
Effective virtual inertia control using inverter optimization method in renewable energy generation
Author(s) -
Shuanbao Niu,
Linan Qu,
Hsiung-Cheng Lin,
Wanliang Fang
Publication year - 2021
Publication title -
energy exploration and exploitation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.435
H-Index - 30
eISSN - 2048-4054
pISSN - 0144-5987
DOI - 10.1177/01445987211021505
Subject(s) - control theory (sociology) , inertia , renewable energy , electric power system , computer science , controller (irrigation) , engineering , power (physics) , control (management) , agronomy , physics , classical mechanics , quantum mechanics , artificial intelligence , electrical engineering , biology
The high-level penetration of intermittent renewable power generation may limit power system inertia, resulting in system frequency instability in increasing power converter-based energy sources. To resolve this problem, virtual inertia control using distributed gray wolf optimization (DGWO) method in a synchronous generator is simulated under a distinct output fluctuation condition. First, the DGWO algorithm was established to achieve a local and global balance solution, and standard test functions were employed to verify the model convergence. Second, the key parameters that determine the effect of the virtual inertia controller in the power grid were analyzed. A DGWO-based optimization strategy to stabilize inertia was also developed. Finally, simulation results using step and random loads under a high permeability level are provided to verify the effectiveness of the proposed model. In the step load disturbance, the system can recover from the disturbance point to the stable point after 3 s under the regulation of the proposed control strategy, which is reduced by 18 s compared with the traditional control method. In the random load test, it takes only 12 s, 63 s less than the traditional one. Accordingly, the power system frequency can be stabilized more quickly from a disturbance state to a stable stage.