Adaptive Neuro Fuzzy Inference System for Cyber-Intrusion Detection in a Smart Grid
- Virginia Tech
- BATTELLE (PACIFIC NW LAB)
The evolution of the power grid has brought increasing deployment of advance metering infrastructure, penetration of intelligent electronic devices, and integration of physical power system components with information and communications technologies. With the fast-expanding connectivity, cyber vulnerabilities arise due to the use of internet-based communication systems. These systems are targets of cyber-intrusions which attempt to disturb the normal power system functions. Traditional intrusion detection algorithms have been developed without an explicit model of the cyber components. In this paper, an algorithm to detect false data injections in the power system is proposed considering both cyber and physical models of the power system. The algorithm is based on an Adaptive Neuro Fuzzy Inference System (ANFIS) which collects information from state variables of the cyber-physical system to meet the performance requirements of the grid. Simulations of the proposed approach using the IEEE 13-bus test system validate the effectiveness of this artificial intelligence-based algorithm.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1638022
- Report Number(s):
- PNNL-SA-147189
- Resource Relation:
- Conference: 20th International Conference on Intelligent Systems Applications to Power Systems (ISAP 2019), December 10-14, 2019, New Delhi, India
- Country of Publication:
- United States
- Language:
- English
Similar Records
Volume fraction determination of the annular three-phase flow of gas-oil-water using adaptive neuro-fuzzy inference system
Data-driven cyber attack detection and mitigation for decentralized wide-area protection and control in smart grids