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Automatic Verification Flow Shop Scheduling of Electric Energy Meters Based on an Improved Q-Learning Algorithm

Author

Listed:
  • Long Peng

    (Meteorology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China)

  • Jiajie Li

    (Meteorology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China)

  • Jingming Zhao

    (Meteorology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China)

  • Sanlei Dang

    (Meteorology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China)

  • Zhengmin Kong

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Li Ding

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

Abstract

Considering the engineering problem of electric energy meter automatic verification and scheduling, this paper proposes a novel scheduling scheme based on an improved Q-learning algorithm. First, by introducing the state variables and behavior variables, the ranking problem of combinatorial optimization is transformed into a sequential decision problem. Then, a novel reward function is proposed to evaluate the pros and cons of the different strategies. In particular, this paper considers adopting the reinforcement learning algorithm to efficiently solve the problem. In addition, this paper also considers the ratio of exploration and utilization in the reinforcement learning process, and then provides reasonable exploration and utilization through an iterative updating scheme. Meanwhile, a decoupling strategy is introduced to address the restriction of over estimation. Finally, real time data from a provincial electric energy meter automatic verification center are used to verify the effectiveness of the proposed algorithm.

Suggested Citation

  • Long Peng & Jiajie Li & Jingming Zhao & Sanlei Dang & Zhengmin Kong & Li Ding, 2022. "Automatic Verification Flow Shop Scheduling of Electric Energy Meters Based on an Improved Q-Learning Algorithm," Energies, MDPI, vol. 15(5), pages 1-11, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1626-:d:755702
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    References listed on IDEAS

    as
    1. Lu, Renzhi & Hong, Seung Ho & Zhang, Xiongfeng, 2018. "A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach," Applied Energy, Elsevier, vol. 220(C), pages 220-230.
    2. Ruiz, Ruben & Maroto, Concepcion, 2005. "A comprehensive review and evaluation of permutation flowshop heuristics," European Journal of Operational Research, Elsevier, vol. 165(2), pages 479-494, September.
    3. Rossit, Daniel Alejandro & Tohmé, Fernando & Frutos, Mariano, 2018. "The Non-Permutation Flow-Shop scheduling problem: A literature review," Omega, Elsevier, vol. 77(C), pages 143-153.
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