable fault management solutions are vital prerequisites. On that front, one of the main objectives of this thesis is to provide a detailed fault signature characterisation of AC and DC SCs and develop effective and practically feasible solutions for the fault management of AC and High Voltage Direct Current (HVDC) grids which incorporate SCs. As the fault management (i.e., fault detection, fault location, and protection) of SCs has proven to be a multi-variable problem, considering the complex structure, the unique features of SCs, and the quenching phenomenon, there is a need for advanced methods with immunity to these factors. In this context, the utilisation of Artificial Intelligence (AI) methods can be considered a very promising solution due to their capability to expose hidden patterns and acquire useful insights from the available data. Specifically, data-driven methods exhibit multifarious characteristics which allow them to provide innovative solutions for complex problems. Given their capacity for advanced learning and extensive data analysis, these methods merit thorough investigation for the fault management of SCs. Their inherent potential to adapt and uncover patterns in large datasets presents a compelling rationale for their exploration in enhancing the reliability and performance of superconducting cable systems. Therefore, this thesis proposes the development of novel, data-driven protection schemes which incorporate fault detection and classification elements for AC and multi-terminal HVDC systems with SCs, by exploiting the advantages of the latest trends in AI applications. In particular this thesis utilises cutting-edge developments and innovations in the field of AI, such as deep learning algorithms (i.e., CNN), and state-of-the-art techniques such as the XGBoost model which is a powerful ensemble learning algorithm. The developed schemes have been validated using simulation-based analysis. The obtained results confirm the enhanced sensitivity, speed, and discrimination capability of the developed schemes under various fault conditions and against other transient events, highlighting their superiority over other proposed methods or existing techniques. Furthermore, the generalisation capability of AI-assisted schemes has been verified against many adverse factors such as high values of fault resistance and noisy measurement. To further evaluate the practical feasibility and assess the time performance of the proposed schemes, real-time Software In the Loop (SIL) testing has been utilised. Another very important task for the effective fault management of AC and DC SCs is the estimation of the accurate fault location. Identifying the precise location of faults is crucial for SCs, given their complex structure and the challenging repair process. As such, this thesis proposes the design of a data-driven fault location scheme for AC systems with SCs. The developed scheme utilises pattern recognition techniques, such as image analysis, for feature extraction. It also incorporates AI algorithms in order to formulate the fault location problem as an AI regression problem. It is demonstrated that the scheme can accurately estimate the fault location along the SCs length and ensure increased reliability against a wide range of fault scenarios and noisy measurements. Further comparative analysis with other data-driven schemes validates the superiority of the proposed approach. In the final chapter the thesis summarises the key observations and outlines potential steps for further research in the field of fault management of superconducting-based systems.With the increasing penetration of renewable energy sources, the immense growth in energy demand and the ageing of existing system infrastructure, future power systems have started to face reliability and resiliency challenges. To mitigate these issues, the need for bulk power corridors which enable the effective sharing of the available power capacity, between countries and from remote renewable energy sources, is rendered imperative. In this context, the deployment of multi-layer Superconducting Cables (SCs) with High Temperature Superconducting (HTS) tapes have been considered as a promising solution towards the modernisation of power systems. As opposed to conventional copper cables, SCs are characterised by a plethora of technically-attractive features such as compact structure, higher current-carrying capability, lower losses, higher power transfer at lower operating voltages and over longer distances, and reduced environmental impact. The performance of SCs is mainly determined by the structure of the cable and the electro-magneto-thermal properties of the HTS tapes, accounting for the critical current, critical temperature and critical magnetic field. Particularly, during steady state conditions, HTS tapes operate in superconducting mode, providing tangible benefits to power system operation such as a current-flowing path with approximately zero resistance. However, under certain transient conditions (e.g., electric faults), when the fault current flowing through HTS tapes reaches values higher than the critical current, HTS tapes start to quench. The quenching phenomenon is accompanied by a rapid increase in the equivalent resistance and temperature of SCs, the generation of Joule heating and the subsequent reduction in fault current magnitudes. Consequently, the transition of SCs from superconducting state to resistive state, during transient conditions, introduces many variables in the fault management of such cable technologies. Therefore, in order to exploit the technological advantages offered by SC applications, accommodate their wide-scale deployment within future energy grids, and accelerate their commercialisation, the detailed evaluation of their transient response and the consequent development of reliable fault management solutions are vital prerequisites. On that front, one of the main objectives of this thesis is to provide a detailed fault signature characterisation of AC and DC SCs and develop effective and practically feasible solutions for the fault management of AC and High Voltage Direct Current (HVDC) grids which incorporate SCs. As the fault management (i.e., fault detection, fault location, and protection) of SCs has proven to be a multi-variable problem, considering the complex structure, the unique features of SCs, and the quenching phenomenon, there is a need for advanced methods with immunity to these factors. In this context, the utilisation of Artificial Intelligence (AI) methods can be considered a very promising solution due to their capability to expose hidden patterns and acquire useful insights from the available data. Specifically, data-driven methods exhibit multifarious characteristics which allow them to provide innovative solutions for complex problems. Given their capacity for advanced learning and extensive data analysis, these methods merit thorough investigation for the fault management of SCs. Their inherent potential to adapt and uncover patterns in large datasets presents a compelling rationale for their exploration in enhancing the reliability and performance of superconducting cable systems. Therefore, this thesis proposes the development of novel, data-driven protection schemes which incorporate fault detection and classification elements for AC and multi-terminal HVDC systems with SCs, by exploiting the advantages of the latest trends in AI applications. In particular this thesis utilises cutting-edge developments and innovations in the field of AI, such as deep learning algorithms (i.e., CNN), and state-of-the-art techniques such as the XGBoost model which is a powerful ensemble learning algorithm. The developed schemes have been validated using simulation-based analysis. The obtained results confirm the enhanced sensitivity, speed, and discrimination capability of the developed schemes under various fault conditions and against other transient events, highlighting their superiority over other proposed methods or existing techniques. Furthermore, the generalisation capability of AI-assisted schemes has been verified against many adverse factors such as high values of fault resistance and noisy measurement. To further evaluate the practical feasibility and assess the time performance of the proposed schemes, real-time Software In the Loop (SIL) testing has been utilised. Another very important task for the effective fault management of AC and DC SCs is the estimation of the accurate fault location. Identifying the precise location of faults is crucial for SCs, given their complex structure and the challenging repair process. As such, this thesis proposes the design of a data-driven fault location scheme for AC systems with SCs. The developed scheme utilises pattern recognition techniques, such as image analysis, for feature extraction. It also incorporates AI algorithms in order to formulate the fault location problem as an AI regression problem. It is demonstrated that the scheme can accurately estimate the fault location along the SCs length and ensure increased reliability against a wide range of fault scenarios and noisy measurements. Further comparative analysis with other data-driven schemes validates the superiority of the proposed approach. In the final chapter the thesis summarises the key observations and outlines potential steps for further research in the field of fault management of superconducting-based systems

CEEME: compensating events based execution monitoring enforcement for Cyber-Physical Systems

Fundamentally, inherently observable events in Cyber-Physical Systems with tight coupling between cyber and physical components can result in a confidentiality violation. By observing how the physical elements react to cyber commands, adversaries can identify critical links in the system and force the cyber control algorithm to make erroneous decisions. Thus, there is a propensity for a breach in confidentiality leading to further attacks on availability or integrity. Due to the highly integrated nature of Cyber-Physical Systems, it is also extremely difficult to map the system semantics into a security framework under existing security models. The far-reaching objective of this research is to develop a science of selfobfuscating systems based on the composition of simple building blocks. A model of Nondeducibility composes the building blocks under Information Flow Security Properties. To this end, this work presents fundamental theories on external observability for basic regular networks and the novel concept of event compensation that can enforce Information Flow Security Properties at runtime --Abstract, page iii

Novel techniques for fault location, voltage profile calculation and visualization of transients

This dissertation addresses three different problems in power systems. The first problem is related to the fault location in complex topologies such as three terminal circuits with series compensation and mutually coupled line sections and distribution networks with distributed generation. Novel methods are presented by using traveling wave approach and wavelet transformation technique to overcome the difficulties introduced by the discontinuities and integrated components such as Metal Oxide Varistor (MOV) protected series capacitors and distributed generation in complex topologies. Simulation results show good correlation between the actual and estimated fault locations for all the studied cases. The second problem concerns the calculation techniques of voltage profiles along transmission lines. A simple yet effective approach to accurately and rapidly obtain the voltage profile along a transmission line during fault transients is presented. The objective of the presented method is to eliminate the need to use wave equations and line parameters provided that an electromagnetic Transients Program (EMTP) type transients simulator is available for generating bus voltage transients for a given fault. This is accomplished by developing a time series model to estimate the voltage at an intermediate point along the transmission line. The model is formed for each intermediate point separately. Once the model is obtained it can be used to predict the transient voltage at that point along the line during any fault in the system. The approach can potentially be useful as a post processor to a transient simulator and can be used by developers of transient animations and movies for illustrating fault-initiated propagation of traveling waves in power systems. The third problem is the lack of powerful visualization and animation methods, which can help understanding the complex behavior of power systems during transients. The goal of this part of the dissertation is to develop new animation and visualization methods for power system electromagnetic transients for both educational and research purposes. Proposed approaches are implemented in different environments such as MATLAB and Microsoft Visual Studio to show the effectiveness of two and three-dimensional visualization of power system transients. The implementations of the proposed methods provide better understanding of the power systems during transient phenomena due to the faults or switchings

Operation, Monitoring, and Protection of Future Power Systems: Advanced Congestion Forecast and Dynamic State Estimation Applications

The electrical power systems are undergoing drastic changes such as increasing levels of renewable energy sources, energy storage, electrification of energy-efficient loads such as heat pumps and electric vehicles, demand-side resources, etc., in the last decade, and more changes will be followed in the near future. The emergence of digitalization and advanced communication in the case of distribution systems to enhance the performance of the electricity infrastructure also adds further complexities. These changes pose challenges such as increased levels of network congestion, voltage variations, protection mis-operations, increased needs for real-time monitoring, and improved planning practices of the system operators. These challenges will require the development of new paradigms to operate the power grids securely, safely, and economically. This thesis attempted to address those challenges and had the following main contributions:First, the thesis started by presenting a comprehensive assessment framework to address the distribution system operators’ future-readiness and help the distribution system operators to determine the current status of their network infrastructures, business models, and policies and thus identify the pathways for the required developments for the smooth transition towards future intelligent distribution grids.Second, the thesis presents an advanced congestion forecast tool that would support the distribution system operators to forecast and visualize network congestion and voltage variations issues for multiple forecasting horizons ranging from close-to-real time to a day-ahead. The tool is based on a probabilistic power flow that incorporates forecasts of solar photovoltaic production and electricity demand, combined with advanced load models and different operating modes of solar photovoltaic inverters. The tool has been integrated to an existing industrial graded distribution management system via an IoT platform Codex Smart Edge of Atos Worldgrid. The results from case studies demonstrated that the tool performs satisfactorily for both small and large networks and can visualise the cumulative probabilities of network congestion and voltage variations for a variety of forecast horizons as desired by the distribution system operator.Third, a dynamic state estimation-based protection scheme for the transmission lines which does not require complicated relay settings and coordination has been demonstrated using an experimental setup at Chalmers power system laboratory. The scheme makes use of the real-time measurements provided by advanced sensors which are developed by Smart State Technology, The Netherlands. The experimental validations of the scheme have been performed under different fault types and conditions, e.g., unbalanced faults, three-phase faults, high impedance faults, hidden failures, inductive load conditions, etc. The results have shown that the scheme performs adequately in both normal and fault conditions and thus the scheme would work for transmission line protection by avoiding relay coordination and settings issues.Finally, the thesis presents a decentralized dynamic state estimation method for estimating the dynamic states of a transmission line in real-time. This method utilizes the sampled measurements from the local end of a transmission line, and thereafter dynamic state estimation is performed by employing an unscented Kalman filter. The advantage of the method is that the remote end state variables of a transmission line can be estimated using only the local end variables and, hence, the need for communication infrastructure is eliminated. Furthermore, an exact nonlinear model of the transmission line is utilized and the dynamic state estimation of one transmission line is independent of the other lines. These features in turn result in reduced complexity, higher accuracy, and easier implementation of the decentralized estimator. The method is envisioned to have potential applications in transmission line monitoring, control, and protection

Emerging Multiport Electrical Machines and Systems: Past Developments, Current Challenges, and Future Prospects

Distinct from the conventional machines with only one electrical and one mechanical port, electrical machines featuring multiple electrical/mechanical ports (the so-called multiport electrical machines) provide a compact, flexible, and highly efficient manner to convert and/or transfer energies among different ports. This paper attempts to make a comprehensive overview of the existing multiport topologies, from fundamental characteristics to advanced modeling, analysis, and control, with particular emphasis on the extensively investigated brushless doubly fed machines for highly reliable wind turbines and power split devices for hybrid electric vehicles. A qualitative review approach is mainly adopted, but strong efforts are also made to quantitatively highlight the electromagnetic and control performance. Research challenges are identified, and future trends are discussed

Intelligent autoreclosing for systems of high penetration of wind generation with real time modelling, development and deployment

COPYRIGHT Attention is drawn to the fact that copyright of this thesis rests with its author. A copy of this thesis has been supplied on condition that anyone who consults it is understood to recognise that its copyright rests with the author and they must not copy it or use material from it except as permitted by law or with the consent of the author. This thesis may be made available for consultation within the University Library and may be photocopied or lent to other libraries for the purposes of consultation. ii This thesis presents investigations into the effect of modern wind farms on grid side short circuits using extensive real time digital simulation. Particular reference is made to adaptive autoreclosing algorithms using artificial neural networks. A section of 132kV transmission grid in Scotland, including DFIG wind farms, is modelled on a real time digital simulator. An algorithm is then developed and tested using this model to show that this autoreclosing technique is feasible in systems with high penetration of wind generation. Although based on an existing technique, an important innovation is the use of two neural networks for the separate tasks of arc presence and extinction. The thesis also describes a low-cost, real time, relay development platform. Executive summary of key achievements- The effect of wind turbines on transmission line short circuit transients, with a comparison of the other significant parameters- Treatment of unbalanced faults and realistic arc modelling in this context- Feasibility studies on RTDS development of AdTAR using primary arcing and inter-circuit coupling- Development of robust AdSPAR autoreclosing algorithm using twin neural networks- A critical discussion of the use of AI in power system protection- A low cost, IEC 61850 compliant, real time relay development platfor