Paper Info
Title
Designing Reactive Power Control Rules for Smart Inverters Using Support Vector Machines
Abstract
Smart inverters have been advocated as a fast-responding mechanism for voltage regulation in distribution grids. Nevertheless, optimal inverter coordination can be computationally demanding, and preset local control rules are known to be subpar. Leveraging tools from machine learning, the design of customized inverter control rules is posed here as a multi-task learning problem. Each inverter control rule is modeled as a possibly nonlinear function of local and/or remote control inputs. Given the electric coupling, the function outputs interact to yield the feeder voltage profile. Using an approximate grid model, inverter rules are designed jointly to minimize a voltage deviation objective based on anticipated load and solar generation scenarios. Each control rule is described by a set of coefficients, one for each training scenario. To reduce the communication overhead between the grid operator and the inverters, we devise a voltage regulation objective that is shown to promote parsimonious descriptions for inverter control rules. Numerical tests using real-world data on a benchmark feeder demonstrate the advantages of the novel nonlinear rules and explore the trade-off between voltage regulation and sparsity in rule descriptions.
Year
DOI
Venue
2020
10.1109/TSG.2019.2942850
IEEE Transactions on Smart Grid
Keywords
Field
DocType
Inverters,Voltage control,Solar power generation,Load modeling,Task analysis,Reactive power control,Computational modeling
Inverter,Mathematical optimization,Remote control,Nonlinear system,Control theory,Voltage,Support vector machine,Voltage regulation,Operator (computer programming),Grid,Mathematics
Journal
Volume
Issue
ISSN
11
2
1949-3053
Citations 
PageRank 
References 
3
0.42
16
Authors
4
Name
Order
Citations
PageRank
Mana Jalali130.42
Vassilis Kekatos224927.11
Nikolaos Gatsis334037.15
Deepjyoti Deka46816.63