Optimization configuration of flexible interconnection device DG based on improved E-C-Kmeans and GA under SOP support

•A hybrid optimization model integrating improved E-C-Kmeans clustering and path-tracing GA was proposed for DG allocation under SOP support.•The E-C-Kmeans algorithm introduces entropy-based feature weighting and density-aware mechanisms, improving clustering robustness for volatile wind–solar data...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Systems and soft computing Ročník 7; s. 200414
Hlavní autori:	Yang, Zhichun, Yang, Lei, He, Junxiong, Yang, Fan, Min, Huaidong, Zhang, Nan, Su, Jing
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.12.2025 Elsevier
Predmet:	Distributed power supply Feature selection weighted k-means clustering algorithm Flexible interconnection devices Genetic algorithm Optimization configuration Distributed power supply Feature selection weighted k-means clustering algorithm Genetic algorithm Flexible interconnection devices Optimization configuration
ISSN:	2772-9419, 2772-9419
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
Shrnutí:	•A hybrid optimization model integrating improved E-C-Kmeans clustering and path-tracing GA was proposed for DG allocation under SOP support.•The E-C-Kmeans algorithm introduces entropy-based feature weighting and density-aware mechanisms, improving clustering robustness for volatile wind–solar data.•A multi-layer benefit coordination framework is established, balancing decision, scheduling, and execution objectives in distributed power systems.•Simulation results show the proposed model achieves higher resource utilization (87.7 %), fault recovery rate (82.6 %), and optimal individual count (183).•The model demonstrates strong adaptability under fluctuating load and weather conditions, providing stable and practical solutions for DG planning. The increasing popularity of flexible interconnected devices has made them a crucial component of distributed power systems. At present, the effective integration and optimized configuration of distributed power sources remain a key focus in power system research. To address this, the study introduces improvements to both the feature selection weighted K-means clustering algorithm and the genetic algorithm, applying them to data preprocessing and configuration optimization. Based on these, an integrated optimization configuration model is proposed. The experimental results demonstrated that the improved clustering algorithm achieved the highest accuracy of 96 %. Under varying wind speeds and light intensities, its minimum sum of squared clustering errors reached 8 and 10, respectively. The improved genetic algorithm generated up to 190 optimal individuals, with a highest accuracy of 91.2 % and a minimum computation time of 6.2 s. Simulation testing further showed that, under support from back-to-back voltage source converters, the decision-making layer achieved a maximum comprehensive income of 1.547 million yuan, an environmental impact of only 41.7 %. The scheduling layer reached the lowest operational cost of 1.627 million yuan. In conclusion, the proposed approach demonstrates significant improvements in distributed power utilization and system operating efficiency.
ISSN:	2772-9419 2772-9419
DOI:	10.1016/j.sasc.2025.200414