TY - JOUR
T1 - Optimal power management for seismic nodes
AU - Duncan, Dauda
AU - Zungeru, Adamu Murtala
AU - Mangwala, Mmoloki
AU - Diarra, Bakary
AU - Chuma, Joseph
AU - Mtengi, Bokani
N1 - Funding Information:
Authors would like to express their sincere acknowledgment to the following: ORDI of the Botswana International University of Science and Technology under the grant R0068. Centre for Geodesy and Geodynamics, Toro, Nigeria. Centre for Atmospheric Research, Anyigba, Nigeria.
Publisher Copyright:
© 2021 Trans Tech Publications Ltd, Switzerland.
PY - 2021
Y1 - 2021
N2 - Estimating the state-of-charge of a lead-acid battery at remote seismic nodes is a key factor in managing the available power. Optimal management enables the continuous acquisition of seismic data of an area. This paper presents the management of lead-acid batteries at remote seismic nodes, using the Neural Network model's historical data to estimate the battery's state-of-charge. Powersim (PSIM) simulation tool was used to implement photovoltaic energy harvesting system with a buck mode converter and maximum power point tracking algorithm to acquire historical data. A backpropagation neural network technique for training the historical dataset of hourly points in 500 days on the Matlab platform is adopted, and a feedforward neural network is employed due to the irregularities of the input data. The neural network model's hidden layer contains the transfer function of the Tansig Function to produce the model output of state-of-charge estimations. Besides, this paper is based on the management of estimating the state-of-charge of the lead-acid battery near-realtime instead of relying on the vendor's lifecycle information. The simulated results show the simplicity and optimal estimations of state-of-charge of the lead-acid battery with RMSE of 0.023%.
AB - Estimating the state-of-charge of a lead-acid battery at remote seismic nodes is a key factor in managing the available power. Optimal management enables the continuous acquisition of seismic data of an area. This paper presents the management of lead-acid batteries at remote seismic nodes, using the Neural Network model's historical data to estimate the battery's state-of-charge. Powersim (PSIM) simulation tool was used to implement photovoltaic energy harvesting system with a buck mode converter and maximum power point tracking algorithm to acquire historical data. A backpropagation neural network technique for training the historical dataset of hourly points in 500 days on the Matlab platform is adopted, and a feedforward neural network is employed due to the irregularities of the input data. The neural network model's hidden layer contains the transfer function of the Tansig Function to produce the model output of state-of-charge estimations. Besides, this paper is based on the management of estimating the state-of-charge of the lead-acid battery near-realtime instead of relying on the vendor's lifecycle information. The simulated results show the simplicity and optimal estimations of state-of-charge of the lead-acid battery with RMSE of 0.023%.
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U2 - 10.4028/www.scientific.net/JERA.56.162
DO - 10.4028/www.scientific.net/JERA.56.162
M3 - Article
AN - SCOPUS:85118942507
SN - 1663-3571
VL - 56
SP - 162
EP - 181
JO - International Journal of Engineering Research in Africa
JF - International Journal of Engineering Research in Africa
ER -