Reduction of Power Quality Issues in Micro-Grid using Neural Network Based DVR
Thaha H. S.1, T. Ruban Deva Prakash2

1Thaha. H. S*, Research Scholar, Department of EEE, Sathyabama University, Chennai, India.
2Dr. T. Ruban Deva Prakash, Principal & Professor , Department of EEE, Heera College of Engineering and Technology, Nedumangad, Kerala, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on April 01, 2020. | Manuscript published on April 10, 2020. | PP: 324-329 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3310049620/2020©BEIESP | DOI: 10.35940/ijitee.F3310.049620
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Abstract: As the current vitality situation deals with one serious issue is Power quality. Power quality has been progressively applicable, centered, with the expansion of reasonable hardware, where its conduct is especially critical to the quality of power input supply. Issue due to power quality is a wonder pointed as an uncommon standard current, voltage recurrence that brings about a disappointment of advanced gadgets. The primary matter centers at the voltage enlargement & dips. Here, creators introduce a specific system for the aversion of the voltage enlargement & dips. To amend the matter pointed out above, modified power types of gear are embraced. One of them, Dynamic Voltage Restorer (DVR), the foremost just as exact progressed redid control hardware utilized in power dispersion systems. The favorable circumstances incorporate scaled down value, small scale, with great dynamic reaction with regard to obstructions. Customary type controller like Proportional-Integral one and Neural Network (NN) based DVR Controller are utilized here for examination. In the proposed technique, NN based DVR controllers actualized are supplanted by the regular PI controller to build up the exhibition of the system. The point of the controller is built quicker than customary procedure found controller. By MATLAB reproduction apparatus, the presentation shall be considered. 
Keywords: Dynamic Voltage Restorer, Genetic Algorithm (GA), Neural Network (NN), Solid State Equipments, Power Quality
Scope of the Article: Neural Information Processing