面向电力系统动态评估的元学习量子传感监测方法

庞先海; 杨赛柯; 刘国平; 王韶鹏; 张凯元

doi:10.13296/j.1001-1609.hva.2026.10.010

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面向电力系统动态评估的元学习量子传感监测方法

研究与分析 | 更新时间：2026-10-14

- 面向电力系统动态评估的元学习量子传感监测方法
- Meta Learning Quantum Sensing Monitoring Method for Dynamic Assessment of Power Systems
- 高压电器 2026年62卷第10期页码：85-91
- 作者机构：
  
  国网河北省电力有限公司电力科学研究院，石家庄 050021
  国网河北省电力有限公司，石家庄 050021
- 作者简介：
  
  庞先海（1983—），男，硕士研究生，教授级高级工程师，研究方向为电力状态检测与试验技术（E-mail：dyy_pangxh@163.com）。
  杨赛柯（1995—），男，博士研究生，工程师，研究方向为电力设备绝缘状态检测与故障诊断（通信作者）（E-mail：yangsk1204@163.com）。
- 基金信息：
  
  国网河北省电力有限公司科技项目资助(kjcb2025-002)
- DOI：10.13296/j.1001-1609.hva.2026.10.010
  中图分类号：
- 收稿：2026-03-03，
  
  修回：2026-05-09，
  
  纸质出版：2026-10-16
- 稿件说明：
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庞先海, 杨赛柯, 刘国平, 等. 面向电力系统动态评估的元学习量子传感监测方法[J]. 高压电器, 2026,62(10):85-91.

PANG Xianhai, YANG Saike, LIU Guoping, et al. Meta Learning Quantum Sensing Monitoring Method for Dynamic Assessment of Power Systems[J]. High Voltage Apparatus, 2026, 62(10): 85-91.
庞先海, 杨赛柯, 刘国平, 等. 面向电力系统动态评估的元学习量子传感监测方法[J]. 高压电器, 2026,62(10):85-91. DOI： 10.13296/j.1001-1609.hva.2026.10.010.

PANG Xianhai, YANG Saike, LIU Guoping, et al. Meta Learning Quantum Sensing Monitoring Method for Dynamic Assessment of Power Systems[J]. High Voltage Apparatus, 2026, 62(10): 85-91. DOI： 10.13296/j.1001-1609.hva.2026.10.010.

摘要

在面向电力系统状态感知的监测框架下，针对电力系统电场信号多模态、宽频带特性易引发频谱混叠，影响后续设备状态辨识与风险评估准确性的问题，提出量子增强多模态电场监测方法。该方法基于金刚石NV色心光纤量子传感器采集电流信号，采用元学习增强的微波频率调制策略，通过多工况先验学习与快速适配机制，动态优化调制频偏与调制频率，确保量子传感始终工作于最优灵敏度点。进一步将校正后的电流序列输入双向长短期记忆网络，利用前向与后向时序特征提取能力，构建正常运行、警戒、紧急、故障4类状态的动态评估模型。测试显示，该调制方法将电流感知灵敏度从86.9 A/GHz提升至1 146.2 A/GHz灵敏度提升了约13.2倍。在0.5高比例噪声干扰下，模型对各状态类型的识别结果与预设状态完全一致，全电流区间感知比差均低于0.1，验证了所提方法在高噪声环境下的鲁棒性与准确性。

Abstract

Within the monitoring framework for power system state awareness

to address the problem that the multimodal and broadband characteristics of electric field signals in power systems are prone to spectral aliasing

which adversely affects the accuracy of subsequent equipment state identification and risk assessment

a quantum-enhanced multi-modal electric field monitoring method is proposed. This method employs a diamond NV-center fiberoptic quantum sensor to acquire current signals

and adopts a meta-learning-enhanced microwave frequency modulation strategy. Through prior learning across multiple operating conditions and a fast adaptation mechanism

the modulation frequency deviation and modulation frequency are dynamically optimized

ensuring that the quantum sensor consistently operates at its optimal sensitivity point.Further

the corrected current sequence is input into the bidirectional long short-term memory network

and the forward and backward temporal feature extraction capabilities are utilized to construct dynamic assessment models for normal operation

alert

emergency

and fault states. The tests show that the modulation method increases the current sensing sensitivity from 86.9 A/GHz to 1 146.2 A/GHz

with increase of approximately 13.2 times. Under a 0.5 high proportion noise interference

the model's recognition results for each state type are in complete agreeement with the preset state

and the perception difference in the full current range is lower than 0.1

verifying the robustness and accuracy of the proposed method in high-noise environments.

关键词

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