Models and Control Strategies for Data Centers in the Smart Grid

2011 ◽  
pp. 223-237
Author(s):  
Luca Parolini ◽  
Bruno Sinopoli ◽  
Bruce H. Krogh
Keyword(s):  
Smart Grid ◽  
Data Centers ◽  
Control Strategies ◽  
And Control

scholarly journals Comparison of Power Hardware-in-the-Loop Approaches for the Testing of Smart Grid Controls

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3381 ◽  
Author(s):  
Falko Ebe ◽  
Basem Idlbi ◽  
David Stakic ◽  
Shuo Chen ◽  
Christoph Kondzialka ◽  
...  
Keyword(s):  
Smart Grid ◽  
Control Strategies ◽  
Load Flow ◽  
Hardware In The Loop ◽  
Renewable Energy Resources ◽  
Operational Conditions ◽  
Actual Usage ◽  
New Methodologies ◽  
And Control

The fundamental changes in the energy sector, due to the rise of renewable energy resources and the possibilities of the digitalisation process, result in the demand for new methodologies for testing Smart Grid concepts and control strategies. Using the Power Hardware-in-the-Loop (PHIL) methodology is one of the key elements for such evaluations. PHIL and other in-the-loop concepts cannot be considered as plug’n’play and, for a wider adoption, the obstacles have to be reduced. This paper presents the comparison of two different setups for the evaluation of components and systems focused on undisturbed operational conditions. The first setup is a conventional PHIL setup and the second is a simplified setup based on a quasi-dynamic PHIL (QDPHIL) approach which involves fast and continuously steady state load flow calculations. A case study which analyses a simple superimposed voltage control algorithm gives an example for the actual usage of the quasi-dynamic setup. Furthermore, this article also provides a comparison and discussion of the achieved results with the two setups and it concludes with an outlook about further research.

Design and control strategies for a modular hydroKinetic smart grid

2018 ◽  
Vol 95 ◽  
pp. 137-145 ◽  
Author(s):  
E. Alvarez Alvarez ◽  
M. Rico-Secades ◽  
E.L. Corominas ◽  
N. Huerta-Medina ◽  
J. Soler Guitart
Keyword(s):  
Smart Grid ◽  
Control Strategies ◽  
And Control

Effective infection prevention and control strategies in a large accredited psychiatric facility in Singapore

2020 ◽  
Author(s):  
Daniel Poremski ◽  
Sandra Henrietta Subner ◽  
Grace Lam Fong Kin ◽  
Raveen Dev Ram Dev ◽  
Mok Yee Ming ◽  
...  
Keyword(s):  
Mental Health ◽  
Prevention And Control ◽  
Infection Prevention ◽  
Control Strategies ◽  
Infection Prevention And Control ◽  
Psychiatric Facility ◽  
Essential Services ◽  
Community Transmission ◽  
And Control

The Institute of Mental Health in Singapore continues to attempt to prevent the introduction of COVID-19, despite community transmission. Essential services are maintained and quarantine measures are currently unnecessary. To help similar organizations, strategies are listed along three themes: sustaining essential services, preventing infection, and managing human and consumable resources.

Optimization and Control Petroleum Refinery Wastewater Treatment Systems — Status, Trends and Needs

1989 ◽  
Vol 24 (3) ◽  
pp. 463-477
Author(s):  
Stephen G. Nutt
Keyword(s):  
Wastewater Treatment ◽  
Process Control ◽  
Petroleum Refinery ◽  
Control Strategies ◽  
Refinery Wastewater ◽  
Statistical Process ◽  
Petroleum Refinery Wastewater ◽  
Optimization And Control ◽  
And Control ◽  
Wastewater Treatment Systems

Abstract Based on discussions in workshop sessions, several recurring themes became evident with respect to the optimization and control of petroleum refinery wastewater treatment systems to achieve effective removal of toxic contaminants. It was apparent that statistical process control (SPC) techniques are finding more widespread use and have been found to be effective. However, the implementation of real-time process control strategies in petroleum refinery wastewater treatment systems is in its infancy. Considerable effort will need to be expended to demonstrate the practicality of on-line sensors, and the utility of automated process control in petroleum refinery wastewater treatment systems. This paper provides a summary of the discussions held at the workshop.

Learning and control

2018 ◽  
Author(s):  
Ivan Herreros
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Brain Function ◽  
Control Strategies ◽  
Learning Problems ◽  
Animal Learning ◽  
Feed Forward Control ◽  
Machine Learning Applications ◽  
And Control

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

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