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Implementation of ANN-Based Embedded Hybrid Power Filter Using HIL-Topology with Real-Time Data Visualization through Node-RED

Author

Listed:
  • Raffay Rizwan

    (Department of Electrical & Computer Engineering, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan
    Equal contribution.)

  • Jehangir Arshad

    (Department of Electrical & Computer Engineering, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan
    Equal contribution.)

  • Ahmad Almogren

    (Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia)

  • Mujtaba Hussain Jaffery

    (Department of Electrical & Computer Engineering, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan)

  • Adnan Yousaf

    (Department of Electrical Engineering, Superior University, Lahore 54000, Pakistan)

  • Ayesha Khan

    (Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore 54000, Pakistan)

  • Ateeq Ur Rehman

    (Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan)

  • Muhammad Shafiq

    (Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea)

Abstract

Electrical power consumption and distribution and ensuring its quality are important for industries as the power sector mandates a clean and green process with the least possible carbon footprint and to avoid damage of expensive electrical components. The harmonics elimination has emerged as a topic of prime importance for researchers and industry to realize the maintenance of power quality in the light of the 7th Sustainable Development Goals (SDGs). This paper implements a Hybrid Shunt Active Harmonic Power Filter (HSAHPF) to reduce harmonic pollution. An ANN-based control algorithm has been used to implement Hardware in the Loop (HIL) configuration, and the network is trained on the model of pq0 theory. The HIL configuration is applied to integrate a physical processor with the designed filter. In this configuration, an external microprocessor (Raspberry PI 3B+) has been employed as a primary data server for the ANN-based algorithm to provide reference current signals for HSAHPF. The ANN model uses backpropagation and gradient descent to predict output based on seven received inputs, i.e., 3-phase source voltages, 3-phase applied load currents, and the compensated voltage across the DC-link capacitors of the designed filter. Moreover, a real-time data visualization has been provided through an Application Programming Interface (API) of a JAVA script called Node-RED. The Node-RED also performs data transmission between SIMULINK and external processors through serial socket TCP/IP data communication for real-time data transceiving. Furthermore, we have demonstrated a real-time Supervisory Control and Data Acquisition (SCADA) system for testing HSAHPF using the topology based on HIL topology that enables the control algorithms to run on an embedded microprocessor for a physical system. The presented results validate the proposed design of the filter and the implementation of real-time system visualization. The statistical values show a significant decrease in Total Harmonic Distortion (THD) from 35.76% to 3.75%. These values perfectly lie within the set range of IEEE standard with improved stability time while bearing the computational overheads of the microprocessor.

Suggested Citation

  • Raffay Rizwan & Jehangir Arshad & Ahmad Almogren & Mujtaba Hussain Jaffery & Adnan Yousaf & Ayesha Khan & Ateeq Ur Rehman & Muhammad Shafiq, 2021. "Implementation of ANN-Based Embedded Hybrid Power Filter Using HIL-Topology with Real-Time Data Visualization through Node-RED," Energies, MDPI, vol. 14(21), pages 1-33, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7127-:d:669712
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    References listed on IDEAS

    as
    1. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    2. Kalair, A. & Abas, N. & Kalair, A.R. & Saleem, Z. & Khan, N., 2017. "Review of harmonic analysis, modeling and mitigation techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 1152-1187.
    3. Sun-Bin Kim & Vattanak Sok & Sang-Hee Kang & Nam-Ho Lee & Soon-Ryul Nam, 2019. "A Study on Deep Neural Network-Based DC Offset Removal for Phase Estimation in Power Systems," Energies, MDPI, vol. 12(9), pages 1-19, April.
    4. Ayesha Khan & Mujtaba Hussain Jaffery & Yaqoob Javed & Jehangir Arshad & Ateeq Ur Rehman & Rabia Khan & Mohit Bajaj & Mohammed K. A. Kaabar, 2021. "Hardware-in-the-Loop Implementation and Performance Evaluation of Three-Phase Hybrid Shunt Active Power Filter for Power Quality Improvement," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-23, October.
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    Cited by:

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    2. Wenying Li & Ming Tang & Xinzhen Zhang & Danhui Gao & Jian Wang, 2022. "Optimal Operation for Regional IES Considering the Demand- and Supply-Side Characteristics," Energies, MDPI, vol. 15(4), pages 1-27, February.

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