PV Parameter Identification using Reduced I-V Data

In this paper, possibility and accuracy of using reduced I-V data in PV parameter identification are discussed. Based on the linear identification method proposed in [1], six I-V points are used instead of the whole I-V curve to identify the PV parameters. The maximum power point (MPP) is then estimated using the identified I-V and P-V characteristics. Validation is done by using different sets of six points on the I-V curve. Experiment results show that the accurate curve fitting (with low RMSE and MPE) and good estimation of MPP can be achieved

Modelling and Parameter Identification Using Reduced I-V Data

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PV panel modeling and identification

In this chapter, the modelling techniques of PV panels from I-V characteristics are discussed. At the beginning, a necessary review on the various methods are presented, where difficulties in mathematics, drawbacks in accuracy, and challenges in implementation are highlighted. Next, a novel approach based on linear system identification is demonstrated in detail. Other than the prevailing methods of using approximation (analytical methods), iterative searching (classical optimization), or soft computing (artificial intelligence), the proposed method regards the PV diode model as the equivalent output of a dynamic system, so the diode model parameters can be linked to the transfer function coefficients of the same dynamic system. In this way, the problem of solving PV model parameters is equivalently converted to system identification in control theory, which can be perfectly solved by a simple integral-based linear least square method. Graphical meanings of the proposed method are illustrated to help readers understand the underlying principles. As compared to other methods, the proposed one has the following benefits: 1) unique solution; 2) no iterative or global searching; 3) easy to implement (linear least square); 4) accuracy; 5) extendable to multi-diode models. The effectiveness of the proposed method has been verified by indoor and outdoor PV module testing results. In addition, possible applications of the proposed method are discussed like online PV monitoring and diagnostics, noncontact measurement of POA irradiance and cell temperature, fast model identification for satellite PV panels, and etc

Analyzing big time series data in solar engineering using features and PCA

In solar engineering, we encounter big time series data such as the satellite-derived irradiance data and string-level measurements from a utility-scale photovoltaic (PV) system. While storing and hosting big data are certainly possible using today’s data storage technology, it is challenging to effectively and efficiently visualize and analyze the data. We consider a data analytics algorithm to mitigate some of these challenges in this work. The algorithm computes a set of generic and/or application-specific features to characterize the time series, and subsequently uses principal component analysis to project these features onto a two-dimensional space. As each time series can be represented by features, it can be treated as a single data point in the feature space, allowing many operations to become more amenable. Three applications are discussed within the overall framework, namely (1) the PV system type identification, (2) monitoring network design, and (3) anomalous string detection. The proposed framework can be easily translated to many other solar engineer applications

A Measurement of Rb using a Double Tagging Method

The fraction of Z to bbbar events in hadronic Z decays has been measured by the OPAL experiment using the data collected at LEP between 1992 and 1995. The Z to bbbar decays were tagged using displaced secondary vertices, and high momentum electrons and muons. Systematic uncertainties were reduced by measuring the b-tagging efficiency using a double tagging technique. Efficiency correlations between opposite hemispheres of an event are small, and are well understood through comparisons between real and simulated data samples. A value of Rb = 0.2178 +- 0.0011 +- 0.0013 was obtained, where the first error is statistical and the second systematic. The uncertainty on Rc, the fraction of Z to ccbar events in hadronic Z decays, is not included in the errors. The dependence on Rc is Delta(Rb)/Rb = -0.056*Delta(Rc)/Rc where Delta(Rc) is the deviation of Rc from the value 0.172 predicted by the Standard Model. The result for Rb agrees with the value of 0.2155 +- 0.0003 predicted by the Standard Model.Comment: 42 pages, LaTeX, 14 eps figures included, submitted to European Physical Journal

Model Reduction for Multiscale Lithium-Ion Battery Simulation

In this contribution we are concerned with efficient model reduction for multiscale problems arising in lithium-ion battery modeling with spatially resolved porous electrodes. We present new results on the application of the reduced basis method to the resulting instationary 3D battery model that involves strong non-linearities due to Buttler-Volmer kinetics. Empirical operator interpolation is used to efficiently deal with this issue. Furthermore, we present the localized reduced basis multiscale method for parabolic problems applied to a thermal model of batteries with resolved porous electrodes. Numerical experiments are given that demonstrate the reduction capabilities of the presented approaches for these real world applications

An improved optimization technique for estimation of solar photovoltaic parameters

The nonlinear current vs voltage (I-V) characteristics of solar PV make its modelling difficult. Optimization techniques are the best tool for identifying the parameters of nonlinear models. Even though, there are different optimization techniques used for parameter estimation of solar PV, still the best optimized results are not achieved to date. In this paper, Wind Driven Optimization (WDO) technique is proposed as the new method for identifying the parameters of solar PV. The accuracy and convergence time of the proposed method is compared with results of Pattern Search (PS), Genetic Algorithm (GA), and Simulated Annealing (SA) for single diode and double diode models of solar PV. Furthermore, for performance validation, the parameters obtained through WDO are compared with hybrid Bee Pollinator Flower Pollination Algorithm (BPFPA), Flower Pollination Algorithm (FPA), Generalized Oppositional Teaching Learning Based Optimization (GOTLBO), Artificial Bee Swarm Optimization (ABSO), and Harmony Search (HS). The obtained results clearly reveal that WDO algorithm can provide accurate optimized values with less number of iterations at different environmental conditions. Therefore, the WDO can be recommended as the best optimization algorithm for parameter estimation of solar PV

Parametric macromodeling of lossy and dispersive multiconductor transmission lines

We propose an innovative parametric macromodeling technique for lossy and dispersive multiconductor transmission lines (MTLs) that can be used for interconnect modeling. It is based on a recently developed method for the analysis of lossy and dispersive MTLs extended by using the multivariate orthonormal vector fitting (MOVF) technique to build parametric macromodels in a rational form. They take into account design parameters, such as geometrical layout or substrate features, in addition to frequency. The presented technique is suited to generate state-space models and synthesize equivalent circuits, which can be easily embedded into conventional SPICE-like solvers. Parametric macromodels allow to perform design space exploration, design optimization, and sensitivity analysis efficiently. Numerical examples validate the proposed approach in both frequency and time domain