WO2019052084A1 - 估计与风力发电机组有关的模型的不确定性的方法和设备 - Google Patents

估计与风力发电机组有关的模型的不确定性的方法和设备 Download PDF

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WO2019052084A1
WO2019052084A1 PCT/CN2017/118992 CN2017118992W WO2019052084A1 WO 2019052084 A1 WO2019052084 A1 WO 2019052084A1 CN 2017118992 W CN2017118992 W CN 2017118992W WO 2019052084 A1 WO2019052084 A1 WO 2019052084A1
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uncertainty
output
parameter
model
parameters
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PCT/CN2017/118992
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English (en)
French (fr)
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王明辉
佩德森·波·约尔
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北京金风科创风电设备有限公司
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    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16ZINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
    • G16Z99/00Subject matter not provided for in other main groups of this subclass

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  • This application relates to the field of wind power generation. More specifically, it relates to a method and apparatus for estimating the uncertainty of a model associated with a wind turbine.
  • a method of estimating uncertainty of a model associated with a wind turbine comprising performing, after each use of the model, an estimating step of acquiring the model at a current time At least one of the inputs received when the second time is used; determining a preset interval in which the acquired at least one parameter falls; respectively, for each of the at least one parameter, counting up to now Each time the model is used, each parameter falls on a determined total number of predetermined intervals into which each of the models falls; and a first uncertainty of the output of the model is determined based on the total number of times.
  • an apparatus for estimating uncertainty of a model related to a wind turbine comprising: an input parameter acquisition unit that acquires the model at a time each time the model is used At least one of the inputs received when the time is used; the interval determining unit determines a preset interval in which the acquired at least one parameter falls; the counting unit is respectively for each of the at least one parameter a parameter for counting the total number of times each of the parameters falls within a predetermined preset interval in which the respective models are used each time the model is used until now; the first estimating unit determines the model based on the total number of times The first uncertainty of the output.
  • Another aspect of the present application provides a system for estimating uncertainty of a model related to a wind turbine, characterized in that the system includes: a processor; a memory storing a computer program when the computer program is processed The method is executed when the device is executed.
  • Another aspect of the present application provides a computer readable storage medium having stored therein a computer program that is executed when the computer program is executed.
  • the uncertainty of the model related to the wind turbine can be accurately evaluated. Furthermore, by using the uncertainty evaluation method and apparatus according to the present application, the output of the model can be corrected to improve the output accuracy or accuracy of the model.
  • FIG. 1 shows a flow chart of a method of estimating uncertainty of a model associated with a wind turbine according to a first embodiment of the present application.
  • FIG. 2 shows a flow chart of a method of estimating uncertainty of a model associated with a wind turbine according to a second embodiment of the present application.
  • FIG. 3 shows a block diagram of an apparatus for estimating uncertainty of a model associated with a wind turbine according to a fourth embodiment of the present application.
  • FIG. 4 shows a block diagram of an apparatus for estimating uncertainty of a model associated with a wind turbine according to a fifth embodiment of the present application.
  • the present application provides methods and apparatus for estimating the uncertainty of a model associated with a wind turbine.
  • the model associated with the wind turbine may be a model for the wind turbine or the components of the wind turbine.
  • These models can be used for various purposes, such as estimating or predicting loads, estimating or predicting fatigue life, estimating or predicting faults, etc., estimating or predicting operating parameters, etc. It should be understood that the models associated with wind turbines are not limited thereto.
  • the uncertainty of the model associated with the wind turbine can be understood as the uncertainty of the output of the model.
  • an estimate of the uncertainty is performed each time the model is used to assess the uncertainty of the output of the model each time it is used. It should be understood that the use of the model refers to the input of a corresponding input to the model to obtain a corresponding output.
  • FIG. 1 shows a flow chart of a method of estimating uncertainty of a model associated with a wind turbine according to a first embodiment of the present application. The method shown in Figure 1 is performed each time the model is used.
  • At step S110 at least one parameter (ie, one or more parameters) of the input received by the model when it is currently used is acquired.
  • the at least one parameter may be all or part of the received input.
  • the at least one parameter is predetermined such that the same type of parameter is acquired each time an estimate of the uncertainty of the model is performed.
  • step S120 it is determined that the acquired at least one parameter falls within a preset interval.
  • a plurality of preset intervals for each parameter are pre-divided for each of the at least one parameter.
  • the at least one parameter is obtained, it is determined which of the plurality of preset intervals corresponding to the parameter belongs to each preset interval. It should be understood that the interval in which the parameter falls is the interval in which the value of the parameter falls.
  • the first number of preset intervals are divided for the first parameter, and the second number of preset intervals are divided for the second parameter.
  • the first number of preset intervals are divided for the first parameter
  • the second number of preset intervals are divided for the second parameter.
  • step S130 for each of the at least one parameter, respectively, counting the total number of times each of the parameters falls within the determined preset interval in which each of the models is used each time until the model is used. .
  • the total number of corresponding parameters falling within each of the determined preset intervals in all of the current uncertainty estimates including this time is counted. That is, it is only for the section determined in step S120 that the total number of times the parameter of the corresponding type in history falls into it is determined.
  • the total number of times the first parameter falls into the interval in all of the uncertainty estimates including the current time is determined. For example, if the interval has only the first parameter in this and the previous uncertainty estimate, the total number of times is 2.
  • step S140 an uncertainty of the output of the model (hereinafter, referred to as a first uncertainty) is determined based on the total number of times.
  • a value indicative of an uncertainty of an output of the model corresponding to the type of the at least one parameter, the total number of times, and the determined preset interval is determined.
  • a function of the type of the uncertainty and the parameter, the preset interval, and the total number of times may be established in advance, or a mapping table of the type of the uncertainty and the parameter, the preset interval, and the total number of times may be established in advance.
  • the uncertainty may be determined by a function or mapping table based on the type of the at least one parameter, the determined preset interval (eg, may be represented by an identifier, label, etc. of the preset interval), the total number of statistics.
  • the predetermined parameters determine which of the plurality of parameters are used for the estimation of the uncertainty in the entered interval determined in step S120. That is, determining a predetermined number of parameters from the plurality of parameters based on a preset interval in which the predetermined one of the plurality of parameters falls, based on a total number of times corresponding to the predetermined number of parameters (ie, the The predetermined number of parameters determines the first uncertainty of the output of the model in the total number of times in step S130 corresponding to the entered interval determined in step S120.
  • the step of determining the first uncertainty of the output of the model based on the total number of times comprises: when a predetermined interval in which the predetermined one of the plurality of parameters falls is a first predetermined preset In the interval, the first uncertainty of the output of the model is determined based only on the total number of times corresponding to the predetermined parameter; when the predetermined parameter of the plurality of parameters falls within the preset interval is the first predetermined When a second predetermined preset interval having different intervals is set, the first uncertainty of the output of the model is determined based on the total number of times corresponding to all the parameters of the plurality of parameters. It should be understood that the number of the first predetermined preset interval or the second predetermined preset interval herein may be one or more.
  • the step of determining the first uncertainty of the output of the model based only on the total number of times corresponding to the predetermined parameter comprises: determining a type of the predetermined parameter, a total number of times corresponding to the predetermined parameter, and the predetermined parameter
  • the preset interval that falls within corresponds to a value indicating the uncertainty of the output of the model.
  • the type of the predetermined parameter, the preset interval into which the predetermined parameter falls (for example, may be represented by an identifier, a label, or the like of the preset section), and the total number of times corresponding to the predetermined parameter may be input, based on the pre-established uncertainty about A function of the type of the parameter and the type of the parameter, the preset interval, the total number of times, or a pre-established uncertainty and the type of the parameter, the preset interval, and the total number of times to obtain a value indicating the uncertainty of the output of the model. .
  • the step of determining a first uncertainty of an output of the model based on a total number of times corresponding to all parameters of the plurality of parameters includes: determining a type of each of the plurality of parameters, corresponding to each parameter The total number of times and the value of the uncertainty indicating the output of the model corresponding to the preset interval in which the various parameters fall.
  • the type of all parameters, the preset interval in which all parameters fall for example, can be represented by an identifier, a label, etc. of a preset interval
  • the total number of times corresponding to all parameters are input, based on pre-established uncertainty.
  • the step of determining a first uncertainty of the output of the model based on a total number of times corresponding to all parameters of the plurality of parameters further comprises: a total number of times corresponding to any one of the plurality of parameters When the threshold is not greater than the number of times corresponding to any one of the parameters, determining a type of each of the plurality of parameters, a total number of times corresponding to the various parameters, and a preset interval in which the various parameters fall A value indicating the uncertainty of the output of the model.
  • the value of the first uncertainty of the output of the model is determined to be a first predetermined value when the total number of times corresponding to each parameter is greater than or equal to the number of times threshold corresponding to each parameter.
  • the first predetermined value may indicate that the output of the model is fully trustworthy.
  • the first predetermined value may be one according to the manner of use of the uncertainty.
  • the threshold number of times is set for all or part of the preset intervals of each parameter, and further determined according to whether the total number of times corresponding to the preset interval determined in the current uncertainty estimation exceeds a corresponding number of times threshold Certainty.
  • the at least one parameter comprises wind speed and/or turbulence intensity.
  • the predetermined parameter mentioned above is the wind speed
  • the lower limit of the first predetermined preset interval is greater than the upper limit of the second predetermined preset interval. It should be understood that this is merely exemplary and the at least one parameter will vary depending on the model.
  • FIG. 2 shows a flow chart of a method of estimating uncertainty of a model associated with a wind turbine according to a second embodiment of the present application. The method shown in Figure 2 is performed each time the model is used.
  • step S210 an output of the model when it is currently used is acquired.
  • the model is output based on the input when it is currently used.
  • a parameter of the distribution of the error of the output is estimated based on the output.
  • the output of the model at the current time of use may be estimated based on the output and a parameter of the distribution of errors of the output (eg, a predetermined type of distribution) of the model when it was last used.
  • the parameters of the distribution of the error e.g, a parameter of the distribution of the error of the output of the model at the time of the current use can be estimated by a recursive algorithm based on the output and the parameter of the distribution of the error of the output of the model when it was last used.
  • the estimation can be performed using various recursive algorithms of existing parameters applicable to the distribution of errors of the output of the model.
  • the distribution of errors refers to the distribution to which the error of the output of the model is subject.
  • the distribution of the error depends on the characteristics of the output of the model, and may be, for example, a normal distribution, a Poisson distribution, or a Weibull distribution, but is not limited thereto.
  • the distribution of the error of the output of the model can be predetermined.
  • the parameters of the distribution include the mean and standard deviation.
  • a method for estimating the mean and standard deviation of the error of the output based on the output proposed in the present application is described below.
  • ⁇ ⁇ ,k represents the mean of the parameters of the distribution of the error of the output of the model as the kth time of the current time
  • ⁇ ⁇ , k-1 indicates that the model is the last kth - the mean of the parameters of the distribution of the error of the output when used once, The output of the model when the model is used for the kth time.
  • ⁇ ⁇ ,k represents the standard deviation in the parameter of the distribution of the error of the output of the model as the kth time of the current time
  • ⁇ ⁇ , k-1 represents the model as the last time
  • the standard deviation in the parameter of the distribution of the error of the output when k-1 times is used, The output of the model when the model is used for the kth time.
  • the method of estimating the mean and standard deviation of the present application is not limited thereto, and other methods are also feasible. Further, the method of estimating the mean and the standard deviation of the present application is not limited to the case where the distribution of the error is a normal distribution, and other distributions using the mean and the standard deviation as parameters are also feasible.
  • step S230 an uncertainty of the output of the model (hereinafter, referred to as a second uncertainty) is obtained based on the parameters of the distribution.
  • the second uncertainty is a sum of a second predetermined value and respective parameters of the distribution.
  • the second predetermined value may represent a predetermined uncertainty.
  • the second predetermined value may be determined according to a difference in model, usage environment, and/or usage manner, and the like.
  • the second predetermined value can be one.
  • the second uncertainty may be expressed as a sum of the third predetermined value, the estimated mean value, and the estimated standard deviation.
  • the present application also provides a method of estimating uncertainty of a model related to a wind turbine according to a third embodiment of the present application.
  • the method includes the methods of the first embodiment and the second embodiment, and the method further includes calculating, when the model is used, a product of the first uncertainty and the second uncertainty as the The third uncertainty of the output of the model.
  • the present application also provides a method of correcting the output of a model associated with a wind turbine.
  • the first uncertainty of the output of the model related to the wind turbine is estimated by the method shown in the first embodiment, or the model related to the wind turbine is estimated by the method shown in the second embodiment.
  • a second uncertainty of the output, or a third uncertainty of the output of the model associated with the wind turbine by the method of the third embodiment is estimated by the method of the third embodiment.
  • the product of the estimated uncertainty and the output can be calculated to correct the original output, and the result of the product is used to update the original output.
  • the present application also provides a system for correcting the output of a model associated with a wind turbine.
  • the system includes a processor and a memory.
  • the memory stores computer readable code, instructions or programs that, when executed by the processor, perform the above-described method of correcting the output of the model associated with the wind turbine.
  • FIG. 1 An apparatus for estimating the uncertainty of a model associated with a wind turbine according to one embodiment of the present application is shown below in conjunction with FIG.
  • FIG. 3 shows a block diagram of an apparatus for estimating uncertainty of a model associated with a wind turbine according to a fourth embodiment of the present application.
  • the apparatus 300 for estimating the uncertainty of a model related to a wind turbine includes an input parameter acquisition unit 310, an interval determination unit 320, a counting unit 330, and a first estimation unit. 340.
  • the device 300 operates each time the model is used to estimate the uncertainty. In other words, each time the model is used, the input parameter acquisition unit 310, the interval determination unit 320, the counting unit 330, and the first estimation unit 340 operate to estimate the uncertainty.
  • the input parameter acquisition unit 310 acquires at least one parameter (ie, one or more parameters) of the input received by the model when it is currently used, each time the model is used.
  • the at least one parameter may be all or part of the received input.
  • the at least one parameter is predetermined such that the same type of parameter is acquired each time an estimate of the uncertainty of the model is performed.
  • the section judging unit 320 determines a preset section in which the at least one parameter acquired by the input parameter acquiring unit 310 falls.
  • a plurality of preset intervals for each parameter are pre-divided for each of the at least one parameter.
  • the at least one parameter is obtained, it is determined which of the plurality of preset intervals corresponding to the parameter belongs to each preset interval.
  • the first number of preset intervals are divided for the first parameter, and the second number of preset intervals are divided for the second parameter.
  • the first number of preset intervals are divided for the first parameter
  • the second number of preset intervals are divided for the second parameter.
  • the counting unit 330 respectively counts, for each of the at least one parameter, the total number of times each of the parameters falls within the determined preset interval in which each of the models is used each time until the model is used.
  • the total number of corresponding parameters falling within each of the determined preset intervals in all of the uncertainty estimates including the current time is counted. That is to say, it is only for the section determined by the section judging unit 320 to determine the total number of times that the parameter of the corresponding type has fallen in history.
  • the total number of times the first parameter falls into the section in all of the uncertainty estimates including the current time is determined. For example, if the interval has only the first parameter in this and the previous uncertainty estimate, the total number of times is 2.
  • the first estimating unit 340 determines a first uncertainty of the output of the model based on the total number of times.
  • a value indicative of an uncertainty of an output of the model corresponding to the type of the at least one parameter, the total number of times, and the determined preset interval is determined.
  • a function of the type of the uncertainty and the parameter, the preset interval, and the total number of times may be established in advance, or a mapping table of the type of the uncertainty and the parameter, the preset interval, and the total number of times may be established in advance.
  • the uncertainty may be determined by a function or a mapping table based on the type of the at least one parameter, the determined preset interval (eg, may be represented by an identifier, label, etc. of the preset interval), the total number of statistics.
  • the input parameter obtaining unit 310 acquires at least one parameter, in the case where the at least one parameter is a plurality of parameters, for some models, not all cases must use all of the plurality of parameters, in this case It is considered that the predetermined parameter among the predetermined parameters determined by the section judging unit 320 determines which of the plurality of parameters are used for the estimation of the uncertainty.
  • the device 300 further includes a selection unit that determines a predetermined number of parameters from the plurality of parameters according to a preset interval into which the predetermined one of the plurality of parameters falls.
  • the first estimating unit 340 determines a first uncertainty of an output of the model based on a total number of times corresponding to the predetermined number of parameters.
  • the determining, based on the total number of times, the first uncertainty of the output of the model comprises: when a predetermined one of the plurality of parameters falls within a preset interval is a first predetermined preset In the interval, the first estimating unit determines the first uncertainty of the output of the model based on the total number of times corresponding to the predetermined parameter; when the predetermined parameter of the plurality of parameters falls within a preset interval When the first predetermined preset interval is different from the second predetermined preset interval, the first estimating unit determines the first uncertainty of the output of the model based on the total number of times corresponding to all the parameters of the plurality of parameters. It should be understood that the number of the first predetermined preset interval or the second predetermined preset interval herein may be one or more.
  • the process of determining the first uncertainty of the output of the model based only on the total number of times corresponding to the predetermined parameter includes determining a type of the predetermined parameter, a total number of times corresponding to the predetermined parameter, and the predetermined parameter
  • the preset interval that falls within corresponds to a value indicating the uncertainty of the output of the model.
  • the type of the predetermined parameter, the preset interval into which the predetermined parameter falls (for example, may be represented by an identifier, a label, or the like of the preset section), and the total number of times corresponding to the predetermined parameter may be input, based on the pre-established uncertainty about A function of the type of the parameter and the type of the parameter, the preset interval, the total number of times, or a pre-established uncertainty and the type of the parameter, the preset interval, and the total number of times to obtain a value indicating the uncertainty of the output of the model. .
  • the process of determining a first uncertainty of an output of the model based on a total number of times corresponding to all parameters of the plurality of parameters includes: determining a type of each of the plurality of parameters, corresponding to each parameter The total number of times and the value of the uncertainty indicating the output of the model corresponding to the preset interval in which the various parameters fall.
  • the type of all parameters, the preset interval in which all parameters fall for example, can be represented by an identifier, a label, etc. of a preset interval
  • the total number of times corresponding to all parameters are input, based on pre-established uncertainty.
  • the process of determining the first uncertainty of the output of the model based on the total number of times corresponding to all of the parameters of the plurality of parameters further comprises: a total number of times corresponding to any one of the plurality of parameters When the threshold is not greater than the number of times corresponding to any one of the parameters, determining a type of each of the plurality of parameters, a total number of times corresponding to the various parameters, and a preset interval in which the various parameters fall A value indicating the uncertainty of the output of the model.
  • the value of the first uncertainty of the output of the model is determined to be a first predetermined value when the total number of times corresponding to each parameter is greater than or equal to the number of times threshold corresponding to each parameter.
  • the first predetermined value may indicate that the output of the model is fully trustworthy.
  • the first predetermined value may be one according to the manner of use of the uncertainty.
  • the threshold number of times is set for all or part of the preset intervals of each parameter, and further determined according to whether the total number of times corresponding to the preset interval determined in the current uncertainty estimation exceeds a corresponding number of times threshold Certainty.
  • the at least one parameter comprises wind speed and/or turbulence intensity.
  • the predetermined parameter mentioned above is the wind speed
  • the lower limit of the first predetermined preset interval is greater than the upper limit of the second predetermined preset interval. It should be understood that this is merely exemplary and the at least one parameter will vary depending on the model.
  • FIG. 1 An apparatus for estimating the uncertainty of a model associated with a wind turbine according to one embodiment of the present application is shown below in conjunction with FIG.
  • FIG. 4 shows a block diagram of an apparatus for estimating uncertainty of a model associated with a wind turbine according to a fifth embodiment of the present application.
  • an apparatus 400 for estimating uncertainty of a model related to a wind turbine includes an output acquisition unit 410, a distribution parameter estimation unit 420, and a second estimation unit 430.
  • the device 400 operates to evaluate the uncertainty each time the model is used.
  • the output acquisition unit 410, the distribution parameter estimation unit 420, and the second estimation unit 430 operate to estimate the uncertainty.
  • the output acquisition unit 410 acquires an output of the model when it is currently used.
  • the model is output based on the input when it is currently used.
  • the distribution parameter estimation unit 420 estimates a parameter of the distribution of the error of the output based on the output.
  • the distribution parameter estimation unit 420 may estimate the error of the output of the model at the time of the current use based on the output and the parameter of the distribution of the error of the output of the model when it was last used.
  • the parameters of the distribution For example, a parameter of the distribution of the error of the output of the model at the time of the current use can be estimated by a recursive algorithm based on the output and the parameter of the distribution of the error of the output of the model when it was last used.
  • the estimation can be performed using various recursive algorithms of existing parameters applicable to the distribution of errors of the output of the model.
  • the distribution of errors refers to the distribution to which the error of the output of the model is subject.
  • the distribution of the error depends on the characteristics of the output of the model, and may be, for example, a normal distribution, a Poisson distribution, or a Weibull distribution, but is not limited thereto.
  • the distribution of the error of the output of the model can be predetermined.
  • the illustrated apparatus 400 further includes a distribution estimation unit that determines a distribution of errors of the output of the model.
  • the parameters of the distribution include a mean and a standard deviation.
  • the mean and standard deviation of the error of the output can be estimated by Equation (1) and Equation (2) above.
  • the second estimating unit 430 obtains a second uncertainty of the output of the model based on the parameters of the distribution.
  • the second uncertainty is a sum of a second predetermined value and respective parameters of the distribution.
  • the second predetermined value may represent a predetermined uncertainty.
  • the second predetermined value may be determined according to a difference in model, usage environment, and/or usage manner, and the like.
  • the second predetermined value can be one.
  • the second uncertainty may be represented as the sum of the second predetermined value, the estimated mean, and the estimated standard deviation.
  • the present application also provides an apparatus for estimating the uncertainty of a model related to a wind turbine according to a sixth embodiment of the present application.
  • the apparatus includes the apparatus 300 and 400 shown in the fourth embodiment and the fifth embodiment, and the apparatus further includes a third estimating unit, the third estimating unit calculates the first uncertainty each time the model is used.
  • the product of the sex and the second uncertainty is the third uncertainty of the output of the model.
  • the present application also provides an apparatus for correcting the output of a model associated with a wind turbine.
  • the apparatus includes the apparatus for estimating the uncertainty of the model related to the wind turbine set shown in the fourth embodiment, the fifth embodiment, or the sixth embodiment.
  • the apparatus further comprises an estimation unit that corrects the output of the model associated with the wind turbine using the first uncertainty, or the second uncertainty, or the third uncertainty as the estimated uncertainty.
  • the estimation unit may calculate a product of the estimated uncertainty and the output to correct the original output, and use the result of the product to update the original output.
  • the present application also provides a system for estimating the uncertainty of a model associated with a wind turbine.
  • the system includes a processor and a memory.
  • the memory stores computer readable code, instructions or programs that, when executed by a processor, perform the methods of the first, second or third embodiments.
  • the method according to an exemplary embodiment of the present application may be implemented as computer code in a computer readable recording medium.
  • the computer code can be implemented by those skilled in the art in accordance with the description of the above method.
  • the above method of the present application is implemented when the computer code is executed in a computer.
  • the uncertainty of the model related to the wind turbine can be accurately evaluated. Furthermore, by using the uncertainty evaluation method and apparatus according to the present application, the output of the model can be corrected to improve the output accuracy or accuracy of the model.

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Abstract

提供一种估计与风力发电机组有关的模型的不确定性的方法和设备,所述方法包括在所述模型每次被使用时执行以下的估计步骤:获取所述模型在当前次被使用时接收的输入中的至少一种参数;确定获取的所述至少一种参数各自所落入的预设区间;分别针对所述至少一种参数中的每种参数,统计至目前为止之所述模型各次被使用时每种参数分别落在确定的各自所落入的预设区间的总次数;基于所述总次数确定所述模型的输出的第一不确定性。

Description

估计与风力发电机组有关的模型的不确定性的方法和设备 技术领域
本申请涉及风力发电领域。更具体地讲,涉及一种估计与风力发电机组有关的模型的不确定性的方法和设备。
背景技术
风能作为一种清洁的可再生能源,越来越受到重视,装机量也不断增加。随着风力发电技术的不断发展,风力发电机组的各种研究也日益深入,各种用途的与风力发电机组有关的模型被提出。
由于这些模型的应用越来越为广泛,涉及到风力发电机组的各个方面,甚至涉及风力发电机组的安全运行。因此,如何准确评估这些模型的输出的不确定性(度)是一个亟待解决的问题。
发明内容
根据本申请的一方面,提供一种估计与风力发电机组有关的模型的不确定性的方法,所述方法包括在所述模型每次被使用时执行以下的估计步骤:获取所述模型在当前次被使用时接收的输入中的至少一种参数;确定获取的所述至少一种参数各自所落入的预设区间;分别针对所述至少一种参数中的每种参数,统计至目前为止之所述模型各次被使用时每种参数分别落在确定的各自所落入的预设区间的总次数;基于所述总次数确定所述模型的输出的第一不确定性。
本申请的另一方面提供一种估计与风力发电机组有关的模型的不确定性的设备,所述设备包括:输入参数获取单元,在所述模型每次被使用时,获取所述模型在当前次被使用时接收的输入中的至少一种参数;区间判断单元,确定获取的所述至少一种参数各自所落入的预设区间;计数单元,分别针对所述至少一种参数中的每种参数,统计至目前为止之所述模型各次被使用时每种参数分别落在确定的各自所落入的预设区间的总次数;第一估计单元,基于所述总次数确定所述模型的输出的第一不确定性。
本申请的另一方面提供一种估计与风力发电机组有关的模型的不确定性的系统,其特征在于,所述系统包括:处理器;存储器,存储有计算机程序,当所述计算机程序被处理器执行时,执行所述方法。
本申请的另一方面提供一种其中存储有计算机程序的计算机可读存储介质,当所述计算机程序被执行时执行所述方法。
根据本申请的估计与风力发电机组有关的模型的不确定性的方法和设备,可以准确地评价与风力发电机组有关的模型的不确定性。此外,通过使用根据本申请的不确定性评价方法和设备,可以校正模型的输出,提高模型的输出精度或准确性。
附图说明
通过下面结合附图进行的详细描述,本申请的上述和其它目的、特点和优点将会变得更加清楚,其中:
图1示出根据本申请的第一实施例的估计与风力发电机组有关的模型的不确定性的方法的流程图。
图2示出根据本申请的第二实施例的估计与风力发电机组有关的模型的不确定性的方法的流程图。
图3示出根据本申请的第四实施例的估计与风力发电机组有关的模型的不确定性的设备的框图。
图4示出根据本申请的第五实施例的估计与风力发电机组有关的模型的不确定性的设备的框图。
具体实施方式
现在,将参照附图更充分地描述不同的示例实施例。
本申请提供估计与风力发电机组有关的模型的不确定性的方法和设备。这里,与风力发电机组有关的模型可以是用于风力发电机组整体或者风力发电机组的零部件的模型。这些模型可以用于各种用途,诸如,估计或预测载荷、估计或预测疲劳寿命、估计或预测故障等、估计或预测运行参数等,应该理解,与风力发电机组有关的模型不限于此。
在一个实施例中,与风力发电机组有关的模型的不确定性可以理解为该模型的输出的不确定性。
在本申请中,在所述模型每次被使用时执行不确定性的估计,以评估所述模型每次被使用时的输出的不确定性。应该理解,使用所述模型是指输入相应的输入至所述模型以得到相应的输出。
下面结合图1示出根据本申请的一个实施例的在所述模型每次被使用时评估不确定性的方法。
图1示出根据本申请的第一实施例的估计与风力发电机组有关的模型的不确定性的方法的流程图。所述模型每次被使用时执行图1所示的方法。
在步骤S110,获取所述模型在当前次被使用时接收的输入中的至少一种参数(即,一种或多种参数)。所述至少一种参数可以是接收的输入中的所有参数或部分参数。所述至少一种参数被预先确定,从而每次执行所述模型的不确定性的估计时都获取同样类型的参数。
在步骤S120,确定获取的所述至少一种参数各自所落入的预设区间。
在本申请中,针对所述至少一种参数中的每种参数,预先划分关于每种参数的多个预设区间。当获取到所述至少一种参数时,确定每种参数落入该种参数所对应的多个预设区间中的哪个预设区间。应该理解,参数落入的区间是指参数的值落入的区间。
在一个实施例中,在所述至少一种参数包括第一参数和第二参数的情况下,针对第一参数划分第一数量的预设区间,针对第二参数划分第二数量的预设区间。在获取到第一参数和第二参数后,确定获取的第一参数落入第一数量的预设区间中的哪个区间,确定获取的第二参数落入第二数量的预设区间中的哪个区间。应该理解,本申请不限于两种参数,上面的描述仅是示例性的。
在步骤S130,分别针对所述至少一种参数中的每种参数,统计至目前为止之所述模型各次被使用时每种参数分别落在确定的各自所落入的预设区间的总次数。
在一个实施例中,针对在步骤S120确定的每个预设区间,统计在包括本次的所有次的不确定性估计中落入确定的每个预设区间的对应参数的总次数。也就是说,仅是针对在步骤S120确定的区间来确定历史上有对应类型的参数落入其中的总次数。
在一个实施例中,针对在步骤S120确定的第一参数所落入的区间,确定在包括本次的所有次的不确定性估计中,第一参数落入到该区间的总次数。 例如,如果该区间仅在本次以及上一次的不确定性估计中有第一参数落入,则总次数为2。
在步骤S140,基于所述总次数确定所述模型的输出的不确定性(以下,称为第一不确定性)。
在一个实施例中,确定与所述至少一种参数的类型、所述总次数以及确定的预设区间对应的指示所述模型的输出的不确定性的值。例如,可预先建立关于不确定性与参数的类型、预设区间、总次数的函数,或者预先建立不确定性与参数的类型、预设区间、总次数的映射表。这样,可基于所述至少一种参数的类型、确定的预设区间(例如,可由预设区间的标识符、标号等表示)、统计的总次数,通过函数或映射表来确定不确定性。
虽然在步骤S110获取了至少一种参数,在所述至少一种参数为多种参数的情况下,针对某些模型,并不是所有情况都要全部使用所述多种参数,此时要考虑其中的预定参数在步骤S120中确定的所落入的区间来确定使用所述多种参数中的哪些参数来用于不确定性的估计。即,根据所述多种参数中的预定参数所落入的预设区间来从所述多种参数中确定预定数量的参数,基于所述预定数量的参数所对应的总次数(即,所述预定数量的参数在步骤S120中确定的所落入的区间所对应的在步骤S130中的总次数)确定所述模型的输出的第一不确定性。在一个实施例中,基于所述总次数确定所述模型的输出的第一不确定性的步骤包括:当所述多种参数中的预定参数所落入的预设区间为第一预定预设区间时,仅基于与所述预定参数对应的总次数确定所述模型的输出的第一不确定性;当所述多种参数中的预定参数所落入的预设区间为与第一预定预设区间不同的第二预定预设区间时,基于与所述多种参数的所有参数对应的总次数确定所述模型的输出的第一不确定性。应该理解,这里的第一预定预设区间或第二预定预设区间的数量可以为一个或多个。
仅基于与所述预定参数对应的总次数确定所述模型的输出的第一不确定性的步骤包括:确定与所述预定参数的类型、与所述预定参数对应的总次数以及所述预定参数所落入的预设区间对应的指示所述模型的输出的不确定性的值。例如,可将预定参数的类型、预定参数所落入的预设区间(例如,可由预设区间的标识符、标号等表示)、预定参数对应的总次数作为输入,基于预先建立的关于不确定性与参数的类型、预设区间、总次数的函数,或者预先建立的不确定性与参数的类型、预设区间、总次数的映射表来获得指示所 述模型的输出的不确定性的值。
基于与所述多种参数的所有参数对应的总次数确定所述模型的输出的第一不确定性的步骤包括:确定与所述多种参数中的各种参数的类型、与各种参数对应的总次数以及各种参数所落入的预设区间对应的指示所述模型的输出的不确定性的值。例如,可将所有参数的类型、所有参数所落入的预设区间(例如,可由预设区间的标识符、标号等表示)、所有参数对应的总次数作为输入,基于预先建立的关于不确定性与参数的类型、预设区间、总次数的函数,或者预先建立的不确定性与参数的类型、预设区间、总次数的映射表来获得指示所述模型的输出的不确定性的值。
此外,基于与所述多种参数的所有参数对应的总次数确定所述模型的输出的第一不确定性的步骤还包括:当与所述多种参数中的任意一种参数对应的总次数不大于所述任意一种参数对应的次数阈值时,确定与所述多种参数中的各种参数的类型、与各种参数对应的总次数以及各种参数所落入的预设区间对应的指示所述模型的输出的不确定性的值。当与每种参数对应的总次数都大于或等于每种参数对应的次数阈值时,将所述模型的输出的第一不确定性的值确定为第一预定值。第一预定值可表示所述模型的输出完全可信赖。例如,根据不确定性的使用方式,所述第一预定值可为1。
在一个实施例中,针对每种参数的所有或部分预设区间设置次数阈值,根据在本次不确定性估计中确定的预设区间所对应的总次数是否超出对应的次数阈值来进一步确定不确定性。
在一个实施例中,所述至少一种参数包括风速和/或湍流强度。在所述至少一种参数包括风速和湍流强度的情况下,上面提到的预定参数为风速,第一预定预设区间的下限大于第二预定预设区间的上限。应该理解,这仅是示例性的,取决于模型的不同,所述至少一种参数也不同。
下面结合图2示出根据本申请的另一个实施例的在所述模型每次被使用时评估不确定性的方法。
图2示出根据本申请的第二实施例的估计与风力发电机组有关的模型的不确定性的方法的流程图。所述模型每次被使用时执行图2所示的方法。
在步骤S210,获取所述模型在当前次被使用时的输出。换言之,所述模型在当前次被使用时基于输入而得到输出。
在步骤S220,基于所述输出估计所述输出的误差的分布的参数。
在一个实施例中,可基于所述输出和所述模型在上一次被使用时的输出的误差的分布(例如,预定类型的分布)的参数,估计所述模型在当前次被使用时的输出的误差的分布的参数。例如,可通过递推算法,基于所述输出和所述模型在上一次被使用时的输出的误差的分布的参数来估计所述模型在当前次被使用时的输出的误差的分布的参数。可利用现有的适用于所述模型的输出的误差的分布的参数的各种递推算法来进行估计。
在一个实施例中,误差的分布是指所述模型的输出的误差所服从的分布。例如,误差的分布取决于所述模型的输出的特点,可以是诸如正态分布、泊松分布、或威布尔分布等,但不限于此。可预先确定所述模型的输出的误差所服从的分布。
例如,在误差的分布为正态分布的情况下,所述分布的参数包括均值和标准差。下面描述本申请所提出的基于所述输出估计所述输出的误差的均值和标准差的方法。
均值通过下面的等式(1)计算:
Figure PCTCN2017118992-appb-000001
其中,μ ∈,k表示所述模型在作为当前次的第k次被使用时的输出的误差的分布的参数中的均值,μ ∈,k-1表示所述模型在作为上一次的第k-1次被使用时的输出的误差的分布的参数中的均值,
Figure PCTCN2017118992-appb-000002
为第k次使用所述模型时模型的输出。
标准差通过下面的等式(2)计算:
Figure PCTCN2017118992-appb-000003
其中,σ ∈,k表示所述模型在作为当前次的第k次被使用时的输出的误差的分布的参数中的标准差,σ ∈,k-1表示所述模型在作为上一次的第k-1次被使用时的输出的误差的分布的参数中的标准差,
Figure PCTCN2017118992-appb-000004
为第k次使用所述模型时模型的输出。
应该理解,本申请的估计均值和标准差的方法不限于此,其他方法也是可行的。另外,本申请的估计均值和标准差的方法不限于适用于误差的分布 为正态分布的情况,其他的使用均值和标准差作为参数的分布也是可行的。
在步骤S230,基于所述分布的参数得到所述模型的输出的不确定性(以下,称为第二不确定性)。
在一个实施例中,第二不确定性为第二预定值与所述分布的各个参数的和。第二预定值可表示预定的不确定性。例如,可根据模型、使用环境、和/或使用方式等的不同来确定第二预定值。例如,在一个示例中,所述第二预定值可为1。
例如,在误差的分布为正态分布的情况下,第二不确定性可被表示为第二预定值、估计的均值、估计的标准差这三者的和。
根据本申请的一个实施例,本申请还提供根据本申请的第三实施例的估计与风力发电机组有关的模型的不确定性的方法。所述方法包括第一实施例和第二实施例所示的方法,并且所述方法还包括所述模型每次被使用时,计算第一不确定性与第二不确定性的乘积作为所述模型的输出的第三不确定性。
根据本申请的一个实施例,本申请还提供一种校正与风力发电机组有关的模型的输出的方法。在该方法中,首先通过第一实施例所示的方法估计与风力发电机组有关的模型的输出的第一不确定性,或者通过第二实施例所示的方法估计与风力发电机组有关的模型的输出的第二不确定性,或者通第三实施例所示的方法估计与风力发电机组有关的模型的输出的第三不确定性;然后使用估计的不确定性校正与风力发电机组有关的模型的输出。具体地说,可计算估计的不确定性与所述输出的乘积来校正原始的输出,使用所述乘积的结果来更新原始的输出。
根据本申请的一个实施例,本申请还提供一种校正与风力发电机组有关的模型的输出的系统。所述系统包括:处理器和存储器。存储器存储有计算机可读代码、指令或程序,当所述计算机可读代码、指令或程序被处理器执行时,执行上述校正与风力发电机组有关的模型的输出的方法。
下面结合图3示出根据本申请的一个实施例的估计与风力发电机组有关的模型的不确定性的设备。
图3示出根据本申请的第四实施例的估计与风力发电机组有关的模型的不确定性的设备的框图。
如图3所示,根据本申请的第四实施例的估计与风力发电机组有关的模型的不确定性的设备300包括输入参数获取单元310、区间判断单元320、计 数单元330、第一估计单元340。
所述设备300在所述模型每次被使用时进行操作以估计所述不确定性。换言之,在所述模型每次被使用时,输入参数获取单元310、区间判断单元320、计数单元330、第一估计单元340进行操作,以估计所述不确定性。
输入参数获取单元310在所述模型每次被使用时,获取所述模型在当前次被使用时接收的输入中的至少一种参数(即,一种或多种参数)。所述至少一种参数可以是接收的输入中的所有参数或部分参数。所述至少一种参数被预先确定,从而每次执行所述模型的不确定性的估计时都获取同样类型的参数。
区间判断单元320确定输入参数获取单元310获取的所述至少一种参数各自所落入的预设区间。
在本申请中,针对所述至少一种参数中的每种参数,预先划分关于每种参数的多个预设区间。当获取到所述至少一种参数时,确定每种参数落入该种参数所对应的多个预设区间中的哪个预设区间。
在一个实施例中,在所述至少一种参数包括第一参数和第二参数的情况下,针对第一参数划分第一数量的预设区间,针对第二参数划分第二数量的预设区间。在获取到第一参数和第二参数后,确定获取的第一参数落入第一数量的预设区间中的哪个区间,确定获取的第二参数落入第二数量的预设区间中的哪个区间。应该理解,本申请不限于两种参数,上面的描述仅是示例性的。
计数单元330分别针对所述至少一种参数中的每种参数,统计至目前为止之所述模型各次被使用时每种参数分别落在确定的各自所落入的预设区间的总次数。
在一个实施例中,针对区间判断单元320确定的每个预设区间,统计在包括本次的所有次的不确定性估计中落入确定的每个预设区间的对应参数的总次数。也就是说,仅是针对区间判断单元320确定的区间来确定历史上有对应类型的参数落入其中的总次数。
在一个实施例中,针对在区间判断单元320确定的第一参数所落入的区间,确定在包括本次的所有次的不确定性估计中,第一参数落入到该区间的总次数。例如,如果该区间仅在本次以及上一次的不确定性估计中有第一参数落入,则总次数为2。
第一估计单元340基于所述总次数确定所述模型的输出的第一不确定性。
在一个实施例中,确定与所述至少一种参数的类型、所述总次数以及确定的预设区间对应的指示所述模型的输出的不确定性的值。例如,可预先建立关于不确定性与参数的类型、预设区间、总次数的函数,或者预先建立不确定性与参数的类型、预设区间、总次数的映射表。这样,可基于所述至少一种参数的类型、确定的预设区间(如,可由预设区间的标识符、标号等表示)、统计的总次数,通过函数或映射表来确定不确定性。
虽然输入参数获取单元310获取了至少一种参数,在所述至少一种参数为多种参数的情况下,针对某些模型,并不是所有情况都要全部使用所述多种参数,此时要考虑其中的预定参数由区间判断单元320确定的所落入的区间来确定使用所述多种参数中的哪些参数来用于不确定性的估计。即,根据所述多种参数中的预定参数所落入的预设区间来从所述多种参数中确定预定数量的参数,基于所述预定数量的参数所对应的总次数(即,所述预定数量的参数的由区间判断单元320确定的所落入的区间所对应的由计数单元330统计的总次数)确定所述模型的输出的第一不确定性。在此情况下,所述设备300还包括选择单元,选择单元根据所述多种参数中的预定参数所落入的预设区间来从所述多种参数中确定预定数量的参数。第一估计单元340基于所述预定数量的参数所对应的总次数确定所述模型的输出的第一不确定性。
在一个实施例中,基于所述总次数确定所述模型的输出的第一不确定性的处理包括:当所述多种参数中的预定参数所落入的预设区间为第一预定预设区间时,第一估计单元仅基于与所述预定参数对应的总次数确定所述模型的输出的第一不确定性;当所述多种参数中的预定参数所落入的预设区间为与第一预定预设区间不同的第二预定预设区间时,第一估计单元基于与所述多种参数的所有参数对应的总次数确定所述模型的输出的第一不确定性。应该理解,这里的第一预定预设区间或第二预定预设区间的数量可以为一个或多个。
仅基于与所述预定参数对应的总次数确定所述模型的输出的第一不确定性的处理包括:确定与所述预定参数的类型、与所述预定参数对应的总次数以及所述预定参数所落入的预设区间对应的指示所述模型的输出的不确定性的值。例如,可将预定参数的类型、预定参数所落入的预设区间(例如,可由预设区间的标识符、标号等表示)、预定参数对应的总次数作为输入,基于 预先建立的关于不确定性与参数的类型、预设区间、总次数的函数,或者预先建立的不确定性与参数的类型、预设区间、总次数的映射表来获得指示所述模型的输出的不确定性的值。
基于与所述多种参数的所有参数对应的总次数确定所述模型的输出的第一不确定性的处理包括:确定与所述多种参数中的各种参数的类型、与各种参数对应的总次数以及各种参数所落入的预设区间对应的指示所述模型的输出的不确定性的值。例如,可将所有参数的类型、所有参数所落入的预设区间(例如,可由预设区间的标识符、标号等表示)、所有参数对应的总次数作为输入,基于预先建立的关于不确定性与参数的类型、预设区间、总次数的函数,或者预先建立的不确定性与参数的类型、预设区间、总次数的映射表来获得指示所述模型的输出的不确定性的值。
此外,基于与所述多种参数的所有参数对应的总次数确定所述模型的输出的第一不确定性的处理还包括:当与所述多种参数中的任意一种参数对应的总次数不大于所述任意一种参数对应的次数阈值时,确定与所述多种参数中的各种参数的类型、与各种参数对应的总次数以及各种参数所落入的预设区间对应的指示所述模型的输出的不确定性的值。当与每种参数对应的总次数都大于或等于每种参数对应的次数阈值时,将所述模型的输出的第一不确定性的值确定为第一预定值。第一预定值可表示所述模型的输出完全可信赖。例如,根据不确定性的使用方式,所述第一预定值可为1。
在一个实施例中,针对每种参数的所有或部分预设区间设置次数阈值,根据在本次不确定性估计中确定的预设区间所对应的总次数是否超出对应的次数阈值来进一步确定不确定性。
在一个实施例中,所述至少一种参数包括风速和/或湍流强度。在所述至少一种参数包括风速和湍流强度的情况下,上面提到的预定参数为风速,第一预定预设区间的下限大于第二预定预设区间的上限。应该理解,这仅是示例性的,取决于模型的不同,所述至少一种参数也不同。
下面结合图4示出根据本申请的一个实施例的估计与风力发电机组有关的模型的不确定性的设备。
图4示出根据本申请的第五实施例的估计与风力发电机组有关的模型的不确定性的设备的框图。
如图4所示,根据本申请的第五实施例的估计与风力发电机组有关的模 型的不确定性的设备400包括输出获取单元410、分布参数估计单元420、第二估计单元430。
所述设备400在所述模型每次被使用时进行操作以估计所述不确定性。换言之,在所述模型每次被使用时,输出获取单元410、分布参数估计单元420、第二估计单元430进行操作,以估计所述不确定性。
输出获取单元410获取所述模型在当前次被使用时的输出。换言之,所述模型在当前次被使用时基于输入而得到输出。
分布参数估计单元420基于所述输出估计所述输出的误差的分布的参数。
在一个实施例中,分布参数估计单元420可基于所述输出和所述模型在上一次被使用时的输出的误差的分布的参数,估计所述模型在当前次被使用时的输出的误差的分布的参数。例如,可通过递推算法,基于所述输出和所述模型在上一次被使用时的输出的误差的分布的参数来估计所述模型在当前次被使用时的输出的误差的分布的参数。可利用现有的适用于所述模型的输出的误差的分布的参数的各种递推算法来进行估计。
在一个实施例中,误差的分布是指所述模型的输出的误差所服从的分布。例如,误差的分布取决于所述模型的输出的特点,可以是诸如正态分布、泊松分布、或威布尔分布等,但不限于此。可预先确定所述模型的输出的误差所服从的分布。此时,所示设备400还包括分布估计单元,分布估计单元确定所述模型的输出的误差的分布。
在一个实施例中,在误差的分布为正态分布的情况下,所述分布的参数包括均值和标准差。例如,可以通过上面的等式(1)和等式(2)估计所述输出的误差的均值和标准差。
第二估计单元430基于所述分布的参数得到所述模型的输出的第二不确定性。
在一个实施例中,第二不确定性为第二预定值与所述分布的各个参数的和。第二预定值可表示预定的不确定性。例如,可根据模型、使用环境、和/或使用方式等的不同来确定第二预定值。例如,在一个示例中,所述第二预定值可为1。
在一个实施例中,在误差的分布为正态分布的情况下,第二不确定性可被表示为第二预定值、估计的均值、估计的标准差这三者的和。
根据本申请的一个实施例,本申请还提供根据本申请的第六实施例的估 计与风力发电机组有关的模型的不确定性的设备。所述设备包括第四实施例和第五实施例所示的设备300和400,并且所述设备还包括第三估计单元,所述模型每次被使用时,第三估计单元计算第一不确定性与第二不确定性的乘积作为所述模型的输出的第三不确定性。
根据本申请的一个实施例,本申请还提供一种校正与风力发电机组有关的模型的输出的设备。该设备包括第四实施例、第五实施例或者第六实施例所示的估计与风力发电机组有关的模型的不确定性的设备。此外,该设备还包括估计单元,估计单元使用第一不确定性、或者第二不确定性、或者第三不确定性作为估计的不确定性,校正与风力发电机组有关的模型的输出。具体地说,估计单元可计算估计的不确定性与所述输出的乘积来校正原始的输出,使用所述乘积的结果来更新原始的输出。
根据本申请的一个实施例,本申请还提供一种估计与风力发电机组有关的模型的不确定性的系统。所述系统包括:处理器和存储器。存储器存储有计算机可读代码、指令或程序,当所述计算机可读代码、指令或程序被处理器执行时,执行第一实施例、第二实施例或第三实施例所示的方法。
此外,应该理解,根据本申请示例性实施例的设备中的各个单元可被实现硬件组件和/或软件组件。本领域技术人员根据限定的各个单元所执行的处理,可以例如使用现场可编程门阵列(FPGA)或专用集成电路(ASIC)来实现各个单元。
此外,根据本申请示例性实施例的方法可以被实现为计算机可读记录介质中的计算机代码。本领域技术人员可以根据对上述方法的描述来实现所述计算机代码。当所述计算机代码在计算机中被执行时实现本申请的上述方法。
根据本申请的估计与风力发电机组有关的模型的不确定性的方法和设备,可以准确地评价与风力发电机组有关的模型的不确定性。此外,通过使用根据本申请的不确定性评价方法和设备,可以校正模型的输出,提高模型的输出精度或准确性。
尽管已经参照其示例性实施例具体显示和描述了本申请,但是本领域的技术人员应该理解,在不脱离权利要求所限定的本申请的精神和范围的情况下,可以对其进行形式和细节上的各种改变。

Claims (24)

  1. 一种估计与风力发电机组有关的模型的不确定性的方法,其特征在于,所述方法包括在所述模型每次被使用时执行以下的估计步骤:
    获取所述模型在当前次被使用时接收的输入中的至少一种参数;
    确定获取的所述至少一种参数各自所落入的预设区间;
    分别针对所述至少一种参数中的每种参数,统计至目前为止之所述模型各次被使用时每种参数分别落在确定的各自所落入的预设区间的总次数;
    基于所述总次数确定所述模型的输出的第一不确定性。
  2. 根据权利要求1所述的方法,其特征在于,基于所述总次数确定所述模型的输出的第一不确定性的步骤包括:确定与所述至少一种参数的类型、所述总次数以及确定的预设区间对应的指示所述模型的输出的不确定性的值。
  3. 根据权利要求1或2所述的方法,其特征在于,所述至少一种参数为多种参数,所述估计步骤还包括:根据所述多种参数中的预定参数所落入的预设区间来从所述多种参数中确定预定数量的参数,
    基于所述总次数确定所述模型的输出的第一不确定性的步骤包括:基于所述预定数量的参数所对应的总次数确定所述模型的输出的第一不确定性。
  4. 根据权利要求3所述的方法,其特征在于,基于所述总次数确定所述模型的输出的第一不确定性的步骤包括:
    当所述多种参数中的预定参数所落入的预设区间为第一预定预设区间时,仅基于与所述预定参数对应的总次数确定所述模型的输出的第一不确定性;
    当所述多种参数中的预定参数所落入的预设区间为与第一预定预设区间不同的第二预定预设区间时,基于与所述多种参数的所有参数对应的总次数确定所述模型的输出的第一不确定性。
  5. 根据权利要求4所述的方法,其特征在于,仅基于与所述预定参数对应的总次数确定所述模型的输出的第一不确定性的步骤包括:确定与所述预定参数的类型、与所述预定参数对应的总次数以及所述预定参数所落入的预设区间对应的指示所述模型的输出的不确定性的值;
    基于与所述多种参数的所有参数对应的总次数确定所述模型的输出的第一不确定性的步骤包括:确定与所述多种参数中的各种参数的类型、与各种参数对应的总次数以及各种参数所落入的预设区间对应的指示所述模型的输 出的不确定性的值。
  6. 根据权利要求4所述的方法,其特征在于,基于与所述多种参数的所有参数对应的总次数确定所述模型的输出的第一不确定性的步骤包括:当与所述多种参数中的任意一种参数对应的总次数不大于所述任意一种参数对应的次数阈值时,确定与所述多种参数中的各种参数的类型、与各种参数对应的总次数以及各种参数所落入的预设区间对应的指示所述模型的输出的不确定性的值。
  7. 根据权利要求6所述的方法,其特征在于,基于与所述多种参数的所有参数对应的总次数确定所述模型的输出的第一不确定性的步骤还包括:当与每种参数对应的总次数都大于或等于每种参数对应的次数阈值时,将所述模型的输出的第一不确定性的值确定为第一预定值。
  8. 根据权利要求1所述的方法,其特征在于,所述估计步骤还包括:
    获取所述模型在当前次被使用时的输出;
    基于所述输出估计所述输出的误差的分布的参数;
    基于所述分布的参数得到所述模型的输出的第二不确定性。
  9. 根据权利要求8所述的方法,所述估计步骤还包括:计算第一不确定性与第二不确定性的乘积作为所述模型的输出的第三不确定性。
  10. 根据权利要求8所述的方法,其特征在于,估计所述误差的分布的参数的步骤包括:基于所述输出和所述模型在上一次被使用时的输出的误差的分布的参数,估计所述模型在当前次被使用时的输出的误差的分布的参数。
  11. 根据权利要求8所述的方法,其特征在于,第二不确定性为第二预定值与所述分布的各个参数的和。
  12. 一种估计与风力发电机组有关的模型的不确定性的设备,其特征在于,所述设备在所述模型每次被使用时进行操作以估计所述不确定性,所述设备包括:
    输入参数获取单元,获取所述模型在当前次被使用时接收的输入中的至少一种参数;
    区间判断单元,确定获取的所述至少一种参数各自所落入的预设区间;
    计数单元,分别针对所述至少一种参数中的每种参数,统计至目前为止之所述模型各次被使用时每种参数分别落在确定的各自所落入的预设区间的总次数;
    第一估计单元,基于所述总次数确定所述模型的输出的第一不确定性。
  13. 根据权利要求14所述的设备,其特征在于,第一估计单元确定与所述至少一种参数的类型、所述总次数以及确定的预设区间对应的指示所述模型的输出的不确定性的值。
  14. 根据权利要求12或13所述的设备,其特征在于,所述至少一种参数为多种参数,所述设备还包括:选择单元,根据所述多种参数中的预定参数所落入的预设区间来从所述多种参数中确定预定数量的参数,
    第一估计单元基于所述预定数量的参数所对应的总次数确定所述模型的输出的第一不确定性。
  15. 根据权利要求14所述的设备,其特征在于,
    当所述多种参数中的预定参数所落入的预设区间为第一预定预设区间时,第一估计单元仅基于与所述预定参数对应的总次数确定所述模型的输出的第一不确定性;
    当所述多种参数中的预定参数所落入的预设区间为与第一预定预设区间不同的第二预定预设区间时,第一估计单元基于与所述多种参数的所有参数对应的总次数确定所述模型的输出的第一不确定性。
  16. 根据权利要求15所述的设备,其特征在于,仅基于与所述预定参数对应的总次数确定所述模型的输出的第一不确定性的处理包括:确定与所述预定参数的类型、与所述预定参数对应的总次数以及所述预定参数所落入的预设区间对应的指示所述模型的输出的不确定性的值;
    基于与所述多种参数的所有参数对应的总次数确定所述模型的输出的第一不确定性的处理包括:确定与所述多种参数中的各种参数的类型、与各种参数对应的总次数以及各种参数所落入的预设区间对应的指示所述模型的输出的不确定性的值。
  17. 根据权利要求15所述的设备,其特征在于,基于与所述多种参数的所有参数对应的总次数确定所述模型的输出的第一不确定性的处理包括:当与所述多种参数中的任意一种参数对应的总次数不大于所述任意一种参数对应的次数阈值时,确定与所述多种参数中的各种参数的类型、与各种参数对应的总次数以及各种参数所落入的预设区间对应的指示所述模型的输出的不确定性的值。
  18. 根据权利要求17所述的设备,其特征在于,基于与所述多种参数的 所有参数对应的总次数确定所述模型的输出的第一不确定性的处理还包括:当与每种参数对应的总次数都大于或等于每种参数对应的次数阈值时,将所述模型的输出的第一不确定性的值确定为第一预定值。
  19. 根据权利要求12所述的设备,其特征在于,所述设备还包括:
    输出获取单元,获取所述模型在当前次被使用时的输出;
    分布参数估计单元,基于所述输出估计所述输出的误差的分布的参数;
    第二估计单元,基于所述分布的参数得到所述模型的输出的第二不确定性。
  20. 根据权利要求19所述的设备,所述设备还包括:第三估计单元,计算第一不确定性与第二不确定性的乘积作为所述模型的输出的第三不确定性。
  21. 根据权利要求19所述的设备,其特征在于,分布参数估计单元基于所述输出和所述模型在上一次被使用时的输出的误差的分布的参数,估计所述模型在当前次被使用时的输出的误差的分布的参数。
  22. 根据权利要求19所述的设备,其特征在于,第二不确定性为第二预定值与所述分布的各个参数的和。
  23. 一种估计与风力发电机组有关的模型的不确定性的系统,其特征在于,所述系统包括:
    处理器;
    存储器,存储有计算机程序,当所述计算机程序被处理器执行时,执行权利要求1至11中的任意一项所述的方法。
  24. 一种其中存储有计算机程序的计算机可读存储介质,当所述计算机程序被执行时执行权利要求1至11中的任意一项所述的方法。
PCT/CN2017/118992 2017-09-18 2017-12-27 估计与风力发电机组有关的模型的不确定性的方法和设备 WO2019052084A1 (zh)

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Citations (4)

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CN102096624A (zh) * 2009-12-09 2011-06-15 联想(北京)有限公司 一种对机群运行状况进行预测的方法和机群平台管理装置
CN103544351A (zh) * 2013-10-25 2014-01-29 北京世纪高通科技有限公司 一种对仿真模型调整参数的方法及装置
CN105991667A (zh) * 2015-01-27 2016-10-05 华为软件技术有限公司 修正资源预测误差的方法和装置
CN106503841A (zh) * 2016-10-17 2017-03-15 东软集团股份有限公司 指标阈值的确定方法和装置

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CN103544351A (zh) * 2013-10-25 2014-01-29 北京世纪高通科技有限公司 一种对仿真模型调整参数的方法及装置
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