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<p>Based on the shortcomings of the current intelligent management of distribution networks, the article designs and implements a remote intelligent detection system for live distribution networks. The article constructs and realises an algorithm based on a genetic algorithm (GA) and agent system. The algorithm is applied to energy-saving intelligent supervision of equipment in the distribution network. The simulation experiment shows that the integrated algorithm based on GA and agent system can accurately detect power quality in real time. At the same time, the algorithm can monitor the energy consumption of equipment in the distribution network under multiple disturbances.</p>
</abstract>

Based on the shortcomings of the current intelligent management of distribution networks, the article designs and implements a remote intelligent detection system for live distribution networks. The article constructs and realises an algorithm based on a genetic algorithm (GA) and agent system. The algorithm is applied to energy-saving intelligent supervision of equipment in the distribution network. The simulation experiment shows that the integrated algorithm based on GA and agent system can accurately detect power quality in real time. At the same time, the algorithm can monitor the energy consumption of equipment in the distribution network under multiple disturbances.

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

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Based on the shortcomings of the current intelligent management of distribution networks, the article designs and implements a remote intelligent detection...

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

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

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

Data publikacji: 27 gru 2021

Zakres stron: 685 - 694

Otrzymano: 16 cze 2021

Przyjęty: 24 wrz 2021

DOI: https://doi.org/10.2478/amns.2021.1.00057

Słowa kluczowe
intelligent identification and judgement, distribution network, energy saving and consumption reduction, monitoring management, genetic algorithm

© 2021 Xue et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Comparison of active power loss of different optimisation algorithms

Comparison results of different optimisation algorithms

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

Data publikacji: 27 gru 2021

Zakres stron: 685 - 694

Otrzymano: 16 cze 2021

Przyjęty: 24 wrz 2021

DOI: https://doi.org/10.2478/amns.2021.1.00057

Słowa kluczoweintelligent identification and judgement, distribution network, energy saving and consumption reduction, monitoring management, genetic algorithm

© 2021 Xue et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Comparison of active power loss of different optimisation algorithms

Comparison results of different optimisation algorithms

Słowa kluczowe
intelligent identification and judgement, distribution network, energy saving and consumption reduction, monitoring management, genetic algorithm