Design of an intelligent hybrid diagnosis scheme for cyber-physical PV systems at the microgrid level
Bibliographic record
Abstract
With focusing on solar photovoltaic (PV) systems operating at microgrid (MG) level, this study investigates the impacts of different physical faults and cyberattacks as well as designing an intelligent faults/attacks detection and diagnosis system. The ability to quickly identify and diagnose faults/attacks enables local controllers and energy management system to accommodate or mitigate the negative effects of physical faults or cyberattacks in a MG. This allows the MG to carry on operating without experiencing any significant interruptions. In this regard, the current paper investigates a hybrid AC/DC MG made up of different distributed energy resources (DER), including solar PV arrays, wind turbines , and battery energy storage systems . An intelligent hybrid diagnosis (IHD) scheme is introduced for online monitoring and diagnosing the data to reflect the real-time status of the PV system operating at the MG level. This hybrid system uses three parallel units, including a “fuzzy inference system (i.e., a rule-based unit)”, a “power spectrum estimator (i.e., a signal-based unit)”, and an “adaptive neuro-fuzzy inference system (i.e., a model-based unit)”. A realistic MG benchmark model with a wide range of operating conditions and dynamic electrical loads in the presence of potential MG disturbances is used to demonstrate the high performance and resilience of the proposed IHD scheme under various types of faults/attacks scenarios.
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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 | 0.001 | 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 | 0.001 | 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".