An improved support vector clustering approach to dynamic aggregation of large wind farms

An improved support vector clustering approach to dynamic aggregation of large wind farms

In this paper, we propose an improved support vector clustering (SVC) algorithm to cluster wind turbines (WTs) in a wind farm (WF). A boundary points (BPs) detecting method based on the grid theory and the connected subdomain (CS) are proposed. Thus the efficiency of SVC is enhanced while maintainin...

Full description

Bibliographic Details
Main Authors: Weijun Teng, Xifan Wang, Yongqing Meng, Wenhui Shi
Format: Article
Language:English
Published: China electric power research institute 2019-06-01
Series:CSEE Journal of Power and Energy Systems
Online Access:https://ieeexplore.ieee.org/document/8735436
id doaj-a45416fdc2594303bd8b24dc80154e8c
record_format Article
spelling doaj-a45416fdc2594303bd8b24dc80154e8c2020-11-25T00:17:38ZengChina electric power research instituteCSEE Journal of Power and Energy Systems2096-00422096-00422019-06-015221522310.17775/CSEEJPES.2016.01600An improved support vector clustering approach to dynamic aggregation of large wind farmsWeijun Teng0Xifan Wang1Yongqing Meng2Wenhui Shi3Department of Electrical Engineering, Xi'an Jiaotong University, Xi'an, ChinaDepartment of Electrical Engineering, Xi'an Jiaotong University, Xi'an, ChinaDepartment of Electrical Engineering, Xi'an Jiaotong University, Xi'an, ChinaChina Electric Power Research Institute, Beijing 100192, ChinaIn this paper, we propose an improved support vector clustering (SVC) algorithm to cluster wind turbines (WTs) in a wind farm (WF). A boundary points (BPs) detecting method based on the grid theory and the connected subdomain (CS) are proposed. Thus the efficiency of SVC is enhanced while maintaining the accuracy of the algorithm. As for the multiwind condition equivalent of WF, a method to determine the number and capacity of each of the aggregated wind turbines (AWTs) based on historical wind data is proposed. Only wind speed (WS) and wind direction (WD) of the WF are needed to calculate the WSs of each AWT. Results demonstrate that the algorithm proposed in this paper can cluster WTs quickly and accurately. And the dynamic aggregated models of the WFs are suitable for both the single-wind condition and multi-wind condition simulations, with high accuracy being obtained.https://ieeexplore.ieee.org/document/8735436
collection DOAJ
language English
format Article
sources DOAJ
author Weijun Teng
Xifan Wang
Yongqing Meng
Wenhui Shi
spellingShingle Weijun Teng
Xifan Wang
Yongqing Meng
Wenhui Shi
An improved support vector clustering approach to dynamic aggregation of large wind farms
CSEE Journal of Power and Energy Systems
author_facet Weijun Teng
Xifan Wang
Yongqing Meng
Wenhui Shi
author_sort Weijun Teng
title An improved support vector clustering approach to dynamic aggregation of large wind farms
title_short An improved support vector clustering approach to dynamic aggregation of large wind farms
title_full An improved support vector clustering approach to dynamic aggregation of large wind farms
title_fullStr An improved support vector clustering approach to dynamic aggregation of large wind farms
title_full_unstemmed An improved support vector clustering approach to dynamic aggregation of large wind farms
title_sort improved support vector clustering approach to dynamic aggregation of large wind farms
publisher China electric power research institute
series CSEE Journal of Power and Energy Systems
issn 2096-0042
2096-0042
publishDate 2019-06-01
description In this paper, we propose an improved support vector clustering (SVC) algorithm to cluster wind turbines (WTs) in a wind farm (WF). A boundary points (BPs) detecting method based on the grid theory and the connected subdomain (CS) are proposed. Thus the efficiency of SVC is enhanced while maintaining the accuracy of the algorithm. As for the multiwind condition equivalent of WF, a method to determine the number and capacity of each of the aggregated wind turbines (AWTs) based on historical wind data is proposed. Only wind speed (WS) and wind direction (WD) of the WF are needed to calculate the WSs of each AWT. Results demonstrate that the algorithm proposed in this paper can cluster WTs quickly and accurately. And the dynamic aggregated models of the WFs are suitable for both the single-wind condition and multi-wind condition simulations, with high accuracy being obtained.
url https://ieeexplore.ieee.org/document/8735436
work_keys_str_mv AT weijunteng animprovedsupportvectorclusteringapproachtodynamicaggregationoflargewindfarms
AT xifanwang animprovedsupportvectorclusteringapproachtodynamicaggregationoflargewindfarms
AT yongqingmeng animprovedsupportvectorclusteringapproachtodynamicaggregationoflargewindfarms
AT wenhuishi animprovedsupportvectorclusteringapproachtodynamicaggregationoflargewindfarms
AT weijunteng improvedsupportvectorclusteringapproachtodynamicaggregationoflargewindfarms
AT xifanwang improvedsupportvectorclusteringapproachtodynamicaggregationoflargewindfarms
AT yongqingmeng improvedsupportvectorclusteringapproachtodynamicaggregationoflargewindfarms
AT wenhuishi improvedsupportvectorclusteringapproachtodynamicaggregationoflargewindfarms
_version_ 1725378759400357888

Similar Items