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

Author

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  • 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
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    References listed on IDEAS

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    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.
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    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.
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    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.

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