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 Application Of Artificial Neural Network For Deloading Control To Support System Integrity
Tác giả hoặc Nhóm tác giả: Hong Viet Phuong Nguyen; Binh Nam Nguyen; Nguyen Van Tan; Thi Bich Thanh Truong; Hieu Nguyen Van; Thi Tinh Minh Le; Quoc Tuan Tran
Nơi đăng: 2023 Asia Meeting on Environment and Electrical Engineering (IEEE); Số: 2023;Từ->đến trang: 1-10;Năm: 2023
Lĩnh vực: Khoa học công nghệ; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
The increase in renewable energy sources presents many challenges to the power system. Specifically, solar power plants have unstable output capacity and inertia contributions, making it difficult to operate the power system. Solar power systems are often controlled to operate with maximum generating capacity, resulting in these not having the reserve capacity to stabilize the grid frequency when there is a power imbalance. Instead of generating the maximum power, this paper proposes a control strategy to improve the inertia of the power grid by deloading-control of the solar power plant through the Artificial Neural Network (ANN) algorithm, while combining methods of droop control. Matlab/Simulink software is used for simulation and evaluation. The results show a much better improvement than conventional methods.
ABSTRACT
The increase in renewable energy sources presents many challenges to the power system. Specifically, solar power plants have unstable output capacity and inertia contributions, making it difficult to operate the power system. Solar power systems are often controlled to operate with maximum generating capacity, resulting in these not having the reserve capacity to stabilize the grid frequency when there is a power imbalance. Instead of generating the maximum power, this paper proposes a control strategy to improve the inertia of the power grid by deloading-control of the solar power plant through the Artificial Neural Network (ANN) algorithm, while combining methods of droop control. Matlab/Simulink software is used for simulation and evaluation. The results show a much better improvement than conventional methods.
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