Auto regressive moving averaging vector model identification and power system adaptive damping control

作者：Lu Chao^*; Wu Chao; Wang Tian; Chen Xiang; Yu Tong Wei

来源：Proceedings of the CSEE, 2010, 30(19): 31-36.

• Adaptive Control
• Ambient signal
• Auto-regressive
• Model identification
• Pole placement

The conventional damping controller designs based on offline parameters and typical system conditions lead to the adaptability difficulties. A novel adaptive control method was proposed to solve the contradiction between identification time and accuracy. An auto regressive moving averaging vector (ARMAV) reduced-order model was identified based on the power system ambient signals. The principles of zeros-poles placement were also summarized with the considerations on identification errors, damping radio and stability margin. Then, these principles were implemented by using genetic algorithm. To validate the performances of the above identification and controller adaptation methods, the physical control system was realized based on the wide area measurement system, and the real time digital simulation (RTDS) tests were also carried out. The experiment results demonstrated the feasibility and the advantages of the proposed approach.

• 出版日期2010
• 单位清华大学