Non autonomous modelling of power systems and Development of Early warning indicators for instabilities

Abstract

Electric power systems are one of the central systems for services such as drinking water, telecommunications and banking. The day-to-day increase in the power demand and lack of simultaneous infrastructure development compel power systems to operate close to the stability margin. Power systems operating close to the stability margin are vulnerable to transitions leading to instability. The intermittent nature of renewable energy resources exacerbates the instability issues. The power generation with renewable resources varies as a function of time, imparting non-autonomous nature to power systems. The stability analysis performed on the power systems considers the system as autonomous. However, the stability regimes of autonomous and non-autonomous systems are different. This creates the need for a non autonomous power system model which is simple enough to investigate the influence of rate dependent variation of system parameters on the stability characteristics. The power system is modelled as a non-autonomous system in the current thesis. Further, the stability characteristics of a non-autonomous power system model are investigated. Further, a comparative study of the stability regimes of autonomous and non-autonomous power system models is presented. The occurrence of subcritical Hopf bifurcation in the power system model was observed for the quasi-static variation of mechanical power. The rate-dependent variation in mechanical power showed a delay in the transition in the power system model. Next, depending on the rate of variation in mechanical power, early transitions with regard to the quasi-static Hopf point are observed. Furthermore, the relationship between the critical rate and the initial conditions is established. Even though the system is quasi-statically stable, systems that are undergoing sub-critical Hopf bifurcation can nevertheless be triggered to the other oscillatory state by noise. Therefore, it is important to investigate the effect of stochasticity and intermittency on the stability..