scholarly journals Power-Efficient Hybrid Energy Storage System for Seismic Nodes

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Dauda Duncan ◽  
Adamu Murtala Zungeru ◽  
Mmoloki Mangwala ◽  
Bakary Diarra ◽  
Bokani Mtengi ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Storage ◽  
Energy Harvesting ◽  
Network Model ◽  
Neural Network Model ◽  
Photovoltaic Module ◽  
Lead Acid Battery ◽  
Pv System ◽  
Energy Harvesting System ◽  
Lead Acid

Recent surveys in the energy harvesting system for seismic nodes show that, most often, a single energy source energizes the seismic system and fails most frequently. The major concern is the limited lifecycle of battery and high routine cost. Simplicity and inexperience have caused intermittent undersizing or oversizing of the system. Optimizing solar cell constraints is required. The hybridization of the lead-acid battery and supercapacitor enables the stress on the battery to lessen and increases the lifetime. An artificial neural network model is implemented to resolve the rapid input variations across the photovoltaic module. The best performance was attained at the epoch of 117 and the mean square error of 1.1176e-6 with regression values of training, test, and validation at 0.99647, 0.99724, and 0.99534, respectively. The paper presents simulations of Nsukka seismic node as a case study and to deepen the understanding of the system. The significant contributions of the study are (1) identification of the considerations of the PV system at a typical remote seismic node through energy transducer and storage modelling, (2) optimal sizing of PV module and lead-acid battery, and, lastly, (3) hybridization of the energy storage systems (the battery and supercapacitor) to enable the energy harvesting system to maximize the available ambient irradiance. The results show the neural network model delivered efficient power with duty cycles across the converter and relatively less complexities, while the supercapacitor complemented the lead-acid battery and delivered an overall efficiency of about 75 % .

scholarly journals MAPPING OF ELECTROCHEMISTRY AND NEURAL NETWORK MODEL APPLIED IN STATE OF CHARGE ESTIMATION FOR LEAD ACID BATTERY USED IN ELECTRIC VEHICLE

2011 ◽  
Vol 11 (2) ◽  
pp. 140-147
Author(s):  
Bambang Sri Kaloko ◽  
Soebagio Soebagio ◽  
Mauridhi H. Purnomo
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
State Of Charge ◽  
Analytical Models ◽  
Lead Acid Battery ◽  
The Neural Network ◽  
Battery Systems ◽  
Product Realization ◽  
Lead Acid

Analytical models have been developed to diminish test procedures for product realization, but they have only been partially successful in predicting the performance of battery systems consistently. The complex set of interacting physical and chemical processes within battery systems has made the development of analytical models of significant challenge. Advanced simulation tools are needed to be more accurately model battery systems which will reduce the time and cost required for product realization. As an alternative approach begun, the development of cell performance modeling using non-phenomenological models for battery systems were based on artificial neural networks (ANN) using Matlab 7.6.0(R2008b). ANN has been shown to provide a very robust and computationally efficient simulation tool for predicting state of charge for Lead Acid cells under a variety of operating conditions. In this study, the analytical model and the neural network model of lead acid battery for electric vehicle were used to determinate the battery state of charge. A precision comparison between the analytical model and the neural network model has been evaluated. The precise of the neural network model has error less than 0.00045 percent.

scholarly journals A Multi-Level World Comprehensive Neural Network Model for Maximum Annual Solar Irradiation on a Flat Surface

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6422
Author(s):  
Ramez Abdallah ◽  
Emad Natsheh ◽  
Adel Juaidi ◽  
Sufyan Samara ◽  
Francisco Manzano-Agugliaro
Keyword(s):  
Neural Network ◽  
Southern Hemisphere ◽  
Northern Hemisphere ◽  
Network Model ◽  
Neural Network Model ◽  
Tilt Angle ◽  
Solar Irradiation ◽  
The South ◽  
Pv System ◽  
The North

With the growing demand for clean and economically feasible renewable energy, solar photovoltaic (PV) system usage has increased. Among many factors, the tilt and azimuth angles are of great importance and influence in determining the photovoltaic panel’s efficiency to generate electricity. Although much research was conducted related to solar PV panels’ performance, this work critically determined the tilt and azimuth angles for PV panels in all countries worldwide. The optimum tilt and azimuth angles are estimated worldwide by the photovoltaic geographic information system (PVGIS). Also, annual and average daily solar irradiation incident on the tilted and oriented plate optimally (AR1 and DR1) are calculated. Besides, annual and average daily solar irradiation incident on plate tilt optimally and oriented because of the south in the northern hemisphere and because of the north in the southern hemisphere (AR2 and DR2) are estimated. PVGIS is also used to calculate the annual and average daily solar irradiation incident on the horizontal plate (AR3 and DR3). The data collected from PVGIS are used to develop an efficient and accurate artificial neural network model based on feed-forward neural network approach. This model is an essential subpart that can be used in an embedded system or an online system for further PV system analysis and optimization. The developed neural model reflected very high accuracy in predicting the PV panels’ optimal tilt and azimuth angles worldwide. The benefit of tilting is generally increased by increasing the latitude. As the latitude increases, the tilt factor (F) increases because of the increase in the optimum tilt angle by increasing the latitude. The optimal orientation is due to the north in the southern hemisphere and due to the south in the northern hemisphere for most cities worldwide. In sum, it can be concluded that the optimum tilt angle is equal to or greater than the latitude until the latitude 30°. The optimum tilt angle becomes less than the latitude, and the difference is increased until it reaches more than 20°. Hence in this study the aim is to develop a simple neural network model which can accurately predict the annual radiation and optimum tilt and azimuth angle in any region of the world and can be easily implemented in a low-cost microcontroller.

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