Open Access10.1109/ICECCME52200.2021.9590931
Solving Reactive Power Optimization Problem Using Weight Improved PSO Algorithm
Shaima Hamdan Shri,Mohammed B. Essa,Ayad Fadhil Mijbas +2 more
- 07 Oct 2021
2
TL;DR: In this article, a new algorithm called Weight Improved Particle Swarm Optimization (WIPSO) algorithm is presented on enhancing the function of weight parameters for solving the problem of reactive power optimization.
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Abstract: The losses in electrical power systems are a significant problem. The proper adjusting of reactive power resources is one of the ways for minimizing the Power Loss (P_L) in any power system. Reactive Power Optimization (RPO) is recorded as a complex optimization problem. The calculations of this problem are a part of Optimal Power Flow (OPF) calculations. In this work, a new algorithm called Weight Improved Particle Swarm Optimization (WIPSO) algorithm is presented on enhancing the function of weight parameters for solving this problem. Weight Improved Particle Swarm Optimization is presented as a useful optimization tool to search for optimal settings of reactive power independent variables during dealing with a number of equality, and inequality constraints at same time by minimizing the goal function (P_L). The Weight Improved Particle Swarm Optimization is tested on the IEEE 14 - bus system. Simulation results obtained WIPSO was effective, and attain the best results and has best convergence characteristic and performance in terms of decreasing (P_L) compared to simple (PSO) algorithm.
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Citations
A data-driven hybrid interval reactive power optimization based on the security limits method and improved particle swarm optimization
Da Lin Chen,Shaoqing Qu,Qian Liu,Wei Xiao,Xiao Liu,Yufeng Luo,Huaizhi Yang,Na Kuang +7 more
TL;DR: In this paper , a data-driven hybrid interval reactive power optimization based on the security limits method (SLM) and the improved particle swarm optimization (IPSO) is proposed to solve the RPOIU problem.
Optimal Power Flow using PSO algorithms based on Artificial Neural Networks
Omar Sagban Al-butti,Mustafa Burunkaya,Javad Rahebi,José Manuel López-Guede +3 more
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