摘要翻译：
本文研究了配电系统同步相量测量误差的在线测量和离线测量。结果表明，同步相量测量误差服从非高斯分布，而不是传统假设的高斯分布。提出了用非高斯模型或混合高斯模型来表示同步相量测量误差的必要性。这些模型比传统的高斯模型更能真实准确地表示误差。这些测量和分析将有助于对配电系统测量特性的理解，也有助于配电系统应用的建模和仿真。
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英文标题：
《Power Distribution System Synchrophasors with Non-Gaussian Errors:
  Real-World Measurements and Analysis》
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作者：
Can Huang, Charanraj A. Thimmisetty, Xiao Chen, Mert Korkali, Vaibhav
  Donde, Emma Stewart, Philip Top, Charles Tong, Liang Min
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  This letter studies the synchrophasor measurement error of electric power distribution systems with on-line and off-line measurements using graphical and numerical tests. It demonstrates that the synchrophasor measurement error follows a non-Gaussian distribution instead of the traditionally-assumed Gaussian distribution. It suggests the need to use non-Gaussian or Gaussian mixture models to represent the synchrophasor measurement error. These models are more realistic to accurately represent the error than the traditional Gaussian model. The measurements and underlying analysis will be helpful for the understanding of distribution system measurement characteristics, and also for the modeling and simulation of distribution system applications.
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PDF链接：
https://arxiv.org/pdf/1803.0623