Education, Science, Technology, Innovation and Life
Open Access
Sign In

Research and Application of Power Distribution Model Based on Simulated Annealing Method

Download as PDF

DOI: 10.23977/jeeem.2023.060306 | Downloads: 3 | Views: 397

Author(s)

Yu Cao 1, Zhiyuan Wang 1, Xin Wang 2, Xiangxi Zhang 1, Maosong Wang 1

Affiliation(s)

1 College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
2 College of Electronic Science and Technology, National University of Defense Technology, Changsha, China

Corresponding Author

Maosong Wang

ABSTRACT

This paper focuses on the power distribution curve and establishes a power distribution model based on the influence of external resistance and the nature of the object. The power supply model was established by the influence of external physical factors on the object using a physical analysis method. Combining the two models, the driver was identified, the initial edge conditions were determined, and a system of differential control equations containing three parameters was established. For the time problem with trajectory, a single-objective optimization model with the minimum time as the objective was established. Firstly, the maximum power provided by the drive is determined; secondly, the constrained objective function is determined as the process limit; secondly, the constraints are simulated and annealed and the power is searched using a circular traversal method.

KEYWORDS

Simulated annealing method, circular traversal, power search, objective optimization

CITE THIS PAPER

Yu Cao, Zhiyuan Wang, Xin Wang, Xiangxi Zhang, Maosong Wang, Research and Application of Power Distribution Model Based on Simulated Annealing Method. Journal of Electrotechnology, Electrical Engineering and Management (2023) Vol. 6: 37-44. DOI: http://dx.doi.org/10.23977/jeeem.2023.060306.

REFERENCES

[1] Nakano Kotaro, Chakraborty Basabi. Real-Time Distraction Detection from Driving Data Based Personal Driving Model Using Deep Learning[J]. International Journal of Intelligent Transportation Systems Research, 2022(prepublish). 
[2] Dong X, Wu Z, Song G, et al. A hybrid optimization algorithm for distribution network coordinated operation with SNOP based on simulated annealing and conic programming[C]// Power & Energy Society General Meeting. IEEE, 2016.
[3] Jamonnak Suphanut, Zhao Ye, Huang Xinyi, Amiruzzaman Md. Geo-Context Aware Study of Vision-Based Autonomous Driving Models and Spatial Video Data. [J]. IEEE transactions on visualization and computer graphics, 2021, PP. 
[4] Jiangling Hong, Hongjie Zhang, Jiaqi Wang, Jinbo Liu, Honglei Liu, Chaodong Tan, Pengkun Hou, Wenrong Song. Data Driven Model of Gas Well Integrity and Its Application[C]//. Proceedings of 2021 5th International Conference on Electrical, Automation and Mechanical Engineering (EAME2021). , 2021:674-681. DOI:10. 26914/c. cnkihy. 2021. 044445. 
[5] Wang Yuqi, Li Yunzhu, Zhang Di, Xie Yonghui. Aerodynamic optimization of a SCO<sub>2</sub> radial-inflow turbine based on an improved simulated annealing algorithm[J]. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 2021, 235(5). 
[6] Almuhaideb Sarab, Altwaijry Najwa, AlMansour Shahad, AlMklafi Ashwaq, AlMojel Khalid AlBandery, AlQahtani Bushra, AlHarran Moshail. Clique Finder: A Self-Adaptive Simulated Annealing Algorithm for the Maximum Clique Problem [J]. International Journal of Applied Metaheuristic Computing (IJAMC), 2022, 13(2). 
[7] Tanha Mozhdeh, Hosseini Shirvani Mirsaeid, Rahmani Amir Masoud. A hybrid meta-heuristic task scheduling algorithm based on genetic and thermodynamic simulated annealing algorithms in cloud computing environments[J]. Neural Computing and Applications, 2021, 33(24). 
[8] Zhang Lifeng, Zhang Menghan. Image reconstruction of electrical capacitance tomography based on optimal simulated annealing algorithm using orthogonal test method [J]. Flow Measurement and Instrumentation, 2021, 80. 
[9] P. Prakasam, T. R. Suresh Kumar, T. Velmurugan, S. Nandakumar. Efficient power distribution model for IoT nodes driven by energy harvested from low power ambient RF signal [J]. Microelectronics Journal, 2020, 95(C). 
[10] R. Maurizio, B. P. Duval, B. Labit, H. Reimerdes, C. Theiler, C. K. Tsui, J. Boedo, H. De Oliveira, O. Février, U. Sheikh, M. Spolaore, K. Verhaegh, N. Vianello, M. Wensing. Conduction-based model of the Scrape-Off Layer power sharing between inner and outer divertor in diverted low-density tokamak plasmas [J]. Nuclear Materials and Energy, 2019, 19. 

Downloads: 2089
Visits: 98238

Sponsors, Associates, and Links


All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.