一种基于动态感知的智能电能表状态自诊断方法及电能表
By constructing a dynamic health baseline model and a Gaussian kernel membership fusion algorithm, combined with time series analysis, the shortcomings of smart meters in environmental adaptability and multi-dimensional feature fusion are solved, achieving high accuracy and robust self-diagnosis and self-repair, and reducing the false alarm rate.
CN122172108BActive Publication Date: 2026-07-17JIANGSU INST OF METROLOGY
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- JIANGSU INST OF METROLOGY
- Filing Date
- 2026-05-12
- Publication Date
- 2026-07-17
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Figure CN122172108B_ABST
Abstract
本发明公开了一种基于动态感知的智能电能表状态自诊断方法及电能表，属于故障诊断技术领域。该方法包括：采集运行状态特征与环境因子数据；基于环境区间划分提取特征分布参数，构建随环境动态变化的健康基线模型；计算实时特征与健康基线的加权偏离度，结合时间序列算法预测漂移趋势；根据偏离度与趋势综合评估健康状态并自适应调整补偿参数。本发明通过环境区间与特征绑定消除环境波动干扰，采用高斯核隶属度融合实现区间边界参数平滑过渡，并引入补偿效能动态衰减机制防止过度补偿掩盖硬件故障，实现复杂环境下电能表状态的精准诊断与安全自修复闭环。
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