Editorial: Recent advances of edge computing for smart grid
Bibliographic record
Abstract
Recent advances of edge computing for smart gridSmart grid is a power system equipped with ICT system to allow for bidirectional flow of electricity and information (Lo Cascio et al., 2021;Song et al., 2022).With the development of Internet of Things (IoT), a large number of terminal devices have been connected to smart grid and generated massive real-time data.The computing and storage resources of edge devices in smart grid are usually limited, and it is difficult to run complex artificial intelligence algorithms to fully process these data.Meanwhile, transferring all data to the cloud server does not only incur significant latency, but also easily leads to data leakage and security issues.Edge computing is an effective way to solve these problems (Song et al., 2023).Edge computing provides services on the side close to the data source, so as to meet the requirements of smart grid in real-time, intelligence and security (Song et al., 2021).This Research Topic aims to collect original papers on the recent advances of edge computing technology applied to the smart grid and present important results in the fields of system optimization, fault detection, intelligent protection, load analysis and forecasting, security and privacy, etc.In this Research Topic, eight papers have been accepted, and all papers were carefully reviewed by at least two reviewers and one guest editor.Details of these papers are as follows.Security is a core issue of smart grid, and Li et al., Liu et al., Xu et al. focused on the lightweight security methods for smart grid. In, Liu et al. proposed a cluster-based scheme for the purpose of preventing wormhole attacks. First, a clustering algorithm was proposed to elect clusterheads, then the elected clusterheads were used to implement the wormhole attacks prevention scheme. Simulation results indicated the proposed method can prevent wormhole attacks efficiently. In, Xu et al. designed an edge computing security support engine and a security monitoring system based on the docker container. In this method, a node security judgment method was proposed combined with a container monitoring and objective weighting method, and a method was proposed to evaluate the security of the unmonitored node. The results proved the efficiency of the proposed method for security protection of the edge power system. In Li et al. designed A 5G network security architecture for smart grid, in which they replaced the commonly used plaintext information in the original system with ciphertext based on Software Defined Network (SDN).Meanwhile, they utilized the defects and differences in devices in smart grid to identify signals and prevent attackers from further damaging the leaked sensitive data.The experimental results showed that the SDN based 5G network anti-attack scheme can avoid the centralized exposure of sensitive data and reduce computational overhead, thus improve system security.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.035 | 0.030 |
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".