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A Wireless Sensor Network (WSN) is comprised of a number of sensor nodes (SNs) that are randomly placed in an open, harsh environment for many applications. Due to the resource-constrained nature of SNs and hostile deployment environments, energy efficiency and security are considered two key factors in designing WSN routing protocols. This paper proposes an Energy Efficient Secure Multipath (EESM) routing protocol to securely construct efficient routes and transmit data packets between SNs and the base station (BS). EESM achieves energy efficiency through minimal task allocation among SNs whereas all computation-intensive tasks such as network information collection, routing table generation, and network maintenance are performed by the BS. The proposed protocol incorporates lightweight security mechanisms including a one-way hash chain, message authentication code, encryption, and clique-based coordinator selection and monitoring schemes to defend against numerous security attacks. Simulation results show that EESM can successfully detect and protect the network against various security attacks such as replay attacks, sybil attacks, sinkhole attacks, spoofing attacks, compromised node attacks, and so on. In terms of energy efficiency, the proposed protocol achieves an up to 37% increase in network lifetime and a 6% increase in throughput over Secure and Energy Efficient Multipath (SEEM) routing, Secure and Reliable Multipath Routing (SRMR), and Reliable and Multipath Encounter Routing (RMER) protocols. The paper implements the protocol in a real environment using Arduino motes to analyze security overheads and network setup time.

A reduced-order, “grey-box” model of an active distribution network, intended for dynamic simulations of the transmission system, is derived. The network hosts inverter-based generators as well as static and motor loads, whose dynamic parameters are affected by uncertainty. This issue is addressed using Monte-Carlo simulations. The parameters of the equivalent are adjusted to match as closely as possible the average of the randomized responses, while their dispersion is accounted for through the weights of the weighted least-square minimization. A procedure is used to remove from the identification the parameters with negligible impact. To avoid over-fitting, the equivalent is tuned for multiple large-disturbance simulations. A recursive procedure is used to select the smallest possible subset of disturbances involved in the least-square minimization. Simulation results with a 75-bus MV test-system are reported. They show that the equivalent is able to reproduce with good accuracy the discontinuous controls of inverter-based generators, such as reactive current injection and tripping.

Abstract The evolutionary algorithm of invasive weed optimization algorithm popularly known as the IWO has been used in this paper, to solve the unit commitment (UC) problem. This integer coded algorithm is based on the colonizing behavior of weed plants and has been developed to minimize the total generation cost over a scheduled time period while adhering to several constraints such as generation limits, meeting load demand, spinning reserves and minimum up and down time. The minimum up/down time constraints have been coded in a direct manner without using the penalty function method. The proposed algorithm was tested and validated using 10 units and 24 h system. The most important merit of the proposed methodology is high accuracy and good convergence speed as it is a derivative free algorithm. The simulation results of the proposed algorithm have been compared with the results of other tested algorithms for UC such as shuffled frog leaping, particle swarm optimization, genetic algorithm and Lagrangian relaxation and bacterial foraging algorithm.

Abstract This paper presents a numerical study of the particle cluster behavior in a riser/downer reactor by means of combined computational fluid dynamics (CFD) and discrete element method (DEM), in which the motion of discrete particles is obtained by solving Newton's equations of motion and the flow of continuum gas by the Navier–Stokes equations. It is shown that the existence of particle clusters, unique to the solid flow behavior in such a reactor, can be predicted from this first principle approach. The results demonstrate that there are two types of clusters in a riser and downer: one is in the near wall region where the velocities of particles are low; the other is in the center region where the velocities of particles are high. While the extent of particle aggregation appears to be similar, the duration time for the first type in a downer is shorter than in a riser. Furthermore, it is demonstrated that the formation of clusters is affected by a range of variables related to operational conditions, particle properties, and bed properties and geometry. The increase of solid volume fraction, sliding and rolling friction between particles or between particles and wall, or damping coefficient can enhance the formation of clusters. The use of multi-sized particles can also promote the formation of clusters. But the increase of gas velocity or use of a wider bed can suppress the formation of clusters. The van der Waals force may enhance the formation of clusters when solid concentration is high but suppress the formation of clusters near the wall region when solid concentration is low.

Lithium-ion batteries are fast becoming the battery of choice in applications such as electric/hybrid electric vehicles (EV/HEV) and renewable energy systems. This increasing usage demands an improved reliability of the battery systems, which in turn heavily relies on the control and optimization algorithms. Of particular importance is ensuring that each lithium-ion cell within a battery pack remains strictly within an acceptable charge range to avoid untimely degradation of the battery pack. Unfortunately, current battery models make the design of charge equalization circuitry difficult due to their limitations. The aim of this paper is to develop a component-wise control-oriented physics-based battery pack model to facilitate implementation of advanced model-based control and optimization algorithms. In the first stage some existing results are used to obtain a simplified electrochemical ODE model of an individual lithium-ion cell. Then, the cell model is used as the building block of the complete battery pack model. Different charge/discharge scenarios are presented to illustrate the potential of the modeling approach in facilitating the implementation of advanced control and optimization algorithms in improved power equalization and hence prolonging the battery pack lifetime.

The present work is dedicated to the steady-state analysis of electronic load controller (ELC) for three phase alternator. In an alternator employed in micro-hydro applications, the voltage is controlled by Automatic Voltage Controller (AVR) so here electronic load controller conforms itself to the control of frequency. It does so by diverting the difference between the rated power and consumer demand to the dump/ballast load for dissipation. The paper presents the mathematical and simulink model of Electronic Load Controller for three phase alternator. The developed mathematical model is first checked for its stability by employing Routh-Hurwitz criterion and then designed in MATLAB/Simulink which is then analyzed for its behavior under steady-state conditions. The controller is being modeled for proportional, proportional plus integral and proportional plus integral plus derivative control and the results are being compared and discussed.

Abstract The expansion of Sargassum muticum in the Danish estuary Limfjorden between 1984 and 1997 was followed by a decrease in abundance of native perennial macroalgae such as Halidrys siliquosa. Although commonly associated with the expansion of exotic species, it is unknown whether such structural changes affect ecosystem properties such as the production and turnover of organic matter and associated nutrients. We hypothesized that S. muticum possesses ‘ephemeral’ traits relative to the species it has replaced, potentially leading to faster and more variable turnover of organic matter. The biomass dynamics of S. muticum and H. siliquosa was therefore compared in order to assess the potential effects of the expansion of Sargassum. The biomass of Sargassum was highly variable among seasons while that of Halidrys remained almost constant over the year. Sargassum grew faster than Halidrys and other perennial algae and the annual productivity was therefore high (P/B = 12 year−1) and exceeded that of Halidrys (P/B = 5 year−1) and most probably also that of other perennial algae in the system. The major grazer on macroalgae in Limfjorden, the sea urchin Psammechinus miliaris, preferred Sargassum to Halidrys, but estimated losses due to grazing were negligible for both species and most of the production may therefore enter the detritus pool. Detritus from Sargassum decomposed faster and more completely than detritus from Halidrys and other slow-growing perennial macrophytes. High productivity and fast decomposition suggest that the increasing dominance of S. muticum have increased turnover of organic matter and associated nutrients in Limfjorden and we suggest that the ecological effects of the invasion to some extent resemble those imposed by increasing dominance of ephemeral algae following eutrophication.

The present research was mainly focused on the production of biodiesel from Annona squamosa oil using a synthesized Ni-doped CaO nanocatalyst. The optimization of the transesterification reaction parameters was studied through response surface methodology. The highest biodiesel yield of 99.1% was achieved with the optimized conditions of 7.86% catalyst concentration, 442 RPM, 15.19:1 molar ratio of methanol to oil, reaction temperature of 55.8°C and reaction time of 63.3 min. The results obtained from reaction kinetics study showed a good fit with a first-order kinetic model. The activation energy and R2value were determined to be 53.7 kJ/mol and 0.90, respectively. The synthesized Ni-doped CaO nanocatalyst was characterized using Scanning Electron Microscope with Energy Dispersive X-ray Spectroscopy which confirms the presence of nickel, calcium and oxygen. Also, the average size of the nanocatalyst was found to be 48.79 nm. The Fourier Transform–Infrared Spectroscopy results showed the occurrence of functional groups such as C-H and C = O bonds in the synthesized Ni-doped CaO nanocatalyst. The presence of fatty acid methyl esters in the produced biodiesel was analyzed through Gas Chromatography-Mass Spectrometry analysis. The obtained results from the current study provides the possibility and insights for sustainable biodiesel production and a greener environment.