A machine learning-based dynamic link power control in wearable sensing devices

2021 ◽  
Author(s):  
Duarte Fernandes ◽  
André G. Ferreira ◽  
Reza Abrishambaf ◽  
José Mendes ◽  
Jorge Cabral
Keyword(s):  
Machine Learning ◽  
Power Control ◽  
Wearable Sensing ◽  
Dynamic Link

Body-posture-based dynamic link power control in wearable sensor networks

2010 ◽  
Vol 48 (7) ◽  
pp. 134-142 ◽  
Author(s):  
Muhannad Quwaider ◽  
Jayanthi Rao ◽  
Subir Biswas
Keyword(s):  
Sensor Networks ◽  
Power Control ◽  
Body Posture ◽  
Wearable Sensor ◽  
Dynamic Link

scholarly journals New intelligent direct power control of DFIG-based wind conversion system by using machine learning under variations of all operating and compensation modes

2021 ◽  
Vol 7 ◽  
pp. 6394-6412
Author(s):  
Younes Sahri ◽  
Salah Tamalouzt ◽  
Farid Hamoudi ◽  
Sofia Lalouni Belaid ◽  
Mohit Bajaj ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Control ◽  
Direct Power ◽  
Direct Power Control ◽  
Conversion System

scholarly journals A Machine Learning Approach for Coordinated Voltage and Reactive Power Control

2020 ◽  
Vol 18 (1) ◽  
pp. 54-60
Author(s):  
Worawat Nakawiro
Keyword(s):  
Machine Learning ◽  
Power Control ◽  
Reactive Power ◽  
Renewable Energy Sources ◽  
Distribution Networks ◽  
Classification Problem ◽  
Reactive Power Control ◽  
Network Information ◽  
Distributed Generators ◽  
Multi Class Classification

Increasing penetration of renewable energy sources in form of distributed generators has brought many technical challenges to distribution networks. Among those, voltage and reactive power control should be revised and improved. Existing and new control resources should be coordinated based on real-time information and in closed loop. To achieve this, machine learning (ML) can be used to map the relationship between the selected network information and the desired control output. In this paper, setting of the shunt compensator operating in capacitive or inductive modes is coordinated with the tap position of substation transformer by the developed ML. Dataset emulating network behaviour during a year operation is constructed for training ML. A multi-class classification problem is formulated. Simulation results show satisfactory accuracy for some classes.

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