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Plants synthesise many secondary metabolites which are of great medicinal value. Costus pictus also known as Insulin plant is considered as valuable medicinal plant in preparation of many commercial pharmaceutical products. The aim of the present study is to identify one such secondary metabolite flavonoid (Rutin) in the leaves of Costus pictus by HPLC method and also to evaluate the antioxidant potential of Ethyl acetate extract of Costus using DPPH and NO scavenging assay. The Extracted materi al was identified as Rutin by comparing with Standard Rutin. Both the samples showed similar retention time of 2.8 minutes. The extract showed maximum DPPH free radical scavenging activity with increase in concentration and IC50 of Nitric acid scavenging activity was found to be more for the Extract than the standard ascorbic acid and Rutin. From this study it is concluded that Costus pictus leaves extract can be used as natural food supplement. Asian J. Med. Biol. Res. June 2020, 6(2): 334-339

Nonhomogeneous irradiation conditions due to environmental changes introduce multiple peaks in nonlinear $P$ – $V$ characteristics. Hence, to operate photovoltaic at the global power point, numerous algorithms have been proposed in the literature. However, due to the insufficient exploitation of control variables, all the maximum power point tracking (MPPT) methods presented in the literature fail to guarantee global maximum power point (GMPP) operation. In this paper, a new detection technology to identify GMPP zones using hybrid enhanced leader particle swarm optimization (ELPSO) assisted by a conventional perturb and observe (P&O) algorithm is proposed. With inherent mutations, ELPSO applied to MPPT excels in exploring global regions at initial stages to determine the global best leader, whereas P&O is reverted back soon after global solution space is detected. The transition from ELPSO to P&O is mathematically verified and allowed only when ELPSO finds the global optimal zone. Adapting this hybrid strategy, the proposed method has produced interesting results under partial shaded conditions. For further validation, the results of the proposed hybrid ELPSO-P&O are compared with the conventional ELPSO and the hybrid PSO-P&O methods. The experimental results along with energy evaluations confirmed the superiority of the ELPSO-P&O method in obtaining the maximum available power under all shaded conditions.

Optimizing the operational performance of maximum power point trackers during multiple peak partial shading conditions (PSCs) prevails as a major challenge in photovoltaic power generation. Though many of the newly evolved soft computing and heuristic methods are compatible during PSCs, the need for such techniques in uniform irradiation levels is uncertain because most of these algorithms produce extensive oscillations before converging to the global maximum power point (GMPP). On the other hand, adopting conventional perturb and observe (P&O) algorithm is meritorious to reduce the transient power and voltage oscillations. Therefore, this article presents a proficient hybrid tracking technology that provides an adequate tradeoff between conventional P&O and advanced soft computing techniques by accurately detecting shade occurrences. For which, the uniqueness in the operating point conductance of P&O at the leftmost power peak in the P–V curve is utilized. Subsequently, the proposed convention utilizes P&O for two major purposes: to track MPP in uniform irradiance, and to detect PSCs. More importantly, even during PSCs, the detection algorithm is well designed to operate at GMPP with the conventional P&O method itself, and, only for extreme shade cases, flower pollination algorithm is introduced to track GMPP. The pre-eminence of the proposed technology to track GMPP with reduced transient oscillations by discriminating shade occurrences is demonstrated via extensive experimental studies in this article.

In recent years, designing reliable and practically feasible Maximum Power Point Tracking (MPPT) techniques to maximize the power output of PV power plants has become a crucial research objective. Further, to counteract the inimitable PV operating conditions, numerous bio-inspired metaheuristic algorithms have been proposed in literature; that are predominantly complex and difficult to implement. On the other hand, the age old Perturb and Observe (PO that has not been proven judicially. Therefore, in this article, a new FPA method assisted by P&O is proposed. A new switching strategy is incorporated and validated in this work to achieve effective utilization of both FPA and P&O algorithms. More importantly, the transition is only initiated when global power regions are initially explored with FPA. Further, for truthful comparison, the results of FPA-P&O are compared with recently proven Enhanced Leader Particle Swarm Optimization (ELPSO) and conventional PSO methods.

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.

The remarkable initiatives taken globally to counteract the ever-increasing energy demand have propelled the utilization of solar power. Even then, efficient and stable rural electrification models are yet to be conceptualized. In this context, off-grid photovoltaic (PV) standalone systems are considered to provide reliable solutions to counteract the existing challenges. However, the selection of DC bus voltage in accordance with the distribution distance as well as cost effective remote monitoring system remains an unsolved issue in rural DC power systems. On this note, this work conducts a detailed performance evaluation on different voltages like 12 V, 24 V, and 48 V rural DC power systems with a viewpoint to derive some formidable guidelines for DC bus voltage selection concerning distribution distance. Considering the capital system cost as the major constraint, the proposed work intends to formulate reliable voltage selection guidelines that establish an efficient trade-off between the battery cost and cost incurred for energy losses.

Globally, almost all PV installations are deployed with maximum power point tracking (MPPT) technique to extract the maximum available power output. However, in the case of an electrically faulty PV arrays, the MPPT operation rapidly optimizes the fault current magnitudes to significantly low values that enforce line-line (LL) and line-ground (LG) faults to remain undetected in the system for longer time periods. Therefore, this paper proposes a sensorless, simple tracking technology to detect any LL/LG faults in a PV array by utilizing MPPT itself. The proposed technique exploits the uniqueness of the rightmost power peak (RPP) in the output characteristics of the PV array to detect LL/LG faults. For implementation, the globally used perturb & observe (P&O) MPPT algorithm is deployed to track the RPP. Experimental results reveal that the proposed methodology is well efficient to detect any LL/LG faults in PV arrays irrespective of the level of mismatch, system type, and the system rating. In addition, faults occurring in low irradiance levels and partial shading conditions (PSCs) can also be detected with incredible accuracy. Furthermore, the proposed technique is proficient to distinguish LL/LG faults from PSCs and temperature variations of a PV array in real-time environment. The merits of the proposed concept are its scalability, simplicity, and practical fitness.

Modelling of Proton Exchange Membrane Fuel Cell (PEMFC) characteristics assumes importance in view of better understanding, analysis and design of high efficient fuel cell systems. Limited by its complexity, strongly coupled behaviour and multivariate characteristics; optimization techniques are attempted to model PEMFC characteristics. Influenced by convergence speed, computational efficiency, level of complexity, dependency on initial solution and ability to locate global optimum; recently evolved Flower Pollination Algorithm is utilized in this work for PEMFC modelling. This method is applied to derive unknown model parameters of fuel cells having different characteristics and rating. Further, to illustrate the superiority of the method, results obtained are compared with some of the recent works. Moreover, to showcase its efficiency; comprehensive comparison is made in terms of model parameter values, sum of squared error, individual absolute error and relative error values.

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.

Flying rover is a rover which is been modified with the attachment of a drone having a H-axis morphing mechanism for the purpose of landing the rover and to fly it through the valleys and peaks, even through the narrow gaps in the mars and titan surface, that are having similar atmosphere conditions like earth. The objective of this flying rover is to overcome the problems in the landing of rover in other planets and satellites and to assist the rover through its entire research period. It is been designed by using CATIA software and fabricated using 3-D printing method. And the structural analysis is been carried out and resultants are plotted by using ANSYS software. By carrying out instrumentation, interface and flight testing of the scaled prototype of flying rover, the paper concludes the landing of rover using this method is simple to operate and can land the rover safely on mars and titan’s surface even in slope surface, valleys or peaks having a major advantage compared to other techniques existing in landing the rovers in the martin’s surface. Combined performance of both drone and rover gives a greater step-over than other landing techniques., To view and download the paper for free, visit: http://pices-journal.com/ojs/index.php/pices/article/view/242

In this paper, a robust adaptive direct power controller (RADPC) is designed for voltage source inverters (VSIs) in grid-connected photovoltaic (PV) systems. The proposed scheme is designed to ensure the robustness of three-phase grid connected PV systems against both parametric uncertainties and external disturbances. The dynamical model of grid-connected PV systems is developed in terms of power (active and reactive) equations where parametric uncertainties are captured by considering all parameters as unknown and measurement noises are considered as external disturbances. The proposed scheme directly controls the active and reactive power injection into the grid using the estimated values of parameters. The proposed RADPC also ensures the desired tracking of the DC-link voltage. Simulation studies are conducted to validate the performance of the proposed scheme and simulation results have revealed that the proposed scheme outperforms the existing sliding mode controller (ESMC).

The present study undertakes the research problem on the optimization of production of biodiesel as a renewable energy resource from the transesterification of soybean oil and ethanol. Predictive modelling and surface analysis techniques were applied based on the artificial neural network search algorithm to correlate the yield of ethyl ester and glycerol and the input parameters. The formulated models accurately predicted the yield of the products with a high coefficient of determination. When the reaction time is low, the ester yield decreases with an increase in temperature and the maximum yield of obtained biodiesel at a very low value of time of reaction and temperature. Plots based on parametric and sensitivity analysis reveals that the yield of ethyl ester can be maximized and that of glycerol minimized at an integrated condition with lower ethanol/oil molar ratio, higher temperature value, higher catalyst concentration value, and longer time of reaction. The global sensitivity analysis reveals that the catalyst concentration and temperature of the reaction influence the yield of ethyl ester the most. In addition, an optimal ethyl ester yield of 95% can be achieved at specific input conditions. Moreover, according to the results of global sensitivity analysis, the catalyst concentration is found to be most significant for both the glycerol and ethyl ester yield.

A nonlinear backstepping controller is designed in this paper for grid-connected photovoltaic (PV) systems with output LC filters to improve the dynamic stability over a large operating region and power quality. The detailed dynamical model of grid-connected PV systems with output LC filters are used to design the proposed backstepping scheme which ensures the desired tracking of all states. Since the proposed scheme uses all nonlinearities within the grid-PV system along with the dynamics of LC filters, the switching control inputs have the ability to ensure the overall dynamic stability and improve the power quality under any circumstance. The concept of control Lyapunov function (CLF) defining the energy of the system is used to analyze the convergence of all errors characterizing the tracking for different states. The performance of the proposed nonlinear backstepping controller (NBC) is evaluated on a gridconnected system with an output LC filter and the same scheme is compared by employing it on a similar system having an L filter.

Modeling of fuel cell characteristics is a major challenge, researchers are addressing day by day to solve due to its non-linear characteristics in nature. Hence, to solve this problem which is in existence for over a decade, the authors have deduced an objective function by employing Artificial Immune System (AIS) optimization technique. The attained results using the proposed approach is compared with the expected results as per the manufactures data sheet and also tested under various operating conditions of temperature and pressure. Furthermore, to provide a comprehensive evaluation, results obtained with AIS algorithm are compared with other renowned algorithms employing the conventional curve fitting technique.

Inspired by the foraging behavior in bacteria (e-coli), Bacterial Foraging Algorithm (BFA) is designed for solving global optimization problems. Being unique in optimization, BFA has already received universal attention from researchers to apply for various engineering application. In order to investigate the BFA performance, mathematical equations for unimodal and multimodal functions are investigated for minimization problem and the optimal results are reported in this regard. Performance indices of BFA method are comprehensively gauged for all functions and a comparative study with standard algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Geometric Search Optimization (GSO) Algorithm is reported. Finding more suitable to optimize non-linear problems, the modified BFA in fusion with nelder- meed method is also introduced. This chapter binds the collective knowledge on BFA that aids the researchers to apply for their research application. Taking BFA as their optimization tool, few authors have attained their research objectives in following areas: (i) PV parameter estimation (2) Selective Harmonic Elimination in inverters and (3) a modified BFA method for optimal power flow to maintain load stability. To have an interior understanding the applications, the simulation results of the above study are also presented more in detail. In all the cases, the due role of BFA to attain accurate results is always appreciated.

The widespread use of Proton Exchange Membrane fuel cell for its unique advantages compelled researchers for precise modelling of its characteristics. Since, modelling becomes extremely important for better understanding, simulation, design, analysis and development of high efficiency fuel cell system. However, due to its non-linearity, multivariate and strongly coupled characteristics; mathematical modelling based on empirical equations was widely adopted. But, the shortage of data, complexity in modelling, and number of unknown parameters favored the use of optimization methods. Many optimization methods have been endeavored to model Proton Exchange Membrane fuel cell characteristics. However, no prior attempt has been made to consolidate the contributions. Hence, this paper comprehensively describes and discusses the various Artificial Intelligence/bio inspired methods applied for fuel cell parameter estimation problem. The methods background theory and its application to the problem is elaborated. It is envisioned that, this review will be a one stop solution to the researchers and engineers working in the area of fuel cell systems.

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.

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.

Partial shading on photovoltaic panels reduces the output power of the PV modules. Due to this P–V curve experiences multiple peaks which makes the maximum power point tracking complex. The row currents are different when the panel experiences partial shading. In the paper, a method is proposed to disperse the shade uniformly along the panel which increases the output power through physical relocation. The method proposed allows the shade to get dispersed all over the array and thus the losses due to mismatch are reduced. The characteristics of the TCT connection are compared with the proposed method. The methodology is performed for the 9 × 9 array for two shading patterns. Finally, simulations on every case are carried out and the characteristics are plotted. The obtained results have better output than the existing TCT methodology.

Due to the environmental threat toward pollution and continuous depletion in fossil fuels, the world has found its alternate source of energy generation through pollution-free solar energy. Due to the scope in solar PV systems, the research interest has considerably increased. In solar PV research thrusts, the accurate modeling of solar I-V characteristics is given prior importance. In this article, the authors have proposed a new FPA-based solar PV parameter extraction. To appreciate the accuracy in the computation, two diode models are preferred. The authors have used RTC France data to experiment with the effectiveness of FPA. Further, the computed root mean square error and relative error for the designed model is compared with the existing Simulated Annealing (SA), Pattern Search (PS), Harmony Search (HS), and Artificial Bee Swarm Optimization (ABSO) techniques.

In upcoming years, energy generated through solar which plays a vital role in power generation has gained importance. The key problem in solar generation is to extract maximum power from solar photovoltaic module at all weather conditions. But due to partial shading conditions caused by bird droppings, soil accumulation and cloud formation, it leads to multiple local peaks making the tracking complex. Since, PSO has gained its significant popularity in the area, maximum power point tracking under partial shading conditions. Analysis of PSO and variants to observe their adaptability for different irradiance conditions such as uniform irradiance, strong shading and weak shading. In this article, a comparative study has been presented for different variants of PSO. The results are analysed based on power oscillation and catching peak through MATLAB simulations.

With the exponential growth in global photovoltaic (PV) power capacity, protection of PV systems has gained prodigious importance in last few decades. Even with the use of standard protection devices in a PV system, faults occurring in a PV array may remain undetected. Inspired by the ever increasing demand for a reliable fault detection technique, several advanced techniques have been proposed in literature; especially in the last few years. Hence, this paper carries out an in depth analysis of various fault occurrences, protection challenges and ramifications due to undetected faults in PV systems. Furthermore, with a widespread literature, the paper critically reviews numerous fault detection algorithms/techniques available for PV systems which are proven to be effective and feasible to implement. The proposed study is not only limited to surveying the existing techniques but also analyzes the performance of each technique with an emphasis on its: 1) Approach, 2) Sensor requirements, 3) Ability to diagnose and localize faults, 4) Integration complexity, 5) Accuracy, 6) Applicability and 7) Implementation cost. Overall, this paper is envisioned to avail the researchers working in the field of PV systems with a valuable resource, which will assist them to enrich their research works.

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.

In recent years, continuous depletion of conventional energy resources and the fluctuating fuel cost have motivated research on alternative source for power generation. To effectively harvest power from the green energy resources, extensive research have been carried out in literature. Solar energy being the foremost among all other renewable energy resources has evolved as a reliable substitute for the conventional power generation. Unlike other sources, real time research on Photovoltaic (PV) Systems is a difficult assignment as it necessitates an accurate PV emulator that can precisely replicate the nonlinear characteristics of a PV cell. PV Researchers have developed emulators of different type utilizing various configurations. This paper attempts to present a comprehensive review on different PV emulator topologies so as to gather all the research activities that are scattered in this arena under a common umbrella. Further this paper provides a detailed analysis of each technique emphasizing on its (i) Realization cost (ii) Accuracy (iii) Level of complexity (iv) Sensitiveness to varying environmental conditions (v) Hardware implementation and (vi) Efficiency.

Shade conditions in PV array invoke multiple power peaks in power-voltage characteristics and as a consequence, mismatch and power loss occur. To attenuate the shade effect, various researches worldwide have been actively engaged to dynamically reconfigure the PV array via different interconnection schemes. In this paper, a new puzzle based reconfiguration scheme is proposed and tested with 5 × 5 PV array. Following physical relocation scheme, the proposed dominance square method configures the PV modules of conventional Total Cross Tied (TCT) interconnection. Thus, row and column wise rearrangement helps to attain uniform row current difference with the method. To test the validity of the proposed scheme, seven shade patterns are critically evaluated. Response for every shade pattern is analyzed with the help of I-V, P-V characteristics and their corresponding losses are evaluated. Moreover, income generated and energy saving analysis for the method is also carried out.

Background: Respiratory tract infections (RTI) are more common in pediatrics. Especially in developing countries like India, RTI causes more death in children. Antibiotics are used to treat RTI even in case of viral infections. Hence rational use of antibiotics is very much needed to avoid antibiotic resistance. In this study we have analysed the prescribing pattern of antibiotics and their drug interactions involved in the management of respiratory tract infections in a tertiary care teaching hospital.Methods: A prospective observational study was conducted in the department of pediatrics at a tertiary care teaching hospital for a period of 6 months (November 2019-April 2020). A total of 144 patients were included in the study and their prescription was analysed for their antibiotic prescribing pattern and interactions.Results: Infants were more prone to RTI infections. Among RTI, lower RTI were more common in children. We have analysed prescribing pattern of antibiotics in which, mono antibiotic therapy were more commonly used. Ampicillin was the frequently prescribed antibiotic. Azithromycin had the most possible interactions among the antibiotics prescribed.Conclusions: More than 336 antibiotics were used for all types of RTI, even in the case of viral infections. So rational use of antibiotics is needed. Among 144 children who were enrolled in the study, none of them were subjected to culture sensitivity test. As of which, all of them were treated empirically. Penicillin and cephalosporin are the mostly prescribed antibiotics in RTI. Cephalosporin prescribed children had less hospital stay and hardly interactions were found.

Solar energy as abundant energy can easily be converted to electricity using the different solar energy technologies. However, the conversion efficiency is highly affected by many factors such soiling phenomenon. This refers to the accumulation of dust particles and other contaminants on the front surface of the system which prevent the sunlight from reaching the cells or the collectors. The present work is mainly focusing on understanding this phenomenon especially for photovoltaic modules. It represents an updated survey on soiling process and its methods of quantitation based on measuring deposited dust, transmittance or by evaluating the real response of the photovoltaic module. It includes as well the different factors influencing soiling, which have been categorized into five categories related to the module characteristics, the surrounding environment, the climatic conditions, the exposure conditions and the soiling properties. The different methods to mitigate its effect mainly natural, manual, mechanical and self-cleaning have been also discussed. It presents as well a summary of the key contributions on this phenomenon in both countries Morocco and India. This will be as a paved guide to help readers to better understand soiling phenomenon and also to get a clear idea about the trend of research in both countries Morocco and India.

To maximize solar photovoltaic (PV) output under dynamic weather conditions, maximum power point tracking (MPPT) controllers are incorporated in solar PV systems. However, the occurrence of multiple peaks due to partial shading adds complexity to the tracking process. Even though conventional and soft computing techniques are widely used to solve MPPT problem, conventional methods exhibit limited performance due to fixed step size, whereas soft computing techniques are restricted by insufficient randomness after reaching the vicinity of maximum power. Hence, in this paper, a new flower pollination algorithm (FPA) with the ability to reach global peak is proposed. Optimization process in FPA method performs global and local search in single stage and it is a key tool for its success in MPPT application. The ruggedness of the algorithm is tested with zero, weak, and strong shade pattern. Further, comprehensive performance estimation via simulation and hardware are carried out for FPA method and are quantified with conventional perturb and observe and particle swarm optimization (PSO) methods. Results obtained with FPA method show superiority in energy saving and proved to be economical.

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&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.

Operating conditions such as temperature and pressure affect the fuel cell performance. Proton Exchange Membrane (PEM) fuel cells work at its maximum efficiency when it is operated within temperature limits. To improve the heat transfer properties of PEM fuel cells, nano coolants are added. Identification of a suitable combination of thermal conductivity, relative humidity, and parametric coefficients for performance assessment of PEM fuel cells in real time is mandatory. Conventional methods follow complex computations and trial and error procedure to model fuel cell without thermal management parameters. Hence, in this paper, the objective of parameter set identification is framed as an optimization problem and solved using Genetic Algorithm method. The performance of the proposed approach is evaluated for different test conditions and validated through simulation findings.