Adaptive Algorithm For Rapidly Optimising The Generator-Tripping Control

The fast optimisation of transient stability emergency control (TSEC) strategy is a key factor of online safeguard for AC-DC interconnected systems. An engineering applicable TSEC algorithm has to obtain the disturbed trajectories of the system by numerical integration, and then do quantised stability knowledge mining. Theoretical analysis and simulation verification reveal that the premise is to identify the unstable mode correctly, and then emergency control decision can be optimised according to the cost performance of the actions. Small integral step is usually needed for accuracy. In order to coordinate the accuracy and computational burden intelligently, this study proposes an algorithm for optimising generator-tripping control, which can adaptively reduce computational burden. If the unstable mode is insensitive to the change of fault parameters (e.g. fault clearing time), this case's time-varying property is more likely to be weak. Only for such cases, large-step Taylor series expansions (LSTSE) can be taken to replace small-step numerical integration (SSNI) and search for the optimal result. Based on its time-varying property, each case can get disturbed trajectories using LSTSE or SSNI adaptively and obtain optimal solution. The excellent performance of this algorithm is verified by simulation of nine Chinese regional power systems under various operating conditions.