Ancillary Services in Hybrid AC/DC Low Voltage Distribution Networks

In the last decade, distribution systems are experiencing a drastic transformation with the advent of new technologies. In fact, distribution networks are no longer passive systems, considering the current integration rates of new agents such as distributed generation, electrical vehicles and energy storage, which are greatly influencing the way these systems are operated. In addition, the intrinsic DC nature of these components, interfaced to the AC system through power electronics converters, is unlocking the possibility for new distribution topologies based on AC/DC networks. This paper analyzes the evolution of AC distribution systems, the advantages of AC/DC hybrid arrangements and the active role that the new distributed agents may play in the upcoming decarbonized paradigm by providing different ancillary services.Ministerio de Economía y Competitividad ENE2017-84813-RUnión Europea (Programa Horizonte 2020) 76409

Protection and fault location schemes suited to large-scale multi-vendor high voltage direct current grids

Recent developments in voltage source converter (VSC) technology have led to an increased interest in high voltage direct current (HVDC) transmission to support the integration of massive amounts of renewable energy sources (RES) and especially, offshore wind energy. VSC-based HVDC grids are considered to be the natural evolution of existing point-to-point links and are expected to be one of the key enabling technologies towards expediting the integration and better utilisation of offshore energy, dealing with the variable nature of RES, and driving efficient energy balance over wide areas and across countries. Despite the technological advancements and the valuable knowledge gained from the operation of the already built multi-terminal systems, there are several outstanding issues that need to be resolved in order to facilitate the deployment of large-scale meshed HVDC grids. HVDC protection is of utmost importance to ensure the necessary reliability and security of HVDC grids, yet very challenging due to the fast nature of development of DC faults and the abrupt changes they cause in currents and voltages that may damage the system components. This situation is further exacerbated in highly meshed networks, where the effects of a DC fault on a single component (e.g. DC cable) can quickly propagate across the entire HVDC grid. To mitigate the effect of DC faults in large-scale meshed HVDC grids, fast and fully selective approaches using dedicated DC circuit breaker and protection relays are required. As the speed of DC fault isolation is one order of magnitude faster than typical AC protection (i.e. less than 10 ms), there is a need for the development of innovative approaches to system protection, including the design and implementation of more advanced protection algorithms. Moreover, in a multi-vendor environment (in which different or the same type of equipment is supplied by various manufacturers), the impact of the grid elements on the DC fault signature may differ considerably from case to case, thus increasing the complexity of designing reliable protection algorithms for HVDC grids. Consequently, there is a need for a more fundamental approach to the design and development of protection algorithms that will enable their general applicability. Furthermore, following successful fault clearance, the next step is to pinpoint promptly the exact location of the fault along the transmission medium in an effort to expedite inspection and repair time, reduce power outage time and elevate the total availability of the HVDC grid. Successful fault location becomes increasingly challenging in HVDC grids due to the short time windows between fault inception and fault clearance that limit the available fault data records that may be utilised for the execution of fault location methods. This thesis works towards the development of protection and fault location solutions, designed specifically for application in large-scale multi-vendor HVDC grids. First, a methodology is developed for the design of travelling wave based non-unit protection algorithms that can be easily configured for any grid topology and parameters. Second, using this methodology, a non-unit protection algorithm based on wavelet transform is developed that ensures fast, discriminative and enhanced protection performance. Besides offline simulations, the efficacy of the wavelet transform based algorithm is also demonstrated by means of real-time simulation, thereby removing key technical barriers that have impeded the use of wavelet transform in practical protection applications. Third, in an effort to reinforce the technical and economic feasibility of future HVDC grids, a thorough fault management strategy is presented for systems that employ efficient modular multilevel converters with partial fault tolerant capability. Finally, a fault location scheme is developed for accurately estimating the fault location in HVDC grids that are characterised by short post-fault data windows due to the utilisation of fast acting protection systems.Recent developments in voltage source converter (VSC) technology have led to an increased interest in high voltage direct current (HVDC) transmission to support the integration of massive amounts of renewable energy sources (RES) and especially, offshore wind energy. VSC-based HVDC grids are considered to be the natural evolution of existing point-to-point links and are expected to be one of the key enabling technologies towards expediting the integration and better utilisation of offshore energy, dealing with the variable nature of RES, and driving efficient energy balance over wide areas and across countries. Despite the technological advancements and the valuable knowledge gained from the operation of the already built multi-terminal systems, there are several outstanding issues that need to be resolved in order to facilitate the deployment of large-scale meshed HVDC grids. HVDC protection is of utmost importance to ensure the necessary reliability and security of HVDC grids, yet very challenging due to the fast nature of development of DC faults and the abrupt changes they cause in currents and voltages that may damage the system components. This situation is further exacerbated in highly meshed networks, where the effects of a DC fault on a single component (e.g. DC cable) can quickly propagate across the entire HVDC grid. To mitigate the effect of DC faults in large-scale meshed HVDC grids, fast and fully selective approaches using dedicated DC circuit breaker and protection relays are required. As the speed of DC fault isolation is one order of magnitude faster than typical AC protection (i.e. less than 10 ms), there is a need for the development of innovative approaches to system protection, including the design and implementation of more advanced protection algorithms. Moreover, in a multi-vendor environment (in which different or the same type of equipment is supplied by various manufacturers), the impact of the grid elements on the DC fault signature may differ considerably from case to case, thus increasing the complexity of designing reliable protection algorithms for HVDC grids. Consequently, there is a need for a more fundamental approach to the design and development of protection algorithms that will enable their general applicability. Furthermore, following successful fault clearance, the next step is to pinpoint promptly the exact location of the fault along the transmission medium in an effort to expedite inspection and repair time, reduce power outage time and elevate the total availability of the HVDC grid. Successful fault location becomes increasingly challenging in HVDC grids due to the short time windows between fault inception and fault clearance that limit the available fault data records that may be utilised for the execution of fault location methods. This thesis works towards the development of protection and fault location solutions, designed specifically for application in large-scale multi-vendor HVDC grids. First, a methodology is developed for the design of travelling wave based non-unit protection algorithms that can be easily configured for any grid topology and parameters. Second, using this methodology, a non-unit protection algorithm based on wavelet transform is developed that ensures fast, discriminative and enhanced protection performance. Besides offline simulations, the efficacy of the wavelet transform based algorithm is also demonstrated by means of real-time simulation, thereby removing key technical barriers that have impeded the use of wavelet transform in practical protection applications. Third, in an effort to reinforce the technical and economic feasibility of future HVDC grids, a thorough fault management strategy is presented for systems that employ efficient modular multilevel converters with partial fault tolerant capability. Finally, a fault location scheme is developed for accurately estimating the fault location in HVDC grids that are characterised by short post-fault data windows due to the utilisation of fast acting protection systems

Enhanced power flow methods in complex plane for VSC-MTDC hybrid AC/DC transmission grids

The power flow problem is composed of phasor variables and quantities and thus can be naturally formulated in the complex domain; however, their applications are commonly developed in the real domain. The solution via the Newton-Raphson method, for example, would be restricted in the real domain once the Taylor series expansion in terms of complex variables alone does not exist. Thanks to the Wirtinger calculus, a Newton-Raphson method based on Taylor series expansions of nonlinear functions of complex variables and their complex conjugates becomes possible. As new technologies are implemented in power systems, such as the incorporation of FACTS devices, the development of power flow applications becomes increasingly intricate, and maintaining their formulations in the real domain is preceded by an arduous algebra task. To overcome this difficulty, a series of power flow solution methods are proposed in this work, specified to solve multiterminal AC/DC hybrid systems, being formulated in the complex plane without any loss of precision. Both sequential and unified approaches for solving hybrid AC/DC power flow are derived in the complex plane. In order to improve the performance of the algorithms, an exact second-order power flow algorithm in the complex domain is also proposed. Such power flow models in the complex plane are naturally developed in Cartesian coordinates; therefore, most constraint equations can be written as quadratic functions. Consequently, the Taylor series expansion stops at its second order and the exact non-linearity of complex quadratic power flow equations is maintained. Minor changes in the code structure are required to transform the Newton-Raphson method into the exact power flow approach in the complex plane. The new algorithm exhibits either a superior behavior in fully AC or hybrid AC/DC networks. In order to show the validity of its formulations, the proposed algorithms are implemented in Matlab for well-established case studies of the IEEE-14, -30, -57 and -118 bus, a modified version of the IEEE Two Area RTS-96, and the Brazilian Southern-equivalent of 1916-buses, termed as SIN-1916. The features and advantages of the proposed algorithms are illustrated through the test systems interconnected across a DC network prone to several scenarios, e.g., topology, voltage control, and interchanging of active power.O problema de fluxo de carga é composto por variáveis e grandezas fasoriais e pode ser naturalmente formulado no domínio complexo; porém, suas aplicações são comumente desenvolvidas no domínio real. A solução via o método de Newton-Raphson, por exemplo, estaria restrita ao domínio real uma vez que a expansão em séries d Taylor em termos somente das variáveis complexas não existe. Mas, graças ao cálculo de Wirtinger, um método de Newton-Raphson baseado em expansões em série de Taylor de funções não lineares de variáveis complexas e seus conjugados complexos se faz possível. A medida em que novas tecnologias são implementadas nos sistemas de potência, como a incorporação de dispositivos FACTS, o desenvolvimento de aplicações de fluxo de carga se torna cada vez mais complexa, e manter suas formulações no domínio real necessita de uma árdua tarefa de álgebra. Para superar esta dificuldade, uma série de métodos de solução de fluxo de potência é proposta neste trabalho, especificados para solucionar sistemas híbridos AC/DC multi-terminal, sendo formuladas no plano complexo sem qualquer perda de precisão. Tanto a abordagem sequencial quanto a unificada para a solução do fluxo de potência híbrido AC/DC são derivadas no plano complexo. Com o objetivo de melhorar o desempenho dos algoritmos, também é proposto um algoritmo exato de fluxo de potência de segunda ordem no domínio complexo. Tais modelos de fluxo de potência no plano complexo são naturalmente desenvolvidos em coordenadas cartesianas; logo, a maioria das equações de restrições pode ser escrita como funções quadráticas. Consequentemente, a expansão em séries de Taylor se encerra na sua segunda ordem e a não linearidade exata das equações complexas quadráticas de fluxo de potência é mantida. Pequenas alterações na estrutura do código são necessárias para transformar o método de Newton-Raphson na abordagem exata do fluxo de potência no plano complexo. O novo algoritmo exibe um comportamento superior em redes totalmente AC ou híbridas AC/DC. A fim de mostrar a validade de suas formulações, os algoritmos propostos são implementados em Matlab para estudos de casos bem estabelecidos dos sistemas teste IEEE-14, -30, -57 e -118 barras, uma versão modificada do sistema de duas áreas IEEE RTS-96, e o sistema interligado nacional SIN-1916 barras. As características e vantagens dos algoritmos propostos são ilustradas através dos sistemas teste interligados através de uma rede DC propensa a vários cenários sob diferentes topologias, controles de tensão e injeções de potência ativa, por exemplo

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

Load Flow Solution of Distribution Systems - A Bibliometric Survey

In this paper, Bibliometric Survey has been carried out on ‘Load Flow Solution of Distribution Systems’ from 2012 to 2021. Scopus database has been used for the analysis. There were total 1711 documents found on this topic. The statistical analysis is carried out source wise, year wise, area wise, Country wise, University wise, author wise, and based on funding agency. Network analysis is also carried out based on Co-authorship, Co-occurrence. Results are presented. During 2020 and 2018, there were 263 documents published which is the highest. ‘IEEE Transactions on Power Systems’ has published 90 documents during the period of study which is the highest in terms of articles under the category of sources. Highest citations were received by the article authored by Hung and Mithulanathan with 484 citations in the collected database with the chosen key words. VOSviewer 1.6.16 is the software that is used for the statistical analysis and network analysis on the database. It provides a very effective way to analyze the co-authorship, co-occurrences, citation and bibliometric analysis etc. The Source for all Tables and figures is www.scopus.com, The data is assessed on 6th July, 2021

Assessment of fault location techniques in voltage source converter based HVDC systems

This paper investigates fault location techniques in high voltage direct current (HVDC) transmission networks utilizing voltage source converters (VSCs). The subject has been extensively researched due to the fault locating actions associated with the supply restoration and the economic loss, and also because of the trending employment of VSC-HVDC transmission systems. However, the fast operation of HVDC protection has made fault localization more challenging as limited measurement data can be extracted. By broadly researching the existing fault locating approaches in such systems, a comprehensive literature review is presented. Then, two selected methods, active impedance method and travelling wave method (using Continuous Wavelet Transformation) are tested. These fault location techniques together with the power system models have been developed using Matlab/Simulink. The results are summarized and systematic comparative analysis of the two fault location techniques is performed