Journal
IEEE TRANSACTIONS ON SMART GRID
Volume 11, Issue 6, Pages 5260-5272Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2020.2997790
Keywords
Damping; Torque; Oscillators; Mathematical model; Generators; Power system stability; Simulation; Ultra-low-frequency oscillations; low frequency oscillation; CPSS; PR-PSS; deep reinforcement learning
Categories
Funding
- Sichuan Distinguished Young Scholars [20JCQN0213]
- Sichuan Key Research and Development Project [2020YFG0312]
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Recent studies have shown that due to the hammer effect of the governor, hydropower units are easily creating negative damping torque at the common mode frequency (below 0.1 Hz). Therefore, there is a risk of ultra low frequency oscillations (ULFO) in hydropower-dominated systems. ULFO is a small-signal frequency oscillation problem, which is quite different from low frequency oscillations (LFO). A conventional power system stabilizer (CPSS) has less effect on suppressing ULFO. To solve this problem, this paper proposes a high-order polynomial structure to replace the CPSS, and combine it with a proportional resonance controller to form a novel PR-PSS. In order to ensure the robustness of PR-PSS, based on the characteristic analysis results of the PR-PSS, a deep reinforcement learning (DRL) algorithm asynchronous advantage actor-critic (A3C) is introduced to train an agent. After training, the proposed agent can provide optimal parameter settings for PR-PSS under various operating conditions. Simulation results verify the effectiveness of the proposed method.
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