Power Determination Method and Device for New Energy Power Station

Abstract

The present invention provides a method and device for determining the power of a new energy station. The method includes: calculating the average value of the real-time target parameters associated with each new energy device in the target new energy station as the average target parameter of the target new energy station; determining the real-time fitting curve parameters corresponding to the average target parameter according to the relationship between the pre-determined fitting curve parameters and the average target parameter; determining the real-time fitting curve of the target new energy station according to the real-time fitting curve parameters; determining the number of new energy devices corresponding to multiple target parameter values of the target new energy station according to the real-time fitting curve; determining the output power corresponding to each target parameter value according to the pre-determined correspondence between the output power of the new energy device and the target parameter; and determining the equivalent power of the target new energy station according to the number of new energy devices and the output power corresponding to each target parameter value. The equivalent power calculated by this solution is very close to the actual power, and the accuracy is relatively high.

Description

Technical Field

[0001] The present application relates to the field of new energy technologies, and particularly to a method and device for determining the power of a new energy power station. Background Art

[0002] New energy, such as wind energy, electrothermal energy, solar energy, etc., can be converted into other forms of energy, such as electrical energy. New energy devices in a new energy power station, such as wind turbines, solar panels, etc., usually have different state characteristics. Therefore, the output characteristics of a new energy power station cannot be directly replaced by a single new energy device. In addition, the capacity of a single new energy device is usually small. A new energy power station is usually composed of hundreds or even more new energy devices. And with the continuous development of new energy, the number of new energy devices in a power station will be increasing. Calculating the power of each new energy device one by one involves a large amount of calculation and it is impossible to obtain the real-time power of the power station in real time. For these two reasons, the method of multi-machine aggregation and equivalence of a new energy power station is usually used to calculate the real-time power.

[0003] However, the error between the equivalent real-time power obtained by the existing multi-machine aggregation and equivalence method and the actual real-time power is relatively large. Summary of the Invention

[0004] The purpose of the present application is to provide a method and device for determining the power of a new energy power station to solve the problem that the error between the equivalent real-time power obtained by the existing multi-machine aggregation and equivalence method and the actual real-time power is relatively large.

[0005] To solve the above technical problems, the first aspect of this specification provides a method for determining the power of a new energy power station, including: calculating the average value of the real-time target parameters associated with each new energy device in the target new energy power station as the average target parameter of the target new energy power station; determining the real-time fitting curve parameter corresponding to the average target parameter according to the relationship between the pre-determined fitting curve parameter and the average target parameter; the real-time fitting parameter curve is the parameter of the fitting curve between the number of new energy devices and the real-time target parameter; determining the real-time fitting curve of the target new energy power station according to the real-time fitting curve parameter; determining the number of new energy devices corresponding to multiple target parameter values of the target new energy power station according to the real-time fitting curve; determining the output power corresponding to each target parameter value according to the pre-determined correspondence between the output power of the new energy device and the target parameter; determining the equivalent power of the target new energy power station according to the number of new energy devices and the output power corresponding to each target parameter value.

[0006] In some embodiments, before calculating the average value of the real-time target parameters associated with each new energy device in the target new energy power station as the average target parameter of the target new energy power station, it further includes: obtaining the real-time target parameters associated with each acquisition moment of each new energy device in the target new energy power station within a first predetermined time period; using a time window of a second predetermined time period to slide within the first predetermined time period according to a preset time length, and each time it slides, calculating the average value of the real-time target parameters associated with each acquisition moment of each new energy device within the time window as the real-time target parameter of the new energy device.

[0007] In some embodiments, the relationship between the fitting curve parameters and the average target parameter, and the fitting curve is obtained through the following method: dividing the sample real-time target parameters associated with each new energy device in the target new energy power station into each predetermined target parameter interval, and counting the number of new energy devices divided into each target parameter interval; for each target parameter interval, perform the following operations: determining the number of new energy devices corresponding to each target parameter value in the current target parameter interval; performing a first fitting on the corresponding relationship between the number of new energy devices and each target parameter value to obtain a first fitting curve; calculating the average value of the sample real-time target parameters divided into the current target parameter interval as the sample average target parameter; performing a second fitting on the corresponding relationship between the parameters of the first fitting curve corresponding to each target parameter interval and the sample average target parameter to obtain a second fitting curve; using the second fitting curve as the relationship curve between the fitting parameters and the average target parameter, and using the first fitting curve as the fitting curve.

[0008] In some embodiments, before dividing the sample real-time target parameters associated with each new energy device in the target new energy power station into each predetermined target parameter interval and counting the number of new energy devices corresponding to each target parameter interval, it further includes: obtaining the values of multiple parameters affecting the output power of the new energy device at multiple moments and the corresponding output power, and taking the values of multiple parameters and the output power at the same moment as a set of values to obtain multiple sets of values; calculating the correlation parameter between each parameter and the output power according to the multiple sets of values; determining the parameters whose correlation parameters with the output power reach a predetermined threshold according to the values of the correlation parameters corresponding to each parameter, and taking the parameters as the target parameters.

[0009] In some embodiments, before performing a first fitting on the number of new energy devices and the current target parameter value to obtain a first fitting curve, it further includes: dividing the number of new energy devices within the current target parameter interval by the maximum value of the number of new energy devices corresponding to the current target parameter interval to normalize the number of new energy devices.

[0010] In some embodiments, after determining the number of new energy devices corresponding to multiple target parameter values of the target new energy power station according to the real-time fitting curve, it further includes: obtaining the correspondence between the maximum number of new energy devices corresponding to each target parameter interval and the target parameter interval value corresponding to the maximum number when the new energy devices are divided into a plurality of predetermined target parameter intervals according to the target parameters; the target parameter interval value is the target parameter value representing the target parameter interval; using the real-time target parameter of the new energy device as the target parameter interval value to determine the maximum number of new energy devices; multiplying the number of each new energy device by the maximum number to obtain the actual value of the number of new energy devices; the actual value refers to an integer value.

[0011] In some embodiments, the relationship between the average target parameter, the predetermined fitting curve parameter and the average target parameter is a quadratic function relationship; and / or, the fitting curve parameter is a parameter of the probability density function of a normal distribution.

[0012] The second aspect of this specification provides a power determination device for a new energy power station, including: a calculation unit for calculating the average value of the real-time target parameters associated with each new energy device in the target new energy power station as the average target parameter of the target new energy power station; a first determination unit for determining the real-time fitting curve parameter corresponding to the average target parameter according to the relationship between the predetermined fitting curve parameter and the average target parameter; the real-time fitting parameter curve is the parameter of the fitting curve between the number of new energy devices and the real-time target parameter; a second determination unit for determining the real-time fitting curve of the target new energy power station according to the real-time fitting curve parameter; a third determination unit for determining the number of new energy devices corresponding to multiple target parameter values of the target new energy power station according to the real-time fitting curve; a fourth determination unit for determining the output power corresponding to each target parameter value according to the correspondence between the output power of the predetermined new energy device and the target parameter; a fifth determination unit for determining the equivalent power of the target new energy power station according to the number of new energy devices and the output power corresponding to each target parameter value.

[0013] In some embodiments, the device further includes: a first acquisition unit for acquiring the real-time target parameters associated with each acquisition moment of each new energy device in the target new energy power station within a first predetermined time period; a sliding unit for sliding within the first predetermined time period using a time window of a second predetermined time period according to a preset time length, and each time it slides, calculating the average value of the real-time target parameters associated with each acquisition moment of each new energy device within the time window as the real-time target parameter of the new energy device.

[0014] In some embodiments, the relationship between the fitting curve parameters and the average target parameters, and the fitting curve are obtained in the following manner: dividing the sample real-time target parameters associated with each new energy device in the target new energy field into each predetermined target parameter interval, and counting the number of new energy devices divided into each target parameter interval; for each target parameter interval, perform the following operations: determining the number of new energy devices corresponding to each target parameter value in the current target parameter interval; performing a first fitting on the corresponding relationship between the number of new energy devices and each target parameter value to obtain a first fitting curve; calculating the average value of each sample real-time target parameter divided into the current target parameter interval as the sample average target parameter; performing a second fitting on the corresponding relationship between the parameters of the first fitting curve corresponding to each target parameter interval and the sample average target parameter to obtain a second fitting curve; taking the second fitting curve as the relationship curve between the fitting parameters and the average target parameters, and taking the first fitting curve as the fitting curve.

[0015] In some embodiments, before dividing the sample real-time target parameters associated with each new energy device in the target new energy field into each predetermined target parameter interval and counting the number of new energy devices corresponding to each target parameter interval, it further includes: obtaining the values of multiple parameters affecting the output power of the new energy device at multiple moments and the corresponding output power, and taking the values of multiple parameters and the output power at the same moment as a set of values to obtain multiple sets of values; calculating the correlation parameter between each parameter and the output power according to the multiple sets of values; determining the parameters whose correlation parameters with the output power reach a predetermined threshold according to the values of the correlation parameters corresponding to each parameter, and taking the parameters as the target parameters.

[0016] In some embodiments, before performing a first fitting on the number of new energy devices and the current target parameter value to obtain a first fitting curve, it further includes: dividing the number of new energy devices in the current target parameter interval by the maximum value of the number of new energy devices corresponding to the current target parameter interval to normalize the number of new energy devices.

[0017] In some embodiments, the device further includes: a second acquisition unit, configured to acquire the corresponding relationship between the maximum number of new energy devices corresponding to each target parameter interval and the target parameter interval value corresponding to the maximum number when the new energy devices are divided into each predetermined target parameter interval according to the target parameters; the target parameter interval value is the target parameter value representing the target parameter interval; a sixth determination unit, configured to determine the maximum number of new energy devices by using the real-time target parameter of the new energy device as the target parameter interval value; a seventh determination unit, configured to multiply the number of each new energy device by the maximum number to obtain the actual value of the number of new energy devices; the actual value refers to an integer value.

[0018] A third aspect of this specification provides an electronic device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor realizes the steps of the method according to any one of the first aspect or the second aspect by executing the computer instructions.

[0019] A fourth aspect of this specification provides a computer storage medium, which stores computer program instructions. When the computer program instructions are executed by a processor, the steps of the method according to any one of the first aspect or the second aspect are realized.

[0020] The power determination method and device for a new energy power station provided in this specification, based on the statistical rules obtained through prior research, first determine the average target parameter corresponding to the real-time target parameter, then determine the fitting curve parameter corresponding to the average target parameter, and then determine the number of new energy devices corresponding to each target parameter value according to the fitting curve. Combining the power of the new energy devices corresponding to each target parameter value, calculate the equivalent power of the new energy power station. The obtained equivalent power is very close to the actual power; this solution takes into account the influence of the average target parameter on the equivalent power, and uses the average target parameter as the basis for grouping, and gives a quantitative curve expression of the statistical rules, making the calculation result of the equivalent power more accurate; for the real-time target parameter at each acquisition moment, this solution can calculate the corresponding equivalent power to achieve real-time dynamic equivalent calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings.

[0022] Figures 1 to 23 It shows a schematic diagram of the wind speed distribution data for 23 time periods;

[0023] Figure 24 It shows a schematic diagram of the first scatter plot and the first fitting curve;

[0024] Figure 25 It shows a schematic diagram of the second scatter plot and the second fitting curve;

[0025] Figure 26 It shows a schematic diagram of the third scatter plot and its fitting curve;

[0026] Figure 27 It shows a schematic diagram of a power determination method for a new energy power station provided in this specification;

[0027] Figure 28 Shows a schematic diagram of the fitting curve of the average power and the average wind speed;

[0028] Figure 29 Shows a comparison diagram of the equivalent power and the actual power obtained by using the power determination method of the new energy power station provided in this specification;

[0029] Figure 30 Shows a schematic diagram of another power determination method of the new energy power station provided in this specification;

[0030] Figure 31 Shows a principle block diagram of the power determination device of the new energy power station provided in this specification;

[0031] Figure 32 Shows a principle block diagram of the electronic device provided in this specification. Specific embodiments

[0032] In order to enable the personnel in the technical field to better understand the technical solutions in this application, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of this application.

[0033] The core problem of multi-machine aggregation equivalence in new energy power stations lies in the effectiveness of the clustering algorithm. The inventor found that the existing multi-machine aggregation equivalence methods for the clustering and equivalence of new energy devices in new energy power stations mainly cluster new energy devices based on the target parameters affecting power, and have not quantitatively described the distribution of target parameters in new energy power stations, and have not considered the influence of the average target parameters of new energy power stations on the equivalent power of the power station, resulting in a large error in the real-time power obtained by the existing multi-machine aggregation equivalence methods.

[0034] For example, for a wind farm, existing research mostly uses grouping indicators such as wind speed, wind energy utilization rate, wake influence factor, fan speed, and fan state variables, and adopts a clustering algorithm to establish a multi-machine equivalence scheme. The research on the clustering and equivalence of wind turbines in a wind farm mainly clusters the wind turbines in the wind farm based on the difference in wind speed, but has not quantitatively described the wind speed distribution in the wind farm, and has not considered the influence of the average wind speed of the wind farm on the equivalent power of the wind farm.

[0035] The inventor has studied the wind speed distribution law of the wind farm. The following specifically introduces the research process.

[0036] S110: Obtain the wind speeds (i.e., real-time wind speeds) collected at each wind speed collection moment of each wind turbine in the wind farm within the first time period. For each wind turbine, calculate the average value of the wind speeds collected at each wind speed collection moment to obtain the average wind speed of the wind turbine. Assuming there are 50 wind turbines in the wind farm, 50 average wind speed values can be obtained.

[0037] It should be noted that the purpose of calculating the average value of multiple real-time wind speeds of a wind turbine is actually to use the average wind speed calculated from multiple real-time wind speeds as a real-time wind speed to avoid the problem that the stability of research results is affected by the abnormality of individual real-time wind speed values of the wind turbine due to interference factors.

[0038] S120: Divide the average wind speeds of each wind turbine into pre-determined wind speed intervals, and count the number of wind turbines in each wind speed interval.

[0039] The wind speed intervals here can be divided according to a predetermined interval length, and the value at the midpoint of the interval is used as the interval wind speed. For example, if the interval length is 1, then the two intervals obtained by this interval division method can be 1 [0.5 m / s, 1.5 m / s], 2 [1.5 m / s, 2.5 m / s], where the value outside the "[]" represents the interval wind speed, the value on the left side in the "[]" represents one end value of the interval, and the value on the right side in the "[]" represents the other end value of the interval.

[0040] S130: Normalize the number of wind turbines corresponding to each wind speed interval within this time period to obtain the wind speed distribution data corresponding to the first time period.

[0041] The specific method of normalization is: obtain the maximum value among the number of wind turbines corresponding to each wind speed interval within this time period, that is, the maximum number of wind turbines, and then divide the number of wind turbines corresponding to each wind speed interval by the maximum number of wind turbines to obtain the normalized number of wind turbines corresponding to each wind speed interval. That is to say, for the number of wind turbines corresponding to each wind speed interval, the following calculation is performed:

[0042]

[0043] Among them, N'|V aver is the normalized number of wind turbines corresponding to the wind speed interval when the average wind speed is V aver , N|V aver is the actual number of wind turbines corresponding to the wind speed interval when the average wind speed is V aver , M|V aver is the maximum value of the number of wind turbines corresponding to each wind speed interval when the average wind speed is V aver .

[0044] Repeat steps S110, S120, and S130 to process the wind speeds collected at each wind turbine in the wind farm at various wind speed acquisition moments during multiple time periods such as the first time period, the second time period, and the third time period.

[0045] S140: Using the interval wind speed as the abscissa and the number of wind turbines as the ordinate, draw a bar chart for the wind speed distribution data of each time period.

[0046] The statistical charts corresponding to each time period are as Figures 1 to 23 shown, where each statistical chart corresponds to a time period. From Figures 1 to 23 it can be seen that the distribution of the number of wind turbines in each time period generally shows the characteristics of a normal distribution. Based on this finding, a Gaussian probability density distribution function can be used to fit the number of wind turbines, that is, perform the following step S150.

[0047] S150: Using the interval wind speed as the independent variable and the number of wind turbines as the dependent variable, use the Gaussian probability density function to fit the wind speed distribution data corresponding to each time period to obtain the fitting parameters μ and σ.

[0048] The expression of the Gaussian probability density function is: where N'|V aver is the normalized number of wind turbines corresponding to the wind speed interval when the average wind speed is V aver , and in the exponent of e , V act represents the interval wind speed, and μ and σ are the parameters of the Gaussian probability density function.

[0049] Figures 1 to 23 The continuous curve in is the fitting curve obtained by fitting using the Gaussian probability density function. From these fitting curves, it can be found that for different time periods, the position of the center axis of the fitting curve and the height of the fitting curve are different, and the average wind speeds corresponding to different time periods are different. Based on this, the relationship between the position of the center axis of the fitting curve (i.e., the parameter μ in the Gaussian probability density function), the height of the fitting curve (i.e., the parameter σ in the Gaussian probability density function), and the average wind speed can be further studied. That is, perform the following step S160.

[0050] The interval wind speed in this specification can refer to the midpoint value of the wind speed interval.

[0051] S160: Using the interval wind speed as the abscissa and the parameter μ of the Gaussian probability density function as the ordinate, draw a first scatter plot and fit the first scatter plot to obtain a first fitting curve; using the interval wind speed as the abscissa and the parameter σ of the Gaussian probability density function as the ordinate, draw a second scatter plot and fit the second scatter plot to obtain a second fitting curve.

[0052] The first scatter plot is as Figure 24 shown, and the second scatter plot is as Figure 25 shown.

[0053] For both the first scatter plot and the second scatter plot, a quadratic function can be used for fitting. The expression of the quadratic function is:

[0054]

[0055] where a1, b1, and c1 are the parameters of the first fitting curve, and a2, b2, and c2 are the parameters of the second fitting curve. Through the two fitting operations in step S160 above, the values of a1, b1, c1, a2, b2, and c2 can be determined, that is, the first fitting curve and the second fitting curve can be uniquely determined. For example, a1 = 1.213e-05, b1 = 1.012, c1 = -0.07344, a2 = -0.002592, b2 = 0.08663, c2 = 0.434.

[0056] From Figures 1 to 23 the wind speed distribution data within the 23 time periods, it can also be seen that for different average wind speeds, the maximum number of wind turbines in each time period is uncertain. In this regard, the relationship between the maximum number of wind turbines and the average wind speed in each time period can be further studied. That is, the following step S170 is executed.

[0057] S170: Using the interval wind speed as the abscissa and the maximum number of wind turbines as the ordinate, plot a third scatter plot and fit this third scatter plot to obtain the relationship curve between the maximum number of wind turbines and the interval wind speed.

[0058] For example, according to the distribution trend of first decreasing and then increasing shown in the scatter plot, a hyperbolic function can be used to fit the third scatter plot. The third scatter plot and its fitting curve are as Figure 26 shown. The expression of the fitting curve can be:

[0059]

[0060] where V aver represents the average wind speed, M|V aver represents the maximum number of wind turbines when the average wind speed is V aver , and a, b, c, and d are fitting parameters. For example, a = 1.282, b = 818.7, c = -10.07, d = -55.1.

[0061] Through the above steps S110 to S170, a quantitative expression of the wind speed distribution law of the wind farm is obtained.

[0062] Based on the above research results, this specification provides a method for determining the power of a new energy power station. The new energy power station in this specification can be a wind farm. Correspondingly, the new energy equipment can be a wind turbine, and the target parameter can be the wind speed. Of course, the new energy power station in this specification can also be other types of power stations, which will not be listed one by one in this specification.

[0063] As Figure 27 shown, the method for determining the power of the new energy power station provided in this specification includes the following steps:

[0064] S210: Calculate the average value of the real-time target parameters associated with each new energy device in the target new energy power station as the average target parameter of the target new energy power station.

[0065] The target parameter is a parameter whose correlation with the output power of the new energy device reaches a predetermined threshold.

[0066] Figure 27 The real-time target parameter in the embodiment shown is equivalent to the average wind speed of a wind turbine described in steps S110 to S170.

[0067] For example, a parameter strongly correlated with the output power of the new energy device. The predetermined threshold here can be a correlation degree of more than 95%. The real-time target parameter is the real-time acquisition value of the target parameter.

[0068] In some embodiments, the target parameter can be determined based on experience. In some embodiments, the target parameter can be determined through the following steps S01, S02, and S03.

[0069] S01: Obtain the values of multiple parameters affecting the output power of the new energy device at multiple moments and the corresponding output power, and use the values of the multiple parameters and the output power at the same moment as a set of values to obtain multiple sets of values.

[0070] S02: Calculate the correlation parameter between each parameter and the output power according to the multiple sets of values.

[0071] S03: Determine the parameter whose correlation parameter with the output power reaches the predetermined threshold according to the values of the correlation parameters corresponding to each parameter, and use the parameter as the target parameter.

[0072] For example, the non-parametric kernel density estimation method is used to obtain the marginal distribution functions of each influencing factor. Based on the copula theory, three joint distribution functions (Frank parameter, Clayton parameter, Gumbel parameter) of wind power output and each related factor are established. The Kendall rank correlation coefficients and Spearman correlation coefficients of the three copula functions are shown in Table 1 below.

[0073] Table 1 Correlation Analysis of Influencing Factors of Wind Power Output

[0074]

[0075] As can be seen from Table 1, among the various factors, the correlation between wind power output and wind speed is extremely strong, and wind speed can be regarded as the key influencing factor of wind power output; while the correlation with wind direction, temperature, humidity, and air pressure is extremely weak and can be regarded as irrelevant. Therefore, wind speed can be used as the target parameter.

[0076] The "real-time target parameter associated with each new energy device" described in this specification refers to the real-time target parameter that affects the output power of the new energy device. For example, the real-time wind speed associated with wind turbine A refers to the real-time wind speed that affects the output power of wind turbine A.

[0077] S220: Determine the real-time fitting curve parameter corresponding to the average target parameter according to the relationship between the pre-determined fitting curve parameter and the average target parameter. The real-time fitting parameter curve is the parameter of the fitting curve between the number of new energy devices and the real-time target parameter.

[0078] The fitting curve parameter here refers to the parameters μ and σ of the Gaussian probability density function in steps S110 to S170. That is to say, step S220 is actually: according to Figure 24 the shown curve, using the real-time target parameter as the average wind speed to obtain the parameter μ; according to Figure 25 the shown curve, using the real-time target parameter as the average wind speed to obtain the parameter σ.

[0079] S230: Determine the real-time fitting curve of the target new energy station according to the real-time fitting curve parameter.

[0080] Step S220 is actually: according to the parameters μ and σ, determine the Gaussian probability density function, that is, determine the Figures 1 to 23 continuous curve in.

[0081] S240: Determine the number of new energy devices corresponding to multiple target parameter values of the target new energy station according to the real-time fitting curve.

[0082] The multiple target parameter values here are also the interval wind speeds in steps S110 to S170. That is to say, the target parameter values are distributed at a predetermined time interval.

[0083] According to Figures 1 to 23 It can be seen that after the curve is determined, each interval wind speed can be determined, that is, the number of new energy devices corresponding to the target parameter value can be determined.

[0084] From Figures 1 to 23It can be seen that the number of wind turbines in the figure has been normalized to the interval [0, 1]. Correspondingly, after obtaining the number of new energy devices according to the real-time fitting curve in step S240, the inverse step of normalization also needs to be performed to convert the number value into the actual number value of a positive integer. Specifically, the following steps can be performed:

[0085] S310: Obtain the correspondence between the maximum number of new energy devices corresponding to each target parameter interval and the target parameter interval value corresponding to the maximum number when the new energy devices are divided into a plurality of predetermined target parameter intervals according to the target parameter; the target parameter interval value is the target parameter value representing the target parameter interval.

[0086] The correspondence between the maximum number and the target parameter interval value in step S310 is also the relationship curve obtained in step S170 above.

[0087] S320: Use the real-time target parameter of the new energy device as the target parameter interval value to determine the maximum number of new energy devices.

[0088] S330: Multiply the number of each new energy device by the maximum number to obtain the actual value of the number of new energy devices; the actual value refers to an integer value.

[0089] S250: Determine the output power corresponding to each target parameter value according to the correspondence between the output power and the target parameter of the new energy device determined in advance.

[0090] In some embodiments, the relationship between the output power and the target parameter that comes with the new energy device when it leaves the factory can be used as the correspondence determined in advance in S250.

[0091] In some embodiments, based on the embodiments shown in steps S110 to S170, the average power of each wind turbine in each wind speed interval can be statistically calculated, and a fourth scatter plot can be drawn with the average wind speed as the abscissa and the average power as the ordinate. Then, the fourth scatter plot is fitted to obtain an expression of the average power and the average wind speed, that is, the correspondence between the average power and the target parameter.

[0092] For example, based on the technical solution described in steps S110 to S170, the fitting curve graph of the average power and the average wind speed obtained by fitting with this method is as Figure 28 shown, and the fitting formula can be

[0093]

[0094] where the parameters k1 = 2027, k2 = 616.1, k3 = 0.8531.

[0095] S260: Calculate the equivalent power of the target new energy field according to the number of new energy devices corresponding to each target parameter value and the output power.

[0096] For example, the equivalent power of the target new energy field can be calculated using the following expression:

[0097]

[0098] Where P represents the equivalent power of the target new energy field, m represents the number of target parameter values, N i represents the number of new energy devices corresponding to the i-th target parameter value, and P i represents the output power of the new energy device corresponding to the i-th target parameter value.

[0099] Figure 29 Fig. shows a comparison chart of the equivalent power and the actual power of the wind farm at each acquisition moment obtained by using the above steps S210 to S260. Among them, the thin line represents the actual power curve, and the thick line represents the equivalent power curve. From Figure 29 it can be seen that the equivalent power is very close to the actual power, indicating that the power determination method for the new energy field provided in this specification has a high accuracy rate.

[0100] The power determination method for the new energy field provided in this specification is based on the statistical law obtained through prior research. First, determine the average target parameter corresponding to the real-time target parameter, then determine the fitting curve parameter corresponding to the average target parameter, and then determine the number of new energy devices corresponding to each target parameter value according to the fitting curve. Combine the power of the new energy devices corresponding to each target parameter value to calculate the equivalent power of the new energy field. The obtained equivalent power is very close to the actual power; this solution takes into account the influence of the average target parameter on the equivalent power and uses the average target parameter as the basis for clustering, giving a quantitative curve expression of the statistical law, making the calculation result of the equivalent power more accurate; for the real-time target parameter at each acquisition moment, this solution can calculate the corresponding equivalent power to achieve real-time dynamic equivalent calculation.

[0101] As Figure 30 shown, in some embodiments, before S210, it may further include:

[0102] S410: Obtain the real-time target parameters associated with each acquisition moment of each new energy device in the target new energy field within the first predetermined time period.

[0103] S420: Set a time window of the second predetermined time period within the first predetermined time period.

[0104] S430: Calculate the average value of the real-time target parameters associated with each acquisition moment of each new energy device within the time window as the real-time target parameter of the new energy device.

[0105] After step S430, execute S210 to S260.

[0106] S440: Slide the time window once along the predetermined direction at the preset step size within the first predetermined duration.

[0107] After step S440, execute S210 to S260.

[0108] S450: Determine whether it is still possible to continue sliding along the predetermined direction at the preset step size. If so, jump to S430 to continue execution; otherwise, end.

[0109] In some embodiments, the relationship between the fitting curve parameters and the average target parameter, and the fitting curve are obtained through the following method:

[0110] S510: Divide the sample real-time target parameters associated with each new energy device in the target new energy field into the predetermined target parameter intervals, and count the number of new energy devices divided into each target parameter interval.

[0111] In some embodiments, to avoid the problem that the stability of research results is affected by the abnormal individual real-time wind speed values of the wind turbines due to interference factors, the average value of the target parameters collected at each acquisition moment of each new energy device within a period of time can also be used as the sample real-time target parameter.

[0112] S520: For each target parameter interval, perform the following operations: determine the number of new energy devices corresponding to each target parameter value in the current target parameter interval; perform a first fitting on the correspondence between the number of new energy devices and each target parameter value to obtain a first fitting curve; calculate the average value of the sample real-time target parameters divided into the current target parameter interval as the sample average target parameter.

[0113] S530: Perform a second fitting on the correspondence between the parameters of the first fitting curve corresponding to each target parameter interval and the sample average target parameter to obtain a second fitting curve.

[0114] S540: Use the second fitting curve as the relationship curve between the fitting parameters and the average target parameter, and use the first fitting curve as the fitting curve.

[0115] Steps S510 to S540 can be understood by referring to steps S110 to 170, and will not be elaborated here.

[0116] In some embodiments, S520 may fit the actual number of new energy devices. However, since the average wind speeds of the overall new energy devices are different, the values of the maximum number of devices vary greatly, resulting in a large difference in the height of the fitting curves and making it difficult to discover the pattern of the fitting curves. Therefore, the actual number of new energy devices can be normalized first and then operations such as fitting can be performed. The specific method of fitting can be: dividing the number of each new energy device by the maximum value of the number of new energy devices corresponding to the current target parameter interval to normalize the number of each new energy device.

[0117] This specification provides a power determination device for a new energy power station, which can be used to implement Figure 27 the power determination method for the new energy power station as shown. As Figure 31 shown, the device includes a calculation unit 10, a first determination unit 20, a second determination unit 30, a third determination unit 40, a fourth determination unit 50, and a fifth determination unit 60.

[0118] The calculation unit 10 is configured to calculate the average value of the real-time target parameters associated with each new energy device in the target new energy power station as the average target parameter of the target new energy power station.

[0119] The first determination unit 20 is configured to determine the real-time fitting curve parameters corresponding to the average target parameter according to the relationship between the pre-determined fitting curve parameters and the average target parameter; the real-time fitting parameter curve is the parameter of the fitting curve of the number of new energy devices and the real-time target parameter.

[0120] The second determination unit 30 is configured to determine the real-time fitting curve of the target new energy power station according to the real-time fitting curve parameters.

[0121] The third determination unit 40 is configured to determine the number of new energy devices corresponding to multiple target parameter values of the target new energy power station according to the real-time fitting curve.

[0122] The fourth determination unit 50 is configured to determine the output power corresponding to each target parameter value according to the pre-determined correspondence between the output power of the new energy device and the target parameter.

[0123] The fifth determination unit 60 is configured to determine the equivalent power of the target new energy power station according to the number of new energy devices and the output power corresponding to each target parameter value.

[0124] In some embodiments, the device further includes a first acquisition unit and a sliding unit.

[0125] The first acquisition unit is used to acquire the real-time target parameters associated with each acquisition moment of each new energy device in the target new energy field within the first predetermined time period. The sliding unit is used to slide within the first predetermined time period with a time window of the second predetermined time period according to a preset time length. Each time it slides, it calculates the average value of the real-time target parameters associated with each acquisition moment of each new energy device within the time window as the real-time target parameter of the new energy device.

[0126] In some embodiments, the relationship between the fitting curve parameters and the average target parameters, and the fitting curve is obtained in the following manner: dividing the sample real-time target parameters associated with each new energy device in the target new energy field into each predetermined target parameter interval, and counting the number of new energy devices divided into each target parameter interval; for each target parameter interval, perform the following operations: determining the number of new energy devices corresponding to each target parameter value in the current target parameter interval; performing a first fitting on the correspondence between the number of new energy devices and each target parameter value to obtain a first fitting curve; calculating the average value of the sample real-time target parameters divided into the current target parameter interval as the sample average target parameter; performing a second fitting on the correspondence between the parameters of the first fitting curve corresponding to each target parameter interval and the sample average target parameters to obtain a second fitting curve; using the second fitting curve as the relationship curve between the fitting parameters and the average target parameters, and using the first fitting curve as the fitting curve.

[0127] In some embodiments, before dividing the sample real-time target parameters associated with each new energy device in the target new energy field into each predetermined target parameter interval and counting the number of new energy devices corresponding to each target parameter interval, it further includes: acquiring the values of multiple parameters affecting the output power of the new energy device at multiple moments and the corresponding output power, and using the values of the multiple parameters and the output power at the same moment as a set of values to obtain multiple sets of values; calculating the correlation parameter between each parameter and the output power according to the multiple sets of values; determining the parameter whose correlation parameter with the output power reaches a predetermined threshold according to the values of the correlation parameters corresponding to each parameter, and using the parameter as the target parameter.

[0128] In some embodiments, before performing a first fitting on the number of new energy devices and the current target parameter value to obtain a first fitting curve, it further includes: dividing the number of new energy devices in the current target parameter interval by the maximum value of the number of new energy devices corresponding to the current target parameter interval to normalize the number of new energy devices.

[0129] In some embodiments, the device further includes a second acquisition unit, a sixth determination unit, and a seventh determination unit.

[0130] The second obtaining unit is configured to obtain the correspondence relationship between the maximum number of new energy devices corresponding to each target parameter interval and the target parameter interval value corresponding to the maximum number when the new energy devices are divided into a plurality of predetermined target parameter intervals according to the target parameters; the target parameter interval value is the target parameter value representing the target parameter interval. The sixth determining unit is configured to determine the maximum number of new energy devices by using the real-time target parameter of the new energy device as the target parameter interval value. The seventh determining unit is configured to multiply the number of each new energy device by the maximum number to obtain the actual value of the number of new energy devices; the actual value refers to an integer value.

[0131] For the description and beneficial effects of the above power determination device of the new energy power station, reference can be made to the description and beneficial effects in the method part, which will not be elaborated here.

[0132] An embodiment of the present invention further provides an electronic device, as Figure 32 shown. The electronic device may include a processor 3201 and a memory 3202, where the processor 3201 and the memory 3202 may be connected through a bus or other means, Figure 32 taking the connection through the bus as an example.

[0133] The processor 3201 may be a central processing unit (CPU). The processor 3201 may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. chips, or a combination of the above types of chips.

[0134] The memory 3202, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the power determination method of the new energy power station in the embodiment of the present invention (for example, Figure 31 shown computing unit 10, first determining unit 31, second determining unit 30, third determining unit 40, fourth determining unit 50, and fifth determining unit 60). By running the non-transitory software programs, instructions, and modules stored in the memory 3202, the processor 3201 executes various functional applications and data classification of the processor, that is, implements the power determination method of the new energy power station in the above method embodiment.

[0135] The memory 3202 may include a program storage area and a data storage area. The program storage area may store an operating system and application programs required for at least one function. The data storage area may store data created by the processor 3201 and the like. In addition, the memory 3202 may include high-speed random access memory and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory 3202 may optionally include a memory remotely disposed relative to the processor 3201, and these remote memories may be connected to the processor 3201 through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0136] The one or more modules are stored in the memory 3202 and, when executed by the processor 3201, perform the power determination method for a new energy power station as described in Figure 27 the embodiments shown.

[0137] Specific details of the above electronic device may be referred to Figure 27 the relevant descriptions and effects in the corresponding embodiments for understanding, and will not be elaborated here.

[0138] This specification provides a computer storage medium storing computer program instructions, which when executed by a processor implement Figure 27 the steps of the method shown.

[0139] Those skilled in the art can understand that to implement all or part of the processes in the above method embodiments, it can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, it may include the processes of the method embodiments as described above. Among them, the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD), etc.; the storage medium may also include a combination of the above types of memories.

[0140] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0141] The systems, devices, modules, or units described in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions.

[0142] For the convenience of description, when describing the above devices, they are divided into various units according to their functions and described separately. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0143] From the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of certain parts of various embodiments of the present application.

[0144] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on.

[0145] The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment, where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0146] Although the present application has been depicted through embodiments, those of ordinary skill in the art know that the present application has many variations and changes without departing from the spirit of the present application. It is hoped that the appended claims will cover these variations and changes without departing from the spirit of the present application.

Claims

1. A method for determining the power of a new energy power station, characterized in that, Including: Calculating the average value of the real-time target parameters associated with each new energy device in the target new energy power station as the average target parameter of the target new energy power station; Determining the real-time fitting curve parameter corresponding to the average target parameter according to the relationship between the pre-determined fitting curve parameter and the average target parameter; the real-time fitting curve parameter is the parameter of the fitting curve between the number of new energy devices and the real-time target parameter; Determining the real-time fitting curve of the target new energy power station according to the real-time fitting curve parameter; Determining the number of new energy devices corresponding to multiple target parameter values of the target new energy power station according to the real-time fitting curve; Determining the output power corresponding to each target parameter value according to the pre-determined correspondence between the output power of the new energy device and the target parameter; Determining the equivalent power of the target new energy power station according to the number of new energy devices corresponding to each target parameter value and the output power; The relationship between the fitting curve parameter and the average target parameter, and the fitting curve are obtained by the following method: Dividing the sample real-time target parameters associated with each new energy device in the target new energy power station into pre-determined target parameter intervals, and counting the number of new energy devices divided into each target parameter interval; For each target parameter interval, perform the following operations: determining the number of new energy devices corresponding to each target parameter value in the current target parameter interval; performing a first fit on the correspondence between the number of new energy devices and each target parameter value to obtain a first fitting curve; Calculating the average value of the sample real-time target parameters divided into the current target parameter interval as the sample average target parameter; Performing a second fit on the correspondence between the parameters of the first fitting curve corresponding to each target parameter interval and the sample average target parameter to obtain a second fitting curve; Taking the second fitting curve as the relationship curve between the fitting parameter and the average target parameter, and taking the first fitting curve as the fitting curve; Before dividing the sample real-time target parameters associated with each new energy device in the target new energy power station into pre-determined target parameter intervals and counting the number of new energy devices corresponding to each target parameter interval, it further includes: Obtaining the values of multiple parameters affecting the output power of the new energy device at multiple moments and the corresponding output power, and taking the values of multiple parameters and the output power at the same moment as a set of values to obtain multiple sets of values; Calculating the correlation parameter between each parameter and the output power according to the multiple sets of values; Determining the parameter whose correlation parameter with the output power reaches a predetermined threshold according to the values of the correlation parameters corresponding to each parameter, and taking the parameter as the target parameter; 2. The method according to claim 1, wherein Before calculating the average value of the real-time target parameters associated with each new energy device in the target new energy power station as the average target parameter of the target new energy power station, it further includes: Obtaining the real-time target parameters associated with each acquisition moment of each new energy device in the target new energy power station within the first predetermined time period; A time window with a second predetermined duration slides within the first predetermined duration according to a preset time length. Each time it slides, the average value of the real-time target parameters associated with each acquisition moment within the time window for each new energy device is calculated as the real-time target parameter of the new energy device.

3. The method according to claim 1, characterized in that, Before performing the first fitting on the number of new energy devices and the current target parameter value to obtain the first fitting curve, it further includes: Dividing the number of new energy devices within the current target parameter range by the maximum value of the number of new energy devices corresponding to the current target parameter range to normalize the number of new energy devices.

4. The method according to claim 1, characterized in that After determining the number of new energy devices corresponding to multiple target parameter values of the target new energy station according to the real-time fitting curve, it further includes: Obtaining the correspondence between the maximum number of new energy devices corresponding to each target parameter range and the target parameter range value corresponding to the maximum number when the new energy devices are divided into multiple predetermined target parameter ranges according to the target parameter; the target parameter range value is the target parameter value representing the target parameter range. Using the real-time target parameter of the new energy device as the target parameter range value to determine the maximum number of new energy devices. Multiplying the number of each new energy device by the maximum number to obtain the actual value of the number of new energy devices; the actual value refers to an integer value.

5. The method according to claim 1, characterized in that The relationship between the average target parameter, the predetermined fitting curve parameter, and the average target parameter is a quadratic function relationship; and / or, the fitting curve parameter is the parameter of the probability density function of the normal distribution.

6. A power determination device for a new energy power station, characterized in that, It includes: A calculation unit for calculating the average value of the real-time target parameters associated with each new energy device in the target new energy station as the average target parameter of the target new energy station. A first determination unit for determining the real-time fitting curve parameter corresponding to the average target parameter according to the relationship between the predetermined fitting curve parameter and the average target parameter. The real-time fitting curve parameter is the parameter of the fitting curve between the number of new energy devices and the real-time target parameter. A second determination unit for determining the real-time fitting curve of the target new energy station according to the real-time fitting curve parameter. A third determination unit for determining the number of new energy devices corresponding to multiple target parameter values of the target new energy station according to the real-time fitting curve. A fourth determination unit for determining the output power corresponding to each target parameter value according to the correspondence between the output power and the target parameter of the predetermined new energy device. A fifth determination unit for determining the equivalent power of the target new energy station according to the number of new energy devices and the output power corresponding to each target parameter value. The relationship between the fitting curve parameter and the average target parameter, and the fitting curve are obtained by the following method: Dividing the sample real-time target parameters associated with each new energy device in the target new energy field into each predetermined target parameter range, and counting the number of new energy devices divided into each target parameter range. For each target parameter range, perform the following operations: determine the number of new energy devices corresponding to each target parameter value in the current target parameter range; perform the first fitting on the correspondence between the number of new energy devices and each target parameter value to obtain the first fitting curve. Calculate the average value of the real-time target parameters of each sample divided into the current target parameter interval as the average target parameter of the sample; Perform a second fitting on the correspondence between the parameters of the first fitting curve corresponding to each target parameter interval and the average target parameter of the sample to obtain a second fitting curve; Use the second fitting curve as the relationship curve between the fitting parameter and the average target parameter, and use the first fitting curve as the fitting curve; The device is further configured to: Before dividing the real-time target parameters of the samples associated with each new energy device in the target new energy field into the predetermined target parameter intervals and counting the number of new energy devices corresponding to each target parameter interval, obtain the values of multiple parameters affecting the output power of the new energy device at multiple moments and the corresponding output power, and use the values of the multiple parameters and the output power at the same moment as a set of values to obtain multiple sets of values; Calculate the correlation parameter between each parameter and the output power according to the multiple sets of values; Determine the parameter whose correlation parameter with the output power reaches a predetermined threshold according to the values of the correlation parameters corresponding to each parameter, and use the parameter as the target parameter.

7. An electronic device, characterized in that, Including: A memory and a processor, the processor and the memory are communicatively connected to each other, the memory stores computer instructions, and the processor realizes the steps of the method according to any one of claims 1 to 5 by executing the computer instructions.

8. A computer storage medium, characterized in that, The computer storage medium stores computer program instructions, and when the computer program instructions are executed by the processor, the steps of the method according to any one of claims 1 to 5 are realized.

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