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

Ying-Yi Hong and Jing-Han Chou
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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

Energies, 2012, vol. 5, issue 7, 1-16

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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