Distribution System Voltage Prediction from Smart Inverters using Decentralized Regression

Autor:	Atkins, Zachary R., Vogl, Christopher J., Madduri, Achintya, Duan, Nan, Miedlar, Agnieszka K., Merl, Daniel
Rok vydání:	2021
Předmět:	Mathematics - Numerical Analysis Computer Science - Distributed Parallel and Cluster Computing Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	As photovoltaic (PV) penetration continues to rise and smart inverter functionality continues to expand, smart inverters and other distributed energy resources (DERs) will play increasingly important roles in distribution system power management and security. In this paper, it is demonstrated that a constellation of smart inverters in a simulated distribution circuit can enable precise voltage predictions using an asynchronous and decentralized prediction algorithm. Using simulated data and a constellation of 15 inverters in a ring communication topology, the COLA algorithm is shown to accomplish the learning task required for voltage magnitude prediction with far less communication overhead than fully connected P2P learning protocols. Additionally, a dynamic stopping criterion is proposed that does not require a regularizer like the original COLA stopping criterion. Comment: This work has been submitted to the IEEE for possible publication
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2101.04816 Zobrazit plný text záznamu View this record from Arxiv