Probabilistic framework for online identification of dynamic behavior of power systems with renewable generation

Papadopoulos, Panagiotis N. and Guo, Tingyan and Milanović, Jovica V. (2018) Probabilistic framework for online identification of dynamic behavior of power systems with renewable generation. IEEE Transactions on Power Systems, 33 (1). pp. 45-54. ISSN 0885-8950

[img]
Preview
Text (Papadopoulos-etal-IEEE-2017-Probabilistic-framework-for-onilne-identification-of-dynamic-behavior)
Papadopoulos_etal_IEEE_2017_Probabilistic_framework_for_onilne_identification_of_dynamic_behavior.pdf
Accepted Author Manuscript

Download (1MB)| Preview

    Abstract

    The paper introduces a probabilistic framework for online identification of post fault dynamic behavior of power systems with renewable generation. The framework is based on decision trees and hierarchical clustering and incorporates uncertainties associated with network operating conditions, topology changes, faults, and renewable generation. In addition to identifying unstable generator groups, the developed clustering methodology also facilitates identification of the sequence in which the groups lose synchronism. The framework is illustrated on a modified version of the IEEE 68 bus test network incorporating significant portion of renewable generation.