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Title: Hybrid Cyber-attack Detection in Photovoltaic Farms

Journal Article · · IEEE Energy Conversion Congress and Exposition (ECCE)
 [1];  [2];  [2];  [3];  [4];  [2]
  1. Eaton Research Laboratory, Golden, CO (United States); University of Georgia
  2. Univ. of Georgia, Athens, GA (United States)
  3. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
  4. Eaton Research Laboratory, Golden, CO (United States)
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Here, to address the cyber-physical security in PV farms, a hybrid cyber-attack detection is proposed in this manuscript. To secure PV farms, the proposed method integrates model-based and data-driven methods by fusing the detection score at the device and system levels. First, a model-based cyber-attack detection method is developed for each PV inverter. A residual between the estimation of the Kalman filter and measurement is calculated. By leveraging the calculated residual from all inverters, a squared Mahalanobis distance is developed for device detection score generation. At the system level, a convolutional neural network (CNN) is proposed to detect cyber-attack using the waveform data at the point of common coupling (PCC) in PV farms. To improve the CNN detection accuracy, a set of well-designed features are extracted from the raw waveform data. Finally, a weighted detection score fusion method is proposed to combine device and system detection scores by using their complementary strength. The feasibility and robustness of the proposed method are validated by testing cases and a comparative experiment.

Research Organization:
Univ. of Arkansas, Fayetteville, AR (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
Grant/Contract Number:
EE0009026
OSTI ID:
2341857
Alternate ID(s):
OSTI ID: 2311312
Journal Information:
IEEE Energy Conversion Congress and Exposition (ECCE), Journal Name: IEEE Energy Conversion Congress and Exposition (ECCE) Vol. 2023; ISSN 2329-3721
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English

References (11)

Sensor fusion potential exploitation-innovative architectures and illustrative applications journal January 1997
Data-Driven Cyber-Attack Detection for PV Farms via Time-Frequency Domain Features journal March 2022
Smart grids cyber-physical security as a malicious data attack: An innovation approach journal August 2017
A Hybrid Framework for Detecting and Eliminating Cyber-Attacks in Power Grids journal September 2021
Dynamic Detection of False Data Injection Attack in Smart Grid using Deep Learning conference February 2019
A Review of Cyber–Physical Security for Photovoltaic Systems journal August 2022
Cyber–Physical Security of a Smart Grid Infrastructure journal January 2012
Cyber-Attack Detection for Active Neutral Point Clamped (ANPC) Photovoltaic (PV) Converter using Kalman Filter conference March 2022
Multi-View Convolutional Neural Network for Data Spoofing Cyber-Attack Detection in Distribution Synchrophasors journal July 2020
Impact and Mitigation of High-Frequency Side-Channel Noise Intrusion on the Low-Frequency Performance of an Inverter journal October 2022
Power plant control in large‐scale photovoltaic plants: design, implementation and validation in a 9.4 MW photovoltaic plant journal January 2016

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Related Subjects

14 SOLAR ENERGY
97 MATHEMATICS AND COMPUTING
Kalman filter
squared Mahalanobis distance
convolutional neural network
score fusion
hybrid detection
cyber-attack
PV farm security