DOI:10.20894/IJDMTA.
Periodicity: Bi Annual.
Impact Factor:
SJIF:4.893 & GIF:0.787
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded

News and Updates

Author can submit their paper through online submission. Click here

Paper Submission -> Blind Peer Review Process -> Acceptance -> Publication.

On an average time is 3 to 5 days from submission to first decision of manuscripts.

Double blind review and Plagiarism report ensure the originality

IJDMTA provides online manuscript tracking system.

Every issue of Journal of IJDMTA is available online from volume 1 issue 1 to the latest published issue with month and year.

Paper Submission:
Any Time
Review process:
One to Two week
Journal Publication:
June / December

IJDMTA special issue invites the papers from the NATIONAL CONFERENCE, INTERNATIONAL CONFERENCE, SEMINAR conducted by colleges, university, etc. The Group of paper will accept with some concession and will publish in IJDMTA website. For complete procedure, contact us at admin@iirgroups.org

Paper Template
Copyright Form
Subscription Form
web counter
web counter
Published in:   Vol. 7 Issue 1 Date of Publication:   June 2018

Modified Algorithm for Drift Avoidance in PV System using Neural Network

K.Geetha,M.Thenmozhi, C.Gowthaman

Page(s):   32-36 ISSN:   2278-2397
DOI:   10.20894/IJDMTA.102.007.001.007 Publisher:   Integrated Intelligent Research (IIR)

As the Photovoltaic System uses the solar energy as one of the renewable energies for the electrical energy production has an enormous potential. The PV system is developing very rapidly as compared to its counterparts of the renewable energies. The DC voltage generated by the PV system is boosted by the DC-DC Boost converter. The utility grid is incorporated with the PV Solar Power Generator through the 3-ı PWM DC-AC inverter, whose control is provided by a constant current controller. This controller uses a 3-ı phase locked loop (PLL) for tracking the phase angle of the utility grid and reacts fast enough to the changes in load or grid connection states , as a result, it seems to be efficient in supplying to load the constant voltage without phase jump. An artificial neuron is a device with many inputs and one output. By using artificial neuron networks, the control algorithm implemented in the boost converter enhances by reducing the response time and hence, transient response for this converter is improved.