A Novel Swarm-based Approach for Load Dispatch of Hydropower Units

被引:0
|
作者
Shen, Jian-Jian [1 ]
机构
[1] Dalian Univ Technol, Inst Hydropower & Hydroinformat, Dalian 116024, Peoples R China
关键词
Swarm-based Approach; Load Dispatch; Hydropower Units; Bee Evolutionary Optimization Algorithm (BEOA);
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
This paper proposes a novel swarm-based approach called bee evolutionary optimization algorithm (BEOA) to solve the load dispatch of hydropower units. The BEOA is developed by simulating the real-world bees mating process. In the BEOA, the diversity of population is improved by introducing the new male bees in the crossover operation. Moreover, the fitness of population is further enhanced using a heuristic function which is inspired by the feeding action of worker bees. The BEOA is tested and validated by dealing with the load dispatch problem of Wujiangdu Hydropower Plant, which plays an important role in the power grid operation. The results from the case study demonstrate that the BEOA can produce practical unit schedules, and is faster than the dynamic programming.
引用
收藏
页码:267 / 271
页数:5
相关论文
共 50 条
  • [41] Ant-based and swarm-based clustering
    Julia Handl
    Bernd Meyer
    Swarm Intelligence, 2007, 1 (2) : 95 - 113
  • [42] Optimal Randomness in Swarm-Based Search
    Wei, Jiamin
    Chen, YangQuan
    Yu, Yongguang
    Chen, Yuquan
    MATHEMATICS, 2019, 7 (09)
  • [43] A novel particle swarm optimization method based on quantum mechanics computation for thermal economic load dispatch problem
    Chakraborty, Shantanu
    Senjyu, Tomonobu
    Saber, Ahmed Yousuf
    Yona, Atsushi
    Funabashi, Toshihisa
    IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2012, 7 (05) : 461 - 470
  • [44] A swarm-based approach to dynamic coverage control of multi-agent systems
    Atinc, Gokhan M.
    Stipanovic, Dusan M.
    Voulgaris, Petros G.
    AUTOMATICA, 2020, 112
  • [45] Particle Swarm-Based Federated Learning Approach for Early Detection of Forest Fires
    Supriya, Y.
    Gadekallu, Thippa Reddy
    SUSTAINABILITY, 2023, 15 (02)
  • [46] A Persian writer identification method using swarm-based feature selection approach
    Ram, Soheila Sadeghi
    Moghaddam, Mohsen Ebrahimi
    INTERNATIONAL JOURNAL OF BIOMETRICS, 2014, 6 (01) : 53 - 74
  • [47] Finding odours across large search spaces: A particle swarm-based approach
    Marques, L
    de Almeida, AT
    CLIMBING AND WALKING ROBOTS, 2005, : 419 - 426
  • [48] A Swarm-based Unmanned Aerial Vehicle Approach for Video Delivery of Mobile Objects
    Medeiros, Iago
    Boukerche, Azzedine
    Cerqueira, Eduardo
    IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021), 2021,
  • [49] Deephive: A Reinforcement Learning Approach for Automated Discovery of Swarm-Based Optimization Policies
    Ikponmwoba, Eloghosa
    Owoyele, Opeoluwa
    ALGORITHMS, 2024, 17 (11)
  • [50] A Novel Image Classification Algorithm Using Swarm-Based Technique for Image Database
    Wahid, Noorhaniza
    UBIQUITOUS COMPUTING AND MULTIMEDIA APPLICATIONS, PT II, 2011, 151 : 460 - 470