Monitoring, Control, and Protection

Permanent URI for this collection


Recent Submissions

Now showing 1 - 9 of 9
  • Item
    An Approach for the Direct Inclusion of Weather Information in the Power Flow
    ( 2023-01-03) Overbye, Thomas ; Safdarian, Farnaz ; Trinh, Wei ; Mao, Zeyu ; Snodgrass, Jonathan ; Yeo, Juhee
    While it is widely recognized that weather impacts the power flow, historically weather information has only been implicitly included. This paper presents an approach for the direct inclusion of weather information in the power flow. Key issues addressed by the paper include the availability of weather information, the mapping of weather information to electric grid components, a flexible and extensible modeling approach for relating weather values to the power flow models, and the visualization of the weather impacts. The approach is demonstrated on several electric grids ranging in size from 7000 to 82,000 buses using weather data over several different years.
  • Item
    Online Tracking of Two Dominant Inter-Area Modes of Oscillation in the Eastern Interconnection
    ( 2023-01-03) Follum, Jim ; Nayak, Neeraj ; Eto, Joseph
    Reliable power system operation requires that small-signal stability be maintained at all times. Mode meters are measurement-based tools that provide operators with situational awareness of the system's stability margin. They operate by continually tracking the inter-area modes of oscillation that govern small-signal stability. This paper reports on the deployment of mode meters for online monitoring of two dominant modes of oscillation in the United States' Eastern Interconnection (EI). The use of measurements from system operators across the interconnection to provide continuous tracking is novel in the EI. Results from over four months of analysis reveal diurnal patterns in the modes and demonstrate that they can be tracked through a variety of system conditions. The results from this study continue to build an understanding of the EI's modes that will inform future modeling and monitoring efforts.
  • Item
  • Item
    Distributed Computing for Scalable Optimal Power Flow in Large Radial Electric Power Distribution Systems with Distributed Energy Resources
    ( 2023-01-03) Sadnan, Rabayet ; Dubey, Anamika
    The increasing penetrations of distributed energy resources (DERs) at the power distribution level augments the complexity of optimally operating the grid edge assets, primarily because of the nonlinearity and scale of the system. An alternative is to solve the relaxed convex or linear-approximated problem, but these methods lead to sub-optimal or power-flow infeasible solutions. This paper proposes a scalable and fast approach to solve the large nonlinear optimal power flow (OPF) problem using a developed distributed method. The full network-level OPF problem is decomposed into multiple smaller sub-problems that are easy to solve - the distributed method attains network-level optimal solutions upon consensus. This effective decomposition technique reduces the number of iterations required for consensus by order of magnitude compared to traditional distributed algorithms. We demonstrate the proposed approach by solving different nonlinear OPF problems (for different problem objectives) for a distribution system with more than fifty-thousands (50,000) problem variables.
  • Item
    A Universal Grid-forming Inverter Model and Simulation-based Characterization Across Timescales
    ( 2023-01-03) Ramasubramanian, Deepak ; Farantatos, Evangelos ; Ajala, Olaoluwapo ; Dhople, Sairaj ; Johnson, Brian
    The evolution of the power grid has given rise to a variety of innovations in inverter control architectures. Among these advances, a class of controllers has emerged with the aim of enabling 100\% inverter-based grids and these are known as grid-forming methods. Since these strategies are still under active development, well validated models are needed by equipment manufacturers as well as system planners and operators. In particular, a system operator may be unable to determine specifications and services that are required from grid forming devices without having the ability to represent them in a simulation environment with trusted models. A universal grid forming model that is portable across multiple simulation domains will be valuable in addressing this issue. In this paper, we develop a practical implementation of such a model that has the ability to represent four different grid-forming methods in a variety of simulation software packages while accurately capturing dynamics across from microsecond to millisecond timescales.
  • Item
    Training Machine Learning Models with Simulated Data for Improved Line Fault Events Classification From 3-Phase PMU Field Recordings
    ( 2023-01-03) Otudi, Hussain ; Mohamed, Taif ; Kezunovic, Mladen ; Hu, Yi ; Obradovic, Zoran
    Utilizing data analytics and machine learning (ML) on phasor measurement units (PMUs) data to analyze faults automatically is the focus of this paper. Insufficient labels and natural uneven distribution of different types of line fault events found in field-recorded PMU data make supervised ML model development challenging. To address this issue, we train off-the-shelf Support Vector Machine (SVM) ML models for line fault classification using simulated PMU data obtained from a combination of 12 physical and virtual PMUs placed on a synthetic IEEE 14-bus system as well as using this simulated data unified with field recordings. A conducted sensitivity study is focused on three factors, 1) the number of PMUs used to train the ML model, 2) the voltage level at which the model is trained, and 3) the vicinity of PMUs to transmission line faults. The ML models trained with simulated and field data are evaluated on one-year field-recorded data collected from 38 PMUs sparsely located in the US Western interconnection. We demonstrate that when training ML models with only simulated data, the performance varies significantly with different number of PMUs, voltage level, and PMU placement in separate areas of the synthetic grid (F1 score of 0.78 to 0.92). We obtained an F1 score of 0.94 using the simulated dataset integrated with field recordings. The performance of a ML model developed using simulated data is also evaluated on the three-phase voltage signals extracted from 188 PMUs in the Eastern interconnection accompanied by imprecise labels, where the majority of the labels do not identify the fault type. On this extremely challenging task, we achieved 77% accuracy solely using synthetic data for ML training.
  • Item
    The Circular Variance as a Visual Summary of Synchronized Voltage Angle Measurements
    ( 2023-01-03) Follum, Jim ; Aksoy, Sinan ; Bhadra, Sraddhanjoli ; Buckheit, John ; Betzsold, Nick ; Yin, Tianzhixi ; Becejac, Tamara
    Phasor measurement units (PMUs) allow voltage angle differences across power grids to be monitored to identify sudden shifts associated with system disturbances. The Eastern Interconnection Situational Awareness and Monitoring System (ESAMS) was developed to identify such wide-area disturbances and summarize them in reports released the following day. Demonstration of ESAMS in North America's Eastern Interconnection revealed the need for an effective visual summary of the disturbance's impact on voltage angle pairs. This paper proposes the use of the circular variance, a measure of dispersion applicable to angular data, for this purpose. Results based on PMU data from North America's Eastern and Western interconnections indicate that the circular variance provides useful summaries of wide-area voltage angle measurements. They also show that the circular variance may have potential uses when applied to historical data to identify unusual grid conditions.
  • Item
    Outer-loop Adaptive Control of Converter-Interfaced Generation for Cyber-Physical Security
    ( 2023-01-03) Roberts, Ciaran ; Callaway, Duncan ; Arnold, Daniel
    The integration of converter-interfaced generation into our power systems is changing how we control and operate these networks. While these fast-acting resources are more controllable than conventional synchronous machines, this additional controllability presents some challenges. One of these challenges is the increased cyber-physical attack surface arising from interactions among the numerous digital control loops of these devices. In this work, we present a supervisory adaptive controller that temporarily increases the outer-loop controller bandwidth of these devices in the event of sustained oscillatory behavior. We design this controller to inherently remain inactive during normal operation and only become active during sustained abnormal operating conditions. We show how this proposed controller can mitigate a cyber-physical attack, even when the attacker has full knowledge of the network model and access to real-time state information for state-feedback control.
  • Item
    Dynamic Response Recovery Using Ambient Synchrophasor Data: A Synthetic Texas Interconnection Case Study
    ( 2023-01-03) Liu, Shaohui ; Zhu, Hao ; Kekatos, Vassilis
    Wide-area dynamic studies are of paramount importance to ensure the stability and reliability of power grids. This paper puts forth a comprehensive framework for inferring the dynamic responses in the small-signal regime using ubiquitous fast-rate ambient data collected during normal grid operations. We have shown that the impulse response between any pair of locations can be recovered in a model-free fashion by cross-correlating angle and power flow data streams collected only at these two locations, going beyond previous work based on frequency data only. The result has been established via model-based analysis of linearized second-order swing dynamics under certain conditions. Numerical validations demonstrate its applicability to realistic power system models including nonlinear, higher-order dynamics. In particular, the case study using synthetic PMU data on a synthetic Texas Interconnection (TI) system strongly corroborates the benefit of using angle PMU data over frequency one for real-world power system dynamic modeling.