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Hybrid Iteration-Based Reinforcement Learning Scheme for Markov Jump Multiarea Interconnected Power Systems

Hao Shen et al.

IEEE Transactions on Systems, Man, and Cybernetics: Systems2026https://doi.org/10.1109/tsmc.2026.3655579article
AJG 3
Weight
0.37

What the paper says

This article presents a novel reinforcement learning (RL)-based load frequency control (LFC) algorithm designed for Markov jump multiarea interconnected power systems (MJMIPSs). Existing LFC methods often require precise dynamic information of the power system, which is challenging to acquire accurately due to the the system complexity, stochastic disturbances, and high modeling costs. Such limitations make it difficult to implement effective control strategies in real-world applications. In response to these challenges, we propose a decentralized hybrid iteration (HI) algorithm that combines the RL scheme with a decentralized control technique to address the LFC problem for MJMIPSs. In contrast to conventional RL schemes, such as policy iteration (PI) and value iteration (VI), the proposed algorithm achieves the controller design without subsystem decomposition (SD) by employing mixed-mode data acquisition and mode-related data classification, while eliminating the requirements on exact system dynamics and initial admissible control policies. Finally, we verify the effectiveness of the proposed method through the power systems.

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https://doi.org/https://doi.org/10.1109/tsmc.2026.3655579

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@article{hao2026,
  title        = {{Hybrid Iteration-Based Reinforcement Learning Scheme for Markov Jump Multiarea Interconnected Power Systems}},
  author       = {Hao Shen et al.},
  journal      = {IEEE Transactions on Systems, Man, and Cybernetics: Systems},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1109/tsmc.2026.3655579},
}

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Hybrid Iteration-Based Reinforcement Learning Scheme for Markov Jump Multiarea Interconnected Power Systems

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.