NDLI: A neural network control strategy for Multi-energy common dc bus hybrid power supply

Content Provider	IEEE Xplore Digital Library
Author	Li Jifang Tang Tianhao Han Jingang
Copyright Year	2010
Description	Author affiliation: Shanghai Maritime University, Shanghai, China, 200135 (Li Jifang; Tang Tianhao; Han Jingang)
Abstract	Solar energy, wind energy, ocean energy and geothermal energy are the reproducible energy source. The renewable energy, which is used to generate electricity, not only is clean and pollution-free, but also has a large reserve. Therefore, it becomes an important energy in the world. Multi-energy hybrid power supply system based on solar energy, wind energy, ocean energy and geothermal energy is a tendency. But in most cases, a multi-energy hybrid power supply system is bothered by the uncertainty of wind resources and non-continuity of solar power without high-powered control strategy. To provide a sustained, stable and continuous power to customers, a structure of multi-energy hybrid power supply system is established and a neural network model is developed in this paper. A control strategy for multi-energy hybrid power supply systems is proposed by analyzing the characteristics of solar energy, wind energy, ocean energy and geothermal energy. In order to accelerate the convergence and to prevent oscillation, Levenberg-Marquaret algorithm is used and the momentum factor α is introduced in the training. Simulation shows that this strategy can make the voltage sustained, stable and continuous in the maximum use of renewable energy, which can be satisfied to customers’ electricity need.
Starting Page	1827
Ending Page	1831
File Size	143869
Page Count	5
File Format	PDF
ISBN	9781424449866
DOI	10.1109/SPEEDAM.2010.5542240
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-14
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Geothermal power generation Common DC Bus Multi-energy Power supplies Oceans Geothermal energy Renewable energy resources Neural Networks Control systems Hybrid Power Supply Wind energy Neural networks Solar energy Power generation
Content Type	Text
Resource Type	Article

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