Manufacturer Datasheet Research Articles

Impact of Temperature and Shading on Performance of Solar Photovoltaic Systems in Telecom Sites

Nigeria's rural mobile network boom strains its power generation. Expensive and polluting diesel generators power off-grid base stations, prompting mobile network operators to explore solar PV systems. However, real-life performance under field conditions is a major concern, as manufacturer datasheets often fall short. This study highlighted the performance of a solar PV system powering a telecommunication station in Uli, Anambra State, Nigeria. We examined temperature, irradiance and shading effect. The findings highlight the impact of shading and temperature on energy yield and performance ratio (PR). While stand-alone and solar tracking systems had similar PRs (66 to 68 percent), tracking systems generated the most energy (4.53 energy yield) due to increased solar exposure. However, this benefit came with a trade-off; higher temperature losses (1559 kWh) caused by temperature rise from direct sunlight. Additionally, the nearby structures significantly impacted tracking systems by causing shading. In conclusion, well-designed solar PV systems for telecommunication stations must consider mitigating factors like shading and temperature. This can achieve substantial advantages: lower operational expenses for operators, improved network availability for rural users, and a reduced environmental impact, aligning with UN Sustainable Development Goals.

Optimization of Production Parameters for Impact Strength of 3D-Printed Carbon/Glass Fiber-Reinforced Nylon Composite in Critical ZX Printing Orientation.

Additive manufacturing (AM) methods are increasingly being adopted as an alternative for mass production. In particular, Fused Deposition Modeling (FDM) technology is leading the way in this field. However, the adhesion of the layers in products produced using FDM technology is an important issue. These products are particularly vulnerable to forces acting parallel to the layers and especially to impact strength. Most products used in the industry have complex geometries and thin walls. Therefore, solid infill is often required in production, and this production must take place in the ZX orientation. This study aims to optimize the impact strength against loads acting parallel to the layers (ZX orientation) of PA6, one of the most widely used materials in the industry. This orientation is critical in terms of mechanical properties, and the mechanical characteristics are significantly lower compared to other orientations. In this study, filaments containing pure PA6 with 15% short carbon fiber and 30% glass fiber were utilized. Additionally, the printing temperature, layer thickness and heat treatment duration were used as independent variables. An L9 orthogonal array was employed for experimental design and then each experiment was repeated three times to conduct impact strength tests. Characterization, Taguchi optimization, and factor analyses were performed, followed by fracture surface characterization by SEM. As a result, the highest impact strength was achieved with pure PA6 at 8.9 kJ/m2, followed by PA6 GF30 at 8.1 kJ/m2, and the lowest impact strength was obtained with PA6 CF15 at 6.258 kJ/m2. Compared to the literature and manufacturer datasheets, it was concluded that the impact strength values had significantly increased and the chosen experimental factors and their levels, particularly nozzle temperature, were effective.

Characteristics of 2N4416 Si JFETs for radiation spectrometer charge sensitive preamplifiers

The input transistors (JFETs) of feedback resistor-less charge sensitive preamplifiers (CSPs), for photon and charged particle counting radiation spectrometers, that use semiconductors as their detectors, are operated in an unusual forward biased mode; consequently, manufacturer datasheets cannot necessarily be relied upon, as they do not specify the characteristics of the JFETs when they are operated with the gate-to-source junction forward biased. This experimental study, in which 165 2N4416 and 2N4416A NJ26 silicon junction field effect transistors (JFETs) were electrically characterised at ambient laboratory temperature, focused on the JFETs’ performances in the context of their use as input transistors of feedback resistor-less charge sensitive preamplifiers (CSPs). The experimental study found evidence which supports much of the common practice of the skilled CSP designer but which had not been documented with results in the open literature until now. This includes, inter alia, that: the 2N4416 performs better than the 2N4416A; not all transistors even of the same type have comparable performance when operated in forward bias mode; only a small proportion of 2N4416 and 2N4416A transistors are of desirable performance characteristics for CSPs; and, individual screening of each candidate JFET is required if an optimally performing CSP is to be produced – even when using JFETs of the same type from the same batch. The methodology and analysis documented here will also be useful to researchers developing new wide bandgap semiconductor JFETs for high temperature tolerant and radiation-hard CSPs.

A robust multi-objective optimization algorithm for accurate parameter estimation for solar cell models

The accuracy of solar cell models is crucial for enhancing the performance of solar photovoltaic (PV) systems. However, existing solar cell models lack precise parameters, and the manufacturer's datasheet does not provide the required information for reliable modeling. Consequently, accurate parameter estimation becomes necessary. This paper presents a simple multi-objective optimization algorithm (Hybrid Particle Swarm Optimization and Rat Search Algorithm (PSORSA)) designed to estimate cell parameters based on this observation. Unlike other optimization algorithms addressing this issue, the proposed algorithm aims to overcome challenges related to local minima and premature convergence, which often lead to suboptimal results. The paper focuses on assessing the reliability of the proposed algorithm by comparing its performance with other well-known optimization algorithms. The proposed optimizing algorithm is tested on the CEC 2019 benchmark function. Experimental results (RMSE), including statistical analysis, validate the algorithm's effectiveness by comparing them with other algorithms. At the end, non-parametric test is performed to justify the outcomes, vouching for the better performance of the proposed algorithm. The findings demonstrate that the proposed algorithms are particularly well-suited for estimating solar PV models. With its simple structure and high accuracy, the proposed algorithm exhibits great potential for various applications in the field of solar energy. Moreover, its computational efficiency and ease of implementation further contribute to its practicality.

Hybrid Tiki Taka and Mean Differential Evolution based Weibull distribution: A comprehensive approach for solar PV modules parameter extraction with Newton-Raphson optimization

Effective parameter extraction is crucial in modeling and analyzing complex systems, particularly photovoltaic (PV) cells/modules. Therefore, it is essential to develop a robust optimization model that can effectively determine the optimal parameters of PV models. This study introduces a novel metaheuristic algorithm, Tiki Taka Algorithm (TTA), inspired by sports strategies, particularly the renowned tiki-taka style. The innovation lies in the hybridization of TTA with the Mean Differential Evolution based on Weibull distribution (MDEW). The resultant optimal parameters obtained from this hybridization serve as the initial guess for Newton-Raphson (NR), a critical step that ensures the estimated current closely aligns with the measured current, leading to a minimized Root Mean Square Error (RMSE). The hybrid approach leverages the excellent exploration capabilities of Weibull distribution and the exploitation strength of the Mean Differential Evolution (MeanDE) mutation, offering a comprehensive solution for navigating complex optimization landscapes. The proposed TTA-MDEW effectively identifies various parameters in PV models, including single diodes, double diodes, and PV modules. The findings show that TTA-MDEW holds its ground against the most sophisticated optimization methods in terms of reliability, accuracy, and speed of convergence. Additionally, data from real-world manufacturer datasheets reveal that our method delivers exceptionally precise results under varying irradiance and temperature conditions. Therefore, these outcomes establish the TTA-MDEW as an effective tool for extracting precise parameters from various PV models, enhancing the modeling of PV systems.

A Kepler optimization algorithm improved using a novel Lévy-Normal mechanism for optimal parameters selection of proton exchange membrane fuel cells: A comparative study

Proton exchange membrane fuel cells (PEMFCs) are considered a promising renewable energy source and have sparked a lot of interest over the last few years due to their robust efficiency, low operating temperature, and longevity. The PEMFC's electrochemical model has seven unknown parameters, which are not given in the manufacturer's datasheets and need to be accurately estimated to present a more accurate model, leading to improved efficiency and performance of the PEMFC systems. The estimation of those unknown parameters has been dealt with as a complex and non-linear optimization problem that needs a powerful optimization algorithm to solve it. The existing optimization algorithms still have some disadvantages, such as falling into local minima and low convergence speed, which make them ineligible to solve this complicated problem with acceptable accuracy and low computational cost. Therefore, this study presents a new parameter estimation technique for estimating the unknown parameters of the PEMFC model more accurately, thereby achieving precise modeling of PEMFCs. This technique called IKOA is based on integrating the Kepler optimization algorithm (KOA) with a novel Lévy-Normal (LN) mechanism to strengthen its exploration and exploitation capabilities against this multimodal optimization problem. The Lévy flight in this mechanism aims to improve the KOA's exploitation operator to accelerate the convergence speed toward the near-optimal solution, thus minimizing the computational cost; meanwhile, the normal distribution is used to strengthen its exploration operator, thereby aiding in the escape of local minima. The proposed IKOA and KOA are herein evaluated against several rival algorithms using six well-known commercial PEMFC stacks to highlight their efficiency and effectiveness. Key performance metrics such as computational cost, fitness measures, and statistical validation through the Wilcoxon rank-sum test are herein used to highlight IKOA's effective performance in enhancing the predictive accuracy and operational efficiency of PEMFCs. The numerical findings show the high superiority of IKOA against all rival optimizers on all solved benchmarks.

Performance of rime-ice algorithm for estimating the PEM fuel cell parameters

The process of employing optimization techniques to identify the optimum unknown variables appropriate for the creation of a precision fuel-cell performance forecasting model is known as parameter identification of a PEM fuel cell (PEMFC). The manufacturer's datasheet may not always provide these parameters, thus it is necessary to ascertain them to precisely estimate and forecast the fuel cell's performance. six unknown parameters of a PEMFC are computed using six optimization techniques: The Rime-Ice algorithm (RIME), the Grey Wolf Optimizer (GWO), the Moth Flam Optimizer (MFO), the Tunicate Swarm Algorithm (TSA), the Sine Cosine Algorithm (SCA), and the Osprey Optimization Algorithm (OOA). These six parameters serve as choice variables during optimization, and the sum square error (SSE) between the estimated and measured cell voltages is the fitness function that needs to be minimized. Ned Stack PS6, a real-world PEM fuel cell model, is used to verify the functionality of all comparator algorithms, including the suggested RIME method. The RIME algorithm yielded an SSE of 1.945417827, which was followed by MFO, GWO, TSA, SCA, and OOA. In addition, it was concluded that RIME's convergence speed was quicker than that of the other methods examined. The comparative analysis is conducted using the same dataset and the same computation burden for each of the several optimization techniques to provide a fair performance evaluation. The performance of the suggested RIME against the alternative optimization algorithms is further examined using statistical analysis. The results demonstrate a good degree of agreement between the experimental data and the estimated voltage-current curves produced by the suggested RIME.

An efficient data sheet based parameter estimation technique of solar PV

This work develops an efficient parameter estimation technique, based on manufacturer datasheet, to obtain unknown parameter of solar photovoltaic (PV), precisely. Firstly, a nonlinear least square objective function, in terms of variables given in manufacturer datasheet, has been developed. Then, two optimization techniques, namely the Particle Swarn Optimization (PSO) and Harmony Search (HS) are applied on the developed objective function to achieve the optimized result. Further, the correctness of the developed technique is tested by estimating the performance indices, namely percentage maximum power deviation index (%MPDI) and overall model deviation index (OMDI), of two different solar PV, viz., Kyocera KD210GH-2PU (poly-crystalline), and Shell SQ85 (mono-crystalline). It is shown that developed method with PSO outperforms the HS. The developed method with PSO gives the values of %MPDI and OMDI of 0.0214% and 0.213, only. Also, the existing methods, based on hybrid, multi-objective function, numerical method, have been considered for the comparative analysis. It is revealed through the comparative studies that the developed method with PSO has smaller value of MPDI (= 0.0041%) and OMDI (0.005) than the other existing methods. Further, the convergence of the developed method has also been estimated to check the speed of estimation. It is shown that the developed technique converges only in 5 s. In addition, the developed technique avoids the need of extensive data as it is based on manufacturer datasheet.

Design optimization of hemodialyzer membrane modelled in chemical reaction module using statistical algorithms and ANOVA testing

High performing hemodialyzers membranes such as high flux membranes, high cut-off membranes and medium cut-off membranes are always at research interest due to their better efficiency than conventional membranes. These membranes provide greater toxin clearance, however retention of essential solutes in a preferable way are still under study. This paper aims at the design of high performing membrane to study the role of its parameters in solute removal and its capability of holding back important molecules. One of the most effective design of experiments (DOE) tool, namely Taguchi Algorithm was used for the formation of fractional factorial design of parameters. The simulation results were benchmarked with that of experimental data from literature and with manufacturers data sheets. Once the benchmarking was done, the error quantification and significance of each design were analysed using statistical method, Analysis of Variance (ANOVA) testing. The most relevant parameters that helped in better clearance in these membranes were thus identified and substantial conclusions were drawn which can be used in the future for designing optimal dialyzer designs. Results shows that clinically used dialyzer membranes such as RevaclearMax and FxCorDiax series on modelling using COMSOL Multiphysics with a blood flow rate of 400 ml/min and dialysate flow of 500 ml/min showed better urea clearance rate of above 300 indicating that the membranes thus designed was superior to the conventional high flux membranes that have a clearance rate of 297 of less for the exact same functional, geometrical, and parametric conditions. The model replication and thus validation of the design helped in understanding the influence of various parameters in toxin clearance. These parameters can be further investigated, and optimal models can be delivered with more of clinical examinations and trials.

Tensile anisotropy of powder bed fusion steel 316L: A practical study on the effect of build orientation

This study investigated the tensile anisotropy of steel 316L fabricated via laser powder bed fusion (L-PBF), built at different orientations. Tensile tests were performed on as-built L-PBF specimens produced at 0°, 15°, 30°, 45°, 60° and 90° angles. The yield strength, ultimate tensile strength and elasticity modulus experienced a decrease with an increasing build angle. Conversely, elongation at fracture increased as the build angle increased. The Elasticity modulus was found to be substantially lower than the nominal values reported in the material data sheet of the L-PBF equipment manufacturer. Fractography performed via Scanning Electron Microscopy (SEM) has found indications of porosity and lack of fusion that may have contributed to lower Elasticity modulus and an overall impacted mechanical performance. A complementary powder quality analysis has offered further insights on this and provided indications on the powder recycling impact.

Simulink model of a bifacial PV module based on the manufacturer datasheet

The paper proposes an update of a mathematical model of a Photo-Voltaic (PV) module, considering the possibility that also the rear face of the PV module produces energy. The proposed approach allows to write a new descriptive equation, whose terms are function of the information always available in the modern datasheet of the PV module’s manufacturers. This implies that no pre-processing of the datasheet’s parameters is needed to use the proposed model, whichever are the solar irradiance and the cell/module temperature. The model considers the total solar radiation (front and back side), such that the produced energy increases. Simulink environment is used to calculate the total solar radiation.

Thermo-economic optimization of a 100 kW air-to-water heat pump for heating purposes in residential units

In order to reach the ambitious objectives of carbon neutrality by 2050 and the limitation of the ambient temperature rise at 1.5 °C, several intensive energy fields such as heating and cooling appliances should be decarbonized. On this regard, electric heat pumps represent the most promising solutions to replace old and inefficient conventional boilers and comply with the ecological transition through the increase of electric energy production by means of renewable sources. In this paper, a thermo-economic optimization analysis of a 100 kW air-to-water electric heat pump is proposed. The appliance is designed to produce hot water at 55 °C. The developed model employs phenomenological equations calibrated on real data and manufacturer datasheets for compressor, heat exchangers, and costs. Different design options, including the heat exchangers size and the choice of the working fluid (among R32, propane R290, ammonia R717, R454C), are presented and discussed. The Pareto analysis is then employed to find the best possible configurations in terms of costs and performance.

Analysis of the Measurement Error From a Low-Cost Ultrasonic Sensor

This study analyzes the error within a low-cost range-finding device known as the HC-SR04 ultrasonic sensor and compares it to what manufacturer datasheets claim as the error measurement in the sensor’s readings. The HC-SR04 ultrasonic sensor is a device that is capable of determining the distance between itself and an object in front of it by using sound waves that reflect off of the object. These sound waves are ultrasonic waves, which have a frequency that cannot be heard by human ears. Common uses for this sensor are motion tracking, object detection, and state estimation – all common capabilities of robots today. This sensor offers an inexpensive option for range-finding which has extensive applications towards the field of mechatronics and robotics; however, there is a major drawback in using this device – the error of its measurements. This error will be analyzed through the distance readings from the HC-SR04 sensor in comparison to the true distance measurements as well as the possible error variation present when the distance from the sensor increases.

Scanning Electron Microscopy and EDX Spectroscopy of Commercial Swabs Used for COVID-19 Lateral Flow Testing.

The chemical composition of COVID test swabs has not been examined beyond the manufacturer's datasheets. The unprecedented demand for swabs to conduct rapid lateral flow tests and nucleic acid amplification tests led to mass production, including 3D printing platforms. Manufacturing impurities could be present in the swabs and, if so, could pose a risk to human health. We used scanning electron microscopy and energy dispersive X-ray (EDX) spectroscopy to examine the ultrastructure of seven assorted brands of COVID test swabs and to identify and quantify their chemical elements. We detected eight unexpected elements, including transition metals, such as titanium and zirconium, the metalloid silicon, as well as post-transition metals aluminium and gallium, and the non-metal elements sulphur and fluorine. Some of the elements were detected as trace amounts, but for others, the amount was close to reported toxicological thresholds for inhalation routes. Experimental studies have shown that the detrimental effects of unexpected chemical elements include moderate to severe inflammatory states in the exposed epithelium as well as proliferative changes. Given the massive testing still being used in the context of the COVID pandemic, we urge caution in continuing to recommend repeated and frequent testing, particularly of healthy, non-symptomatic, individuals.

Preliminary design of an OWC wave energy converter battery charger

This paper introduces a low-power off-grid oscillating water column wave energy converter with an internal battery bank. The research aims at the preliminary design and devising of the control strategy of a power electronics interface between the turbo generator and the battery bank. The converter comprises a spar buoy, a biradial turbine, a permanent magnet generator, a full-wave bridge rectifier, a braking chopper, a DC-to-DC step-down converter, and a lead-acid battery bank. The power-take-off system was modelled in Simulink/MATLAB, and its performance was assessed with steady-state simulations, considering a wave climate characteristic of Leixões, Portugal. The chamber pressure, the turbine, generator and rectifier performance were taken from experimental data sets. A simple battery model was derived from the manufacturer's datasheet. An ideal step-down DC-to-DC converter operating in discontinuous conduction mode regulates the battery charging current. This converter, in parallel with the braking chopper, adjusts the generator counter torque by regulating the current through the rectifier. Twelve system variables were recorded for selected pairs of input pressure and step-down converter design coefficient. The power at the rectifier's output terminals was mapped for the rotational speed and input pressure. The results show a system rating of 1.4 kW with 400 W of electrical power at 200 rad/s for the most frequent sea states. The range of the duty cycle, the inductance and the braking resistance were derived. Two closed-loop controllers were proposed for managing the step-down converter and the braking chopper. Their set points and saturation limits were derived from the simulation results.

An efficient method for predicting PV modules performance based on the two-diode model and adaptable to the single-diode model

This paper introduces a simplified and accurate modeling approach to model photovoltaic modules. The current method uses a hybrid technique combining numerical and analytical approaches to predict the current-voltage characteristics of PV generators by calculating the parameters of the double-diode model. First, to reduce the complexity of the current equation and minimize the calculation time, some simplifications reducing the research space from seven to only five unknowns are taken into consideration. Second, three of these five parameters are extracted using simple analytical equations derived from the output current's equation based on the available values of the typical points on the manufacturer's datasheet. Then, the remaining two parameters are extracted using a combination of numerical approach and slope adjustment technique at the short-circuit point. The technique is adapted to the single-diode model in order to study the effect of the second diode on the simulating accuracy. The effectiveness is tested over six PV modules matching different PV technologies, and the results are compared with a great number of modeling methods based on different approaches, and the adjustment technique has shown the highest accuracy levels (RMSE values less than 0.045A) at the least computational times (compilation time less than 0.084 s).

Part load operation analysis of a biomass steam generator integrated with a Linear Fresnel solar field

Hybrid systems involving the use of biomass and solar energy have been proposed as a promising alternative to reduce greenhouse gas emissions and provide a way to overcome the inherited shortcomings of solar energy, such as intermittent and instability. Nevertheless, some plant components will operate in the off-design condition once solar energy is added to the cycle. In this regard, the objective of this work is to evaluate the performance of an existing biomass steam generator at partial loads, integrated with a Fresnel solar field in fuel-saving mode. The equipment consists of a biomass steam generator with a capacity to produce up to 50 t/h of superheated steam at 420 ℃/45 bar(a) to feed a cogeneration power plant. Measurement data were collected on-site to calculate the boiler efficiency at part loads according to the ASME PTC4 standard. The main components of the boiler were modeled using the OpenModelica® tool and the ThermoSysPro® library. The model accuracy was verified at different loads with the available measurement data and manufacturer datasheets. In conclusion, the implemented model allowed to reproduce the boiler performance in different loads, being able to evaluate the impact of the solar field integration and the hybrid boiler operation.

Numerical model of heat transfer for protected steel beam with cavity under ISO 834 standard fire

The temperature distribution of a member is an essential factor in determining the structural behavior at high temperatures. This paper presents a methodology for predicting the temperature distribution of a steel beam with a membrane enclosure type of protection under the ISO 834 standard fire conditions using an Abaqus finite element model. In this research, to enhance the reliability of the Abaqus simulation model, the Fire Dynamic Simulator (FDS) was used to simulate the convection process within the cavity between the protection cover and the steel beam. Thermal properties of steel and protection cover were adopted from Eurocode 3 and manufacturer's datasheets. The effects of damage to the fire protection cover on the beam at elevated temperatures were also discussed. It was found that using Abaqus in conjunction with FDS to consider the convective heat transfer within the cavity would predict the steel beam temperature more accurately than without considering the convective heat transfer.

Optimal Parameter Estimation of Solar Photovoltaics Through Nature Inspired Metaheuristic and Hybrid Approaches

Estimation of solar photovoltaic (PV) equivalent model parameters through the manufacturer's datasheet or experimental performance current voltage (I-V) characteristics is an apprehension among researchers. In this paper, two approaches are instigated and compared i.e. metaheuristic pheromone value black widow optimisation (pv-BWO) and hybrid sailfish optimisation (h-SFO) for the estimation of a single-diode model (SDM) and double-diode model (DDM) parameters for the accurate solar PV modelling. Four distinct case studies that incorporate four different PV modules i.e. RTC France, PWP 201, SHARP ND-R250A5, and EIL 75W PV are investigated, where the validation of the proposed methods is carried out through the experimental performance characteristics of solar PV under test i.e. the I-V and power-voltage (P-V) performance characteristics. Moreover, the performance is evaluated on various error functions, and experimental I-V and P-V characteristics, where the results are also compared with the various techniques of parameter estimation in the literature. It is important to underline that the closeness of the objective function to zero exhibits the congruence of estimated parameters with the exact parameters, where the exact parameter value is not accessible. Therefore, the preciseness rests on the experimental data only, where any significant reduction in OF i.e. RMSE considered as an improvement towards the exactness of real unknown parameter values. The pv-BWO exhibits an average of 69.89% of lower error results when compared with few existing methods, whereas h-SFO recorded a higher RMSE of about 93% when compared with the pv-BWO which suggests the suitability of pv-BWO over h-SFO and few existing methods.

Optimization of pv cells/modules parameters using a modified quasi-oppositional logistic chaotic rao-1 (QOLCR) algorithm.

Since the behavior of photovoltaic (PV) modules under different operational conditions is highly nonlinear, predicting the performance of PV systems in industrial applications is becoming a major challenge issue. Moreover, the most important information required to configure an optimal PV system is unavailable in all manufacturer's datasheets. In this context, a novel method is recommended to optimize PV cells/module parameters with the ability to correctly characterize the I-V and P-V curves of different PV models. In the present article, a chaotic map is incorporated in the so-called quasi-oppositional Rao-1 algorithm to improve its efficiency, and the resulting algorithm is named quasi-oppositional logistic chaotic Rao-1 (QOLCR) algorithm. Numerical results indicate that the QOLCR algorithm has presented very good performance in terms of accuracy and robustness. The idea is to minimize the root mean square error (RMSE) between the estimated and the actual data. Simulation results in the single diode model give an RMSE of value [Formula: see text], and in the double diode model, an RMSE of value [Formula: see text] has been reached as the minimum value among the other compared optimization methods. Hence, the QOLCR approach also converges faster than the basic Rao-1 algorithm and its other variants. Moreover, the modified QO Rao-1 algorithm shows its perfectness and could be involved as tools for optimal designing of PV systems.