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Research on non-intrusive unknown load identification technology based on deep learning

机译：基于深度学习的非侵入式载荷识别技术研究

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摘要

With the development of smart grid technology, optimization of energy structure, improvement of power efficiency and reduction of power consumption have become the major development trends. As a key process of power structure analysis, load identification is gaining increasing attention in smart grids. Although lots of load identification methods have been proposed, the introduction of unknown states or new load types is still a challenge for precise identification. In this paper, a similarity calculation method of the space convex hull overlap rate is proposed to deal with the identification of unknown loads based on the low-dimensional feature space model of Siamese neural networks. Moreover, transfer learning is introduced to realize the category-added learning of load pre-identification model for unknown load. The ultimate aim was to establish the identification and rapid modeling of unknown loads. The public datasets Plaid is used to verify the performance of the proposed method. As a result, the high accuracy of load identification for unknown load is proved.
机译：随着智能电网技术的发展，能源结构的优化，功率效率提高以及功耗降低已成为主要的发展趋势。作为电力结构分析的关键过程，负载识别在智能电网中增加了越来越关注。虽然已经提出了许多负载识别方法，但是引入未知状态或新负载类型仍为精确识别的挑战。本文提出了一种基于暹罗神经网络的低维特征空间模型来处理空间凸船重叠率的相似性计算方法。此外，引入了转移学习，以实现对未知负载的负载预识别模型的类别添加学习。最终目标是建立未知负荷的识别和快速建模。 PILP数据集格库格用于验证所提出的方法的性能。结果，证明了未知负载的负载识别的高精度。

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