Performance of Controller Designs in Small-Disturbance Angle Stability of Power Systems with Parametric Uncertainties

The electric power system is a complicated dynamic system with a range of operating states and parametric uncertainties, especially due to change of the network topology, load increment and generation scheduling. Under this circumstance, traditional power system transient stability analysis methods may not always be appropriate. This paper presents the development of a computational methodology for evaluating the effect of parametric uncertainties on the small-signal stability analysis of power systems. A probabilistic approach is applied as a metric for the dynamic performance of the damping ratio of critical eigenvalues. The method is based on a Monte Carlo simulation for the analysis of automatic control of generation. The methodology is used for the performance evaluation of three classical controller tuning techniques: Frequency Response, Approximate Method and Ziegler-Nichols. The results show that the methodology is valid and potentially useful for quantifying the effect of parametric uncertainties in power systems dynamics simulations.

Automated summary

This paper presents a computational methodology for evaluating the effect of parametric uncertainties on the small-signal stability analysis of power systems. A probabilistic approach is applied as a metric for the dynamic performance. The results show the potential usefulness of the method for quantifying the effect of parametric uncertainties in power systems dynamics simulations.