Detection of FDI Attacks on Voltage Regulation of PV-Integrated Distribution Grids Using Machine Learning Methods
Bibliographic record
Abstract
The integration of photovoltaic (PV) panels in power distribution systems (PDSs) has provided the grid with the capability to regulate voltage through the injection or absorption of reactive power. The deployment of information and communication technologies (ICTs), which is required for this voltage regulation scheme, has made the PDS prone to a variety of cyber attacks, e.g., false data injection (FDI) attacks. To counter these attacks, this paper proposes a data-driven detection framework to identify FDI attacks against voltage regulation of PV-integrated PDS. To regulate the voltage at a desired location, e.g., the point of common coupling (PCC), the voltage measurements are sent to a centralized controller and the calculated control signals are transmitted back to PVs to be added to their internal control schemes. During this transmission of data, an attacker manipulates the measurement data and launches an FDI attack leading to an unacceptable voltage profile and operation of protection systems. To detect these attacks, a machine learning (ML)-based framework based on support vector machine (SVM) is developed in this research. In this regard, a dataset of different operating points, e.g., loading conditions, is collected to train this supervised framework. The performance of the trained framework for attack detection has been compared with other supervised and unsupervised ML-based techniques in the case of FDI attacks against modified IEEE 33-bus PDS. The obtained results demonstrate the superior performance of the proposed framework in detecting FDI attacks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".