Events

Cyber Physical Systems for Smart Grids – Attack Generation and Detection

  • 22

    Nov

    2022


Name of the Speaker: Mr. Himanshu Goyel (EE19S025)
Name of the Guide: Dr. Shanti Swarup K
Link: https://meet.google.com/jbp-bvas-qxz?pli=1
Date/Time: 22.11.2022 (Tuesday), 2.30 PM - 3:30 PM

Abstract
Cyber-Physical Systems (CPS) for Modern Digital Power Networks are important from the point of view of false data attacks or manipulations on digital devices like smart meters, phasor measurement units, intelligent electronic devices, etc. The study presents cyber-physical modeling of a power system from an attack detection and mitigation point of view. The research deals with generating attack vectors to spoof the bad data detection strategies and detect them using machine learning techniques. In this work, we present three problems relevant to the smart grids, which are the i) attack vector generation problem (data attack), and ii) attack detection (protection) iii) Placement of the detection algorithm on the network. This first and second part provides a novel technique to generate and detect data integrity attacks in smart grids. It also gives an optimization algorithm for generating FDIA against state estimation algorithms present at the control center. The formulation for generating AC state estimation attack with full information and limited information along with DC state estimation attacks is given. It further proposes a combining technique for the voting based ensemble learning technique (MVCC) to detect FDIA in smartgrids. The model is then tested on an IEEE 24 bus system and 39 bus new England system by generating false data injection attacks and detecting them. The detection strategy is compared with most of the existing state of the art Machine learning algorithms, ensemble algorithms and conventional weighted least square algorithm and is found to have a better performance. Finally in the third part an optimal position of a centralized controller is found, for a smart grid network to place the algorithm. The model takes into consideration the delay associated with each communication line along with data congestion and node degree. This model is tested with IEEE 24 bus system, and a comparison is made based on different locations of the controllers. We identified node availability, and their respective power flows during communication line contingencies.