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摘要翻译：
本文是一个两部分系列的第二部分，讨论了一种鲁棒无迹卡尔曼滤波器(UKF)的实现问题和测试结果，该滤波器用于含非高斯同步相量测量噪声的电力系统动态状态估计。提出并系统地讨论了广义最大似然型鲁棒UKF（GM-UKF）的参数整定问题。通过对IEEE39节点系统的仿真，评估了该系统在不同情况下的性能，包括：i）在厚尾分布下出现两种不同类型的噪声，即真实功率和无功功率测量的拉普拉斯或柯西概率分布；㈡观察和创新异常值的出现；iii）由于通信故障导致PMU测量损失的发生；iv）网络攻击；强系统非线性。并与UKF和广义最大似然型鲁棒迭代EKF（GM-IEKF）进行了比较。仿真结果表明，GM-UKF在所有场景下的性能都优于GM-IEKF和UKF。特别地，当系统在应力条件下运行时，由于系统非线性，GM-IEKF和UKF发生发散，而我们的GM-UKF收敛。此外，当功率测量噪声服从Cauchy分布时，我们的GM-UKF收敛到一个状态估计向量，表现出比GM-IEKF更高的统计效率；相比之下，UKF未能收敛。最后，在结束语部分讨论了拟议的GM-UKF的潜在应用和未来的工作。
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英文标题：
《Robust Power System Dynamic State Estimator with Non-Gaussian
Measurement Noise: Part II--Implementation and Results》
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作者：
Junbo Zhao and Lamine Mili
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最新提交年份：
2017
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Systems and Control 系统与控制
分类描述：cs.SY is an alias for eess.SY. This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
cs.sy是eess.sy的别名。本部分包括理论和实验研究，涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制，以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Systems and Control 系统与控制
分类描述：This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
本部分包括理论和实验研究，涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制，以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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英文摘要：
This paper is the second of a two-part series that discusses the implementation issues and test results of a robust Unscented Kalman Filter (UKF) for power system dynamic state estimation with non-Gaussian synchrophasor measurement noise. The tuning of the parameters of our Generalized Maximum-Likelihood-type robust UKF (GM-UKF) is presented and discussed in a systematic way. Using simulations carried out on the IEEE 39-bus system, its performance is evaluated under different scenarios, including i) the occurrence of two different types of noises following thick-tailed distributions, namely the Laplace or Cauchy probability distributions for real and reactive power measurements; ii) the occurrence of observation and innovation outliers; iii) the occurrence of PMU measurement losses due to communication failures; iv) cyber attacks; and v) strong system nonlinearities. It is also compared to the UKF and the Generalized Maximum-Likelihood-type robust iterated EKF (GM-IEKF). Simulation results reveal that the GM-UKF outperforms the GM-IEKF and the UKF in all scenarios considered. In particular, when the system is operating under stressed conditions, inducing system nonlinearities, the GM-IEKF and the UKF diverge while our GM-UKF does converge. In addition, when the power measurement noises obey a Cauchy distribution, our GM-UKF converges to a state estimate vector that exhibits a much higher statistical efficiency than that of the GM-IEKF; by contrast, the UKF fails to converge. Finally, potential applications and future work of the proposed GM-UKF are discussed in concluding remarks section.
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PDF链接：
https://arxiv.org/pdf/1703.05991
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