Methodology for Real-Time Calculation of Temperature Rise and Dynamic Ratings for Distribution System Duct Banks

1982 ◽  
Vol PER-2 (12) ◽  
pp. 44-44
Author(s):  
G. Z. Ben-Yaacov ◽  
J. G. Bohn
Keyword(s):  
Real Time ◽  
Temperature Rise ◽  
Distribution System ◽  
Time Calculation

scholarly journals Research on the Real-time Calculation Model for Transient Temperature Rise of Duct-Laid Cable

2019 ◽  
Vol 631 ◽  
pp. 052034
Author(s):  
Wenrong Si ◽  
Chenzhao Fu ◽  
Honglei Li ◽  
Yongchun Liang ◽  
Zhenpeng Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Temperature Rise ◽  
Calculation Model ◽  
Transient Temperature ◽  
The Real ◽  
Time Calculation

scholarly journals Research on fast real-time calculation model for transient temperature rise of power cables in ducts

2021 ◽  
Vol 7 ◽  
pp. 239-245
Author(s):  
Chenzhao Fu ◽  
Yongchun Liang ◽  
Ying Sun ◽  
Qingquan Li ◽  
Ziyu Zhao ◽  
...  
Keyword(s):  
Real Time ◽  
Temperature Rise ◽  
Calculation Model ◽  
Transient Temperature ◽  
Power Cables ◽  
Time Calculation

A distribution system outage dispatch by data base method with real-time revision

1989 ◽  
Vol 4 (1) ◽  
pp. 515-523 ◽  
Author(s):  
C.E. Ling ◽  
Y.-W. Huang ◽  
H.L. Chow ◽  
C.L. Huang
Keyword(s):  
Data Base ◽  
Real Time ◽  
Distribution System ◽  
Base Method

scholarly journals Comparison of Searching Behaviour of Three Evolutionary Algorithms Applied to Water Distribution System Design Optimization

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 695 ◽  
Author(s):  
Weiwei Bi ◽  
Yihui Xu ◽  
Hongyu Wang
Keyword(s):  
Evolutionary Algorithms ◽  
Real Time ◽  
Distribution System ◽  
Optimization Design ◽  
Distribution Systems ◽  
Water Distribution ◽  
Optimal Solutions ◽  
Great Ability ◽  
Searching Behaviour ◽  
Run Time

Over the past few decades, various evolutionary algorithms (EAs) have been applied to the optimization design of water distribution systems (WDSs). An important research area is to compare the performance of these EAs, thereby offering guidance for the selection of the appropriate EAs for practical implementations. Such comparisons are mainly based on the final solution statistics and, hence, are unable to provide knowledge on how different EAs reach the final optimal solutions and why different EAs performed differently in identifying optimal solutions. To this end, this paper aims to compare the real-time searching behaviour of three widely used EAs, which are genetic algorithms (GAs), the differential evolution (DE) algorithm and the ant colony optimization (ACO). These three EAs are applied to five WDS benchmarking case studies with different scales and complexities, and a set of five metrics are used to measure their run-time searching quality and convergence properties. Results show that the run-time metrics can effectively reveal the underlying searching mechanisms associated with each EA, which significantly goes beyond the knowledge from the traditional end-of-run solution statistics. It is observed that the DE is able to identify better solutions if moderate and large computational budgets are allowed due to its great ability in maintaining the balance between the exploration and exploitation. However, if the computational resources are rather limited or the decision has to be made in a very short time (e.g., real-time WDS operation), the GA can be a good choice as it can always identify better solutions than the DE and ACO at the early searching stages. Based on the results, the ACO performs the worst for the five case study considered. The outcome of this study is the offer of guidance for the algorithm selection based on the available computation resources, as well as knowledge into the EA’s underlying searching behaviours.

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