Abstract
With the management reform of power grid enterprises and the construction of “big marketing” system, higher requirements are put forward for the data management and collaborative operation of power system engineering. Particle Swarm Optimization (PSO) is a new and advanced algorithm. Because of its simple, easy-to-operate, general-purpose, and parallel processing advantages, it can perform data analysis on power system engineering construction. In view of this, this paper designs an improved particle swarm algorithm (PPSO) based on the basic particle swarm algorithm, and conducts research on the analysis of power system engineering construction data. In this paper, the basic particle swarm algorithm is summarized first, and then the principle of the algorithm is researched and analyzed. Based on again, the improved particle swarm algorithm designed in this paper is proposed. And combined with the current status of power system engineering construction data analysis, based on its existing problems and deficiencies, data analysis of power system engineering construction. This article systematically expounds the system architecture, core module realization and forecasting model construction of the construction of power data analysis system. And using comparative method, field survey method and other research forms to carry out research on the theme of this article. Experimental research shows that compared with the traditional power construction data analysis system, the power construction data analysis system based on the PPSO algorithm designed in this paper is superior in many aspects, especially the status analysis is more than 15% higher, which fully reflects this article Research the feasibility of the theme.
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Zhou, Z., Xin, C., Tian, S., Zhang, Y., Ran, X. (2022). Data Analysis of Power System Engineering Construction Based on PPSO Algorithm. In: Macintyre, J., Zhao, J., Ma, X. (eds) The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIoT 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 98 . Springer, Cham. https://doi.org/10.1007/978-3-030-89511-2_11
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DOI: https://doi.org/10.1007/978-3-030-89511-2_11
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