The output power of distributed energy resources (DERs) may experience irregular fluctuations due to variations of renewable sources, which need to be monitored in order to reliably control the grid. This paper proposes a novel approach for centralized detection of such irregularities based on the time-series analysis of the data reported by phasor measurement units (PMUs). In this approach, a network controller constructs datasets of time-aligned real/reactive powers for different zones. The datasets are transformed into sequences of short-time local outlier probability (ST-LOP) that are analyzed to identify the DER events. The network controller estimates features such as the average duration and the similarity degree that is a measure of spatio-temporal correlation between the DER events. As a use case, event-triggered control of solar photovoltaic (PV) systems with energy storage devices is investigated. The simulation results for the IEEE 123-bus network corroborate the effectiveness of the developed analytics for detection and mitigation of ramp-rate solar power fluctuations. Smart microgrids and active distribution networks can employ the developed analytics to improve a range of diagnostic and control functionalities.

中文翻译：

---

智能电网中使用 PMU 数据分析检测分布式能源输出功率的不规则性


分布式能源（DER）的输出功率可能会因可再生能源的变化而出现不规则波动，需要对其进行监控以便可靠地控制电网。本文提出了一种基于相量测量单元（PMU）报告的数据的时间序列分析来集中检测此类不规则性的新方法。在这种方法中，网络控制器为不同区域构建时间对齐的有功/无功功率数据集。数据集被转换为短时局部离群概率 (ST-LOP) 序列，并对其进行分析以识别 DER 事件。网络控制器估计平均持续时间和相似度等特征，相似度是 DER 事件之间时空相关性的度量。作为一个用例，我们研究了具有储能设备的太阳能光伏 (PV) 系统的事件触发控制。 IEEE 123 总线网络的仿真结果证实了所开发的分析方法在检测和缓解太阳能发电爬坡率波动方面的有效性。智能微电网和主动配电网络可以利用开发的分析技术来改进一系列诊断和控制功能。