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This research introduces an Efficient Broad Learning (EBL) framework designed to accelerate harmonic analysis in power grids affected by electric vehicle charging, leveraging quantized FPGA implementations for ultra-low latency. By utilizing closed-form solutions instead of backpropagation, the method achieves 17.4× faster predictions while consuming significantly fewer logic resources than current state-of-the-art estimators. The work represents a significant advancement in applying machine learning architectures to real-time, hardware-constrained infrastructure monitoring and control.
EBL: Efficient Broad Learning for Distributed Adaptive Harmonic Analysis
arXiv:2609.16358v1 Announce Type: cross Abstract: Renewable energy systems and electrified transport have found widespread adoption in recent years. The integration of these non-linear loads, dominated by electric vehicle (EV) charging, however, has introduced severe harmonic distortion into the power grid, impacting the efficiency and lifetime of substation equipment and switchgear in the distribution network. Rapid and high-precision harmonic analysis has hence become a prerequisite for effective harmonic control at the source of injection. This paper proposes an Efficient Broad Learning (EBL) framework for distributed adaptive harmonic estimation. As a quantised FPGA acceleration framework for BLS-style harmonic estimation, it offers high-accuracy estimation with half-cycle input, reconfigurable flexibility enabled by the FPGA implementation, and ultra-low latency, achieving 17.4 $\times$ faster predictions than the nearest reported FPGA method. For harmonic prediction across multi-scenario charging and discharging nodes, the online transfer learning based on a closed-form solution rather than backpropagation in EBL demonstrates rapid adaptability. By exploiting bespoke quantisation and sparsity, the approach consumes 5.9\% of the LUTs on the Zynq Ultrascale+ ZU7EV FPGA, using $\approx$ 82\% of the LUTs required by the state-of-the-art FPGA-accelerated estimator.
Abstract:Renewable energy systems and electrified transport have found widespread adoption in recent years. The integration of these non-linear loads, dominated by electric vehicle (EV) charging, however, has introduced severe harmonic distortion into the power grid, impacting the efficiency and lifetime of substation equipment and switchgear in the distribution network. Rapid and high-precision harmonic analysis has hence become a prerequisite for effective harmonic control at the source of injection. This paper proposes an Efficient Broad Learning (EBL) framework for distributed adaptive harmonic estimation. As a quantised FPGA acceleration framework for BLS-style harmonic estimation, it offers high-accuracy estimation with half-cycle input, reconfigurable flexibility enabled by the FPGA implementation, and ultra-low latency, achieving 17.4 $\times$ faster predictions than the nearest reported FPGA method. For harmonic prediction across multi-scenario charging and discharging nodes, the online transfer learning based on a closed-form solution rather than backpropagation in EBL demonstrates rapid adaptability. By exploiting bespoke quantisation and sparsity, the approach consumes 5.9\% of the LUTs on the Zynq Ultrascale+ ZU7EV FPGA, using $\approx$ 82\% of the LUTs required by the state-of-the-art FPGA-accelerated estimator.
Hardware Architecture (cs.AR); Machine Learning (cs.LG)
arXiv:2609.16358 [cs.AR]
(or arXiv:2609.16358v1 [cs.AR] for this version)
https://doi.org/10.48550/arXiv.2609.16358 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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