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Related Concept Videos

Real-World Application of Classical Conditioning01:15

Real-World Application of Classical Conditioning

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Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
Higher-order, or second-order, conditioning occurs when a neutral stimulus becomes associated with an already established conditioned stimulus through repeated pairings. For instance, if a dog has been...
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  11. Deep Reinforcement Learning Approach For Dynamic Distribution Network Reconfiguration Based On Sequential Masking.
  1. Home
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  11. Deep Reinforcement Learning Approach For Dynamic Distribution Network Reconfiguration Based On Sequential Masking.

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Deep Reinforcement Learning Approach for Dynamic Distribution Network Reconfiguration Based on Sequential Masking.

Ruoheng Wang, Xiaowen Bi, Siqi Bu

    IEEE Transactions on Neural Networks and Learning Systems
    |June 11, 2025

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces a novel sequential masking strategy for dynamic distribution network reconfiguration (DDNR) using deep reinforcement learning (DRL). The method effectively handles complex action spaces, improving scalability and performance for secure and economic power grid operation.

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    Area of Science:

    • Electrical Engineering
    • Artificial Intelligence
    • Power Systems

    Background:

    • Dynamic distribution network reconfiguration (DDNR) is crucial for secure and economic power distribution network (PDN) operation, especially with high renewable energy source (RES) penetration.
    • Data-driven solutions, particularly deep reinforcement learning (DRL), are gaining traction for DDNR due to enhanced data availability in PDNs.
    • Existing DRL methods struggle with the vast and sparse action space of DDNR, often due to the radiality constraint, limiting scalability and optimality.

    Purpose of the Study:

    • To address the challenges of scalability and optimality in DRL-based DDNR.
    • To propose a novel sequential masking strategy to decompose the complex action space of DDNR.
    • To develop a data-efficient, safety-guaranteed, and scalable DRL solution for DDNR.

    Main Methods:

    • A sequential masking strategy is proposed to decompose the DDNR problem's complex action space into manageable sub-action spaces.
    • A gated recurrent unit (GRU)-based agent is designed to process sequential data.
    • An adapted soft actor critic (SAC) algorithm is employed to handle the decomposed action spaces.

    Main Results:

    • The proposed method demonstrates superior algorithmic performance and scalability compared to existing data-driven approaches.
    • Case studies confirm the effectiveness of the sequential masking strategy in handling the radiality constraint.
    • The GRU-based agent and adapted SAC algorithm provide a data-efficient and safety-guaranteed DRL solution.

    Conclusions:

    • The developed DRL approach, utilizing a sequential masking strategy, offers a significant advancement in solving the DDNR problem.
    • This method overcomes the limitations of existing DRL techniques in terms of scalability and optimality for large-scale power distribution networks.
    • The findings highlight the potential of this approach for enhancing the secure and economic operation of modern power grids with high RES integration.