Every Moment Counts: Synchrophasors for Distribution Networks with Variable Resources

Chapter 34 in the textbook, "Renewable Energy Integration: Practical Management of Variability, Uncertainty and Flexibility

Chapter 34 - Every Moment Counts: Synchrophasors for Distribution Networks with Variable Resources

Historically, with mostly radial power distribution and one-way power flow, it was only necessary to evaluate the envelope of design conditions, e.g., peak loads or fault currents, rather than continually observe the operating state. But the growth of distributed energy resources introduces variability, uncertainty, and opportunities to recruit diverse resources for grid services. This chapter addresses how the direct measurement of voltage phase angle might enable new strategies for managing distribution networks with diverse, active components.Comment: 14 pages, Chapter, Renewable Energy Integration, Academic, 201

Real-time estimation and damping of SSR in a VSC-HVDC connected series-compensated system

Infrastructure reinforcement using high-voltage direct-current (HVDC) links and series compensation has been proposed to boost the power transmission capacity of existing ac grids. However, deployment of series capacitors may lead to subsynchronous resonance (SSR). Besides providing bulk power transfer, voltage source converter (VSC)-based HVDC links can be effectively used to damp SSR. To this end, this paper presents a method for the real-time estimation of the subsynchronous frequency component present in series-compensated transmission lines-key information required for the optimal design of damping controllers. A state-space representation has been formulated and an eigenvalue analysis has been performed to evaluate the impact of a VSC-HVDC link on the torsional modes of nearby connected thermal generation plants. Furthermore, the series-compensated system has been implemented in a real-time digital simulator and connected to a VSC-HVDC scaled-down test-rig to perform hardware-in-the-loop tests. The efficacy and operational performance of the ac/dc network while providing SSR damping is tested through a series of experiments. The proposed estimation and damping method shows a good performance both in time-domain simulations and laboratory experiments

Power Spectrum Estimation for Frequency Domain Ambient Modal Analysis

This paper studies the effect of Power Spectrum Density (PSD) estimation techniques on the accuracy of Fast Frequency Domain Decomposition (FFDD) modal analysis. FFDD utilizes ambient synchrophasor measurements to estimate characteristics of dominant system modes and oscillations by analyzing the PSD estimates from multiple synchrophasor measurements. In this paper, the impact of three different methods for PSD estimation on the accuracy of FFDD modal estimates is investigated: PWelch, MultiTaper Method (MTM) using Slepian Tapers, and MTM using Sine Tapers. Tests are done using synthetic and archived synchrophasor data. All three PSD methods are shown to work well for oscillation detection of sustained oscillations using FFDD. However, for ambient modal analysis, it is shown that FFDD based on MTM with Slepian Tapers has the most reliable modal estimations. FFDD using both MTM with Sine Tapers and PWelch have bias issues in estimating well-damped system modes, requiring more research for them to be suitable for FFDD

Performance Improvement of Wide-Area-Monitoring-System (WAMS) and Applications Development

Wide area monitoring system (WAMS), as an application of situation awareness, provides essential information for power system monitoring, planning, operation, and control. To fully utilize WAMS in smart grid, it is important to investigate and improve its performance, and develop advanced applications based on the data from WAMS. In this dissertation, the work on improving the WAMS performance and developing advanced applications are introduced.To improve the performance of WAMS, the work includes investigation of the impacts of measurement error and the requirements of system based on WAMS, and the solutions. PMU is one of the main sensors for WAMS. The phasor and frequency estimation algorithms implemented highly influence the performance of PMUs, and therefore the WAMS. The algorithms of PMUs are reviewed in Chapter 2. To understand how the errors impact WAMS application, different applications are investigated in Chapter 3, and their requirements of accuracy are given. In chapter 4, the error model of PMUs are developed, regarding different parameters of input signals and PMU operation conditions. The factors influence of accuracy of PMUs are analyzed in Chapter 5, including both internal and external error sources. Specifically, the impacts of increase renewables are analyzed. Based on the analysis above, a novel PMU is developed in Chapter 6, including algorithm and realization. This PMU is able to provide high accurate and fast responding measurements during both steady and dynamic state. It is potential to improve the performance of WAMS. To improve the interoperability, the C37.118.2 based data communication protocol is curtailed and realized for single-phase distribution-level PMUs, which are presented in Chapter 7.WAMS-based applications are developed and introduced in Chapter 8-10. The first application is to use the spatial and temporal characterization of power system frequency for data authentication, location estimation and the detection of cyber-attack. The second application is to detect the GPS attack on the synchronized time interval. The third application is to detect the geomagnetically induced currents (GIC) resulted from GMD and EMP-E3. These applications, benefited from the novel PMU proposed in Chapter 6, can be used to enhance the security and robust of power system

Measurement based method for online characterization of generator dynamic behaviour in systems with renewable generation

This paper introduces a hybrid-methodology for online identification and clustering of generator oscillatory behavior, based on measured responses. The dominant modes in generator measured responses are initially identified using a mode identification technique and then introduced, in the next step, as input into a clustering algorithm. Critical groups of generators that exhibit poorly or negatively damped oscillations are identified, in order to enable corrective control actions and stabilize the system. The uncertainties associated with operation of modern power systems, including Renewable Energy Sources (RES) are investigated, with emphasis on the impact of the dynamic behavior of power electronic interfaced RES

Control of voltage source converters connected to variable impedance grids

The increase in new renewable energy resources is key to achieving carbon reduction targets, however it also introduces new grid integration challenges. The best renewable resource in Scotland is found in remote parts of the country, and as a result new renewable based generation is increasingly subjected to high and variable levels of impedance. Impedances that cause resonances are also increasingly common, given the higher order characteristics of impedance when transformers, filters, subsea cables, compensators and so on are present in the network. For a better understanding of impedance related stability issues, the estimation of the grid impedance using both Thévenin equivalent and wide spectrum techniques is studied in this thesis and integrated into the converter’s control. These estimations inform the controller of the grid conditions, allowing for controller adaptation. In instances where weak grid conditions are severe and the local grid impedance is dominant, a disturbance rejection mechanism called the pre-emptive voltage decoupler (PVD) is proposed. The PVD feeds forward the active current reference and measured voltage, and adapts the reactive current reference as a function of the impedance estimation, to pre-emptively compensate the local voltage for changes in active power transfer. This is justified through small signal analysis using linearised state space models and validated in the laboratory using large inductors and a converter. The control is also made more resilient with an instability detector, proposed to prevent instability when significant grid disturbances occur. Through early detection of sudden power angle changes, stability can be maintained. This is achieved by momentarily reducing the power reference and re-establishing grid parameters. The implementation of the proposed changes improves the steady state stability region from -0.75 – 0.55 pu to -0.85 – 0.75 pu. Further, the nonlinear transient performance is much more resilient, and uninterrupted power flow can be maintained. When the local grid is not dominant, and higher order grid impedances cause undesired resonances, a detection of the resonant frequency allows for an adaptation of the outer loop gains, thus damping the resonances and improving stability. Such grids are also prone to instability, but a reduction of the power reference does not improve stability, on the contrary the reduction of the power reference shifts eigenvalues into the right hand plane. A better preventative measure is to reduce the outer loop gains, and once the frequency of the problematic resonances is identified, final decisions on outer loop tuning can be taken. With this implementation, the stability of the system is maintained and the power output can be recovered within about 1 second.The increase in new renewable energy resources is key to achieving carbon reduction targets, however it also introduces new grid integration challenges. The best renewable resource in Scotland is found in remote parts of the country, and as a result new renewable based generation is increasingly subjected to high and variable levels of impedance. Impedances that cause resonances are also increasingly common, given the higher order characteristics of impedance when transformers, filters, subsea cables, compensators and so on are present in the network. For a better understanding of impedance related stability issues, the estimation of the grid impedance using both Thévenin equivalent and wide spectrum techniques is studied in this thesis and integrated into the converter’s control. These estimations inform the controller of the grid conditions, allowing for controller adaptation. In instances where weak grid conditions are severe and the local grid impedance is dominant, a disturbance rejection mechanism called the pre-emptive voltage decoupler (PVD) is proposed. The PVD feeds forward the active current reference and measured voltage, and adapts the reactive current reference as a function of the impedance estimation, to pre-emptively compensate the local voltage for changes in active power transfer. This is justified through small signal analysis using linearised state space models and validated in the laboratory using large inductors and a converter. The control is also made more resilient with an instability detector, proposed to prevent instability when significant grid disturbances occur. Through early detection of sudden power angle changes, stability can be maintained. This is achieved by momentarily reducing the power reference and re-establishing grid parameters. The implementation of the proposed changes improves the steady state stability region from -0.75 – 0.55 pu to -0.85 – 0.75 pu. Further, the nonlinear transient performance is much more resilient, and uninterrupted power flow can be maintained. When the local grid is not dominant, and higher order grid impedances cause undesired resonances, a detection of the resonant frequency allows for an adaptation of the outer loop gains, thus damping the resonances and improving stability. Such grids are also prone to instability, but a reduction of the power reference does not improve stability, on the contrary the reduction of the power reference shifts eigenvalues into the right hand plane. A better preventative measure is to reduce the outer loop gains, and once the frequency of the problematic resonances is identified, final decisions on outer loop tuning can be taken. With this implementation, the stability of the system is maintained and the power output can be recovered within about 1 second

Machine learning approach for dynamic event identification in power systems with wide area measurement systems

Orientador: Prof. Dr. Alexandre Rasi AokiCoorientador: Prof. Dr. Ricardo SchumacherDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 13/02/2023Inclui referênciasResumo: Ao longo dos últimos dez anos, a disponibilidade de WAMS (Wide Area Measurement Systems) tem constantemente aumentado e, com isso, a necessidade de se otimizar seu uso em relação a uma ampla gama de capacidades requeridas nos centros de operação. Concorrentemente, o sistema brasileiro tem observado diversos eventos em múltiplos níveis de criticalidade e, portanto, formas de rapidamente identificar irregularidades na rede elétrica têm sido requisitadas pelos operadores. Todavia, mesmo com tal diversidade de eventos registrados por PMUs (Phasor Measurement Unit), há dificuldades em se consolidar um banco de dados de eventos e, ademais, sistemas diferem uns dos outros - isto é, os volumes de dados requeridos para machine learning e a especificidade de cada sistema criam desafios para a construção de aplicações para detecção e identificação de eventos em uma dada rede. De tal maneira, o presente trabalho propõe uma forma de endereçar tais restrições e habilitar o uso de modelos de machine learning na vida real ao modelar um sistema real, simular uma grande quantia de eventos (como medição de PMU) e executar o processo de aprendizado de máquina com esses dados simulados. Tendo posse de qualquer conjunto de dados que contenha medições de evento da mesma PMU simulada, uma validação da aplicabilidade e performance do modelo obtido pode ser feita. Assim, um processo reprodutível e escalável foi definido pelo trabalho a partir de um estudo de caso no corredor Salto Caxias, um subsistema da rede elétrica paranaense operado pela COPEL, que forneceu três conjuntos de dados contendo eventos registrados em uma PMU dessa área. Alguns componentes como barras, linhas de transmissão, transformadores, geradores, PSSs, excitadores e controles de turbina foram modelados dentro da Power System Toolbox, embasada em MATLAB, para simulação de eventos. O algoritmo de machine learning selecionado para provar o conceito estabelecido foi rede neural artificial, definindo-se quatro classes possíveis para reconhecimento - "Curto-circuito", "Perda de Carga", "Perda de Linha" e "Normal". Com o modelo de machine learning definido e treinado, se aplicaram os dados de eventos reais nele. Os resultados mostram que as métricas da rede neural no processo de aprendizado foram geralmente suficientes para aplicação em vida real, mas que sua performance nos conjuntos de dados de eventos reais foi abaixo da registrada com os dados simulados. Todavia, considerando-se que os dados reais providenciados são de eventos longínquos à PMU observada e ao próprio sistema modelado, distorções e atenuações de sinal são inerentes. Assim, pode-se dizer que o método proposto é aplicável, com mais etapas de pré-processamento de dados, a qualquer dado sistema - caso ele seja minuciosamente modelado e haja disponibilidade de conjuntos de dados de eventos internos ao sistema.Abstract: Over the last ten years, the availability of WAMS (Wide Area Measurement Systems) has steadily increased and, with it, the need to optimize its usage concerning a large array of capabilities required at the operation centers. Concurrently, the Brazilian system has witnessed various events at multiple levels of criticality, and, thus, ways to quickly identify irregularities in the grid have been more and more requested by power transmission and distribution companies. The introduction of machine learning models and algorithms in such a context has been explored by the scientific community. However, even with such a diversity of events and their PMU (Phasor Measurement Unit) measurements, there is hardship in consolidating an event database and systems differ from each other - that is, the data volume required for machine learning and the specificity of each power system create challenges in constructing applications for detection and identification of events in a given grid. As such, the present work proposes a way to address those constraints and further enable the real-life application of machine learning models in a power system with WAMS through the modeling of a real-life system, simulating a large database of events as if they were registered through a PMU in said system and training machine learning models on this simulated data. If one has any dataset containing event measurements from the same PMU (which was simulated), a validation of model performance and applicability can be performed. A reproducible and scalable process was defined to achieve this through one case study for the Salto Caxias subsystem of the Paraná state grid, operated by COPEL, who provided the author with three event datasets captured from a PMU in the aforementioned system. Some components of the system were modeled in MATLAB-based Power System Toolbox for dynamic simulation, such as generators, PSSs, exciters, and turbine governors in addition to buses, transmission lines, and transformers. The selected algorithm for this proof-of-concept was artificial neural network, defining four distinct possible classes it can recognize - "Short-circuit", "Load Loss", "Line Loss" and "Normal". With the machine learning model defined and trained, its application was executed on real event datasets. The results show that the metrics of the neural network model on the learning process were generally sufficient for real-life solutions, but its performance on the real event datasets was below that of the performance on simulated data. However, considering that the provided datasets were from events that happened far away from the selected PMU and its modeled system, signal distortions and attenuations are present. Thus, it can be stated that the proposed method is applicable, with further data preprocessing, to any given system - as long as it is thoroughly modeled and there is availability of datasets of events that happened within it