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The Optimization Study about Fault Self-Healing Restoration of Power Distribution Network Based on Multi-Agent Technology

Fuquan Huang1, Zijun Liu1, Tinghuang Wang1, Haitai Zhang2, *, Tony Yip3

1 Shenzhen Power Utility Co., Ltd., Shenzhen, 518000, China.
2 Shandong Kehui Electric Power Automation Co., Ltd., Zibo, 255000, China.
3 Kehui International Ltd., Studio 206, Mill Studio Business Centre, Crane Mead, Ware, Herts, SG12 9PY, UK.

* Corresponding Author: Haitai Zhang. Email: email.

Computers, Materials & Continua 2020, 65(1), 865-878. https://doi.org/10.32604/cmc.2020.010724

Abstract

In order to quickly and accurately locate the fault location of the distribution network and increase the stability of the distribution network, a fault recovery method based on multi-objective optimization algorithm is proposed. The optimization of the power distribution network fault system based on multiagent technology realizes fast recovery of multi-objective fault, solve the problem of network learning and parameter adjustment in the later stage of particle swarm optimization algorithm falling into the local extreme value dilemma, and realize the multi-dimensional nonlinear optimization of the main grid and the auxiliary grid. The system proposed in this study takes power distribution network as the goal, applies fuzzy probability algorithm, simplifies the calculation process, avoids local extreme value, and finally realizes the energy balance between each power grid. Simulation results show that the Multi-Agent Technology enjoys priority in restoring important load, shortening the recovery time of power grid balance, and reducing the overall line loss rate of power grid. Therefore, the power grid fault self-healing system can improve the safety and stability of the important power grid, and reduce the economic loss rate of the whole power grid.

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Cite This Article

F. Huang, Z. Liu, T. Wang, H. Zhang and T. Yip, "The optimization study about fault self-healing restoration of power distribution network based on multi-agent technology," Computers, Materials & Continua, vol. 65, no.1, pp. 865–878, 2020. https://doi.org/10.32604/cmc.2020.010724

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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