Machine Learning-Based Cyber-Attack Detection in Photovoltaic Farms

Abstract

In this article, a machine learning technique is proposed for the detection of cyber-attacks in Photovoltaic (PV) farms using point of common coupling (PCC) sensors alone. A comprehensive cyber-attack model of a PV farm is first developed to consider operating conditions variability. The attack model specifically includes two types of cyber-attacks that are historically more difficult to detect. A Convolutional Neural Network (CNN) using $\mu$PMU plus figures of merit is proposed and compared with other machine learning techniques using raw electric waveform and micro-phase measurement units ($\mu$PMU), respectively. Finally, a cyber-physical security testbed of an IEEE 37-bus distributed grid with PV farms is developed. A real-time simulation, detection, and visualization framework is designed to demonstrate the feasibility of the proposed method in a real-world application. Results show that the proposed machine learning methods can achieve adequate detection accuracy and robustness under various attack scenarios.

Keywords

• Grid-tied power electronics converters
• photovoltaic farms
• cyber-attack detection
• waveform
• micro-phasor measurement units