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    Extremum Seeking for Plants With a Time-Varying Disturbance: Application to Photovoltaic Maximum Power Point Tracking

    Source: Journal of Dynamic Systems, Measurement, and Control:;2019:;volume( 141 ):;issue: 001::page 11011
    Author:
    Kehs, Michelle A.
    ,
    Fathy, Hosam K.
    DOI: 10.1115/1.4041297
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This paper presents an extremum seeking controller for photovoltaic maximum power point tracking (MPPT). The controller belongs to the broad family of “perturb and observe” algorithms, where the terminal voltage of a photovoltaic system is adjusted to maximize its output power. One critical challenge with these algorithms is that it can be difficult to distinguish between changes in photovoltaic power resulting from changes in irradiation versus the control input. With regard to this challenge, we develop an extremum seeking algorithm that uses least-squares estimation to explicitly separate the effect of the control input from the effect of time-varying disturbances. While the use of least-squares estimation in the context of extremum seeking is not new, our separation of time-varying effects is. In addition, our formulation retains much of the structure of traditional extremum seeking, thereby allowing us to perform a stability analysis comparable to the existing literature. This stability analysis assumes the time-varying disturbance to be slow, but we test the controller beyond this limit in simulation for photovoltaic MPPT. We compare our controller to two benchmarks (a similar controller that does not separate time-varying effects and a traditional extremum seeking controller), and our controller outperforms both.
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      Extremum Seeking for Plants With a Time-Varying Disturbance: Application to Photovoltaic Maximum Power Point Tracking

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    contributor authorKehs, Michelle A.
    contributor authorFathy, Hosam K.
    date accessioned2019-03-17T09:45:26Z
    date available2019-03-17T09:45:26Z
    date copyright9/26/2018 12:00:00 AM
    date issued2019
    identifier issn0022-0434
    identifier otherds_141_01_011011.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4255664
    description abstractThis paper presents an extremum seeking controller for photovoltaic maximum power point tracking (MPPT). The controller belongs to the broad family of “perturb and observe” algorithms, where the terminal voltage of a photovoltaic system is adjusted to maximize its output power. One critical challenge with these algorithms is that it can be difficult to distinguish between changes in photovoltaic power resulting from changes in irradiation versus the control input. With regard to this challenge, we develop an extremum seeking algorithm that uses least-squares estimation to explicitly separate the effect of the control input from the effect of time-varying disturbances. While the use of least-squares estimation in the context of extremum seeking is not new, our separation of time-varying effects is. In addition, our formulation retains much of the structure of traditional extremum seeking, thereby allowing us to perform a stability analysis comparable to the existing literature. This stability analysis assumes the time-varying disturbance to be slow, but we test the controller beyond this limit in simulation for photovoltaic MPPT. We compare our controller to two benchmarks (a similar controller that does not separate time-varying effects and a traditional extremum seeking controller), and our controller outperforms both.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleExtremum Seeking for Plants With a Time-Varying Disturbance: Application to Photovoltaic Maximum Power Point Tracking
    typeJournal Paper
    journal volume141
    journal issue1
    journal titleJournal of Dynamic Systems, Measurement, and Control
    identifier doi10.1115/1.4041297
    journal fristpage11011
    journal lastpage011011-10
    treeJournal of Dynamic Systems, Measurement, and Control:;2019:;volume( 141 ):;issue: 001
    contenttypeFulltext
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