WO2022021470A1 - Prediction error distribution estimation method for frequency modulation potential of wind turbines - Google Patents

Prediction error distribution estimation method for frequency modulation potential of wind turbines Download PDF

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WO2022021470A1
WO2022021470A1 PCT/CN2020/107727 CN2020107727W WO2022021470A1 WO 2022021470 A1 WO2022021470 A1 WO 2022021470A1 CN 2020107727 W CN2020107727 W CN 2020107727W WO 2022021470 A1 WO2022021470 A1 WO 2022021470A1
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fan
wind
frequency modulation
wind speed
frequency regulation
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French (fr)
Chinese (zh)
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汤奕
阎诚
戴剑丰
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南京东博智慧能源研究院有限公司
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A30/00—Adapting or protecting infrastructure or their operation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects

Definitions

  • the invention relates to a wind power frequency regulation potential prediction error distribution estimation method, which belongs to the technical field of frequency stability control of power systems.
  • the technical problem to be solved by the present invention is to provide a method for estimating the error distribution of wind power frequency regulation potential prediction, which can accurately reflect the fluctuation range of wind power frequency regulation potential, optimize the spare capacity of the unit, and reduce the operation risk.
  • the present invention designs a method for estimating the error distribution of wind power frequency regulation potential prediction, which includes the following steps:
  • Step A For the fan, obtain the fan rotor speed, fan power measured data, fan predicted wind speed data, and fan measured wind speed data at each historical moment in the specified historical time period, and preprocess the obtained data, and then enter step B;
  • Step B Calculate and obtain the prediction error of the frequency regulation potential of the wind farm corresponding to each historical moment, as each sample, form a sample set, and then enter step C;
  • Step C According to each sample, based on the principle of maximum entropy, establish a probability density model of frequency modulation potential prediction error corresponding to the fan, and then enter step D;
  • Step D According to the frequency regulation potential prediction error probability density model, for the target wind speed data of the wind turbine, calculate and obtain the frequency regulation potential prediction interval of the wind farm under different confidence levels.
  • preprocessing is performed as follows for the fan rotor speed, fan power measured data, fan predicted wind speed data, and fan actual measured wind speed data at each historical moment;
  • the fan exits the frequency modulation, and deletes the historical moment and the corresponding fan rotor speed and fan power measured data , Fan predicted wind speed data, fan measured wind speed data; if the fan's measured wind speed data is greater than the wind speed lower limit of the fan participating in the frequency modulation operation, the fan participates in the frequency modulation, and retains the historical moment, and the corresponding fan rotor speed, fan power measured data, fan Predicted wind speed data, fan measured wind speed data.
  • step B in the step B, the following steps B1 to B7 are performed for each historical moment, respectively, to obtain the prediction error of the frequency regulation potential of the wind farm corresponding to each historical moment, as each sample, constitute sample collection;
  • ⁇ P df of the wind power frequency regulation potential where T del represents the time that the wind turbine continuously injects power into the grid, H represents the inherent inertia time constant of the wind turbine, and ⁇ f represents the wind speed measured by the wind turbine.
  • Step B6 According to the following formula:
  • the step C includes the following steps:
  • Step C2 According to the sample set X corresponding to the preset origin moments of each order n, the maximum entropy model of the sample set X is as follows:
  • s represents the variable
  • H(s) represents the information entropy of the sample set X
  • p(s) represents the probability density distribution to be obtained; then enter step C3;
  • Step C3 According to the maximum entropy model of the sample set X, according to the following formula:
  • Step C4 According to the Lagrangian function FL satisfying the conditions, the probability density distribution p(s) to be obtained is as follows:
  • Step C5. Substitute the probability density distribution p(s) to be obtained into the constraints in step C2, and obtain the following:
  • the Lagrangian multipliers ⁇ 0 , ..., ⁇ n , ..., ⁇ N are obtained, and then the probability density distribution p(s) to be obtained is obtained by solving the solution, that is, the corresponding FM Potential Forecast Error Probability Density Model.
  • the step D includes the following steps D1 to D2;
  • Step D1 According to the probability density model of the prediction error of the frequency regulation potential corresponding to the fan, calculate and obtain the confidence interval of the prediction error under different confidence levels, and then enter the step D2;
  • Step D2. Calculate and obtain the predicted value of the wind power frequency regulation potential corresponding to the target predicted wind speed data of the wind turbine, and superimpose it with the confidence interval of the prediction error under different confidence levels to obtain the prediction interval under different confidence levels.
  • the wind power frequency regulation potential prediction error distribution estimation method designed in the present invention adopts a new strategy design to obtain the wind farm frequency regulation potential prediction interval under different confidence levels. Accurate evaluation of the frequency regulation potential error interval and accurate estimation of the forecast fluctuation interval are of great significance for optimizing the reserve capacity of traditional units, alleviating the frequency regulation pressure of the grid, and improving the stable operation of the power system. It can accurately reflect the fluctuation range of wind power frequency regulation potential and optimize the unit reserve. capacity and reduce operational risk.
  • Fig. 1 is the schematic flow chart of designing the method for estimating the error distribution of wind power frequency regulation potential forecast according to the present invention
  • Fig. 2 is the fan frequency modulation potential error distribution after adopting the method proposed by the present invention
  • Fig. 3 is the prediction interval of the frequency regulation potential of the fan after adopting the method proposed in the present invention.
  • the present invention designs a method for estimating the error distribution of wind power frequency regulation potential prediction.
  • the following steps A to D are specifically performed.
  • Step A For the fan, obtain the fan rotor speed, fan power measured data, fan predicted wind speed data, and fan measured wind speed data at each historical moment in the specified historical time period, and preprocess the obtained data, and then enter step B.
  • step A the rotor speed of the fan, the measured data of the fan power, the predicted wind speed data of the fan, and the measured wind speed data of the fan at each historical moment are preprocessed as follows.
  • the fan exits the frequency modulation, and deletes the historical moment and the corresponding fan rotor speed and fan power measured data , Fan predicted wind speed data, fan measured wind speed data; if the fan's measured wind speed data is greater than the wind speed lower limit of the fan participating in the frequency modulation operation, the fan participates in the frequency modulation, and retains the historical moment, and the corresponding fan rotor speed, fan power measured data, fan Predicted wind speed data, fan measured wind speed data.
  • Step B For each historical moment, perform the following steps B1 to B7 to obtain the wind farm frequency modulation potential prediction error corresponding to each historical moment, as each sample, form a sample set, and then proceed to step C.
  • ⁇ P df of the wind power frequency regulation potential where T del represents the time that the wind turbine continuously injects power into the grid, H represents the inherent inertia time constant of the wind turbine, and ⁇ f represents the wind speed measured by the wind turbine.
  • Step B6 According to the following formula:
  • Step C According to each sample, based on the principle of maximum entropy, establish a probability density model of the frequency modulation potential prediction error corresponding to the fan, and then go to step D.
  • step C specifically executes the following steps C1 to C5.
  • Step C2 According to the sample set X corresponding to the preset origin moments of each order n, the maximum entropy model of the sample set X is as follows:
  • s represents the variable
  • H(s) represents the information entropy of the sample set X
  • p(s) represents the probability density distribution to be obtained; then go to step C3.
  • Step C3 According to the maximum entropy model of the sample set X, according to the following formula:
  • Step C4 According to the Lagrangian function FL satisfying the conditions, the probability density distribution p(s) to be obtained is as follows:
  • Step C5. Substitute the probability density distribution p(s) to be obtained into the constraints in step C2, and obtain the following:
  • the Lagrangian multipliers ⁇ 0 , ..., ⁇ n , ..., ⁇ N are obtained, and then the probability density distribution p(s) to be obtained is obtained by solving the solution, that is, the corresponding FM Potential Forecast Error Probability Density Model.
  • Step D According to the frequency regulation potential prediction error probability density model, for the target wind speed data of the wind turbine, calculate and obtain the frequency regulation potential prediction interval of the wind farm under different confidence levels.
  • step D specifically executes the following steps D1 to D2.
  • Step D1 According to the probability density model of the prediction error of the frequency regulation potential corresponding to the wind turbine, calculate and obtain the confidence interval of the prediction error under different confidence levels, and then enter the step D2.
  • Step D2. Calculate and obtain the predicted value of the wind power frequency regulation potential corresponding to the target predicted wind speed data of the wind turbine, and superimpose it with the confidence interval of the prediction error under different confidence levels to obtain the prediction interval under different confidence levels.
  • Step A Select the SCADA collection data and wind speed prediction data of a single wind turbine in a 1.5MW wind farm in a certain place in China from June 15 to 19, 2015, with a sampling interval of 5 minutes, and a total of 1440 sampling points.
  • the inherent inertia time constant of the fan is 5.04
  • the lower limit of the wind speed of the fan participating in the frequency modulation operation is 7m/s
  • the rated wind speed of the fan is 12m/s.
  • the fan exits the frequency modulation, and discards the historical measured data of rotor speed and power at the corresponding moment; when the measured value of the wind speed is greater than the lower limit of the wind speed for the fan participating in the frequency modulation operation, the fan participates in the frequency modulation, and the corresponding time is reserved.
  • the historical measured data of rotor speed and power of 1028 times are obtained after screening.
  • Step B Perform steps B1 to B7 for each historical moment to obtain the wind farm frequency modulation potential prediction error corresponding to each historical moment as each sample to form a sample set, and then proceed to step C.
  • Step C According to each sample, based on the principle of maximum entropy, perform steps C1 to C5 to establish the probability density model of the frequency modulation potential prediction error corresponding to the fan, and then go to step D.
  • the distribution of the fan frequency regulation potential error is shown in Figure 2.
  • Step D Calculate the frequency regulation potential prediction interval of the wind farm under different confidence levels, and calculate the prediction error confidence interval under different confidence levels according to the frequency regulation potential prediction error distribution probability density.
  • the predicted value of frequency regulation potential is calculated by the forecast data of the given wind speed series, and the prediction interval under different confidence levels is obtained by superimposing with the prediction error interval. shown.
  • the wind power frequency regulation potential prediction error distribution estimation method designed in the present invention adopts a new strategy design to obtain the wind farm frequency regulation potential prediction interval under different confidence levels. Accurate evaluation of the frequency regulation potential error interval and accurate estimation of the forecast fluctuation interval are of great significance for optimizing the reserve capacity of traditional units, alleviating the frequency regulation pressure of the grid, and improving the stable operation of the power system. It can accurately reflect the fluctuation range of wind power frequency regulation potential and optimize the unit reserve. capacity and reduce operational risk.

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Wind Motors (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

A prediction error distribution estimation method for the frequency modulation potential of wind turbines. In the method, the frequency modulation potential prediction intervals of a wind power plant at different confidence levels are obtained, and the execution process does not rely on prior knowledge of the distribution shape of error samples of the frequency modulation potential, so that for a wind generation set, the accurate estimation of an error interval and accurate estimation of a prediction fluctuation interval of the frequency modulation potential can be realized. The method is of great significance in optimizing the backup capacity of a traditional generating unit, relieving the frequency modulation pressure of a power grid and improving the stable operation of an electrical power system, and can accurately reflect a fluctuation range of the frequency modulation potential of wind turbines, optimize the backup capacity of a unit, and reduce an operational risk.

Description

一种风电调频潜力预测误差分布估计方法An estimation method of wind power frequency regulation potential forecast error distribution 技术领域technical field
本发明涉及一种风电调频潜力预测误差分布估计方法，属于电力系统频率稳定控制技术领域。The invention relates to a wind power frequency regulation potential prediction error distribution estimation method, which belongs to the technical field of frequency stability control of power systems.
背景技术Background technique
随着大规模风电接入电力系统，风电的功率波动性、非平稳性、以及不确定性对系统稳定运行带来了巨大的挑战，提高了系统频率失稳风险和调频需求。变流器附加频率控制技术使风电机组能够主动响应电网频率变化，在系统频率跌落瞬间提供额外的有功支撑，因此充分挖掘风电机组参与系统调频潜力，实时评估风电调频裕度，对促进源网荷双端柔性互动具有重要意义。With the integration of large-scale wind power into the power system, the power fluctuation, non-stationarity, and uncertainty of wind power have brought huge challenges to the stable operation of the system, increasing the risk of system frequency instability and the need for frequency regulation. The additional frequency control technology of the converter enables wind turbines to actively respond to grid frequency changes and provide additional active support at the moment when the system frequency drops. Therefore, the potential of wind turbines to participate in system frequency regulation is fully exploited, and the wind power frequency regulation margin is evaluated in real time, which is conducive to promoting the load on the source grid. Two-terminal flexible interaction is of great significance.
现有的研究主要集中在风电参与调频的有功控制策略制定以及一次调频备用功率的研究，对考虑风电场内风机调频潜力的研究还比较匮乏，并且也未涉及风电参与系统调频潜力预测误差分布的研究。Existing researches mainly focus on the formulation of active power control strategies for wind power participation in frequency regulation and the research on primary frequency regulation reserve power. There is still a lack of research considering the frequency regulation potential of wind turbines in wind farms, and it does not involve wind power participation in the system. Frequency regulation potential prediction error distribution. Research.
发明内容SUMMARY OF THE INVENTION
本发明所要解决的技术问题是提供一种风电调频潜力预测误差分布估计方法，能够准确反映风电调频潜力的波动范围，优化机组备用容量，降低运行风险。The technical problem to be solved by the present invention is to provide a method for estimating the error distribution of wind power frequency regulation potential prediction, which can accurately reflect the fluctuation range of wind power frequency regulation potential, optimize the spare capacity of the unit, and reduce the operation risk.
本发明为了解决上述技术问题采用以下技术方案：本发明设计了一种风电调频潜力预测误差分布估计方法，包括如下步骤：In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention designs a method for estimating the error distribution of wind power frequency regulation potential prediction, which includes the following steps:
步骤A.针对风机，获取指定历史时间段中各历史时刻的风机转子转速、风机功率实测数据、风机预测风速数据、风机实测风速数据，并针对所获数据进行预处理，然后进入步骤B；Step A. For the fan, obtain the fan rotor speed, fan power measured data, fan predicted wind speed data, and fan measured wind speed data at each historical moment in the specified historical time period, and preprocess the obtained data, and then enter step B;
步骤B.计算获得各历史时刻分别所对应的风电场调频潜力预测误差，作为各个样本，构成样本集合，然后进入步骤C；Step B. Calculate and obtain the prediction error of the frequency regulation potential of the wind farm corresponding to each historical moment, as each sample, form a sample set, and then enter step C;
步骤C.根据各个样本，基于最大熵原理，建立风机所对应的调频潜力预测误差概率密度模型，然后进入步骤D；Step C. According to each sample, based on the principle of maximum entropy, establish a probability density model of frequency modulation potential prediction error corresponding to the fan, and then enter step D;
步骤D.根据调频潜力预测误差概率密度模型，针对风机目标预测风速数据，计算获得不同置信水平下风电场调频潜力预测区间。Step D. According to the frequency regulation potential prediction error probability density model, for the target wind speed data of the wind turbine, calculate and obtain the frequency regulation potential prediction interval of the wind farm under different confidence levels.
作为本发明的一种优选技术方案：所述步骤A中，针对各历史时刻的风机转子转速、风机功率实测数据、风机预测风速数据、风机实测风速数据，按如下进行预处理；As a preferred technical solution of the present invention: in the step A, preprocessing is performed as follows for the fan rotor speed, fan power measured data, fan predicted wind speed data, and fan actual measured wind speed data at each historical moment;
分别针对各历史时刻的风机实测风速数据，若风机实测风速数据小于或等于风机参与 调频运行风速下限时，则风机退出调频，并删除该历史时刻、以及所对应的风机转子转速、风机功率实测数据、风机预测风速数据、风机实测风速数据；若风机实测风速数据大于风机参与调频运行风速下限时，则风机参与调频，并保留该历史时刻、以及所对应的风机转子转速、风机功率实测数据、风机预测风速数据、风机实测风速数据。For the measured wind speed data of the fan at each historical moment, if the measured wind speed data of the fan is less than or equal to the lower limit of the wind speed of the fan participating in the frequency modulation operation, the fan exits the frequency modulation, and deletes the historical moment and the corresponding fan rotor speed and fan power measured data , Fan predicted wind speed data, fan measured wind speed data; if the fan's measured wind speed data is greater than the wind speed lower limit of the fan participating in the frequency modulation operation, the fan participates in the frequency modulation, and retains the historical moment, and the corresponding fan rotor speed, fan power measured data, fan Predicted wind speed data, fan measured wind speed data.
作为本发明的一种优选技术方案：所述步骤B中，分别针对各历史时刻，执行如下步骤B1至步骤B7，获得各历史时刻分别所对应的风电场调频潜力预测误差，作为各个样本，构成样本集合；As a preferred technical solution of the present invention: in the step B, the following steps B1 to B7 are performed for each historical moment, respectively, to obtain the prediction error of the frequency regulation potential of the wind farm corresponding to each historical moment, as each sample, constitute sample collection;
步骤B1.根据如下公式：Step B1. According to the following formula:
Figure PCTCN2020107727-appb-000001
Figure PCTCN2020107727-appb-000001
获得风机叶尖速比λ、风机桨距角β，以及对应风机叶尖速比λ与风机桨距角β的风能利用系数C p(λ,β)，其中，v表示风机输入风速，ω表示风机转子转速，然后进入步骤B2； Obtain the fan tip speed ratio λ, the fan pitch angle β, and the wind energy utilization coefficient C p (λ, β) corresponding to the fan tip speed ratio λ and the fan pitch angle β, where v represents the input wind speed of the fan, ω represents fan rotor speed, and then enter step B2;
步骤B2.根据如下公式：Step B2. According to the following formula:
Figure PCTCN2020107727-appb-000002
Figure PCTCN2020107727-appb-000002
获得风机输出功率实际值P r，其中，ρ表示空气密度，R表示风轮半径；然后进入步骤B2； Obtain the actual value P r of the output power of the fan, where ρ represents the air density, and R represents the radius of the rotor; then go to step B2;
步骤B3.根据如下公式：Step B3. According to the following formula:
Figure PCTCN2020107727-appb-000003
Figure PCTCN2020107727-appb-000003
获得风机输出功率预测值P f，其中，C p(λ opt,β)表示对应最优风机叶尖速比λ opt与风机桨距角β的风能利用系数，然后进入步骤B4； Obtain the predicted value P f of the output power of the fan, wherein C p (λ opt , β) represents the wind energy utilization coefficient corresponding to the optimal fan tip speed ratio λ opt and the fan pitch angle β, and then enter step B4;
步骤B4.根据如下公式：Step B4. According to the following formula:
Figure PCTCN2020107727-appb-000004
Figure PCTCN2020107727-appb-000004
获得调频结束时刻风机输出功率P 0，其中，ω 0表示风机参与调频运行风速下限、根据风机最优叶尖速比对应得到的风机参与调频运行转速下限，C p(ω 0R/v,β)表示对应最优风机叶尖速比ω 0R/v与风机桨距角β的风能利用系数，然后进入步骤B5； Obtain the output power P 0 of the fan at the end of frequency modulation, where ω 0 represents the lower limit of the wind speed of the fan participating in the frequency modulation operation, and the lower limit of the speed of the fan participating in the frequency modulation operation obtained according to the optimal tip speed ratio of the fan, C p (ω 0 R/v,β ) represents the wind energy utilization coefficient corresponding to the optimal fan tip speed ratio ω 0 R/v and the fan pitch angle β, and then enters step B5;
步骤B5.根据如下公式：Step B5. According to the following formula:
Figure PCTCN2020107727-appb-000005
Figure PCTCN2020107727-appb-000005
获得风电调频潜力的预测值ΔP df，其中，T del表示风机向电网持续注入功率时间，H表示风机固有惯性时间常数，ω f表示风机实测风速数据根据风机最优叶尖速比对应得到的转子转速，ω 0表示风机参与调频运行风速下限、根据风机最优叶尖速比对应得到的风机参与调频运行转速下限；然后进入步骤B6； Obtain the predicted value ΔP df of the wind power frequency regulation potential, where T del represents the time that the wind turbine continuously injects power into the grid, H represents the inherent inertia time constant of the wind turbine, and ω f represents the wind speed measured by the wind turbine. The rotor corresponding to the optimal tip speed ratio of the wind turbine Rotation speed, ω 0 represents the lower limit of the wind speed of the fan participating in the frequency modulation operation, and the lower limit of the speed of the fan participating in the frequency modulation operation obtained according to the optimal blade tip speed ratio of the fan; then go to step B6;
步骤B6.根据如下公式：Step B6. According to the following formula:
Figure PCTCN2020107727-appb-000006
Figure PCTCN2020107727-appb-000006
获得风电调频潜力的实际值ΔP d，其中，ω r表示风机转子转速；然后进入步骤B7； Obtain the actual value ΔP d of the wind power frequency regulation potential, where ω r represents the rotor speed of the fan; then go to step B7;
步骤B7.根据如下公式：Step B7. According to the following formula:
Figure PCTCN2020107727-appb-000007
Figure PCTCN2020107727-appb-000007
计算风电调频潜力预测误差ΔP err，其中，P N为风机额定输出功率。 Calculate the wind power frequency regulation potential prediction error ΔP err , where P N is the rated output power of the wind turbine.
作为本发明的一种优选技术方案，所述步骤C包括如下步骤：As a preferred technical solution of the present invention, the step C includes the following steps:
步骤C1.根据如下公式：Step C1. According to the following formula:
Figure PCTCN2020107727-appb-000008
Figure PCTCN2020107727-appb-000008
计算获得样本集合X分别对应预设各阶n的原点矩，其中，n＝{0、...、N}，N表示预设最大阶数，L表示样本集合X中样本的个数，
Figure PCTCN2020107727-appb-000009
表示样本集合X中第l个样本的n次方，然后进入步骤C2； Calculate and obtain the origin moments of the sample set X corresponding to each preset order n, where n={0, . . . , N}, N represents the preset maximum order, L represents the number of samples in the sample set X,
Figure PCTCN2020107727-appb-000009
Represents the nth power of the lth sample in the sample set X, and then enters step C2;
步骤C2.根据样本集合X分别对应预设各阶n的原点矩，则样本集合X的最大熵模型如下：Step C2. According to the sample set X corresponding to the preset origin moments of each order n, the maximum entropy model of the sample set X is as follows:
max H(s)＝-∫p(s)ln p(s)dsmax H(s)=-∫p(s)ln p(s)ds
s.t.∫p(s)s nds＝a n st∫p(s)s n ds=a n
式中，s表示变量，H(s)表示样本集合X的信息熵，p(s)表示待求概率密度分布；然后进入步骤C3；In the formula, s represents the variable, H(s) represents the information entropy of the sample set X, and p(s) represents the probability density distribution to be obtained; then enter step C3;
步骤C3.根据样本集合X的最大熵模型，按如下公式：Step C3. According to the maximum entropy model of the sample set X, according to the following formula:
Figure PCTCN2020107727-appb-000010
Figure PCTCN2020107727-appb-000010
构造拉格朗日函数F L，其中，λ 0、...、λ n、...、λ N表示各个拉格朗日乘子，然后进入步骤C4； Construct the Lagrangian function FL , where λ 0 , ..., λ n , ..., λ N represent each Lagrangian multiplier, and then enter step C4;
步骤C4.根据拉格朗日函数F L满足条件，则待求概率密度分布p(s)如下： Step C4. According to the Lagrangian function FL satisfying the conditions, the probability density distribution p(s) to be obtained is as follows:
Figure PCTCN2020107727-appb-000011
Figure PCTCN2020107727-appb-000011
然后进入步骤C5；Then enter step C5;
步骤C5.将待求概率密度分布p(s)代入步骤C2中的约束条件，获得如下：Step C5. Substitute the probability density distribution p(s) to be obtained into the constraints in step C2, and obtain the following:
Figure PCTCN2020107727-appb-000012
Figure PCTCN2020107727-appb-000012
通过求解以上非线性方程组，获得拉格朗日乘子λ 0、...、λ n、...、λ N，进而求解获得待求概率密度分布p(s)，即风机所对应的调频潜力预测误差概率密度模型。 By solving the above nonlinear equations, the Lagrangian multipliers λ 0 , ..., λ n , ..., λ N are obtained, and then the probability density distribution p(s) to be obtained is obtained by solving the solution, that is, the corresponding FM Potential Forecast Error Probability Density Model.
作为本发明的一种优选技术方案：所述步骤D包括如下步骤D1至步骤D2；As a preferred technical solution of the present invention: the step D includes the following steps D1 to D2;
步骤D1.根据风机所对应的调频潜力预测误差概率密度模型，计算获得不同置信水平下预测误差置信区间，然后进入步骤D2；Step D1. According to the probability density model of the prediction error of the frequency regulation potential corresponding to the fan, calculate and obtain the confidence interval of the prediction error under different confidence levels, and then enter the step D2;
步骤D2.计算获得风机目标预测风速数据所对应的风电调频潜力预测值，并与不同置信水平下预测误差置信区间叠加，获得不同置信水平下的预测区间。Step D2. Calculate and obtain the predicted value of the wind power frequency regulation potential corresponding to the target predicted wind speed data of the wind turbine, and superimpose it with the confidence interval of the prediction error under different confidence levels to obtain the prediction interval under different confidence levels.
本发明所述一种风电调频潜力预测误差分布估计方法，采用以上技术方案与现有技术相比，具有以下技术效果：The method for estimating the error distribution of wind power frequency regulation potential prediction according to the present invention adopts the above technical solution and has the following technical effects compared with the prior art:
本发明所设计风电调频潜力预测误差分布估计方法，采用全新策略设计，获得不同置信水平下风电场调频潜力预测区间，执行过程不依赖调频潜力误差样本分布形状的先验知识，可以实现风电机组的调频潜力误差区间的准确评估、以及预测波动区间的准确估计，为优化传统机组备用容量、缓解电网调频压力和提升电力系统稳定运行具有重要意义，能够准确反映风电调频潜力的波动范围，优化机组备用容量，降低运行风险。The wind power frequency regulation potential prediction error distribution estimation method designed in the present invention adopts a new strategy design to obtain the wind farm frequency regulation potential prediction interval under different confidence levels. Accurate evaluation of the frequency regulation potential error interval and accurate estimation of the forecast fluctuation interval are of great significance for optimizing the reserve capacity of traditional units, alleviating the frequency regulation pressure of the grid, and improving the stable operation of the power system. It can accurately reflect the fluctuation range of wind power frequency regulation potential and optimize the unit reserve. capacity and reduce operational risk.
附图说明Description of drawings
图1为本发明设计风电调频潜力预测误差分布估计方法的流程示意图；Fig. 1 is the schematic flow chart of designing the method for estimating the error distribution of wind power frequency regulation potential forecast according to the present invention;
图2为采用本发明所提方法后风机调频潜力误差分布；Fig. 2 is the fan frequency modulation potential error distribution after adopting the method proposed by the present invention;
图3为采用本发明所提方法后风机调频潜力预测区间。Fig. 3 is the prediction interval of the frequency regulation potential of the fan after adopting the method proposed in the present invention.
具体实施方式detailed description
下面结合说明书附图对本发明的具体实施方式作进一步详细的说明。The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.
本发明设计了一种风电调频潜力预测误差分布估计方法，实际应用当中，如图1所示，具体执行如下步骤A至步骤D。The present invention designs a method for estimating the error distribution of wind power frequency regulation potential prediction. In practical application, as shown in FIG. 1 , the following steps A to D are specifically performed.
步骤A.针对风机，获取指定历史时间段中各历史时刻的风机转子转速、风机功率实测数据、风机预测风速数据、风机实测风速数据，并针对所获数据进行预处理，然后进入步骤B。Step A. For the fan, obtain the fan rotor speed, fan power measured data, fan predicted wind speed data, and fan measured wind speed data at each historical moment in the specified historical time period, and preprocess the obtained data, and then enter step B.
上述步骤A在实际应用中，针对各历史时刻的风机转子转速、风机功率实测数据、风机预测风速数据、风机实测风速数据，按如下进行预处理。In the practical application of the above step A, the rotor speed of the fan, the measured data of the fan power, the predicted wind speed data of the fan, and the measured wind speed data of the fan at each historical moment are preprocessed as follows.
分别针对各历史时刻的风机实测风速数据，若风机实测风速数据小于或等于风机参与调频运行风速下限时，则风机退出调频，并删除该历史时刻、以及所对应的风机转子转速、风机功率实测数据、风机预测风速数据、风机实测风速数据；若风机实测风速数据大于风机参与调频运行风速下限时，则风机参与调频，并保留该历史时刻、以及所对应的风机转子转速、风机功率实测数据、风机预测风速数据、风机实测风速数据。For the measured wind speed data of the fan at each historical moment, if the measured wind speed data of the fan is less than or equal to the lower limit of the wind speed of the fan participating in the frequency modulation operation, the fan exits the frequency modulation, and deletes the historical moment and the corresponding fan rotor speed and fan power measured data , Fan predicted wind speed data, fan measured wind speed data; if the fan's measured wind speed data is greater than the wind speed lower limit of the fan participating in the frequency modulation operation, the fan participates in the frequency modulation, and retains the historical moment, and the corresponding fan rotor speed, fan power measured data, fan Predicted wind speed data, fan measured wind speed data.
步骤B.分别针对各历史时刻，执行如下步骤B1至步骤B7，获得各历史时刻分别所对应的风电场调频潜力预测误差，作为各个样本，构成样本集合，然后进入步骤C。Step B. For each historical moment, perform the following steps B1 to B7 to obtain the wind farm frequency modulation potential prediction error corresponding to each historical moment, as each sample, form a sample set, and then proceed to step C.
步骤B1.根据如下公式：Step B1. According to the following formula:
Figure PCTCN2020107727-appb-000013
Figure PCTCN2020107727-appb-000013
获得风机叶尖速比λ、风机桨距角β，以及对应风机叶尖速比λ与风机桨距角β的风能利用系数C p(λ,β)，其中，v表示风机输入风速，ω表示风机转子转速，然后进入步骤B2。 Obtain the fan tip speed ratio λ, the fan pitch angle β, and the wind energy utilization coefficient C p (λ, β) corresponding to the fan tip speed ratio λ and the fan pitch angle β, where v represents the input wind speed of the fan, ω represents fan rotor speed, and then go to step B2.
步骤B2.根据如下公式：Step B2. According to the following formula:
Figure PCTCN2020107727-appb-000014
Figure PCTCN2020107727-appb-000014
获得风机输出功率实际值P r，其中，ρ表示空气密度，R表示风轮半径；然后进入步骤B2。 Obtain the actual value P r of the output power of the fan, where ρ represents the air density, and R represents the radius of the wind wheel; then go to step B2.
步骤B3.根据如下公式：Step B3. According to the following formula:
Figure PCTCN2020107727-appb-000015
Figure PCTCN2020107727-appb-000015
获得风机输出功率预测值P f，其中，C p(λ opt,β)表示对应最优风机叶尖速比λ opt与风机 桨距角β的风能利用系数，然后进入步骤B4。 Obtain the predicted value P f of the output power of the fan, wherein C p (λ opt , β) represents the wind energy utilization coefficient corresponding to the optimal fan tip speed ratio λ opt and the fan pitch angle β, and then enter step B4.
步骤B4.根据如下公式：Step B4. According to the following formula:
Figure PCTCN2020107727-appb-000016
Figure PCTCN2020107727-appb-000016
获得调频结束时刻风机输出功率P 0，其中，ω 0表示风机参与调频运行风速下限、根据风机最优叶尖速比对应得到的风机参与调频运行转速下限，C p(ω 0R/v,β)表示对应最优风机叶尖速比ω 0R/v与风机桨距角β的风能利用系数，然后进入步骤B5。 Obtain the output power P 0 of the fan at the end of frequency modulation, where ω 0 represents the lower limit of the wind speed of the fan participating in the frequency modulation operation, and the lower limit of the speed of the fan participating in the frequency modulation operation obtained according to the optimal tip speed ratio of the fan, C p (ω 0 R/v,β ) represents the wind energy utilization coefficient corresponding to the optimal fan tip speed ratio ω 0 R/v and the fan pitch angle β, and then goes to step B5.
步骤B5.根据如下公式：Step B5. According to the following formula:
Figure PCTCN2020107727-appb-000017
Figure PCTCN2020107727-appb-000017
获得风电调频潜力的预测值ΔP df，其中，T del表示风机向电网持续注入功率时间，H表示风机固有惯性时间常数，ω f表示风机实测风速数据根据风机最优叶尖速比对应得到的转子转速，ω 0表示风机参与调频运行风速下限、根据风机最优叶尖速比对应得到的风机参与调频运行转速下限；然后进入步骤B6。 Obtain the predicted value ΔP df of the wind power frequency regulation potential, where T del represents the time that the wind turbine continuously injects power into the grid, H represents the inherent inertia time constant of the wind turbine, and ω f represents the wind speed measured by the wind turbine. The rotor corresponding to the optimal tip speed ratio of the wind turbine Rotation speed, ω 0 represents the lower limit of the wind speed of the fan participating in the frequency modulation operation, and the lower limit of the fan participating in the frequency modulation operation corresponding to the optimal tip speed ratio of the fan; then go to step B6.
步骤B6.根据如下公式：Step B6. According to the following formula:
Figure PCTCN2020107727-appb-000018
Figure PCTCN2020107727-appb-000018
获得风电调频潜力的实际值ΔP d，其中，ω r表示风机转子转速；然后进入步骤B7。 Obtain the actual value ΔP d of the wind power frequency regulation potential, where ω r represents the rotor speed of the wind turbine; then go to step B7.
步骤B7.根据如下公式：Step B7. According to the following formula:
Figure PCTCN2020107727-appb-000019
Figure PCTCN2020107727-appb-000019
计算风电调频潜力预测误差ΔP err，其中，P N为风机额定输出功率。 Calculate the wind power frequency regulation potential prediction error ΔP err , where P N is the rated output power of the wind turbine.
步骤C.根据各个样本，基于最大熵原理，建立风机所对应的调频潜力预测误差概率密度模型，然后进入步骤D。Step C. According to each sample, based on the principle of maximum entropy, establish a probability density model of the frequency modulation potential prediction error corresponding to the fan, and then go to step D.
实际应用当中，上述步骤C具体执行如下步骤C1至步骤C5。In practical applications, the above step C specifically executes the following steps C1 to C5.
步骤C1.根据如下公式：Step C1. According to the following formula:
Figure PCTCN2020107727-appb-000020
Figure PCTCN2020107727-appb-000020
计算获得样本集合X分别对应预设各阶n的原点矩，其中，n＝{0、...、N}，N表示预设最大阶数，L表示样本集合X中样本的个数，
Figure PCTCN2020107727-appb-000021
表示样本集合X中第l个样本的n次方，然后进入步骤C2。 Calculate and obtain the origin moments of the sample set X corresponding to each preset order n, where n={0, . . . , N}, N represents the preset maximum order, L represents the number of samples in the sample set X,
Figure PCTCN2020107727-appb-000021
Represents the nth power of the lth sample in the sample set X, and then goes to step C2.
步骤C2.根据样本集合X分别对应预设各阶n的原点矩，则样本集合X的最大熵模型如下：Step C2. According to the sample set X corresponding to the preset origin moments of each order n, the maximum entropy model of the sample set X is as follows:
max H(s)＝-∫p(s)ln p(s)dsmax H(s)=-∫p(s)ln p(s)ds
s.t.∫p(s)s nds＝a n st∫p(s)s n ds=a n
式中，s表示变量，H(s)表示样本集合X的信息熵，p(s)表示待求概率密度分布；然后进入步骤C3。In the formula, s represents the variable, H(s) represents the information entropy of the sample set X, and p(s) represents the probability density distribution to be obtained; then go to step C3.
步骤C3.根据样本集合X的最大熵模型，按如下公式：Step C3. According to the maximum entropy model of the sample set X, according to the following formula:
Figure PCTCN2020107727-appb-000022
Figure PCTCN2020107727-appb-000022
构造拉格朗日函数F L，其中，λ 0、...、λ n、...、λ N表示各个拉格朗日乘子，然后进入步骤C4。 Construct the Lagrangian function FL , where λ 0 , . . . , λ n , .
步骤C4.根据拉格朗日函数F L满足条件，则待求概率密度分布p(s)如下： Step C4. According to the Lagrangian function FL satisfying the conditions, the probability density distribution p(s) to be obtained is as follows:
Figure PCTCN2020107727-appb-000023
Figure PCTCN2020107727-appb-000023
然后进入步骤C5。Then go to step C5.
步骤C5.将待求概率密度分布p(s)代入步骤C2中的约束条件，获得如下：Step C5. Substitute the probability density distribution p(s) to be obtained into the constraints in step C2, and obtain the following:
Figure PCTCN2020107727-appb-000024
Figure PCTCN2020107727-appb-000024
通过求解以上非线性方程组，获得拉格朗日乘子λ 0、...、λ n、...、λ N，进而求解获得待求概率密度分布p(s)，即风机所对应的调频潜力预测误差概率密度模型。 By solving the above nonlinear equations, the Lagrangian multipliers λ 0 , ..., λ n , ..., λ N are obtained, and then the probability density distribution p(s) to be obtained is obtained by solving the solution, that is, the corresponding FM Potential Forecast Error Probability Density Model.
步骤D.根据调频潜力预测误差概率密度模型，针对风机目标预测风速数据，计算获得不同置信水平下风电场调频潜力预测区间。Step D. According to the frequency regulation potential prediction error probability density model, for the target wind speed data of the wind turbine, calculate and obtain the frequency regulation potential prediction interval of the wind farm under different confidence levels.
实际应用当中，上述步骤D具体执行如下步骤D1至步骤D2。In practical applications, the above step D specifically executes the following steps D1 to D2.
步骤D1.根据风机所对应的调频潜力预测误差概率密度模型，计算获得不同置信水平下预测误差置信区间，然后进入步骤D2。Step D1. According to the probability density model of the prediction error of the frequency regulation potential corresponding to the wind turbine, calculate and obtain the confidence interval of the prediction error under different confidence levels, and then enter the step D2.
步骤D2.计算获得风机目标预测风速数据所对应的风电调频潜力预测值，并与不同置信水平下预测误差置信区间叠加，获得不同置信水平下的预测区间。Step D2. Calculate and obtain the predicted value of the wind power frequency regulation potential corresponding to the target predicted wind speed data of the wind turbine, and superimpose it with the confidence interval of the prediction error under different confidence levels to obtain the prediction interval under different confidence levels.
将本发明所设计风电调频潜力预测误差分布估计方法，实际应用当中，具体执行如下 步骤。In the practical application of the method for estimating the error distribution of wind power frequency regulation potential forecasting designed by the present invention, the following steps are specifically performed.
步骤A.选取中国某地1.5MW风电场内单台风机2015年6月15日至19日的SCADA采集数据和风速预测数据，采样间隔5min，共计1440采样点。风机固有惯性时间常数为5.04，风机参与调频运行风速下限为7m/s，风机额定风速为12m/s。根据历史风速实测值判断风机是否能参与调频，对风机转子转速和功率实测数据进行筛选。当风速实测值小于等于风机参与调频运行风速下限时，风机退出调频，弃置对应时刻的历史转子转速和功率实测数据；当风速实测值大于风机参与调频运行风速下限时，风机参与调频，保留对应时刻的历史转子转速和功率实测数据，经筛选后得到1028个时刻对应采样数据。Step A. Select the SCADA collection data and wind speed prediction data of a single wind turbine in a 1.5MW wind farm in a certain place in China from June 15 to 19, 2015, with a sampling interval of 5 minutes, and a total of 1440 sampling points. The inherent inertia time constant of the fan is 5.04, the lower limit of the wind speed of the fan participating in the frequency modulation operation is 7m/s, and the rated wind speed of the fan is 12m/s. According to the measured value of historical wind speed, it is judged whether the fan can participate in frequency regulation, and the measured data of rotor speed and power of the fan is screened. When the measured value of wind speed is less than or equal to the lower limit of the wind speed of the fan participating in the frequency modulation operation, the fan exits the frequency modulation, and discards the historical measured data of rotor speed and power at the corresponding moment; when the measured value of the wind speed is greater than the lower limit of the wind speed for the fan participating in the frequency modulation operation, the fan participates in the frequency modulation, and the corresponding time is reserved The historical measured data of rotor speed and power of 1028 times are obtained after screening.
步骤B.分别针对各历史时刻，执行步骤B1至步骤B7，获得各历史时刻分别所对应的风电场调频潜力预测误差，作为各个样本，构成样本集合，然后进入步骤C。Step B. Perform steps B1 to B7 for each historical moment to obtain the wind farm frequency modulation potential prediction error corresponding to each historical moment as each sample to form a sample set, and then proceed to step C.
步骤C.根据各个样本，基于最大熵原理，执行步骤C1至步骤C5，建立风机所对应的调频潜力预测误差概率密度模型，然后进入步骤D。Step C. According to each sample, based on the principle of maximum entropy, perform steps C1 to C5 to establish the probability density model of the frequency modulation potential prediction error corresponding to the fan, and then go to step D.
实际应用中，选取N＝5，风电调频潜力预测误差最大熵分布模型参数如下表1所示。风机调频潜力误差分布如图2所示。In practical applications, N=5 is selected, and the model parameters of the maximum entropy distribution model of wind power frequency regulation potential prediction error are shown in Table 1 below. The distribution of the fan frequency regulation potential error is shown in Figure 2.
Figure PCTCN2020107727-appb-000025
Figure PCTCN2020107727-appb-000025
表1Table 1
步骤D.计算不同置信水平下风电场调频潜力预测区间，根据调频潜力预测误差分布概率密度计算不同置信水平下预测误差置信区间。通过给定风速序列的预测数据计算调频潜力预测值，与预测误差区间叠加得到不同置信水平下的预测区间，选取该风电场2015年6月20日风速预测数据，计算调频潜力预测区间如图3所示。Step D. Calculate the frequency regulation potential prediction interval of the wind farm under different confidence levels, and calculate the prediction error confidence interval under different confidence levels according to the frequency regulation potential prediction error distribution probability density. The predicted value of frequency regulation potential is calculated by the forecast data of the given wind speed series, and the prediction interval under different confidence levels is obtained by superimposing with the prediction error interval. shown.
本发明所设计风电调频潜力预测误差分布估计方法，采用全新策略设计，获得不同置信水平下风电场调频潜力预测区间，执行过程不依赖调频潜力误差样本分布形状的先验知识，可以实现风电机组的调频潜力误差区间的准确评估、以及预测波动区间的准确估计，为优化传统机组备用容量、缓解电网调频压力和提升电力系统稳定运行具有重要意义，能够准确反映风电调频潜力的波动范围，优化机组备用容量，降低运行风险。The wind power frequency regulation potential prediction error distribution estimation method designed in the present invention adopts a new strategy design to obtain the wind farm frequency regulation potential prediction interval under different confidence levels. Accurate evaluation of the frequency regulation potential error interval and accurate estimation of the forecast fluctuation interval are of great significance for optimizing the reserve capacity of traditional units, alleviating the frequency regulation pressure of the grid, and improving the stable operation of the power system. It can accurately reflect the fluctuation range of wind power frequency regulation potential and optimize the unit reserve. capacity and reduce operational risk.
上面结合附图对本发明的实施方式作了详细说明，但是本发明并不限于上述实施方式，在本领域普通技术人员所具备的知识范围内，还可以在不脱离本发明宗旨的前提下做出各种变化。The embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned embodiments, and can also be made within the scope of knowledge possessed by those of ordinary skill in the art without departing from the purpose of the present invention. Various changes.

Claims (5)

  1. 一种风电调频潜力预测误差分布估计方法，其特征在于，包括如下步骤：A method for estimating error distribution of wind power frequency regulation potential prediction, characterized in that it includes the following steps:
    步骤A.针对风机，获取指定历史时间段中各历史时刻的风机转子转速、风机功率实测数据、风机预测风速数据、风机实测风速数据，并针对所获数据进行预处理，然后进入步骤B；Step A. For the fan, obtain the fan rotor speed, fan power measured data, fan predicted wind speed data, and fan measured wind speed data at each historical moment in the specified historical time period, and preprocess the obtained data, and then enter step B;
    步骤B.计算获得各历史时刻分别所对应的风电场调频潜力预测误差，作为各个样本，构成样本集合，然后进入步骤C；Step B. Calculate and obtain the prediction error of the frequency regulation potential of the wind farm corresponding to each historical moment, as each sample, form a sample set, and then enter step C;
    步骤C.根据各个样本，基于最大熵原理，建立风机所对应的调频潜力预测误差概率密度模型，然后进入步骤D；Step C. According to each sample, based on the principle of maximum entropy, establish a probability density model of frequency modulation potential prediction error corresponding to the fan, and then enter step D;
    步骤D.根据调频潜力预测误差概率密度模型，针对风机目标预测风速数据，计算获得不同置信水平下风电场调频潜力预测区间。Step D. According to the probability density model of the frequency regulation potential prediction error, for the target wind speed data of the wind turbine, calculate and obtain the frequency regulation potential prediction interval of the wind farm under different confidence levels.
  2. 根据权利要求1所述一种风电调频潜力预测误差分布估计方法，其特征在于：所述步骤A中，针对各历史时刻的风机转子转速、风机功率实测数据、风机预测风速数据、风机实测风速数据，按如下进行预处理；A method for estimating the error distribution of wind power frequency regulation potential prediction according to claim 1, characterized in that: in the step A, for each historical moment of the fan rotor speed, fan power measured data, fan predicted wind speed data, fan measured wind speed data , preprocessed as follows;
    分别针对各历史时刻的风机实测风速数据，若风机实测风速数据小于或等于风机参与调频运行风速下限时，则风机退出调频，并删除该历史时刻、以及所对应的风机转子转速、风机功率实测数据、风机预测风速数据、风机实测风速数据；若风机实测风速数据大于风机参与调频运行风速下限时，则风机参与调频，并保留该历史时刻、以及所对应的风机转子转速、风机功率实测数据、风机预测风速数据、风机实测风速数据。For the measured wind speed data of the fan at each historical moment, if the measured wind speed data of the fan is less than or equal to the lower limit of the wind speed of the fan participating in the frequency modulation operation, the fan exits the frequency modulation, and deletes the historical moment and the corresponding fan rotor speed and fan power measured data , Fan predicted wind speed data, fan measured wind speed data; if the fan's measured wind speed data is greater than the wind speed lower limit of the fan participating in the frequency modulation operation, the fan participates in the frequency modulation, and retains the historical moment, and the corresponding fan rotor speed, fan power measured data, fan Predicted wind speed data, fan measured wind speed data.
  3. 根据权利要求1所述一种风电调频潜力预测误差分布估计方法，其特征在于：所述步骤B中，分别针对各历史时刻，执行如下步骤B1至步骤B7，获得各历史时刻分别所对应的风电场调频潜力预测误差，作为各个样本，构成样本集合；The method for estimating the error distribution of wind power frequency regulation potential prediction according to claim 1, characterized in that: in the step B, the following steps B1 to B7 are performed for each historical moment, respectively, to obtain the wind power corresponding to each historical moment. The field frequency modulation potential prediction error, as each sample, constitutes a sample set;
    步骤B1.根据如下公式：Step B1. According to the following formula:
    Figure PCTCN2020107727-appb-100001
    Figure PCTCN2020107727-appb-100001
    获得风机叶尖速比λ、风机桨距角β，以及对应风机叶尖速比λ与风机桨距角β的风能利用系数C p(λ,β)，其中，v表示风机输入风速，ω表示风机转子转速，然后进入步骤B2； Obtain the fan tip speed ratio λ, the fan pitch angle β, and the wind energy utilization coefficient C p (λ, β) corresponding to the fan tip speed ratio λ and the fan pitch angle β, where v represents the input wind speed of the fan, ω represents fan rotor speed, and then enter step B2;
    步骤B2.根据如下公式：Step B2. According to the following formula:
    Figure PCTCN2020107727-appb-100002
    Figure PCTCN2020107727-appb-100002
    获得风机输出功率实际值P r，其中，ρ表示空气密度，R表示风轮半径；然后进入步骤B2； Obtain the actual value P r of the output power of the fan, where ρ represents the air density, and R represents the radius of the rotor; then go to step B2;
    步骤B3.根据如下公式：Step B3. According to the following formula:
    Figure PCTCN2020107727-appb-100003
    Figure PCTCN2020107727-appb-100003
    获得风机输出功率预测值P f，其中，C p(λ opt,β)表示对应最优风机叶尖速比λ opt与风机桨距角β的风能利用系数，然后进入步骤B4； Obtain the predicted value P f of the output power of the fan, wherein C p (λ opt , β) represents the wind energy utilization coefficient corresponding to the optimal fan tip speed ratio λ opt and the fan pitch angle β, and then enter step B4;
    步骤B4.根据如下公式：Step B4. According to the following formula:
    Figure PCTCN2020107727-appb-100004
    Figure PCTCN2020107727-appb-100004
    获得调频结束时刻风机输出功率P 0，其中，ω 0表示风机参与调频运行风速下限、根据风机最优叶尖速比对应得到的风机参与调频运行转速下限，C p(ω 0R/v,β)表示对应最优风机叶尖速比ω 0R/v与风机桨距角β的风能利用系数，然后进入步骤B5； Obtain the output power P 0 of the fan at the end of frequency modulation, where ω 0 represents the lower limit of the wind speed of the fan participating in the frequency modulation operation, and the lower limit of the speed of the fan participating in the frequency modulation operation obtained according to the optimal tip speed ratio of the fan, C p (ω 0 R/v,β ) represents the wind energy utilization coefficient corresponding to the optimal fan tip speed ratio ω 0 R/v and the fan pitch angle β, and then enters step B5;
    步骤B5.根据如下公式：Step B5. According to the following formula:
    Figure PCTCN2020107727-appb-100005
    Figure PCTCN2020107727-appb-100005
    获得风电调频潜力的预测值ΔP df，其中，T del表示风机向电网持续注入功率时间，H表示风机固有惯性时间常数，ω f表示风机实测风速数据根据风机最优叶尖速比对应得到的转子转速，ω 0表示风机参与调频运行风速下限、根据风机最优叶尖速比对应得到的风机参与调频运行转速下限；然后进入步骤B6； Obtain the predicted value ΔP df of the wind power frequency regulation potential, where T del represents the time that the wind turbine continuously injects power into the grid, H represents the inherent inertia time constant of the wind turbine, and ω f represents the wind speed measured by the wind turbine. The rotor corresponding to the optimal tip speed ratio of the wind turbine Rotation speed, ω 0 represents the lower limit of the wind speed of the fan participating in the frequency modulation operation, and the lower limit of the speed of the fan participating in the frequency modulation operation obtained according to the optimal blade tip speed ratio of the fan; then go to step B6;
    步骤B6.根据如下公式：Step B6. According to the following formula:
    Figure PCTCN2020107727-appb-100006
    Figure PCTCN2020107727-appb-100006
    获得风电调频潜力的实际值ΔP d，其中，ω r表示风机转子转速；然后进入步骤B7； Obtain the actual value ΔP d of the wind power frequency regulation potential, where ω r represents the rotor speed of the fan; then go to step B7;
    步骤B7.根据如下公式：Step B7. According to the following formula:
    Figure PCTCN2020107727-appb-100007
    Figure PCTCN2020107727-appb-100007
    计算风电调频潜力预测误差ΔP err，其中，P N为风机额定输出功率。 Calculate the wind power frequency regulation potential prediction error ΔP err , where P N is the rated output power of the wind turbine.
  4. 根据权利要求1所述一种风电调频潜力预测误差分布估计方法，其特征在于，所述步骤C包括如下步骤：The method for estimating error distribution of wind power frequency regulation potential prediction according to claim 1, wherein the step C comprises the following steps:
    步骤C1.根据如下公式：Step C1. According to the following formula:
    Figure PCTCN2020107727-appb-100008
    Figure PCTCN2020107727-appb-100008
    计算获得样本集合X分别对应预设各阶n的原点矩，其中，n＝{0、…、N}，N表示预设最大阶数，L表示样本集合X中样本的个数，
    Figure PCTCN2020107727-appb-100009
    表示样本集合X中第l个样本的n次方，然后进入步骤C2；     Calculate and obtain the origin moments of the sample set X corresponding to each preset order n respectively, where n={0,...,N}, N represents the preset maximum order, L represents the number of samples in the sample set X,
    Figure PCTCN2020107727-appb-100009
    Represents the nth power of the lth sample in the sample set X, and then enters step C2;
    步骤C2.根据样本集合X分别对应预设各阶n的原点矩，则样本集合X的最大熵模型如下：Step C2. According to the sample set X corresponding to the preset origin moments of each order n, the maximum entropy model of the sample set X is as follows:
    max H(s)＝-∫p(s)ln p(s)dsmax H(s)=-∫p(s)ln p(s)ds
    s.t. ∫p(s)s nds＝a n st ∫p(s)s n ds=a n
    式中，s表示变量，H(s)表示样本集合X的信息熵，p(s)表示待求概率密度分布；然后进入步骤C3；In the formula, s represents the variable, H(s) represents the information entropy of the sample set X, and p(s) represents the probability density distribution to be obtained; then enter step C3;
    步骤C3.根据样本集合X的最大熵模型，按如下公式：Step C3. According to the maximum entropy model of the sample set X, according to the following formula:
    Figure PCTCN2020107727-appb-100010
    Figure PCTCN2020107727-appb-100010
    构造拉格朗日函数F L，其中，λ 0、…、λ n、…、λ N表示各个拉格朗日乘子，然后进入步骤C4； Construct the Lagrangian function FL , where λ 0 , ..., λ n , ..., λ N represent each Lagrangian multiplier, and then go to step C4;
    步骤C4.根据拉格朗日函数F L满足条件，则待求概率密度分布p(s)如下： Step C4. According to the Lagrangian function FL satisfying the conditions, the probability density distribution p(s) to be obtained is as follows:
    Figure PCTCN2020107727-appb-100011
    Figure PCTCN2020107727-appb-100011
    然后进入步骤C5；Then enter step C5;
    步骤C5.将待求概率密度分布p(s)代入步骤C2中的约束条件，获得如下：Step C5. Substitute the probability density distribution p(s) to be obtained into the constraints in step C2, and obtain the following:
    Figure PCTCN2020107727-appb-100012
    Figure PCTCN2020107727-appb-100012
    通过求解以上非线性方程组，获得拉格朗日乘子λ 0、…、λ n、…、λ N，进而求解获得待求概率密度分布p(s)，即风机所对应的调频潜力预测误差概率密度模型。 By solving the above nonlinear equations, the Lagrangian multipliers λ 0 , ..., λ n , ..., λ N are obtained, and then the probability density distribution p(s) to be obtained is obtained, that is, the prediction error of the frequency modulation potential corresponding to the fan Probability Density Model.
  5. 根据权利要求1所述一种风电调频潜力预测误差分布估计方法，其特征在于：所述步骤D包括如下步骤D1至步骤D2；The method for estimating the error distribution of wind power frequency regulation potential prediction according to claim 1, wherein the step D comprises the following steps D1 to D2;
    步骤D1.根据风机所对应的调频潜力预测误差概率密度模型，计算获得不同置信水平下预测误差置信区间，然后进入步骤D2；Step D1. According to the probability density model of the prediction error of the frequency regulation potential corresponding to the fan, calculate and obtain the confidence interval of the prediction error under different confidence levels, and then enter the step D2;
    步骤D2.计算获得风机目标预测风速数据所对应的风电调频潜力预测值，并与不同置信水平下预测误差置信区间叠加，获得不同置信水平下的预测区间。Step D2. Calculate and obtain the predicted value of the wind power frequency regulation potential corresponding to the target predicted wind speed data of the wind turbine, and superimpose it with the confidence interval of the prediction error under different confidence levels to obtain the prediction interval under different confidence levels.
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