Algorithms to Improve Performance of Wide Area Measurement Systems of Electric Power Systems

Power system operation has become increasingly complex due to high load growth and increasing market pressure. The occurrence of major blackouts in many power systems around the world has necessitated the use of synchrophasor based Wide Area Measurement Systems (WAMS) for grid monitoring. Synchrophasor technology is comparatively new in the area of power systems. Phasor measurement units (PMUs) and phasor data concentrators (PDCs) are new to the substations and control centers. Even though PMUs have been installed in many power grids, the number of installed PMUs is still low with respect to the number of buses or lines. Currently, WAMS systems face many challenges. This thesis is an attempt towards solving some of the technical problems faced by the WAMS systems. This thesis addresses four problems related to synchrophasor estimation, synchrophasor quality detection, synchrophasor communication and synchrophasor application. In the first part, a synchrophasor estimation algorithm has been proposed. The proposed algorithm is simple, requires lesser computations, and satisfies all the steady state and dynamic performance criteria of the IEEE Standard C37.118.1-2011 and also suitable for protection applications. The proposed algorithm performs satisfactorily during system faults and it has lower response time during larger disturbances. In the second part, areas of synchrophasor communication which can be improved by applying compressive sampling (CS) are identified. It is shown that CS can reduce bandwidth requirements for WAMS networks. It is also shown that CS can successfully reconstruct system dynamics at higher rates using synchrophasors reported at sub-Nyquist rate. Many synchrophasor applications are not designed to use fault/switching transient synchrophasors. In this thesis, an algorithm has been proposed to detect fault/switching transient synchrophasors. The proposed algorithm works satisfactorily during smaller and larger step changes, oscillations and missing data. Fault transient synchrophasors are not usable in WAMS applications as they represent a combination of fault and no-fault scenario. In the fourth part, two algorithms have been proposed to extract fault synchrophasor from fault transient synchrophasor in PDC. The proposed algorithms extract fault synchrophasors accurately in presence of noise, off-nominal frequencies, harmonics, and frequency estimation errors

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

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

Decentralized and Fault-Tolerant Control of Power Systems with High Levels of Renewables

Inter-area oscillations have been identified as a major problem faced by most power systems and stability of these oscillations are of vital concern due to the potential for equipment damage and resulting restrictions on available transmission capacity. In recent years, wide-area measurement systems (WAMSs) have been deployed that allow inter-area modes to be observed and identified.Power grids consist of interconnections of many subsystems which may interact with their neighbors and include several sensors and actuator arrays. Modern grids are spatially distributed and centralized strategies are computationally expensive and might be impractical in terms of hardware limitations such as communication speed. Hence, decentralized control strategies are more desirable.Recently, the use of HVDC links, FACTS devices and renewable sources for damping of inter-area oscillations have been discussed in the literature. However, very few such systems have been deployed in practice partly due to the high level of robustness and reliability requirements for any closed loop power system controls. For instance, weather dependent sources such as distributed winds have the ability to provide services only within a narrow range and might not always be available due to weather, maintenance or communication failures.Given this background, the motivation of this work is to ensure power grid resiliency and improve overall grid reliability. The first consideration is the design of optimal decentralized controllers where decisions are based on a subset of total information. The second consideration is to design controllers that incorporate actuator limitations to guarantee the stability and performance of the system. The third consideration is to build robust controllers to ensure resiliency to different actuator failures and availabilities. The fourth consideration is to design distributed, fault-tolerant and cooperative controllers to address above issues at the same time. Finally, stability problem of these controllers with intermittent information transmission is investigated.To validate the feasibility and demonstrate the design principles, a set of comprehensive case studies are conducted based on different power system models including 39-bus New England system and modified Western Electricity Coordinating Council (WECC) system with different operating points, renewable penetration and failures

A new approach to the fault location problem: using the fault's transient intermediate frequency response

The fault location problem has been tackled mainly through impedance-based techniques, the travelling wave principle and more recently by machine learning algorithms. These techniques require both current and voltage measurements. In the case of impedance-based methods they can provide multiples solutions. In the case of the travelling wave approach it usually requires high sampling and synchronized frequency measurements together with sophisticated identification algorithms. Machine learning techniques require training data and re-tuning for different grid topologies. In this work we propose a new fault location method based on the fault's transient intermediate frequency response of the system immediately after a fault occurs. The transient response immediately after the occurrence of a fault is characterized by the travelling wave phenomenon together with intermediate frequencies of oscillation in the range of 5 to 500 kHz. These intermediate frequencies of oscillations are associated with the natural response of the cable/line system to the fault event. Their frequencies of oscillation are dependent on the faulted section and the fault location within that section. The proposed fault location methodology aims to leverage on that dependency, by firstly identifying these intermediate frequencies for different fault location scenarios for a given network. This process is performed offline using a linear time invariant (LTI) representation of the network. To compute this LTI representation, as part of this work an impedance representation in the modal domain is established for cable/line sections, which is able to capture the frequency-dependence and distributed nature of its electrical parameters. The offline methodology identifies these intermediate frequencies for different fault location scenarios, and then proceeds to fit the fault location dependence of each intermediate frequency using a polynomial regression. An online methodology is also proposed to perform the fault location in real time by solving the polynomial regressions computed during the offline methodology using measurements of the intermediate frequencies present in the frequency spectrum of transient signals. The fault location is thus solved by using voltage or current measurements of the fault’s transient response at different locations in the network, together with simple signal processing techniques such as the Fast Fourier Transform. The full method is tested with an EMT simulation in PSCAD, using the detailed frequency dependent model for underground cables, together with realistic load models in a low voltage distribution network test system.Open Acces

Synchronized measurement data conditioning and real-time applications

Phasor measurement units (PMU), measuring voltage and current phasor with synchronized timestamps, is the fundamental component in wide-area monitoring systems (WAMS) and reveals complex dynamic behaviors of large power systems. The synchronized measurements collected from power grid may degrade due to many factors and impacts of the distorted synchronized measurement data are significant to WAMS. This dissertation focus on developing and improving applications with distorted synchronized measurements from power grid. The contributions of this dissertation are summarized below. In Chapter 2, synchronized frequency measurements of 13 power grids over the world, including both mainland and island systems, are retrieved from Frequency Monitoring Network (FNET/GridEye) and the statistical analysis of the typical power grids are presented. The probability functions of the power grid frequency based on the measurements are calculated and categorized. Developments of generation trip/load shedding and line outage events detection and localization based on high-density PMU measurements are investigated in Chapters 3 and 4 respectively. Four different types of abnormal synchronized measurements are identified from the PMU measurements of a power grid. The impacts of the abnormal synchronized measurements on generation trip/load shedding events detection and localization are evaluated. A line outage localization method based on power flow measurements is proposed to improve the accuracy of line outage events location estimation. A deep learning model is developed to detect abnormal synchronized measurements in Chapter 5. The performance of the model is evaluated with abnormal synchronized measurements from a power grid under normal operation status. Some types of abnormal synchronized measurements in the testing cases are recently observed and reported. An extensive study of hyper-parameters in the model is conducted and evaluation metrics of the model performance are presented. A non-contact synchronized measurements study using electric field strength is investigated in Chapter 6. The theoretical foundation and equation derivations are presented. The calculation process for a single circuit AC transmission line and a double circuit AC transmission line are derived. The derived method is implemented with Matlab and tested in simulation cases

Big data analytics in smart grids: a review

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Fast Simulation of Electromagnetic Transients in Power Systems:Numerical Solvers and their Coupling with the Electromagnetic Time Reversal Process

The development of modern and future power systems is associated with the definition of new approaches for their simulation, control, and protection. To give an example, the increasing connection of massive renewable energy conversion systems is justifying the integration of DC infrastructures (eventually, multi-terminal HVDC) in the current AC power grids. Furthermore, the existing passive distribution networks are evolving by integration of decentralized and intermittent generation units which results in Active Distribution Networks (ADNs). As a consequence, complex power system topologies are emerging requiring adequate simulation tools capable to reproduce, possibly in real-time, their dynamic behavior. In this context, future operation/protection practices of power networks might rely on the availability of chip-scale real-time simulators (RTS) that will enable the implementation of efficient protection/fault location processes that, in principle, should be capable to comply with the restrictive constraints associated with these complex systems. Within this context, the work presented in the thesis contributes to the integration of new concepts of the fault location in AC/DC systems that can be deployed in chip-scale real-time simulation hardware represented by Field Programmable Gate Arrays (FPGAs). The development of the proposed fault location platform is done in two steps. First, an original fault location method based on the Electromagnetic Time Reversal (EMTR) theory is proposed. The proposed method is validated for the case of various power networks topologies and its performance is assessed. Compared to the existing fault location methods, the proposed approach is suitably applicable to different topologies including MTDCs and ADNs. Next, a new automated FPGA-based solver for RTS is proposed. The developed FPGA-RTS uses a specific automated procedure to couple the simulation platform with an offline simulation environment (EMTR-RV) without the need for Hardware Description Language (HDL). It is able to simulate both power electronics converters and power system grids and thanks to the use of particular parallel computational algorithms, it can accurately simulate, in real-time, Electromagnetic Transient (EMT) phenomena taking place in power converters and travelling wave propagation along multi-conductor transmission lines within very small simulation time steps (in the order of some hundreds of nanoseconds). To overcome the limitations associated with the Fixed Admittance Matrix Nodal Method (FAMNM), a method to assess the optimal value of the parameter of the Associated Discrete Circuit (ADC) switch model used by FAMNM is proposed. Finally, a specific application of the developed FPGA-RTS is explored for the development of a fault location platform by leveraging the EMTR theory. To this end, the proposed EMTR-based fault location method is integrated with the FPGA-RTS to develop an efficient fault location platform. Thanks to the fast EMT simulation capability of the FPGA-RTS, the developed fault location platform is able to estimate the accurate fault location within very short time scales. Moreover, the developed platform is compatible with the constraints characterizing complex topologies such as MTDC networks (e.g., the ultra-fast operation of the protection systems). The developed fault location platform is validated by making reference to an MTDC grid and an ADN, and it is shown to exhibit remarkable fault location accuracy as well as robustness against uncertainties such as fault type, the presence of noise, measurement systems delay, and fault impedance