Fast online identification of power system dynamic behaviour

Papadopoulos, Panagiotis N. and Bhui, Pratyasa and Milanovic, Jovica V. and Senroy, Nilanjan (2017) Fast online identification of power system dynamic behaviour. In: 2017 IEEE Power and Energy Society General Meeting, PESGM 2017, 2017-07-16 - 2017-07-20.

[thumbnail of Papadopoulos-etal-PESGM2017-Fast-online-identification-of-power-system-dynamic-behaviour]
Text (Papadopoulos-etal-PESGM2017-Fast-online-identification-of-power-system-dynamic-behaviour)
Accepted Author Manuscript

Download (833kB)| Preview


    This paper discusses the methodology for fast prediction of power system dynamic behavior. A combination of features that can be obtained from PMU data is proposed, that can improve the prediction time while keeping high accuracy of prediction. Several combinations of features including generator rotor angles, kinetic energy, acceleration and energy margin are used to train and test decision trees for the online identification of unstable generator groups. The predictor importance for trained decision trees is also calculated to highlight in more detail the effect of using different predictors.

    ORCID iDs

    Papadopoulos, Panagiotis N. ORCID logoORCID:, Bhui, Pratyasa, Milanovic, Jovica V. and Senroy, Nilanjan;