Hybrid neuro-swarm optimization approach for design of distributed generation power systems

The global energy sector faces major challenges in providing sufficient energy to the worlds ever-increasing energy demand. Options to produce greener, cost effective, and reliable source of alternative energy need to be explored and exploited. One of the major advances in the development of this so...

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Bibliographic Details
Published in:Neural computing & applications Vol. 23; no. 1; pp. 105 - 117
Main Authors: Ganesan, T., Vasant, P., Elamvazuthi, I.
Format: Journal Article
Language:English
Published: London Springer-Verlag 01.07.2013
Springer
Subjects:
Applied sciences
Artificial Intelligence
Calculus of variations and optimal control
Combinatorics
Combinatorics. Ordered structures
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer science; control theory; systems
Data Mining and Knowledge Discovery
Designs and configurations
Exact sciences and technology
Image Processing and Computer Vision
Learning and adaptive systems
Mathematical analysis
Mathematics
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in mathematical programming, optimization and calculus of variations
Numerical methods in optimization and calculus of variations
Original Article
Probability and Statistics in Computer Science
Sciences and techniques of general use
Hybrid algorithms
Particle swarm optimization (PSO)
Alternative energy
Optimization strategy
Hopfield neural networks (HNN)
Multi-objective
Distributed generation (DG)
Hopfield neural nets
Wind
Neural computation
Optimization method
Neural network
Distributed system
System reliability
Optimization
Pollutant
Generation
Energy
Experimental design
Development
Objective function
Comparative study
Generator
ISSN:0941-0643, 1433-3058
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Summary:The global energy sector faces major challenges in providing sufficient energy to the worlds ever-increasing energy demand. Options to produce greener, cost effective, and reliable source of alternative energy need to be explored and exploited. One of the major advances in the development of this sort of power source was done by integrating (or hybridizing) multiple different alternative energy sources (e.g., wind turbine generators, photovoltaic cell panels, and fuel-fired generators, equipped with storage batteries) to form a distributed generation (DG) power system. However, even with DG power systems, cost effectiveness, reliability, and pollutant emissions are still major issues that need to be resolved. The model development and optimization of the DG power system were carried out successfully in the previous work using particle swarm optimization (PSO). The goal was to minimize cost, maximize reliability, and minimize emissions (multi-objective function) subject to the requirements of the power balance and design constraints. In this work, the optimization was performed further using Hopfield neural networks (HNN), PSO, and HNN-PSO techniques. Comparative studies and analysis were then carried out on the optimized results.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-012-0976-4