Bibliographic record
Abstract
The penetration of distributed energy resources (DERs) in smart grids significantly increases the number of field devices owned and controlled by consumers, aggregators, third parties, and utilities. As the interface between DER and power grids, DER inverters are becoming smarter with various grid-support functions and communication capabilities. Meanwhile, the cybersecurity risks of smart inverters are also on the rise due to the extensive utilization of information and communication technologies. The potential negative impacts of cyberattacks on smart inverters have attracted significant attention from scholars and organizations. To advance the research on smart inverter cybersecurity and provide insights into its technical achievements, barriers, and future directions, this article will give a comprehensive review of critical attacks and defense strategies for smart inverters and inverter-based systems like microgrids. We start this survey with an overview of the smart inverter introduction, including device- and grid-level architectures, grid-support functions, and communication protocols. We then review various cyberattacks and defense strategies in different categories and scenarios tailed with discussions including their feasibility and remaining gaps. Finally, we discuss the opportunities and challenges of emerging technologies that can secure smart inverters. We hope this survey can inspire efforts to close research gaps and develop more mature cybersecurity solutions for smart inverters in the smart grid.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".