Dynamic Security Analysis of Power Systems by a Sampling-Based Algorithm

Abstract

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 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 paper, 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 face of disruptions.

Split-Based Sequential Sampling for Realtime Security Assessment

“…However, large systems require large amounts of generated data, and most data adds little knowledge to the database. For instance, Jafar [37] uses the Latin hypercube sampling (LHS) approach to uniformly sample the entire search space, and researchers in [38] sample within the feasible neighbourhood of OCs, while researchers in [39] proposed an outer approximation to convexify the original nonconvex feasible space, then sample from the convex region to generate samples close to the security boundary. Venzke [40] uses infeasibility certificates based on separating hyperplanes to discard large portions of the input space as infeasible.…”