An Overview of Cyber-Resilient Smart Inverters Based on Practical Attack Models
Not Available
97 MATHEMATICS AND COMPUTING↗
SEARCH · Engineering Papers
Results for “Cyberattack”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Detecting False Data Injection Attacks in Smart Grids: A Semi-Supervised Deep Learning Approach
The dependence on advanced information and communication technology increases the vulnerability in smart grids under cyber-attacks. Recent research on unobservable false data injection attacks (FDIAs) reveals the high risk of secure system operation, since these attacks can bypass current bad data detection mechanisms. To mitigate this risk, this paper proposes a data-driven learning-based algorithm for detecting unobservable FDIAs in distribution systems. We use autoencoders for efficient dimension reduction and feature extraction of measurement datasets. Further, we integrate the autoencoders into an advanced generative adversarial network (GAN) framework, which successfully detects anomalies under FDIAs by capturing the unconformity between abnormal and secure measurements. Also, considering that the datasets collected from practical power systems are partially labeled due to expensive labeling costs and missing labels, the proposed method only requires a few labeled measurement data in addition to unlabeled data for training. Numerical simulations in three-phase unbalanced IEEE 13-bus and 123-bus distribution systems validate the detection accuracy and efficiency of this method.
97 MATHEMATICS AND COMPUTING↗
Machine Learning-based Intrusion Detection for Smart Grid Computing: A Survey
Machine learning (ML)-based intrusion detection system (IDS) approaches have been significantly applied and advanced the state-of-the-art system security and defense mechanisms. In smart grid computing environments, security threats have been significantly increased as shared networks are commonly used, along with the associated vulnerabilities. However, compared to other network environments, ML-based IDS research in a smart grid is relatively unexplored, although the smart grid environment is facing serious security threats due to its unique environmental vulnerabilities. In this article, we conducted an extensive survey on ML-based IDS in smart grids based on the following key aspects: (1) The applications of the ML-based IDS in transmission and distribution side power components of a smart power grid by addressing its security vulnerabilities; (2) dataset generation process and its usage in applying ML-based IDSs in the smart grid; (3) a wide range of ML-based IDSs used by the surveyed papers in the smart grid environment; (4) metrics, complexity analysis, and evaluation testbeds of the IDSs applied in the smart grid; and (5) lessons learned, insights, and future research directions.
SCADA↗
Emission Based Monitoring for Cyber Threat Detection of DER Systems
Explore the source record for details and available documents.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Fast-Tracking Technology Solutions to Boost Cybersecurity in Virtual Power Plant and Aggregator Operations
This presentation was presented at the Aggregation and Grid Security Workshop - held on June 17-18, 2025, at NREL in Golden, Colorado. The goal of the two-day workshop was to address the critical cybersecurity challenges for the future electric grid. This presentation overviews NREL's work in evaluating cybersecurity technology solutions for virtual power plant and aggregator operations.
24 POWER TRANSMISSION AND DISTRIBUTION↗
A Risk Assessment Framework for Cyber-Physical Security in Distribution Grids with Grid-Edge DERs
Integration of inverter-based distributed energy resources (DERs) is reshaping the landscape of distribution grids to fulfill the socioeconomic, environmental, and sustainability goals. Addressing the technological challenges of DER grid integration requires an adaptive communication layer for efficient DER management and control. This transition has given rise to a cyberphysical system (CPS) architecture within the distribution system, causing new vulnerabilities for cyberphysical attacks. To better address potential threats, this paper presents a comprehensive risk assessment framework for cyberphysical security in distribution grids with grid-edge DERs. The framework incorporates a detailed CPS model accounting for dynamic DER characteristics within the distribution grid. It identifies vulnerabilities in DER communication systems, models attack scenarios, and addresses communication latency crucial for inverter control timescales. Subsequently, the quantification of attack impacts employs an attack probability model including both the vulnerability and criticality of cyber components. The proposed risk assessment framework was validated through testing on the modified IEEE 13-node and 123-node test feeders.
cyberattack↗
Caribbean Energy Sector Cybersecurity Forum: Modernizing and Securing the Grid
This presentation on Modernizing and Securing the Grid brought together regional and NREL cybersecurity expertise for USAID's Caribbean Energy Sector Cybersecurity Forum. The presentation's overall purpose was to provide a deep dive into the changes underway in the electric grid, including the greater integration of renewables and distributed energy resources. The presentation also reviewed best practices for building cybersecurity into modern energy systems, including in the areas of vendor assessment and supply chain cybersecurity.
Caribbean↗
Covert Cognizance: A Novel Predictive Modeling Paradigm for Securing Unattended High-Valued Systems
Explore the source record for details and available documents.
42 ENGINEERING↗