期刊
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
卷 12, 期 3, 页码 1640-1650出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2021.3060546
关键词
Estimation; Broadband communication; Power cables; Observability; Smart meters; Sensors; Low voltage; Cable television network; distributed photovoltaics; low-voltage estimation; random forests; secondary distribution system
资金
- National Renewable Energy Laboratory (NREL)
- U.S. Department of Energy (DOE) [DE-AC36-08GO28308]
The study utilizes sensors from CATV networks for low-voltage estimation to enhance observability of distributed energy resources in distribution grids. By considering spatial-temporal observation factors, the method is able to estimate single-phase voltage magnitudes at non-monitored low-voltage buses, demonstrating highly accurate voltage estimation results on simulated data.
Present distribution grids generally have limited sensing capabilities and are therefore characterized by low observability. Improved observability is a prerequisite for increasing the hosting capacity of distributed energy resources such as solar photovoltaics (PV) in distribution grids. In this context, this paper presents learning-aided low-voltage estimation using untapped but readily available and widely distributed sensors from cable television (CATV) networks. The cable broadband sensors offer timely local voltage magnitude sensing with 5-minute resolution and can provide an order of magnitude more data on the time-varying state of a secondary distribution system than currently deployed utility sensors. The proposed solution incorporates voltage readings from neighboring CATV sensors, taking into account spatio-temporal aspects of the observations, and estimates single-phase voltage magnitudes at all non-monitored low-voltage buses using random forests. The effectiveness of the proposed approach was demonstrated using a multi-phase 1572-bus feeder from the SMART-DS data set for two case studies - passive distribution feeder (without PV) and active distribution feeder (with PV). The analysis was conducted on simulated data, and the results show voltage estimates with a high degree of accuracy, even at extremely low percentages of observable nodes.
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