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Energies 2012, 5(7), 2578-2593; doi:10.3390/en5072578
Article

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

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Received: 3 May 2012; in revised form: 11 July 2012 / Accepted: 12 July 2012 / Published: 18 July 2012
(This article belongs to the Special Issue Smart Grid and the Future Electrical Network)
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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.
Keywords: microgrid; nonintrusive energy monitoring; harmonics; self-organizing feature mapping microgrid; nonintrusive energy monitoring; harmonics; self-organizing feature mapping
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Hong, Y.-Y.; Chou, J.-H. Nonintrusive Energy Monitoring for Microgrids Using Hybrid Self-Organizing Feature-Mapping Networks. Energies 2012, 5, 2578-2593.

AMA Style

Hong Y-Y, Chou J-H. Nonintrusive Energy Monitoring for Microgrids Using Hybrid Self-Organizing Feature-Mapping Networks. Energies. 2012; 5(7):2578-2593.

Chicago/Turabian Style

Hong, Ying-Yi; Chou, Jing-Han. 2012. "Nonintrusive Energy Monitoring for Microgrids Using Hybrid Self-Organizing Feature-Mapping Networks." Energies 5, no. 7: 2578-2593.


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