Online Dynamic Total Transfer Capability Estimation Using Cotraining-Style Semi-Supervised Regression

With the increasing integration of wind power, the operating condition of the power system varies more rapidly. As the total transfer capability (TTC) of the transmission interface changes with the operating condition, the offline TTC estimation has become less suitable for online security control....

Full description

Bibliographic Details
Main Authors: Yuwei Zhang, Wenying Liu, Yue Huan, Qiang Zhou, Ningbo Wang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9096358/
id doaj-7bbe626e969246afa9af5f1ac118a71c
record_format Article
spelling doaj-7bbe626e969246afa9af5f1ac118a71c2021-03-30T03:00:29ZengIEEEIEEE Access2169-35362020-01-018940549406410.1109/ACCESS.2020.29956209096358Online Dynamic Total Transfer Capability Estimation Using Cotraining-Style Semi-Supervised RegressionYuwei Zhang0https://orcid.org/0000-0002-0787-2050Wenying Liu1https://orcid.org/0000-0001-5130-0765Yue Huan2https://orcid.org/0000-0001-9182-7692Qiang Zhou3https://orcid.org/0000-0002-1294-2834Ningbo Wang4https://orcid.org/0000-0002-7816-9784State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, ChinaState Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, ChinaState Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, ChinaState Grid Gansu Provincial Electric Power Research Institute, Lanzhou, ChinaState Grid Gansu Provincial Electric Power Research Institute, Lanzhou, ChinaWith the increasing integration of wind power, the operating condition of the power system varies more rapidly. As the total transfer capability (TTC) of the transmission interface changes with the operating condition, the offline TTC estimation has become less suitable for online security control. In this paper, an efficient online dynamic TTC estimation method using semi-supervised learning approach is proposed. First, considering the high-order uncertainties of wind and load, a sample database of expected operating conditions with or without corresponding TTCs is generated. Then, the pivotal features which greatly correlate with the TTC are selected. Finally, the relationship between the TTC and pivotal features is learned, using the cotraining-style semi-supervised regression algorithm (COREG), thus the dynamic TTC estimation model is established. With real-time data inputting the model, the TTC can be estimated. The proposed method is validated on Gansu Province Power Grid in China, and the results and accuracy and efficiency comparison with other typical existing methods indicate that, the proposed method can provide accurate TTC estimation, and because of the high efficiency of the semi-supervised learning approach, the whole process of model establishment and TTC estimation can be refreshed every 15 minutes. Therefore, the proposed method of online dynamic TTC estimation is suitable for online security control.https://ieeexplore.ieee.org/document/9096358/Online dynamic TTC estimationCOREGhigh-order uncertaintywind power
collection DOAJ
language English
format Article
sources DOAJ
author Yuwei Zhang
Wenying Liu
Yue Huan
Qiang Zhou
Ningbo Wang
spellingShingle Yuwei Zhang
Wenying Liu
Yue Huan
Qiang Zhou
Ningbo Wang
Online Dynamic Total Transfer Capability Estimation Using Cotraining-Style Semi-Supervised Regression
IEEE Access
Online dynamic TTC estimation
COREG
high-order uncertainty
wind power
author_facet Yuwei Zhang
Wenying Liu
Yue Huan
Qiang Zhou
Ningbo Wang
author_sort Yuwei Zhang
title Online Dynamic Total Transfer Capability Estimation Using Cotraining-Style Semi-Supervised Regression
title_short Online Dynamic Total Transfer Capability Estimation Using Cotraining-Style Semi-Supervised Regression
title_full Online Dynamic Total Transfer Capability Estimation Using Cotraining-Style Semi-Supervised Regression
title_fullStr Online Dynamic Total Transfer Capability Estimation Using Cotraining-Style Semi-Supervised Regression
title_full_unstemmed Online Dynamic Total Transfer Capability Estimation Using Cotraining-Style Semi-Supervised Regression
title_sort online dynamic total transfer capability estimation using cotraining-style semi-supervised regression
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description With the increasing integration of wind power, the operating condition of the power system varies more rapidly. As the total transfer capability (TTC) of the transmission interface changes with the operating condition, the offline TTC estimation has become less suitable for online security control. In this paper, an efficient online dynamic TTC estimation method using semi-supervised learning approach is proposed. First, considering the high-order uncertainties of wind and load, a sample database of expected operating conditions with or without corresponding TTCs is generated. Then, the pivotal features which greatly correlate with the TTC are selected. Finally, the relationship between the TTC and pivotal features is learned, using the cotraining-style semi-supervised regression algorithm (COREG), thus the dynamic TTC estimation model is established. With real-time data inputting the model, the TTC can be estimated. The proposed method is validated on Gansu Province Power Grid in China, and the results and accuracy and efficiency comparison with other typical existing methods indicate that, the proposed method can provide accurate TTC estimation, and because of the high efficiency of the semi-supervised learning approach, the whole process of model establishment and TTC estimation can be refreshed every 15 minutes. Therefore, the proposed method of online dynamic TTC estimation is suitable for online security control.
topic Online dynamic TTC estimation
COREG
high-order uncertainty
wind power
url https://ieeexplore.ieee.org/document/9096358/
work_keys_str_mv AT yuweizhang onlinedynamictotaltransfercapabilityestimationusingcotrainingstylesemisupervisedregression
AT wenyingliu onlinedynamictotaltransfercapabilityestimationusingcotrainingstylesemisupervisedregression
AT yuehuan onlinedynamictotaltransfercapabilityestimationusingcotrainingstylesemisupervisedregression
AT qiangzhou onlinedynamictotaltransfercapabilityestimationusingcotrainingstylesemisupervisedregression
AT ningbowang onlinedynamictotaltransfercapabilityestimationusingcotrainingstylesemisupervisedregression
_version_ 1724184208761421824