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- W3094762211 abstract "In this paper, the mechanism of ultra-low frequency oscillation (ULFO) is studied according to the damping torque theory and the influence of proportional, integral and differential parts (PID) parameter settings of hydro-governor on ULFO is investigated. After that, a deep Q network (DQN) based method is proposed for the hydro-governor PID parameter settings self-tuning, which considering the uncertainty of the hydraulic turbine operating conditions. Simulation results show that the proposed DQN-based method can provide the optimal parameters for the hydro-governor PID under all operating conditions, which achieves better performance in preventing ULFO in comparison with other methods." @default.
- W3094762211 created "2020-11-09" @default.
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- W3094762211 date "2020-09-12" @default.
- W3094762211 modified "2023-10-17" @default.
- W3094762211 title "Deep Reinforcement Learning Based Optimization Strategy for Hydro-Governor PID Parameters to Suppress ULFO" @default.
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- W3094762211 doi "https://doi.org/10.1109/icpre51194.2020.9233119" @default.
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