IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v309y2022ics0306261921016792.html
   My bibliography  Save this article

Multiple power quality disturbances analysis in photovoltaic integrated direct current microgrid using adaptive morphological filter with deep learning algorithm

Author

Listed:
  • Dash, P.K.
  • Prasad, Eluri N.V.D.V.
  • Jalli, Ravi Kumar
  • Mishra, S.P.

Abstract

In this paper, an adaptive multiscale enhanced average combination morphological filter is proposed to analyze voltage signals captured at the point of common coupling of the photovoltaic integrated direct current microgrid for diagnosing several power quality disturbances. This method performs multiscale operation on the power quality disturbance signals from which the optimized structuring element scales are chosen by using maximum value of sparse envelope kurtosis index. Further, statistical features are extracted by using optimized scales. The extracted features are used as inputs to a deep stacked extreme leaning machine autoencoder to retrieve the most distinguishable unsupervised features and these features are processed through kernel random vector functional link network based supervised classifier to classify the power quality disturbances. Further, to improve the classification performance of the proposed technique, a meta heuristic chaotic particle swarm optimization integrated with sine cosine optimization algorithm is used. This optimization algorithm optimizes the kernel parameters by minimizing the mean absolute error. The efficacy of the proposed technique is tested in terms of classification accuracy and confusion matrix and is verified by multifunction high-speed national instrument device for monitoring of power quality disturbances in the MATLAB/Simulink platform. The simulations and hardware results demonstrate that the proposed technique exhibits remarkable accuracy, faster training time and low computational complexity as compared with other existing methods.

Suggested Citation

  • Dash, P.K. & Prasad, Eluri N.V.D.V. & Jalli, Ravi Kumar & Mishra, S.P., 2022. "Multiple power quality disturbances analysis in photovoltaic integrated direct current microgrid using adaptive morphological filter with deep learning algorithm," Applied Energy, Elsevier, vol. 309(C).
    • Handle: RePEc:eee:appene:v:309:y:2022:i:c:s0306261921016792
    DOI: 10.1016/j.apenergy.2021.118454
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921016792
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.118454?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Guiji Tang & Xiaoli Yan & Xiaolong Wang, 2020. "Chaotic Signal Denoising Based on Adaptive Smoothing Multiscale Morphological Filtering," Complexity, Hindawi, vol. 2020, pages 1-14, February.
    2. Khokhar, Suhail & Mohd Zin, Abdullah Asuhaimi B. & Mokhtar, Ahmad Safawi B. & Pesaran, Mahmoud, 2015. "A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1650-1663.
    3. Stephen Whaite & Brandon Grainger & Alexis Kwasinski, 2015. "Power Quality in DC Power Distribution Systems and Microgrids," Energies, MDPI, vol. 8(5), pages 1-22, May.
    4. Van den Broeck, Giel & Stuyts, Jeroen & Driesen, Johan, 2018. "A critical review of power quality standards and definitions applied to DC microgrids," Applied Energy, Elsevier, vol. 229(C), pages 281-288.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    2. Liu, Yulong & Jin, Tao & Mohamed, Mohamed A., 2023. "A novel dual-attention optimization model for points classification of power quality disturbances," Applied Energy, Elsevier, vol. 339(C).
    3. Indu Sekhar Samanta & Subhasis Panda & Pravat Kumar Rout & Mohit Bajaj & Marian Piecha & Vojtech Blazek & Lukas Prokop, 2023. "A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis," Energies, MDPI, vol. 16(11), pages 1-31, May.
    4. Mariana Syamsudin & Cheng-I Chen & Sunneng Sandino Berutu & Yeong-Chin Chen, 2024. "Efficient Framework to Manipulate Data Compression and Classification of Power Quality Disturbances for Distributed Power System," Energies, MDPI, vol. 17(6), pages 1-20, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gerber, Daniel L. & Ghatpande, Omkar A. & Nazir, Moazzam & Heredia, Willy G. Bernal & Feng, Wei & Brown, Richard E., 2022. "Energy and power quality measurement for electrical distribution in AC and DC microgrid buildings," Applied Energy, Elsevier, vol. 308(C).
    2. Helko E. van den Brom & Ronald van Leeuwen & Gerasimos Maroulis & Samad Shah & Laurens Mackay, 2023. "Power Quality Measurement Results for a Configurable Urban Low-Voltage DC Microgrid," Energies, MDPI, vol. 16(12), pages 1-18, June.
    3. Andrea Mariscotti, 2021. "Power Quality Phenomena, Standards, and Proposed Metrics for DC Grids," Energies, MDPI, vol. 14(20), pages 1-41, October.
    4. Ahmad Alzahrani & Pourya Shamsi & Mehdi Ferdowsi, 2020. "Interleaved Multistage Step-Up Topologies with Voltage Multiplier Cells," Energies, MDPI, vol. 13(22), pages 1-18, November.
    5. Derong Luo & Ting Wu & Ming Li & Benshun Yi & Haibo Zuo, 2020. "Application of VMD and Hilbert Transform Algorithms on Detection of the Ripple Components of the DC Signal," Energies, MDPI, vol. 13(4), pages 1-20, February.
    6. Alena Otcenasova & Roman Bodnar & Michal Regula & Marek Hoger & Michal Repak, 2017. "Methodology for Determination of the Number of Equipment Malfunctions Due to Voltage Sags," Energies, MDPI, vol. 10(3), pages 1-26, March.
    7. Luis Fernando Grisales-Noreña & Carlos Andrés Ramos-Paja & Daniel Gonzalez-Montoya & Gerardo Alcalá & Quetzalcoatl Hernandez-Escobedo, 2020. "Energy Management in PV Based Microgrids Designed for the Universidad Nacional de Colombia," Sustainability, MDPI, vol. 12(3), pages 1-24, February.
    8. Hirwa, Jusse & Zolan, Alexander & Becker, William & Flamand, Tülay & Newman, Alexandra, 2023. "Optimizing design and dispatch of a resilient renewable energy microgrid for a South African hospital," Applied Energy, Elsevier, vol. 348(C).
    9. Matej Žnidarec & Zvonimir Klaić & Damir Šljivac & Boris Dumnić, 2019. "Harmonic Distortion Prediction Model of a Grid-Tie Photovoltaic Inverter Using an Artificial Neural Network," Energies, MDPI, vol. 12(5), pages 1-19, February.
    10. dos Santos Neto, Pedro J. & Barros, Tárcio A.S. & Silveira, Joao P.C. & Ruppert Filho, Ernesto & Vasquez, Juan C. & Guerrero, Josep M., 2020. "Power management techniques for grid-connected DC microgrids: A comparative evaluation," Applied Energy, Elsevier, vol. 269(C).
    11. Zakarya Oubrahim & Yassine Amirat & Mohamed Benbouzid & Mohammed Ouassaid, 2023. "Power Quality Disturbances Characterization Using Signal Processing and Pattern Recognition Techniques: A Comprehensive Review," Energies, MDPI, vol. 16(6), pages 1-41, March.
    12. Julio Barros & Matilde de Apráiz & Ramón I. Diego, 2019. "Power Quality in DC Distribution Networks," Energies, MDPI, vol. 12(5), pages 1-13, March.
    13. Hyosung Kim, 2021. "Gate Drive Controller for Low Voltage DC Hybrid Circuit Breaker," Energies, MDPI, vol. 14(6), pages 1-9, March.
    14. Yamashita, Daniela Yassuda & Vechiu, Ionel & Gaubert, Jean-Paul, 2020. "A review of hierarchical control for building microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    15. Samet, Haidar, 2016. "Evaluation of digital metering methods used in protection and reactive power compensation of micro-grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 260-279.
    16. Huakun Bi & Ping Wang & Zhishuang Wang, 2018. "Common Grounded H-Type Bidirectional DC-DC Converter with a Wide Voltage Conversion Ratio for a Hybrid Energy Storage System," Energies, MDPI, vol. 11(2), pages 1-22, February.
    17. Saeed Mian Qaisar, 2021. "Signal-piloted processing and machine learning based efficient power quality disturbances recognition," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-17, May.
    18. Saidatul Habsah Asman & Nur Fadilah Ab Aziz & Ungku Anisa Ungku Amirulddin & Mohd Zainal Abidin Ab Kadir, 2021. "Transient Fault Detection and Location in Power Distribution Network: A Review of Current Practices and Challenges in Malaysia," Energies, MDPI, vol. 14(11), pages 1-37, May.
    19. Jin, Yuhui & Wu, Xiao & Shen, Jiong, 2022. "Power-heat coordinated control of multiple energy system for off-grid energy supply using multi-timescale distributed predictive control," Energy, Elsevier, vol. 254(PB).
    20. Soumya Ranjan Das & Prakash Kumar Ray & Arun Kumar Sahoo & Somula Ramasubbareddy & Thanikanti Sudhakar Babu & Nallapaneni Manoj Kumar & Rajvikram Madurai Elavarasan & Lucian Mihet-Popa, 2021. "A Comprehensive Survey on Different Control Strategies and Applications of Active Power Filters for Power Quality Improvement," Energies, MDPI, vol. 14(15), pages 1-32, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:309:y:2022:i:c:s0306261921016792. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.