基于云计算的IoT自适应PMU状态估计
Adaptive PMU-based Distribution System State Estimation exploiting the Cloud-based IoT paradigm
摘要：
本文提出了一种基于云计算的物联网模式的自适应配电系统状态估计（DSSE）。该方法在估计过程的执行速率方面是自适应的，该估计过程根据分布式测量系统的指示而变化。该系统尤其由相量测量单元（PMU）组成。PMU相对于物理设备被虚拟化，并且对应的虚拟化模块在通信网络边缘（即，更靠近物理对象）中运行。PMU被设置为较高的测量速率，而估计过程在正常操作条件下以给定的较慢速率工作，例如每秒一次。在虚拟化模块中实现的本地决策算法监控测量的量，以检测和解决可能的意外动态。特别是，可以应用不同的度量：均方根电压值的变化和变化趋势，以及监测信号的频率变化率（ROCOF），可以用于触发DSSE中的速率变化。如果检测到动态，则测量数据以更高的速率发送到DSSE，从而在更精细的时间尺度上运行估计过程。在所考虑的系统中，只有应用级实体位于云中，从而允许获得带宽效率和智能数据传输。在一个13节点系统上得到的结果证明了所提出的方法的优越性。
英文摘要：
This paper presents an adaptive Distribution System State Estimation (DSSE) which relies on a Cloud-based IoT paradigm. The methodology is adaptive in terms of the rate of execution of the estimation process which varies depending on the indications of the distributed measurement system. The system is composed, in particular, of Phasor Measurement Units (PMUs). PMUs are virtualized with respect to the physical devices and the corresponding virtualizing modules run in the communication network edge (i.e. closer to the physical objects). PMUs are set at a higher measurement rate, while the estimation process works at a given slower rate, for example once per second, in normal operative conditions. A local decision algorithm implemented in the virtualized module, monitors the measured quantities in order to detect and address possible unexpected dynamics. In particular, different metrics can be applied: the variations and the trend of variation of the rms voltage values, but also the Rate Of Change Of Frequency (ROCOF) of the monitored signals can be used to trigger rate variation in the DSSE.In case dynamics are detected, the measurement data is sent to the DSSE at higher rates and the estimation process runs consequently on a finer time scale. In the considered system only application level entities are located in the Cloud, thus allowing to obtain a bandwidth-efficient and smart data transmission. The results obtained on a 13-bus systems prove the goodness of the proposed methodologies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
