Detection and Localization of Data Forgery Attacks in Automatic Generation Control

Abstract

Automatic Generation Control (AGC) is a key control system to maintain the power system’s balance between load and supply by maintaining its frequency in a specific range. It collects the tie-line power flow and frequency measurements of each control area to calculate the Area Control Error (ACE) and then adjusts power generation based on the calculated ACE. However, malicious frequency or tie-line power flow measurements can be injected and then AGC is misled to make false power generation adjustments which will harm power system operations. Such attacks can be carefully designed to pass the power system’s existing bad data detection schemes. In this work, we propose Long Short Term Memory (LSTM) neural network-based methods and a Fourier Transform-based method to detect and localize such data forgery attacks in AGC. These methods only utilize historical data, which are already available in existing AGC systems, making them easy to deploy in the real world. They learn normal data patterns from historical data and detect abnormal patterns caused by attacks. To make it easier for users to use the solution, we also propose methods to automatically find the proper detection threshold based on user needs. These methods are tested both on real and simulated datasets and show high detection and localization accuracy.

Keywords

• Power grid
• automatic generation control
• data forgery
• deep learning
• attack detection
• attack localization