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Record W4206229105 · doi:10.1109/tii.2022.3142755

A Proof-of-Authority Blockchain-Based Distributed Control System for Islanded Microgrids

2022· article· en· W4206229105 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Industrial Informatics · 2022
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsUniversity of Calgary
FundersNational Research Foundation Singapore
KeywordsMicrogridComputer scienceBlockchainControl (management)Process (computing)Distributed computingQuality (philosophy)Node (physics)Distributed generationControl systemProtocol (science)Computer networkDistributed control systemDecentralised systemPower (physics)Control theory (sociology)Computer securityEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Control systems are significant to the microgrid as they regulate performance parameters such as frequency, active power, and voltage. Distributed control systems allow direct communication between the secondary controllers and controls the parameters efficiently. To secure each distributed control process and ensure a good quality of control results, a proof-of-authority private blockchain is applied in this article to defend the distributed control system against various types of cyber-attacks such as false data injection. A four-distributed generation islanded microgrid is tested with the implementation of the blockchain. Smart contracts are created to calculate the control feedback and return the value to corresponding secondary controllers. All of the four nodes are initially assigned as the authority nodes to share the mining burden, but according to the proof-of-authority consensus protocol, the authority role could be excluded if the node behaves illegally and causes damage to the control system. In addition, different attacking scenarios are categorized and analyzed with their respective solutions. Finally, a case study is introduced to verify the corresponding solutions and proves that the proposed method is able to secure the distributed control system while ensuring the control quality. Numerical results show the effectiveness and feasibility of the proposed approach.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.239
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it