Parameter Extraction Approach Research Articles

Comparative analysis of the hybrid gazelle‐Nelder–Mead algorithm for parameter extraction and optimization of solar photovoltaic systems

AbstractThe pressing need for sustainable energy solutions has driven significant research in optimizing solar photovoltaic (PV) systems which is crucial for maximizing energy conversion efficiency. Here, a novel hybrid gazelle‐Nelder–Mead (GOANM) algorithm is proposed and evaluated. The GOANM algorithm synergistically integrates the gazelle optimization algorithm (GOA) with the Nelder–Mead (NM) algorithm, offering an efficient and powerful approach for parameter extraction in solar PV models. This investigation involves a thorough assessment of the algorithm's performance across diverse benchmark functions, including unimodal, multimodal, fixed‐dimensional multimodal, and CEC2020 benchmark functions. Notably, the GOANM consistently outperforms other optimization approaches, demonstrating enhanced convergence speed, accuracy, and reliability. Furthermore, the application of the GOANM is extended to the parameter extraction of the single diode and double diode models of RTC France solar cell and PV model of Photowatt‐PWP201 PV module. The experimental results consistently demonstrate that the GOANM outperforms other optimization approaches in terms of accurate parameter estimation, low root mean square values, fast convergence, and alignment with experimental data. These results emphasize its role in achieving superior performance and efficiency in renewable energy systems.

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

A Learning-Based Signal Parameter Extraction Approach for Multi-Source Frequency-Hopping Signal Sorting

A Learning-Based Signal Parameter Extraction Approach for Multi-Source Frequency-Hopping Signal Sorting

A novel hybrid numerical with analytical approach for parameter extraction of photovoltaic modules

• A Novel hybrid approach analytical/Numerical technique is proposed and validated. • The minimum value of ARE is obtained using our model and equal to 1.14%. • The obtained RMSE is 0.0031 A , it’s considered as the smallest among the values found by other model. • Low computational time is carried using our Hybrid approach compared with the others models. • The parameter extraction method is superior in terms of accuracy and convergence. Building an accurate mathematical model of photovoltaic modules is an essential issue for providing reasonable analysis, control and optimization of photovoltaic energy systems. Therefore, this study provides a new accurate model of photovoltaic Panels based on single diode Model. In this case, the proposed model is the link between two models which are the ideal model and the resistance network. All parameters are estimated based on hybrid Analytical/Numerical approach: three parameters photocurrent, reverse saturation current and ideality factor are obtained using an Analytical approach based on the datasheet provided by the manufacturer under Standard Test Conditions. The series and shunt resistances are obtained by using a Numerical approach similar to the Villalva's method in order to achieve the purpose of modeling the resistance network part. Our model is tested with data from the manufacturer of three different technologies namely polycrystalline, Mono-crystalline silicon modules and thin-film based on Copper Indium Diselenide, and for more accurate performance evaluation we are introducing the Average Relative Error and the Root Mean Square Error. The simulated Current-Voltage and Power-Voltage curves are in accordance with experimental characteristics, and there is a strong agreement between the proposed model and the experimental characteristics. The computation time is 0.23 s lower than those obtained using others approach, and all obtained results under real environment conditions are also compared with different models and indicated that the proposed model outperforms the others approach such as villalva’s and kashif’s method.

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.

Characterization of a solar concentration thermoelectric generator

In this work we developed a didactic linear parabolic solar concentrator for use with Peltier cooling modules for power generation. The main idea is to maximize the temperature gradient between the hot and cold sides to provide maximum power for this system and use that in class for experimental explanations. The cold temperature is provided by a flow of water pumped from a cold water reservoir and the hot temperature provided by solar radiation which could reach up to 200 °C, but is limited to the Peltier modules allowed temperature of T < 140 °C. This system is compact and lightweight when compared to conventional solar electric generators and can be easily assembled and used in lectures. For this system, we have dedicated to IV measurements and adjustment models to precisely extract the model parameters in a robust simultaneous autonomous adjustment of three equations. As far as we know, our parameter extraction approach was applied to thermoelectric generation for the first time.

A single-diode model for photovoltaic panels in variable environmental conditions: Investigating dust impacts with experimental evaluation

The current study utilized a single-diode equivalent electrical circuit with a non-iterative parameter extraction approach and innovative modifications to predict the photovoltaic modules' electrical behavior in variable environmental conditions. The model's outputs were validated using both the presented data in the module's datasheet and experimental investigations under more than 500 different real conditions. For the first time, besides irradiation and temperature, dust accumulation effects on the single-diode model's main parameters have been investigated to predict the module's electrical behavior under real environmental conditions. Comparing and simulating more than 13,200 different cases, it was shown that by considering dust effects, respectively, the series resistance, parallel resistance, ideality factor, reverse saturation current, and photocurrent source could be defined with up to 25%, 40%, 9%, 35%, and 40% more accuracy. The trend and dependencies of the prediction improvements for each parameter were also investigated thoroughly. Finally, compared with experimental results in more than 500 different cases showed that, on average, the proposed model improved the prediction of the module's output electrical behavior by 25% to 35%. The proposed model could be easily adjusted and applied to predict photovoltaic modules' output behavior under diverse environmental conditions.

Parameter extraction and modelling of the MOS transistor by an equivalent resistance

ABSTRACT During the analysis of multi-transistor circuits, the need arises to evaluate the time delay or the power consumption of the circuit. Due to the complexity of the transistor model, several complicated equations arise from which a compact-form solution cannot be obtained and a suitable physical insight cannot be drawn. With this regard, two contributions are presented in this paper. The first one is a fully analytical parameter extraction approach to be applied on the MOS transistors. The second one is a quantitative method for simplifying the analysis of MOS circuits by modelling the MOS transistor by a suitable equivalent resistance adopting the time-delay or the power-consumption equivalence criteria. The parameter-extraction method is verified by using the extracted parameters in the derived expressions according to the second contribution. Compared to other representations, the agreement of the proposed model with the simulation results is very good.

A reinforcement learning based approach for on-line adaptive parameter extraction of photovoltaic array models

At present, most methods for the fault detection and diagnosis (FDD) of the photovoltaic (PV) array strongly rely on comparing the on-line measured electrical parameters with the modeled reference ones, which are challenging the on-line accuracy and time cost of the parameter extraction for modeling the current-voltage (I-V) curves of the PV array. In this paper, a reinforcement learning (RL) based approach for on-line adaptive parameter extraction of PV array models is proposed. The model parameters, including the ideality factor, series and shunt resistance, and the compensated irradiance for the uncalibrated pyranometer, are extracted. Corresponding environmental states, actions, rewards, and the entire framework for the on-line adaptive parameter extraction are reasonably designed and investigated. The annual experimental results verify that the proposed RL-based approach can obtain higher on-line accuracy for modeling the I-V curve of PV array with fast extraction speed, compared with the conventional meta-heuristic-based approach and the analytical approach for parameter extraction. The annual experimental results reveal that the proposed approach can guarantee the 50% probability for obtaining the root mean square error (RMSE) less than 0.1, and 90% probability for obtaining the RMSE less than 0.25. The average computational time cost of the proposed approach is approximate 38.12 ms. In addition, the annual trend of extracted model parameters is analyzed. The annual results also show that the series and shunt resistance have the inverse seasonal trend. Besides, the measurement error of the pyranometer can be identified statistically. The proposed RL-based approach can also be integrated with the presented on-line FDD method, which realizes the on-line training of RL agents and the FDD of PV array simultaneously.

Direct parameter extraction method for InP heterojunction bipolar transistors based on the combination of T- and π-models up to 110 GHz

A parameter-extraction approach for determination of the small-signal equivalent circuit model for InP/InGaAs double heterojunction bipolar transistors is presented in this paper. This method combines the advantages of conventional T- and π-type equivalent-circuit topologies. All the equivalent circuit elements are only extracted analytically from S-parameter data without any numerical optimization. The agreements between the measured and the model-calculated data are excellent in the frequency range of 0.1–110 GHz.

A Multi-Threshold Segmentation for Tree-Level Parameter Extraction in a Deciduous Forest Using Small-Footprint Airborne LiDAR Data

The development of new approaches to tree-level parameter extraction for forest resource inventory and management is an important area of ongoing research, which puts forward high requirements for the capabilities of single-tree segmentation and detection methods. Conventional methods implement segmenting routine with same resolution threshold for overstory and understory, ignoring that their lidar point densities are different, which leads to over-segmentation of the understory trees. To improve the segmentation accuracy of understory trees, this paper presents a multi-threshold segmentation approach for tree-level parameter extraction using small-footprint airborne LiDAR (Light Detection And Ranging) data. First, the point clouds are pre-processed and encoded to canopy layers according to the lidar return number, and multi-threshold segmentation using DSM-based (Digital Surface Model) method is implemented for each layer; tree segments are then combined across layers by merging criteria. Finally, individual trees are delineated, and tree parameters are extracted. The novelty of this method lies in its application of multi-resolution threshold segmentation strategy according to the variation of LiDAR point density in different canopy layers. We applied this approach to 271 permanent sample plots of the University of Kentucky’s Robinson Forest, a deciduous canopy-closed forest with complex terrain and vegetation conditions. Experimental results show that a combination of multi-resolution threshold segmentation based on stratification and cross-layer tree segments merging method can provide a significant performance improvement in individual tree-level forest measurement. Compared with DSM-based method, the proposed multi-threshold segmentation approach strongly improved the average detection rate (from 52.3% to 73.4%) and average overall accuracy (from 65.2% to 76.9%) for understory trees. The overall accuracy increased from 75.1% to 82.6% for all trees, with an increase of the coefficient of determination R2 by 20 percentage points. The improvement of tree detection method brings the estimation of structural parameters for single trees up to an accuracy level: For tree height, R2 increased by 5.0 percentage points from 90% to 95%; and for tree location, the mean difference decreased by 23 cm from 105 cm to 82 cm.

Modeling and Parameter Extraction of OFET Compact Models Using Metaheuristics-Based Approach

Research on the fabrication of organic field effect transistors (OFETs) has been dramatically increased in the last decade, considering their lightweight and flexible structure as well as their practical and low-cost production. In the next step of fabrication, building compact models and developing parameter extraction methods have critical importance in designing electronic circuits using fabricated OFETs. In this paper, we propose a parameter extraction approach which benefits from the power of metaheuristics. Although direct extraction tools offer analytical solutions in successive steps to extract model parameters, metaheuristics-based global optimization methods can find all parameters at once by exploring a wide range of parameters. Direct extraction tools are cumbersome and need human expertise. On the other hand, global optimization methods are very flexible, adaptive and can be automated as parameter extraction tools of any compact model. In this study, we introduce three different global optimization algorithms, namely, a genetic algorithm (GA), a hybrid artificial bee colony (h-ABC) algorithm, and bacterial foraging optimization (BFO) algorithm, for the parameter extraction. To the best of our knowledge, h-ABC and BFO algorithms have been used for the first time in extracting parameters of OFET compact models. We use two OFET compact models developed by Estrada et al. and Marinov et al. for two different datasets of OFET transistors, both having pentacene as organic semiconductor. While one of the dataset of transistor (T1) is available in literature, the dataset of the other transistor fabricated in our laboratory (T2) is generated as a new dataset. In order to tune control parameters of the developed algorithms, Taguchi's orthogonal experimental design (OED) method is used. Experimental results show that the proposed metaheuristics-based approach can extract model parameters successfully and can perform better than direct extraction methods. The studied OFET compact models fit to the experimental data with these parameters and predict similar output characteristic curves. The algorithms show a good agreement with the experimental data of T1 and T2, having normalized root-mean-square error values under 3.70%, and 8.74% for the models of Estrada et al. and Marinov et al., respectively. It is shown that h-ABC and BFO algorithms perform better than GA on average. It is also observed that the compact model by Estrada et al. performs better for both T1 and T2 compared to the model of Marinov et al.

A Quantitative Approach to Characterize Total Ionizing Dose Effect of Periphery Device for 65 nm Flash Memory

To evaluate the total ionizing dose (TID) response of periphery devices with 65 nm flash memory, the TID effects of the main and parasitic transistor have been investigated based on the proposed novel parameter extraction approach. By analyzing post-radiation behavior of the device's drain current and interface trap density, it has been proven that the parasitic transistor demonstrates stronger radiation dependence than the main transistor. With the proposed approach, the roles of the parasitic transistor and main transistor in the TID effect are quantitatively characterized. For a W =10 μm HVN device, the main transistor Vth shows a shift of &lt;0.1 V with a TID of 100 krad (Si), while the parasitic transistor shows shift &gt;0.5 V with 100 krad (Si) radiation. It is concluded that the net positive charge accumulating in the shallow trench isolation oxide is responsible for the TID induced leakage and the Vth shift in the flash technology periphery device.

Determination of charge transport activation energy and injection barrier in organic semiconductor devices

Charge carrier transport in organic semiconductor devices is thermally activated with characteristic activation energies in the range of 0.2–0.6 eV, leading to strongly temperature-dependent behaviour. For designing efficient organic semiconductor materials and devices, it is therefore indispensable to understand the origin of these activation energies. We propose that in bilayer organic light-emitting diodes (OLEDs) employing a polar electron transport layer, as well as in metal-insulator-semiconductor (MIS) devices, the hole injection barrier Einj and the hole mobility activation energy Eμ can be decoupled from each other if temperature-dependent capacitance-frequency (C-f-T) and MIS-CELIV (charge extraction by linearly increasing voltage) experiments are combined. While the C-f-T signal contains information of both injection and transport, the CELIV current is expected to be insensitive to the electrode injection properties. We employ numerical drift-diffusion simulations to investigate the accuracy of this analytical parameter extraction approach and to develop criteria for its validity. We show that the implicit assumption of constant charge density and field profiles leads to systematic errors in determining the activation energies. Thus, one should be aware of the intrinsic limitations of the analytical Arrhenius fit, and for more accurate parameter determination a full drift-diffusion modelling is advised. Applying the analytical method to a standard bilayer OLED, we find that the total activation energy of 0.5 eV for the hole current can be split into contributions of ≈0.25 eV each for injection barrier and mobility. Finally, we also discuss the broader applicability of this method for other device stacks and material combinations.

Stiffness and Damping Properties of a Swing Clamp: Model and Experiment

The dynamics of a workpiece, and hence its behaviour in a machining process, is significantly influenced by the fixture system. In such assembled systems, contacts between the parts have great impact on the vibration characteristics of the complete structure. Prediction of the dynamic behaviour of a workpiece-fixture-system can help to avoid undesirable results like poor workpiece surface quality and thus a reduction of rejects can be achieved. This paper introduces experimental studies and a model for describing the tangential stiffness and damping properties of a swing clamp. A modification of an existing material model was used to represent the transfer function, in terms of force and relative displacement, between the contact surfaces. An experimental setup for assessing the model parameters was established. An approach for parameter extraction is also provided. The proposed model can be adapted for other clamping mechanisms.

CRLH Transmission Lines for Telecommunications: Fast and Effective Modeling

A different parameter extraction approach based on zero immittances for composite right/left-handed (CRLH) structure is presented. For lossless unit cell equivalent circuit model, LC parameters of series and parallel branches are extracted according to series resonance frequency and parallel resonance frequency, respectively. This approach can be applied to symmetric and unbalanced CRLH structures. The parameter extraction procedure is provided and validated by T-type unit cell model. The responses of distributed prototype and extracted equivalent LC circuit model are in good agreement. The equivalent circuit modeling can improve the degree of freedom in the CRLH TLs design. This parameter extraction method provides an effective and straightforward way in CRLH metamaterials design and applications in telecommunication systems.

Development of Temperature-Dependent SPICE Computer Models of Photovoltaic Cells

Parameterized computer models of photovoltaic (PV) cells and modules are proposed in the paper. Four-and five-parameter one-diode models, two-diode model, as well as behavioral models of the PV cell are considered. The temperature-dependence of the model parameters is taken into account. Parameter extraction approach is developed to determine the model parameters, based on nullor equivalent circuit and datasheet characteristics of the PV cells and modules. PSpice description of the developed PV model is given in the form of parameterized block. The accuracy of the computer PV models is investigated by comparison to the datasheet characteristics.

Novel neural approach for parameter extraction of microwave transistor noise models

AbstractA novel approach for parameter extraction of microwave transistor noise models based on artificial neural networks is proposed in this work. Neural networks are applied to determine parameters of the noise model directly from the measured noise and small‐signal scattering parameters without any optimization procedure. Moreover, unlike the similar existing procedures, development of the extraction procedure does not require any measured data or optimizations in a circuit simulator, making the procedure more efficient, as described in detail in the paper. The approach has been applied to extraction of the Pospieszalski's noise model parameters for a specific pseudomorphic high‐electron‐mobility transistor (pHEMT) device working under different temperatures. Copyright © 2015 John Wiley &amp; Sons, Ltd.

A novel physical parameter extraction approach for Schottky diodes**Project supported by the Joint Fund of the National Natural Science Foundation of China and the China Academy of Engineering Physics (Grant No. U1230112).

Parameter extraction is an important step for circuit simulation methods that are based on physical models of semiconductor devices. A novel physical parameter extraction approach for Schottky diodes is proposed in this paper. By employing a set of analytical formulas, this approach extracts all of the necessary physical parameters of the diode chip in a unique way. It then extracts the package parasitic parameters with a curve-fitting method. To validate the proposed approach, a model HSMS-282c commercial Schottky diode is taken as an example. Its physical parameters are extracted and used to simulate the diode’s electrical characteristics. The simulated results based on the extracted parameters are compared with the measurements and a good agreement is obtained, which verifies the feasibility and accuracy of the proposed approach.

Erratum: An approach for determining equivalent circuit model of on‐chip inductors

A parameter-extraction approach for the spiral on-chip inductors, which combines the analytical approach and the empirical optimization procedure, is developed in this article. The model parameters determined from the analytical expressions are considered as an initial guess of a subsequent optimization procedure leading to the final model parameters. Scaling rule for the inductances of spiral on-chip inductors has been developed, and very useful for predicting the performance of the on-chip inductors with larger size. Good agreement is obtained between simulated and measured results for the spiral inductors with different size on silicon in the frequency range of 500 MHz–40 GHz. © 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:2363–2370, 2013