Open AccessOptimization of controlling parameters of DFIG and battery energy storage combined frequency modulation based on PSO
Author(s) -
Changyan Lei,
Qi Wang,
Shun Wang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1311/1/012030
Subject(s) - frequency deviation , wind power , control theory (sociology) , automatic frequency control , energy storage , frequency response , particle swarm optimization , frequency grid , electric power system , frequency modulation , computer science , engineering , power (physics) , electrical engineering , voltage , radio frequency , control (management) , physics , quantum mechanics , artificial intelligence , machine learning
Wind power has randomicity and intermittence. As the wind power penetration rate increases, it poses a threat to the frequency of the power grid. In the power systems with high-permeability of wind power, relying solely on conventional crew tuning is no longer sufficient to ensure that the frequency is within safe limits. The battery energy storage has a fast response speed, but it is expensive. The frequency regulation provided by the wind farm itself has a short inertial control support time, the pitch control response is slow, and both frequency modulation methods are subject to wind speed. In order to improve the frequency adjustment capability of the wind farm, the battery energy storage and the wind farm’s own frequency modulation means are combined to adjust the system frequency. However, how to set the battery energy storage output to make the system frequency characteristics optimal hasn’t been studied. In this paper, the particle swarm optimization algorithm is used to select the optimal parameters, so that the frequency characteristics are optimal. The example shows that compared with the unoptimized energy storage frequency modulation coefficient, the optimal frequency modulation coefficient found by the PSO algorithm can greatly reduce the maximum frequency drop of the power system and improve the frequency stability of the power system.