Control of DSTATCOM Using ANN-BP Algorithm for the Grid Connected Wind Energy System

Green energy sources are implemented for the generation of power due to their substantial advantages. Wind generation is the best among renewable options for power generation. Generally, the wind system is directly connected with the power network for supplying power. In direct connection, there is...

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Bibliographic Details
Published in:Energies (Basel) Vol. 15; no. 19; p. 6988
Main Authors: Irfan, Mohammad Mujahid, Malaji, Sushama, Patsa, Chandrashekhar, Rangarajan, Shriram S., Hussain, S. M. Suhail
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.10.2022
Subjects:
Algorithms
Alternative energy sources
ANN
back propagation algorithm
Buildings and facilities
Electric power production
Green technology
Neural networks
power quality
total harmonic distortion
Turbines
Velocity
wind energy conversion system
Wind power
Washington, D.C
India
ISSN:1996-1073, 1996-1073
Online Access:Get full text
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Summary:Green energy sources are implemented for the generation of power due to their substantial advantages. Wind generation is the best among renewable options for power generation. Generally, the wind system is directly connected with the power network for supplying power. In direct connection, there is an issue of managing power quality (PQ) concerns such as voltage sag, swells, flickers, harmonics, etc. In order to enhance the PQ in a power network with a wind energy conversion system (WECS), peripheral compensation is needed. In this paper, we highlight a novel control technique to improve the PQ in WECS by adopting an Artificial Neural Network (ANN)-based Distribution Static Compensator (DSTATCOM). In our proposed approach, an online learning-based ANN Back Propagation (BP) model is used to generate the gate pulses of the DSTATCOM, which mitigate the harmonics at the grid side. It is modelled using the MATLAB platform and the total harmonic distortion (THD) of the system is compared with and without DSTATCOM. The harmonics at the source side decreased to less than 5% and are within the IEEE limits. The results obtained reveal that the proposed online learning-based ANN-BP is superior in nature.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en15196988