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FPGA-Based Semisupervised Multifusion RDCNN of Process Robust VMD Data With Online Kernel RVFLN for Power Quality

Mrutyunjaya Sahani, Pradipta Kishore Dash

    IEEE Transactions on Neural Networks and Learning Systems
    |October 19, 2020
    PubMed
    Summary

    This study introduces a novel method combining Variational Mode Decomposition (VMD) and deep learning for accurate power quality event (PQE) classification. The approach enhances feature extraction and classification speed, even in noisy conditions.

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    Area of Science:

    • Electrical Engineering
    • Signal Processing
    • Artificial Intelligence

    Background:

    • Power quality events (PQEs) are critical in electrical systems, requiring accurate detection and classification.
    • Existing methods often struggle with complex, combined PQEs and noisy environments.
    • Advanced signal processing and machine learning are needed for robust PQE analysis.

    Purpose of the Study:

    • To develop an integrated method for efficient extraction and classification of single and combined PQEs.
    • To improve the accuracy and speed of PQE recognition, particularly in challenging conditions.
    • To validate the proposed method's feasibility for real-time online monitoring.

    Main Methods:

    • Integration of improved particle swarm optimization with Variational Mode Decomposition (VMD) to extract band-limited intrinsic mode functions (BLIMFs).
    • Utilizing Robust VMD (RVMD) and Fourier Magnitude Spectrum (FMS) for feature extraction via a Reduced Deep Convolutional Neural Network (RDCNN).
    • Employing an Online Kernel Random Vector Functional Link Network (OKRVFLN) for supervised classification of complex PQEs.

    Main Results:

    • The proposed RVMD-FMS-RDCNN-OKRVFLN method demonstrated superior feature extraction with minimal overlap.
    • Achieved excellent recognition capability and classification accuracy in both noise-free and noisy environments.
    • The method exhibited higher learning speed and robust anti-noise performance compared to baseline methods.

    Conclusions:

    • The developed RVMD-FMS-RDCNN-OKRVFLN method offers a highly effective solution for PQE monitoring.
    • Its unique BLIMF selection, feature extraction, and classification capabilities are significant advancements.
    • Implementation on FPGA validated the method's practicality for online PQE monitoring systems.