A missing-data tolerant method for data-driven short-term voltage stability assessment of power systems

• Main Authors: Zhang, Yuchen, Xu, Yan, Zhang, Rui, Dong, Zhao Yang
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Electrical and electronic engineering; Ensemble Learning; Feature Selection
• Online Access: https://hdl.handle.net/10356/151496
• Institution: Nanyang Technological University

With the widespread deployment of phasor measurement units (PMU), synchronized measurements of the power system has opened opportunities for data-driven short-term voltage stability (STVS) assessment. The existing intelligent system-based methods for data-driven stability assessment assume full and complete data input is always available. However, in practice, after a fault occurs in the system, some PMU data may not be fully available due to PMU loss and/or fault-induced topology change, which deteriorates the stability assessment performance. To address this issue, this paper proposes a missing-data tolerant method for post-fault STVS assessment. The buses in the system are strategically grouped to maintain a high level of grid observability for the stability assessment model under any PMU loss and/or topology change scenario, and a structure-adaptive ensemble learning model is designed to adapt its structure to only use available feature inputs for real-time STVS assessment. By marked contrast to existing methods, the proposed method demonstrates much stronger missing-data tolerance and can maintain a high STVS assessment accuracy even when a large portion of measurements are missing.