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 % .

Seismic data is one of the most important data for analysis and interpretation of subsurface, but remote seismic node fails frequently. This is one of the main constraints that limits seismic network to acquire continuous seismic data and near real-time prediction of seismicity of an area. There are several ambient energy sources at the remote seismic nodes. Optimization of their energy transducers and DC-DC converters is inevitable. In this study, solar and thermal sources are utilized with maximum power point tracking (MPPT) algorithm. The algorithm is based on the Neural Network model to supply the duty cycle across the converter optimally. Historical data were generated from Perturb and Observe algorithm in PSIM for the Neural Network Model to train the data and predict the duty cycles for both within and outside the data. The proposed system delivered an over 75% conversion rate of the Photovoltaic module's power. The system was modeled in Simulink under the ideal conditions of its components. It could face few constraints during prototype implementation due to the unusual characteristics of the thermoelectric elements. However, certain additional electrical energy was achieved for a low duty power load, such as a remote seismic node. The significant contributions are to identify operating constraints and design optimal hybrid energy harvesting systems at a remote seismic node.

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We have developed an optimal Photovoltaic Energy Harvesting System at the remote seismic node to sustain the remote seismic node. This node is a continuous application for monitoring the geodynamics of the earth for long-term and persistent. However, due to the constraints of solar cells and low funding of seismic installations, a simple and optimal energy harvesting system is required at the remote seismic node. So, this novel design focuses on using fitting curve equations as models to extract parameters of the photovoltaic module. This is to predict instantaneous duty cycle levels across synchronous buck DC-DC converter for optimal energy conversion. The converter regulates the supercapacitor as energy storage to deliver longer runtime at the remote seismic node. The conventional techniques concentrate on improving the hardware and charging to sustain power at the remote seismic node. Yet, energy is lost by the DC-DC converter, and the lead-acid battery usually used exhibits energy leakage and a shorter lifecycle. Thus, the contribution of this work is to design a relatively less computational maximum power point tracking based on a simple curve fitting technique to sustain the lifecycle of the node. In this proposed design, selected light-dependent resistors to mimic a pyranometer and formulate equations from specific photovoltaic module characteristics are considered. This correlates the optimal duty cycle levels with the ambient irradiance and temperature variables, while validation was done with an experimental setup of Computer Controlled Photovoltaic System. The non-linear characteristics relationship between the irradiance, temperature, and voltage levels is linearized with the selected PV module parameters in curve fitting equation models. These models are implemented in C programming as an algorithm to be processed by 8-bit microcontroller and deliver duty cycle levels across pulse width modulation to drive the converter. The performance of our approach is evaluated with the Computer Controlled Photovoltaic System with a deviation between 2.5 % to 5 % based on tabulated results and graphs.

Global concerns over the inappropriate utilization of abundant renewable energy sources, the damages due to instability of fuel prices, and fossil fuels' effect on the environment have led to an increased interest in green energy (natural power generation) from renewable sources. In renewable energy, photovoltaic is relatively the dominant technique and exhibits non-linearities, leading to inefficiencies. Maximum Power Point is required to be tracked rapidly and improve the power output levels. The target is to use a Neural network controller by training historical data of ambient irradiance and temperature levels as inputs and voltage levels as output for the photovoltaic module to predict duty cycles across the DC-DC converter. The DC-DC converter is the electrical power conditioner at the Botswana International University of Science and Technology, Palapye Off-Grid photovoltaic system. Perturb and Observe algorithm on PSIM environment is only implemented to acquire the historical data for the training and Matlab for the modeling of the network. Relatively long period ambient irradiance and temperature data of Palapye were acquired from the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) WeatherNet in Botswana. Matlab environment was used for the simulation of the backpropagation algorithm for training. The Neural network's feedforward to optimize the non-linear nature of the PV module input and output relationship with relatively fewer processes is required. The results show promising, and the Mean Errors appear to be typically about 0.1 V, and the best performance is 193.5812 at Epoch 13, while the regression delivered a relatively low measured error. The maximum power delivered by the duty cycles from the model with 90 % prediction accuracy. The article demonstrates Neural Network controller is more efficient than the conventional Perturb and Observe Maximum Power Point algorithm.