Open AccessSmall‐signal stability analysis and particle swarm optimisation self‐tuning frequency control for an islanding system with DFIG wind farm
Author(s) -
Yang JhihSiang,
Chen YiWei,
Hsu YuanYih
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.2018.6101
Subject(s) - islanding , particle swarm optimization , doubly fed electric machine , control theory (sociology) , signal (programming language) , stability (learning theory) , wind power , automatic frequency control , control signal , swarm behaviour , control (management) , engineering , computer science , control system , power (physics) , electric power system , ac power , physics , voltage , electrical engineering , quantum mechanics , machine learning , artificial intelligence , programming language
Linearised model for frequency control of an islanding system comprising an equivalent synchronous generator and an equivalent doubly fed induction generator (DFIG) is derived. Small signal stability analysis is performed to determine the stable regions for DFIG supplementary droop controller under various wind velocities. In order to achieve better dynamic frequency response under different system parameters, the DFIG droop gain is adapted in real‐time using a self‐tuning controller designed based on particle swarm optimisation technique. In order to validate the results from small signal analysis and to demonstrate the effectiveness of the proposed self‐tuning frequency controller, digital simulations using MATLAB/SIMULINK are performed on a local power system in central Taiwan. It is concluded from the simulation results that the proposed self‐tuning frequency controller offer better dynamic frequency response than the fixed‐gain droop controller.