A Simple Parameter Estimation Approach to Modeling of Photovoltaic Modules Based on Datasheet Values

2016 ◽  
Vol 138 (5) ◽  
Author(s):  
Xiangsai Feng ◽  
Xiangyun Qing ◽  
C. Y. Chung ◽  
Hongqiao Qiao ◽  
Xunchun Wang ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Optimal Parameter ◽  
Standard Test ◽  
Least Square ◽  
Model Parameters ◽  
Electrical Model ◽  
Characteristic Model ◽  
Test Conditions ◽  
Two Parameters ◽  
Simple Parameter

This work presents a simple parameter estimation approach for a photovoltaic (PV) module using a single-diode five-parameter electrical model. The proposed approach only uses the information from manufacturer datasheet without requiring a specific experimental procedure or a curve extractor. The number of parameters to be determined is first reduced from five to two by gaining insight into electrical equations of the model at the standard test conditions (STCs). A nonlinear least square (NLS) objective function is then constructed and minimized by a complete scan for all possible values of the two parameters within some specific ranges based on their physical meaning. Consequently, the single-diode five-parameter electrical model at the STC is determined based on two optimal parameter values. Besides, a PV full characteristic model with consideration of both the irradiance and temperature dependencies is also constructed by using the data at the nominal operating cell temperature (NOCT) test conditions. The proposed approach is easy to implement and free of the convergence problem. The evaluations on several PV modules show that the proposed approach is capable of extracting accurate estimates of the model parameters.

scholarly journals Data assimilation in integrated hydrological modelling in the presence of observation bias

2015 ◽  
Vol 12 (8) ◽  
pp. 8131-8173 ◽  
Author(s):  
J. Rasmussen ◽  
H. Madsen ◽  
K. H. Jensen ◽  
J. C. Refsgaard
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Stream Flow ◽  
Groundwater Modeling ◽  
Optimal Parameter ◽  
Estimation Methods ◽  
Model Parameters ◽  
Stream Discharge ◽  
Groundwater Head ◽  
Observation Bias

Abstract. The use of bias-aware Kalman filters for estimating and correcting observation bias in groundwater head observations is evaluated using both synthetic and real observations. In the synthetic test, groundwater head observations with a constant bias and unbiased stream discharge observations are assimilated in a catchment scale integrated hydrological model with the aim of updating stream discharge and groundwater head, as well as several model parameters relating to both stream flow and groundwater modeling. The Colored Noise Kalman filter (ColKF) and the Separate bias Kalman filter (SepKF) are tested and evaluated for correcting the observation biases. The study found that both methods were able to estimate most of the biases and that using any of the two bias estimation methods resulted in significant improvements over using a bias-unaware Kalman Filter. While the convergence of the ColKF was significantly faster than the convergence of the SepKF, a much larger ensemble size was required as the estimation of biases would otherwise fail. Real observations of groundwater head and stream discharge were also assimilated, resulting in improved stream flow modeling in terms of an increased Nash-Sutcliffe coefficient while no clear improvement in groundwater head modeling was observed. Both the ColKF and the SepKF tended to underestimate the biases, which resulted in drifting model behavior and sub-optimal parameter estimation, but both methods provided better state updating and parameter estimation than using a bias-unaware filter.

scholarly journals MODELING AND CHARACTERIZATION OF OSCILLATOR CIRCUITS BY VAN DER POL MODEL USING PARAMETER ESTIMATION

2012 ◽  
Vol 21 (05) ◽  
pp. 1250043 ◽  
Author(s):  
IULIA DUMITRESCU ◽  
SMAIL BACHIR ◽  
DAVID CORDEAU ◽  
JEAN-MARIE PAILLOT ◽  
MIHAI IORDACHE
Keyword(s):  
Parameter Estimation ◽  
Optimal Parameter ◽  
Estimation Method ◽  
Van Der Pol Oscillator ◽  
Space Representation ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Van Der Pol ◽  
Oscillator Circuit

In this paper, we present a new method for the modeling and characterization of oscillator circuit with a Van Der Pol (VDP) model using parameter identification. We also discussed and investigated the problem of estimation in nonlinear system based on time domain data. The approach is based on an appropriate state space representation of Van der Pol oscillator that allows an optimal parameter estimation. Using sampled output voltage signal, model parameters are obtained by an iterative identification algorithm based on Output Error method. Normalization issues are fixed by an appropriate transformation allowing a quickly global minimum search. Finally, the proposed estimation method is tested and validated using simulation data from a 1 GHz oscillator circuit in GaAs technology.

scholarly journals Direct inversion of the apparent complex‐resistivity spectrum

Geophysics ◽  
2001 ◽  
Vol 66 (5) ◽  
pp. 1399-1404 ◽  
Author(s):  
J. Xiang ◽  
N. B. Jones ◽  
D. Cheng ◽  
F. S. Schlindwein
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Optimal Parameter ◽  
Geophysical Methods ◽  
Inversion Method ◽  
Model Parameters ◽  
Complex Resistivity ◽  
Direct Inversion ◽  
Optimal Parameter Estimation ◽  
Cole Model

Cole‐Cole model parameters are widely used to interpret electrical geophysical methods and are obtained by inverting the induced polarization (IP) spectrum. This paper presents a direct inversion method for parameter estimation based on multifold least‐squares estimation. Two algorithms are described that provide optimal parameter estimation in the least‐squares sense. Simulations demonstrate that both algorithms can provide direct apparent spectral parameter inversion for complex resistivity data. Moreover, the second algorithm is robust under reasonably high noise.

scholarly journals Improved Hybrid Parameters Extraction of a PV Module Using a Moth Flame Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2798
Author(s):  
Safi Allah Hamadi ◽  
Aissa Chouder ◽  
Mohamed Mounir Rezaoui ◽  
Saad Motahhir ◽  
Ameur Miloud Kaddouri
Keyword(s):  
Extraction Procedure ◽  
Parameter Extraction ◽  
Standard Test ◽  
Maximum Power ◽  
Model Parameters ◽  
Unknown Parameters ◽  
Operating Condition ◽  
Pv Module ◽  
Test Conditions ◽  
Parameters Extraction

The identification of actual photovoltaic (PV) model parameters under real operating condition is a crucial step for PV engineering. An accurate and trusted model depends mainly on the accuracy of the model parameters. In this paper, an accurate and enhanced methodology is intended for PV module parameters extraction in outdoor conditions. The proposed methodology combines numerical methods and analytical formulations of the one diode model to derive the five unknown parameters in any operating condition of irradiance and temperature. First, the measured I-V curves at a random weather condition are translated to standard test conditions (i.e., G = 1000 W/m2, T = 25 °C), using translation equations. The second step consists of using an optimization algorithm namely the moth flame algorithm (MFO) to find out the five parameters at standard test conditions. Analytical formulations, at a random irradiance and temperature, are then used to express the unknown parameters at any irradiance and temperature. The proposed approach is validated under outdoor conditions against measured I-V curves at different irradiances and temperatures. The validation has also been performed under dynamic operation where the measured maximum power point coordinates (MPP) are compared to the predicted maximum power points. The obtained results from the adopted hybrid methodology confirm the accuracy of the parameter extraction procedure.

scholarly journals Parameter Estimation for HVAC System Models From Standard Test Data

2016 ◽  
Author(s):  
Hannah Luthman ◽  
John F. Gardner
Keyword(s):  
Building Energy ◽  
Energy Performance ◽  
Standard Test ◽  
Model Parameters ◽  
Vapor Compression ◽  
Heating Systems ◽  
Test Conditions ◽  
Vapor Compression Cycle ◽  
Compression Cycle ◽  
Modeling Errors

Nearly all cooling systems, and an increasing proportion of heating systems, utilize the vapor compression cycle (VCC) to provide and remove heat from conditioned spaces. Even though the application of VCC’s throughout the built environment is ubiquitous, effective and accessible models of the performance of these systems remains elusive. Such models could be important tools for equipment and building designers, and building energy managers and those who are attempting to optimize building energy performance through the use of model-based control systems. A quasi-steady state, spreadsheet-based model has been developed which requires knowledge of 10 system-specific parameters. A method utilizing manufacturer’s test conditions to derive these values is presented. The model is applied to three commercially available units and a subset of test conditions is used to identify the model parameters. The model is validated over the entire range of conditions with modeling errors ranging from 2 to 4%.

scholarly journals Selection and Configuration of Sorption Isotherm Models in Soils Using Artificial Bees Guided by the Particle Swarm

2017 ◽  
Vol 2017 ◽  
pp. 1-22
Author(s):  
Tadikonda Venkata Bharat
Keyword(s):  
Parameter Estimation ◽  
Sorption Isotherms ◽  
Initial Guess ◽  
Performance Comparison ◽  
Measured Data ◽  
Least Square ◽  
Isotherm Model ◽  
Isotherm Models ◽  
Model Parameters ◽  
Local Optima

A precise estimation of isotherm model parameters and selection of isotherms from the measured data are essential for the fate and transport of toxic contaminants in the environment. Nonlinear least-square techniques are widely used for fitting the isotherm model on the experimental data. However, such conventional techniques pose several limitations in the parameter estimation and the choice of appropriate isotherm model as shown in this paper. It is demonstrated in the present work that the classical deterministic techniques are sensitive to the initial guess and thus the performance is impeded by the presence of local optima. A novel solver based on modified artificial bee-colony (MABC) algorithm is proposed in this work for the selection and configuration of appropriate sorption isotherms. The performance of the proposed solver is compared with the other three solvers based on swarm intelligence for model parameter estimation using measured data from 21 soils. Performance comparison of developed solvers on the measured data reveals that the proposed solver demonstrates excellent convergence capabilities due to the superior exploration-exploitation abilities. The estimated solutions by the proposed solver are almost identical to the mean fitness values obtained over 20 independent runs. The advantages of the proposed solver are presented.

Investigation on the accurate parameter estimation of photovoltaic (PV) module based on simplified two-diode model

2016 ◽  
Vol 13 (3) ◽  
pp. 225-233
Author(s):  
B. Chitti Babu ◽  
Suresh Gurjar ◽  
Tomas Cermak
Keyword(s):  
Parameter Estimation ◽  
Design Methodology ◽  
Standard Test ◽  
Computational Time ◽  
Unknown Parameters ◽  
Partial Shading ◽  
Content Type ◽  
Pv Module ◽  
Test Conditions ◽  
Proposed Model

Purpose This paper aims to present a detailed investigation on the parameter estimation of a photovoltaic (PV) module by using a simplified two-diode model. Design/methodology/approach The studied PV module in this paper resembles an ideal two-diode model, and to reduce the computational time, the proposed model has a photocurrent source and two ideal-diodes and neglects the series and shunt resistances. Hence, for calculating the unknown parameters, only four parameters are required from the datasheet. Moreover, the studied model is simple and uses an easy modeling approach which is free from complexities. Findings The performance of the PV module is analyzed under non-standard test conditions by considering partial shading at different shaded levels, and it is found that the model has less computational time and gives accurate results. Originality/value The usefulness of this PV model is demonstrated with the help of several illustrative figures, and the performance of the PV module is evaluated.

scholarly journals Adjusting the Single-Diode Model Parameters of a Photovoltaic Module with Irradiance and Temperature

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3226 ◽  
Author(s):  
Nader Anani ◽  
Haider Ibrahim
Keyword(s):  
Numerical Technique ◽  
Standard Test ◽  
Climatic Conditions ◽  
Operating Conditions ◽  
Photovoltaic Module ◽  
Model Parameters ◽  
Pv System ◽  
Pv Module ◽  
Test Conditions ◽  
Parameter Modification

This paper presents a concise discussion and an investigation of the most literature-reported methods for modifying the lumped-circuit parameters of the single-diode model (SDM) of a photovoltaic (PV) module, to suit the prevailing climatic conditions of irradiance and temperature. These parameters provide the designer of a PV system with an essential design and simulation tool to maximize the efficiency of the system. The parameter modification methods were tested using three commercially available PV modules of different PV technologies, namely monocrystalline, multicrystalline, and thin film types. The SDM parameters of the three test modules were extracted under standard test conditions (STC) using a well-established numerical technique. Using these STC parameters as reference values, the parameter adjustment methods were subsequently deployed to calculate the modified parameters of the SDM under various operating conditions of temperature and irradiance using MATLAB-based software. The accuracy and effectiveness of these methods were evaluated by a comparison between the calculated and measured values of the modified parameters.

scholarly journals Data assimilation in integrated hydrological modelling in the presence of observation bias

2016 ◽  
Vol 20 (5) ◽  
pp. 2103-2118 ◽  
Author(s):  
Jørn Rasmussen ◽  
Henrik Madsen ◽  
Karsten Høgh Jensen ◽  
Jens Christian Refsgaard
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Optimal Parameter ◽  
Estimation Methods ◽  
Model Parameters ◽  
Bias Estimation ◽  
Catchment Scale ◽  
Stream Discharge ◽  
Groundwater Head ◽  
Observation Bias

Abstract. The use of bias-aware Kalman filters for estimating and correcting observation bias in groundwater head observations is evaluated using both synthetic and real observations. In the synthetic test, groundwater head observations with a constant bias and unbiased stream discharge observations are assimilated in a catchment-scale integrated hydrological model with the aim of updating stream discharge and groundwater head, as well as several model parameters relating to both streamflow and groundwater modelling. The coloured noise Kalman filter (ColKF) and the separate-bias Kalman filter (SepKF) are tested and evaluated for correcting the observation biases. The study found that both methods were able to estimate most of the biases and that using any of the two bias estimation methods resulted in significant improvements over using a bias-unaware Kalman filter. While the convergence of the ColKF was significantly faster than the convergence of the SepKF, a much larger ensemble size was required as the estimation of biases would otherwise fail. Real observations of groundwater head and stream discharge were also assimilated, resulting in improved streamflow modelling in terms of an increased Nash–Sutcliffe coefficient while no clear improvement in groundwater head modelling was observed. Both the ColKF and the SepKF tended to underestimate the biases, which resulted in drifting model behaviour and sub-optimal parameter estimation, but both methods provided better state updating and parameter estimation than using a bias-unaware filter.

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