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深度转移学习方法用于单个和多个功率质量干扰的分类

Uvesh Sipai1, Rajendrasinh Jadeja2, Nishant Kothari2

  • 1Department of Electrical Engineering, Marwadi University, Rajkot, Gujarat, 360003, India. uvesh.sipai@marwadieducation.edu.in.

Scientific reports
|October 3, 2025
PubMed
概括

本研究引入了一种深度转移学习方法,用于分类功率质量干扰 (PQD). 谷歌网络模型在识别各种PQD方面取得了很高的准确性,即使在噪音和现实场景中,也提高了电网的可靠性.

关键词:
连续波形变换连续波形变换.深度学习是一种深度学习.电力质量障碍 电力质量障碍转移学习转移学习

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