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Proceedings ArticleDOI

An innovative method for solar pv parameter extraction for double diode model

TL;DR: In this paper, a novel objective function based on derivative of maximum power with respect to voltage for solar PV double diode model is proposed for parameter extraction using Artificial Immune System (AIS).
Abstract: The potential of renewable energy sources is massive as they can in principle meet many times the world's energy demand. In this paper, Solar PV parameter extraction is performed using Artificial Immune System (AIS). A novel objective function based on derivative of maximum power with respect to voltage for solar PV double diode model is proposed. For validation and performance evaluation, the results obtained using the proposed approach employing AIS, are compared with Genetic algorithm (GA) and particle swarm optimization (PSO). As compared to GA and PSO, AIS using the proposed approach has the least error. The results also showcase that the proposed approach with AIS outclasses GA and PSO in terms of absolute error for all the three PV models under various irradiation conditions.
Citations
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Journal ArticleDOI
TL;DR: To enable an optimal PV design parameter estimation there is an inevitable need to incorporate either evolutionary computation schemes or apply an efficient multi-objective optimization measures.
Abstract: Solar energy has been one of the environmental friendly sources of energy. The low cost solution with minimal maintenance motivates towards photovoltaic (PV) cells based energy harnessing methods to meet energy demands. However, majority of conventional PV systems suffer from low energy conversion ratio (ECR) due to improper selection of the PV parameters and maximum power point tracking (MPPT) algorithm. Even ECR is adversely affected under varying environmental conditions. Therefore, accurate estimation of PV parameter can be of paramount significance for efficient PV model design. In addition, the development of a robust MPPT algorithm in conjunction with the effective PV design parameter can enable optimal ECR achievement. In this review paper, a number of literatures pertaining to PV parameter estimation and MPPT algorithms are discussed. Different methods including analytical, iterative and evolutionary computing algorithms are assessed for their efficacy towards PV parameter estimation. This review paper revealed that the analytical approaches suffer from singularity problem as well as limited mathematical calculation that confine its efficacy for optimal PV parameter estimation under dynamic irradiation pattern. The iterative approaches too are limited due to dynamic environment conditions. Our study has revealed that the evolutionary computing approaches, such as genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), etc. have played vital role in PV design parameter estimation and classical approaches suffer from local minima and convergence issues. This manuscript reveals that to enable an optimal PV design parameter estimation there is an inevitable need to incorporate either evolutionary computation schemes or apply an efficient multi-objective optimization measures. This as a result can not only alleviate local minima and convergence issues but can also enable swift and precise parameter estimation to assist optimal PV design and augmented ECR performance.

62 citations

Journal ArticleDOI
TL;DR: In this paper, a shuffled complex evolution (SCE) technique was used for extracting the intrinsic parameters of a photovoltaic (PV) generator by using shuffled complexity evolution (SCE) technique for a double-diode PV model.
Abstract: This paper proposes a method for extracting the intrinsic parameters of a photovoltaic (PV) generator by using shuffled complex evolution (SCE) technique for a double-diode PV model. The characteristic equation of a double-diode PV presents a nonlinear behavior and the determination of the intrinsic parameters from a $I \times V$ experimental curve requires the use of nonlinear optimization methods. To evaluate the accuracy of the SCE technique for extracting the intrinsic PV parameters, a comparison with other well-known methods is presented; in particular, analytic method, Levenberg–Marquardt, genetic algorithms (GA), differential evolution (DE), and particle swarm optimization (PSO) are considered. This comparison is performed by using statistical analysis and by estimating the relative error of parameter values; it has been applied to an unknown PV module and to a known PV cell. The obtained results showed that, compared with other evolutionary methods (GA, DE and PSO), the SCE presents the lowest computational time and requires less iterations/generations to converge. All the results prove that the proposed method is feasible, faster, and presents better results than the conventional ones.

62 citations

Journal ArticleDOI
TL;DR: In this article, the particle swarm optimization (PSO) approach was used to estimate the single-diode model parameters of the photovoltaic modules, which require five parameters.

9 citations

Journal ArticleDOI
TL;DR: In this article , a new noise-scaled Euclidean distance (NSED) metric is proposed as a weighted variation of ED, which is shown to fetch the maximum likelihood estimates (MLE) of the model parameters at any noise conditions.
Abstract: This article revisits the objective function (or metric) used in the extraction of photovoltaic (PV) model parameters. A theoretical investigation shows that the widely used current distance (CD) metric does not yield the maximum likelihood estimates (MLE) of the model parameters when there is noise in both voltage and current samples. It demonstrates that the Euclidean distance (ED) should be used instead, when the voltage and current noise powers are equal. For the general case, a new noise-scaled Euclidean distance (NSED) metric is proposed as a weighted variation of ED, which is shown to fetch the MLE of the parameters at any noise conditions. This metric requires the noise ratio (i.e., ratio of the two noise variances) as an additional input, which can be estimated by a new noise estimation (NE) method introduced in this study. One application of the new metric is to employ NSED regression as a follow-up step to existing parameter extraction methods toward fine-tuning of their outputs. Results on synthetic and experimental data show that the so-called NSED regression “add-on” improves the accuracy of five such methods and validate the merits of the NSED metric.

5 citations

References
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Journal ArticleDOI
TL;DR: In this article, an exact closed-form solution based on Lambert W -function is presented to express the transcendental currentvoltage characteristic containing parasitic power consuming parameters like series and shunt resistances.

449 citations

Journal ArticleDOI
TL;DR: In this paper, an application of pattern search optimization technique for extracting the parameters of different solar cell models is presented, where the solar cell parameters estimation is viewed and formulated as a multivariate nonlinear optimization problem.

322 citations

Journal ArticleDOI
TL;DR: In this article, particle swarm optimization (PSO) was applied to extract the solar cell parameters from illuminated currentvoltage characteristics, and the performance of the PSO was compared with the genetic algorithms (GAs) for the single and double diode models.
Abstract: In this article, particle swarm optimization (PSO) was applied to extract the solar cell parameters from illuminated current-voltage characteristics. The performance of the PSO was compared with the genetic algorithms (GAs) for the single and double diode models. Based on synthetic and experimental current-voltage data, it has been confirmed that the proposed method can obtain higher parameter precision with better computational efficiency than the GA method. Compared with conventional gradient-based methods, even without a good initial guess, the PSO method can obtain the parameters of solar cells as close as possible to the practical parameters only based on a broad range specified for each of the parameters.

306 citations

Journal ArticleDOI
TL;DR: In this paper, an improved modeling technique for a photovoltaic (PV) module was proposed, utilizing the optimization ability of a genetic algorithm, with different parameters of the PV module being computed via this approach.

271 citations

Journal ArticleDOI
TL;DR: Bacterial Foraging Algorithm is proposed to model the solar PV characteristics accurately and the best computational technique is derived based on performance criteria such as accuracy, consistency, speed of convergence and absolute error.

250 citations