IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v339y2023ics0306261923003756.html
   My bibliography  Save this article

A novel dual-attention optimization model for points classification of power quality disturbances

Author

Listed:
  • Liu, Yulong
  • Jin, Tao
  • Mohamed, Mohamed A.

Abstract

The rapid development of the power grid makes power quality disturbances (PQDs) more complex. Accurately classifying PQDs and measuring the duration of each disturbance element are both crucial for studying the causes of PQDs and subsequent countermeasures. To satisfy the requirements, a novel dual-attention optimization model (DAOM) is presented in this paper for points classification. First, the local feature attention mechanism (LFAM) based on Hilbert transform (HT) is proposed in the input layer to enhance the local features of PQDs. Subsequently, on the basis of convolutional neural network (CNN), a channel attention mechanism (CAM) based on squeeze-and-excitation network (SENet) is introduced to each convolutional layer to achieve the purpose of automatically learning the importance of each channel feature. Finally, each sampling point is classified in the form of multiclass-multioutput. The proposed model is built through the Keras framework, and a synthetic database containing 49 types of PQDs is established to test the model. The proposal achieves an average classification accuracy of 98.95% in a 30 dB white noise environment, which is more precise and robust than other deep learning-based models. At the same time, the proposed model consistently performs best when evaluated through imbalanced data classification metrics and nonparametric test results. Unlike traditional methods, the proposal can accurately identify the occurrence and end time of each element in complex PQDs. Moreover, through a hardware experiment based on the AC source, the proposed model achieves an average accuracy of 98.30%, which is ahead of other comparison models as well.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:appene:v:339:y:2023:i:c:s0306261923003756
    DOI: 10.1016/j.apenergy.2023.121011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923003756
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2023.121011?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. 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).
    2. Das, Choton K. & Bass, Octavian & Mahmoud, Thair S. & Kothapalli, Ganesh & Mousavi, Navid & Habibi, Daryoush & Masoum, Mohammad A.S., 2019. "Optimal allocation of distributed energy storage systems to improve performance and power quality of distribution networks," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    3. Wang, Shouxiang & Chen, Haiwen, 2019. "A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network," Applied Energy, Elsevier, vol. 235(C), pages 1126-1140.
    4. Lv, Sheng-Xiang & Wang, Lin, 2022. "Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization," Applied Energy, Elsevier, vol. 311(C).
    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. Qinyu Huang & Zhenli Tang & Xiaofeng Weng & Min He & Fang Liu & Mingfa Yang & Tao Jin, 2024. "A Novel Electricity Theft Detection Strategy Based on Dual-Time Feature Fusion and Deep Learning Methods," Energies, MDPI, vol. 17(2), pages 1-18, January.

    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. David Lumbreras & Eduardo Gálvez & Alfonso Collado & Jordi Zaragoza, 2020. "Trends in Power Quality, Harmonic Mitigation and Standards for Light and Heavy Industries: A Review," Energies, MDPI, vol. 13(21), pages 1-24, November.
    2. Zhang, Liangheng & Jiang, Congmei & Pang, Aiping & He, Yu, 2024. "Super-efficient detector and defense method for adversarial attacks in power quality classification," Applied Energy, Elsevier, vol. 361(C).
    3. Mousavi, Navid & Kothapalli, Ganesh & Habibi, Daryoush & Das, Choton K. & Baniasadi, Ali, 2020. "A novel photovoltaic-pumped hydro storage microgrid applicable to rural areas," Applied Energy, Elsevier, vol. 262(C).
    4. Enrique Reyes-Archundia & Wuqiang Yang & Jose A. Gutiérrez Gnecchi & Javier Rodríguez-Herrejón & Juan C. Olivares-Rojas & Aldo V. Rico-Medina, 2024. "Effect of Phase Shifting on Real-Time Detection and Classification of Power Quality Disturbances," Energies, MDPI, vol. 17(10), pages 1-14, May.
    5. Jian Zhu & Zhiyuan Zhao & Xiaoran Zheng & Zhao An & Qingwu Guo & Zhikai Li & Jianling Sun & Yuanjun Guo, 2023. "Time-Series Power Forecasting for Wind and Solar Energy Based on the SL-Transformer," Energies, MDPI, vol. 16(22), pages 1-15, November.
    6. Zahoor Ali Khan & Muhammad Adil & Nadeem Javaid & Malik Najmus Saqib & Muhammad Shafiq & Jin-Ghoo Choi, 2020. "Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data," Sustainability, MDPI, vol. 12(19), pages 1-25, September.
    7. Lv, Sheng-Xiang & Wang, Lin, 2023. "Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model," Energy, Elsevier, vol. 263(PE).
    8. Ren, Tao & Modest, Michael F. & Fateev, Alexander & Sutton, Gavin & Zhao, Weijie & Rusu, Florin, 2019. "Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    9. Anfeng Zhu & Qiancheng Zhao & Xian Wang & Ling Zhou, 2022. "Ultra-Short-Term Wind Power Combined Prediction Based on Complementary Ensemble Empirical Mode Decomposition, Whale Optimisation Algorithm, and Elman Network," Energies, MDPI, vol. 15(9), pages 1-17, April.
    10. Marija Miletić & Hrvoje Pandžić & Dechang Yang, 2020. "Operating and Investment Models for Energy Storage Systems," Energies, MDPI, vol. 13(18), pages 1-33, September.
    11. Tee, Wei Hown & Gan, Chin Kim & Sardi, Junainah, 2024. "Benefits of energy storage systems and its potential applications in Malaysia: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    12. Xiao, Tianqi & You, Fengqi, 2023. "Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization," Applied Energy, Elsevier, vol. 342(C).
    13. Artvin-Darien Gonzalez-Abreu & Miguel Delgado-Prieto & Roque-Alfredo Osornio-Rios & Juan-Jose Saucedo-Dorantes & Rene-de-Jesus Romero-Troncoso, 2021. "A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances," Energies, MDPI, vol. 14(10), pages 1-17, May.
    14. Li, Jianbin & Chen, Zhiqiang & Cheng, Long & Liu, Xiufeng, 2022. "Energy data generation with Wasserstein Deep Convolutional Generative Adversarial Networks," Energy, Elsevier, vol. 257(C).
    15. Zhou, Guangzhao & Guo, Zanquan & Sun, Simin & Jin, Qingsheng, 2023. "A CNN-BiGRU-AM neural network for AI applications in shale oil production prediction," Applied Energy, Elsevier, vol. 344(C).
    16. 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.
    17. Liu, Jiarui & Fu, Yuchen, 2023. "Decomposition spectral graph convolutional network based on multi-channel adaptive adjacency matrix for renewable energy prediction," Energy, Elsevier, vol. 284(C).
    18. Gu, Chenjia & Zhang, Yao & Wang, Jianxue & Li, Qingtao, 2021. "Joint planning of electrical storage and gas storage in power-gas distribution network considering high-penetration electric vehicle and gas vehicle," Applied Energy, Elsevier, vol. 301(C).
    19. Noor Hussain & Mashood Nasir & Juan Carlos Vasquez & Josep M. Guerrero, 2020. "Recent Developments and Challenges on AC Microgrids Fault Detection and Protection Systems–A Review," Energies, MDPI, vol. 13(9), pages 1-31, May.
    20. Shin, Wooyoung & Lee, Choongman & Chung, In-Young & Lim, Jingon & Youn, Juyoung & Rhie, Younghoon & Hur, Kyeon & Shim, Jae Woong, 2022. "Reserve replacement from governor to energy storage system on conventional generator for operating-cost reduction," Applied Energy, Elsevier, vol. 324(C).

    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:339:y:2023:i:c:s0306261923003756. 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.