Influence degree prediction method and device, storage medium and computing equipment

Abstract

Embodiments of the invention provide an influence degree prediction method and apparatus, a storage medium and a computing device. The method comprises the steps of obtaining a plurality of synchronous machine influence parameters; based on the plurality of synchronous machine influence parameters, predicting a control influence index of the influence of the fan on the synchronous machine, and generating a control influence index prediction value; and based on the control influence index prediction value, predicting the influence degree and generating a predicted influence degree, thereby quantifying the influence of the fan on the synchronous machine, and obtaining the control influence index prediction value by quantifying the influence of the fan at the next moment on the synchronous machine. The influence of the fan on the synchronous machine at the next moment is determined according to the control influence index predicted value, the predicted influence degree is obtained, the influence degree of the fan on the synchronous machine at the next moment can be accurately predicted, and the accuracy and reliability of predicting the influence of the fan on the synchronous machine are improved; and the fan operation at the next moment can be adjusted based on the predicted influence degree.

Description

Technical Field

[0001] The embodiments of this specification relate to the technical field of wind turbines, and in particular to a method for predicting the degree of impact. Background Art

[0002] In modern power systems, wind power generation has played an increasingly important role, but its intermittent and unpredictable nature has brought challenges to the stable operation of the power grid. The equipment required for wind power generation includes wind turbines, synchronous machines, etc. Wind turbines include wind turbine pitch control, etc. Wind turbine pitch control is an adjustment device inside the wind turbine. Its function is to adjust the angle of the blades in the wind turbine according to the changes in wind speed and wind direction, so as to capture wind energy to the greatest extent. The frequency of the power grid is determined by the balance between total demand and total supply. As one of the main frequency adjustment devices of the power grid, the synchronous machine can adjust the frequency of the power grid. Therefore, the frequency stability of the synchronous machine is crucial to the reliability and safety of the entire power system. However, when the wind turbine is connected to the grid, it has a certain impact on the frequency control of the synchronous machine. For example, the power fluctuation of the wind turbine may cause an imbalance between supply and demand, thereby causing the frequency fluctuation of the synchronous machine.

[0003] Therefore, in order to maintain the stability of the power grid, it is necessary to have an accurate prediction of the impact of wind turbines on synchronous machines on the frequency control of the power grid. This prediction can help power grid operators take timely control measures, such as adjusting spare capacity or other regulation services. Prediction methods usually include statistical models, machine learning techniques, or complex system dynamics models, which can predict the impact of wind turbine pitch operation on synchronous machine frequency control. However, the existing prediction methods for the impact of wind turbines on synchronous machines on power grid frequency control have deficiencies such as uncertainty, variability, model simplification, and insufficient data, which reduce the accuracy and reliability of predicting the impact of wind turbines on synchronous machines. Summary of the invention

[0004] In view of this, the embodiments of this specification provide a method for predicting the degree of influence. One or more embodiments of this specification also relate to a device for predicting the degree of influence, a computing device, a computer-readable storage medium, and a computer program to solve the technical defects of the prior art in predicting the influence of a wind turbine on a synchronous machine, such as reduced accuracy and reliability.

[0005] According to a first aspect of an embodiment of this specification, a method for predicting an impact degree is provided, comprising:

[0006] Acquire a plurality of synchronous machine influencing parameters, wherein the synchronous machine influencing parameters are influencing parameters of the fan on the synchronous machine within a preset time period;

[0007] Based on the multiple synchronous machine influencing parameters, predicting the control influence index of the fan affecting the synchronous machine, and generating a control influence index prediction value;

[0008] Based on the control influence index prediction value, the influence degree is predicted to generate a predicted influence degree, so as to control the operation of the wind turbine based on the predicted influence degree, wherein the predicted influence degree is the influence degree of the wind turbine on the synchronous machine.

[0009] In a possible implementation manner, after generating the predicted impact degree, the method further includes:

[0010] Based on the predicted impact degree, generating wind turbine operation prediction parameters;

[0011] The operation of the fan is controlled based on the predicted operation parameter of the fan.

[0012] In a possible implementation, the preset time period includes a plurality of collection moments, and the synchronizer influencing parameter corresponds to the collection moments;

[0013] Correspondingly, the control influence index of the wind turbine affecting the synchronous machine is predicted based on the multiple synchronous machine influence parameters to generate a control influence index prediction value, including:

[0014] generating a control influence index based on a plurality of said synchronous machine influence parameters;

[0015] Normalizing the plurality of synchronous machine influencing parameters to generate a normalized parameter corresponding to each synchronous machine influencing parameter;

[0016] Based on the normalized parameters corresponding to each of the acquisition moments, a coefficient for controlling the synchronous machine is predicted to generate a prediction coefficient corresponding to each of the acquisition moments;

[0017] The control influence index prediction value is generated based on the control influence index, the plurality of prediction coefficients and the plurality of normalization parameters.

[0018] In a possible implementation, the normalizing the plurality of synchronization machine influencing parameters to generate a normalized parameter corresponding to each synchronization machine influencing parameter includes:

[0019] Finding out the maximum value and the minimum value of the synchronous machine influencing parameter from the plurality of synchronous machine influencing parameters;

[0020] Generate a first difference value corresponding to each synchronous machine influencing parameter based on each synchronous machine influencing parameter and the minimum synchronous machine influencing parameter, and generate a second difference value based on the maximum synchronous machine influencing parameter and the minimum synchronous machine influencing parameter;

[0021] The ratio of the first difference value corresponding to the synchronous machine influencing parameter to the second difference value is used as a normalization parameter corresponding to the synchronous machine influencing parameter.

[0022] In a possible implementation, the preset time period is a period starting from the first moment and ending at the second moment, and the wind turbine operation prediction parameter includes a wind turbine power prediction value;

[0023] Accordingly, generating wind turbine operation prediction parameters based on the predicted impact degree includes:

[0024] Determining a power coefficient corresponding to the predicted impact degree based on the predicted impact degree;

[0025] A predicted value of wind turbine power is generated based on the power coefficient and the wind turbine power at the second moment.

[0026] In a possible implementation, predicting the impact degree based on the control impact index prediction value to generate the predicted impact degree includes:

[0027] The predicted impact degree is determined based on the control impact index predicted value and a target threshold range corresponding to the control impact index predicted value.

[0028] In a possible implementation, determining the predicted impact degree based on the control impact index predicted value and a target threshold range corresponding to the control impact index predicted value includes:

[0029] When the target threshold range is greater than or equal to the first threshold range, based on the control influence index prediction value being greater than or equal to the first threshold, determining that the predicted influence degree is a greater influence degree;

[0030] When the target threshold range is greater than or equal to the second threshold and less than the first threshold, based on the control influence index prediction value being greater than or equal to the second threshold and less than the first threshold, determining that the predicted influence degree is a small influence degree;

[0031] When the target threshold range is smaller than the second threshold range, based on the fact that the predicted value of the control influence index is smaller than the second threshold, it is determined that the predicted influence degree is a no influence degree.

[0032] According to a second aspect of an embodiment of this specification, there is provided a device for predicting the degree of influence, including:

[0033] An acquisition module is configured to acquire a plurality of synchronous machine influencing parameters, wherein the synchronous machine influencing parameters are influencing parameters of the fan on the synchronous machine within a preset time period;

[0034] A first prediction module is configured to predict a control influence index of the wind turbine on the synchronous machine based on a plurality of synchronous machine influence parameters, and generate a control influence index prediction value;

[0035] The second prediction module is configured to predict the degree of influence based on the control influence index prediction value, generate a predicted degree of influence, and control the operation of the wind turbine based on the predicted degree of influence, wherein the predicted degree of influence is the degree of influence of the wind turbine on the synchronous machine.

[0036] According to a third aspect of an embodiment of this specification, a computing device is provided, including:

[0037] Memory and processor;

[0038] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the above-mentioned method for predicting the degree of influence are implemented.

[0039] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided, which stores computer-executable instructions, and when the instructions are executed by a processor, the steps of the above-mentioned method for predicting the degree of influence are implemented.

[0040] According to a fifth aspect of the embodiments of this specification, a computer program is provided, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned method for predicting the degree of influence.

[0041] An embodiment of the present specification implements a method, device, storage medium and computing device for predicting the degree of influence, obtaining multiple synchronous machine influence parameters, wherein the synchronous machine influence parameter is the influence parameter of the fan on the synchronous machine within a preset time period; based on the multiple synchronous machine influence parameters, predicting the control influence index of the fan on the synchronous machine, generating a control influence index prediction value; based on the control influence index prediction value, predicting the degree of influence, generating a predicted degree of influence, so as to control the operation of the fan based on the predicted degree of influence, wherein the predicted degree of influence is the degree of influence of the fan on the synchronous machine, thereby quantifying the influence of the fan on the synchronous machine, and obtaining a control influence index prediction value by quantifying the influence of the fan on the synchronous machine at the next moment, and determining the influence of the fan on the synchronous machine at the next moment according to the control influence index prediction value to obtain the predicted degree of influence, which can accurately predict the degree of influence of the fan on the synchronous machine at the next moment, thereby improving the accuracy and reliability of predicting the influence of the fan on the synchronous machine; and can also adjust the operation of the fan at the next moment based on the predicted degree of influence, which helps to solve the problem of decreased frequency adjustment capability of the synchronous machine caused by the uncertainty of the fan output. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a flow chart of a method for predicting the degree of influence provided by an embodiment of this specification;

[0043] Figure 2 It is a structural schematic diagram of an impact degree prediction device provided by an embodiment of this specification;

[0044] Figure 3 It is a structural block diagram of a computing device provided by an embodiment of this specification. DETAILED DESCRIPTION

[0045] Many specific details are described in the following description to facilitate a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of this specification, so this specification is not limited to the specific implementation disclosed below.

[0046] The terms used in one or more embodiments of this specification are only for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of this specification. The singular forms of "a" and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0047] It should be understood that although the terms first, second, etc. may be used to describe various information in one or more embodiments of this specification, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0048] In this specification, a method for predicting the degree of influence is provided. This specification also relates to an apparatus for predicting the degree of influence, a computing device, and a computer-readable storage medium, which are described in detail one by one in the following embodiments.

[0049] Figure 1 is a flow chart of a method for predicting the degree of influence provided by an embodiment of this specification, such as Figure 1 As shown, the method includes:

[0050] Step 101: A computing device obtains a plurality of synchronous machine influencing parameters, wherein the synchronous machine influencing parameters are influencing parameters of a fan on the synchronous machine within a preset time period.

[0051] In some embodiments, the computing device includes but is not limited to a server, a laptop computer, a desktop computer, a mobile phone, a wearable device, etc. The preset time period may be from the first time t1 to the second time t m The period of time that ends, wherein m is a natural number greater than 1. The synchronous machine includes a synchronous machine connected to the wind turbine grid, for example, the synchronous machine is a grid-connected megawatt (MW) synchronous machine.

[0052] Since the wind turbine will have a certain impact on the frequency control of the synchronous machine when it is connected to the grid, causing the frequency fluctuation of the synchronous machine, the synchronous machine influencing parameters are the parameters related to the frequency fluctuation of the synchronous machine. The synchronous machine influencing parameters include but are not limited to the wind turbine output power P nd , total load power P to , synchronous machine power factor H sy , fan output frequency F of , grid frequency F gn , wind turbine pitch speed V rs , Wind farm wind speed V wn Or load mutation Δ L For example, multiple synchronous machine influencing parameters include wind turbine output power P nd , total load power P to , synchronous machine power factor H sy , fan output frequency F of , grid frequency F gn , wind turbine pitch speed V rs , Wind farm wind speed V wn With load mutation Δ L , the computing device obtains the time from the first time t1 to the second time t m Output power of m fans to m total load power to m synchronous machine power factor to m fan output frequency to m grid frequencies to m wind turbine pitch speed to m wind farm wind speed to With m load mutations to

[0053] Step 102: The computing device predicts the control influence index of the wind turbine on the synchronous machine based on the multiple synchronous machine influence parameters, and generates a control influence index prediction value.

[0054] In some embodiments, the computing device predicts t m+1 The influence of the fan on the synchronous machine at the moment is quantified and expressed in the form of a control influence index prediction value. The control influence index prediction value represents the contribution or influence of the fan on the frequency control of the synchronous machine in the preset time period, which can be used as the next moment (t m+1 Quantification of the influence of the fan at (time) on the frequency control of the synchronous machine.

[0055] Step 103: The computing device predicts the degree of influence based on the predicted value of the control influence index and generates a predicted degree of influence to control the operation of the wind turbine based on the predicted degree of influence, wherein the predicted degree of influence is the degree of influence of the wind turbine on the synchronous machine.

[0056] In some embodiments, the wind turbine includes a wind turbine pitch control, and the computing device can predict the degree of influence of the wind turbine pitch control on the frequency control of the synchronous machine from the control influence index prediction value. For example, the predicted influence degree can be a greater influence degree, a smaller influence degree, or a no influence degree, etc. The greater influence degree indicates that the frequency control of the synchronous machine at the next moment is predicted to be more affected by the wind turbine, and the computing device can adjust the operation of the wind turbine according to the greater influence degree to reduce the influence of the wind turbine on the frequency control of the synchronous machine; the smaller influence degree indicates that the frequency control of the synchronous machine at the next moment is predicted to be less affected by the wind turbine, and the computing device can adjust the operation of the wind turbine according to the smaller influence degree to reduce the influence of the wind turbine on the frequency control of the synchronous machine; the no influence degree indicates that the frequency control of the synchronous machine at the next moment is predicted to be not affected by the wind turbine, and the computing device can maintain the operation of the wind turbine according to the no influence degree to keep the wind turbine from affecting the frequency control of the synchronous machine. In this way, the influence of the wind turbine on the synchronous machine can be accurately predicted, which improves the accuracy and reliability of predicting the influence of the wind turbine on the synchronous machine.

[0057] However, the embodiments of the present invention simply exemplify several situations of the predicted impact degree, but the predicted impact degree can also be expressed in a graded form. For example, the predicted impact degree includes the first-level predicted impact degree, the second-level predicted impact degree, etc. The predicted impact degree is not limited in the embodiments of the present invention.

[0058] An embodiment of the present specification provides a method for predicting the degree of influence, wherein a computing device obtains multiple synchronous machine influence parameters, wherein the synchronous machine influence parameter is an influence parameter of the fan on the synchronous machine within a preset time period; based on the multiple synchronous machine influence parameters, a control influence index of the fan's influence on the synchronous machine is predicted, and a control influence index prediction value is generated; based on the control influence index prediction value, the degree of influence is predicted, and a predicted degree of influence is generated, so as to control the operation of the fan based on the predicted degree of influence, wherein the predicted degree of influence is the degree of influence of the fan on the synchronous machine, so that the influence of the fan on the synchronous machine can be quantified, and the control influence index prediction value is obtained by quantifying the influence of the fan on the synchronous machine at the next moment, and the influence of the fan on the synchronous machine at the next moment is determined according to the control influence index prediction value to obtain the predicted degree of influence, so that the degree of influence of the fan on the synchronous machine at the next moment can be accurately predicted, thereby improving the accuracy and reliability of predicting the influence of the fan on the synchronous machine; the operation of the fan at the next moment can also be adjusted based on the predicted degree of influence, which helps to solve the problem of decreased frequency adjustment capability of the synchronous machine caused by the uncertainty of the fan output.

[0059] The following combination Figure 1 , the prediction method of the degree of impact provided in this specification is further explained.

[0060] In a possible implementation, step 101 includes: a computing device acquires a plurality of synchronization machine influencing parameters arranged in a time series.

[0061] In some embodiments, assuming that the measurement interval is 0.5 seconds (second, s), m = 200, multiple synchronous machine influencing parameters include wind turbine output power Total load power Synchronous machine power factor Fan output frequency Grid frequency Wind turbine pitch speed Wind farm wind speed Load mutation The computing device has a data collection function, which collects the influencing parameters of the synchronous machine every 0.5s and collects the output power of 200 fans. 200 total load power 200 synchronous machine power factor 200 fan output frequencies 200 grid frequencies 200 wind turbine pitch speeds Wind speed of 200 wind farms With 200 load mutations The influence parameters of multiple synchronous machines arranged in time series are

[0062] In a possible implementation, the preset time period includes a plurality of collection moments, and the synchronizer influencing parameters correspond to the collection moments; accordingly, step 102 may specifically include:

[0063] Step 1021: The computing device generates a control influence index based on the multiple synchronous machine influence parameters.

[0064] In some embodiments, when the preset time period includes t1, ..., t m , the collection time is t1,…,t m At any time in , n is any natural number from 1 to m, n∈[1,m], and the acquisition time can be expressed as t n .

[0065] When multiple synchronous machines affect the parameters including the fan output power P nd , total load power P to , synchronous machine power factor H sy , fan output frequency F of , grid frequency F gn With wind turbine pitch speed V rs When the computing device controls the influence index formula, based on each collection time t n Corresponding fan output power Total load power Synchronous machine power factor Fan output frequency Grid frequency With wind turbine pitch speed Generate each acquisition time t n Corresponding collection control impact index; based on multiple collection times t n The corresponding acquisition control impact index generates the control impact index W sy .

[0066] The computing device generates each acquisition time t n The corresponding collection control influence index includes: the computing device finds the maximum output power P of the fan from the m fan output powers nd,max The minimum output power of the fan P nd,min , find the maximum total load power P from the m total load powers to,max and the minimum total load power P to,min , find the maximum value of synchronous machine power factor H from m synchronous machine power factors sy,max The minimum power factor of the synchronous machine is H sy,min , find the maximum fan output frequency F from the m fan output frequencies of,max The fan output frequency minimum value F of,min, and find the maximum grid frequency F from the m grid frequencies gn,max With the minimum grid frequency F gn,min ; Through the control influence index formula, based on the maximum output power P of the fan nd,max , Minimum fan output power P nd,min With t n The fan output power corresponding to the time Get the fan output power ratio, based on the maximum total load power P to,max , minimum total load power P to,min With t n Total load power corresponding to the moment Get the total load power ratio, based on the maximum value of the synchronous machine power factor H sy,max , Minimum value of synchronous machine power factor H sy,min With t n The synchronous machine power factor corresponding to the time Get the synchronous machine power factor ratio value based on the maximum output frequency F of the fan of,max , fan output frequency minimum value F of,min With t n The fan output frequency corresponding to the time Get the fan output frequency ratio, and find the maximum grid frequency F based on the grid frequency gn,max , minimum grid frequency F gn,min With t n The grid frequency corresponding to the time Get the grid frequency ratio; based on the wind turbine output power ratio, total load power ratio, synchronous machine power factor ratio, wind turbine output frequency ratio, grid frequency ratio and t n The wind turbine pitch speed corresponding to the moment Generate collection time t n The corresponding acquisition control impact index.

[0067] For example, the control impact index formula is The fan output power ratio is The total load power ratio is The power factor ratio of the synchronous machine is The fan output frequency ratio is The grid frequency ratio is Collection time t n The corresponding acquisition control impact index is

[0068]

[0069] Step 1022: The computing device normalizes the plurality of synchronous machine influencing parameters to generate a normalized parameter corresponding to each synchronous machine influencing parameter.

[0070] In some embodiments, step 1022 may specifically include: the computing device finds the maximum value and the minimum value of the synchronizer influence parameter from the multiple synchronizer influence parameters; generates a first difference corresponding to each synchronizer influence parameter based on each synchronizer influence parameter and the minimum value of the synchronizer influence parameter, and generates a second difference based on the maximum value and the minimum value of the synchronizer influence parameter; and uses the ratio of the first difference corresponding to the synchronizer influence parameter to the second difference as the normalized parameter corresponding to the synchronizer influence parameter.

[0071] Normalized parameters include but are not limited to normalized fan output power P′ d , Normalized total load power P′ o , Normalized synchronous machine power factor H′ sy , Normalized fan output frequency F′ of , normalized grid frequency F′ gn , Normalized wind turbine pitch speed V′ rs , Normalized wind farm wind speed V′ wn Or normalized load mutation Δ′ L wait.

[0072] The computing device may perform normalization processing on the synchronous machine influencing parameters through a normalization formula to obtain normalized parameters corresponding to the synchronous machine influencing parameters.

[0073] For example, the normalization formula is

[0074] Among them, V wn,max Represents the maximum wind speed of the wind farm, which is the maximum value among the wind speeds of m wind farms; V mn,min Indicates the minimum wind speed of the wind farm, which is the minimum wind speed of m wind farms. L,max Indicates the maximum value of load mutation, which is the maximum value among m load mutations; Δ L,min Indicates the minimum value of load mutation, which is the minimum value among m load mutations. rs,max Indicates the maximum value of the wind turbine pitch speed, which is the maximum value among the m wind turbine pitch speeds; V rs,min Indicates the minimum value of the wind turbine pitch speed, which is the minimum value among the m wind turbine pitch speeds.

[0075] Step 1023: The computing device predicts the coefficients for controlling the synchronous machine based on the normalized parameters corresponding to each of the acquisition moments, and generates prediction coefficients corresponding to each of the acquisition moments.

[0076] In some embodiments, when the acquisition time t n The corresponding multiple normalized parameters include normalized fan output power Normalized total load power Normalized synchronous machine power factor Normalized fan output frequency Normalized grid frequency Normalized wind turbine pitch speed Normalized wind farm wind speed Normalized load mutation When the calculation device uses the prediction coefficient formula, according to the maximum wind speed V of the wind farm wn,max , minimum wind speed V of wind farm wn,min Normalized wind farm wind speed Generate a normalized wind farm ratio; according to the normalized wind turbine output frequency Normalized grid frequency Minimum fan output frequency F of,min With the minimum grid frequency F gn,min Generate a normalized frequency ratio; according to the normalized wind farm ratio and normalized load mutation Normalized wind turbine pitch speed Fan damping coefficient k r The first numerator is generated by the normalized frequency ratio and the normalized fan output power is calculated based on Normalized total load power Normalized synchronous machine power factor Generate the first denominator; take the ratio of the first numerator to the first denominator as the acquisition time t n The corresponding prediction coefficient O iwn Among them, the fan damping coefficient k r It indicates the damping effect of the fan after being subjected to external disturbances, and reflects the shock absorption capacity of the fan when encountering external forces or disturbances. r The larger the fan is, the stronger its resistance to external disturbances is; the fan damping coefficient k r It is an inherent coefficient and is set when the fan leaves the factory.

[0077] For example, the prediction coefficient formula is The normalized wind farm ratio is The normalized frequency ratio is The first molecule is The first denominator is Collection time t n and the prediction coefficient O iwn correspond.

[0078] Step 1024: The computing device generates a predicted value of the control influence index based on the control influence index, the plurality of prediction coefficients and the plurality of normalization parameters.

[0079] In some embodiments, the computing device obtains the collection time t by controlling the influence index prediction formula. n The corresponding target normalized wind turbine output power Target normalized total load power Target normalized synchronous machine power factor Target normalized fan output frequency Target normalized grid frequency Target normalized wind turbine pitch speed Target normalized wind farm wind speed Normalized load mutation with target Normalized wind turbine output power based on target Target normalized total load power Target normalized synchronous machine power factor Target normalized fan output frequency Target normalized grid frequency Target normalized wind turbine pitch speed Target normalized wind farm wind speed Normalized load mutation with target Generate a second numerator and normalize the wind turbine output power based on the target Target normalized total load power Target normalized wind turbine pitch speed Target normalized wind farm wind speed and control influence index W sy Generate the second denominator; take the ratio of the second numerator to the second denominator as the acquisition time t n The corresponding acquisition ratio; the acquisition time t n The corresponding acquisition ratio and acquisition time t n The corresponding prediction coefficient O iwn The product of is taken as the acquisition time t n The corresponding acquisition product; based on the acquisition product corresponding to multiple acquisition moments, the control influence index prediction value W is generated foc .

[0080] The computing device generates a second numerator, including: the computing device normalizes the wind turbine output power based on the target Target normalized total load power Normalized load mutation with target Generate a first ratio based on the target normalized grid frequency Normalized wind farm wind speed with target Generate a second ratio based on the target normalized turbine pitch speed Normalized fan output frequency with target Generate a third ratio and normalize the wind farm speed based on the target Normalized grid frequency with target Generate a fourth ratio; based on the first ratio, the target normalized synchronous machine power factor The second ratio, the third ratio, and the fourth ratio generate a second numerator.

[0081] The computing device generates the second denominator including: the computing device normalizes the wind turbine output power based on the target Target normalized total load power Target normalized wind turbine pitch speed Normalized wind farm wind speed with target Generate a fifth ratio; based on the fifth ratio and the control influence index W sy Generate the second denominator.

[0082] For example, the control impact index prediction formula is

[0083] In the embodiment of the present invention, the execution order of step 1021 is not limited, and step 1021 may be executed before step 1022, or before step 1023 or step 1024.

[0084] In a possible implementation, step 103 may specifically include: a computing device determines the predicted impact degree based on the control impact index prediction value and a target threshold range corresponding to the control impact index prediction value.

[0085] In some embodiments, the threshold range includes a range greater than or equal to the first threshold, a range greater than or equal to the second threshold and less than the first threshold, or a range less than the second threshold. foc The corresponding threshold range is used as the target threshold range. When the target threshold range is a range greater than or equal to the first threshold, the computing device determines that the predicted impact degree is a large impact degree based on the control impact index predicted value being greater than or equal to the first threshold; when the target threshold range is a range greater than or equal to the second threshold and less than the first threshold, the computing device determines that the predicted impact degree is a small impact degree based on the control impact index predicted value being greater than or equal to the second threshold and less than the first threshold; when the target threshold range is a range less than the second threshold, the computing device determines that the predicted impact degree is no impact degree based on the control impact index predicted value being less than the second threshold.

[0086] For example, the first threshold is 0.621 and the second threshold is 0.315. focIf the control influence index prediction value W is greater than or equal to 0.621, it is considered that the wind turbine pitch control has a greater impact on the frequency control of the synchronous machine, and the predicted impact degree is a greater impact degree; when the control influence index prediction value W foc If the control influence index prediction value W is greater than or equal to 0.315 and less than 0.621, it is considered that the wind turbine pitch control has little influence on the frequency control of the synchronous machine, and the predicted influence degree is small. foc If it is less than or equal to 0.315, it is considered that the wind turbine pitch control has no effect on the frequency control of the synchronous machine, and the predicted impact level is no impact.

[0087] In a possible implementation, step 103 further includes: step 104: the computing device generates wind turbine operation prediction parameters based on the predicted impact degree.

[0088] In some embodiments, the fan output power of the fan can be predicted so as to adjust the fan operation by adjusting the fan output power. The preset time period is from the first time t1 to the second time t m During the period of time that is terminated, the wind turbine operation prediction parameters include a wind turbine power prediction value; accordingly, step 104 may specifically include: a computing device determines a power coefficient corresponding to the predicted impact degree based on the predicted impact degree; and generates a wind turbine power prediction value based on the power coefficient and the wind turbine power at the second moment.

[0089] The predicted impact degree includes a greater impact degree, a smaller impact degree or no impact degree. The power coefficient includes a first power coefficient, a second power coefficient or a third power coefficient, wherein the first power coefficient is less than the second power coefficient, and the second power coefficient is less than the third power coefficient.

[0090] When the predicted impact degree is a large impact degree, it indicates that the frequency control of the synchronous machine is greatly affected by the wind turbine pitch change; the wind turbine pitch change needs to be adjusted to a large extent to reduce the wind turbine output power, thereby reducing the impact of the wind turbine pitch change on the frequency control of the synchronous machine. The large impact degree corresponds to the first power coefficient.

[0091] When the predicted impact degree is a small impact degree, it indicates that the frequency control of the synchronous machine is less affected by the wind turbine pitch change; but the wind turbine pitch change needs to be adjusted to a small extent to reduce the wind turbine output power, thereby reducing the impact of the wind turbine pitch change on the frequency control of the synchronous machine. The small impact degree corresponds to the second power coefficient.

[0092] When the predicted impact degree is no impact degree, it indicates that the frequency control of the synchronous machine is tends to be not affected by or is not affected by the wind turbine pitch change; the wind turbine pitch change only needs to be slightly adjusted to reduce the wind turbine output power, or the wind turbine pitch change is not adjusted and the wind turbine output power is not changed, and the impact of the wind turbine pitch change on the frequency control of the synchronous machine tends to be no or does not affect the frequency control of the synchronous machine. The no impact degree corresponds to the third power coefficient.

[0093] The predicted impact degree corresponds to the target threshold range, or step 104 may also include: the computing device determines the power coefficient corresponding to the target threshold range based on the target threshold range; and generates a predicted wind turbine power value based on the power coefficient and the wind turbine power at the second moment. The computing device may use the product of the power coefficient and the wind turbine power at the second moment as the predicted wind turbine power value.

[0094] For example, the first power coefficient is 0.5, the second power coefficient is 0.7, and the third power coefficient is 1. The correspondence between the target threshold range and the power coefficient, as well as the evaluation process of the wind turbine power prediction value, can be expressed in the form of a wind turbine power prediction formula.

[0095] The wind turbine power prediction formula is:

[0096] Step 105: The computing device controls the operation of the wind turbine based on the predicted operation parameters of the wind turbine.

[0097] In some embodiments, the computing device is based on the wind turbine power prediction value The fan output power is controlled by adjusting the fan pitch.

[0098] An embodiment of the present specification provides a method for predicting the degree of influence, wherein a computing device obtains a plurality of synchronous machine influence parameters, wherein the synchronous machine influence parameter is an influence parameter of a fan on a synchronous machine within a preset time period; based on the plurality of synchronous machine influence parameters, a control influence index of the fan on the synchronous machine is predicted to generate a control influence index prediction value; based on the control influence index prediction value, the degree of influence is predicted to generate a predicted degree of influence, so as to control the operation of the fan based on the predicted degree of influence, wherein the predicted degree of influence is the degree of influence of the fan on the synchronous machine, so that the influence of the fan on the synchronous machine can be quantified, and a control influence index prediction value is obtained by quantifying the influence of the fan on the synchronous machine at the next moment, and the influence of the fan on the synchronous machine at the next moment is determined according to the control influence index prediction value to obtain the predicted degree of influence, so that the degree of influence of the fan on the synchronous machine at the next moment can be accurately predicted, thereby improving the accuracy and reliability of predicting the influence of the fan on the synchronous machine; the operation of the fan at the next moment can also be adjusted based on the predicted degree of influence, so as to reduce or maintain the influence of the fan on the frequency control of the synchronous machine, which helps to solve the problem of decreased frequency adjustment capability of the synchronous machine caused by the uncertainty of the fan output.

[0099] Corresponding to the above method embodiment, this specification also provides an embodiment of a device for predicting the degree of influence. Figure 2 is a schematic diagram of a structure of an impact prediction device provided by an embodiment of this specification, such as Figure 2 As shown, the device includes: an acquisition module 201, a first prediction module 202 and a second prediction module 203. The acquisition module 201 is connected to the first prediction module 202, and the first prediction module 202 is connected to the second prediction module 203.

[0100] The acquisition module 201 is configured to acquire multiple synchronous machine influencing parameters, wherein the synchronous machine influencing parameters are the influencing parameters of the fan on the synchronous machine within a preset time period; the first prediction module 202 is configured to predict the control influence index of the fan on the synchronous machine based on the multiple synchronous machine influencing parameters, and generate a control influence index prediction value; the second prediction module 203 is configured to predict the influence degree based on the control influence index prediction value, and generate a predicted influence degree, so as to control the operation of the fan based on the predicted influence degree, wherein the predicted influence degree is the influence degree of the fan on the synchronous machine.

[0101] In a possible implementation, the device further includes: a generation module 204 and a control module 205. The generation module 204 is connected to the second prediction module 203 and the control module 205.

[0102] The generating module 204 is configured to generate a wind turbine operation prediction parameter based on the predicted impact degree; the controlling module 205 is configured to control the wind turbine operation based on the wind turbine operation prediction parameter.

[0103] In a possible implementation, the preset time period includes multiple collection moments, and the synchronous machine influencing parameters correspond to the collection moments; the first prediction module 202 is also configured to generate a control influence index based on the multiple synchronous machine influencing parameters; normalize the multiple synchronous machine influencing parameters to generate normalized parameters corresponding to each of the synchronous machine influencing parameters; predict the coefficient for controlling the synchronous machine based on the normalized parameters corresponding to each of the collection moments, and generate a prediction coefficient corresponding to each of the collection moments; generate a control influence index prediction value based on the control influence index, the multiple prediction coefficients and the multiple normalized parameters.

[0104] In a possible implementation, the first prediction module 202 is also configured to find out the maximum value and the minimum value of the synchronizer influence parameter from the multiple synchronizer influence parameters; generate a first difference corresponding to each synchronizer influence parameter based on each synchronizer influence parameter and the minimum value of the synchronizer influence parameter, and generate a second difference based on the maximum value and the minimum value of the synchronizer influence parameter; and use the ratio of the first difference corresponding to the synchronizer influence parameter to the second difference as the normalization parameter corresponding to the synchronizer influence parameter.

[0105] In one possible implementation, the preset time period is a period of time starting from a first moment and ending at a second moment, and the wind turbine operation prediction parameters include a wind turbine power prediction value; the generation module 204 is also configured to determine a power coefficient corresponding to the predicted impact degree based on the predicted impact degree; and generate a wind turbine power prediction value based on the power coefficient and the wind turbine power at the second moment.

[0106] In a possible implementation, the second prediction module 203 is further configured to determine the predicted impact degree based on the control impact index prediction value and a target threshold range corresponding to the control impact index prediction value.

[0107] In one possible implementation, the second prediction module 203 is further configured to, when the target threshold range is a range greater than or equal to the first threshold, determine that the predicted impact degree is a greater impact degree based on the control impact index predicted value being greater than or equal to the first threshold; when the target threshold range is a range greater than or equal to the second threshold and less than the first threshold, determine that the predicted impact degree is a smaller impact degree based on the control impact index predicted value being greater than or equal to the second threshold and less than the first threshold; when the target threshold range is a range less than the second threshold, determine that the predicted impact degree is no impact degree based on the control impact index predicted value being less than the second threshold.

[0108] An embodiment of the present specification provides a prediction device for the degree of influence, wherein an acquisition module is configured to acquire a plurality of synchronous machine influence parameters, wherein the synchronous machine influence parameters are the influence parameters of the fan on the synchronous machine within a preset time period; a first prediction module is configured to predict the control influence index of the fan on the synchronous machine based on the plurality of synchronous machine influence parameters, and generate a control influence index prediction value; a second prediction module is configured to predict the degree of influence based on the control influence index prediction value, and generate a predicted degree of influence, so as to control the operation of the fan based on the predicted degree of influence, wherein the predicted degree of influence is the influence of the fan on the synchronous machine. The degree of influence of the synchronous machine can be used to quantify the influence of the fan on the synchronous machine. By quantifying the influence of the fan on the synchronous machine at the next moment, a control influence index prediction value is obtained. The influence of the fan on the synchronous machine at the next moment is determined according to the control influence index prediction value to obtain the predicted influence degree. The influence of the fan on the synchronous machine at the next moment can be accurately predicted, thereby improving the accuracy and reliability of predicting the influence of the fan on the synchronous machine; the fan operation at the next moment can also be adjusted based on the predicted influence degree to reduce or maintain the influence of the fan on the frequency control of the synchronous machine, which helps to solve the problem of decreased frequency adjustment ability of the synchronous machine caused by the uncertainty of the fan output.

[0109] The above is a schematic scheme of a device for predicting the degree of influence of this embodiment. It should be noted that the technical scheme of the device for predicting the degree of influence and the technical scheme of the method for predicting the degree of influence belong to the same concept, and the details of the technical scheme of the device for predicting the degree of influence that are not described in detail can all be referred to the description of the technical scheme of the method for predicting the degree of influence.

[0110] Figure 3 The block diagram of a computing device 300 according to an embodiment of the present specification is shown. The components of the computing device 300 include but are not limited to a memory 310 and a processor 320. The processor 320 is connected to the memory 310 via a bus 330, and the database 350 is used to store data.

[0111] The computing device 300 also includes an access device 340 that enables the computing device 300 to communicate via one or more networks 360. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 340 may include one or more of any type of network interface (e.g., a network interface card (NIC)) that is wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a world-wide interoperability for microwave access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, and a near field communication (NFC).

[0112] In one embodiment of the present specification, the above components of the computing device 300 and Figure 3 Other components not shown in the figure may also be connected to each other, for example, via a bus. It should be understood that Figure 3 The computing device structure block diagram shown is only for the purpose of illustration, and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0113] The computing device 300 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 300 may also be a mobile or stationary server.

[0114] The processor 320 is used to execute the following computer executable instructions, which implement the steps of the above-mentioned method for predicting the degree of influence when executed by the processor. The above is a schematic scheme of a computing device of this embodiment. It should be noted that the technical scheme of the computing device and the technical scheme of the above-mentioned method for predicting the degree of influence belong to the same concept. For the details not described in detail in the technical scheme of the computing device, please refer to the description of the technical scheme of the above-mentioned method for predicting the degree of influence.

[0115] An embodiment of the present specification further provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the above-mentioned method for predicting the degree of influence.

[0116] The above is a schematic scheme of a computer-readable storage medium of this embodiment. It should be noted that the technical scheme of the storage medium and the technical scheme of the above-mentioned method for predicting the degree of influence belong to the same concept, and the details not described in detail in the technical scheme of the storage medium can be referred to the description of the technical scheme of the above-mentioned method for predicting the degree of influence.

[0117] An embodiment of the present specification further provides a computer program, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned method for predicting the degree of influence.

[0118] The above is a schematic scheme of a computer program of this embodiment. It should be noted that the technical scheme of the computer program and the technical scheme of the above-mentioned method for predicting the degree of influence belong to the same concept, and the details not described in detail in the technical scheme of the computer program can be referred to the description of the technical scheme of the above-mentioned method for predicting the degree of influence.

[0119] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0120] The computer instructions include computer program codes, which may be in source code form, object code form, executable files or some intermediate forms, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0121] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of this specification are not limited by the order of the actions described, because according to the embodiments of this specification, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of this specification.

[0122] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0123] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The optional embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that technicians in the relevant technical field can well understand and use this specification. This specification is only limited by the claims and their full scope and equivalents.

Claims

1. A method for predicting the degree of influence, characterized in that: include: Acquire a plurality of synchronous machine influencing parameters, wherein the synchronous machine influencing parameters are influencing parameters of the fan on the synchronous machine within a preset time period; Based on the multiple synchronous machine influencing parameters, predicting the control influence index of the fan affecting the synchronous machine, and generating a control influence index prediction value; Based on the control influence index prediction value, the influence degree is predicted to generate a predicted influence degree, so as to control the operation of the wind turbine based on the predicted influence degree, wherein the predicted influence degree is the influence degree of the wind turbine on the synchronous machine.

2. The method according to claim 1, characterized in that After generating the predicted impact, the method further includes: Based on the predicted impact degree, generating wind turbine operation prediction parameters; The operation of the fan is controlled based on the predicted operation parameter of the fan.

3. The method according to claim 1, characterized in that The preset time period includes a plurality of collection moments, and the synchronous machine influencing parameters correspond to the collection moments; Correspondingly, the control influence index of the wind turbine affecting the synchronous machine is predicted based on the multiple synchronous machine influence parameters to generate a control influence index prediction value, including: generating a control influence index based on a plurality of said synchronous machine influence parameters; Normalizing the plurality of synchronous machine influencing parameters to generate a normalized parameter corresponding to each synchronous machine influencing parameter; Based on the normalized parameters corresponding to each of the acquisition moments, a coefficient for controlling the synchronous machine is predicted to generate a prediction coefficient corresponding to each of the acquisition moments; The control influence index prediction value is generated based on the control influence index, the plurality of prediction coefficients and the plurality of normalization parameters.

4. The method according to claim 3, characterized in that The normalizing the plurality of synchronous machine influencing parameters to generate a normalized parameter corresponding to each synchronous machine influencing parameter includes: Finding out the maximum value and the minimum value of the synchronous machine influencing parameter from the plurality of synchronous machine influencing parameters; generating a first difference value corresponding to each synchronous machine influencing parameter based on each synchronous machine influencing parameter and the minimum synchronous machine influencing parameter, and generating a second difference value based on the maximum synchronous machine influencing parameter and the minimum synchronous machine influencing parameter; The ratio of the first difference value corresponding to the synchronous machine influencing parameter to the second difference value is used as a normalization parameter corresponding to the synchronous machine influencing parameter.

5. The method according to claim 2, characterized in that: The preset time period is a period of time starting from the first moment and ending at the second moment, and the wind turbine operation prediction parameter includes a wind turbine power prediction value; Accordingly, generating wind turbine operation prediction parameters based on the predicted impact degree includes: Determining a power coefficient corresponding to the predicted impact degree based on the predicted impact degree; A predicted value of wind turbine power is generated based on the power coefficient and the wind turbine power at the second moment.

6. The method according to claim 1, characterized in that The step of predicting the degree of influence based on the predicted value of the control influence index to generate the predicted degree of influence includes: The predicted impact degree is determined based on the control impact index predicted value and a target threshold range corresponding to the control impact index predicted value.

7. The method according to claim 6, characterized in that The determining the predicted impact degree based on the control impact index predicted value and a target threshold range corresponding to the control impact index predicted value includes: When the target threshold range is greater than or equal to the first threshold range, based on the control influence index prediction value being greater than or equal to the first threshold, determining that the predicted influence degree is a greater influence degree; When the target threshold range is greater than or equal to the second threshold and less than the first threshold, based on the control influence index prediction value being greater than or equal to the second threshold and less than the first threshold, determining that the predicted influence degree is a small influence degree; When the target threshold range is smaller than the second threshold range, based on the fact that the predicted value of the control influence index is smaller than the second threshold, it is determined that the predicted influence degree is a no influence degree.

8. A device for predicting the degree of influence, characterized in that: include: An acquisition module is configured to acquire a plurality of synchronous machine influencing parameters, wherein the synchronous machine influencing parameters are influencing parameters of the fan on the synchronous machine within a preset time period; A first prediction module is configured to predict a control influence index of the wind turbine on the synchronous machine based on a plurality of synchronous machine influence parameters, and generate a control influence index prediction value; The second prediction module is configured to predict the degree of influence based on the control influence index prediction value, generate a predicted degree of influence, and control the operation of the wind turbine based on the predicted degree of influence, wherein the predicted degree of influence is the degree of influence of the wind turbine on the synchronous machine.

9. A computing device, characterized in that include: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the method for predicting the degree of influence described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for predicting the degree of influence as claimed in any one of claims 1 to 7.

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