The inaccurate I-V curve generation in solar PV modeling introduces less efficiency and on the other hand, accurate simulation of PV characteristics becomes a mandatory obligation before experimental validation. Although many optimization methods in literature have attempted to extract accurate PV parameters, all of these methods do not guarantee their convergence to the global optimum. Hence, the authors of this paper have proposed a new hybrid Bee pollinator Flower Pollination Algorithm (BPFPA) for the PV parameter extraction problem. The PV parameters for both single diode and double diode are extracted and tested under different environmental conditions. For brevity, the I01, I02, Ipvfor double diode and I0,Ipvfor single diode models are calculated analytically where the remaining parameters ‘Rs, Rp, a1, a2’ are optimized using BPFPA method. It is found that, the proposed Bee Pollinator method has all the scope to create exploration and exploitation in the control variable to yield a less RMSE value even under lower irradiated conditions. Further for performance validation, the parameters arrived via BPFPA method is compared with Genetic Algorithm (GA), Pattern Search (PS), Harmony Search (HS), Flower Pollination Algorithm (FPA) and Artificial Bee Swarm Optimization (ABSO). In addition, various outcomes of PV modeling and different parameters influencing the accurate PV modeling are critically analyzed. © 2016 Elsevier Ltd

Rajasekar N

Department of Energy and Power Electronics

School of Electrical Engineering

Vellore Campus

Ram J.P

Babu T.S

Dragicevic T

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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&nbsp;

VIT University

Energy Conversion and Management

This study presents a reply on a reply to comments on “Important notes on parameter estimation of the solar photovoltaic cell”, by Gnetchejo et al. [Energy Conversion and Management,https://doi.org/10.1016/j.enconman.2019.111870] introduced by Gnetchejo et al. It is noteworthy that, Gnetchejo et al. proposed their criteria in computing RMSE to prove their results in Algorithm 2 and flowchart in their reply. By inspecting the listed calculation in Algorithm 2 and flowchart, as described in this comment, one can note that their explanations are wrong. © 2019 Elsevier Ltd

Reply on “Reply to comment on Important notes on parameter estimation of solar photovoltaic cell”, by Gnetchejo et al. [Energy Conversion and Management, https://doi.org/10.1016/ j.enconman.2019.111870]

This study comments on a manuscript “Important notes on parameter estimation of solar photovoltaic cell”, [Energy conversion and management, 2019.111870.] [1]introduced by Gnetchejo et al. It is noteworthy that, in [1], another approach is proposed for computing the objective function; root mean square error (RMSE) computations that depends on the measured current in all of its terms unlike the literature where it is difference between measured and estimated currents. Moreover, the flowchart for computing the RMSE shows the authors took the value of RMSE at only one point of the results (last point). Additionally, the steps proposed to compute the RMSE are incorrect. In addition, by using the same method as proposed by the authors in [1], it has been found that their results are misleading. Moreover, the algorithms classified as accurate, according to authors classification, gives wrong results. © 2019 Elsevier Ltd

Comment on “Important notes on parameter estimation of solar photovoltaic cell”, by Gnetchejo et al. [Energy Conversion and Management, https://doi.org/10.1016/j.enconman.2019.111870]

Array reconfiguration methods for PV arrays generally follow puzzle based mathematical methods for relocating PV modules. Being size constrained, no array reconfiguration techniques in literature is a reliable solution for effective shade dispersion in large sized PV arrays. Moreover, most of the physical relocation methods do not possess a well-defined and proven methodology to disperse the shade effect of a PV array irrespective of the system architecture. Hence, in this paper, a new two phase method compatible for PV arrays of any size is outlined and tested for effective shade dispersion. Having two different phases in shade dispersion process, the two-phase technology is found highly commendable in relocating PV panels such that illustrious shade dispersion is attained. To confirm its contribution in physical relocation scheme, four shade cases are considered and tested with other methods in literature. Furthermore, based on row current calculation and bypass effect, a quantitative comparison is also performed with popular physical relocation (SuDoKu), electrical reconfiguration (PSO) and conventional (TCT) interconnection schemes. As the results suggest, the fill factor attainment for the proposed two phase method for short wide case, long wide, short narrow and long narrow cases are 72%, 62%, 81% and 75% respectively. Furthermore, except for a single pattern, the power attainment with proposed method has benchmarked minimal of 700 W power improvement compared to TCT in all shade cases. © 2018 Elsevier Ltd

Design and testing of two phase array reconfiguration procedure for maximizing power in solar PV systems under partial shade conditions (PSC)

Large solar power plants in recent years has gained a momentary attraction since, the demand estimated for next few years has benchmarked has a monumental requirement for electrical energy. However, the solar power parks are often concerned with the shade occurrence resulting in power reduction. Hence, array reconfiguration schemes have became extremely popular to minimize the power loss under shade occurrences. In this paper, a new column index method based on one time physical relocation scheme is proposed. The proposed method possesses the advantage of having simple rewiring requirements since; the panels are rearranged in the respective column itself. Various parameters like mismatch loss and power loss are effectively measured and fill factor for four various shade occurrences are calculated. Simulation verification along with theoretical validation is performed for four existing schemes in literature. To strengthen the analysis, very recent Dominance Square (DS) method is also critically analyzed and compared. Finally, a qualitative comparison with various methods in literature is performed to contribute for quality check. © 2018 Elsevier Ltd

A simple, sensorless and fixed reconfiguration scheme for maximum power enhancement in PV systems

Precise model parameters being the prerequisite for realizing accurate PV models, parameter identification techniques have gained immense interest over the years among the researchers specializing in PV systems. The application of various promising metaheuristic algorithms to optimize the model parameters have lightened up the scope of further enhancements in this field. Ever since, numerous metaheuristic algorithms have deployed for this purpose. With handful of techniques available in this regard, this paper takes up an initiative to review the existing metaheuristic algorithms based parameter extraction techniques with an emphasis on their compatibility, accuracy, convergence speed, range of parameters set and their validating environment. Based on the analysis conducted, accurate models available for 17 different industrial solar cells/modules are identified. Inspired by this review, an unidentified gateway between parameter extraction and fault detection in PV systems have been identified; and has further extended this review to differentiate some models that can help the researchers to achieve accurate, efficient and rapid fault detection. This review is a valuable gathering of statistics from the various researches carried out in PV parameter extraction which can assist enhanced researches for fault detection in PV systems as well. © 2017 Elsevier Ltd

Renewable and Sustainable Energy Reviews

Metaheuristic algorithms for PV parameter identification: A comprehensive review with an application to threshold setting for fault detection in PV systems

The double diode model for photovoltaic (PV) modules is currently less adopted than one-diode model because of the difficulty in the extraction of its seven unknown parameters IPV,I01,I02, Rs, Rp, a1 and a2, which is a serious inverse problem. This paper proposes application of the Fireworks Algorithm (FWA) for the accurate identification of these unknown parameters in such a way to solve effectively this modeling problem. In particular, firstly, the FWA has been comprehensively tested with two different technologies of Mono-Crystalline (SM55 &amp; SP70) and Multi-Crystalline (Kyocera200GT) PV modules. In addition, further statistical and error analysis for three different panels are exclusively carried out to validate the suitability of proposed methodology. The results of proposed algorithm are benchmarked with popular Genetic Algorithm and Particle Swarm Optimization (PSO) methods. Fitness convergence curves or FWA method for SM55, SP70 and Kyocera200GT produce very less objective function as 2.2498E−07, 2.85765E−08 and 4.0075E−08 respectively. This illustrates the wise and accurate validation of FWA method. Calculated curve-fit via FWA in agreement to datasheet curve strongly suggest the FWA can constitute the core of suitable optimization code for two diode PV parameter extraction. © 2016 Elsevier Ltd

Solar Energy

Parameter extraction of two diode solar PV model using Fireworks algorithm

In this paper, a new formulation based on the derivative of maximum power with respect to current is proposed for fuel cell parameter extraction. Compared to conventional extraction methods, the proposed method has various features such as faster convergence, higher efficiency and accuracy. In this work, the problem of parameter extraction is formulated as an optimization task and it is solved using simple GA. Explanations about the new formulation and its implementation using GA are elaborated. To validate the candidature of the proposed formulation, performance analysis is done with two sets of initial value ranges and the results are compared with the existing curve fitting method in terms of accuracy, convergence characteristics and objective function value. The results show that the proposed formulation employing GA is capable of extracting fuel cell parameters accurately with lesser computational steps and time. © 2015 Faculty of Engineering, Ain Shams University.

Fulltext

Ain Shams Engineering Journal

Comparative study of PEM fuel cell parameter extraction using Genetic Algorithm

Accuracy of fuel cell characteristics mainly depends on its parameters. Generally, estimation is done in a curve fitting procedure. In this paper, the problem of fuel cell parameter extraction is formulated as an objective function and solved using simple Genetic Algorithm. Explanations about the new formulation and its implementation using GA is elaborated. The candidature of the proposed method is explained. The results are compared with existing curve fitting method. © 2015 The Authors. Published by Elsevier Ltd.

Energy Procedia

Critical Evaluation of Genetic Algorithm Based Fuel Cell Parameter Extraction

The abundance and non-polluting nature of solar energy has aroused the interest of many researchers. This worldwide attention of photovoltaic panels has led to the need of generating accurate model for solar photovoltaic (PV) module before proceeding to the installation part. However, accurate modeling of solar PV characteristics is difficult; since the manufacturer's datasheet provides only four values such as Vmp, Imp, Voc, and Isc. Further, for accurate modeling precise estimation of model parameters at different environmental conditions are very essential. On the other hand, optimization technique is a very powerful tool to obtain solutions to complex non-linear problems. Hence, in this paper, Bacterial Foraging Algorithm is proposed to model the solar PV characteristics accurately. A new equation has been evolved to determine the values of Voc, Vmp accurately; since these values decides the closeness of the simulated characteristics. Model parameters are extracted for three different types of solar PV panels. A systematic evaluation and performance comparison of Bacterial Foraging Algorithm with other optimization techniques such as Genetic Algorithm and Artificial Immune System has been done and the best computational technique is derived based on performance criteria such as accuracy, consistency, speed of convergence and absolute error. Extensive computations are carried out for the proposed method, as well as for Genetic Algorithm and Artificial Immune System to substantiate the findings. © 2013 Elsevier Ltd.

Journal	Data powered by TypesetEnergy Conversion and Management
Publisher	Data powered by TypesetElsevier BV
ISSN	0196-8904
Open Access	0