Open AccessAdaBoost classifiers for phasor measurements‐based security assessment of power systems
Author(s) -
Thirugnanasambandam Venkatesh,
Jain Trapti
Publication year - 2018
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2017.0013
Subject(s) - support vector machine , computer science , boosting (machine learning) , adaboost , electric power system , artificial intelligence , pattern recognition (psychology) , classifier (uml) , phasor , artificial neural network , machine learning , data mining , power (physics) , physics , quantum mechanics
Power system security is a major concern in real‐time operation. It is essential to protect the system from blackout by taking proper control actions. This study proposes a boosting algorithm for the precise and accurate prediction of static security assessment of power systems using synchronised measurements. In addition to security status, the proposed approach also predicts the type of violations which may be either line overload/voltage violation or both of the insecure operating conditions. To overcome the computational complexity, the number of input phasor measurements is reduced by a statistical approach based on class separability and correlation coefficient indices. In the classification stage, support vector machines (SVMs) are used as weak classifiers and a strong classifier is constructed as the linear combination of many weak SVM classifiers. The performance of the Adaptive Boosting (AdaBoost) algorithm is further improved by a new weight updation strategy using fuzzy clustering thresholding technique. The efficiency of the proposed approach is demonstrated on IEEE 14‐bus, IEEE 30‐bus, and Indian 246‐bus systems. Further, the test results reveal that the proposed method of security assessment performs better than the other traditional classifiers viz. SVM, feed forward neural network and k‐nearest neighbour classifier.