In experimental tests, besides data in range of allowable error, the experimenters usually get some unexpected wrong data called bad points. In usual experimental data processing, the method of bad points exclusion ba...In experimental tests, besides data in range of allowable error, the experimenters usually get some unexpected wrong data called bad points. In usual experimental data processing, the method of bad points exclusion based on automatic programming is seldom taken into consideration by researchers. This paper presents a new method to reject bad points based on Hough transform, which is modified to save computational and memory consumptions. It is fit for linear data processing and can be extended to process data that is possible to be transformed into and from linear form; curved lines, which can be effectively detected by Hough transform. In this paper, the premise is the distribution of data, such as linear distribution and exponential distribution, is predetermined. Steps of the algorithm start from searching for an approximate curve line that minimizes the sum of parameters of data points. The data points, whose parameters are above a self-adapting threshold, will be deleted. Simulation experiments have manifested that the method proposed in this paper performs efficiently and robustly.展开更多
配电网环境复杂,配电网同步相量测量装置(distribution network synchronous phasor measurement unit, D-PMU)容易受到干扰而产生坏数据,进一步影响基于测量数据的应用效果。为了提高D-PMU数据质量,提出一种不依赖系统拓扑的基于密度...配电网环境复杂,配电网同步相量测量装置(distribution network synchronous phasor measurement unit, D-PMU)容易受到干扰而产生坏数据,进一步影响基于测量数据的应用效果。为了提高D-PMU数据质量,提出一种不依赖系统拓扑的基于密度的噪场应用空间聚类(density-based spatial clustering of applications with noise, DBSCAN)的配电网同步测量坏数据检测方法。首先利用基于密度的聚类算法DBSCAN进行异常数据检测。通过轮廓系数和邓恩指数对DBSCAN的聚类结果进行综合评价。利用麻雀搜索算法实现自适应参数调整,解决检测时需要预先处理训练、标记数据的问题。在此基础上,将时间序列聚类的K-Medoids算法和动态时间规整算法相结合,通过衡量不同时间序列之间的相似性,解决了D-PMU在电气联系较弱时对扰动数据与坏数据的区分问题,增强了数据处理的准确性与噪声环境下的稳健性。仿真和实际数据的测试结果表明,所提方法能有效区分真实扰动数据并准确识别D-PMU坏数据。展开更多
为提高状态估计的抗差性,提出一种基于最大指数绝对值目标函数的状态估计(maximum exponential absolute value state estimation,MEAV)方法。首先给出了MEAV的基本模型,并介绍了其理论基础和数学性质。由于MEAV基本模型的目标函数并非...为提高状态估计的抗差性,提出一种基于最大指数绝对值目标函数的状态估计(maximum exponential absolute value state estimation,MEAV)方法。首先给出了MEAV的基本模型,并介绍了其理论基础和数学性质。由于MEAV基本模型的目标函数并非处处可导,因而无法利用基于梯度的方法进行求解。为此,给出了MEAV基本模型的等价模型,并详细推导了基于原-对偶内点算法的MEAV等价模型的求解方法。算例分析表明,MEAV在估计过程中可自动抑制多个强相关不良数据,显示了良好的抗差性和较高的计算效率,因而具有良好的工程应用前景。展开更多
文摘In experimental tests, besides data in range of allowable error, the experimenters usually get some unexpected wrong data called bad points. In usual experimental data processing, the method of bad points exclusion based on automatic programming is seldom taken into consideration by researchers. This paper presents a new method to reject bad points based on Hough transform, which is modified to save computational and memory consumptions. It is fit for linear data processing and can be extended to process data that is possible to be transformed into and from linear form; curved lines, which can be effectively detected by Hough transform. In this paper, the premise is the distribution of data, such as linear distribution and exponential distribution, is predetermined. Steps of the algorithm start from searching for an approximate curve line that minimizes the sum of parameters of data points. The data points, whose parameters are above a self-adapting threshold, will be deleted. Simulation experiments have manifested that the method proposed in this paper performs efficiently and robustly.
文摘配电网环境复杂,配电网同步相量测量装置(distribution network synchronous phasor measurement unit, D-PMU)容易受到干扰而产生坏数据,进一步影响基于测量数据的应用效果。为了提高D-PMU数据质量,提出一种不依赖系统拓扑的基于密度的噪场应用空间聚类(density-based spatial clustering of applications with noise, DBSCAN)的配电网同步测量坏数据检测方法。首先利用基于密度的聚类算法DBSCAN进行异常数据检测。通过轮廓系数和邓恩指数对DBSCAN的聚类结果进行综合评价。利用麻雀搜索算法实现自适应参数调整,解决检测时需要预先处理训练、标记数据的问题。在此基础上,将时间序列聚类的K-Medoids算法和动态时间规整算法相结合,通过衡量不同时间序列之间的相似性,解决了D-PMU在电气联系较弱时对扰动数据与坏数据的区分问题,增强了数据处理的准确性与噪声环境下的稳健性。仿真和实际数据的测试结果表明,所提方法能有效区分真实扰动数据并准确识别D-PMU坏数据。
文摘为提高状态估计的抗差性,提出一种基于最大指数绝对值目标函数的状态估计(maximum exponential absolute value state estimation,MEAV)方法。首先给出了MEAV的基本模型,并介绍了其理论基础和数学性质。由于MEAV基本模型的目标函数并非处处可导,因而无法利用基于梯度的方法进行求解。为此,给出了MEAV基本模型的等价模型,并详细推导了基于原-对偶内点算法的MEAV等价模型的求解方法。算例分析表明,MEAV在估计过程中可自动抑制多个强相关不良数据,显示了良好的抗差性和较高的计算效率,因而具有良好的工程应用前景。
