IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v5y2012i7p2578-2593d18941.html
   My bibliography  Save this article

Nonintrusive Energy Monitoring for Microgrids Using Hybrid Self-Organizing Feature-Mapping Networks

Author

Listed:
  • Ying-Yi Hong

    (Chung Yuan Christian University, 200 Chung Pei Road, Chung Li 320, Taiwan)

  • Jing-Han Chou

    (Chung Yuan Christian University, 200 Chung Pei Road, Chung Li 320, Taiwan)

Abstract

Microgrids can increase power penetration from distributed generation (DG) in the power system. The interface ( i.e. , the point of common coupling, PCC) between the microgrid and the power utility must satisfy certain standards, such as IEEE Sd. 1547. Energy monitoring of the microgrid at the PCC by the power utility is crucial if the utility cannot install advanced meters at different locations in the microgrid (e.g., a factory). This paper presents a new nonintrusive energy monitoring method using a hybrid self-organizing feature-mapping neural network (SOFMNN). The components of the FFT spectra for voltage, current, kW and kVAR, measured at the PCC, serve as the signatures for the hybrid SOFMNN inputs. The nonintrusive energy monitoring at the PCC identifies different load levels for individual linear/nonlinear loads and output levels for wind power generators in the microgrid. Using this energy monitoring result, the power utility can establish an energy management policy. The simulation results from a microgrid, consisting of a diesel generator, a wind-turbine-generator, a rectifier and a cyclo-converter, show the practicability of the proposed method.

Suggested Citation

  • Ying-Yi Hong & Jing-Han Chou, 2012. "Nonintrusive Energy Monitoring for Microgrids Using Hybrid Self-Organizing Feature-Mapping Networks," Energies, MDPI, vol. 5(7), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:7:p:2578-2593:d:18941
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/5/7/2578/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/5/7/2578/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jong-Yul Kim & Hak-Man Kim & Seul-Ki Kim & Jin-Hong Jeon & Heung-Kwan Choi, 2011. "Designing an Energy Storage System Fuzzy PID Controller for Microgrid Islanded Operation," Energies, MDPI, vol. 4(9), pages 1-18, September.
    2. Hong, Ying-Yi & Chang, Huei-Lin & Chiu, Ching-Sheng, 2010. "Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs," Energy, Elsevier, vol. 35(9), pages 3870-3876.
    3. Christina N. Papadimitriou & Nicholas A. Vovos, 2010. "Transient Response Improvement of Microgrids Exploiting the Inertia of a Doubly-Fed Induction Generator (DFIG)," Energies, MDPI, vol. 3(6), pages 1-18, June.
    4. Zhe Xiao & Tinghua Li & Ming Huang & Jihong Shi & Jingjing Yang & Jiang Yu & Wei Wu, 2010. "Hierarchical MAS Based Control Strategy for Microgrid," Energies, MDPI, vol. 3(9), pages 1-17, September.
    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. Luis Hernandez & Carlos Baladrón & Javier M. Aguiar & Belén Carro & Antonio J. Sanchez-Esguevillas & Jaime Lloret, 2013. "Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks," Energies, MDPI, vol. 6(3), pages 1-24, March.
    2. Kuei-Hsiang Chao & Min-Sen Yang & Chin-Pao Hung, 2013. "Islanding Detection Method of a Photovoltaic Power Generation System Based on a CMAC Neural Network," Energies, MDPI, vol. 6(8), pages 1-18, August.
    3. Hsueh-Hsien Chang, 2012. "Non-Intrusive Demand Monitoring and Load Identification for Energy Management Systems Based on Transient Feature Analyses," Energies, MDPI, vol. 5(11), pages 1-21, November.
    4. Aggelos S. Bouhouras & Paschalis A. Gkaidatzis & Konstantinos C. Chatzisavvas & Evangelos Panagiotou & Nikolaos Poulakis & Georgios C. Christoforidis, 2017. "Load Signature Formulation for Non-Intrusive Load Monitoring Based on Current Measurements," Energies, MDPI, vol. 10(4), pages 1-21, April.

    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. Wang, Jianzhou & Xiong, Shenghua, 2014. "A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China," Energy, Elsevier, vol. 76(C), pages 526-541.
    2. Jing Wang & Longhua Mu & Fan Zhang & Xin Zhang, 2017. "A Parallel Restoration for Black Start of Microgrids Considering Characteristics of Distributed Generations," Energies, MDPI, vol. 11(1), pages 1-18, December.
    3. Niu, Tong & Wang, Jianzhou & Zhang, Kequan & Du, Pei, 2018. "Multi-step-ahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy," Renewable Energy, Elsevier, vol. 118(C), pages 213-229.
    4. Burgaç, Alper & Yavuz, Hakan, 2019. "Fuzzy Logic based hybrid type control implementation of a heaving wave energy converter," Energy, Elsevier, vol. 170(C), pages 1202-1214.
    5. Colak, Ilhami & Sagiroglu, Seref & Yesilbudak, Mehmet, 2012. "Data mining and wind power prediction: A literature review," Renewable Energy, Elsevier, vol. 46(C), pages 241-247.
    6. Kuei-Hsiang Chao & Min-Sen Yang & Chin-Pao Hung, 2013. "Islanding Detection Method of a Photovoltaic Power Generation System Based on a CMAC Neural Network," Energies, MDPI, vol. 6(8), pages 1-18, August.
    7. Ratnam Kamala Sarojini & Kaliannan Palanisamy & Enrico De Tuglie, 2022. "A Fuzzy Logic-Based Emulated Inertia Control to a Supercapacitor System to Improve Inertia in a Low Inertia Grid with Renewables," Energies, MDPI, vol. 15(4), pages 1-23, February.
    8. Thai-Thanh Nguyen & Hyeong-Jun Yoo & Hak-Man Kim & Huy Nguyen-Duc, 2018. "Direct Phase Angle and Voltage Amplitude Model Predictive Control of a Power Converter for Microgrid Applications," Energies, MDPI, vol. 11(9), pages 1-21, August.
    9. Nie, Ying & Liang, Ni & Wang, Jianzhou, 2021. "Ultra-short-term wind-speed bi-forecasting system via artificial intelligence and a double-forecasting scheme," Applied Energy, Elsevier, vol. 301(C).
    10. Liu, Hui & Duan, Zhu & Li, Yanfei & Lu, Haibo, 2018. "A novel ensemble model of different mother wavelets for wind speed multi-step forecasting," Applied Energy, Elsevier, vol. 228(C), pages 1783-1800.
    11. Ambach, Daniel & Schmid, Wolfgang, 2015. "Periodic and long range dependent models for high frequency wind speed data," Energy, Elsevier, vol. 82(C), pages 277-293.
    12. Jiang, Yu & Song, Zhe & Kusiak, Andrew, 2013. "Very short-term wind speed forecasting with Bayesian structural break model," Renewable Energy, Elsevier, vol. 50(C), pages 637-647.
    13. Korprasertsak, Natapol & Leephakpreeda, Thananchai, 2019. "Robust short-term prediction of wind power generation under uncertainty via statistical interpretation of multiple forecasting models," Energy, Elsevier, vol. 180(C), pages 387-397.
    14. Vasiliki Vita & Georgios Fotis & Christos Pavlatos & Valeri Mladenov, 2023. "A New Restoration Strategy in Microgrids after a Blackout with Priority in Critical Loads," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    15. Chin-Hsing Cheng, 2016. "Implementation of a Small Type DC Microgrid Based on Fuzzy Control and Dynamic Programming," Energies, MDPI, vol. 9(10), pages 1-12, September.
    16. Khaizaran Abdulhussein Al Sumarmad & Nasri Sulaiman & Noor Izzri Abdul Wahab & Hashim Hizam, 2022. "Microgrid Energy Management System Based on Fuzzy Logic and Monitoring Platform for Data Analysis," Energies, MDPI, vol. 15(11), pages 1-19, June.
    17. Yancai Xiao & Tieling Zhang & Zeyu Ding & Chunya Li, 2016. "The Study of Fuzzy Proportional Integral Controllers Based on Improved Particle Swarm Optimization for Permanent Magnet Direct Drive Wind Turbine Converters," Energies, MDPI, vol. 9(5), pages 1-17, May.
    18. Wang, H.Z. & Wang, G.B. & Li, G.Q. & Peng, J.C. & Liu, Y.T., 2016. "Deep belief network based deterministic and probabilistic wind speed forecasting approach," Applied Energy, Elsevier, vol. 182(C), pages 80-93.
    19. Hocine, Labar & Mounira, Mekki, 2011. "Effect of nonlinear energy on wind farm generators connected to a distribution grid," Energy, Elsevier, vol. 36(5), pages 3255-3261.
    20. Danxiang Wei & Jianzhou Wang & Kailai Ni & Guangyu Tang, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting," Energies, MDPI, vol. 12(18), pages 1-38, September.

    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:5:y:2012:i:7:p:2578-2593:d:18941. 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.