Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158942
Title: Data-analytics for power system stability assessment
Authors: Tang, Yuchi
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Tang, Y. (2022). Data-analytics for power system stability assessment. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158942
Abstract: With the increasing integration of phasor measurement units (PMUs) and supervisory control and data acquisition (SCADA) in power systems, intelligent data-analytics for short-term voltage stability assessment becomes achievable. This task requires fast response and accurate conclusion, especially, to avoid wrong conclusions for the actual unstable cases. Given this, an intelligent post-fault short-term voltage stability (STVS) assessment method is proposed in this research. By introducing Gramian Angular Field (GAF) transform, two-dimensional convolutional neural network (2D-CNN), and adaptive confidence interval (ACI), the proposed method shows better performance to carry out the task. The related tests are based on the New England 10-machine 39-bus system with an obtained 6536-case dataset.
URI: https://hdl.handle.net/10356/158942
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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