A Proactive Intrusion Detection and Mitigation System for Grid-Connected Photovoltaic Inverters
Bibliographic record
Abstract
The breach of data confidentiality, integrity, and availability due to cyberattacks can adversely impact the operation of grid-connected Photovoltaic (PV) inverters. Detecting such attacks based on their signatures or behavior-based analytics and adopting corrective actions to prevent security breaches for grid-connected PV systems requires the implementation of an intelligent Intrusion Detection System (IDS). In this paper, a Proactive Intrusion Detection and Mitigation System (PIDMS) based on real-time stability boundary identification at the Point of Common Coupling (PCC) for grid-connected PV systems is presented to identify the potentially compromised grid-connected PV systems in Cyber-Physical Power and Energy Systems (CPPES). The proposed PIDMS correlates the variations in the active power and reactive power measurements to power grids voltage at the PCC in real-time and accurately identifies compromised grid-connected PV systems, and enhances the resilience of CPPES. The performance of the proposed PIDMS is validated through dynamic simulations under different operating conditions. The obtained results verify the applicability and effectiveness of the proposed PIDMS.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".