The Haiyuan fault is a major seismogenic fault in north-central China where the 1920 Haiyuan earthquake of magnitude 8.5 occurred, resulting in more than 220000 deaths. The fault zone can be divided into three segment...The Haiyuan fault is a major seismogenic fault in north-central China where the 1920 Haiyuan earthquake of magnitude 8.5 occurred, resulting in more than 220000 deaths. The fault zone can be divided into three segments based on their geometric patterns and associated geomorphology. To study paleoseismology and recurrent history of devastating earthquakes along the fault, we dug 17 trenches along different segments of the fault zone. Although only 10 of them allow the paleoearthquake event to be dated, together with the 8 trenches dug previously they still provide adequate information that enables us to capture major paleoearthquakes oc- curring along the fault during the past geological time. We discovered 3 events along the eastern segment during the past 14000 a, 7 events along the middle segment during the past 9000 a, and 6 events along the western segment during the past 10000 a. These events clearly depict two temporal clusters. The first cluster occurs from 4600 to 6400 a, and the second occurs from 1000 to 2800 a, approximately. Each cluster lasts about 2000 a. Time period between these two clus- ters is also about 2000 a. Based on fault geometry, segmentation pattern, and paleoearthquake events along the Haiyuan fault we can identify three scales of earthquake rupture: rupture of one segment, cascade rupture of two segments, and cascade rupture of entire fault (three segments). Interactions of slip patches on the surface of the fault may cause rupture on one patch or ruptures of more than two to three patchs to form the complex patterns of cascade rupture events.展开更多
Phasor measurement units(PMUs) can provide real-time measurement data to construct the ubiquitous electric of the Internet of Things. However, due to complex factors on site, PMU data can be easily compromised by inte...Phasor measurement units(PMUs) can provide real-time measurement data to construct the ubiquitous electric of the Internet of Things. However, due to complex factors on site, PMU data can be easily compromised by interference or synchronization jitter. It will lead to various levels of PMU data quality issues, which can directly affect the PMU-based application and even threaten the safety of power systems. In order to improve the PMU data quality, a data-driven PMU bad data detection algorithm based on spectral clustering using single PMU data is proposed in this paper. The proposed algorithm does not require the system topology and parameters. Firstly, a data identification method based on a decision tree is proposed to distinguish event data and bad data by using the slope feature of each data. Then, a bad data detection method based on spectral clustering is developed. By analyzing the weighted relationships among all the data, this method can detect the bad data with a small deviation. Simulations and results of field recording data test illustrate that this data-driven method can achieve bad data identification and detection effectively. This technique can improve PMU data quality to guarantee its applications in the power systems.展开更多
从用户侧电流数据中发现用电事件,对挖掘用户用电行为模式,提高用户侧用电管理水平具有重要意义。为及时有效地检测出单个电器下电流数据中蕴含的用户用电事件,设计基于聚类用户用电事件辨识模型。该模型在用户用电电流数据高频在线监...从用户侧电流数据中发现用电事件,对挖掘用户用电行为模式,提高用户侧用电管理水平具有重要意义。为及时有效地检测出单个电器下电流数据中蕴含的用户用电事件,设计基于聚类用户用电事件辨识模型。该模型在用户用电电流数据高频在线监测基础上,构建固定宽度电流序列片段,将电流序列中用电事件辨识问题视为电流序列片段集的聚类划分问题,并使用轮廓系数和精度2个指标进行性能评估。实验结果表明,相较基于k均值聚类、层次式聚类以及SOM(Self-Organizing Map)聚类等实现的用户用电事件辨识模型,基于DBSCAN(Density-Based Spatial Clustering of Applications with Noise)算法用户用电事件辨识模型可以高效辨识出高频电流序列中的用户用电事件。展开更多
为保证同步相量测量装置(phasor measurement unit,PMU)采集数据的准确应用,须排除其量测值中的异常数据。现有PMU异常数据辨识算法存在算法复杂度高、难以在线更新、多源数据难以校准、依赖多源数据应用难度大等不足。为此,文中从PMU...为保证同步相量测量装置(phasor measurement unit,PMU)采集数据的准确应用,须排除其量测值中的异常数据。现有PMU异常数据辨识算法存在算法复杂度高、难以在线更新、多源数据难以校准、依赖多源数据应用难度大等不足。为此,文中从PMU事件数据和异常数据模型及PMU异常数据判别信息熵定义出发,提出基于该信息熵的异常数据辨识框架。在此框架基础上,基于利用层次方法的平衡迭代规约和聚类(balanced iterative reducing and clustering using hierarchies,BIRCH)算法提出PMU异常数据辨识算法;然后,对所提出的算法进行原型实现,并针对某变电站的PMU采集数据集进行算法实验验证。实验结果表明,与一类支持向量机(one-class support vector machine,OCSVM)算法与间隙统计算法相比,文中算法的准确度及实时性均具有较强的优势。展开更多
基金supported by National Key Bas ic Research Program(Grant No.G1998040701)Joint Foundation of Earthquake Sciences(Grant No.95087421).
文摘The Haiyuan fault is a major seismogenic fault in north-central China where the 1920 Haiyuan earthquake of magnitude 8.5 occurred, resulting in more than 220000 deaths. The fault zone can be divided into three segments based on their geometric patterns and associated geomorphology. To study paleoseismology and recurrent history of devastating earthquakes along the fault, we dug 17 trenches along different segments of the fault zone. Although only 10 of them allow the paleoearthquake event to be dated, together with the 8 trenches dug previously they still provide adequate information that enables us to capture major paleoearthquakes oc- curring along the fault during the past geological time. We discovered 3 events along the eastern segment during the past 14000 a, 7 events along the middle segment during the past 9000 a, and 6 events along the western segment during the past 10000 a. These events clearly depict two temporal clusters. The first cluster occurs from 4600 to 6400 a, and the second occurs from 1000 to 2800 a, approximately. Each cluster lasts about 2000 a. Time period between these two clus- ters is also about 2000 a. Based on fault geometry, segmentation pattern, and paleoearthquake events along the Haiyuan fault we can identify three scales of earthquake rupture: rupture of one segment, cascade rupture of two segments, and cascade rupture of entire fault (three segments). Interactions of slip patches on the surface of the fault may cause rupture on one patch or ruptures of more than two to three patchs to form the complex patterns of cascade rupture events.
基金supported by the National Key R&D Program (No.2017YFB0902901)the National Natural Science Foundation of China (No.51627811,No.51725702,and No.51707064)。
文摘Phasor measurement units(PMUs) can provide real-time measurement data to construct the ubiquitous electric of the Internet of Things. However, due to complex factors on site, PMU data can be easily compromised by interference or synchronization jitter. It will lead to various levels of PMU data quality issues, which can directly affect the PMU-based application and even threaten the safety of power systems. In order to improve the PMU data quality, a data-driven PMU bad data detection algorithm based on spectral clustering using single PMU data is proposed in this paper. The proposed algorithm does not require the system topology and parameters. Firstly, a data identification method based on a decision tree is proposed to distinguish event data and bad data by using the slope feature of each data. Then, a bad data detection method based on spectral clustering is developed. By analyzing the weighted relationships among all the data, this method can detect the bad data with a small deviation. Simulations and results of field recording data test illustrate that this data-driven method can achieve bad data identification and detection effectively. This technique can improve PMU data quality to guarantee its applications in the power systems.
文摘从用户侧电流数据中发现用电事件,对挖掘用户用电行为模式,提高用户侧用电管理水平具有重要意义。为及时有效地检测出单个电器下电流数据中蕴含的用户用电事件,设计基于聚类用户用电事件辨识模型。该模型在用户用电电流数据高频在线监测基础上,构建固定宽度电流序列片段,将电流序列中用电事件辨识问题视为电流序列片段集的聚类划分问题,并使用轮廓系数和精度2个指标进行性能评估。实验结果表明,相较基于k均值聚类、层次式聚类以及SOM(Self-Organizing Map)聚类等实现的用户用电事件辨识模型,基于DBSCAN(Density-Based Spatial Clustering of Applications with Noise)算法用户用电事件辨识模型可以高效辨识出高频电流序列中的用户用电事件。
文摘为保证同步相量测量装置(phasor measurement unit,PMU)采集数据的准确应用,须排除其量测值中的异常数据。现有PMU异常数据辨识算法存在算法复杂度高、难以在线更新、多源数据难以校准、依赖多源数据应用难度大等不足。为此,文中从PMU事件数据和异常数据模型及PMU异常数据判别信息熵定义出发,提出基于该信息熵的异常数据辨识框架。在此框架基础上,基于利用层次方法的平衡迭代规约和聚类(balanced iterative reducing and clustering using hierarchies,BIRCH)算法提出PMU异常数据辨识算法;然后,对所提出的算法进行原型实现,并针对某变电站的PMU采集数据集进行算法实验验证。实验结果表明,与一类支持向量机(one-class support vector machine,OCSVM)算法与间隙统计算法相比,文中算法的准确度及实时性均具有较强的优势。
