Measurement Based Method for Online Characterization of Generator Dynamic Behaviour in Systems With Renewable Generation

Abstract

This paper introduces a two-step methodology for online identification of the participation of generators in power system oscillatory modes, based on measured responses. The dominant modes in generator measured responses are initially identified using a mode identification technique and then introduced, in the next step, as input into a clustering algorithm. Critical groups of generators that exhibit poorly or negatively damped oscillations are identified, in order to enable corrective control actions and stabilize the system. The uncertainties associated with the operation of modern power systems with Renewable Energy Sources (RES) are investigated as well as the impact of the dynamic behavior of power electronic interfaced RES.

Surrogate‐assisted optimal re‐dispatch control for risk‐aware regulation of dynamic total transfer capability

“…However, it is necessary to strictly verify the predefined contingencies from the stability aspect according to N‐1 criteria [3], especially for the heavily loaded and highly interconnected power systems. Essentially, the TTC calculation can be modelled as transient stability constrained OPF (TSC‐OPF) problem [4–11]. To address TSC‐OPF, an alternative is to directly incorporate the discretized swing equations into the optimization problem by adopting the implicit integration rule [4, 5].…”

“…Although it consumes less time to optimize the TSC‐OPF problem than other methods, this method is likely to sacrifice accuracy due to the local linearization [9]. Towards an online and accurate TSC‐OPF, data‐driven techniques, such as nonparametric estimation [10] and machine learning [11], have demonstrated their feasibility. Besides, based on the data‐driven methods, online proactive protection schemes for power system security can be exploited without sacrificing the solution accuracy and computing efficiency.…”