Patents by Inventor Kevin W. Warren
Kevin W. Warren has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 10732319Abstract: A method, computer system, and computer program product. Weather forecast data is generated with respect to an area encompassing a location of a solar farm by a computer system. Solar power output by the solar farm is forecasted by the computer system based on the generated weather forecast data. Forecasted solar power output data is generated by the computer system based on the forecasted solar power output by the solar farm. A power grid operation, including one or both of a power grid balancing operation and a power grid optimization operation, is performed based on the forecasted solar power output data.Type: GrantFiled: August 30, 2017Date of Patent: August 4, 2020Assignee: International Business Machines CorporationInventors: Minwei Feng, Ildar Khabibrakhmanov, Tarun Kumar, Mark A. Lavin, Kevin W. Warren, Rui Zhang, Wei Zhang
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Patent number: 10598157Abstract: Historical electrical power output measurements of a wind turbine for a time period immediately preceding a specified past time are received. Historical wind speed micro-forecasts for the geographic location of the wind turbine, for a time period immediately preceding the specified past time and for a time period immediately following the specified past time are received. Based on the historical electrical power output measurements and the historical wind speed micro-forecasts, a trained machine learning model for predicting wind power output of the wind turbine is generated. Real-time electrical power output measurements of the wind turbine and real-time wind speed micro-forecasts for the geographic location of the wind turbine are received. Using the trained machine learning model with the real-time electrical power output measurements of the wind turbine and the real-time wind speed micro-forecasts, a wind power output forecast for the wind turbine at a future time is outputted.Type: GrantFiled: February 7, 2017Date of Patent: March 24, 2020Assignee: International Business Machines CorporationInventors: Varun Badrinath Krishna, Younghun Kim, Tarun Kumar, Wander S. Wadman, Kevin W. Warren
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Patent number: 10566835Abstract: Detecting an outage in an alternating current (AC) electrical network. One or more time-stamped and location-stamped data packets, each data packet including magnetic sensor data collected by one or more non-contact magnetic sensors in a mobile device in proximity to the AC electrical network are received. Based on the magnetic sensor data, it is determined that an outage exists in the AC electrical network.Type: GrantFiled: July 22, 2016Date of Patent: February 18, 2020Assignee: International Business Machines CorporationInventors: Younghun Kim, Jayant K. Taneja, Kevin W. Warren
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Patent number: 10330081Abstract: Historical power output measurements of a wind turbine for a time period immediately preceding a specified past time are received. Historical wind speed micro-forecasts for the wind turbine for a time period immediately preceding the specified past time and for a time period immediately following the specified past time are received. The historical wind speed micro-forecasts are converted to wind power values. Based on the historical power output measurements and the wind power output values, a machine learning model for predicting wind power output is trained. Real-time power output measurements of the wind turbine and real-time wind speed micro-forecasts for the wind turbine are received. The real-time wind speed micro-forecasts are converted to real-time wind power values. Using the machine learning model with the real-time power output measurements and the real-time wind power values, a wind power output forecast for the wind turbine at a future time is outputted.Type: GrantFiled: February 7, 2017Date of Patent: June 25, 2019Assignee: International Business Machines CorporationInventors: Varun Badrinath Krishna, Younghun Kim, Tarun Kumar, Wander S. Wadman, Kevin W. Warren
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Patent number: 10302066Abstract: Historical power output measurements of a wind turbine for a time period immediately preceding a specified time are received. Historical wind speed micro-forecasts for the wind turbine for a time periods immediately preceding the specified past time and immediately following the specified past time are received. The historical wind speed micro-forecasts are converted to wind power values. Based on the historical power output measurements and the wind power output values, a machine learning model for predicting wind power output is trained. Real-time power output measurements of the wind turbine and real-time wind speed micro-forecasts for the wind turbine are received. The real-time wind speed micro-forecasts are converted to real-time wind power values. Using the machine learning model with the real-time power output measurements and the real-time wind power values, a wind power output forecast for the wind turbine at a future time is outputted.Type: GrantFiled: April 27, 2018Date of Patent: May 28, 2019Assignee: International Business Machines CorporationInventors: Varun Badrinath Krishna, Younghun Kim, Tarun Kumar, Wander S. Wadman, Kevin W. Warren
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Patent number: 10288038Abstract: Historical power output measurements of a wind turbine for a time period immediately preceding a specified time are received. Historical wind speed micro-forecasts for the wind turbine for a time periods immediately preceding the specified past time and immediately following the specified past time are received. The historical wind speed micro-forecasts are converted to wind power values. Based on the historical power output measurements and the wind power output values, a machine learning model for predicting wind power output is trained. Real-time power output measurements of the wind turbine and real-time wind speed micro-forecasts for the wind turbine are received. The real-time wind speed micro-forecasts are converted to real-time wind power values. Using the machine learning model with the real-time power output measurements and the real-time wind power values, a wind power output forecast for the wind turbine at a future time is outputted.Type: GrantFiled: April 27, 2018Date of Patent: May 14, 2019Assignee: International Business Machines CorporationInventors: Varun Badrinath Krishna, Younghun Kim, Tarun Kumar, Wander S. Wadman, Kevin W. Warren
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Publication number: 20190064392Abstract: A method, computer system, and computer program product. Weather forecast data is generated with respect to an area encompassing a location of a solar farm by a computer system. Solar power output by the solar farm is forecasted by the computer system based on the generated weather forecast data. Forecasted solar power output data is generated by the computer system based on the forecasted solar power output by the solar farm. A power grid operation, including one or both of a power grid balancing operation and a power grid optimization operation, is performed based on the forecasted solar power output data.Type: ApplicationFiled: August 30, 2017Publication date: February 28, 2019Inventors: MINWEI FENG, ILDAR KHABIBRAKHMANOV, TARUN KUMAR, MARK A. LAVIN, KEVIN W. WARREN, RUI ZHANG, WEI ZHANG
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Publication number: 20180223812Abstract: Historical electrical power output measurements of a wind turbine for a time period immediately preceding a specified past time are received. Historical wind speed micro-forecasts for the geographic location of the wind turbine, for a time period immediately preceding the specified past time and for a time period immediately following the specified past time are received. Based on the historical electrical power output measurements and the historical wind speed micro-forecasts, a trained machine learning model for predicting wind power output of the wind turbine is generated. Real-time electrical power output measurements of the wind turbine and real-time wind speed micro-forecasts for the geographic location of the wind turbine are received. Using the trained machine learning model with the real-time electrical power output measurements of the wind turbine and the real-time wind speed micro-forecasts, a wind power output forecast for the wind turbine at a future time is outputted.Type: ApplicationFiled: February 7, 2017Publication date: August 9, 2018Inventors: Varun Badrinath Krishna, Younghun Kim, Tarun Kumar, Wander S. Wadman, Kevin W. Warren
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Publication number: 20180223806Abstract: Historical power output measurements of a wind turbine for a time period immediately preceding a specified time are received. Historical wind speed micro-forecasts for the wind turbine for a time periods immediately preceding the specified past time and immediately following the specified past time are received. The historical wind speed micro-forecasts are converted to wind power values. Based on the historical power output measurements and the wind power output values, a machine learning model for predicting wind power output is trained. Real-time power output measurements of the wind turbine and real-time wind speed micro-forecasts for the wind turbine are received. The real-time wind speed micro-forecasts are converted to real-time wind power values. Using the machine learning model with the real-time power output measurements and the real-time wind power values, a wind power output forecast for the wind turbine at a future time is outputted.Type: ApplicationFiled: April 27, 2018Publication date: August 9, 2018Inventors: Varun Badrinath Krishna, Younghun Kim, Tarun Kumar, Wander S. Wadman, Kevin W. Warren
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Publication number: 20180223814Abstract: Historical electrical power output measurements of a wind turbine for a time period immediately preceding a specified past time are received. Historical wind speed micro-forecasts for the geographic location of the wind turbine, for a time period immediately preceding the specified past time and for a time period immediately following the specified past time are received. Based on the historical electrical power output measurements and the historical wind speed micro-forecasts, a trained machine learning model for predicting wind power output of the wind turbine is generated. Real-time electrical power output measurements of the wind turbine and real-time wind speed micro-forecasts for the geographic location of the wind turbine are received. Using the trained machine learning model with the real-time electrical power output measurements of the wind turbine and the real-time wind speed micro-forecasts, a wind power output forecast for the wind turbine at a future time is outputted.Type: ApplicationFiled: December 13, 2017Publication date: August 9, 2018Inventors: Varun Badrinath Krishna, Younghun Kim, Tarun Kumar, Wander S. Wadman, Kevin W. Warren
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Publication number: 20180223805Abstract: Historical power output measurements of a wind turbine for a time period immediately preceding a specified time are received. Historical wind speed micro-forecasts for the wind turbine for a time periods immediately preceding the specified past time and immediately following the specified past time are received. The historical wind speed micro-forecasts are converted to wind power values. Based on the historical power output measurements and the wind power output values, a machine learning model for predicting wind power output is trained. Real-time power output measurements of the wind turbine and real-time wind speed micro-forecasts for the wind turbine are received. The real-time wind speed micro-forecasts are converted to real-time wind power values. Using the machine learning model with the real-time power output measurements and the real-time wind power values, a wind power output forecast for the wind turbine at a future time is outputted.Type: ApplicationFiled: December 13, 2017Publication date: August 9, 2018Inventors: Varun Badrinath Krishna, Younghun Kim, Tarun Kumar, Wander S. Wadman, Kevin W. Warren
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Publication number: 20180223807Abstract: Historical power output measurements of a wind turbine for a time period immediately preceding a specified time are received. Historical wind speed micro-forecasts for the wind turbine for a time periods immediately preceding the specified past time and immediately following the specified past time are received. The historical wind speed micro-forecasts are converted to wind power values. Based on the historical power output measurements and the wind power output values, a machine learning model for predicting wind power output is trained. Real-time power output measurements of the wind turbine and real-time wind speed micro-forecasts for the wind turbine are received. The real-time wind speed micro-forecasts are converted to real-time wind power values. Using the machine learning model with the real-time power output measurements and the real-time wind power values, a wind power output forecast for the wind turbine at a future time is outputted.Type: ApplicationFiled: April 27, 2018Publication date: August 9, 2018Inventors: Varun Badrinath Krishna, Younghun Kim, Tarun Kumar, Wander S. Wadman, Kevin W. Warren
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Publication number: 20180223804Abstract: Historical power output measurements of a wind turbine for a time period immediately preceding a specified past time are received. Historical wind speed micro-forecasts for the wind turbine for a time period immediately preceding the specified past time and for a time period immediately following the specified past time are received. The historical wind speed micro-forecasts are converted to wind power values. Based on the historical power output measurements and the wind power output values, a machine learning model for predicting wind power output is trained. Real-time power output measurements of the wind turbine and real-time wind speed micro-forecasts for the wind turbine are received. The real-time wind speed micro-forecasts are converted to real-time wind power values. Using the machine learning model with the real-time power output measurements and the real-time wind power values, a wind power output forecast for the wind turbine at a future time is outputted.Type: ApplicationFiled: February 7, 2017Publication date: August 9, 2018Inventors: Varun Badrinath Krishna, Younghun Kim, Tarun Kumar, Wander S. Wadman, Kevin W. Warren
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Patent number: 10041475Abstract: Historical power output measurements of a wind turbine for a time period immediately preceding a specified time are received. Historical wind speed micro-forecasts for the wind turbine for a time periods immediately preceding the specified past time and immediately following the specified past time are received. The historical wind speed micro-forecasts are converted to wind power values. Based on the historical power output measurements and the wind power output values, a machine learning model for predicting wind power output is trained. Real-time power output measurements of the wind turbine and real-time wind speed micro-forecasts for the wind turbine are received. The real-time wind speed micro-forecasts are converted to real-time wind power values. Using the machine learning model with the real-time power output measurements and the real-time wind power values, a wind power output forecast for the wind turbine at a future time is outputted.Type: GrantFiled: December 13, 2017Date of Patent: August 7, 2018Assignee: International Business Machines CorporationInventors: Varun Badrinath Krishna, Younghun Kim, Tarun Kumar, Wander S. Wadman, Kevin W. Warren
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Publication number: 20180024205Abstract: Detecting an outage in an alternating current (AC) electrical network. One or more time-stamped and location-stamped data packets, each data packet including magnetic sensor data collected by one or more non-contact magnetic sensors in a mobile device in proximity to the AC electrical network are received. Based on the magnetic sensor data, it is determined that an outage exists in the AC electrical network.Type: ApplicationFiled: July 22, 2016Publication date: January 25, 2018Inventors: Younghun Kim, Jayant K. Taneja, Kevin W. Warren
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Patent number: 6914604Abstract: A method of (and system for) of displaying information, includes an extended bus bridge, a graphics adaptor coupled to the extended bridge, and a monitor coupled to the graphics adaptor to display the information, such that the graphics adaptor is localized to the monitor.Type: GrantFiled: August 7, 2000Date of Patent: July 5, 2005Assignee: International Business Machines CorporationInventors: Sameh W. Asaad, Kevin W. Warren
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Patent number: 6823415Abstract: A computer system, includes a mobile computer, a docking station for receiving the mobile computer, a bridge having a first side coupled to the mobile computer and a second side coupled to the docking station, and a flat panel display formed with the docking station for being coupled to the mobile computer via the docking station. The docking station includes a dock housing coupled to a desktop display and including a first bus, and a bridge coupled between the first bus and a second bus, the first bus residing in the dock housing and the second bus for being coupled to the mobile computer.Type: GrantFiled: August 7, 2000Date of Patent: November 23, 2004Assignee: International Business Machines CorporationInventors: Sameh W. Asaad, Nicholas R. Dono, Ernest Nelson Mandese, Bengt-Olaf Schneider, Kevin W. Warren
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Patent number: 6754761Abstract: A method of (and system for) of transporting a sideband signal through a physical layer of an extended bridge, includes on a first node of the extended bridge, providing an interface to a sideband component coupled to a side of the extended bridge, encoding a first data stream being output from the sideband component with a unique header to identify the data output from the sideband component, and multiplexing the first data stream from the sideband component with a second data stream from a principal signal port, and outputting the multiplexed first and second data streams to another node of the extended bridge.Type: GrantFiled: August 7, 2000Date of Patent: June 22, 2004Assignee: International Business Machines CorporationInventors: Sameh W. Asaad, Kevin W. Warren
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Patent number: 6675237Abstract: A computer network system includes a plurality of computers each including a central processing unit (CPU), a memory and at least one peripheral device, a connection fabric having selectable first and second sides, the first side being coupled to a first computer of the plurality of computers and the second side being coupled to at least a second computer of the plurality of computers. Each of the first and second computers performs a negotiation to determine which one of the first and second computers controls resources of the other of the first and second computers.Type: GrantFiled: August 7, 2000Date of Patent: January 6, 2004Assignee: International Business Machines CorporationInventors: Sameh W. Asaad, Nicholas R. Dono, Ernest Nelson Mandese, Bengt-Olaf Schneider, Kevin W. Warren
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Publication number: 20020075251Abstract: The present invention provides for a software routine executable by an image display system that controls at least one video timing signal (for example, the pixel clock signal) supplied to a display subsystem. More specifically, the software routine, adjusts the video timing signal in response to detection that the system is switching between power modes. The software routine preferably updates the video timing signal such that the system conserves power in response to detection that the system is switching from AC-powered operation to limited DC-powered operation.Type: ApplicationFiled: March 23, 1998Publication date: June 20, 2002Inventors: STEVEN E. MILLMAN, KEVIN W. WARREN