Enabling Characterisation of Dynamic Interactions With Probabilistic Small-Signal Analysis in Converter-Integrated Power Systems

This paper proposes a framework for probabilistic small-signal analysis which enables the characterisation of new interactions in power systems incorporating converter-interfaced generators (CIGs) as well as understanding their probabilistic behaviour. The uptake of CIGs and subsequent reduction of synchronous generation is causing significant changes to power system dynamic behaviour, characterised by increasing dependence on converter control and variable renewable energy resources. Consequently, single-operating point deterministic studies are insufficient to fully understand power system stability and the characteristics of emerging interactions. The increasing uncertainty in possible operating points, and resultant impact on the modes of small-signal eigenvalue analysis, necessitates probabilistic approaches. However, the plethora of data obtained through such studies, especially considering the detailed models required in CIG-dominated systems, makes it difficult to identify the key distinct interactions present across the operating range, especially with respect to their state participation factor-based characteristics. Such limitations are addressed using the framework proposed in this paper. This includes a systematic characterisation procedure as well as modal clustering and the introduction of a Stability-Weighted Participation Index. A Monte Carlo approach is adopted and applied to a modified IEEE 68-bus test system, offering an insight into emerging interactions involving CIGs, including sub-synchronous oscillations.