State Monitoring Method for High-Voltage Circuit Breakers Based on Quantum Sensing and Edge Intelligence

YANG Saike; PANG Xianhai; LIU Guoping; GU Chaomin; ZHANG Kaiyuan

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State Monitoring Method for High-Voltage Circuit Breakers Based on Quantum Sensing and Edge Intelligence

更新时间：2026-10-16

- State Monitoring Method for High-Voltage Circuit Breakers Based on Quantum Sensing and Edge Intelligence
- High VoltageApparatus (2026)
- 作者机构：
  
  1.国网河北省电力有限公司电力科学研究院，河北石家庄 050021
  2.国网河北省电力有限公司，河北石家庄 050021
- 作者简介：
- 基金信息：
  
  State Grid Hebei Electric Power Research Institute(kjcb2025-002)
- DOI：
  CLC： TP393
- Received：03 July 2026，
  
  Revised：2026-09-07，
  
  Accepted：07 September 2026，
- 稿件说明：
移动端阅览
YANG Saike, PANG Xianhai, LIU Guoping, et al. State Monitoring Method for High-Voltage Circuit Breakers Based on Quantum Sensing and Edge Intelligence[J/OL]. High VoltageApparatus, 2026.
DOI：

YANG Saike, PANG Xianhai, LIU Guoping, et al. State Monitoring Method for High-Voltage Circuit Breakers Based on Quantum Sensing and Edge Intelligence[J/OL]. High VoltageApparatus, 2026. DOI：

摘要

复杂电磁环境下，高压断路器的微弱瞬态故障特征信号易被强工频干扰及空间电磁耦合淹没。为此，提出一种基于量子传感与边缘智能的高压断路器状态监测方法。该方法利用金刚石NV色心量子传感技术采集磁场数据，求解自旋能级哈密顿量实现高精度磁测量；引入滑动窗口自适应阈值滤波与指数加权平滑模型，剔除原始数据流中的异常值并进行趋势平滑处理；将处理后数据输入融合物理约束的卷积神经网络，提取特征并学习磁场数据与设备状态之间的复杂非线性映射关系，实现运行状态的准确识别。实验结果表明，强电磁干扰环境下，该方法的信噪比稳定在30 dB以上，状态识别率达96.2%，误报率仅2.3%，能有效识别高压断路器多参数异常，为复杂电磁环境下的电力设备状态监测提供了一种可行方案。

Abstract

In complex electromagnetic environments

the weak transient fault characteristic signals of high-voltage circuit breakers are easily overwhelmed by strong power-frequency interference and spatial electromagnetic coupling. To address this issue

this paper proposes a state monitoring method for high-voltage circuit breakers based on quantum sensing and edge intelligence. This method utilizes diamond NV center quantum sensing technology to acquire magnetic field data and solves the spin energy level Hamiltonian to achieve high-precision magnetic measurements; it incorporates a sliding window adaptive threshold filtering mechanism and an exponential weighted smoothing model to eliminate outliers from the raw data stream and perform trend smoothing; finally

the processed data is fed into a convolutional neural network incorporating physical constraints to extract features and learn the complex nonlinear mapping relationship between magnetic field data and equipment status

thereby enabling accurate identification of the operating condition. Experimental results demonstrate that in environments with strong electromagnetic interference

the signal-to-noise ratio of this method remains stable at above 30 dB

the state identification rate reaches 96.2%

and the false alarm rate is only 2.3%; this approach can effectively identify multi-parameter anomalies in high-voltage circuit breakers

providing a feasible solution for the state monitoring of electrical equipment in complex electromagnetic environments.

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