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11.发明申请公开(公告)号：US20210042452A1
公开(公告)日：2021-02-11
申请号：US16942760
申请日：2020-07-29
IPC分类号： G06F30/20
摘要： Generator dynamic model parameter estimation and tuning using online data and subspace state space models are disclosed. According to one embodiment, a system comprises a sensor, a data acquisition network in communication with the sensor; a user console and an identification and tuning engine in communication with the data acquisition network, the user console, and a database. The database comprises one or more generator models, and the identification and tuning engine identifies and tunes parameters associated with a selected generator model.
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公开(公告)号：US20180081327A1
公开(公告)日：2018-03-22
申请号：US15475065
申请日：2017-03-30
IPC分类号： G05B13/04
CPC分类号： G05B13/04
摘要： 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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公开(公告)号：US09875324B2
公开(公告)日：2018-01-23
申请号：US14461356
申请日：2014-08-15
发明人： Farrokh Shokooh , Tanuj Khandelwal
IPC分类号： G06F17/50
CPC分类号： G06F17/5009 , G06F17/5095 , G06F2217/78
摘要： Systems and methods are provided for simulating fraction power and control in transportation systems under design conditions and/or utilizing real-time data.
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公开(公告)号：US20140172125A1
公开(公告)日：2014-06-19
申请号：US14042539
申请日：2013-09-30
IPC分类号： G05B13/04
CPC分类号： G05B13/04
摘要： 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.
摘要翻译： 公开了使用粒子群优化的动态参数调整。 根据一个实施例，一种用于动态调整包括控制单元的参数的系统; 以及用于接收由控制单元调谐的参数的系统。 控制单元作为输入接收模型选择和定义，并通过使用修改的粒子群优化方法动态调整每个参数的值。 改进的粒子群优化方法包括基于粒子的惯性，经验，全局知识和调谐因子来移动粒子位置。 控制单元输出每个参数的动态调整值。
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公开(公告)号：US20240348088A1
公开(公告)日：2024-10-17
申请号：US18611466
申请日：2024-03-20
CPC分类号： H02J13/00034 , G01R31/088 , G01R31/2836 , H02H3/042 , H02J13/00002
摘要： A system for estimating faulted area in an electric distribution system. The system includes a database storing input data, a fault detection module to estimate, based on the input data, if a new faulted area estimation process is required, a condition estimation module to estimate condition of metered protective devices, un-metered protective devices, and metered devices (PMDs), an upstream to downstream module to assess condition of each metered protective device, un-metered protective device, and metered device (PMD), starting from a feeder circuit breaker towards feeder downstream, to estimate a tripped protective device and a last metered device upstream of a fault, and a downstream to upstream module configured to assess outaged electric loads or elements towards network upstream to find the common interrupting protective device.
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公开(公告)号：US20230012972A1
公开(公告)日：2023-01-19
申请号：US17873013
申请日：2022-07-25
发明人： Farrokh Shokooh , Tanuj Khandelwal
IPC分类号： G06F30/20 , B32B3/26 , B32B7/12 , B32B9/04 , B32B15/04 , B32B15/20 , B32B17/00 , B32B27/06 , B32B27/28 , C09D143/04 , C09J4/00 , C09J9/00 , C09J11/06 , C09J143/04 , C09J183/04 , H01L51/00 , H01L51/52 , G06F30/15
摘要： Systems and methods are provided for simulating traction power and control in transportation systems under design conditions and/or utilizing real-time data.
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17.发明申请公开(公告)号：US20220283215A1
公开(公告)日：2022-09-08
申请号：US17702615
申请日：2022-03-23
IPC分类号： G01R31/12
摘要： Provided are embodiments of systems, devices and methods to create and visualize an arc-flash incident energy or arc fault thermal energy on a TCC plot. In some embodiments, the system may use an area shape or region (of any form) on a TCC plot. The bounded area may represent a reference constant or variable arc fault energy or arc flash incident energy value.
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公开(公告)号：US20210255591A1
公开(公告)日：2021-08-19
申请号：US17145280
申请日：2021-01-08
IPC分类号： G05B13/04
摘要： 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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公开(公告)号：US20210173977A1
公开(公告)日：2021-06-10
申请号：US17109150
申请日：2020-12-02
发明人： Farrokh Shokooh , Tanuj Khandelwal
IPC分类号： G06F30/20 , B32B3/26 , B32B7/12 , B32B9/04 , B32B15/04 , B32B15/20 , B32B17/00 , B32B27/06 , B32B27/28 , C09D143/04 , C09J4/00 , C09J9/00 , C09J11/06 , C09J143/04 , C09J183/04 , H01L51/00 , H01L51/52 , G06F30/15
摘要： Systems and methods are provided for simulating traction power and control in transportation systems under design conditions and/or utilizing real-time data.
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公开(公告)号：US20200161860A1
公开(公告)日：2020-05-21
申请号：US16586871
申请日：2019-09-27
发明人： Farrokh Shokooh , Tanuj Khandelwal
IPC分类号： H02J3/14
摘要： A power control system utilizing real-time power system operating data to effectuate predictive load shedding so as to accurately predict the need for and the optimal type of responsive action to a contingency—before the contingency actually occurs.
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