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.
Highlights
Renewable energies, such as solar, wind, geothermal, biomass, tidal, and hydropower, are classified as distributed electricity resources and have recently been the subject of much attention as alternative sources of electricity
If the PCC cannot comply with the standard, the microgrid is not allowed to connect with the main grid
This paper proposes a novel method to achieve nonintrusive energy monitoring at the PCC between the main power grid and the microgrid, based on a hybrid self-organizing feature-mapping neural network
Summary
Renewable energies, such as solar, wind, geothermal, biomass, tidal, and hydropower, are classified as distributed electricity resources and have recently been the subject of much attention as alternative sources of electricity. The nonintrusive monitoring estimates the number and nature of the individual appliances, their individual energy consumptions and other related statistics (e.g., time-of-use variations). (1) NEM must consider the intermittent nature of renewable energies (e.g., wind-turbine) in a micro grid; a NALM-based residential home generally does not have a power resource. (3) The purpose of NEM in a microgrid is to monitor power generation from distributed generators and power consumption by individual loads; the purpose of NALM in a residential home is to determine the energy consumption pattern of individual appliances. This paper proposes a novel method to achieve nonintrusive energy monitoring at the PCC between the main power grid and the microgrid, based on a hybrid self-organizing feature-mapping neural network (hybrid SOFMNN).
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