Abstract
With the rapid development of the social economy, the rapid development of all social circles places higher demands on the electricity industry. As a fundamental industry supporting the salvation of the national economy, society, and human life, the electricity industry will face a significant improvement and the restructuring of the network as an important part of the power system should also be optimised. This paper first introduces the development history of swarm intelligence algorithm and related research work at home and abroad. Secondly, it puts forward the importance of particle swarm optimization algorithm for power system network reconfiguration and expounds the basic principle, essential characteristics, and basic model of the particle swarm optimization algorithm. This paper completes the work of improving PSO through the common improved methods of PSO and the introduction of mutation operation and tent mapping. In the experimental simulation part, the improved particle swarm optimization algorithm is used to simulate the 10-machine 39-bus simulation system in IEEE, and the experimental data are compared with the chaos genetic algorithm and particle swarm optimization discrete algorithm. Through the experimental data, we can know that the improved particle swarm optimization algorithm has the least number of actions in switching times, only 4 times, and the chaos genetic algorithm and discrete particle swarm optimization algorithm are 5 times; compared with the other two algorithms, the improved particle swarm optimization algorithm has the fastest convergence speed and the highest convergence accuracy. The improved particle swarm optimization algorithm proposed in this paper provides an excellent solution for power system network reconfiguration and has important research significance for power system subsequent optimization and particle swarm optimization algorithm improvement.
Highlights
Electricity is the main energy base of a country, related to saving the national economy. e development of modern electricity has entered a period of multiple services, which is linked to global resources, environmental protection, and sustainable development
In order to make the improved particle swarm optimization algorithm more suitable for power system network reconfiguration, this paper introduces a variety of algorithms based on the abovementioned improved particle swarm optimization algorithm and uses the self-made parameters of the experimental simulation system to verify the performance of the improved particle swarm optimization algorithm
Rough the above comparison, it can be seen that the improved particle swarm optimization algorithm has a certain degree of improvement in the number of iterations and the solution accuracy. e recovery scheme obtained is better than other algorithms, the switching operation cost is low, and the convergence performance is improved, which can ensure that a better recovery scheme can be obtained
Summary
Electricity is the main energy base of a country, related to saving the national economy. e development of modern electricity has entered a period of multiple services, which is linked to global resources, environmental protection, and sustainable development. E requirements of “safety, reliability, economy, high quality, and environmental protection” in the power supply are constantly improving, and the power system is developing in the direction of automation, optimization, adaptation, intelligence, coordination, and regionalization. The particle swarm optimization algorithm has the advantages of fast convergence speed, multiparticle parallel processing, and easy application, which can solve the optimization problem of the power system. American scholars Eberhart and Kennedy studied the foraging behavior of birds and proposed particle swarm optimization (PSO). Since the particle swarm optimization was proposed, in order to improve the performance of the algorithm, a large number of researchers at home and abroad have improved it. E model of analysis of convergence theory and the improved particle optimization algorithm shall be used to study the reformulation of the power grid. Is article is mainly concerned with improving the PSO learning method and the optimization algorithm. e model of analysis of convergence theory and the improved particle optimization algorithm shall be used to study the reformulation of the power grid. e main purpose of this work is to propose a more appropriate and valuable algorithm for the restructuring of the power grid
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