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- W4226250679 abstract "The multi-source and heterogeneous characteristics of the distribution network have become increasingly prominent, and accurate line parameters are of great significance to its operation and control. The determination of on-site inherent parameters often relies on empirical data and classical mathematical models to derive, without considering the actual operating conditions and environmental factors’ influence on the parameters. The data-driven multi-source distribution network line parameter identification method has received widespread attention, and it is important for timely and accurate control. The line parameters and operating characteristics of the distribution network are of great significance. The paper compares and analyzes traditional online and offline parameter identification methods, and analyzes the advantages and disadvantages of common algorithms in detail. Due to the difficulty of obtaining physical models, it discusses several typical data-driven methods such as machine learning, deep learning, reinforcement learning, and migration learning. Based on the multi-source, massive, and heterogeneous characteristics presented by the measurement data in the multi-source distribution network, the data preprocessing and feature extraction techniques in the data-driven intelligent algorithm are analyzed; the data is discussed The current status of research on the application of intelligent algorithms in line parameter identification under the driving force, and the structure of line parameter identification of distribution network is given; by discussing the challenges and problems faced by the current data-driven intelligent algorithm in the line parameter identification of distribution network, the corresponding solution ideas are proposed." @default.
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- W4226250679 date "2021-12-23" @default.
- W4226250679 modified "2023-09-30" @default.
- W4226250679 title "Research on Intelligent Algorithms for Line Parameter Identification of Multi-source Distribution Network Based on Data Driven" @default.
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- W4226250679 doi "https://doi.org/10.1109/ispec53008.2021.9735606" @default.
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