Open AccessSmall‐signal stability analysis and control of stochastic time‐variant power system through differential inclusion theory
Author(s) -
Chen Hongkun,
Hu Pan,
Zhu Xiaohang,
Chen Lei
Publication year - 2019
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.2018.5934
Subject(s) - control theory (sociology) , lyapunov function , stability (learning theory) , electric power system , computer science , mathematical optimization , monte carlo method , stochastic differential equation , differential inclusion , mathematics , power (physics) , control (management) , statistics , nonlinear system , physics , quantum mechanics , artificial intelligence , machine learning
Here, small‐signal stability issue associated with multi‐scale uncertainty excitation is investigated by using differential inclusion theory. Specifically, a polytopic linear differential inclusion (PLDI) model for large‐scale uncertainty system is developed, in which a modified non‐sequence Monte Carlo method is introduced to identify a series of time‐variant operation states. Additionally, a simplified small‐signal model of renewable energy resources (RES) is proposed, the outputs of RES are modelled as time‐varying elements in PLDI model to reflect the uncertainty in the linearised matrix. The stability criterion for the stochastic time‐varying system is mathematically deduced based on convex hull Lyapunov function (CHLF). The criterion is then utilised to design robust stabilisers for stochastic system. Simulation, utilising two‐area four‐machine system and New England 39‐bus test system, demonstrates the benefits of the proposed model in describing system stochastic characteristics, designing additional controllers and reducing computational burden.