Open AccessProbabilistic framework for transient stability contingency ranking of power grids with active distribution networks: application in post disturbance security assessment
Author(s) -
Tajdinian Mohsen,
Allahbakhshi Mehdi,
Mohammadpourfard Mostafa,
Mohammadi Behnam,
Weng Yang,
Dong Zhaoyang
Publication year - 2020
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.2019.0840
Subject(s) - transient (computer programming) , electric power system , stability (learning theory) , probabilistic logic , control theory (sociology) , ac power , fault (geology) , computer science , rotor (electric) , contingency , grid , engineering , power (physics) , reliability engineering , voltage , mathematics , machine learning , artificial intelligence , mechanical engineering , physics , control (management) , geometry , quantum mechanics , seismology , geology , electrical engineering , operating system , linguistics , philosophy
The conventional distribution network is becoming an active grid due to widespread integration of distributed energy resources (DERs). This integration raises great concerns about security of the power system considering the growing interactions between the transmission and active distribution systems. One way to ensure stability of the integrated power system is prediction of critical contingencies which can jeopardize the power system security. Therefore, this paper aims to investigate transient stability contingency ranking in power grids considering the uncertainties raised by DERs of active distribution networks. To this end, probabilistic transient stability prediction framework is provided, in which the probability density function of transient stability condition is calculated based on the normalized stability indicators. The normalized stability indicators are based on the rotor angle and rotor speed. The normalized stability indicators are only utilized in the case of using fault data for prediction. Based on the proposed scheme, the instability of generators is anticipated through the proposed probabilistic transient stability prediction framework. Also, the effects of the probable topology changes on the line overload and also the voltage profile are investigated. Finally, the performance of the proposed method is validated and compared with the time domain simulation under various operating conditions.