Comparative studies of particle swarm optimization techniques for reactive power allocation planning in power systems

2005 ◽  
Vol 153 (1) ◽  
pp. 34-41 ◽  
Author(s):  
Yoshikazu Fukuyama
Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5679
Author(s):  
Mohamed A. M. Shaheen ◽  
Dalia Yousri ◽  
Ahmed Fathy ◽  
Hany M. Hasanien ◽  
Abdulaziz Alkuhayli ◽  
...  

The appropriate planning of electric power systems has a significant effect on the economic situation of countries. For the protection and reliability of the power system, the optimal reactive power dispatch (ORPD) problem is an essential issue. The ORPD is a non-linear, non-convex, and continuous or non-continuous optimization problem. Therefore, introducing a reliable optimizer is a challenging task to solve this optimization problem. This study proposes a robust and flexible optimization algorithm with the minimum adjustable parameters named Improved Marine Predators Algorithm and Particle Swarm Optimization (IMPAPSO) algorithm, for dealing with the non-linearity of ORPD. The IMPAPSO is evaluated using various test cases, including IEEE 30 bus, IEEE 57 bus, and IEEE 118 bus systems. An effectiveness of the proposed optimization algorithm was verified through a rigorous comparative study with other optimization methods. There was a noticeable enhancement in the electric power networks behavior when using the IMPAPSO method. Moreover, the IMPAPSO high convergence speed was an observed feature in a comparison with its peers.


2013 ◽  
Vol 811 ◽  
pp. 666-671
Author(s):  
Yan Wen Liu ◽  
Ke Yin Jia ◽  
Hao Wang ◽  
Yan Hua Wang

Reactive power optimization is very important to power systems economic operation and nowadays, the research about it gets more and more popular. The paper presents a reactive power optimization algorithm of particle swarm optimization combined with sensitivity analysis. The paper first builds the mathematic model of reactive power optimization and introduces particle swarm optimization. Then, presents the sensitivity method in detail and talks about the process of computing the sensitivity. Finally, take the algorithm into practical application and the results proves that sensitivity analysis could improve the particle swarm optimization algorithm.


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