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.
Surface sediment samples were collected from a source water reservoir in Zhejiang Province, East of China to investigate pollution characteristics and potential ecological risk of heavy metals. The BCR sequential extraction method was used to determine the four chemical fractions of heavy metals such as acid soluble, easily reducible, easily oxidizable and residual fractions. The heavy metals pollution and potential ecological risk were evaluated systematically using geoaccumulation index (I geo ) and Hakanson potential ecological risk index (H′). The results showed that the sampling sites from the estuaries of tributary flowing through downtowns and heavy industrial parks showed significantly (p < 0.05) higher average concentrations of heavy metals in the surface sediments, as compared to the other sampling sites. Chemical fractionation showed that Mn existed mainly in acid extractable fraction, Cu and Pb were mainly in reducible fraction, and As existed mainly in residual fraction in the surface sediments despite sampling sites. The sampling sites from the estuary of tributary flowing through downtown showed significantly (p < 0.05) higher proportions of acid extractable and reducible fractions than the other sampling sites, which would pose a potential toxic risk to aquatic organisms as well as a potential threat to drinking water safety. As, Pb, Ni and Cu were at relatively high potential ecological risk with high I geo values for some sampling locations. Hakanson potential ecological risk index (H′) showed the surface sediments from the tributary estuaries with high population density and rapid industrial development showed significantly (p < 0.05) higher heavy metal pollution levels and potential ecological risk in the surface sediments, as compared to the other sampling sites.
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