Open AccessNew phasor‐based approach for online and fast prediction of generators grouping using decision tree
Author(s) -
Rezaeian Koochi Mohammad Hossein,
Esmaeili Saeid,
Fadaeinedjad Roohollah
Publication year - 2017
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.2016.1448
Subject(s) - phasor , computer science , decision tree , islanding , classifier (uml) , generator (circuit theory) , electric power system , artificial intelligence , data mining , subtractive color , pattern recognition (psychology) , machine learning , power (physics) , physics , quantum mechanics , art , visual arts
Fast and accurate identification of coherent generator groups is helpful in dynamic and transient stability analysis as well as other applications such as controlled islanding. In this study, a new method is presented for predicting the generators’ grouping scheme based on the data measured before and in a short time after the disturbance occurrence. To do that, a classifier model is trained using a training dataset. In the training dataset, the input is the attributes, which are obtained directly or indirectly from the data measured by phasor measurement units. On the other hand, the target in the training dataset is the generators’ grouping, which in this study is calculated using a new method called subtractive technique. In subtractive technique, coherent generator groups are determined based on the generator density values. When the classifier model is built using the training dataset, it can be used for online applications. In this study, the well‐known 68‐bus, 16‐machine power system as well as the Iranian 400 and 230 kV south east regional grid are used as the test systems for investigating the efficiency of proposed coherent group prediction method. Results show that the proposed method can predict the generators’ grouping scheme with high accuracy.