Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162511
Title: Anomaly detection in smart grids using machine learning
Authors: Li, Xiang
Keywords: Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Li, X. (2022). Anomaly detection in smart grids using machine learning. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162511
Abstract: Today’s smart power system is threatened by an increasing number of cyberattack events, fast and accurate detection of attack events is essential for the safe and reliable operation of the smart grid. In this dissertation, anomaly detection strategies based on reinforcement learning (RL) are proposed. Moreover, a cyber attack location method in the power system is presented. Various tests are performed with IEEE 14-bus systems, and results illustrate the effectiveness of the proposed algorithms of accurate and delay reduction of detection against cyber attacks targeting the smart grid. In addition, the impact of multiple solutions to the security-constrained economic dispatch is investigated, and apply an approach to determine the optimal attack vector, which can lead to a significant increase in the operation cost. The optimal false data injection (FDI) attack vector can be determined by solving a bi-level linear programming problem (LP), but in this approach, the attack vector only needs to solve one LP. The result of the simulation test on the IEEE 14-bus system verifies the effectiveness of this model.
URI: https://hdl.handle.net/10356/162511
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
Dissertation_LiXiang.pdf
  Restricted Access
2.15 MBAdobe PDFView/Open

Page view(s)

46
Updated on Dec 7, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.