Campus Units
Electrical and Computer Engineering
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
5-18-2020
Journal or Book Title
IEEE Transactions on Smart Grid
DOI
10.1109/TSG.2020.2995313
Abstract
In an interconnected multi-area power system, wide-area measurement based damping controllers are used to damp out inter-area oscillations, which jeopardize grid stability and constrain the power flows below to their transmission capacity. The effect of wide-area damping control (WADC) significantly depends on both power and cyber systems. At the cyber system layer, an adversary can inflict the WADC process by compromising either measurement signals, control signals or both. Stealthy and coordinated cyber-attacks may bypass the conventional cybersecurity measures to disrupt the seamless operation of WADC. This paper proposes an anomaly detection (AD) algorithm using supervised Machine Learning and a model-based logic for mitigation. The proposed AD algorithm considers measurement signals (input of WADC) and control signals (output of WADC) as input to evaluate the type of activity such as normal, perturbation (small or large signal faults), attack and perturbation-and-attack. Upon anomaly detection, the mitigation module tunes the WADC signal and sets the control status mode as either wide-area mode or local mode. The proposed anomaly detection and mitigation (ADM) module works inline with the WADC at the control center for attack detection on both measurement and control signals and eliminates the need for ADMs at the geographically distributed actuators. We consider coordinated and primitive data-integrity attack vectors such as pulse, ramp, relay-trip and replay attacks. The performance of the proposed ADM algorithms was evaluated under these attack vector scenarios on a testbed environment for 2-area 4-machine power system. The ADM module shows effective performance with 96.5% accuracy to detect anomalies.
Rights
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Copyright Owner
IEEE
Copyright Date
2020
Language
en
File Format
application/pdf
Recommended Citation
Ravikumar, Gelli and Govindarasu, Manimaran, "Anomaly Detection and Mitigation for Wide-Area Damping Control using Machine Learning" (2020). Electrical and Computer Engineering Publications. 250.
https://lib.dr.iastate.edu/ece_pubs/250
Comments
This is a manuscript of an article published as Ravikumar, Gelli, and Manimaran Govindarasu. "Anomaly Detection and Mitigation for Wide-Area Damping Control using Machine Learning." IEEE Transactions on Smart Grid (2020). DOI: 10.1109/TSG.2020.2995313. Posted with permission.