Dynamic security analysis is an important problem of power systems on ensuring safe operation and stable power supply even when certain faults occur. No matter if such faults are caused by vulnerabilities of system components, physical attacks, or cyber-attacks that are more related to cyber-security, they eventually affect the physical stability of a power system. Examples of the loss of physical stability include the Northeast Blackout of 2003 in North America and the 2015 system-wide blackout in Ukraine. The nonlinear hybrid nature, that is, nonlinear continuous dynamics integrated with discrete switching, and the high degree of freedom property of power system dynamics make it challenging to conduct the dynamic security analysis. In this article, we use the hybrid automaton model to describe the dynamics of a power system and mainly deal with the index-1 differential-algebraic equation models regarding the continuous dynamics in different discrete states. The analysis problem is formulated as a reachability problem of the associated hybrid model. A sampling-based algorithm is then proposed by integrating modeling and randomized simulation of the hybrid dynamics to search for a feasible execution connecting an initial state of the post-fault system and a target set in the desired operation mode. The proposed method enables the use of existing power system simulators for the synthesis of discrete switching and control strategies through randomized simulation. The effectiveness and performance of the proposed approach are demonstrated with an application to the dynamic security analysis of the New England 39-bus benchmark power system exhibiting hybrid dynamics. In addition to evaluating the dynamic security, the proposed method searches for a feasible strategy to ensure the dynamic security of the system in the face of disruptions.
Generally, state-of-the-art DSA tools rely heavily on deterministic computation methods @cite_4 , such as the aforementioned EEAC, BCU, SBS, etc. Security limits are searched based on exhaustively examining many predefined contingencies using a rigorous approach. Advantages of such methods are the accurate results available at the expense of computation time, since precise models of the system dynamics are used as the basis. With the development of data science, the probabilistic methods, including machine learning and artificial intelligence @cite_43 @cite_51 , are recently proposed again to DSA, utilizing accumulated knowledge and data. Some probabilistic models for the causal relation could be trained using certain learning methods based on data obtained via simulation, and the built models are further employed to conduct the on-line DSA. The advantage of such methods are the rapid results, while the reliability of the obtained models is still in study, and simulation still plays a fundamental role on supplying the data. In addition, a rigorous and concrete strategy to maintain the system security is not provided in the state-of-the-art tools and methods.
@cite_51 @cite_43 @cite_4
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Real-time transient stability prediction is an essential and challenging step of response-based transient stability emergency controls. Machine learning methods including decision trees and artificial neural networks have the potential to be applied to the problem. To counter the inefficiency of common machine learning methods in learning new information, an incremental learning algorithm is employed to train an artificial neural network for real-time transient stability prediction. The resulted learning framework can readily be integrated into on-line dynamic security assessment. The effectiveness of such prediction model is demonstrated by the simulation results of a practical power system. In recent years, complex operating conditions have greatly reduced the predictability of electric grid operations and hence, there is an urgent need to improve grid security more than ever before. The best approach would be to improve grid intelligence rather than simply hardening the grid. An implementation of dynamic security assessment (DSA) would require carrying out time-domain simulations that are computationally too involved to be performed in real-time. This paper presents an approach using machine learning (ML) techniques that would enable the grid to assess its current dynamic state instantaneously. A database consisting of steady-state operating points of the power system and outputs of time-domain simulations is generated in order to train and test the algorithm. A few operating points termed as “landmarks” are identified through a ranking methodology proposed in this paper. Finally, it is shown that prediction accuracy improves through use of such landmarks. Security refers to the degree of risk in a power system's ability to survive imminent disturbances (contingencies) without interruption to customer service. It relates to robustness of the system to imminent disturbances and, hence, depends on the system operating condition as well as the contingent probability of disturbances. DSA refers to the analysis required to determine whether or not a power system can meet specified reliability and security criteria in both transient and steady-state time frames for all credible contingencies. Ensuring security in the new environment requires the use of advanced power system analysis tools capable of comprehensive security assessment with due consideration to practical operating criteria. These tools must be able to model the system appropriately, compute security limits in a fast and accurate manner, and provide meaningful displays to system operators. Online dynamics security assessment can provide the first line of defense against widespread system disturbances by quickly scanning the system for potential problems and providing operators with actionable results. With the development of emerging technologies, such as wide-area PMs and ISs, online DSA is expected to become a dominant weapon against system blackouts.