Machine learning-based online stability assessment of power systems

• Main Author: Zheng, Hongfei
• Other Authors: Xu Yan
• Format: Thesis-Master by Coursework
• Language: English
• Published: Nanyang Technological University 2021
• Subjects: Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/152475
• Institution: Nanyang Technological University

The assessment of power system stability is of great significance to the research in power system operating status and power supply reliability. Under the concept of power system stability, small-signal stability, which usually occurs in the form of low-frequency oscillation, determines the power transmission capability in many power systems. The research goal of this dissertation is to construct a machine learning-based power system small-signal stability assessment method to evaluate the system's oscillation and small disturbance stability. This dissertation uses three main machine learning methods, including DT (decision tree), RF (random forest), and SVM (support vector machine). By adopting the adequate technologies in feature selection, validation and testing, we succeed in building the evaluation model for power system small-signal stability. And then we select the appropriate model evaluation indicators to optimize the parameters and compare the performance in different models. A database generated from the IEEE New England 10-machine 39-bus system is used for the above processes.