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Distribution network monitoring and management system based on intelligent recognition and judgement

Yiwei Xue

Yiwei Xue

Guangzhou Huangpu Power Supply Bureau of Guangdong Power Grid Co. LtdGuangdong Guangzhou, China
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Xue, Yiwei
, 
Qizhen Sun

Qizhen Sun

Guangzhou Power Supply Bureau of Guangdong Power Grid Co. LtdGuangdong Guangzhou, China
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Sun, Qizhen
, 
Chendi Li

Chendi Li

Guangzhou Huangpu Power Supply Bureau of Guangdong Power Grid Co. LtdGuangdong Guangzhou, China
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Li, Chendi
, 
Weijun Dang

Weijun Dang

Guangzhou Huangpu Power Supply Bureau of Guangdong Power Grid Co. LtdGuangdong Guangzhou, China
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Dang, Weijun
 e 
Fangzhou Hao

Fangzhou Hao

Guangzhou Power Supply Bureau of Guangdong Power Grid Co. LtdGuangdong Guangzhou, China
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Hao, Fangzhou
  
27 dic 2021
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Pubblicato online: 27 dic 2021
Pagine: 685 - 694
Ricevuto: 16 giu 2021
Accettato: 24 set 2021
DOI: https://doi.org/10.2478/amns.2021.1.00057
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© 2021 Xue et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

The environment structure diagram of an Agent.
The environment structure diagram of an Agent.

Fig. 2

Flowchart of the GA-MAS algorithm.GA-MAS, genetic algorithm multi-agent system.
Flowchart of the GA-MAS algorithm.GA-MAS, genetic algorithm multi-agent system.

Fig. 3

Structural diagram of the integrated management system for energy saving and consumption reduction of a distribution network based on multi-agents. PV, xxx.
Structural diagram of the integrated management system for energy saving and consumption reduction of a distribution network based on multi-agents. PV, xxx.

Fig. 4

GA-MAS and PSO algorithm iteration curve. GA-MAS, genetic algorithm multi-agent system; PSO, particle swarm optimisation.
GA-MAS and PSO algorithm iteration curve. GA-MAS, genetic algorithm multi-agent system; PSO, particle swarm optimisation.

Fig. 5

Comparison curve before and after node voltage optimisation.
Comparison curve before and after node voltage optimisation.

Comparison of active power loss of different optimisation algorithms

Optimisation Total active power loss, pu Number of energy-saving equipment invested
PSO 0.138 5
GA-MAS 0.135 3

Comparison results of different optimisation algorithms

Compare items PSO GA-MAS
Maximum generator active power, pu 1.998 1.972
Minimum generator active power, pu 0.462 0.458
Maximum generator reactive power, pu 0.245 0.102
Minimum generator reactive power, pu 0.031 0.025
Maximum voltage distortion rate THDu, % 4.02 3.63
Minimum voltage distortion rate THDu, % 2.97 2.89
Maximum node voltage, pu 1.085 1.05
Minimum node voltage, pu 0.989 0.952
Lingua:
Inglese
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1 volte all'anno
Argomenti della rivista:
Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro
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