Photovoltaic Energy Harvesting System Research Articles

Improving photovoltaic energy harvesting systems with hybrid fuzzy logic-PI MPPT optimized by PSO under normal and partial shading conditions

Improving photovoltaic energy harvesting systems with hybrid fuzzy logic-PI MPPT optimized by PSO under normal and partial shading conditions

Simulation and forecasting of power by energy harvesting method in photovoltaic panels using artificial neural network

Heat is an important efficiency reducing factor in photovoltaic systems. Although many studies in the literature are related to the removal of waste heat from photovoltaic panels, the disadvantages such as installation and maintenance of cooling systems should not be forgotten. A study has shown that with a new approach, energy can be produced with the method of energy harvesting from waste heat. The results of the study were limited to a geographical region and climatic conditions in Aksaray, Turkey. In this article, the results of the study, which was limited to a region, were extended using the finite element method analysis and the artificial neural network model. Ten different cases were determined and temperature gradient was found by finite element analysis. The forecasting algorithm was developed with artificial neural network and estimates the harvestable power based on the temperature gradient. The accuracy of the algorithm was tested with the MSE and nRMSE statistical metrics which were calculated as 59.1423 mW and 13.6189 %, respectively. The training data accuracy of the network was 0.93987 and the combined accuracy was 0.94364. The results of this study are important to be a reference for researchers who want to establish a photovoltaic panel energy harvesting system.

Building Blocks of Photovoltaic Energy Harvesting Systems [Women-In-Circuits Column

Building Blocks of Photovoltaic Energy Harvesting Systems [Women-In-Circuits Column

Optimization of Building integrated photovoltaic and thermoelectric hybrid energy harvesting system for different climatic regions

As energy saving emerges as a critical factor for the sustainable development of humankind, the importance of Zero-Energy Buildings (ZEB) is gradually increasing. Therefore, much research is being conducted on high-efficiency renewable energy technologies that can be applied in urban areas. However, increasing the efficiency of BIPV were proposed and studied because of the need for the renewable energy source. Such as using phase change material (PCM), heat fin, wavelength selection, PV surface temperature decreasing or using a thermoelectric generator (TEG), and convection cooling utilizing the waste heat from the PV. In most previous studies, the performance when each method was analyzed through experiments or simulations. However, the design analysis for maximum performance still needs to be conducted. Therefore, a BIPV combined with a PCM and TEG (BIPV-TEG-PCM) design is analyzed in this study. Herein, the three variables (phase change temperature of the PCM, heat fin spacing in the PCM container, and TEG arrangement) were analyzed through computational fluid dynamics (CFD)-based simulations. Moreover, through multi-objective optimization, the BIPV-TEG-PCM system of the optimal design was derived. Analysis results of the appropriate melting temperature of PCM, heat fin interval, and arrangement of TEG for the proposed system are 40 °C, 12.4 mm, and 187 mm, respectively.

Maximum output power point tracking for photovoltaic energy harvesting systems: Mathematical model and circuit implementation

Maximizing the power transferred to the load is a key feature in any energy harvesting system. Contrary to traditional approaches, this paper mathematically demonstrates that performing a maximum power point tracking on the power delivered to the load instead of on the photogenerated power allows to harvest up to 25% more power due to setting less demanding operating conditions. A circuit implementation of a system that successfully maximizes its output by exclusively taking measurements of the output voltage is designed and demonstrated using a 180 nm commercial CMOS process. The system operates at μW range and achieves a peak power conversion efficiency of 79.04% at 30.74 μW output.

A Low Q Three-Phase Series Resonant Converter for PV Applications

In this paper, a soft-switched three-phase resonant converter with a low quality factor (Q) design is proposed for Photovoltaic (PV) energy harvesting systems with a very wide range of operating conditions. Due to the low quality factor (Q ≤ 1), the voltage stress across the capacitors is drastically reduced. However, a major challenge of using a low Q design is the wide switching frequency range required for operation over a wide range of load and input conditions. In order to reduce the switching frequency range, this paper introduces a hybrid modulation technique employing asymmetric pulse width modulation (APWM) along with variable frequency modulation. The proposed modulation scheme also substantially extends the soft-switching range of the converter from full-load upto 10–35% load condition over a wide range of line voltages. To sustain soft-switching in the entire operating region of the converter, the converter is operated in a hybrid structure with single-phase and burst modes at light load conditions. A comprehensive time-domain analysis of the proposed converter is presented, which greatly improves accuracy over conventional frequency-domain modeling. Experimental results from a 1 kW prototype are presented to verify the performance of the converter and validate the theoretical analysis.

A 1.4mW to 119mW, Wide Output Power Range Energy Harvesting System With 2-D Fast MPPT Based on HC for 1k to 50k Illuminated Solar Cell

A photovoltaic (PV) energy harvesting system with 2-Dimensional Fast Maximum Power Point Tracking (2-D FMPPT), based on the hill climbing (HC) MPPT technique is proposed. The 3-step MPPT modes for the energy harvesting system are employed to obtain KVOC that is independent of environmental effects such as irradiance and radiated temperature. The system consists of a dynamic peak transducer power sensor (DPTPS), Coarse (1st) FMPPT mode, Ton Increment/Decrement Mode, Fine (2nd) mode, MPPT logics, and a controller. The system can handle a voltage range of 0.5 V to 2.4 V and a power range of 1.4 mW to 119 mW. A peak MPPT efficiency of 99.4% and over 96.1% MPPT efficiency in 1 k to 50 k illuminance are achieved. The proposed system is fabricated on 0.18- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu m$ </tex-math></inline-formula> CMOS process and occupies an active area of 1.4 mm2.

A revolutionary Partial Resonant Inverter and doubler rectifier with MPPT based on Sliding Mode Controller for harvesting Solar photovoltaic sources

This article presents a novel solar photovoltaic energy harvesting system for charging the high voltage Electric Vehicle (E.V.) battery using a Partial Resonant Inverter (PRI) driven doubler rectifier circuit. The Maximum Power Point Tracking (MPPT) uses a Sliding Mode Controller (SMC). The proposed system has been circuit modelled in the MATLAB SIMULINK environment and validated using an experimental prototype. The performance of the proposed system has been compared against existing electric vehicle battery charging systems, and it has been established that the proposed idea outperforms the existing systems in terms of power conversion efficiency, power quality, cost, and size.

Development of an organic photovoltaic energy harvesting system for wireless sensor networks; application to autonomous building information management systems and optimisation of OPV module sizes for future applications

The emergence of internet of things (IoT) has motivated research into developing Organic Photovoltaic (OPV) devices that can efficiently convert indoor light into electricity. In this work, the performance and operation of an OPV-powered Wireless Sensor network (WSN) for Building Information management system is provided through a case study. Results are shown for the operation of the WSN and how data can be acquired to build machine learning algorithms that can forecast the indoor conditions of a building, when the system is linked to an external weather station. Remarkably, our data indicates only minor degradation of the OPV when tested under indoor conditions over a 21-month period; at a luminance level of 1000 Lux, only a −10% relative drop in performance was measured. Finally, the field data is used to optimise the size of the OPV and battery for future indoor applications which possess different energy loads. Based on the energy efficiency model, the loss of power supply probability (LPSP) of the indoor applications system is calculated for different size combinations of PV, battery sizes and load energies. This model provides a method to calculate the required OPV output power to ensure remote operation of other IoT electronics. • First report of OPV powered indoor WSN. • Forecasting of indoor conditions possible. • Degradation of indoor operated OPV shown to be minor. • Optimisation of OPV/battery size conducted.

Perovskite PV Energy Harvesting System for Uninterrupted IoT Device Applications

The performance of perovskite solar cells (PSCs) has been improved throughout the years. These photovoltaic (PV) cells can be used to power Internet of Things (IoT) devices for indoor applications. A perovskite PV energy harvesting system with a stand-by battery that continuously powers an IoT device is developed in this work. The battery is required to complement the PSCs when the latter have difficulties in power delivery during low or no irradiance. The performance of the energy harvesting circuit as well as the battery charge and discharge scenarios are investigated. Voltage matching between the PSC and the battery is achieved by a boost converter. The PSC energy harvesting system uses fractional open-circuit voltage (FOCV) based maximum power point tracking (MPPT), which utilizes a Sample and Hold (S&amp;H) circuit. The FOCV technique is based on a comparison of the perovskite PV open circuit voltages and the maximum power points. For each irradiance level, the maximum power point is unique, and this work uses a light-dependent resistor (LDR) to adjust the scaling constant in MPPT. Case studies include various scenarios under 1000 lux fluorescent light and 1 sun irradiance as well as a consideration of different battery states.

Optimal Power Management for Seismic Nodes

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%.

Implementation of Different MPPT Techniques in Solar PV Tree under Partial Shading Conditions

This paper presents the design and analytical modeling of the proposed solar photovoltaic standalone system under varying environmental conditions. The proposed system consists of a unique structure of a solar PV-tree, maximum power point tracking (MPPT) technique, and DC–DC converter. The output voltage acquired from the solar PV tree is low. A DC–DC boost converter is utilized to step-up the required amount of voltage level. In this paper, the appropriate duty cycle is obtained for extracting the optimum power from the solar PV tree by using various MPPT mechanisms such as perturb and observe (P&amp;O), incremental conductance (INC), and a radial basis function network (RBFN)-based neural network (NN). The proposed solar photovoltaic tree-based energy harvesting system is designed and validated by using MATLAB/SIMULINK software and real-time application. The simulation results of the above-mentioned three techniques are compared with each other in order to show the effectiveness of the proposed system with RBFN. The RBFN-MPPT provides a significant improvement in tracking efficiency of 6.0% and 5.72% as compared with the P&amp;O method and the INC method at 1000 W/m2 irradiance condition. From the simulation and real-time results, it is concluded that the RBFN-based NN provides better tracking efficiency and less oscillation as compared with the other two algorithms.

An Adaptive TE-PV Hybrid Energy Harvesting System for Self-Powered IoT Sensor Applications.

In this paper, an integrated thermoelectric (TE) and photovoltaic (PV) hybrid energy harvesting system (HEHS) is proposed for self-powered internet of thing (IoT)-enabled wireless sensor networks (WSNs). The proposed system can run at a minimum of 0.8 V input voltage under indoor light illumination of at least 50 lux and a minimum temperature difference, ∆T = 5 °C. At the lowest illumination and temperature difference, the device can deliver 0.14 W of power. At the highest illumination of 200 lux and ∆T = 13 °C, the device can deliver 2.13 W. The developed HEHS can charge a 0.47 F, 5.5 V supercapacitor (SC) up to 4.12 V at the combined input voltage of 3.2 V within 17 s. In the absence of any energy sources, the designed device can back up the complete system for 92 s. The sensors can successfully send 39 data string to the webserver within this time at a two-second data transmission interval. A message queuing telemetry transport (MQTT) based IoT framework with a customised smartphone application ‘MQTT dashboard’ is developed and integrated with an ESP32 Wi-Fi module to transmit, store, and monitor the sensors data over time. This research, therefore, opens up new prospects for self-powered autonomous IoT sensor systems under fluctuating environments and energy harvesting regimes, however, utilising available atmospheric light and thermal energy.

Comprehensive evaluation of a photovoltaic energy harvesting system in smart clothing for mountain rescuers

A photovoltaic energy harvesting system has been developed for application in smart clothing for mountain rescuers. The generator has been assembled from flexible organic photovoltaic modules and integrated with a specially designed clothing. A power conversion and storage system has been prototyped, providing a USB standard output for supplying active clothing components or standalone devices. The clothing with the integrated energy harvester has been comprehensively tested in respect of both electrical performance and ergonomics. An electrical test methodology has been developed, based on the characteristics recorded in the field and their simulation in a laboratory. This enabled repetitive tests under identical real-life conditions. Ergonomic tests involved diverse physical activities performed by mountain rescuers, simulating their true operations, but conducted in a training room for the sake of standardisation. The prototype system has substantially extended the operating time of a realistic load (equivalent to a smartphone) by providing an extra 1.91 Wh of energy in the best case of irradiance in autumn and 0.77 Wh for a typical case with varying irradiance and temperature. Ergonomics ratings by end users have been good to very good and they have been generally in favour of using power harvesters integrated with their clothing.

A study of light amplification and dispersion by surface tension on water droplet above taro (Colocasia Esculenta) leaf

Hydrophobic characteristics are widely used in the development of new materials, especially to be applied to the surface coating of materials. This involves amplification of the light exposure on the hydrophobic surface for energy-efficient buildings and photovoltaic energy harvesting systems. The paper discusses the role super-hydrophobic nature of light dispersion falling on the water droplet of the taro leaf surface. Camera and video modes were used to get infrared ray images shot at the water droplet, in dark and bright spaces, with variations in the angle of rays incidence and the volume of droplet. The result shows that the waxy layer surface of the taro leaf has the main structure of alkanes/alkyne with active phenol and aldehydes groups, that peak in 2,648/cm. These active groups bind the atoms (free) of the leaf surface when the leaf surface is in normal conditions. The presence of water bubbles on the surface of the taro leaf causes air to be trapped in the cavity of the lump, forms a silvery layer, resulting in chemical reactions with Mg and K atoms, and dehydrogenation of hydrocarbons. These reactions form metal oxides and hydrogen gas. When the bubble is hit by light, the dispersion tends to strengthen at the light angle greater than 40°, due to the silvery coating of magnesium and potassium oxides and the activity of hydrogen gas, that lead to stronger surface tension and the electron mobility and strengthening water molecules bonds. The activities of these products accelerate atomic movement that amplifies the light energy into white light. This study is expected to be a consideration for the new hydrophobic materials design. Applications for surface coating that can amplify light irradiated on the super-hydrophobic surface are promising for energy-efficient buildings and photovoltaic energy harvesting.

An Autonomous Wearable Sensor Node for Long-Term Healthcare Monitoring Powered by a Photovoltaic Energy Harvesting System

In this paper, an autonomous wearable sensor node is developed for long-term continuous healthcare monitoring. This node is used to monitor the body temperature and heart rate of a human through a mobile application. Thus, it includes a temperature sensor, a heart pulse sensor, a low-power microcontroller, and a Bluetooth low energy (BLE) module. The power supply of the node is a lithium-ion rechargeable battery, but this battery has a limited lifetime. Therefore, a photovoltaic (PV) energy harvesting system is proposed to prolong the battery lifetime of the sensor node. The PV energy harvesting system consists of a flexible photovoltaic panel, and a charging controller. This PV energy harvesting system is practically tested outdoor under lighting intensity of 1000 W/m 2 . Experimentally, the overall power consumption of the node is 4.97 mW and its lifetime about 246 hours in active-sleep mode. Finally, the experimental results demonstrate long-term and sustainable operation for the wearable sensor node.

A Single-Inductor Dual-Input Dual-Output DC–DC Converter for Photovoltaic and Piezoelectric Energy Harvesting Systems

A single-inductor dual-input dual-output (SI-DIDO) dc–dc converter for photovoltaic and piezoelectric energy harvesting systems is proposed. The SI-DIDO dc–dc converter uses buck-boost topology. A new algorithm is proposed to share the single inductor between all the inputs and outputs in one switch cycle. Light-load or heavy-load mode is determined by current-mode pulse width modulation (PWM) control. Moreover, a delay lock loop (DLL)-based zero-current detector (ZCD) is proposed to lock the pre-opening time of the comparator. Measurement results show that the peak efficiency of the proposed SI-DIDO buck-boost dc–dc converter is 88.1%.

Photovoltaic Energy Harvesting System Adapted for Different Environmental Operation Conditions: Analysis, Modeling, Simulation and Selection of Devices.

The present research work proposes a photovoltaic energy harvester and an appropriate direct current (DC)/DC converter for a harvesting system after the study of the devices and taking the operation conditions. Parameters such as power, efficiency and voltage are taken into account under different environment conditions of illumination and temperature in order to obtain the best possible response. For this reason, suitable metal-oxide semiconductor field-effect transistor (MOSFET), diode, coil, frequency, duty-cycle and load are selected and analyzed for a DC/DC converter with boost architecture.

An integrated four port bidirectional dc-dc converter for photovoltaic energy harvesting systems

An integrated four port bidirectional dc-dc converter for photovoltaic energy harvesting systems

An Electromagnetism-like Mechanism Algorithm Approach for Photovoltaic System Optimization

&lt;span lang="EN-GB"&gt;Solar energy has become one of the most studied topic in the field of renewable energy. In this paper, an artificial intelligent approach is proposed for the optimization of a photovoltaic solar energy harvesting system. An Electromagnetism-Like Mechanism Algorithm (EM) has been developed to search for the hourly optimum tilt angles for photovoltaic panels. In order to investigate the effect of the search step size on the efficiency and overall accuracy of the algorithm, the EM has also been modified into several variants with different search step size settings. Experimental findings show that EM with bigger search lengths has the advantage of reaching a near optimum tilt angle in earlier iterations but less accurate. EM with smaller step lengths, on the other hand, can hit a relatively more optimum tilt angle in the process. During the peak of the power generation at noon, EM with smaller search stes found an optimum tilt angle which yielded additional 3.17W of power compared to a fixed panel. We thus conclude that the proposed EM performs well in optimizing the tilt angle of a photovoltaic solar energy harvesting system.&lt;/span&gt;