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

Abstract

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 impedancebased analysis, coupled with a descriptive analytic method. We segregate the events that are caused by the solar farm, i.e., locallyinduced 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.

Improving Fault Detection in Industrial Processes by Event-Driven Data Acquisition

“…From the MDR results obtained from the statistics T 2 and Q with PCA for the fixed-time approach (2nd column of Tables 2 and 3), the set of twenty-one faults of the Tennessee benchmark problem can be grouped into four subsets according to the level of detection difficulty. Group 1 refers to easy faults (1,2,4,6,7,8,12,13,14); group 2, to intermediate faults (11,17,18); group 3, to hard faults (5,10,16,19,20,21); and group 4, to very hard faults (3,9,15). First, significant MDR gains were verified for eight faults, all related to the Q statistic.…”

“…The authors reported a gain in network security and bandwidth. [12] described an eventbased analysis of a solar distribution feeder integrated into distribution substations. The authors highlighted an improvement in event detection, analysis of the impact of solar production, and understanding of the dynamics of the solar feeder control system.…”

Automated Event Region Identification and Its Data-Driven Applications in Behind-the-Meter Solar Farms Based on Micro-PMU Measurements

“…In [4], visual inspection is used to cross-compare the measurements from micro-PMUs on two different load feeders to identify the local events for each feeder. In [16]- [18], visual inspection is used to investigate the simultaneous impact of specific events, such as lightning strikes, on various components of a power distribution network, such as solar power inverters. For example, they visually verified the results from impedancebased event region identification methods.…”

“…This section briefly discusses the conventional impedancebased method [18], [19] to solve the event region identification problem. We apply this method to micro-PMU data from the…”

“…We shall add that we did not observe any such severe locally-induced event in our data set. Other examples of visual inspection are discussed in the literature, e.g., in [4], [17], [18]. However, the problem with this approach is the need for constant and real-time human supervision of the micro-PMU measurements, which is costprohibitive.…”