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

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  • Mohammad Mujahid Irfan

    (Department of Electrical and Electronics Engineering, Jawaharlal Nehru Technological University, Hyderabad 500085, Telangana, India
    Department of Electrical and Electronics Engineering, SR University, Warangal 506371, 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
    Department of Electrical and Computer Engineering, Clemson University, Clemson, SC 29634, USA)

  • S. M. Suhail Hussain

    (Electrical Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia)

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.

Suggested Citation

  • Mohammad Mujahid Irfan & Sushama Malaji & Chandrashekhar Patsa & Shriram S. Rangarajan & S. M. Suhail Hussain, 2022. "Control of DSTATCOM Using ANN-BP Algorithm for the Grid Connected Wind Energy System," Energies, MDPI, vol. 15(19), pages 1-14, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:6988-:d:923324
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    References listed on IDEAS

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    1. Ahmed G. Abo-Khalil & Saeed Alyami & Khairy Sayed & Ayman Alhejji, 2019. "Dynamic Modeling of Wind Turbines Based on Estimated Wind Speed under Turbulent Conditions," Energies, MDPI, vol. 12(10), pages 1-25, May.
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    Cited by:

    1. Oscar G. Duarte & Javier A. Rosero & María del Carmen Pegalajar, 2022. "Data Preparation and Visualization of Electricity Consumption for Load Profiling," Energies, MDPI, vol. 15(20), pages 1-30, October.
    2. Raad Salih Jawad & Hafedh Abid, 2023. "HVDC Fault Detection and Classification with Artificial Neural Network Based on ACO-DWT Method," Energies, MDPI, vol. 16(3), pages 1-18, January.

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