Optimal Power Management for Seismic Nodes

Abstract:

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%.

• Periodical: International Journal of Engineering Research in Africa (Volume 56)
• Pages: 162-181
• DOI: https://doi.org/10.4028/www.scientific.net/JERA.56.162
• Online since: October 2021
• Authors: Dauda Duncan, Adamu Murtala Zungeru*, Mmoloki Mangwala, Bakary Diarra, Joseph Chuma, Bokani Mtengi
• Keywords: Battery, Data, Energy Harvesting, MPPT, Neural Network, Photovoltaic, Seismic Node, SOC

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