Optimal placement of Distributed Generation in power distribution systems using Neuro-genetic Algorithm

被引:0
|
作者
Kazeem, Bakare [1 ]
Alor, Mike [1 ]
Okafor, E. N. C. [2 ]
机构
[1] Enugu State Univ Sci & Technol ESUT, Dept Elect & Elect Engn, Enugu, Enugu State, Nigeria
[2] FUTO, Dept Elect & Elect Engn, Owerri, Imo State, Nigeria
关键词
Distributed Generation; Neuro-genetic algorithm; artificial neural network; optimal DG location; optimal DG size;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The deployment of distributed generation (DG) is gaining more acceptances in improving power distribution networks. Hence the need for more research and development in the optimal placement of DGs. Sub-optimal DG placement in distribution systems has the potentials to increase power losses degrade voltage profile and cause stability problems. Due to the nonlinear and complex dynamics of the power systems, this paper proposes a neuro-genetic technique for the optimal placement of DGs. Evolutionary processes are used in the design of artificial neural network (ANA). The DG placement is formulated as an optimization problem solved using the neuron-genetic network. The proposed method is applied to the South-East Nigeria 60-bus 33KV distribution system. Results from MA TLAB simulation showed significant reduction in active power losses, improvement in voltage profile and enhancement of voltage stability. Comparative analysis carried showed that the proposed neuro-genetic method performed better than genetic algorithm (GA) DG placement.
引用
收藏
页码:898 / 904
页数:7
相关论文
共 50 条
  • [1] Optimal Placement of Distributed Generators and Reconfiguration of Distribution Systems for Loss Reduction Using Genetic Algorithm
    Jouybari, Bahram Rashidi
    Hosseini, Mehdi
    Byagowi, Zaki
    [J]. INTERNATIONAL REVIEW OF ELECTRICAL ENGINEERING-IREE, 2011, 6 (02): : 1007 - 1012
  • [2] Optimal Placement of UPFC in Power Systems Using Genetic Algorithm
    Arabkhaburi, D.
    Kazemi, A.
    Yari, M.
    Aghaei, J.
    [J]. 2006 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY, VOLS 1-6, 2006, : 1732 - +
  • [3] Optimal Placement and Sizing of Distributed Generation and Capacitor Banks in Distribution Systems Using Water Cycle Algorithm
    Abou El-Ela, Adel A.
    El-Sehiemy, Ragab A.
    Abbas, Ahmed Samir
    [J]. IEEE SYSTEMS JOURNAL, 2018, 12 (04): : 3629 - 3636
  • [4] Optimal Sizing and Placement of Distributed Generation in Egyptian Radial Distribution Systems Using Crow Search Algorithm
    Ismael, Sherif M.
    Aleem, Shady H. E. Abdel
    Abdelaziz, Almoataz Y.
    [J]. PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON INNOVATIVE TRENDS IN COMPUTER ENGINEERING (ITCE' 2018), 2018, : 332 - 337
  • [5] Optimal Sizing and Placement of Capacitor Banks and Distributed Generation in Distribution Systems Using Spring Search Algorithm
    Dehghani, Mohammad
    Montazeri, Zeinab
    Malik, O. P.
    [J]. INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS, 2020, 21 (01)
  • [6] A distributed neuro-genetic programming tool
    Russo, Marco
    [J]. SWARM AND EVOLUTIONARY COMPUTATION, 2016, 27 : 145 - 155
  • [7] Optimal Power Grid Integration With Distributed Generation Using Genetic Algorithm
    Moloi, K.
    Jordaan, J. A.
    Hamam, Y.
    [J]. 2021 SOUTHERN AFRICAN UNIVERSITIES POWER ENGINEERING CONFERENCE/ROBOTICS AND MECHATRONICS/PATTERN RECOGNITION ASSOCIATION OF SOUTH AFRICA (SAUPEC/ROBMECH/PRASA), 2021,
  • [8] A novel method for optimal placement of distributed generation in distribution systems using HSDO
    Kollu, Ravindra
    Rayapudi, Srinivasa Rao
    Sadhu, Venkata Lakshmi Narasimham
    [J]. INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS, 2014, 24 (04): : 547 - 561
  • [10] Optimal Sizing and Placement of Distributed Generation Using Genetic Algorithm Based on Bayesian Network
    Wu, Keru
    Wang, Hongtao
    Zou, Bin
    [J]. 2019 IEEE 3RD INTERNATIONAL CONFERENCE ON GREEN ENERGY AND APPLICATIONS (ICGEA 2019), 2019, : 75 - 79