用纯量子电路实时优化柴油机频率控制，提升响应速度与稳定性。
Quantum Machine Learning for Secondary Frequency Control
- 采用纯变分量子电路独立运行，避免经典-量子通信延迟。
- 预测准确率超90%，显著减少频率波动和系统恢复时间。
- 适合电力系统实时控制场景，尤其关注低延迟高可靠需求。
电力系统的频率控制对维持稳定、防止停电至关重要。传统方法如元启发式算法和机器学习在实时性与可扩展性方面存在局限。本文提出一种基于纯变分量子电路（VQC）的实时二级频率控制新方法，用于柴油发电机。与混合经典-量子模型不同，该VQC在执行时完全独立，消除了经典-量子数据交换带来的延迟。VQC通过监督学习训练，将历史频率偏差映射到最优比例积分（PI）控制器参数，使用预计算的查找表。仿真结果表明，当测量次数充足时，VQC预测准确率超过90%，且在多种测试事件中具有良好泛化能力。经量子优化的PI参数显著改善了系统暂态响应，减少了频率波动和调节时间。
原文摘要 · Abstract (English)
Frequency control in power systems is critical to maintaining stability and preventing blackouts. Traditional methods like meta-heuristic algorithms and machine learning face limitations in real-time applicability and scalability. This paper introduces a novel approach using a pure variational quantum circuit (VQC) for real-time secondary frequency control in diesel generators. Unlike hybrid classical-quantum models, the proposed VQC operates independently during execution, eliminating latency from classical-quantum data exchange. The VQC is trained via supervised learning to map historical frequency deviations to optimal Proportional-Integral (PI) controller parameters using a pre-computed lookup table. Simulations demonstrate that the VQC achieves high prediction accuracy (over 90%) with sufficient quantum measurement shots and generalizes well across diverse test events. The quantum-optimized PI parameters significantly improve transient response, reducing frequency fluctuations and settling time.
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