Open AccessGalerkin method‐based model predictive control for mid‐long term voltage stability enhancement
Author(s) -
Xia Bingqing,
Wu Hao,
Shen Danfeng,
Zheng Xiang,
Hua Wen,
Song Yonghua
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.2019.1952
Subject(s) - control theory (sociology) , model predictive control , galerkin method , stability (learning theory) , polynomial , electric power system , term (time) , trajectory , voltage , computation , computer science , function (biology) , power (physics) , mathematical optimization , mathematics , control (management) , engineering , algorithm , finite element method , artificial intelligence , mathematical analysis , physics , structural engineering , electrical engineering , quantum mechanics , astronomy , machine learning , evolutionary biology , biology
Mid‐long term voltage stability refers to voltage stability concerning the dynamics of the slow dynamic elements and protection devices in power systems. To improve the mid‐long term voltage stability, this paper proposes a scheme of model predictive control (MPC) based on Galerkin method. The basic principle of MPC is to solve the optimization problem regarding voltage stability control based on the prediction model periodically. By applying the optimal control scheme to power systems, the mid‐long term voltage stability can be enhanced. The Galerkin method‐based polynomial approximation approximates the relationship between mid‐long term trajectories and control parameters by a polynomial function. The polynomial function acts as a prediction model to predict future system trajectories under different values of control parameters in MPC. The advantage of the proposed method is that its prediction accuracy is relatively high and thus the control efficiency of MPC is improved, as the polynomial function can embody the non‐linear characteristics of the power system. In the case study, the 3‐machine 10‐bus system and the New England 39‐bus system are taken as examples to compare the MPC based on the proposed method and the trajectory sensitivity method in terms of prediction accuracy, computation time and control results.