公开(公告)号：US11074522B2

公开(公告)日：2021-07-27

申请号：US16916013

申请日：2020-06-29

申请人： Roger N. Anderson ,  Boyi Xie ,  Leon L. Wu ,  Arthur Kressner

发明人： Roger N. Anderson ,  Boyi Xie ,  Leon L. Wu ,  Arthur Kressner

IPC分类号： G06N20/00

摘要： Electric Grid Analytics Learning Machine, EGALM, is a machine learning based, “brutally empirical” analysis system for use in all energy operations. EGALM is applicable to all aspects of the electricity operations from power plants to homes and businesses. EGALM is a data-centric, computational learning and predictive analysis system that uses open source algorithms and unique techniques applicable to all electricity operations in the United States and other foreign countries.

公开(公告)号：US07945524B2

公开(公告)日：2011-05-17

申请号：US12178553

申请日：2008-07-23

申请人： Roger N. Anderson ,  Albert Boulanger ,  David L. Waltz ,  Phil Long ,  Marta Arias ,  Philip Gross ,  Hila Becker ,  Arthur Kressner ,  Mark Mastrocinque ,  Matthew Koenig ,  John A. Johnson

发明人： Roger N. Anderson ,  Albert Boulanger ,  David L. Waltz ,  Phil Long ,  Marta Arias ,  Philip Gross ,  Hila Becker ,  Arthur Kressner ,  Mark Mastrocinque ,  Matthew Koenig ,  John A. Johnson

IPC分类号： G06F15/18

CPC分类号： G06N99/005

摘要： A machine learning system creates failure-susceptibility rankings for feeder cables in a utility's electrical distribution system. The machine learning system employs martingale boosting algorithms and Support Vector Machine (SVM) algorithms to generate a feeder failure prediction model, which is trained on static and dynamic feeder attribute data. Feeders are dynamically ranked by failure susceptibility and the rankings displayed to utility operators and engineers so that they can proactively service the distribution system to prevent local power outages. The feeder rankings may be used to redirect power flows and to prioritize repairs. A feedback loop is established to evaluate the responses of the electrical distribution system to field actions taken to optimize preventive maintenance programs.

摘要翻译： 机器学习系统为公用事业的配电系统中的馈线电缆创建故障敏感性排名。 机器学习系统采用mart升级算法和支持向量机（SVM）算法，生成馈线故障预测模型，对静态和动态馈线属性数据进行了培训。 馈线由故障敏感性和对公用事业运营商和工程师的排名进行动态排名，以便他们可以主动为服务分配系统，以防止局部停电。 馈线排名可用于重定向电力流量并优先进行维修。 建立了一个反馈回路，以评估配电系统对优化预防性维护计划所采取的现场行动的响应。

公开(公告)号：US20090157573A1

公开(公告)日：2009-06-18

申请号：US12178553

申请日：2008-07-23

申请人： Roger N. Anderson ,  Albert Boulanger ,  David L. Waltz ,  Phil Long ,  Arias Marta ,  Philip Gross ,  Hila Becker ,  Arthur Kressner ,  Mark Mastrocinque ,  Matthew Koenig ,  John A. Johnson

发明人： Roger N. Anderson ,  Albert Boulanger ,  David L. Waltz ,  Phil Long ,  Arias Marta ,  Philip Gross ,  Hila Becker ,  Arthur Kressner ,  Mark Mastrocinque ,  Matthew Koenig ,  John A. Johnson

IPC分类号： G06F15/18

CPC分类号： G06N99/005

摘要： A machine learning system creates failure-susceptibility rankings for feeder cables in a utility's electrical distribution system. The machine learning system employs martingale boosting algorithms and Support Vector Machine (SVM) algorithms to generate a feeder failure prediction model, which is trained on static and dynamic feeder attribute data. Feeders are dynamically ranked by failure susceptibility and the rankings displayed to utility operators and engineers so that they can proactively service the distribution system to prevent local power outages. The feeder rankings may be used to redirect power flows and to prioritize repairs. A feedback loop is established to evaluate the responses of the electrical distribution system to field actions taken to optimize preventive maintenance programs.

摘要翻译： 机器学习系统为公用事业的配电系统中的馈线电缆创建故障敏感性排名。 机器学习系统采用mart升级算法和支持向量机（SVM）算法，生成馈线故障预测模型，对静态和动态馈线属性数据进行了培训。 馈线由故障敏感性和对公用事业运营商和工程师的排名进行动态排名，以便他们可以主动为服务分配系统，以防止局部停电。 馈线排名可用于重定向电力流量并优先进行维修。 建立了一个反馈回路，以评估配电系统对优化预防性维护计划所采取的现场行动的响应。