PROGRAMMABLE MICROGRID CONTROL SYSTEM

    公开(公告)号:US20220057769A1

    公开(公告)日:2022-02-24

    申请号:US17466763

    申请日:2021-09-03

    Abstract: Provided herein are embodiments of a programmable microgrid control system that includes programming software tools capable of modeling, analyzing and monitoring power system especially AC, DC and hybrid microgrids. The monitoring feature of the software tool allows tools to communicate with a real system to acquire online data of power system assets such as conventional and renewable energy sources, transformers, and electrical loads.

    DYNAMIC PARAMETER TUNING USING MODIFIED PARTICLE SWARM OPTIMIZATION

    公开(公告)号:US20210255591A1

    公开(公告)日:2021-08-19

    申请号:US17145280

    申请日:2021-01-08

    Abstract: Dynamic parameter tuning using particle swarm optimization is disclosed. According to one embodiment, a system for dynamically tuning parameters comprising a control unit; and a system for receiving parameters tuned by the control unit. The control unit receives as input a model selection and definitions, and dynamically tunes a value for each parameter by using a modified particle swarm optimization method. The modified particle swarm optimization method comprises moving particle locations based on a particle's inertia, experience, global knowledge, and a tuning factor. The control unit outputs the dynamically tuned value for each parameter.

    DYNAMIC PARAMETER TUNING USING PARTICLE SWARM OPTIMIZATION

    公开(公告)号:US20200096956A1

    公开(公告)日:2020-03-26

    申请号:US16370708

    申请日:2019-03-29

    Abstract: Dynamic parameter tuning using particle swarm optimization is disclosed. According to one embodiment, a system for dynamically tuning parameters comprising a control unit; and a system for receiving parameters tuned by the control unit. The control unit receives as input a model selection and definitions, and dynamically tunes a value for each parameter by using a modified particle swarm optimization method. The modified particle swarm optimization method comprises moving particle locations based on a particle's inertia, experience, global knowledge, and a tuning factor. The control unit outputs the dynamically tuned value for each parameter.

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