Data-Driven Congestion Control of Micro Smart Sensor Networks for Transparent Substations

Micro smart sensors and sensor networks are the key bases for building a fully visible, perceptible and controllable transparent substation in power grid. However, the massive deployment of micro smart sensors often leads to network congestion and directly affects the quality of information transmis...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ke Zhou, Xiaoming Wang, Xiaoyin Qiu, Wenwei Li
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Koopman operator network congestion micro smart sensor network extended state observer Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/6aba666aaf0c45599dcc292306d940a6
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:6aba666aaf0c45599dcc292306d940a6
record_format	dspace
spelling	oai:doaj.org-article:6aba666aaf0c45599dcc292306d940a62021-11-18T00:05:28ZData-Driven Congestion Control of Micro Smart Sensor Networks for Transparent Substations2169-353610.1109/ACCESS.2021.3123967https://doaj.org/article/6aba666aaf0c45599dcc292306d940a62021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9592790/https://doaj.org/toc/2169-3536Micro smart sensors and sensor networks are the key bases for building a fully visible, perceptible and controllable transparent substation in power grid. However, the massive deployment of micro smart sensors often leads to network congestion and directly affects the quality of information transmission. In practice, control design for micro sensor networks is often disturbed by factors such as nonlinearity, time delay and time-varying parameters, and the mathematical model is inaccurate. To solve these problems, this paper proposes a Koopman operator based data-driven congestion control scheme without using any system model information, based on model predictive control (MPC) and extended state observer (ESO). Firstly, according to the Koopman operator theory, a data-driven linear Koopman model for micro smart sensor networks is derived, only using the input/output data. Then an ESO based MPC scheme is designed to obtain the optimal packet dropping probability in the micro smart sensor node buffer area. Specifically, ESO performs real-time online estimation of the modeling errors, such as time-varying parameters, time delay and the external disturbances. Then the estimated modeling errors are viewed as total disturbances and are compensated in MPC, so that the queue length in the node buffer can quickly stabilize at the set value. Extensive simulations are conducted to verify the accuracy of Koopman model, and the effectiveness of the proposed control system design.Ke ZhouXiaoming WangXiaoyin QiuWenwei LiIEEEarticleKoopman operatornetwork congestionmicro smart sensor networkextended state observerElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 148625-148634 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Koopman operator network congestion micro smart sensor network extended state observer Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Koopman operator network congestion micro smart sensor network extended state observer Electrical engineering. Electronics. Nuclear engineering TK1-9971 Ke Zhou Xiaoming Wang Xiaoyin Qiu Wenwei Li Data-Driven Congestion Control of Micro Smart Sensor Networks for Transparent Substations
description	Micro smart sensors and sensor networks are the key bases for building a fully visible, perceptible and controllable transparent substation in power grid. However, the massive deployment of micro smart sensors often leads to network congestion and directly affects the quality of information transmission. In practice, control design for micro sensor networks is often disturbed by factors such as nonlinearity, time delay and time-varying parameters, and the mathematical model is inaccurate. To solve these problems, this paper proposes a Koopman operator based data-driven congestion control scheme without using any system model information, based on model predictive control (MPC) and extended state observer (ESO). Firstly, according to the Koopman operator theory, a data-driven linear Koopman model for micro smart sensor networks is derived, only using the input/output data. Then an ESO based MPC scheme is designed to obtain the optimal packet dropping probability in the micro smart sensor node buffer area. Specifically, ESO performs real-time online estimation of the modeling errors, such as time-varying parameters, time delay and the external disturbances. Then the estimated modeling errors are viewed as total disturbances and are compensated in MPC, so that the queue length in the node buffer can quickly stabilize at the set value. Extensive simulations are conducted to verify the accuracy of Koopman model, and the effectiveness of the proposed control system design.
format	article
author	Ke Zhou Xiaoming Wang Xiaoyin Qiu Wenwei Li
author_facet	Ke Zhou Xiaoming Wang Xiaoyin Qiu Wenwei Li
author_sort	Ke Zhou
title	Data-Driven Congestion Control of Micro Smart Sensor Networks for Transparent Substations
title_short	Data-Driven Congestion Control of Micro Smart Sensor Networks for Transparent Substations
title_full	Data-Driven Congestion Control of Micro Smart Sensor Networks for Transparent Substations
title_fullStr	Data-Driven Congestion Control of Micro Smart Sensor Networks for Transparent Substations
title_full_unstemmed	Data-Driven Congestion Control of Micro Smart Sensor Networks for Transparent Substations
title_sort	data-driven congestion control of micro smart sensor networks for transparent substations
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/6aba666aaf0c45599dcc292306d940a6
work_keys_str_mv	AT kezhou datadrivencongestioncontrolofmicrosmartsensornetworksfortransparentsubstations AT xiaomingwang datadrivencongestioncontrolofmicrosmartsensornetworksfortransparentsubstations AT xiaoyinqiu datadrivencongestioncontrolofmicrosmartsensornetworksfortransparentsubstations AT wenweili datadrivencongestioncontrolofmicrosmartsensornetworksfortransparentsubstations
_version_	1718425229198360576

Data-Driven Congestion Control of Micro Smart Sensor Networks for Transparent Substations

Ejemplares similares