Open AccessA novel approach for early detection of impending voltage collapse events based on the support vector machine
Author(s) -
Nguyen Duc Huy,
Kamwa Innocent,
Dessaint LouisA,
CaoDuc Huy
Publication year - 2017
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/etep.2375
Subject(s) - support vector machine , voltage , classifier (uml) , electric power system , ac power , computer science , engineering , control theory (sociology) , power (physics) , artificial intelligence , data mining , reliability engineering , electrical engineering , physics , control (management) , quantum mechanics
Summary This paper proposes an approach to detect the possibility of long‐term voltage instability, based on online measurement of system bus voltages. An optimization framework is proposed to determine the maximum loading points, with different load increase patterns and different levels of reactive power output. The operating conditions so obtained are used as the training database for an artificial intelligence classifier based on the support vector machines. In an online application, the support vector machine classifier helps in detecting the probability of some generators operating at high reactive power output, which is an important indicator of an impending voltage collapse. The proposed framework is tested with the IEEE 39 bus and the Nordic 32 bus systems. The test results demonstrate that the proposed scheme gives reliable prediction of the power system long‐term voltage stability.