Event-Based Analysis of Solar Power Distribution Feeder Using Micro-PMU Measurements

• Published in: arXiv.org
• Main Authors: Khaledian, Parviz, Aligholian, Armin, Mohsenian-Rad, Hamed
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 26.11.2020
• Subjects: Electric industries; Electric power distribution; Feeders; Machine learning; Photovoltaic cells; Solar energy; Solar power generation; Substations; Utilities

Solar distribution feeders are commonly used in solar farms that are integrated into distribution substations. In this paper, we focus on a real-world solar distribution feeder and conduct an event-based analysis by using micro-PMU measurements. The solar distribution feeder of interest is a behind-the-meter solar farm with a generation capacity of over 4 MW that has about 200 low-voltage distributed photovoltaic (PV) inverters. The event-based analysis in this study seeks to address the following practical matters. First, we conduct event detection by using an unsupervised machine learning approach. For each event, we determine the event's source region by an impedance-based analysis, coupled with a descriptive analytic method. We segregate the events that are caused by the solar farm, i.e., locally-induced events, versus the events that are initiated in the grid, i.e., grid-induced events, which caused a response by the solar farm. Second, for the locally-induced events, we examine the impact of solar production level and other significant parameters to make statistical conclusions. Third, for the grid-induced events, we characterize the response of the solar farm; and make comparisons with the response of an auxiliary neighboring feeder to the same events. Fourth, we scrutinize multiple specific events; such as by revealing the dynamics to the control system of the solar distribution feeder. The results and discoveries in this study are informative to utilities and solar power industry.