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Model Predictive Control for Virtual Inertia Synthesis
Kerdphol T.a, Rahman F.S.b, Watanabe M.a, Mitani Y.a
a Department of Electrical and Electronic Engineering, Kyushu Institute of Technology, Kitakyushu, Japan
b School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2021, Springer Nature Switzerland AG.Nowadays, a new concept of modern power systems (i.e., smart/micro-grids), which includes various components such as smart meters, smart appliances, renewable energy sources (RESs), distributed generators (DGs), and controllable loads has increased attention worldwide due to its energy efficiency and environmental concerns. Such a modern system requires the employment of real-time application and intelligent control. To properly utilize the virtual inertia control regarding an intelligent ability in future predictions, the model predictive control (MPC) is necessary. The MPC has a fine performance in delivering fast dynamic response with robustness against disturbance and uncertainty, while keeping future control variables in account. Thus, it has been implemented in a wide range of industrial applications, including real-time measurement and control. In this chapter, the design of decentralized MPC-based virtual inertia control is introduced to emulate the suitable virtual inertia power, while predicting the future behavior or event regarding inertia control-based frequency regulation. The MPC controller applies a feedforward control technique to reject the disturbances from RES/DG and load penetration as well as system parameter uncertainty, ensuring rapid dynamic response with the robustness of system operation. The proposed MPC-based virtual inertia control is verified through a nonlinear control area system with high RESs/DGs penetration including the extended communication delay time.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Fast dynamic response,Frequency control,Inertia control,Model predictive control (MPC),Nonlinear control,Parameter tuning,Predictive model,Real-time control,Robustness,Uncertainty,Virtual inertia constant,Virtual inertia synthesis[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1007/978-3-030-57961-6_6[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]