It has recently been shown that state estimation (SE), which is the most important real-time function in modern energy management systems (EMSs), is vulnerable to false data injection attacks, due to the undetectability of those attacks using standard bad data detection techniques, which are typically based on normalized measurement residuals. Therefore, it is of the utmost importance to develop novel and efficient methods that are capable of detecting such malicious attacks. In this paper, we propose using the unscented Kalman filter (UKF) in conjunction with a weighted least square (WLS) based SE algorithm in real-time, to detect discrepancies between SV estimates and, as a consequence, to identify false data attacks. After an attack is detected and an appropriate alarm is raised, an operator can take actions to prevent or minimize the potential consequences. The proposed algorithm was successfully tested on benchmark IEEE 14-bus and 300-bus test systems, making it suitable for implementation in commercial EMS software.

This paper describes a new class of system identification procedures that are tailored to electric power systems, in particular to synchronous generators (SGs) and other dynamic components. Our procedure builds on computational advances in differential geometry, and offers a new, global characterization of challenges frequently encountered in system identification of electric power systems. The approach also benefits from increasing availability of high-quality measurements. While the proposed procedure is illustrated on SG example in a multimachine benchmark (IEEE 14-bus and real-world 441-bus power systems), it is equally applicable to identification of other system components, such as loads.

The paper describes an algorithm for parameter identification of a dynamic equivalent for an external subsystem, based solely on the available online measurements in boundary buses and branches. Static equivalent part is represented by equivalent impedances from boundary buses (ones that separate the internal and external subsystems) and calculated using the modified (minimum loss) radial, equivalent, and independent method. Parameter identification of synchronous generator (SG)-based equivalent (for predominantly production external areas), dynamic load (DL)-based equivalent (for predominantly load external areas), or (SG + DL)-based equivalent (for mixed external areas) in fictitious buses is performed by Levenberg-Marquardt weighted least-square nonlinear optimization, which minimizes the variances between available online measurements and transient responses of the reduced power system. The IEEE 14-bus and 441-bus real-world test systems are used to illustrate and test the proposed power system equivalent derivation technique.

This paper describes a data-driven symbolic regression identification method tailored to power systems and demonstrated on different synchronous generator (SG) models. In this work, we extend the sparse identification of nonlinear dynamics (SINDy) modeling procedure to include the effects of exogenous signals (measurements), nonlinear trigonometric terms in the library of elements, equality, and boundary constraints of expected solution. We show that the resulting framework requires fairly little in terms of data, and is computationally efficient and robust to noise, making it a viable candidate for online identification in response to rapid system changes. The SINDy-based model identification is integrated with the manifold boundary approximation method (MBAM) for the reduction of the differential-algebraic equations (DAE)-based SG dynamic models (decrease in the number of states and parameters). The proposed procedure is illustrated on an SG example in a real-world 441-bus and 67-machine benchmark.

This paper presents an analysis of three typical microgrid operation modes. First is the analysis of the grid-connected mode, in which the main goal is to optimize the microgrid operation (in terms of economics, carbon emissions and/or reliability) when it is connected with the main grid. Second is the analysis of the planned island mode, in which the main goal is to provide a stable and smooth transition from grid-connected to island mode while keeping both the frequency and the voltages within the required limits. Finally, the paper presents the analysis of the emergency island mode, which occurs after sudden islanding, in which the main goal is to maintain the energization of critical customers. The objectives and the operation strategies of microgrid management are different in each operation mode. This paper proposes a novel, centralized, multi-agent-based, microgrid controller architecture, which provides the coordination of all three operation modes and enables the easy configuration/combination of optimization goals that are subject to a given set of operational constraints. The microgrid controller consists of a real-time agent (responsible for closed loop control and maintenance of the system's frequency), an operation planning agent [responsible for near-term (15 minutes up to 24 hours) and short-term (1 to 7 days) optimization] and a state agent (responsible for overall monitoring, management and coordination of the other two agents). A microgrid controller with the aforementioned agents has been developed and tested. The simulation results are presented for a typical microgrid test example. Copyright © 2016 John Wiley & Sons, Ltd.

The article explores the analysis of transient phenomena in large-scale power systems subjected to major disturbances from the aspect of interleaving, coordinating, and refining physics- and data-driven models. Major disturbances can lead to cascading failures and ultimately to the partial power system blackout. Our primary interest is in a framework that would enable coordinated and seamlessly integrated use of the two types of models in engineered systems. Parts of this framework include: 1) optimized compressed sensing, 2) customized finite-dimensional approximations of the Koopman operator, and 3) gray-box integration of physics-driven (equation-based) and data-driven (deep neural network-based) models. The proposed three-stage procedure is applied to the transient stability analysis on the multimachine benchmark example of a 441-bus real-world test system, where the results are shown for a synchronous generator with local measurements in the connection point.