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Application of reinforcement learning to electrical power system closed-loop emergency control

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Academic year: 2021

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Figure

Fig. 2. Topology of the Itaipu transmission system (60 Hz)
Fig. 4. MW not rejected versus evolution of learning for α = C st (= 0.2) We have observed that the second technique (α = n s,a1 ) converges better and faster than the first technique (α = C st )
Fig. 5. MW not rejected versus evolution of learning for α = n s,a 1

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