针对当前用户侧用电设备非侵入式辨识中负荷边沿检测方法准确率不高的问题,该文在研究阈值检测算法（threshold detection algorithm,TDA）、暂态能量启动算法（transient energy to start the algorithm,TEA）、微分算子（method of di...针对当前用户侧用电设备非侵入式辨识中负荷边沿检测方法准确率不高的问题,该文在研究阈值检测算法（threshold detection algorithm,TDA）、暂态能量启动算法（transient energy to start the algorithm,TEA）、微分算子（method of differential algorithm,MDA）以及拟合方法的基础上,提出一种基于高斯滤波器和工业检测累加求和（cumulative sum,CUSUM）算法的边沿检测方法,解决传统算法考虑因素单一、精度不高等问题。该方法采用自适应高斯滤波器能有效过滤噪声,同时保留突变点信息的特点,通过检测去噪后的突变点波动,提升检测的准确性。结合CUSUM算法对设备状态与工作模式变化检测灵敏的特点,提升设备模态的检测速度和准确性。通过搭建的非侵入式负荷辨识平台,对提出的方法进行仿真和实验验证,显示所提出的方法能有效提高用电设备的检测速度和准确率。展开更多
In order to support the perception and defense of the operation risk of the medium and low voltage distribution system, it is crucial to conduct data mining on the time series generated by the system to learn anomalou...In order to support the perception and defense of the operation risk of the medium and low voltage distribution system, it is crucial to conduct data mining on the time series generated by the system to learn anomalous patterns, and carry out accurate and timely anomaly detection for timely discovery of anomalous conditions and early alerting. And edge computing has been widely used in the processing of Internet of Things (IoT) data. The key challenge of univariate time series anomaly detection is how to model complex nonlinear time dependence. However, most of the previous works only model the short-term time dependence, without considering the periodic long-term time dependence. Therefore, we propose a new Hierarchical Attention Network (HAN), which introduces seven day-level attention networks to capture fine-grained short-term time dependence, and uses a week-level attention network to model the periodic long-term time dependence. Then we combine the day-level feature learned by day-level attention network and week-level feature learned by week-level attention network to obtain the high-level time feature, according to which we can calculate the anomaly probability and further detect the anomaly. Extensive experiments on a public anomaly detection dataset, and deployment in a real-world medium and low voltage distribution system show the superiority of our proposed framework over state-of-the-arts.展开更多
文摘针对当前用户侧用电设备非侵入式辨识中负荷边沿检测方法准确率不高的问题,该文在研究阈值检测算法（threshold detection algorithm,TDA）、暂态能量启动算法（transient energy to start the algorithm,TEA）、微分算子（method of differential algorithm,MDA）以及拟合方法的基础上,提出一种基于高斯滤波器和工业检测累加求和（cumulative sum,CUSUM）算法的边沿检测方法,解决传统算法考虑因素单一、精度不高等问题。该方法采用自适应高斯滤波器能有效过滤噪声,同时保留突变点信息的特点,通过检测去噪后的突变点波动,提升检测的准确性。结合CUSUM算法对设备状态与工作模式变化检测灵敏的特点,提升设备模态的检测速度和准确性。通过搭建的非侵入式负荷辨识平台,对提出的方法进行仿真和实验验证,显示所提出的方法能有效提高用电设备的检测速度和准确率。
基金supported by the Science and Technology Project named“Research on Risk Perception and Defense System for Medium and Low Voltage Distribution System Operation Based on Data Mining”of State Grid Beijing Electric Power Company(No.520202220002).
文摘In order to support the perception and defense of the operation risk of the medium and low voltage distribution system, it is crucial to conduct data mining on the time series generated by the system to learn anomalous patterns, and carry out accurate and timely anomaly detection for timely discovery of anomalous conditions and early alerting. And edge computing has been widely used in the processing of Internet of Things (IoT) data. The key challenge of univariate time series anomaly detection is how to model complex nonlinear time dependence. However, most of the previous works only model the short-term time dependence, without considering the periodic long-term time dependence. Therefore, we propose a new Hierarchical Attention Network (HAN), which introduces seven day-level attention networks to capture fine-grained short-term time dependence, and uses a week-level attention network to model the periodic long-term time dependence. Then we combine the day-level feature learned by day-level attention network and week-level feature learned by week-level attention network to obtain the high-level time feature, according to which we can calculate the anomaly probability and further detect the anomaly. Extensive experiments on a public anomaly detection dataset, and deployment in a real-world medium and low voltage distribution system show the superiority of our proposed framework over state-of-the-arts.
