Over a period of years, Maximum Power Point Tracking has become a mandatory requirement for Solar Photo Voltaic (PV) systems. Being dependent to environmental changes, the PV power constantly fluctuates due to change in irradiation. Under such conditions, large PV array connected in interconnection will experience non-uniform irradiation thus results multiple peaks in P-V characteristics. Although many conventional and soft computing techniques have been proposed in literature, the ability to identify global peak under strong shading conditions is not guaranteed. Particularly, local peak in close agreement to global peak makes most of the algorithms to get trapped in local peaks. This condition often occurs due to insufficient randomness in algorithm hence, a new Flower Pollination Algorithm (FPA) is investigated in this research. Proposed method has dual mode search ability which creates required randomness in every iteration is the key reason to suit FPA for MPPT. Simulation and experimental results verified with different patterns portray FPA excellence under all irradiated conditions. Further performance of FPA is verified with Particle swarm Optimization method and conventional P&amp;O method. © 2016 Elsevier Ltd

Rajasekar N

Department of Energy and Power Electronics

School of Electrical Engineering

Vellore Campus

Prasanth Ram J

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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VIT University

Energy

Solar PV arrays are often subjected to partial shading that imposes substantial reduction in maximum power extraction from PV based power plants. For which, the principal reason is the exhibition of uneven row current levels that eventually creates multiple power peaks in the output characteristics of a PV array. In general, to mitigate the abovementioned issue, array reconfiguration schemes adopting either physical relocation or electrical rewiring are effectively utilized in the technical literature. Typically, most of the existing physical relocation methods have been conceptualized based on mathematical puzzles owing to its high shade dispersion capability. However, most of these methods are not compatible to both symmetrical and non-symmetrical PV arrays. In this work, a new physical relocation alternative deploying Skyscraper puzzle suitable for all PV arrays is designed, implemented and tested. For validation and performance evaluation of the proposed method, both simulation and experimental studies are carried out. The results demonstrate that, in comparison with the existing methods in literature, Skyscraper method provides better shade dispersion over the entire PV array and subsequently, reduces the mismatch loss to improve power reduction. As performance indices, the power values obtained from the I-V and P-V characteristics of the proposed Skyscraper method is compared with TCT (Total Cross Tie) connection, DS (Dominance Square) and Sudoku methods. 9 × 9 and 5 × 5 PV arrays are utilized for simulations and real-time experiments respectively. In summary, the Skyscraper method shows uniform characteristics and enhanced power output compared to the existing techniques in literature. © 2019 International Solar Energy Society

Solar Energy

Enhanced power production in PV arrays using a new skyscraper puzzle based one-time reconfiguration procedure under partial shade conditions (PSCs)

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

Energy Conversion and Management

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

The ever growing interest on solar energy has augmented large solar power parks in the contemporary time. Even though the most commonly used Total Cross Tie (TCT) interconnection scheme minimizes mismatch losses, shade occurrence in large PV arrays drastically reduces the power output and invokes multiple power peaks in the P-V characteristics. To counteract the multiple peak issue, PV arrays are reconfigured either via electrical or physical reconfiguration techniques. In this regard, physical relocation methods are proven to be efficient and economical in achieving better shade dispersion since, it neither demands complex switching arrangements or high end controllers for reconfiguration. Therefore, this paper introduces an innovative Competence Square (CS) technique for physical rearrangement of PV panels in a TCT interconnection scheme. The method devised is a onetime relocation technique that follows a unique number pattern for relocating the PV panels. Moreover, various test results elucidate the effectiveness of the CS technique in effectively dispersing the shade over the entire PV array. For validation, four typical shade cases are considered and the results are comprehensively compared with the recently proved TCT and Dominance Square (DS) techniques. In addition, the proposed method is computationally easy and is adaptable for PV arrays of any size. Overall, CS technique has showed its eminence in obtaining smoother output characteristics, better fill factor, power enhancement and increased energy savings. © 2018 Elsevier Ltd

A novel Competence Square based PV array reconfiguration technique for solar PV maximum power extraction

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

Mounting demand for energy and accumulation of hazardous nuclear wastes has invited the world to reduce their addiction to conventional power generation. Having established its global potential to replace fossil fuels, solar PV is increasingly installed worldwide. Yet PV is ill-starred due to its non-linear characteristics and hence, PV systems employ Maximum Power Point (MPP) controllers. Even though enough research progress is kept at the forefront in the MPP research area, the necessity to improvise the existing methods becomes mandatory to improve the energy conversion efficiency. Hence, in this paper, a global maximum power point tracking (GMPPT) algorithm based on Leader Particle Swarm Optimization (LPSO) is proposed for PV system. Apart from the conventional PSO, exclusive mutations strategies are employed to obtain the global best leader that helps the algorithm to differentiate between local and global MPPs. The simulation results are validated under numerous test conditions in which partial shading conditions are analyzed over a wide extent and in validation, the results of LPSO method is compared with PSO and P&amp;O methods as well. Interestingly LPSO method has an inherent exploration and exploitation quality that made it to produce hasty converge within 0.5 s under any shade conditions. Acknowledging to the promise shown in simulation, the mutation based LPSO method has managed to excel even in hardware experimentation which in turn justifies its suitability for MPPT application. © 2017 Elsevier Ltd

Applied Energy

A new robust, mutated and fast tracking LPSO method for solar PV maximum power point tracking under partial shaded conditions

Generation of electricity from solar energy has gained worldwide acceptance due to its abundant availability and eco-friendly nature. Even though the power generated from solar looks to be attractive; its availability is subjected to variation owing to many factors such as change in irradiation, temperature, shadow etc. Hence, extraction of maximum power from solar PV using Maximum Power Point Tracking (MPPT) method was the subject of study in the recent past. Among many methods proposed, Hill Climbing and Incremental Conductance MPPT methods were popular in reaching Maximum Power under constant irradiation. However, these methods show large steady state oscillations around MPP and poor dynamic performance when subjected to change in environmental conditions. On the other hand, bio-inspired algorithms showed excellent characteristics when dealing with non-linear, non-differentiable and stochastic optimization problems without involving excessive mathematical computations. Hence, in this paper an attempt is made by applying modifications to Particle Swarm Optimization technique, with emphasis on initial value selection, for Maximum Power Point Tracking. The key features of this method include ability to track the global peak power accurately under change in environmental condition with almost zero steady state oscillations, faster dynamic response and easy implementation. Systematic evaluation has been carried out for different partial shading conditions and finally the results obtained are compared with existing methods. In addition, simulations results are validated via built-in hardware prototype. © 2015 Elsevier B.V.

Applied Soft Computing

Modified Particle Swarm Optimization technique based Maximum Power Point Tracking for uniform and under partial shading condition

In this paper, a meta-heuristics algorithm Artificial Immune System (AIS) is used for solar PV parameter estimation. Solar PV Double diode model parameter estimation is carried out by applying the new method. Estimated parameter values are substituted in developed MATLAB model and characteristics are obtained. For performance analysis, the results obtained using AIS are compared with Genetic algorithm (GA) and Particle Swarm Optimization (PSO). For further validation of the proposed method, error graphs are plotted for two panels using AIS, GA and PSO. The results confirm that the proposed method employing AIS outperforms GA and PSO in terms of convergence characteristics and absolute error. © 2015 The Authors. Published by Elsevier Ltd.

Fulltext

Energy Procedia

Solar PV Modelling and Parameter Extraction Using Artificial Immune System

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
Publisher	Data powered by TypesetElsevier BV
ISSN	0360-5442
Open Access	0