Evaluation Method of Wind Turbine Group Classification Based on Calinski Harabasz-Reference-Cited by-同舟云学术

Evaluation Method of Wind Turbine Group Classification Based on Calinski Harabasz

Published:2021-10-22 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2)
language:
Short-container-title:

Author:

Wang Yingpei¹,Xu Yanhui²,Gao Tianchu²

Affiliation:

1. Global Energy Interconnection Research Institute Co. Ltd,State Key Laboratory of Advanced Power Transmission Technology,Beijing,China

2. North China Electric Power University,School of Electrical & Electronic Engineering,Beijing,China

Publisher

IEEE

Link

Reference17 articles.

1. Feature Analysis Based Equivalent Modeling for Large-Scale Wind Farms[J];wei;Power System Technology,2013

2. Dynamic Equivalence for Wind Farms Based on Clustering Algorithm[J];shuyong;Proceedings of the CSEE,0

3. K-means algorithm based on improved artificial bee colony algorithm[J];yu;Control and Decision,2018

4. K-means clustering algorithm based on improved quantum particle swarm optimization and its application[J/OL];yue;Control and Decision,2021

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Optimizing neural network using clustering for resource constrained environment;Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI;2024-06-07

4. Web-based E-Commerce Customer Segmentation System Using RFM and K-Means Model;2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT);2023-11-20

5. Applying K-Means Clustering for User Profiling in Retail: A Department Store Case Study;Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023);2023-10-07