Issue |
E3S Web of Conf.
Volume 540, 2024
1st International Conference on Power and Energy Systems (ICPES 2023)
|
|
---|---|---|
Article Number | 13005 | |
Number of page(s) | 8 | |
Section | Other Renewable Energies | |
DOI | https://doi.org/10.1051/e3sconf/202454013005 | |
Published online | 21 June 2024 |
Real Time Machine Learning Based Voltage Regulation Model for Smart Agriculture
Associate Professor, Department of CS & IT, Kalinga University, Naya Raipur, Chhattisgarh, India .
* ku.abhijeetmadhukarhaval@kalingauniversity.ac.in
** ku.akankshamishra@kalingauniversity.ac.in
The problem of voltage regulation in smart agriculture unit is well studied. There exists number of approaches to support voltage regulation in agriculture sector. However, the methods consider only the number of motors share the voltage as the key in regulating the artificial voltage to the agriculture unit. The methods suffer to achieve higher performance in smart agriculture. To solve this issue, an efficient Machine Learning Based Voltage Regulation Model (MLVRM) is presented in this paper. The method maintains the agriculture trace and uses them to compute mean voltage utilization (MVU) at various duty cycles. With the information like no of smart motors connected, average voltage utilization of motors, and other features, the method computes MVU value. The method trains the neural network with the features extracted. The network is designed with number of intermediate layers where each layer neuron computes the value of MVU according to the features available. The output layer neurons produces number of MVU value. Based on the MVU values obtained, the method computes Optimal Regulation Voltage (ORV) for the current input voltage according to the required voltage for the smart motor connected. The proposed model improves the performance of voltage regulation and smart agriculture.
Key words: Smart Agriculture / Voltage Regulation / ORV / MVU / MLVRM
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.