| Summary: | 碩士 === 中原大學 === 電機工程研究所 === 99 === Microgrids can increase usage of renewable energies and avoid power penetration caused by distributed generation in the power system. The interface (i.e., point of common coupling, PCC) between the microgrid and power utility should satisfy some standards, e.g., IEEE Sd. 1547. Monitoring the microgrid loads at the PCC by the power utility becomes crucial because the utility cannot install advanced meters at different locations in the microgrid. This paper presents a new nonintrusive load monitoring method using artificial neural network. The fundamental component, characteristic and characteristic harmonic currents /voltage measured at the PCC serve as the signatures for the artificial neural network inputs.
The nonintrusive load monitoring at the PCC is addressed to identify different load levels for individual linear/nonlinear loads in the microgrid. With the help of load monitoring results, the power utility can make further load management policy. Simulation results obtained from a microgrid consisting of diesel generation, wind-turbine-generator, converter, and cycle-converter show the applicability of the proposed method.
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