发表机构
Trinity College Dublin（都柏林圣三一学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出EBL框架，一种基于量化FPGA的BLS式谐波估计方法，实现半周期高精度、低延迟（快17.4倍）及快速在线迁移，资源消耗仅为先进方法的82%。
AI 中文摘要
近年来，可再生能源系统和电气化交通得到了广泛采用。然而，这些非线性负载（以电动汽车充电为主）的接入，给电网引入了严重的谐波畸变，影响了配电网中变电站设备和开关设备的效率与寿命。因此，快速且高精度的谐波分析已成为在注入源进行有效谐波控制的前提。本文提出了一种用于分布式自适应谐波估计的高效宽度学习（EBL）框架。作为一种基于BLS式谐波估计的量化FPGA加速框架，它提供了半周期输入下的高精度估计、由FPGA实现带来的可重构灵活性，以及超低延迟，其预测速度比最近报道的FPGA方法快17.4倍。对于多场景充放电节点的谐波预测，EBL中基于闭式解而非反向传播的在线迁移学习展现了快速的自适应性。通过利用定制量化和稀疏性，该方法在Zynq Ultrascale+ ZU7EV FPGA上仅消耗5.9%的LUT，约为最先进的FPGA加速估计器所需LUT的82%。
英文摘要
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.
CommentsAccepeted by ICECS'26
