New multivariate linear regression real and reactive branch flow models for volatile scenarios
Electric load flow; Least squares approximations; Regression analysis; Branch flow; flexible; Multivariate linear regressions; Power flow equations; Power systems operation; Prediction accuracy; robust; Underlying factors; Linear regression
Saved in:
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE Computer Society
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1833413516517703680 |
|---|---|
| author | Appalasamy S. Jones O.D. Moin N.H. Sin T.C. |
| author2 | 57092686500 |
| author_facet | 57092686500 Appalasamy S. Jones O.D. Moin N.H. Sin T.C. |
| author_sort | Appalasamy S. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Electric load flow; Least squares approximations; Regression analysis; Branch flow; flexible; Multivariate linear regressions; Power flow equations; Power systems operation; Prediction accuracy; robust; Underlying factors; Linear regression |
| format | Conference Paper |
| id | my.uniten.dspace-22242 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | IEEE Computer Society |
| record_format | dspace |
| spelling | my.uniten.dspace-222422023-05-29T13:59:48Z New multivariate linear regression real and reactive branch flow models for volatile scenarios Appalasamy S. Jones O.D. Moin N.H. Sin T.C. 57092686500 57205913427 6507487566 55363559700 Electric load flow; Least squares approximations; Regression analysis; Branch flow; flexible; Multivariate linear regressions; Power flow equations; Power systems operation; Prediction accuracy; robust; Underlying factors; Linear regression Nonlinearity of power flow equations is one of the major underlying factors in a power systems operation complexity. The need for a robust and less complex models rises in a volatile, dynamic and real time scenario. This paper introduces new empirical models using multivariate linear regression (MLR) methods with least squares for both real and reactive branch flows. The models do not make prior assumptions and do not depend on a particular base case. Instead they are trained on either simulated or historical data. Tests using the IEEE 14 bus system show that given similar input variables to DC models, the MLR models performs significantly better. They also show that the MLR models have good prediction accuracy in scenarios with high volatility. � 2015 IEEE. Final 2023-05-29T05:59:48Z 2023-05-29T05:59:48Z 2015 Conference Paper 10.1109/PESGM.2015.7285669 2-s2.0-84956854686 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84956854686&doi=10.1109%2fPESGM.2015.7285669&partnerID=40&md5=1b99eebb0482525366daa30ceae3c770 https://irepository.uniten.edu.my/handle/123456789/22242 2015-September 7285669 IEEE Computer Society Scopus |
| spellingShingle | Appalasamy S. Jones O.D. Moin N.H. Sin T.C. New multivariate linear regression real and reactive branch flow models for volatile scenarios |
| title | New multivariate linear regression real and reactive branch flow models for volatile scenarios |
| title_full | New multivariate linear regression real and reactive branch flow models for volatile scenarios |
| title_fullStr | New multivariate linear regression real and reactive branch flow models for volatile scenarios |
| title_full_unstemmed | New multivariate linear regression real and reactive branch flow models for volatile scenarios |
| title_short | New multivariate linear regression real and reactive branch flow models for volatile scenarios |
| title_sort | new multivariate linear regression real and reactive branch flow models for volatile scenarios |
| url_provider | http://dspace.uniten.edu.my/ |