Search for: All records

The paper proposes an approach for fast small signal stability assessment on a short data window using deep learning algorithms. This paper shows that the proposed deep convolutional neural networks (CNNs)-based assessment approach is faster than traditional methods (i.e. Prony’s method). The evaluated CNNs are fully convolutional network (FCN), CNN with sub-sampling steps performed through max pooling (Time LeNet), time CNN, fully convolutional network with attention mechanism (Encoder), and CNN with a shortcut residual connection (ResNet). The proposed approach is validated on different synthetic measurement data sets generated from the IEEE 9-bus system that is used as a reference, and further applied to a 769-bus system representing a region in the U. S. Eastern Interconnection. We show that precision and recall are more informative metrics than accuracy for the reliability of the stability assessment process using the proposed methodology. In addition, the method’s efficiency is compared to classical Prony method. 

Enhancing grid resilience is proposed through the integration of distributed energy resources (DERs) with microgrids. Due to the diverse nature of DERs, there is a need to explore the optimal combined operation of these energy sources within the framework of microgrids. As such, this paper presents the design, implementation and validation of a Model Predictive Control (MPC)-based secondary control scheme to tackle two challenges: optimal islanded operation, and optimal re-synchronization of a microgrid. The MPC optimization algorithm dynamically adjusts input signals, termed manipulated variables, for each DER within the microgrid, including a gas turbine, an aggregate photovoltaic (PV) unit, and an electrical battery energy storage (BESS) unit. To attain optimal islanded operation, the secondary-level controller based on Model Predictive Control (MPC) was configured to uphold microgrid functionality promptly following the islanding event. Subsequently, it assumed the task of power balancing within the microgrid and ensuring the reliability of the overall system. For optimal re-synchronization, the MPC-based controller was set to adjust the manipulated variables to synchronize voltage and angle with the point of common coupling of the system. All stages within the microgrid operation were optimally achieved through one MPC-driven control system, where the controller can effectively guide the system to different goals by updating the MPC’s target reference. More importantly, the results show that the MPC-based control scheme is capable of controlling different DERs simultaneously, mitigating potentially harmful transient rotor torques from the re-synchronization as well as maintaining the microgrid within system performance requirements. 

This paper describes the development of a phasor-based campus microgrid model utilizing the Modelica language and the OpenIPSL library. The phasor-based modeling approach was chosen because the resulting microgrid model would yield faster simulation run times when compared to models developed using electromagnetic transient (EMT) methods. Beyond the benefits of simulation performance, this becomes necessary when attempting to understand dynamic phenomena arising under emergency conditions across time scales ranging from milliseconds to hours, which will aid in developing resiliency improvement plans for the real-world campus microgrid that the model represents. Considering the increasing number of distributed energy sources (DERs) being added to power grids across the world and the paradigm shift on how electrical grids can operate with more DERs, the implementation of such a microgrid campus model can help in the development and testing new control strategies to support new operational approaches while guaranteeing system stability and resiliency. The added benefit of having the microgrid model in Modelica is that it can be simulated in any Modelica complaint tool (both proprietary or not), preserving an open-source code, unlocked for the user to explore and adjust the implementation as well as observe and edit the mathematical formulation. This enables not only nonlinear time simulation, but also linear analysis techniques and other approaches to be applied.