Abstract: The system and method described herein relate to production of power from the wind farm that incorporate tunable power production forecasts for optimal wind farm performance, where the wind farm power production is controlled at least in part by the power production forecasts. The system and method use a tunable power forecasting model to generate tunable coefficients based on asymmetric loss function applied on actual power production data, along with tuning factor(s) that tune forecast towards under forecasting or over forecasting. The power production forecasts are generated using the tunable coefficients 34 and power characteristic features that are derived from actual power production data. The power production forecasts are monitored for any degradation, and a control action to regenerate the coefficients or retune the model is undertaken if degradation is observed.

Abstract: A method for operating a wind turbine includes determining at least one wind condition of the wind turbine for a plurality of time intervals. The method also includes determining a status of the wind turbine at the beginning of each of the plurality of time intervals. Further, the method includes determining at least one vibration parameter of the wind turbine for one or more preceding time intervals of the plurality of time intervals. Moreover, the method includes predicting whether a trip event is imminent based on the at least one wind condition, the status of the wind turbine at the beginning of each of the plurality of time intervals, and the vibration parameter. Thus, the method further includes implementing a control action for the wind turbine so as to prevent the trip event.

Abstract: The present disclosure is directed to a system and method for forecasting a farm-level power output of a wind farm having a plurality of wind turbines. The method includes collecting actual operational data and/or site information for the wind farm. The method also includes predicting operational data for the wind farm for a future time period. Further, the method includes generating a model-based power output forecast based on the actual operational data, the predicted operational data, and/or the site information. In addition, the method includes measuring real-time operational data from the wind farm and adjusting the power output forecast based on the measured real-time operational data. Thus, the method also includes forecasting the farm-level power output of the wind farm based on the adjusted power output forecast.

Abstract: A method for operating a wind turbine includes determining at least one wind condition of the wind turbine for a plurality of time intervals. The method also includes determining a status of the wind turbine at the beginning of each of the plurality of time intervals. Further, the method includes determining at least one vibration parameter of the wind turbine for one or more preceding time intervals of the plurality of time intervals. Moreover, the method includes predicting whether a trip event is imminent based on the at least one wind condition, the status of the wind turbine at the beginning of each of the plurality of time intervals, and the vibration parameter. Thus, the method further includes implementing a control action for the wind turbine so as to prevent the trip event.

Abstract: The present disclosure is directed to a system and method for forecasting a farm-level power output of a wind farm having a plurality of wind turbines. The method includes collecting actual operational data and/or site information for the wind farm. The method also includes predicting operational data for the wind farm for a future time period. Further, the method includes generating a model-based power output forecast based on the actual operational data, the predicted operational data, and/or the site information. In addition, the method includes measuring real-time operational data from the wind farm and adjusting the power output forecast based on the measured real-time operational data. Thus, the method also includes forecasting the farm-level power output of the wind farm based on the adjusted power output forecast.

Abstract: The system and method described herein relate to production of power from the wind farm that incorporate tunable power production forecasts for optimal wind farm performance, where the wind farm power production is controlled at least in part by the power production forecasts. The system and method use a tunable power forecasting model to generate tunable coefficients based on asymmetric loss function applied on actual power production data, along with tuning factor(s) that tune forecast towards under forecasting or over forecasting. The power production forecasts are generated using the tunable coefficients 34 and power characteristic features that are derived from actual power production data. The power production forecasts are monitored for any degradation, and a control action to regenerate the coefficients or retune the model is undertaken if degradation is observed.

Abstract: Early detection of high volume swarms in a peer-to-peer network, including a data feed of peer-to-peer swarm activity, and an analytics engine processing the data feed and identifying the high volume swarms that have parameters that exceed a threshold. The system can include a pre-processing section for conditioning the swarm data for the analytics section. There can also be a verification section that confirms that the peer download file matches the target file. The early detection provides for enhanced anti-piracy efforts, improved allocation of network resources, and better business decision-making.

Abstract: A monitoring system for use with a turbine engine is described herein. The turbine engine includes a compressor and at least one compressor bleed valve coupled to the compressor. The monitoring system includes at least one sensor that is for sensing an operational parameter of the compressor bleed valve, and a controller that is coupled to the sensor and the compressor bleed valve. The controller is configured to determine an operating mode of the turbine engine and receive a monitoring signal indicative of the sensed operational parameter. The controller is also configured to calculate at least one operational value of the compressor bleed valve based at least in part on the sensed operational parameter. The controller is further configured to determine a condition of the turbine engine based at least in part on the calculated compressor bleed valve operational value.

Abstract: Battery management systems and methods related to predicting power-delivery performance are provided. In one embodiment, a method for predicting power-delivery performance for a battery includes retrieving a plurality of battery operating parameters for a selected discharge cycle, calculating an offset indicative of a difference between a modeled internal resistance of the battery and an observed internal resistance generated from a calibration discharge cycle of the battery prior to the selected discharge cycle, and outputting a battery power-delivery performance prediction based on an offset-corrected internal resistance indicative of a difference between a modeled internal resistance based on the plurality of battery operating parameters for the selected discharge cycle and the offset.

Abstract: A monitoring system for use with a turbine engine is described herein. The turbine engine includes a compressor and at least one compressor bleed valve coupled to the compressor. The monitoring system includes at least one sensor that is for sensing an operational parameter of the compressor bleed valve, and a controller that is coupled to the sensor and the compressor bleed valve. The controller is configured to determine an operating mode of the turbine engine and receive a monitoring signal indicative of the sensed operational parameter. The controller is also configured to calculate at least one operational value of the compressor bleed valve based at least in part on the sensed operational parameter. The controller is further configured to determine a condition of the turbine engine based at least in part on the calculated compressor bleed valve operational value.

Abstract: Battery management systems and methods related to predicting power-delivery performance are provided. In one embodiment, a method for predicting power-delivery performance for a battery includes retrieving a plurality of battery operating parameters for a selected discharge cycle, calculating an offset indicative of a difference between a modeled internal resistance of the battery and an observed internal resistance generated from a calibration discharge cycle of the battery prior to the selected discharge cycle, and outputting a battery power-delivery performance prediction based on an offset-corrected internal resistance indicative of a difference between a modeled internal resistance based on the plurality of battery operating parameters for the selected discharge cycle and the offset.

Abstract: Early detection of high volume swarms in a peer-to-peer network, including a data feed of peer-to-peer swarm activity, and an analytics engine processing the data feed and identifying the high volume swarms that have parameters that exceed a threshold. The system can include a pre-processing section for conditioning the swarm data for the analytics section. There can also be a verification section that confirms that the peer download file matches the target file. The early detection provides for enhanced anti-piracy efforts, improved allocation of network resources, and better business decision-making.

Abstract: Tracking digital asset infringement activities and infringers on peer-to-peer networks. One method for assessing notice effectiveness of unauthorized distributors of content on a peer-to-peer network comprises processing peer data from a plurality of peers on the network, wherein the peer data aids in identification of individual unauthorized distributors. The embodiment includes creating a trial with a trial base population of the unauthorized distributors and a trial capture window, determining metrics for measuring the notice effectiveness, selecting a randomization methodology for the trial, performing the trial with the trial base population over the trial capture window according to the randomization methodology and issuing notices to some of the unauthorized distributors.

Abstract: Material creation systems and methods for quickly identifying which existing experimental run, or newly-created material, best matches a desired set of properties are described so that product development time can be minimized. Users may input the properties they desire in a material, the acceptable values of those properties, the goals for each property, and a priority value for each property. Preliminary matching existing experimental runs may be retrieved from an experimental run database. One of four desirability functions may then be utilized to calculate a scored property value for each property of each existing experimental run. The scored property value may then be weighted to account for the priority value assigned to each property. The results may then be sorted in descending order based on their overall match scores, and output to the user so the best matching existing experimental run(s) is readily identifiable by the user.

Abstract: A method for applying design for reliability into design for Six Sigma is described. The method includes establishing an appropriate model for reliability as a function of time; determining a reliability transfer function; calculating defects per opportunity per unit of time; entering said defects per opportunity per unit of time into a calculation of value of sigma; selecting one or more noise factors likely to have an impact on reliability; and performing a closed form analytical solution of said impact on reliability using a Monte Carlo analysis. The noise parameters may include one or more assumptions of the hours of usage per year, temperature of use, material quality, part quality, layout of components, extrinsic stresses, supplier quality, interconnection quality, test coverage, shipping damage, installation errors, errors in instructions, customer misuse or other noise factors beyond the control of the designer.

Abstract: An exemplary embodiment of the invention is a method and apparatus for calculating at least one confidence interval. The method comprises activating a calculator. The calculator prompts the user to enter at least three pairs of calibration data. The user then specifies a reference for the calibration data. The calculator then generates a list of the calibration data. The calculator then calculates a linear calibration curve derived from the calibration data. The calculator then calculates a residual calibration value plot derived from the calibration data. The user enters an unknown sample output measurement. The calculator calculates a back-calculated unknown sample input measurement. The calculator lastly calculates a confidence interval for the back-calculated unknown sample input measurement.

Abstract: A computer-based method and processor for determining the equivalence of the respective mean values of physical processes used in the production of goods is provided. The method allows for inputting respective values of various parameters. A storing step allows for storing each inputted value into a memory unit, and a computing step allows for computing, based on the stored values, respective sample sizes required by the processes to determine whether the respective means of the processes are equivalent to one another within a selected equivalence range and within sufficiently acceptable probabilities. The magnitude of the respective sample sizes is sufficiently small to allow a user to experimentally demonstrate the equivalence of the respective means of the processes.

Abstract: The present invention discloses a system and method for estimating a change point time in a manufacturing process. The change point estimate is estimated by a change point estimator after receiving an alarm from a statistical process control chart. The change point estimate is estimated by estimating the point of a process before and after a particular point in time and declaring the point with the maximum absolute difference to be the change point time. In the present invention there are three versions for estimating the change point estimate. After estimating the change point time any other likely change point times are then determined and used to match the dates of those changes to any possible changes in the product variables that occurred during the manufacturing process. A source causing the change in the process is then identified, allowing an engineer to provide the necessary corrective action to remedy the change.