Probabilistic tuning of Power System Stabilizers considering the wind farm generation uncertainty

Abstract

The increasing use of wind farms and the uncertainties in their generation levels have led to new challenges in system dynamic behaviors and Small Signal Stability (SSS). This paper proposes a probabilistic method to investigate the impact of the wind farm generation uncertainty on power system SSS, based on the Monte Carlo simulation. The proposed method is stochastic in contrast to conventional deterministic methods, which only analyze the system dynamic behaviors in one operating point. It determines several operating points, based on wind farm generation levels and calculates the Probability Density Function (PDF) of critical eigenvalues. Then, Genetic Algorithm (GA) is used to optimize the Power System Stabilizers (PSS) parameters. A case study is carried out on the IEEE 16-machine system to demonstrate the effectiveness and validity of the proposed algorithms. The results of probabilistic stability analysis are compared with those of deterministic analysis. It is shown that variation of the wind farm generation levels can cause the instability, even though the system is stable according to the deterministic analysis. Also, the time domain simulation results show how such tunings can effectively improve the SSS performance under the wind farm generation uncertainties.