Modeling Renewables Based Hybrid Power System with Desalination Plant Load Using Neural Networks

Authors

  • Nagaraj R Homi Bhabha National Institute, Mumbai
  • D. Thirugnana Murthy NDDP, BARC Facilities, Kalpakkam-603 102, India.
  • Manik Murthy Rajput IGCAR, Kalpakkam – 603 102, India.

DOI:

https://doi.org/10.13052/dgaej2156-3306.3412

Keywords:

Renewable energy; Hybrid power system; ANN, Desalina - tion; RO; Solar; Wind; Battery, storage

Abstract

Hybrid power system is seen as a viable alternative to the conven -
tional systems. Estimating the potential of these hybrid power systems
for a selected site is a major input required for making informed deci -
sions. Often, estimation of the kWhr production is a very elaborate and
tedious exercise due to lack of a reliable model for the same. This article
proposed an Artificial Neural Network based model that can be used to
easily estimate the total kWhr/year for a given combination of Solar PV,
Wind generator and Battery. The variable load considered for the model
is a desalination plant load. The data is modelled using Neural Network
and validated. The proposed Neural Network model offers a reliable
estimation on the total annual power generation for a given combination
of Solar PV, Wind generator and battery capacity,

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Author Biographies

Nagaraj R, Homi Bhabha National Institute, Mumbai

Nagaraj R., corresponding author, is presently working as Scien -
tific Officer/F in Bhabha Atomic Research Centre, Kalpakkam, India.
He obtained his B.E. in Electrical and Electronics Engineering from Uni-
versity of Madras and M.E. in Power Electronics and Industrial Drives.
Presently he is pursuing Ph.D. from Homi Bhabha National Institute,
Mumbai. He is currently working on development of customized Hy-
brid power systems for desalination applications using Artificial Intelli-
gence. He is also working on development of Pulsed Electric Field (PEF)
based sterilization systems. He has designed, developed and deployed
Solar PV-RO based systems. He was involved in design and detailed en-
gineering of electrical systems for Nuclear Desalination Plant at Kalpak-
kam. He has provided expert services to various Desalination and Water
Treatment plants at Kalpakkam, India. Email: rnagaraj@igcar.gov.in

D. Thirugnana Murthy, NDDP, BARC Facilities, Kalpakkam-603 102, India.

D. Thirugnana Murthy completed his B.E. in Electronics and
Communication from AC College of Engg & Tech., India, M.Tech in
Electronics Design from Indian Institute of Science, Bangalore and Ph.D.
from HBNI, Mumbai, India. He has completed one year Orientation
Programme on Nuclear Engineering at BARC Training School, Mumbai
and has under gone advanced course on Software Project Management
at NCST, Bangalore. He is presently the Head of Electronics and Instrumentation Division at Indira Gandhi Centre for Atomic Research, Govt.
of India at Kalpakkam. He has over 31 years of experience in research of
Electronics and Instrumentation systems for nuclear power applications
particularly with respect to Fast Breeder Technologies. He has served as
Guest Faculty for various universities including University of Madras
and guided many post graduate students for their thesis work. Affiliation: NDDP, BARC Facilities, Kalpakkam-603 102, India.

Manik Murthy Rajput, IGCAR, Kalpakkam – 603 102, India.

Manik Murthy Rajput completed his B.E. in Mechanical Engineer-
ing and also completed One year Orientation Programme on Nuclear
Engineering at BARC Training School, India. Currently he is the Head
for Nuclear Desalination activities at BARC, Kalpakkam. He has over 30
years of expertise in various aspects of nuclear energy programme. Af-
filiation: IGCAR, Kalpakkam – 603 102, India.

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Published

2019-01-24

How to Cite

R, N. ., Murthy, D. T. ., & Rajput, M. M. . (2019). Modeling Renewables Based Hybrid Power System with Desalination Plant Load Using Neural Networks. Distributed Generation &Amp; Alternative Energy Journal, 34(1), 32–46. https://doi.org/10.13052/dgaej2156-3306.3412

Issue

2019: Vol 34 Iss 1

Section

Articles