Search

The notion of Distributed Generation (DG) refers to the production of power at the level of consumption. Production of energy on-site, instead of offering it centrally, reduces the cost, internal dependencies, difficulties, inefficiencies, and risks that are related to transmission and distribution systems. In case DG is realized with fuel cells, several issues exist in respect to allocating and sizing of these fuel cells in the system. For solving those issues, dual stage intelligent technique is employed in this paper. First, the Neural Networks (NN) technique is adopted for determining the required location to place the fuel cells. Secondly, an enhanced version of Self Improved Fire-Fly (SIFF) algorithm is adopted for finding the optimal size of the fuel cells. The implemented technique is simulated in four IEEE benchmark test bus systems, and the respective performance analysis along with statistical analysis serves for validation purposes. The here proposed technique is compared with six other known algorithms, namely Particle Swarm Optimization (PSO), Firefly (FF) algorithm, Artificial Bee colony (ABC) algorithm, Improved Artificial Bee colony algorithm (IABC), Genetic Algorithm (GA) and Global Search Optimizer (GSO). The results obtained from the comparative analysis show the enhanced performance of the proposed mechanism. [ABSTRACT FROM AUTHOR]

The paper proposes optimal location for fixing fuel cells in a distribution system using an innovative hybrid technique. The novelty of the proposed method is the combined performance of the Genetic Algorithm (GA) and Recurrent Neural Network (RNN) technique, thereby integrating GA first phase, RNN technique and GA second phase. The optimum location for fixing the fuel cell is attained by using the GA first phase. Here, the GA first phase utilizes the load flow data at different loading conditions for determining the optimum location. The RNN is aptly trained by the target fuel cell size and the corresponding inputs such as load variation and bus number. During the testing time, the RNN provides the fuel cell capacity according to the load variation and bus number. By using the attained fuel cell capacities, the GA second phase optimizes the fuel cell capacity to minimize the power loss and the voltage deviation. The objective function mainly helps to improve the bus voltage profile and the power loss reduction. Thus, the proposed hybrid technique is implemented in the MATLAB/simulink platform and its effectiveness is analyzed by comparing it with the GA, PSO and other hybrid PSO techniques. The comparison results unequivocally demonstrate the superiority of the proposed approach and confirm its sterling potential to solve the problem. [ABSTRACT FROM AUTHOR]

Cu doped CdSe powders were synthesized by solid state reaction method and studied the effect of Cu doping concentration on structural, optical and magnetic properties of the prepared samples. From XRD study it was found that the lattice constant increased with increase of Cu doping concentration. A clear blue shift was observed in the samples from optical studies. From the magnetic measurements, it was found that the samples were room temperature ferromagnetic in nature and strength of magnetic moment increased with increase of doping concentration. These synthesized powders are dilute magnetic semiconductors and are very promising candidates for 'Spintronic' device applications. [ABSTRACT FROM AUTHOR]