Power System Frequency Measurement Based Data Analytics and Situational Awareness

This dissertation presents several measurement-based research from power system wide-area dynamics data analytics to real-time situational awareness application development. All the research are grounded on the power system phasor measurements provided by wide-area Frequency Monitoring Network (FNET/GridEye), which collects the Global Positioning System (GPS) signal synchronized power system phasor measurements at distribution networks. The synchronized frequency measurement at FNET/GridEye enables real-time monitoring of bulk power systems (BPSs) and allows the dynamics interpretation of power system disturbances. Research on both the dynamic and ambient frequency measurements are conducted in this dissertation.The dynamics refer to the frequency measurement when the system is experiencing sudden contingencies. This dissertation focuses on two types of contingency: generation trip and oscillation and conducts both data analytics and corresponding real-time applications. Historical generation trip events in North America are analyzed in purpose to develop a frequency measurement based indicator of power systems low inertia events. Then the frequency response study is extended to bulk power systems worldwide to derive its association with system capacity size. As an essential parameter involved in the frequency response, the magnitude of the power imbalances is estimated based on multiple linear regression for improved accuracy. With respect to situational awareness, a real-time FNET/GridEye generation trip detection tool is developed for PMU use at power utilities and ISOs, which overcomes several challenges brought by different data situations.Regarding the oscillation dynamics, statistical analysis is accomplished on power system inter-area oscillations demonstrating the yearly trend of low-frequency oscillations and the association with system load. A novel real-time application is developed to detect power systems sustained oscillation in large area. The application would significantly facilitate the power grid situational awareness enhancement and system resiliency improvement.Furthermore, an additional project is executed on the ambient frequency measurement at FNET/GridEye. This project discloses the correlation between power system frequency and the electric clock time drift. In practice, this technique serves to track the time drifts in traffic signal systems

Advanced Wide-Area Monitoring System Design, Implementation, and Application

Wide-area monitoring systems (WAMSs) provide an unprecedented way to collect, store and analyze ultra-high-resolution synchrophasor measurements to improve the dynamic observability in power grids. This dissertation focuses on designing and implementing a wide-area monitoring system and a series of applications to assist grid operators with various functionalities. The contributions of this dissertation are below: First, a synchrophasor data collection system is developed to collect, store, and forward GPS-synchronized, high-resolution, rich-type, and massive-volume synchrophasor data. a distributed data storage system is developed to store the synchrophasor data. A memory-based cache system is discussed to improve the efficiency of real-time situation awareness. In addition, a synchronization system is developed to synchronize the configurations among the cloud nodes. Reliability and Fault-Tolerance of the developed system are discussed. Second, a novel lossy synchrophasor data compression approach is proposed. This section first introduces the synchrophasor data compression problem, then proposes a methodology for lossy data compression, and finally presents the evaluation results. The feasibility of the proposed approach is discussed. Third, a novel intelligent system, SynchroService, is developed to provide critical functionalities for a synchrophasor system. Functionalities including data query, event query, device management, and system authentication are discussed. Finally, the resiliency and the security of the developed system are evaluated. Fourth, a series of synchrophasor-based applications are developed to utilize the high-resolution synchrophasor data to assist power system engineers to monitor the performance of the grid as well as investigate the root cause of large power system disturbances. Lastly, a deep learning-based event detection and verification system is developed to provide accurate event detection functionality. This section introduces the data preprocessing, model design, and performance evaluation. Lastly, the implementation of the developed system is discussed

Wide-Area Synchrophasor Measurement Applications and Power System Dynamic Modeling

The use of synchrophasor measurements system-wide has been providing significant assistance for grid dynamic monitoring, situation awareness and reliability improvement. Frequency Monitoring Network (FNET), as an academia-run synchrophasor measurement system, utilizes a large number of Internet-connected low-cost Frequency Disturbance Recorders (FDRs) installed at the distribution level to measure power system dynamics and provide both online and off-line applications, such as event detection, oscillation modes estimation, event replay, etc. This work aims to further explore applications of the FNET measurements and utilize measurement-based method in dynamic modeling. Measurement-based dynamic reduction is an important application of synchrophasor measurement, especially considering the fact that when the system model is large, measurements provide a precise insight of system dynamics in order to determine equivalent regions. Another important application is to investigate Super Bowl games as an example to evaluate the influence of synchronized human activities on the power system. Featured characteristics drawn from the frequency data detected during the Super Bowl games are discussed. Increased penetration levels of wind generation and retirements of conventional plants have caused concerns about a decline of system inertia and primary frequency response. This work evaluates the impact of wind power on the system inertial response, simulation scenarios with different wind penetration levels are developed based on the U.S. Northeast Power Coordinating Council (NPCC) system. A user-defined electrical control model is also introduced to provide inertia and governor control to wind generations. Except for wind generation, frequency regulation can also be achieved by supplementary control of High Voltage Direct Current (HVDC) transmission line. A multi-terminal Voltage Source Converter (VSC) HVDC model is constructed to prove the effective control. In order to transmit large amount of intermittent and remote renewable energy over long distance to load centers, a potential solution is to upgrade the transmission system at a higher voltage by constructing an overlay HVDC grid on top of the original transmission system. The VSC HVDC model is utilized to build the HVDC overlay grid, and the overlay grid is tested with interconnection models. Conclusions and possible future research topics are given in the end

Electromechanical Dynamics of High Photovoltaic Power Grids

This dissertation study focuses on the impact of high PV penetration on power grid electromechanical dynamics. Several major aspects of power grid electromechanical dynamics are studied under high PV penetration, including frequency response and control, inter-area oscillations, transient rotor angle stability and electromechanical wave propagation.To obtain dynamic models that can reasonably represent future power systems, Chapter One studies the co-optimization of generation and transmission with large-scale wind and solar. The stochastic nature of renewables is considered in the formulation of mixed-integer programming model. Chapter Two presents the development procedures of high PV model and investigates the impact of high PV penetration on frequency responses. Chapter Three studies the impact of PV penetration on inter-area oscillations of the U.S. Eastern Interconnection system. Chapter Four presents the impacts of high PV on other electromechanical dynamic issues, including transient rotor angle stability and electromechanical wave propagation. Chapter Five investigates the frequency response enhancement by conventional resources. Chapter Six explores system frequency response improvement through real power control of wind and PV. For improving situation awareness and frequency control, Chapter Seven studies disturbance location determination based on electromechanical wave propagation. In addition, a new method is developed to generate the electromechanical wave propagation speed map, which is useful to detect system inertia distribution change. Chapter Eight provides a review on power grid data architectures for monitoring and controlling power grids. Challenges and essential elements of data architecture are analyzed to identify various requirements for operating high-renewable power grids and a conceptual data architecture is proposed. Conclusions of this dissertation study are given in Chapter Nine

Data Mining and Machine Learning Applications of Wide-Area Measurement Data in Electric Power Systems

Wide-area measurement systems (WAMS) are quickly becoming an important part of modern power system operation. By utilizing the Global Positioning System, WAMS offer highly accurate time-synchronized measurements that can reveal previously unobtainable insights into the grid’s status. An example WAMS is the Frequency Monitoring Network (FNET), which utilizes a large number of Internet-connected low-cost Frequency Disturbance Recorders (FDRs) that are installed at the distribution level. The large amounts of data collected by FNET and other WAMS present unique opportunities for data mining and machine learning applications, yet these techniques have only recently been applied in this domain. The research presented here explores some additional applications that may prove useful once WAMS are fully integrated into the power system. Chapter 1 provides a brief overview of the FNET system that supplies the data used for this research. Chapter 2 reviews recent research efforts in the application of data mining and machine learning techniques to wide-area measurement data. In Chapter 3, patterns in frequency extrema in the Eastern and Western Interconnections are explored using cluster analysis. In Chapter 4, an artificial neural network (ANN)-based classifier is presented that can reliably distinguish between different types of power system disturbances based solely on their frequency signatures. Chapter 5 presents a technique for constructing electromechanical transient speed maps for large power systems using FNET data from previously detected events. Chapter 6 describes an object-oriented software framework useful for developing FNET data analysis applications. In the United States, recent environmental regulations will likely result in the removal of nearly 30 GW of oil and coal-fired generation from the grid, mostly in the Eastern Interconnection (EI). The effects of this transition on voltage stability and transmission line flows have previously not been studied from a system-wide perspective. Chapter 7 discusses the results of power flow studies designed to simulate the evolution of the EI over the next few years as traditional generation sources are replaced with greener ones such as natural gas and wind. Conclusions, a summary of the main contributions of this work, and a discussion of possible future research topics are given in Chapter 8

Measurement-Based Monitoring and Control in Power Systems with High Renewable Penetrations

Power systems are experiencing rapid changes in their generation mixes because of the increasing integration of inverter-based resources (IBRs) and the retirement of traditional generations. This opens opportunities for a cleaner energy outlook but also poses challenges to the safe operation of the power networks. Enhanced monitoring and control based on the increasingly available measurements are essential in assisting stable operation and effective planning for these evolving systems. First, awareness of the evolving dynamic characteristics is quintessential for secure operation and corrective planning. A quantified monitoring study that keeps track of the inertial response and primary frequency response is conducted on the Eastern Interconnection (EI) for the past decade with field data. Whereas the inertia declined by at least 10%, the primary frequency response experienced an unexpected increase. The findings unveiled in the trending analysis also led to an improved event MW size estimation method, as well as discussions about regional dynamics. Experiencing a faster and deeper renewable integration, the Continental Europe Synchronous Area (CESA) system has been threatened by more frequent occurrences of inter-area oscillations during light-load high-renewable periods. A measurement-based oscillation damping control scheme is proposed for CESA with reduced reliance on system models. The design, implementation, and hardware-in-the-loop (HIL) testing of the controller are discussed in detail. Despite the challenges, the increasing presence of IBRs also brings opportunities for fast and efficient controls. Together with synchronized measurement, IBRs have the potential to flexibly complement traditional frequency and voltage control schemes for improved frequency and voltage recovery. The design, implementation, and HIL testing of the measurement-based frequency and voltage control for the New York State Grid are presented. In addition to the transmission level development, IBRs deployed in distribution networks can also be valuable assets in emergency islanding situations if controlled properly. A power management module is proposed to take advantage of measurements and automatically control the electric boundaries of islanded microgrids for maximized power utilization and improved frequency regulation. The module is designed to be adaptive to arbitrary non-meshed topologies with multiple source locations for increased flexibility, expedited deployment, and reduced cost

Data-Driven Situation Awareness for Power System Frequency Dynamics

As the penetration of renewable energy increases, system inertia decreases, causing changes in system frequency dynamics. The power industry desires situation awareness of power system frequency dynamics to ensure secure and economic operation and control. Moreover, FNET/Grideye has abundant measured data from power systems, making it possible to conduct data-driven situation awareness studies on power system frequency dynamics. This doctoral dissertation proposes several contributions: (a) Two accurate generator trip event MW estimation methods are proposed, in which one is based on long window RoCoF and another is based on multi-Beta values; (b) Two real-time system inertia estimation approaches are developed using ambient frequency fluctuation and pump turn-off events, along with techniques for improving RoCoF calculation in event-based inertia estimation; (c) An adaptive PV reserve estimation algorithm is established to provide PV reserve while saving energy for PV resources; (d) A practical load composition estimation tool is built for the industry to easily obtain essential load model parameters. Although conducting research using actual data from power systems for practical application is challenging and compilated, the proposed data-driven situation awareness methods in this doctoral dissertation solve practical problems and offer clear theoretical explanations for the industry. These methods address one of the key challenges for operating a high-renewable power grid and pave the way for the U.S. carbon-free power sector by 2035