Enhancing power system loadability and optimal load shedding based on TCSC allocation using improved moth flame optimization algorithm

被引:11
|
作者
Sayed, Fatma [1 ]
Kamel, Salah [1 ]
Taher, Mahrous Ahmed [1 ]
Jurado, Francisco [2 ]
机构
[1] Aswan Univ, Fac Engn, Elect Engn Dept, Aswan 81542, Egypt
[2] Univ Jaen, Dept Elect Engn, Jaen 23700, Spain
关键词
TCSC; Load shedding; Loading margin stability; Improved moth flame optimization; Continuation power flow; MITIGATE BLACKOUT; OPTIMAL PLACEMENT; VOLTAGE COLLAPSE; INTELLIGENCE; CONTROLLER; PREVENTION; DESIGN; UPFC; FLOW;
D O I
10.1007/s00202-020-01072-w
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 power systems become operate closer to loadability limits; hence, the power systems static voltage stability assessment becomes an essential task in planning and operating for electric power systems to prevent voltage instability. In this paper, the improved moth flame optimization (IMFO) technique is applied for optimal location and size of (TCSC) with the aim of reducing load shedding, preventing voltage collapse, and enhancing the power system loadability. IMFO is developed to avoid the stagnating in local optima and improve the convergence characteristics of the conventional moth flame optimization. The loadability of the system is obtained using continuation power flow (CPF). The proposed approach is formulated by merging CPF with IMFO incorporated with TCSC. Multi-objective function is solved for minimization of loadability, load shedding, voltage stability index, and severity index. A contingency analysis is implemented on power system as two scenarios: The first scenario is outage of generator and the second scenario is outage line. Placement of TCSC has been determined by power flow analysis. The developed approach is tested on standard IEEE-30 bus system in normal operation, and contingency cases of generator and bus outage. The IMFO is compared to recent and well-known optimization techniques. The results reveal the efficiency of the proposed algorithm to reduce load shedding, continuous energy service to the customers and prevent occurrence of voltage collapse.
引用
收藏
页码:205 / 225
页数:21
相关论文
共 50 条
  • [21] An improved moth flame optimization for optimal DG and battery energy storage allocation in distribution systems
    Elseify, Mohamed A.
    Kamel, Salah
    Nasrat, Loai
    [J]. CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2024, 27 (10): : 14767 - 14810
  • [22] Optimal Capacitor Placement and Sizing Considering Load Profile Variations Using Moth-Flame Optimization Algorithm
    Ceylan, Oguzhan
    Paudyal, Sumit
    [J]. 2017 7TH INTERNATIONAL CONFERENCE ON MODERN POWER SYSTEMS (MPS), 2017,
  • [23] Optimal test sequence generation in state based testing using moth flame optimization algorithm
    Sharma, Rashmi
    Saha, Anju
    [J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2018, 35 (05) : 5203 - 5215
  • [24] Optimal Allocation of IaaS Cloud Resources through Enhanced Moth Flame Optimization (EMFO) Algorithm
    Thiruvenkadam, Srinivasan
    Kim, Hyung-Jin
    Ra, In-Ho
    [J]. ELECTRONICS, 2022, 11 (07)
  • [25] Fuzzy adaptive tuning control of power system based on moth-flame optimization algorithm
    Gao, Hongliang
    Li, Jun
    Xiong, Lang
    Zhang, Hongcong
    Ma, Shuangbao
    [J]. TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2024, 46 (03) : 513 - 523
  • [26] Optimal dispatching of microgrid based on improved moth-flame optimization algorithm based on sine mapping and Gaussian mutation
    Zhang, Yu
    Wang, Peng
    Yang, Hongwan
    Cui, Qi
    [J]. SYSTEMS SCIENCE & CONTROL ENGINEERING, 2022, 10 (01) : 115 - 125
  • [27] LVCI approach for optimal allocation of distributed generations and capacitor banks in distribution grids based on moth–flame optimization algorithm
    Mohamed A. Tolba
    Ahmed A. Zaki Diab
    Vladimir N. Tulsky
    Almoataz Y. Abdelaziz
    [J]. Electrical Engineering, 2018, 100 : 2059 - 2084
  • [28] Power System Stability Enhancement Using a Novel Hybrid Algorithm Based on the Water Cycle Moth-Flame Optimization
    Boucetta, Ikram
    Naimi, Djemai
    Salhi, Ahmed
    Abujarad, Saleh
    Zellouma, Laid
    [J]. ENERGIES, 2022, 15 (14)
  • [29] A Node Deployment Optimization Algorithm of WSNs Based on Improved Moth Flame Search
    Yao, Yindi
    Hu, Shanshan
    Li, Ying
    Wen, Qin
    [J]. IEEE SENSORS JOURNAL, 2022, 22 (10) : 10018 - 10030
  • [30] Single- and Multiobjective Optimal Power Flow with Stochastic Wind and Solar Power Plants Using Moth Flame Optimization Algorithm
    Pandya, Sundaram
    Jariwala, Hitesh R.
    [J]. SMART SCIENCE, 2022, 10 (02) : 77 - 117