论文标题
基于LASSO的多线停电在部分可观察到的电源系统中
LASSO-Based Multiple-Line Outage Identification In Partially Observable Power Systems
论文作者
论文摘要
相量测量单元（PMU）为现代电力系统创造了充足的实时监控机会。其中，线路停电检测和识别仍然是一项至关重要但具有挑战性的任务。当前有关中断识别的工作在完整的PMU部署和单线断电中成功。然而，绩效降低了多线断电，并具有部分系统可观察性。我们提出了一个使用部分节点电压测量值的多线断电识别的新框架。使用交流电流（AC）功率流模型，提取中断的相角签名，并将其用于将最小诊断群组分组为最小的。然后将识别提出为Lasso解决的不确定的稀疏回归问题。在IEEE 39-BUS系统上测试了25％和50％PMU覆盖范围，拟议的识别方法对于单线和双线停机时间为93％和80％。我们的研究表明，AC功率流在捕获停电模式和牺牲某些精度方面更好地提高了识别准确性。这些发现可能有助于开发未来的控制方案，这些方案有助于实时抵抗和从停电中断中恢复。
Phasor measurement units (PMUs) create ample real-time monitoring opportunities for modern power systems. Among them, line outage detection and identification remains a crucial but challenging task. Current works on outage identification succeed in full PMU deployment and single-line outages. Performance however degrades for multiple-line outage with partial system observability. We propose a novel framework of multiple-line outage identification using partial nodal voltage measurements. Using alternating current (AC) power flow model, phase angle signatures of outages are extracted and used to group lines into minimal diagnosable clusters. Identification is then formulated into an underdetermined sparse regression problem solved by lasso. Tested on IEEE 39-bus system with 25% and 50% PMU coverage, the proposed identification method is 93% and 80% accurate for single- and double-line outages. Our study suggests that the AC power flow is better at capturing outage patterns and sacrificing some precision could yield substantial improvement in identification accuracy. These findings could contribute to the development of future control schemes that help power systems resist and recover from outage disruptions in real time.
