Technological Perspective of Cyber Secure Smart Inverters Used in Power Distribution System: State of the Art Review
Bibliographic record
Abstract
The purpose of smart grid architecture as compared to the conventional grid is to ensure more stability, reliability and bi-directional communication between the utility and the consumer. The deployment of the same has succeeded in improving the efficiency of the distribution systems and effective co-ordination and interoperability among the different components of the grid. Smart inverters play a major role in seamless grid integration, control and conversion of power when the renewable energy sources are present. However, they come with several security challenges as well, which are of considerable concern. Certain cyber threats include physical and cyber attacks, natural phenomena which in turn can lead to grid failure, blackouts, commercial energy losses, privacy and safety issues, etc. Therefore, there is a need for critical examination of all these issues which must be considered for designing cyber secure smart inverters at the distribution level. In this comprehensive review, keeping the technological perspective in mind, the existing gaps and the necessity for the same are highlighted. The various topologies, IEEE protocols and the control strategy are presented in detail. This will enable prospective researchers to address the design issues of smart inverters with greater focus on security and reliability aspects.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".