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arXiv Machine Learning By Changhong Li, Georgios Floros, Biswajit Basu, Shreejith Shanker

EBL: Efficient Broad Learning for Distributed Adaptive Harmonic Analysis

Read the original on arXiv Machine Learning →

The paper introduces EBL, an Efficient Broad Learning framework designed for distributed adaptive harmonic estimation in power grids affected by electric vehicle charging. It leverages a quantised FPGA implementation to provide high‑accuracy, half‑cycle input harmonic predictions with ultra‑low latency, outperforming existing FPGA methods by 17.4×. The online transfer learning component enables rapid adaptation across multiple charging scenarios, while bespoke quantisation and sparsity reduce resource usage to just 5.9% of the LUTs on a Zynq Ultrascale+ FPGA, compared to 82% of the state‑of‑the‑art accelerator.

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