Сучасні протоколи передачі шкали часу інтелектуальних електроенергетичних систем зі зниженою аварійністю
Bibliographic record
Abstract
UDC 681.516.75:631.234 TIME-SCALE DISTRIBUTION MODERN PROTOCOLS FOR SMART GRID POWER SYSTEMS WITH LOW ACCIDENT RATE O. Velychko, V. Koval, O. Samkov, I. Shkliarevskyi In the last about 10 years , in different counties the reasons for power failures or blackouts in which more than one power utility is involved must be analyzed. The so-called cascading emergency power outages, which entail serious social consequences (emergency stops of trains in underground tunnels, trams and trolleybuses streets, elevators in high-rise buildings, disconnect from the electric power electrical devices, hospitals, kindergartens , enterprises with continuous production, etc.) have appeared for the general public. Examples of some power outgates are shown in the Table 1. Table 1. Year Country or region Involved population , millions 2003 USA, Canada 55 2003 Italy 56 2005 Indonesia 100 2005 Russia 3 2011 USA, Mexico 5 2012 India 620 The North American Blackout back in August 2003 visualized how painful and time consuming it can be to align data, whose time stamps are derived from inaccurate time references. As a result the task force investigating the blackout demanded a regulation that ensures a minimum absolute accuracy for time stamped disturbance event data. Analysis of cascading outages of electricity, which recently took place in the world [1],[2] can show, in addition to the purely technical, the presence of other, more common causes of such events. First of all, it's deregulation of the electricity market, making this market extremely highly, but less manageable. In order to prevent undesirable consequences specified problematic situation in the energy sector developed the concept of intellectual power grid (Smart Grid), which is an important part of that is associated with continuous monitoring of grid stability parameters linked to real-time with microsecond precision. In Ukraine there is a public service Time and Frequencies Reference for coordination and implementation of activities aimed ensuring the uniformity of time and frequency measurements, creating and maintaining a national time-scale confirmed to the best national scales the world. But Ukraine's existing time and frequency distribution technical means and methods do not form a single system and can not meet the requirements of all customers time-frequency information that encourages them to use synchro-information from other states’ sources (GPS or GLONASS), which threatens the national security and increases the risks of loss of traceability time and frequency within the state. Implementation of the Precision Time Protocol (PTP), defined in the IEEE 1588 standard provides a very accurate and safe way to distribute time references throughout the power station’s Ethernet networks. PTP allows distributing reference time information in a local area network like NTP does, but due to the innovative protocol it allows to reach accuracies in the sub-microsecond range. There’s a PTP supporting equipment named SU-1588, developed and manufactured in Ukraine, which provides the necessary services distributing the national time-scale from the Secondary Time and Frequency Reference located in Ukrmetrteststandart enterprise in Kiev. The direct connection 30 minutes interval stability between different manufacturers’ equipment was tested in [4]. Table 2 shows some results from [4] combining with some SU-1588 results provided by it’s manufacturer. Table 2. Slave/ Master Slave A Slave B Slave C Slave F SU-1588S (slave) Information source Master A - 705 304 798 - [4] Master C 715 156 - 43 - [4] Master D 670 255 232 200 - [4] OSA-5535 - - - - 320 IST Ltd SU-1588M - - - - 250 IST Ltd Conclusion A modern power field needs precise time-stamping for new monitoring and measurement methods for Smart Grid’s implementation. The SU-1588 equipment supporting “power” profile of IEEE 1588 protocol, is able to provide time-scale microseconds level distribution for power industry.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.029 |
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".