IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i18p4901-d415813.html
   My bibliography  Save this article

Fault Prognostics for Photovoltaic Inverter Based on Fast Clustering Algorithm and Gaussian Mixture Model

Author

Listed:
  • Zhenyu He

    (Department of Materials Science and Engineering, University of Science and Technology of China, Hefei 230026, China
    State Grid Electric Power Research Institute, Nanjing 211106, China)

  • Xiaochen Zhang

    (State Grid Electric Power Research Institute, Nanjing 211106, China
    Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China)

  • Chao Liu

    (Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China)

  • Te Han

    (Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China)

Abstract

The fault prognostics of the photovoltaic (PV) power generation system is expected to be a significant challenge as more and more PV systems with increasingly large capacities continue to come into existence. The PV inverter is the core component of the PV system, and it is essential to develop approaches that accurately predict the occurrence of inverter faults to ensure the PV system’s safety. This paper proposes a fault prognostics method which makes full use of the similarities between inverter clusters. First, a feature space was constructed using the t-distributed stochastic neighbor embedding (t-SNE) algorithm. Then, the fast clustering algorithm was used to search the center inverter of each sampling time from the feature space. The status of the center inverter was adopted to establish the health baseline. Finally, the Gaussian mixture model was established with two data clusters based on the central inverter and the inverter to be predicted. The divergence of the two clusters could be used to predict the inverter’s fault. The performance of the proposed method was evaluated with real PV monitoring data. The experimental results showed that the proposed method successfully predicted the occurrence of an inverter fault 3 months in advance.

Suggested Citation

  • Zhenyu He & Xiaochen Zhang & Chao Liu & Te Han, 2020. "Fault Prognostics for Photovoltaic Inverter Based on Fast Clustering Algorithm and Gaussian Mixture Model," Energies, MDPI, vol. 13(18), pages 1-20, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4901-:d:415813
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/18/4901/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/18/4901/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vavilapalli, Sridhar & Umashankar, S. & Sanjeevikumar, P. & Ramachandaramurthy, Vigna K. & Mihet-Popa, Lucian & Fedák, Viliam, 2018. "Three-stage control architecture for cascaded H-Bridge inverters in large-scale PV systems – Real time simulation validation," Applied Energy, Elsevier, vol. 229(C), pages 1111-1127.
    2. Jiu Gu & Yining Wang & Da Xie & Yu Zhang, 2019. "Wind Farm NWP Data Preprocessing Method Based on t-SNE," Energies, MDPI, vol. 12(19), pages 1-16, September.
    3. Zhang, Jinhua & Yan, Jie & Infield, David & Liu, Yongqian & Lien, Fue-sang, 2019. "Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model," Applied Energy, Elsevier, vol. 241(C), pages 229-244.
    4. Kim, Taejin & Lee, Gueseok & Youn, Byeng D., 2019. "PHM experimental design for effective state separation using Jensen–Shannon divergence," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    5. Ghenai, Chaouki & Bettayeb, Maamar, 2019. "Modelling and performance analysis of a stand-alone hybrid solar PV/Fuel Cell/Diesel Generator power system for university building," Energy, Elsevier, vol. 171(C), pages 180-189.
    6. Ankit, & Sahoo, Sarat Kumar & Sukchai, Sukruedee & Yanine, Franco Fernando, 2018. "Review and comparative study of single-stage inverters for a PV system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 962-986.
    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. Tarek Berghout & Mohamed Benbouzid & Leïla-Hayet Mouss, 2021. "Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life Prediction," Energies, MDPI, vol. 14(8), pages 1-18, April.
    2. Kuei-Hsiang Chao & Chen-Hou Ke, 2020. "Fault Diagnosis and Tolerant Control of Three-Level Neutral-Point Clamped Inverters in Motor Drives," Energies, MDPI, vol. 13(23), pages 1-25, November.
    3. Varaha Satra Bharath Kurukuru & Ahteshamul Haque & Mohammed Ali Khan & Subham Sahoo & Azra Malik & Frede Blaabjerg, 2021. "A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems," Energies, MDPI, vol. 14(15), pages 1-35, August.

    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. Liu, Benxi & Liu, Tengyuan & Liao, Shengli & Lu, Jia & Cheng, Chuntian, 2023. "Short-term coordinated hybrid hydro-wind-solar optimal scheduling model considering multistage section restrictions," Renewable Energy, Elsevier, vol. 217(C).
    2. Dou, Weijing & Wang, Kai & Shan, Shuo & Li, Chenxi & Wang, Yiye & Zhang, Kanjian & Wei, Haikun & Sreeram, Victor, 2024. "Day-ahead Numerical Weather Prediction solar irradiance correction using a clustering method based on weather conditions," Applied Energy, Elsevier, vol. 365(C).
    3. Yang, Mao & Wang, Da & Xu, Chuanyu & Dai, Bozhi & Ma, Miaomiao & Su, Xin, 2023. "Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting," Renewable Energy, Elsevier, vol. 211(C), pages 582-594.
    4. Han, Yongming & Liu, Shuang & Cong, Di & Geng, Zhiqiang & Fan, Jinzhen & Gao, Jingyang & Pan, Tingrui, 2021. "Resource optimization model using novel extreme learning machine with t-distributed stochastic neighbor embedding: Application to complex industrial processes," Energy, Elsevier, vol. 225(C).
    5. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    6. Ruben Hidalgo-Leon & Fernando Amoroso & Javier Urquizo & Viviana Villavicencio & Miguel Torres & Pritpal Singh & Guillermo Soriano, 2022. "Feasibility Study for Off-Grid Hybrid Power Systems Considering an Energy Efficiency Initiative for an Island in Ecuador," Energies, MDPI, vol. 15(5), pages 1-25, February.
    7. Jia, Mengshuo & Huang, Shaowei & Wang, Zhiwen & Shen, Chen, 2021. "Privacy-preserving distributed parameter estimation for probability distribution of wind power forecast error," Renewable Energy, Elsevier, vol. 163(C), pages 1318-1332.
    8. Savolainen, Rebecka & Lahdelma, Risto, 2022. "Optimization of renewable energy for buildings with energy storages and 15-minute power balance," Energy, Elsevier, vol. 243(C).
    9. Meng, Anbo & Zhang, Haitao & Yin, Hao & Xian, Zikang & Chen, Shu & Zhu, Zibin & Zhang, Zheng & Rong, Jiayu & Li, Chen & Wang, Chenen & Wu, Zhenbo & Deng, Weisi & Luo, Jianqiang & Wang, Xiaolin, 2023. "A novel multi-gradient evolutionary deep learning approach for few-shot wind power prediction using time-series GAN," Energy, Elsevier, vol. 283(C).
    10. Işık, Cem & Kuziboev, Bekhzod & Ongan, Serdar & Saidmamatov, Olimjon & Mirkhoshimova, Mokhirakhon & Rajabov, Alibek, 2024. "The volatility of global energy uncertainty: Renewable alternatives," Energy, Elsevier, vol. 297(C).
    11. Hanifi, Shahram & Cammarono, Andrea & Zare-Behtash, Hossein, 2024. "Advanced hyperparameter optimization of deep learning models for wind power prediction," Renewable Energy, Elsevier, vol. 221(C).
    12. Meng, Anbo & Zhu, Zibin & Deng, Weisi & Ou, Zuhong & Lin, Shan & Wang, Chenen & Xu, Xuancong & Wang, Xiaolin & Yin, Hao & Luo, Jianqiang, 2022. "A novel wind power prediction approach using multivariate variational mode decomposition and multi-objective crisscross optimization based deep extreme learning machine," Energy, Elsevier, vol. 260(C).
    13. Md Ohirul Qays & Yonis Buswig & Md Liton Hossain & Ahmed Abu-Siada, 2020. "Active Charge Balancing Strategy Using the State of Charge Estimation Technique for a PV-Battery Hybrid System," Energies, MDPI, vol. 13(13), pages 1-16, July.
    14. Deepika Bishnoi & Harsh Chaturvedi, 2022. "Optimal Design of a Hybrid Energy System for Economic and Environmental Sustainability of Onshore Oil and Gas Fields," Energies, MDPI, vol. 15(6), pages 1-21, March.
    15. Wang, Qin & Yao, Wei & Fang, Jiakun & Ai, Xiaomeng & Wen, Jinyu & Yang, Xiaobo & Xie, Hailian & Huang, Xing, 2020. "Dynamic modeling and small signal stability analysis of distributed photovoltaic grid-connected system with large scale of panel level DC optimizers," Applied Energy, Elsevier, vol. 259(C).
    16. Yu, Binbin & Li, Jianjing & Liu, Che & Sun, Bo, 2022. "A novel short-term electrical load forecasting framework with intelligent feature engineering," Applied Energy, Elsevier, vol. 327(C).
    17. Vargas Gil, Gloria Milena & Bittencourt Aguiar Cunha, Rafael & Giuseppe Di Santo, Silvio & Machado Monaro, Renato & Fragoso Costa, Fabiano & Sguarezi Filho, Alfeu J., 2020. "Photovoltaic energy in South America: Current state and grid regulation for large-scale and distributed photovoltaic systems," Renewable Energy, Elsevier, vol. 162(C), pages 1307-1320.
    18. Tanzim Meraj, Sheikh & Zaihar Yahaya, Nor & Hasan, Kamrul & Hossain Lipu, M.S. & Madurai Elavarasan, Rajvikram & Hussain, Aini & Hannan, M.A. & Muttaqi, Kashem M., 2022. "A filter less improved control scheme for active/reactive energy management in fuel cell integrated grid system with harmonic reduction ability," Applied Energy, Elsevier, vol. 312(C).
    19. Gu, Bo & Zhang, Tianren & Meng, Hang & Zhang, Jinhua, 2021. "Short-term forecasting and uncertainty analysis of wind power based on long short-term memory, cloud model and non-parametric kernel density estimation," Renewable Energy, Elsevier, vol. 164(C), pages 687-708.
    20. Lin, Zi & Liu, Xiaolei, 2020. "Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network," Energy, Elsevier, vol. 201(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:gam:jeners:v:13:y:2020:i:18:p:4901-:d:415813. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.