A risk-based machine learning approach for probabilistic transient stability enhancement incorporating wind generation

Abstract

ABSTRACT Power systems are becoming more complex than ever and are consequently operating close to their limit of stability. Considering its significance in power system security, it is important to propose a novel approach for enhancing the transient stability, considering uncertainties. Current deterministic industry practices of transient stability assessment ignore the probabilistic nature of variables. Moreover, the time-domain simulation approach for transient stability evaluation can be very computationally intensive, especially for a large-scale system. The impact of wind penetration on transient stability is critical to investigate, as it does not possess the inherent inertia of synchronous generators. Thus, this paper proposes a risk-based, machine learning decision-making approach, for probabilistic transient stability enhancement, by replacing circuit breakers, including the impact of wind generation. The IEEE 14-bus test system was used to test and validate the effectiveness of the proposed approach. DIgSILENT PowerFactory and MATLAB were utilised for transient stability simulations (for obtaining training data for machine learning), and applying machine learning algorithms, respectively.