Manufacturer Datasheet Research Articles

A fully automated measurement system for the characterization of micro thermoelectric devices near room temperature

In this work, we report the customized design and development of a fully automated measurement system to characterize the temperature-dependent properties of microscale thermoelectric devices. The featured system can characterize the devices up to a dimension of 8.79 × 8.79 mm2 in an atmospheric environment. Commercially available bismuth telluride-based thermoelectric modules (both thermoelectric generator and thermoelectric cooler) are used to validate the feasibility and accuracy of the developed system. Their performance data is analysed and compared with the available manufacturer datasheet. The measured data from these devices are found to be in good agreement with the anticipated values and showed an acceptable deviation of less than 4% in the output performances. The developed setup is simple to operate and suitable for performance evaluation of both macro and micro-thermoelectric devices. The system accurately reproduces application conditions the module may be subjected to in a real-world environment.

A New Approach for Solar Photovoltaic Parameter Extraction Using Metaheuristic Algorithms From Manufacturer Datasheet

A New Approach for Solar Photovoltaic Parameter Extraction Using Metaheuristic Algorithms From Manufacturer Datasheet

Experimental Modelling and Simulation of Stand-Alone Experimental Photovoltaic Array for Different Climatic Conditions in Bauchi Metropolis

Standalone renewable energy based on photovoltaic systems accompanied with battery storage system are beginning to play an important role over the world to supply power to remote areas. The current state and the future potentials of renewable energy have increased globally to minimize the usage of other resources such as fossil fuel, which affect the environment. AbubakarTatari Ali Polytechnic, Bauchi (ATAP) is a case where renewable energy will count for 75% of its energy consumption. Solar energy is abundantly availabl.so we have to extract and utilize it in a very efficient way. In this research, an improved yet simple model that can simulate and accurately predict the output power of an installed photovoltaic array in school of Engineering of the AbubakarTatari Ali Polytechnic, Bauchi and for different climatic conditions was developed. The result shows the field measurement based analytical model and RFBNN architecture for PV performance evaluation yielded superior results when compared with that of the manufacturer datasheet based mathematical model.

Comparison of decision tree based ensemble methods for prediction of photovoltaic maximum current

• Irradiance can be used as a direct reference generator to avoid tracking oscillations. • Extracting the parasitic resistances in Single Diode Model can assess aging. • Solar panel maximum current is directly proportional to irradiance variations. • AdaBoost is sensitive to noisy datasets and extreme outliers. The intermittent nature of the output power of photovoltaic (PV) systems, in addition to the fast-varying solar irradiance, has prompted the development of fast, accurate, and reliable forecasting techniques. This paper presents a comparative study of five ensemble machine learning methods based on bootstrap aggregating and gradient boosting for PV applications, namely AdaBoost, LightGBM, XGBoost, Random Forest, and CatBoost. A dataset of fast-varying environmental conditions was collected, and the terminal current of the experimental setup was augmented by applying a mathematical model, along with an evolutionary algorithm to extract the parasitic resistances found in the Single Diode Model (SDM) to accommodate for the aging effect. The mathematical model was evaluated for several irradiance and temperature levels against manufacturer Standard Test Conditions (STC), and then variations of environmental conditions were adjusted based on the manufacturer datasheet. CatBoost showed the lowest overall absolute error distribution (with respect to the mean and standard deviation) of all methods, and the best performance in terms of the absolute error (0.25%) and its standard deviation (0.85%) relative to the mathematical model. The AdaBoost method had the highest absolute error (34.5%) and a standard deviation of (15.8%). After hyperparameters tuning, CatBoost still outperformed other methods and showed consistency of high accuracy above 99% in performance with respect to a testing dataset, in addition, to having the largest area under the curve using the trapezoidal rule. Therefore, the CatBoost prediction method is expected to be an effective technique for maximum power point tracking schemes under fast-varying environmental conditions.

A Novel Metaheuristic Approach for Solar Photovoltaic Parameter Extraction Using Manufacturer Data

Solar photovoltaic (PV) panel parameter estimation is vital to manage solar-based microgrid operations, for which several techniques have been developed. Solar cell modeling using metaheuristic algorithms is found to be one of the accurate techniques. However, it requires experimental datasets, which may not be available for most of the industrial modules. Therefore, this study proposed a new model to estimate the solar parameters for two types of PV panels using manufacturer datasheets only. In addition, two optimization techniques called particle swarm optimization (PSO) and genetic algorithm (GA) were also investigated for solving this problem. The predicted results showed that GA is more accurate than PSO, but PSO is faster. The new model was tested under different solar radiation conditions and found to be accurate under all conditions, with an error which varied between 7.6212 × 10−4 under standard testing conditions and 0.0032 at 200 W/m2 solar radiation. Further comparison of the proposed method with other methods in the literature showed its capability to compete with other models despite not using experimental datasets. The study is of significance for the sustainable energy management of newly established commercial PV micro grids.

Evaluation of Anaesthetic Effect of Commercial Basil Ocimum basilicum on Zebrafish (Danio rerio) Embryos

Essential oils (EOs) are natural compounds used in various fields such as traditional medicine, gastronomy, and agriculture, often used against plant and animal parasites. In the present study, the toxic and pathological effects of basil (Ocimum basilicum) essential oil on the development of zebrafish embryos was investigated. The manufacturer datasheet reported linalool and eugenol as major compounds. The effects of the essential oil were evaluated through a zebrafish embryo toxicity test (ZFET) following the OECD n°236 guidelines. Embryos were exposed to different essential oil concentrations (50, 100, and 200 μL/L). Results showed mortality effects in the experimental groups in a dose-dependent manner. Moreover, zebrafish exposed to the higher concentration showed several pathological alterations; particularly, delayed hatching, pericardial edema, and a significant reduction of heart rate after 96 h post-fertilization (hpf) compared to the control group. Results reported here suggest an anaesthetic effect of the O. basilicum at low concentrations, due to the possible synergist effect of the main components such as linalool and eugenol which act on the GABAergic system evoking bradycardia.

Diffusive solute transport in hollow fiber dialyzers is not affected by variable feed viscosity

Dialyzer clearance (K) for hemodialysis is usually predicted from the mass transfer area product (K0A) provided in manufacturer data sheets without accounting for elevated feed-viscosity when treating blood. The boundary layer model for mass transport across hollow fiber membranes, however, predicts an increase in mass transfer resistance (1/K0) and a decrease in K with increasing feed-viscosity.The effect of increased feed-side viscosity relative to baseline crystalloid viscosity on small solute K and 1/K0 was therefore examined in commercial high- (HF) and low-flux (LF) dialyzers in lab-bench studies using standard dialysis equipment in the normal operating range. Homogeneous colloid solutions and bovine plasma were used to simulate the range of relative viscosities (ηrel) and oncotic pressures expected under in-vivo conditions. Internal filtration (IF) was quantified by a mathematical model to obtain diffusive transport characteristics (K’, 1/K0′).An up to 5-fold increase in ηrel caused a small increase in K and a small decrease in 1/K0 in HF, but not in LF dialyzers. After correction for a small convective contribution by IF, K’ and 1/K0′ remained constant in both LF and HF dialyzers.Diffusive transport characteristics of commercial HF and LF dialyzers are independent of variable feed-side viscosity. This suggests an insignificant contribution of the feed-side boundary layer resistance in dialyzers optimized for operation in hemodialysis. Increasing the feed-side viscosity, however, increases the convective component of dialyzer solute transport because of IF. Diffusive dialyzer clearance predicted from the dialyzer K0A is independent of elevated feed-viscosity.

Comparative analysis on parametric estimation of a PEM fuel cell using metaheuristics algorithms

One of the primary issues in the modelling of fuel cell is the determination of specific boundary conditions often deduced from the manufacturer of the fuel cell. Realistically, not all data is available from the manufacturer's data sheet; hence, to improve the accuracy as well as predict the performance of the cell, all these information need to be determined. This investigation however advanced the concept of using five different algorithms (Grey Wolf Optimization(GWO), Particle Swarm Optimization(PSO), Slime Mould Algorithm(SMA), Harris Hawk Optimiser (HHO), artificial ecosystem-based algorithm(AEO)) to ascertaining seven (ξ1,ξ2,ξ3,ξ4,R,B,λ) unknow parameters that affect the mathematical modelling of the cell. The unknown parameters were used as the modelling variables. A minimum fitness function implied a good correlation between the measured/experimental data and the predicted/modelled data. The study had to rank the performance of the algorithms from the best value to the worse value, average and standard deviation. The artificial ecosystem-based algorithm showed the best results compared to the PSO, SMA, GWO and HHO algorithms.

Accurate Efficiency and Power Densities Optimization of Output Inductor of Buck Derived Converters

In this article, inductor loss models are developed based on the experimental characterization of off-the-shelf components. The modelling steps and techniques are described and validated. It is shown that the model exhibits fairly good accuracy over a large range of ripple current frequency and magnitude. Then, these models are used, as an illustration, in order to present the possible optimization process of the tradeoff between switching frequency-current ripple magnitude and output inductors value in the case of a Buck-derived converter. This optimization has shown that a large current ripple may lead to minimized losses in some cases. The developed modelling technique aims to represent Joules and iron losses, as well as DC and AC losses of inductors. It is not based on physical behaviour description but on mathematical equations based on a set of experimental characterizations. The modelling technique is not suitable for designing the component itself but is useful for selecting the best component value in the manufacturer’s series of components. Since it remains difficult with the manufacturer datasheet to estimate AC losses accurately with respect to frequency and ripple current magnitude, a specific characterization is carried out to complement the available data.

Machine Learning to Predict Junction Temperature Based on Optical Characteristics in Solid-State Lighting Devices: A Test on WLEDs.

While junction temperature control is an indispensable part of having reliable solid-state lighting, there is no direct method to measure its quantity. Among various methods, temperature-sensitive optical parameter-based junction temperature measurement techniques have been used in practice. Researchers calibrate different spectral power distribution behaviors to a specific temperature and then use that to predict the junction temperature. White light in white LEDs is composed of blue chip emission and down-converted emission from photoluminescent particles, each with its own behavior at different temperatures. These two emissions can be combined in an unlimited number of ways to produce diverse white colors at different brightness levels. The shape of the spectral power distribution can, in essence, be compressed into a correlated color temperature (CCT). The intensity level of the spectral power distribution can be inferred from the luminous flux as it is the special weighted integration of the spectral power distribution. This paper demonstrates that knowing the color characteristics and power level provide enough information for possible regressor trainings to predict any white LED junction temperature. A database from manufacturer datasheets is utilized to develop four machine learning-based models, viz., k-Nearest Neighbor (KNN), Radius Near Neighbors (RNN), Random Forest (RF), and Extreme Gradient Booster (XGB). The models were used to predict the junction temperatures from a set of dynamic opto-thermal measurements. This study shows that machine learning algorithms can be employed as reliable novel prediction tools for junction temperature estimation, particularly where measuring equipment limitations exist, as in wafer-level probing or phosphor-coated chips.

Output performance prediction of PV modules based on power-law model from manufacturer datasheet

The current–voltage (I–V) equation in the equivalent circuit model of the photovoltaic (PV) module is implicit, and the dependence of model parameters on environmental conditions is uncertain, which causes inconvenience in output performance prediction. In this paper, a novel method based on the power-law model (PLM) is proposed to predict the I–V characteristics and output power of PV modules under varying operating conditions. The relationship between parameters in the PLM and manufacturer datasheet information is established. The irradiance and temperature dependences of shape parameters in PLM are obtained and investigated thoroughly. Due to inherent simplicity and explicit expression of PLM, the proposed method predicts the I–V characteristics and output power without using any iterative process, which reduces the computational complexity. The proposed method is validated by different types PV modules and under a wide range of environmental conditions. Comparing with traditional methods based on a single-diode model, the proposed method has better agreements with experimental results in all irradiance and temperature intervals. The accuracy and effectiveness are verified both in short-term and long-term output power prediction. The proposed method is simple and suitable to predict the actual output properties of PV modules under varying operating conditions.

A novel black widow optimization approach to improve the precision in parameter estimation problem of solar photovoltaic electrical model

AbstractMathematical modeling of solar photovoltaic (PV) cell/module or array is to assess its unknown modeling parameters with the experimental current–voltage (I–V) characteristic or from the manufacturer's datasheet, where exploitation of implicit and precise solution is a stern concern among researchers for the designing of an optimized solar PV system. Hence, in this article, a robust pheromone value black widow optimization (pv‐BWO) algorithm is proposed to estimate precise modeling parameters of equivalent solar PV single‐diode model (SDM), double‐diode model (DDM), and three‐diode model (TDM). The proposed algorithm is well investigated in coordination with three case studies that hold polycrystalline and monocrystalline PV modules. The suggested technique is validated through the analysis of I–V and power–voltage (P–V) performance characteristics that are meticulously analyzed under various environmental constraints. The first two case studies are widely investigated in the literature also, whereas, to affirm the suitability of pv‐BWO under real implementation the third case study is carried out in an experimental environment. Furthermore, the pv‐BWO method for the aforesaid case studies are validated and compared with the experimental data along with various well‐established parameter estimation techniques in literature, where the proposed method promises a more reliable, stable, and precise solution for the parameter estimation problem.

A Novel Adaptive Filtering Algorithm Based Parameter Estimation Technique for Photovoltaic System

This paper offers a hybrid analytical and estimation based technique to determine the photovoltaic (PV) system parameter in a systematic way. The new model formulation is based on the datasheet parameters under standard test condition and normal operating cell temperature environment, forming the analytical approach's foundation. The estimation part is based on the adaptive filtering algorithm, which shows superiority in estimated parameters compared to existing techniques applied in the photovoltaic system. The proposed approach is made available for a single diode PV model and scaled up aggregated model in extracting the model parameters of two real PV modules in the market, representing the exact <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</i> - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i> characteristics of the manufacturer datasheet. A rigorous comparative analysis is carried out between two adaptive and two optimization based estimations for performance evaluation and recommendation purposes.

Parameterization of photovoltaic solar cell double-diode model based on improved arithmetic optimization algorithm

After avoiding the Standard Test Condition (STC) related to the manufacturer of PV cells, accurate solar system modeling is considered the most important task to study this system. Several mathematical developments based-approaches or algorithms were considered valuable tools to parameterize the electrical circuit of the photovoltaic models. Thus, these parameters are well determined using optimization algorithms. To achieve this goal, an experimental database or manufacturer's datasheet is requested. This paper proposes a novel Improved Arithmetic Optimization Algorithm (IAOA) to extract the solar cell/panel parameters. Also, practical tests are presented for obtaining the measured illustration of the I-V and P-V characteristics. Thus, the Double Diode Model (DDM) of the electrical circuit of PV panel is studied considering various statistical analyzes, to know the Minimum (MinFobj,DDM), the Maximum (MaxFobj,DDM), the Average (AveFobj,DDM), and the Standard Deviation (SDFobj,DDM). The estimated I-V and P-V curves using IAOA is compared with eight other recently published algorithms. In addition, the evaluation of the proposed IAOA proves that this proposal has a satisfactory performance than all tested algorithms. The obtained results show that the IAOA is considered first in terms of effectiveness and accuracy by 9.537e−06 of standard deviation. Moreover, considering the variability of operating conditions, the proposed IAOA shows its high superiority to optimize the electrical parameters of the solar cell TDM Where it has the lowest errors by 3.623e−05 of RMSE, 4.183e−06 of MAE, and 1.397e−05 of RMSD.

Separable Nonlinear Least Squares Search of Parameter Values in Photovoltaic Models

This article presents a new numerical approach for parameter extraction in five-parameter single-diode or double-diode photovoltaic models, through a separable nonlinear least squares treatment of the problem. The parameter separation technique realizes a reduction of the number of independent parameters as well as the computational complexity. Then, a penalty-assisted direct search algorithm is proposed to solve the optimization problem. The proposed method is applicable to current <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">–</i> voltage ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I–V</i> ) curves with at least 4 points or manufacturer datasheets with 3 key points, which breaks the 5-point limitation in previous numerical optimization methods. In two commonly-used case studies that are considered as the benchmark test for single-diode model, the proposed method shows a high accuracy close to the most accurate methods. After an additional curve-fitting refinement of the obtained parameters, the best accuracy documented until now is achieved in the case studies. Furthermore, the robustness of the proposed method is validated through a large-scale data repository containing more than one million <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I–V</i> curves. Finally, to facilitate the application of the proposed method, a graphical easy-to-use program has been developed and available to public users.

A new method to fit logistic functions with wind turbines power curves using manufacturer datasheets

The literature contains different methods for estimating logistic functions parameters to fit the power-speed characteristics of a wind turbine (WT). However, their disadvantages are: (1) they require a large amount of supervisory control and data acquisition data; (2) the parameter range needed for constrained optimization is not systematically determined; and/or (3) they do not guarantee monotonically increasing relationship between wind speed and predicted WT output power. This paper proposes a systematic approach to fitting the five- and the six-parameter logistic functions to power-speed data of WTs. The authors introduce new limits on the parameters of these functions, which guarantee their monotonicity. Most of these limits are determined analytically and are obtained from manufacturer datasheets. Afterwards, these limits are passed as optimization constraints for subspace trust region algorithm. The results show that the authors' method generally provides better accuracy with mean absolute percentage error values below 0.02 for the five-parameter logistic function and below 0.005 for the six-parameter logistic function. The authors present accurate fits for a group of WTs of different ratings (from 275 kW to 3000 kW) and 12 unique manufacturers, which proves the versatility of the authors' method.

Accelerating Time–Current Curve Computation of Induction Motor from Manufacturer Data

Accelerating time–current (ATC) curves of induction motors are normally used for setting, selecting, and coordinating their overcurrent protective devices against heating. Conventionally, the ATC curves can be computed using commercial software tools via a dynamic simulation. This approach always needs a completed set of motor circuit-model parameters, which are typically unavailable to users. To eliminate this problem, this article addresses a direct approach to calculate the ATC curves from commonly available manufacturer data sheets of motors without using the sophisticated simulation tool. Mathematical models of motor’s torque and current curves, taking a nonlinear deep-bar effect into account, are proposed for the ATC determination. The computed torque and current profiles are validated by comparison with those generated from the Matlab simulation tool, published equivalent-circuit parameters, and field measurements. An accuracy of the obtained ATC curves is also assessed against those of the dynamic simulations. The satisfactory results are entirely obtained.

Double-objective optimization-based firefly algorithm of a stand-alone photovoltaic/water pumping system for water supply in rural and remote areas: a case study

A techno-economic study of a stand-alone PV water pumping system for water supply is done in this paper. An optimal design of the system is realized thanks to a double-objective optimization based on a proposed operational strategy of the system and on firefly algorithm (FA). The two objective functions simulated simultaneously using FA are: the function defining the cost of water (COW) and the function which defines the loss of water supply probability (LWSP). The system is designed to supply water to around 328 households in Cameroon, each composed by an average of six (06) persons. For different LWSP, the double-objective simulation determines the optimal configurations of the system with their related cost. The optimal and reliable (LWSP of 0%) system configuration obtained is composed by a number of 7 PV modules, a reservoir volume of 98.4 m3 with 2 days autonomy, a total dynamic head of 40 m, and a pump power of around 1 kW. The related COW corresponding to this configuration is 0.1185 $/m3, and the total investment cost is 27,506 $. The sensitivity analysis of the system revealed that the PV size, the volume of reservoir storage, and the efficiency of the pump influence the system both technically and economically, whereas the total dynamic head only influences the system economically. The validation of the optimized system has been done by comparison with manufacturer datasheets. It is demonstrated in this paper that the techno-economic analysis of energy systems based on multi-objective optimization using firefly algorithm is a powerful tool for decision-making.

An Improved Method for Parameter Identification and Performance Estimation of PV Modules From Manufacturer Datasheet Based On Temperature-Dependent Single-Diode Model

The single-diode model (SDM) is widely used in photovoltaic (PV) system modeling and output power estimation because of its simplicity and accuracy. This article proposed an improved and comprehensive method for parameter identification and performance estimation by using manufacturer datasheet information for PV module. The temperature dependence of all physical parameters in SDM of PV module is comprehensively considered and investigated for varying operating conditions. In the proposed method, all physical parameters under reference condition and their temperature coefficients are calculated from information in manufacturer datasheet, which can be used for parameter calculation and output power estimation under varying operating conditions. To improve the accuracy and efficiency, the analytical and optimization methods are combined in calculation procedure of parameter extraction. By using analytical derivation from I–V equations at key operating points and their temperature coefficients, it reduces the dimensions of the search space and save computational cost for parameter optimization. The accuracy and effectiveness of the proposed method are validated by massive experimental data under varying operating conditions for different type PV modules. The proposed processes are simple and especially useful to calculate the actual performances of PV modules under operating conditions, making it scalable for direct online application.

Modeling, simulation and experimental validation of flow rate of electro-hydraulic hitch control valve of agricultural tractor

Advances in non-linear control theory have made it possible to develop controllers for non-linear dynamic systems in their nature. The low-cost proportional control valves of the hydraulic control system are examples of such a system where there is no linearity due to the structure of the valves and the flaws in the spool. However, the non-linear analysis and regulation of these hydraulic systems cannot be done without a proper valve model. This paper presents a methodology for the development of an efficient unified model of a three-point hitch (TPH) electro-hydraulic proportional control valve control system for agricultural tractors by means of a parameter estimation technique. Modeling and simulation of the proportional control valve was performed using MATLAB Simulink software. Parameter estimation methodology was used to optimize the effective orifice opening of the solenoid valve to meet the flow characteristics available in the manufacturer's technical data sheet for the proportional control valve model parameters. Such unified Simulink models are useful for simulation, practical capability testing, and non-linear control design. Modeling and simulation of electro-hydraulic hitch (EHH) control valve were made and simulation results were compared with actual experimental results. The simulation results of the TPH lifting and lowering time were found to vary 4 and 12.5 %, respectively with the experimental results. This type of parameterized valve models facilitates their implementation in dynamic simulation models of complex hydraulic systems.