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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.

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.

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.

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.

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.

Photovoltaic Electrical Power Generation System (PV-EPGS) is gaining more importance due to its benefits like no fuel cost, eco-friendliness and less maintenance. However, harnessing the maximum power from large PV-EPGS has become difficult due to the occurrence of a number of peaks in P-V characteristics under partial shaded conditions. This paper presents simulation study of MPPT under partially shaded conditions using the modified version of Particle Swarm Optimization method known as Enhanced Leader Particle Swarm Optimization (EL-PSO). Proposed ELPSO method has advantages over PSO like fast convergence, better dynamic performance, easy implementation and high efficiency. To evaluate the proposed method simulation result of PV panel, Siemens S75 is provided for three different shaded conditions.

Increased penetration of wind and solar PV system in Distributed Generation (DG) and isolated micro grid environment necessitates the use of maximum power point tracking method for wind and solar PV resources. Considering the change in environmental conditions and non-linearity, a variety of publications reporting various MPPT algorithms for solar and wind energy systems are put forward in recent times. But the review reports on common MPPT techniques used for solar and wind applications for hybrid power generation have not yet been reported. Hence, in this paper, conventional techniques and artificial intelligent techniques found extensively used in the power generation platform are peerly reviewed and compared. Historical MPPT methods like Perturb & Observe (P&O) / Hill Climbing, Incremental Conductance (INC), Fuzzy and Neural Network methods benchmarked in MPPT province are comprehensively compared in a common platform. In addition to the common existing techniques, recent swarm intelligence and bio-inspired techniques in solar PV and sensor less adaptive techniques in wind MPPT are also been reviewed provided for quality assessment. Finally an economic analysis is arrived for MPPT methods based on (i) Capacity utilization factor (ii) Cost (iii) Energy Savings (iv) payback period (v) Income generated and (vi) stability.

Organic PV cells have gained tremendous attention in recent years for its advantage of having tailored molecular properties and economic manufacturing process. Along with the rigorous and rapid researchesprogressing in the field of organic photovoltaic cells, accurate modelling of organic PV cells has gained extreme importance. Hence in this paper, modelling of organic solar photovoltaic cells based on an improved reverse double diode model is presented. Apart from all other complex mathematical models available in literature, the modelling is performed by introducing simple circuit equations. Furthermore, the model is highly capable of realizing the ideal and non-ideal current-voltage characteristics of an organic PV cell. The eight unknown parameters of the model are extracted using Genetic Algorithm(GA) and the extracted parameters are used to predict the I-V characteristics of a nav-100 organic solar cell. The main aim of this paper is to study the behaviour of organic solar cells and to realize an accurate PV model to replicate the exact I-V characteristics.

Partial shading of PV arrays is one of the most discussed and worked upon problem in the field of solar photovoltaic as it reduces the output power and exhibits multiple peaks in the PV and IV characteristics. As, a result the modules have to be reconfigured to get a maximum power output. This paper presents an optimization based approach for Total cross tied (TCT) connected modules in a PV array. The physical locations of the modules remain unchanged while the electrical connections are altered. The proposed technique utilizes, standard deviation genetic algorithm (SDGA) as an optimization tool, which gives the final connection matrix for the new electrical interconnection which extracts the maximum power from the PV array. This is done to obtain uniform shade dispersion throughout the panel. The proposed method has been tested and simulated in Matlab-Simulink environment under partial shading conditions. Results of the simulation show that the proposed reconfiguration technique exhibit superior results as compared to the traditional TCT interconnection scheme.

Aims Insulin/insulin receptor signaling is involved in cognitive functions. Clinical studies have shown that intranasal insulin administration improves memory functions. However, the molecular mechanisms associated with improvement in memory functions are largely unexplored. Therefore, we investigated the protective effect of intranasal insulin in intracerebroventricular (ICV) streptozotocin (STZ) induced memory impairment in rats. Main methods Rats were injected with STZ (3 mg/kg, ICV) bilaterally twice, on days 1 and 3 and intranasal insulin (2 IU/rat/day) was given for 14 days. Memory was assessed by Morris water maze test. Cerebral blood flow (CBF) was measured by laser-Doppler flowmetry. The biochemical and molecular studies were done in cortex and hippocampus of rat brain. Key findings STZ (ICV) administration caused memory impairment along with the reduction of CBF, ATP level, and Nrf-2 expression. Treatment with intranasal insulin significantly improved memory functions as well as restored CBF, ATP content and Nrf-2 expression in STZ injected rats. STZ administration stimulated oxidative-nitrosative stress as evidenced by a significant increase in ROS, malondialdehyde, NO level and inducible nitric oxide synthase expression and the decrease in glutathione level; which was normalized by intranasal insulin delivery. STZ-induced cholinergic dysfunction (AChE activity and α7-nAChR expression), and mitochondrial hypofunction was largely prevented by treatment with intranasal insulin. Intranasal insulin delivery successfully restored BDNF level and pCREB expression in STZ injected rats. Significance The study shows the beneficial effects of intranasal insulin against STZ-induced memory impairment, which attributed to improved CBF, cholinergic function, brain energy metabolism, BDNF, Nrf-2 expression and antioxidative action.

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, Ipv for double diode and I0,Ipv for 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.

In recent years solar energy has received worldwide attention in the field of renewable energy systems. Among the various research thrusts in solar PV, the most proverbial area is extracting maximum power from solar PV system. Application dof Maximum Power Point Tracking (MPPT) for extracting maximum power is very much appreciated and holds the key in developing efficient solar PV system. In this paper, a state of the art review on various maximum power point techniques for solar PV systems covering timeworn conventional methods and latest soft computing algorithms is presented. To date critical analysis on each of the method in terms of (1) tracking speed, (2) algorithm complexity, (3) Dynamic tracking under partial shading and (4) hardware implementation is not been carried out. In this regard the authors have attempted to compile a comprehensive review on various solar PV MPPT techniques based on the above criteria. Further, it is envisaged that the information presented in this review paper will be a valuable gathering of information for practicing engineers as well as for new researchers.

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&O method.

Alzheimer’s disease (AD) is associated with reduced insulin level and impairment of insulin receptor (IR) signaling in the brain, which correlates to amyloid pathology, neuroinflammation, and synaptic neurotoxicity. Clinical studies show that intranasal insulin improves memory in AD patients without peripheral hypoglycemia. However, neuroprotective molecular mechanism of the beneficial effect of intranasal insulin in AD pathology is unexplored. Therefore, we investigated the role of intranasal insulin on intracerebroventricular (ICV) streptozotocin (STZ)-induced memory impairment in rats as evaluated in the Morris water maze test. STZ (ICV) treated rats had shown memory impairment along with a significant decrease in IR signaling molecules (IR, pIRS-1, pAkt, and pGSK-3α/β expression) and IDE expression in both hippocampus and cerebral cortex. Intranasal insulin delivery prevented these changes. Moreover, intranasal insulin was found to inhibit significantly glial cell activation (GFAP and Iba-1 expression), neuroinflammation (COX-2 expression, NFκB translocation, TNF-α, and IL-10 level) and amyloidogenic protein expression (BACE-1 and Aβ1–42 expression) in STZ (ICV)-injected rats. STZ (ICV)-induced caspase activation and postsynaptic neurotoxicity were also prevented by treatment with intranasal insulin. Our findings reveal that insulin has the neuroprotective effect and clearly signifies the potential use of intranasal insulin delivery for the treatment of AD.

A new method based on the concept of voltage band for efficient Distributed Maximum Power Point Tracking (DMPPT) of PV module is proposed. This novel formulation exhibits many advantages including faster convergence, improved tracking efficiency, zero steady state oscillations and simplicity in implementation. The paper unveils the possibility of utilizing a narrow voltage band for effective MPP tracking which has not been discussed so far in the literature. Extensive analysis is done and simulations are carried out in MATLAB/SIMULINK environment. To showcase the superiority of the proposed method, comparison is done with conventional methods for step change as well as gradual change in irradiation.

A nearly accurate solar photovoltaic emulator using DC-DC buck converter is proposed. A mathematical model developed using MATLAB/SIMULINK (The MathWorks, Natick, Massachusetts, USA) serving as reference is built for emulating solar photovoltaic characteristics. Hardware implementation is done using a dSPACE ds1104 controller board (dSPACE GmbH, Paderborn, Germany), and a closed-loop control system is constructed using proportional integral (PI) controller. The proposed emulator has the following characteristics:(1) faster dynamic response with easy interfacing, (2) the advantage of real-time control, (3) a DC-DC buck converter with higher bandwidth, (4) highly stable output with lower response time, and (5) lower output voltage and current ripple. Experimentations are done under different operating conditions, and the results obtained are presented. Further, to demonstrate the capability of the proposed emulator, maximum power point tracking using the hill-climbing method is tested on a DC-DC boo...

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.

A new problem formulation for effective identification of fuel cell parameters is proposed. The proposed formulation is solved by applying Genetic Algorithm optimization technique. The algorithm steps are coded in MATLAB and the objective function is solved for PEM fuel cell. In order to estimate the performance of the proposed formulation; extensive simulations are performed with both the proposed and conventional objective functions and the results obtained are compared. Further, a comprehensive evaluation based on objective function value, convergence speed and absolute voltage error value is also made to prove the superiority of the proposed formulation over the conventional curve fitting approach.

For large photovoltaic power generation plants, number of panels are interconnected in series and parallel to form a photovoltaic (PV) array. In this configuration, partial shade will result in decrease in power output and introduce multiple peaks in the P–V curve. As a consequence, the modules in the array will deliver different row currents. Therefore, to maximize the power extraction from PV array, the panels need to be reconfigured for row current difference minimization. Row current minimization via Su Do Ku game theory do physical relocation of panels may cause laborious work and lengthy interconnecting ties. Hence, in this paper, an alternative to physical relocation based on particle swarm optimization (PSO) connected modules is proposed. In this method, the physical location of the modules remains unchanged, while its electrical connections are altered. Extensive simulations with different shade patterns are carried out and thorough analysis with the help of I–V , P–V curves is carried out to support the usefulness of the proposed method. The effectiveness of proposed PSO technique is evaluated via performance analysis based on energy saving and income generation. Further, a comprehensive comparison of various electrical array reconfiguration based is performed at the last to examine the suitability of proposed array reconfiguration method.

Majority of the appliances in a typical home such as LED lights, laptops, LED TVs, washing machines, computers, use a final DC input whereas our supply which we are using today is AC. The DC approach will help to eliminate or reduce the high utilization of AC-DC converters embedded in several appliances and are replaced with a centralized high efficiency rectifier and a couple of DC-DC converters thus by reducing losses to 10%–15% from the existing 20%–40%. As all the DC-DC converters are non-linear in nature different types of controllers have been designed according to the system in order to maintain entire system stable in all conditions. The total system is coordinated with the Energy management system for its efficient working and to maintain constant voltage from solar pv and battery sources. Energy management system decides the modes of operation of the converters. It generally works in two modes one in constant voltage mode and the other in MPPT mode. This method is adopted to control the power consumed by the system and also to regulate the DC bus voltage by using battery source. The other advantage of the proposed system is its higher compatibility of DC power with storage. The simulation results of EMS has been verified along with theoretical results.

In the 21st century dearth of energy and degradation of environment are among the major two problems. Thermoelectric devices provide a solution to such problems. Thermo electric devices are very small in size and therefore finds a wide range of application in areas like military, marine, space rovers, automobile industry, food and packaging industry etc. This paper begins with fundamental concepts of Thermoelectric followed by simplified modelling equations of a Thermo Electric Cooler (TEC). An easy to use interface of MATLAB/ Simulink model has been developed. The creditability of this paper lies in newly defined mathematical equations that results in a figure of merit and power consumption level that are much better as compared to outputs obtained by using existing methods of TEC Modelling. Using this concept as a base, an improved version of TEC, in practical terms, can be made in the future.

Network reconfiguration which is constrained non linear optimization problem has been solved for loss minimization, load balancing, etc. for last two decades using various heuristic search evolutionary algorithms like binary particle swarm optimization, neuro-fuzzy techniques, etc. The contribution of this paper lies in considering distributed generation which are smaller power sources like solar photovoltaic cells or wind turbines connected in the customer roof top. This new connection in the radial network has made unidirectional current flow to become bidirectional there by increasing the efficiency but sometimes reducing stability of the system. Modified plant growth simulation algorithm has been applied here successfully to minimize real power loss because it does not require barrier factors or cross over rates because the objectives and constraints are dealt separately. The main advantage of this algorithm is continuous guiding search along with changing objective function because power from distributed generation is continuously varying so this can be applied for real time applications with required modifications. This algorithm here is tested for a standard 33 bus radial distribution system for loss minimization and test results here shows that this algorithm is efficient and suitable for real time applications.

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.

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.

Hypertension is a risk factor for cognitive impairment. Furthermore, neuroinflammation and neurodegeneration are intricately associated with memory impairment. Therefore, the present study aimed to explore the involvement of hypertension and angiotensin system in neurodegeneration and memory dysfunction in the presence of neuroinflammatory stimulus. Memory impairment was induced by chronic neuroinflammation that was developed by repeated intracerebroventricular (ICV) injections of lipopolysaccharide (LPS) on the 1st, 4th, 7th, and 10th day. Memory functions were evaluated by the Morris water maze (MWM) test on days 13–15, followed by biochemical and molecular studies in the cortex and hippocampus regions of rat brain. LPS at the dose of 25 μg ICV caused memory impairment in spontaneously hypertensive rats (SHRs) but not in normotensive Wistar rats (NWRs). Memory deficit was obtained with 50 μg of LPS (ICV) in NWRs. Control SHRs already exhibited increased angiotensin converting enzyme (ACE) activity and expression, neuroinflammation (increased TNF-α, GFAP, COX-2 and NF-kB), oxidative stress (increased iNOS, ROS and nitrite levels), TLR-4 expression and TUNEL positive cells as compared to control NWRs. Further, LPS (25 μg ICV) exaggerated inflammatory response, oxidative stress and apoptosis in SHRs but similar effects were witnessed at 50 μg of LPS (ICV) in NWRs. Oral administration of perindopril (ACE inhibitor), at non-antihypertensive dose (0.1 mg/kg), for 15 days attenuated LPS induced deleterious changes in both NWRs and SHRs. Our data suggest that susceptibility of the brain for neurodegeneration and memory impairment induced by neuroinflammation is enhanced in hypertension, and that can be protected by ACE inhibition.

Pulse Width Modulation (PWM) techniques are increasingly employed for PWM inverter fed induction motor drive. Among various popular PWM methods used, Selective Harmonic Elimination PWM (SHEPWM) has been widely accepted for its better harmonic elimination capability. In addition, using SHEPWM, it is also possible to maintain better voltage regulation. Hence, in this paper, an attempt has been made to apply Bacterial Foraging Algorithm (BFA) for solving selective harmonic elimination problem. The problem of voltage harmonic elimination together with output voltage regulation is drafted as an optimization task and the solution is sought through proposed method. For performance comparison of BFA, the results obtained are compared with other techniques such as derivative based Newton-Raphson method, and Genetic Algorithm. From the comparison, it can be observed that BFA based approach yields better results. Further, it provides superior convergence, reduced computational burden, and guaranteed global optima. The simulation results are validated through experimental findings.

The contribution of renewable energy to the field of energy markets has been substantial over the last few years. A large number of PV array installations show the increasing contribution of solar energy to the renewable energy. Partial shading of the PV arrays is one of the most discussed and worked upon concept for the simple reasons that it decreases the power output of the PV array installations and exhibits multiple peaks in the I–V characteristics. As a result, the modules have to be reconfigured to get the maximum power output. This papers presents an optimization based approach for Total cross tied (TCT) connected modules in a PV array. The physical locations of the modules remain unchanged while the electrical connections are altered. The genetic algorithm (GA) as an optimization tool, gives the connection matrix for the new electrical interconnection which fetches the maximum power output from the PV array. This is done to obtain uniform dispersion of shadow throughout the panel.

In recent years, most of the loads in a typical home operate at DC voltage. Some of the loads to mention are LED lights, laptops, LED T. Vs, washing machines, and computers. The use of DC voltage at load minimizes the power losses that happen with centralized power generation. Moreover, tendency of using DC voltage provoke the path for smart micro-girds that form local distributed system and supply the loads locally. Further it encourages use of renewable energy resources as well. However, power conversion units such as DC-DC converter for obtaining desired voltage introduces non-linearity. Hence controllers/compensators are designed to maintain the system stabile at all operating conditions. In this work, a Home DC microgrid system fed from solar Photovoltaic array and battery is built and its performance is studied. A robust Energy Management System is designed to regulate the system voltage levels. Three different converters such as Boost, Bidirectional and Buck converters are used to serve the purpose. Various simulation studies are performed in MATLAB/SIMULINK to understand the system stability.

Internet of Things (IoT) is one of the emerging trends and has great potential to bring advances in electrical engineering and renewable energy. It is estimated that by 2020, IoT will be a $20 trillion industry. Hence, this paper proposes the method of incorporating IoT in solar energy and photovoltaic research at the laboratory level. The proposed method includes IoT’s divine application in (a) MPPT tracking; (b) partial shading; (c) security switch for turning ON Arduino’s serial monitor; and finally, (d) building up the direct communication between professor (sitting in the cabin) and the research scholar performing experiment (in the laboratory which is at some particular distance from the professor’s cabin). The whole setup is based on the newly developed Indian microcontroller-BOLT (An Internet of Things platform) launched on 21 February 2016.

The utilization of renewable energy nowadays has a tremendous growth over the past few years. The important factor which reduces the power output of solar PV array is partial shading. Further the mismatch losses in solar PV array are created by uneven shading. This affects the characteristics of PV array by creating multiple peaks which further reduces the output power. To reduce the effect of partial shading, the shading pattern has to be distributed uniformly which can reduce the mismatch losses as well as to increase the output power. In this article, the PSO algorithm has been proposed to mitigate the problems by partial shading to increase the output yield.

Recent times have witnessed a significant positive trend in adoption of renewable energy sources and DC-based loads, which questions the efficiency and reliability of existing structure of electrical power system. DC microgrids are identified as potential solutions for addressing India's and other developing nations' rural electrification. This paper presents an energy management technique for isolated DC microgrids for academic readers, as well as provides an introduction to not-familiarized readers for understanding the common design challenges for implementing off-grid power systems. The principle of the proposed technique is based on law of energy conservation defined by the energy balance equation and is implemented by synthesizing the instantaneous current reference and hysteresis control with source priority, which was validated by rigors simulation studies in Matlab/Simulink. Furthermore, in order to demonstrate the modularity and scalability of the algorithm, it was tested on a prototype hardware test setup at the Solar Energy Research Centre, VIT University.

Surmount demand requirement in electrical energy has given a provision of integrating renewable energy to the three-phase grid-connected system. In particular, the penetration of distributed generation system has contributed more in electrical power generation. But the system connected produces adverse effects in grid due to the unpredictable atmospheric conditions. To handle such condition, in this research article, PV system is considered under study and a new two-stage CPG-P&O MPPT method is used for interfacing PV with grid systems. In addition, a wide range of feed-in power to grid capacity at inverter level is briefly addressed. Further, the algorithm is designed to reduce the increased thermal stresses in switch and to get rid of the grid overshoot and power loss. The desired objective was acknowledged by the simulation results by ensuring delivery of optimal performance.

This paper proposes a near accurate Solar PV Emulator using DC-DC Buck converter. A mathematical model serving as a reference is built using MATLAB/SIMULINK for emulating Solar PV characteristics. In the proposed Emulator by changing model parameter values it is possible to emulate any Solar PV characteristics. Hardware implementation is done using dSPACE ds1104 controller board and a closed loop control system is built using PI controller. The proposed Emulator has the following characteristics: 1. Fast and accurate Solar PV Emulator with easy interfacing and real-time control 2. Features to extend the system for other Solar PV panels 3. DC-DC Buck converter having high bandwidth. 4. Faster dynamic response and highly stable output with lower response time. 5. Lower output voltage and current ripple. Experimentations are done under different operating conditions and the results obtained are compared with the existing Emulators.

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.

The generation of electricity from solar energy has gained worldwide acceptance and serves as a critical objective for the future due to its abundance and eco-friendly nature. However, the output power extracted from solar PV module varies based on the variation in environmental conditions such as irradiance, temperature, shadow etc. Therefore, Maximum Power Point Tracking (MPPT) algorithms are implemented for the proper utilization of the available photovoltaic energy. This paper proposes an algorithm using Particle Swarm Optimization technique which involves a simple and effective method to calculate the required duty cycle. The key feature of this method is its ability to track the maximum power accurately with almost zero steady state oscillations which in turn improves the performance of the tracking system. The effectiveness of this algorithm has been evaluated under uniform change in environmental conditions in MATLAB/SIMULINK. Moreover, superiority of the proposed method is verified by comparing its results with the conventional algorithms such as Hill Climbing and Incremental Conductance in terms of tracking speed and steady state oscillations.

Obtaining optimal power flow solution is a strenuous task for any power system engineer. The inclusion of FACTS devices in the power system network adds to its complexity. The dual objective of OPF with fuel cost minimization along with FACTS device location for IEEE 30 bus is considered and solved using proposed Enhanced Bacterial Foraging algorithm (EBFA). The conventional Bacterial Foraging Algorithm (BFA) has the difficulty of optimal parameter selection. Hence, in this paper, BFA is enhanced by including Nelder-Mead (NM) algorithm for better performance. A MATLAB code for EBFA is developed and the problem of optimal power flow with inclusion of FACTS devices is solved. After several run with different initial values, it is found that the inclusion of FACTS devices such as SVC and TCSC in the network reduces the generation cost along with increased voltage stability limits. It is also observed that, the proposed algorithm requires lesser computational time compared to earlier proposed algorithms.

Recently a variety of advanced PLL methods have been proposed for single phase grid connected applications. For such applications, the correct generation of reference signals is of the utmost importance. To ensure this, an accurate and fast estimation of the phase angle and the frequency of the utility voltage is essential. Among the methods proposed to achieve this, the Park PLL is the most commonly utilized method due to its simple design, low computational load, and optimal performance under distorted grid conditions. Despite the wide acceptance and usage of this method, there hasn't been a definitive method to fine tune and optimize its parameters reported yet. In this paper an improved Park based PLL is proposed which utilizes genetic algorithm in order to fine tune these parameters, which guarantees a fast response and a robust performance. The key point to implement this PLL is to generate the orthogonal signal accurately and quicker, even under distorted grids which may consist of harmonics, noise and DC components. Finally, the response of the proposed PLL has been evaluated using MATLAB and its results are presented to support the theoretical analysis.

Boost converter is most widely used DC-DC converter for renewable energy applications. The operation of high frequency switching DC-DC converters causes Electro Magnetic Interference (EMI), which is degrading the normal operation of the converter and other adjacent systems. Therefore power converters must comply with the Electro Magnetic Compatibility standards in order to ensure the proper operation of converters and not to pollute the nearby systems. This paper discusses an integrated conducted EMI suppression approach, the chaotic soft switching technique based on Field Programmable Gate Array (FPGA) technology to provide a significant EMI noise attenuation in solar fed DC-DC boost converter. The solar fed DCDC boost converter is designed, simulated and implemented in hardware. The results obtained and the FFT analysis are strongly substantiate the proposed approach. The obtained results also compared with the traditional DC-DC boost converter results. The proposed system may replace the bulky passive EMI filters wherein which the space is the major concern.

Due to the regular availability, pollution free and eco-friendly nature, power generation from solar energy has gained considerable attention. Although, power generation from solar is attractive; the environmental factors such as irradiation and temperature makes it challenging. In particular, it is a demanding task to researchers to extract maximum power different shading conditions. However, to extract maximum power various MPPT techniques have been proposed in conventional method and Evolutionary algorithm implemented methods. In the paper, by considering the limitations of aforementioned methods, a new nature inspired flower pollination algorithm (FPA) is proposed. Compared to the inability in conventional methods under partial shaded conditions, the FPA yields very good performance. Further, different shading patterns are tested and the results are compared with P&O and HC (hill climbing) methods. The results of FPA acknowledges its potential in attaining good convergence, high efficiency and less complexity.