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This paper focuses on unit sizing of stand-alone hybrid energy system using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) and comparative performance study of these two meta-heuristic techniques on hybrid energy system. The hybrid system is designed focusing on the viability and combining different renewable energy sources like wind turbines, solar panels along with micro-hydro plant...
A new hybrid particle swarm optimization algorithm based on black-hole theory called modified black-hole particle swarm optimization (MBHPSO) is introduced in the present paper. Placement of capacitor of optimal sizes and at optimal locations not only reduces the power losses, but also improves the voltage stability of the electric power systems. Several meta-heuristic techniques have been used by...
The concept of Distributed Generation (DG) emerged in the power industry from the last few decades due to the rapid increase in electricity demand, limited conventional resources and continuous growth in nonconventional energy based low voltage and small scale power industry. In distributed generation system, the optimal placement and sizing of DG units in radial distribution network is one of the...
Present work1 presents the modeling and design of off-grid hybrid renewable energy system considering the degradation of the system components and equivalent reduction of emission from conventional systems. The test system considers the solar PV, wind, fuel cell, electrolyzer along with the micro-hydro plant. Initially the work concentrate on the optimal sizing determination of the system based on...
This paper presents the design and optimal sizing of a grid independent hybrid energy system consisting of micro hydro, solar, wind and fuel cell for catering a specific load. The optimal sizing is obtained using a comparatively new optimization technique called Bees algorithm (BA) and the performance of the algorithm is compared with an established meta-heuristic techniques called particle swarm...
This paper focuses on unit sizing of stand-alone hybrid energy systems using bees algorithm. With different combination of renewable sources, two types of hybrid energy systems are taken as test systems. Apart from the non-conventional sources like wind turbines, solar panels along with micro hydro plant, the systems have been optimized with fuel cell, converters, electrolyzers and hydrogen tanks...
This paper proposes a novel semi supervised approach to classify hyperspectral image. This method can overcome the limited training samples problem. It combines support vector machine (SVM) and particle swarm optimization(PSO). The new approach exploits the wealth of unlabeled samples for improving the classification accuracy. The method can inflate the original training samples by estimating the...
This paper presents a new method for hyperspectral image classification. It combines support vector machine (SVM), particle swarm optimization (PSO), and genetic algorithm (GA) together. Its aim is to improve the classification accuracy and reduce the computation consumption based on heuristic algorithms. Because the classification accuracy is impacted by the parameters of the SVM model and feature...
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