Power system stabilizers tuning for probabilistic small-signal stability enhancement using particle swarm optimization and unscented transformation

In modern power systems, uncertainties related to loads and renewable energy sources increase the need for reliable tools for steady-state and dynamic studies. These uncertainties are commonly modeled using probability density functions. In the context of the probabilistic small-signal stability analysis of power systems, the challenge lies in developing computationally efficient tools that accurately calculate the probability of ensuring security (minimum damping ratio greater than or equal to a desired value) and stability (negative spectral abscissa) requirements. This paper proposes an optimization approach for designing power system stabilizers to maximize the probability of meeting these security and stability requirements. The innovation of this approach is the integration of the unscented transformation (UT) with the particle swarm optimization (PSO). The UT is advantageous, as it requires a smaller number of samples to compute the mean and standard deviation of the output variables, especially compared to the Monte Carlo simulation (MCS), whereas PSO provides high-quality solutions. The New-England test system is employed in a case study to validate the proposed approach. This case study highlights the method’s accuracy and computational efficiency advantages, showcasing its potential to address the challenges posed by increasing uncertainties in modern power systems.