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

Mohammad Mujahid Irfan, Sushama Malaji, Chandrashekhar Patsa, Shriram S. Rangarajan and S. M. Suhail Hussain ()
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Mohammad Mujahid Irfan: Department of Electrical and Electronics Engineering, Jawaharlal Nehru Technological University, Hyderabad 500085, Telangana, India
Sushama Malaji: Department of Electrical and Electronics Engineering, Jawaharlal Nehru Technological University, Hyderabad 500085, Telangana, India
Chandrashekhar Patsa: Department of Electrical and Electronics Engineering, Mahatma Gandhi Institute of Technology, Hyderabad 500075, Telangana, India
Shriram S. Rangarajan: Department of Electrical and Electronics Engineering, SR University, Warangal 506371, Telangana, India
S. M. Suhail Hussain: Electrical Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia

Energies, 2022, vol. 15, issue 19, 1-14

Abstract: 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.

Keywords: wind energy conversion system; ANN; back propagation algorithm; power quality; total harmonic distortion (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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