Owing to the fast and large power regulating the capacity of the HVDC system, the wide-area measurement system (WAMS) based high voltage direct current (HVDC) system has been regarded as a prospective solution to deal with the low-frequency oscillation issue. However, due to the vulnerability of the WAMS communication, WAMS based HVDC system control can be a prime target of malicious penetrations that could lead to disastrous events. To remediate this adverse effect, an improved WAMS based HVDC damping control framework is proposed. First, a lightweight network named Attack Shuffle convolutional neural Networks (ASNet) is proposed to learn the characteristics of cyber attacks. Then, a model-free-based cyber attack defense framework is introduced to quickly identify the attack types based on the continuous wavelet transform and ASNet. Additionally, an improved control framework of the WAMS and HVDC-based wide-area power oscillation damping control (WH-PODC) is developed to provide different response control for mitigation of the impact of cyber attacks. Finally, the performance of the proposed WH-PODC control framework is evaluated with real PMU data in multiple scenarios in RTDS, where the results indicate that the response intensity can be kept under multiple types of cyber attacks while providing similar effectiveness in oscillation suppression to conventional controls.

In contrast with conventional microgrids (MGs) with fixed boundaries, a smart and flexible MG with dynamic boundary is introduced in this paper. Such a MG can dynamically change its boundary by picking up or shedding load sections of a distribution feeder depending on its available power, leading to more flexible operation, better utilization of renewables, smaller size of energy storage system, higher reliability, and lower cost. To achieve a flexible MG, the main challenges in MG design are addressed, including recloser placement, MG asset sizing considering resilience, system grounding design, and protection system design. Meanwhile, a hierarchical structure is employed to design and implement the MG controller. On top of the functions defined in IEEE 2030.7-2018, a few new functions, e.g., online topology identification and PQ balance, are added, while the planned/unplanned islanding and reconnection functions are enhanced. The controller is implemented on a CompactRIO, a general-purpose hardware platform provided by National Instruments (NI), and tested on a controller hardware-in-the-loop setup based on an OPAL-RT real-time simulator and a reconfigurable power electronic converter-based hardware testbed. The test results have validated the performance of the developed controllers. Such a flexible MG and its controller have been deployed at a municipal utility, and part of the controller’s functions have been tested on-site.

The transition from conventional to modern power systems is causing an increase in integration of inverter-based resources (IBRs). This generally leads to a decrease in total system inertia, which in-turn increases the system’s rate-of-change-of-frequency (RoCoF) during disturbances. This poses a threat to the frequency stability of the system and may falsely trigger protective devices. To monitor system status and plan for integrating renewable energy sources like photovoltaic, wind, and energy storage systems, a realistic study of inertia estimation and analysis in the United States (US) over the past decade is needed. This paper uses field-measured phasor measurement unit (PMU) data collected throughout the US from 2013 to 2023 via the Frequency Monitoring Network (FNET/GridEye) operated by the University of Tennessee, Knoxville (UTK) and Oak Ridge National Laboratory (ORNL). The collected PMU frequency data is utilized to estimate the system inertia of the three US interconnections: Eastern, Western, and Texas. Various RoCoF time windows are investigated for estimating the inertia of each interconnection by maximizing the correlation coefficient between the measured RoCoF and power mismatch. The resulting inertia trends over the past decade show approximately a 6% decline in inertia in the Eastern interconnection, a 15% decline in inertia in the Western interconnection, and a 16% increase in inertia in Texas. Key insights into how inertia is changing amidst the complex energy landscape are extracted using the fuel mix trend data. This provides valuable information for future energy strategies and planning.

Large‐scale power systems exhibit more complex dynamics due to the increasing integration of inverter‐based resources (IBRs). Therefore, there is an urgent need to enhance the situational awareness capability for better monitoring and control of power grids dominated by IBRs. As a pioneering Wide‐Area Measurement System, FNET/GridEye has developed and implemented various advanced applications based on the collected synchrophasor measurements to enhance the situational awareness capability of large‐scale power grids. This study provides an overview of the latest progress of FNET/GridEye. The sensors, communication, and data servers are upgraded to handle ultra‐high density synchrophasor and point‐on‐wave data to monitor system dynamics with more details. More importantly, several artificial intelligence (AI)‐based advanced applications are introduced, including AI‐based inertia estimation, AI‐based disturbance size and location estimation, AI‐based system stability assessment, and AI‐based data authentication.

The dynamic boundary concept enables more flexible and efficient operation of microgrids with distributed energy resources (DER) that are intermittent in nature. As the integration of renewables continues to accelerate, an adaptive power management module that enables dynamic boundary operations in microgrids with an increasing number of source locations is essential for the fast and low-cost deployment of microgrid controllers. The power management module introduced in this paper is capable of handling the increased complexity in topological variations and transitions stemming from dynamic boundaries and multiple source locations. This includes real-time operation of multiple islands with dynamic boundaries, initiation of topological transitions (merging and separation of islands), and automatic source coordination for power sharing and frequency regulation. All functions in the power management module are designed to be automatically adaptable to arbitrary microgrids with non-meshed topologies so that the deployment of the controller at new microgrid sites can be expedited with a reduced cost. The module has been implemented on NI’s CompactRIO system as an essential part of an MG controller and tested on a converter-based hardware testbed (HTB). Testing results validated the effectiveness of the algorithms under various operating conditions.