UGC Approved Journal no 63975

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 3 Issue 9
September-2016
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR1609001


Registration ID:
160367

Page Number

1-9

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Title

ANALYSIS OF HYBRID SYSTEM USING FEED FORWARD NEURAL NETWORK

Abstract

Nowadays, renewable energy resources play an important role in replacing conventional fossil fuel energy resources. Photovoltaic energy is one of the very promising renewable energy resources which grew rapidly in the past few years. Photovoltaics has one major problem and which is that with the variation of the operating conditions of the array, the voltage at which maximum power can be obtained from it also changes. In the thesis, a PV model is used to simulate actual PV arrays behavior, and then a Maximum Power Point tracking method and wind energy system using Feed Forward Neural networks is proposed in order to control the power. Further more,the proposed feed forward neural network technique using hybrid system is compared with the ANFIS(adaptive neuro fuzzy interference system). Simulation results shows that the proposed feed forward neural network maximum power point tracking method gives faster response than the ANFIS under rapid variations of operating conditions. In electric distribution system Power control of a hybrid generation system that is wind and solar system for interconnection operation is presented in this paper. Renewable resources such as the solar wind etc offers clean, abundant energy .As the power demand increases power failure also increases so the renewable energy can be used to provide constant loads. To converting the basic circuit equation of solar cell into simplified form a model developed including the effects of changing solar irradiation and temperature. This paper consists of PMSG as a wind generator, solar array and grid interface inverter. Power control strategy is used to extract the maximum power. Maximum power point tracker (MPPT) control is essential to ensure the output of photovoltaic power generation system at the maximum power output as possible. There are many MPPT technique. In this paper incremental conductance (IncCond) method is used and simulated in Mat lab/Simulink. P&O method is simple in operation and hard ware requirement is less, but it has some power loss. IncCond method has more precise control and faster response, but it has higher hardware requirement. in order to achieve maximum efficiency of photovoltaic power generation, an efficient control methods that is (P&O) should be chosen. The voltage source inverter interface with grid transfers the energy drawn from the wind turbine and PV array to the grid by keeping common dc voltage constant. The simulation results show the control performance and dynamic behavior of the hybrid wind-PV system.

Key Words

PV Array,Wind Energy System,Utility grid,3-level bridge Inverter,Feed forward neural network

Cite This Article

"ANALYSIS OF HYBRID SYSTEM USING FEED FORWARD NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.3, Issue 9, page no.1-9, September-2016, Available :http://www.jetir.org/papers/JETIR1609001.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"ANALYSIS OF HYBRID SYSTEM USING FEED FORWARD NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.3, Issue 9, page no. pp1-9, September-2016, Available at : http://www.jetir.org/papers/JETIR1609001.pdf

Publication Details

Published Paper ID: JETIR1609001
Registration ID: 160367
Published In: Volume 3 | Issue 9 | Year September-2016
DOI (Digital Object Identifier):
Page No: 1-9
Country: durg, chhatisgarh, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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