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One of the primary issues in the world of electrical power dispatch that still getting higher because of the most complicated and essential problem in the power system operation is economic dispatch. Economic dispatch is a vast subject in modern power system because of its non linearity constraints. Due to this reason economic dispatch becomes a great challenge for researchers and engineers. The principle target of Economic dispatch is to make the producing electrical power inside points of confinement to meet the necessities of demand request with least cost simultaneously fulfilling the various constraints. In this work we have proposed a new PSO technique for the optimization of economic dispatch with the properties of time varying acceleration coefficients. The suggested technique is tested for various IEEE transmission bus system of economic dispatch; its results are evaluated with prominent technology presents in literature.

A new nature inspired algorithm, that simulates the mating behavior of the bumble bees, the Bumble Bees Mating Optimization (BBMO) algorithm, is proposed in this work for optimization of economic load dispatch. Economic dispatch is a method to evaluate the performance of the generating units to fulfill the load demand on minimum fuel cost. The proposed method bumble bees mating optimization (BBMO), work on different three modes namely the queen, the workers and the drones (males). For the evaluation of performance this study consider case study of forty generating unit data. The case study data is tested in various algorithms like Ant colony optimization, particle swarm optimization and genetic algorithm along with BBMO. The performance of all considered algorithm in this work is compared and it is found that minimum operating cost of the forty generating unit system is evaluated by BBMO. Convergence rate of BBMO is also very fast as compared to other considered methods.

Monitoring biomass over large geographic regions for changes in vegetation and cropping patterns is important for many applications. Changes in vegetation happen due to reasons ranging from climate change and damages to new government policies and regulations. Remote sensing imagery (multi-spectral and multi-temporal) is widely used in change pattern mapping studies. Existing bi-temporal change detection techniques are better suited for multi-spectral images and time series based techniques are more suited for analyzing multi-temporal images. A key contribution of this work is to define change as hierarchical rather than boolean. Based on this definition of change pattern, we developed a novel time series similarity based change detection framework for identifying inter-annual changes by exploiting phenological properties of growing crops from satellite time series imagery. The proposed framework consists of three components: hierarchical clustering tree construction, nearest neighbor based classification, and change detection using similarity hierarchy. Though the proposed approach is unsupervised, we present evaluation using manually induced change regions embedded in the real dataset. We compare our method with the widely used K-Means clustering and evaluation shows that K-Means over-detects changes in comparison to our proposed method.

This paper proposes Z (Impedance)-source inverter for single phase grid connected photovoltaic system. Unlike traditional voltage source inverter, Z-source inverter uses shoot-through (ST) duty cycle and modulation index to control output voltage. By modifying the traditional pulse width modulation (PWM) techniques, shoot through duty is accommodated to buck or boost the input voltage. Z-source network provides single stage power conversion. This paper summarizes brief calculation for circuit capacitors and inductors. The design parameter are validated by simulating the performance of an example photovoltaic (PV) module under MATLAB/Simulink environment.

Impedance source converters, are very fast growing technology for electrical energy conversion and particularly appropriate for distributed energy generation. These network avoid limitation associated with traditional voltage and current source converter by adding additional state, called as shoot-through state. These shoot-through states can be controlled by various control methods and pulse width modulation (PWM) techniques to control output voltage and current at required level. This paper provides broad review on various modulation techniques used in Z-source network for ac-dc and dc-dc power conversion. This paper also provides comparison between various parameter related to PWM techniques.

In this paper, we study the radio propagation characteristics in a typical forest environment in central Europe. Understanding propagation in such environment is important not just for remote monitoring and military applications, but also for vehicular networks of cars passing by the forest. We have conducted measurement campaigns covering the frequencies of 750MHz and 2170 MHz. We have also covered longer link distances than usually reported in the literature. We show how proposed forest propagation models perform in the case of our forest environment.

Land use land cover changes (LULCC) are expected to occur due to population growth, climate change and bioenergy demand. It is imperative that we develop better data analysis and techniques to estimate these changes to better predict the consequences and impacts of such changes on the environment. In this study we analyze time series MODIS land cover data to estimate patterns of spatio-temporal cropland and grassland change by analyzing nine years of consistent land cover data. Our results show that the rate of displacement of cropland and grassland by each other declined for the fist half of the last decade while it increased in the second half of the decade. Most of these changes were concentrated in the Great Plain regions and included states in the Corn Belt.

The economic load dispatch (ELD) is the online process for allocating the generation among the available generating unit to fulfill the load demand in such a way to minimize the total generation cost and satisfying the equality and inequality constraints. In literature many papers have used the particle swarm optimization to solve the economic load dispatch problem with emission constraint which is a population based optimization technique inspired by sociological behavior of bird flocking, It can solve ELD problem to a wide range but it lacks global search ability in the last stage of iterations. So it is difficult to get the global optimal solution for the ELD problem by using PSO. At the time of generation of large power, fossil fuel burns at power plants which produces many toxic gases and pollute the environment. The main objective of this paper is to minimize the total generation cost of thermal units as well as to minimize the pollutant emission emitted by toxic gases. This paper, a novel PSO with a moderate random search strategy called as moderate-random particle swarm optimization (MRPSO) is used for solving ELD problem with emission as constraints. MRPSO enhances the ability of particles to explore the solution spaces more effectively and increases their convergence rates. The validation of the proposed MRPSO algorithm is demonstrated through its application for six generator systems with emission constraints for various load demand.

Economic load dispatch (ELD) is an important optimization task in power system. It is the process of allocating generation among the committed units such that the constraints imposed are satisfied and the fuel cost is minimized. Particle swarm optimization (PSO) is a population-based optimization technique that can be applied to a wide range of problems but it lacks global search ability in the last stage of iterations. This paper used a novel PSO with a moderate-random-search strategy (MRPSO), which enhances the ability of particles to explore the solution spaces more effectively and increases their convergence rates. In this paper the power and usefulness of the MRPSO algorithm is demonstrated through its application to three and six generator systems with ramp rate limit constraints.

There has been a growing debate on the effects of the increase in demands of biofuels on land use land cover (LULC) change with apprehension in some quarters that the growing demand forf bioenergy as a clean fuel will result in widespread direct and indirect LULC change. However estimating both direct and indirect LULC change is challenging and will require development of accurate high frequency, high resolution (temporal and spatial) land use land cover data as well as new LULC models which can be used to locate, quantify and predict these changes. To assess whether the demand for biofuel has caused LULC we used MODIS land cover data (MCD12Q1) from 2001 to 2008 along with cropland data layer (CDL) to estimate cropland and grassland changes in United States for the years 2002–2008 as well as its correlation with biofuel growth.

We present a fast and statistically principled approach for land cover change detection. The approach is illustrated with a geographic application that involves analyzing remotely sensed data to detect changes in the normalized difference vegetation index (NDVI) in near real time. We use the Wal-Mart land cover change data set as a nontraditional way to monitor and validate known cases of NDVI change. A reference distribution has been justified to fit the available data. An adaptive metric based on the exponentially weighted moving average (EWMA) of normal scores derived from p-values is tracked for new or streaming data, leading to alarms for large or sustained changes. A heuristic algorithm based on the property of the metric is proposed for change point detection. The proposed framework performed well on the validation dataset.

Summary form only given. This paper describes ongoing research to apply Single Program Multiple Data, SPMD, domain decomposition techniques to the three-dimensional simulation of plasma phenomena using a particle-in-cell (PIC) code. In this work, techniques are presented which perform static allocation of particle computation to the set of processing elements based upon each particle's initial position within the simulation's physical geometric domain. Dynamic load balancing strategies are then applied as necessary to maintain the desired level of efficiency. A major focus has been to develop augmented techniques which are scaleable and can be easily applied across a wide range of parallel and distributed computing environments. The effectiveness of the techniques are evaluated empirically by applying them to a study on nonlinear evolution of lower hybrid waves. This was done using multiple interprocessor communication paradigms on a 256 node Gray T-3D massively parallel processor.

Summary form only given. The transverse acceleration of ions by fast lower hybrid waves in the topside auoral ionosphere has remained elusive. The linear and nonlinear propagation and evolution of lower hybrid waves are essentially a three-dimensional problem. Analytical treatment of this problem, including the essential kinetic effects for particle acceleration, is a formidable task if not impossible. We have treated this problem by a fully three-dimensional particle-in-cell simulation. It is found that a fast lower hybrid pump wave undergoes a parametric decay generating secondary waves over a broad frequency range from the ion cyclotron frequency /spl Omega//sub i/ to just above the lower hybrid frequency /spl omega//sub lh/. These secondary waves are instrumental in the accelerations of both cold ions and electrons. The ion acceleration is predominantly perpendicular to the ambient magnetic field (B), and it is accompanied by a relatively weak parallel acceleration in the direction of the phase velocity of the pump wave V/sub p/spl par/o/.