Modified current space vector based powerline communication method for distributed inverters interfaced smart grids

The following paper presents a Powerline Communication (PLC) Method for grid interfaced inverters, for smart grid application. The PLC method is based on the concept of the composite vector which involves multiple components rotating at different harmonic frequencies. The pulsed information is modulated on the fundamental component of the grid current as a specific repeating sequence of a particular harmonic. The principle of communication is same as that of power flow, thus reducing the complexity. The power flow and information exchange are simultaneously accomplished by the interfacing inverters based on current programmed vector control, thus eliminating the need for dedicated hardware. Simulation results have been shown for inter-inverter communication, both under ideal and distorted conditions, using various harmonic modulating signals.

Holistic Measures for Evaluating Prediction Models in Smart Grids

The performance of prediction models is often based on ``abstract metrics'' that estimate the model's ability to limit residual errors between the observed and predicted values. However, meaningful evaluation and selection of prediction models for end-user domains requires holistic and application-sensitive performance measures. Inspired by energy consumption prediction models used in the emerging ``big data'' domain of Smart Power Grids, we propose a suite of performance measures to rationally compare models along the dimensions of scale independence, reliability, volatility and cost. We include both application independent and dependent measures, the latter parameterized to allow customization by domain experts to fit their scenario. While our measures are generalizable to other domains, we offer an empirical analysis using real energy use data for three Smart Grid applications: planning, customer education and demand response, which are relevant for energy sustainability. Our results underscore the value of the proposed measures to offer a deeper insight into models' behavior and their impact on real applications, which benefit both data mining researchers and practitioners.

A Networked Control Systems Perspective for Wide-Area Monitoring Control of Smart Power Grids

This paper presents a networked control systems (NCS) framework for wide area monitoring control of smart power grids. We consider a scenario in which wide area measurements are transmitted to controllers at remote locations. We model the effects of delays and packet dropouts due to limited communication capabilities in the grid. We also design a robust networked controller to damp wide-area oscillations based on information obtained from Wide Area Monitoring Systems (WAMS), and analyze the improvement in system stability due to networked control. With communication integration being an important feature of the smart grid, detailed consideration of the effects of communication is essential in the control design for future power systems. We believe that this work is an essential step in this direction.

Grids with multiple batch systems for performance enhancement of multi-component and parameter sweep parallel applications

In this work, we evaluate the benefits of using Grids with multiple batch systems to improve the performance of multi-component and parameter sweep parallel applications by reduction in queue waiting times. Using different job traces of different loads, job distributions and queue waiting times corresponding to three different queuing policies(FCFS, conservative and EASY backfilling), we conducted a large number of experiments using simulators of two important classes of applications. The first simulator models Community Climate System Model (CCSM), a prominent multi-component application and the second simulator models parameter sweep applications. We compare the performance of the applications when executed on multiple batch systems and on a single batch system for different system and application configurations. We show that there are a large number of configurations for which application execution using multiple batch systems can give improved performance over execution on a single system.

Analysis of DNA sequence transformations on grids

Study of the evolution of species or organisms is essential for various biological applications. Evolution is typically studied at the molecular level by analyzing the mutations of DNA sequences of organisms. Techniques have been developed for building phylogenetic or evolutionary trees for a set of sequences. Though phylogenetic trees capture the overall evolutionary relationships among the sequences, they do not reveal fine-level details of the evolution. In this work, we attempt to resolve various fine-level sequence transformation details associated with a phylogenetic tree using cellular automata. In particular, our work tries to determine the cellular automata rules for neighbor-dependent mutations of segments of DNA sequences. We also determine the number of time steps needed for evolution of a progeny from an ancestor and the unknown segments of the intermediate sequences in the phylogenetic tree. Due to the existence of vast number of cellular automata rules, we have developed a grid system that performs parallel guided explorations of the rules on grid resources. We demonstrate our techniques by conducting experiments on a grid comprising machines in three countries and obtaining potentially useful statistics regarding evolutions in three HIV sequences. In particular, our work is able to verify the phenomenon of neighbor-dependent mutations and find that certain combinations of neighbor-dependent mutations, defined by a cellular automata rule, occur with greater than 90% probability. We also find the average number of time steps for mutations for some branches of phylogenetic tree over a large number of possible transformations with standard deviations less than 2.

Anion directed template synthesis of Cu(II) complexes of a N,N,O donor mono-condensed Schiff base ligand: A molecular scaffold forming highly ordered H-bonded rectangular grids

Anion directed, template syntheses of two dinuclear copper(II) complexes of mono-condensed Schiff base ligand Hdipn (4-[(3-aminopentylimino)-methyl]-benzene-1,3-diol) involving 2,4- dihydroxybenzaldehyde and 1,3-diaminopentane were realized in the presence of bridging azide and acetate anions. Both complexes, [Cu-2(dipn)(2)(N-3)(2)] (1) and [Cu-2(dip(n))(2)(OAc)(2)] (2) have been characterized by X-ray crystallography. The two mononuclear units are joined together by basal-apical, double end-on azido bridges in complex 1 and by basal-apical, double mono-atomic acetate oxygen-bridges in 2. Both complexes form rectangular grid-like supramolecular structures via H-bonds connecting the azide or acetate anion and the p-hydroxy group of 2,4- dihydroxybenzaldehyde. Variable-temperature (300-2 K) magnetic susceptibility measurements reveal that complex 1 has antiferromagnetic coupling (J = -2.10 cm (1)) through the azide bridge while 2 has intra-dimer ferromagnetic coupling through the acetate bridge and inter-dimer antiferromagnetic coupling through H-bonds (J = 2.85 cm (1), J' = -1.08 cm (1)). (C) 2009 Elsevier B. V. All rights reserved.

KFVS (Kinetic Flux Vector Splitting) on moving grids for unsteady aerodynamics

Accurate numerical solutions to the problems in fluid-structure (aeroelasticity) interaction are becoming increasingly important in recent years. The methods based on FCD (Fixed Computational Domain) and ALE (Alternate Lagrangian Eulerian) to solve such problems suffer from numerical instability and loss of accuracy. They are not general and can not be extended to the flowsolvers on unstructured meshes. Also, global upwind schemes can not be used in ALE formulation thus leads to the development of flow solvers on moving grids. The KFVS method has been shown to be easily amenable on moving grids required in unsteady aerodynamics. The ability of KFMG (Kinetic Flux vector splitting on Moving Grid) Euler solver in capturing shocks, expansion waves with small and very large pressure ratios and contact discontinuities has been demonstrated.

Smart applications for energy harvested WSNs

A Wireless Sensor Network (WSN) powered using harvested energies is limited in its operation by instantaneous power. Since energy availability can be different across nodes in the network, network setup and collaboration is a non trivial task. At the same time, in the event of excess energy, exciting node collaboration possibilities exist; often not feasible with battery driven sensor networks. Operations such as sensing, computation, storage and communication are required to achieve the common goal for any sensor network. In this paper, we design and implement a smart application that uses a Decision Engine, and morphs itself into an energy matched application. The results are based on measurements using IRIS motes running on solar energy. We have done away with batteries; instead used low leakage super capacitors to store harvested energy. The Decision Engine utilizes two pieces of data to provide its recommendations. Firstly, a history based energy prediction model assists the engine with information about in-coming energy. The second input is the energy cost database for operations. The energy driven Decision Engine calculates the energy budgets and recommends the best possible set of operations. Under excess energy condition, the Decision Engine, promiscuously sniffs the neighborhood looking for all possible data from neighbors. This data includes neighbor's energy level and sensor data. Equipped with this data, nodes establish detailed data correlation and thus enhance collaboration such as filling up data gaps on behalf of nodes hibernating under low energy conditions. The results are encouraging. Node and network life time of the sensor nodes running the smart application is found to be significantly higher compared to the base application.

Evaluation of bearing capacity improvement using composite grids

Recently, composite reinforcements in which combinations of materials and material forms such as strips, grids, and strips and anchors, depending on requirements have proven to be effective in various ground improvement applications. Composite geogrids studied in this paper belong to the category of composite reinforcements and are useful for bearing capacity improvement. The paper presents evaluation of results of bearing capacity tests conducted oil a composite geogrid, made of composite reinforcement consisting of steel and cement mortar. The study shows that the behavior of composite reinforcements follows the general trends observed in the case of conventional geogrids, with reference to the depth of first layer below the footing, number of layers of reinforcement, and vertical spacing of the reinforcement. Results show that the performance is comparable to that of a conventional polymer geogrid.

Strategies for Rescheduling Tightly-Coupled Parallel Applications in Multi-Cluster Grids

As computational Grids are increasingly used for executing long running multi-phase parallel applications, it is important to develop efficient rescheduling frameworks that adapt application execution in response to resource and application dynamics. In this paper, three strategies or algorithms have been developed for deciding when and where to reschedule parallel applications that execute on multi-cluster Grids. The algorithms derive rescheduling plans that consist of potential points in application execution for rescheduling and schedules of resources for application execution between two consecutive rescheduling points. Using large number of simulations, it is shown that the rescheduling plans developed by the algorithms can lead to large decrease in application execution times when compared to executions without rescheduling on dynamic Grid resources. The rescheduling plans generated by the algorithms are also shown to be competitive when compared to the near-optimal plans generated by brute-force methods. Of the algorithms, genetic algorithm yielded the most efficient rescheduling plans with 9-12% smaller average execution times than the other algorithms.

Executing Long-running Multi-component Applications on Batch Grids

Computational grids are increasingly being used for executing large multi-component scientific applications. The most widely reported advantages of application execution on grids are the performance benefits, in terms of speeds, problem sizes or quality of solutions, due to increased number of processors. We explore the possibility of improved performance on grids without increasing the application’s processor space. For this, we consider grids with multiple batch systems. We explore the challenges involved in and the advantages of executing long-running multi-component applications on multiple batch sites with a popular multi-component climate simulation application, CCSM, as the motivation.We have performed extensive simulation studies to estimate the single and multi-site execution rates of the applications for different system characteristics.Our experiments show that in many cases, multiple batch executions can have better execution rates than a single site execution.

Phylogenetic Predictions on Grids

A phylogenetic or evolutionary tree is constructed from a set of species or DNA sequences and depicts the relatedness between the sequences. Predictions of future sequences in a phylogenetic tree are important for a variety of applications including drug discovery, pharmaceutical research and disease control. In this work, we predict future DNA sequences in a phylogenetic tree using cellular automata. Cellular automata are used for modeling neighbor-dependent mutations from an ancestor to a progeny in a branch of the phylogenetic tree. Since the number of possible ways of transformations from an ancestor to a progeny is huge, we use computational grids and middleware techniques to explore the large number of cellular automata rules used for the mutations. We use the popular and recurring neighbor-based transitions or mutations to predict the progeny sequences in the phylogenetic tree. We performed predictions for three types of sequences, namely, triose phosphate isomerase, pyruvate kinase, and polyketide synthase sequences, by obtaining cellular automata rules on a grid consisting of 29 machines in 4 clusters located in 4 countries, and compared the predictions of the sequences using our method with predictions by random methods. We found that in all cases, our method gave about 40% better predictions than the random methods.