Control of TCSC and SVC using Least Square Support Vector Regression (LS-SVR) to improve voltage stability

Rony Seto Wibowo, Aw Bahrowi, K. Anam, M. Abdillah, Adi Soeprijanto, Ontoseno Penangsang, Naoto Yorino

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

Abstract

This paper proposes the application of Least Square Support Vector Regression (LS-SVR) for controlling Flexible AC Transmission Systems (FACTS) in order to meet voltage stability requirement. Transient voltage stability is a very fast phenomenon. Therefore, the proposed approach is aimed to provide a quick response to prevent voltage collapse. Generally, time response consists of two parts. Firstly, control center receives signals from the field and then process those signals to determine the appropriate setting of FACTS devices according to load level and location of fault. Secondly, FACTS devices react based on the signals sent by control center to prevent voltage collapse. The total response time should be shorter than the time to voltage collapse. Two kinds of FACTS devices, Thyristor Controlled Series Capacitor (TCSC) and Static VAR Compensator (SVC), are used to represent series and shunt type devices, respectively. To prove the effectiveness of the proposed approach, IEEE 14 buses is used as test system. In addition, comparison study between application of LS-SVR and Extreme Learning Machine (ELM) is also presented.

Original languageEnglish
Pages337-342
Number of pages6
DOIs
Publication statusPublished - 2013
Event2013 5th International Conference on Information Technology and Electrical Engineering, ICITEE 2013 - Yogyakarta, Indonesia
Duration: 7 Oct 20138 Oct 2013

Conference

Conference2013 5th International Conference on Information Technology and Electrical Engineering, ICITEE 2013
Country/TerritoryIndonesia
CityYogyakarta
Period7/10/138/10/13

Keywords

  • Contingency
  • Extreme Learning Machines
  • Support Vector Machines
  • Voltage Stability

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