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Research on a Non-Intrusive Load Recognition Algorithm Based on High-Frequency Signal Decomposition with Improved VI Trajectory and Background Color Coding

Author

Listed:
  • Jiachuan Shi

    (Shandong Key Laboratory of Intelligent Buildings Technology, Shandong Jianzhu University, Jinan 250101, China
    These authors contributed equally to this work.)

  • Dingrui Zhi

    (School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China
    These authors contributed equally to this work.)

  • Rao Fu

    (School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China)

Abstract

Against the backdrop of the current Chinese national carbon peak and carbon neutrality policies, higher requirements have been put forward for the construction and upgrading of smart grids. Non-intrusive Load Monitoring (NILM) technology is a key technology for advanced measurement systems at the end of the power grid. This technology obtains detailed power information about the load without the need for traditional hardware deployment. The key step to solve this problem is load decomposition and identification. This study first utilized the Long Short-Term Memory Denoising Autoencoder (LSTM-DAE) to decompose the mixed current signal of a household busbar and obtain the current signals of the multiple independent loads that constituted the mixed current. Then, the obtained independent current signals were combined with the voltage signals to generate multicycle colored Voltage–Current (VI) trajectories, which were color-coded according to the background. These color-coded VI trajectories formed a feature library. When the Convolutional Neural Network (CNN) was used for load recognition, in light of the influence of the hyperparameters on the recognition results, the Bayesian Optimization Algorithm (BOA) was used for optimization, and the optimized CNN network was employed for VI trajectory recognition. Finally, the proposed method was validated using the PLAID dataset. The experimental results show that the proposed method exhibited better performance in load decomposition and identification than current methods.

Suggested Citation

  • Jiachuan Shi & Dingrui Zhi & Rao Fu, 2023. "Research on a Non-Intrusive Load Recognition Algorithm Based on High-Frequency Signal Decomposition with Improved VI Trajectory and Background Color Coding," Mathematics, MDPI, vol. 12(1), pages 1-20, December.
  • Handle: RePEc:gam:jmathe:v:12:y:2023:i:1:p:30-:d:1305340
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    References listed on IDEAS

    as
    1. Pengfei Yang & Xianbo Sun & Li Zhu & Yuhan Wu & Baofu Dai & Nouman Ali, 2022. "Load Identification Method Based on ISMA-GRNN," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-11, March.
    2. Petros Papageorgiou & Dimitra Mylona & Konstantinos Stergiou & Aggelos S. Bouhouras, 2023. "A Time-Driven Deep Learning NILM Framework Based on Novel Current Harmonic Distortion Images," Sustainability, MDPI, vol. 15(17), pages 1-14, August.
    3. Li, Dandan & Li, Jiangfeng & Zeng, Xin & Stankovic, Vladimir & Stankovic, Lina & Xiao, Changjiang & Shi, Qingjiang, 2023. "Transfer learning for multi-objective non-intrusive load monitoring in smart building," Applied Energy, Elsevier, vol. 329(C).
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