A virtual power plant data tracking and detection method and system

Abstract

The present invention relates to the field of electrical digital data processing technology, and specifically to a virtual power plant data tracking and detection method and system, including real-time collection of wind speed and power time series; analyzing and confirming the wind speed and power time series to obtain a wind speed-generation power function model; confirming the authenticity evaluation value of each power data based on the wind speed-generation power function model, the wind speed and power time series; assigning different weights based on a vector weighting algorithm and the authenticity evaluation value of each power data and predicting to obtain predicted power data; comparing the predicted power data with the power data corresponding to the predicted moment, and tracking and detecting the virtual power plant based on the comparison result. In the present invention, by comparing the predicted power data with the actual power corresponding to the moment, the non-steady-state change of the wind turbine can be quickly confirmed, and the continuous tracking and detection abnormality of the wind turbine of the virtual power plant can be quickly confirmed.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical digital data processing, and in particular to a virtual power plant data tracking and detection method and system. Background Art

[0002] A virtual power plant (VPP) is a conceptual model or entity that integrates multiple distributed energy resources (such as solar, wind, and energy storage systems) and implements flexible scheduling strategies to achieve electricity supply. Among these resources, wind power holds a significant position as a low-cost, renewable source of electricity. Harnessing wind power not only helps reduce dependence on traditional fossil fuels and lowers carbon footprints, but also aligns with environmental policies and sustainable development goals.

[0003] The predictability of wind energy is crucial to the stability of the power system. Effectively tracking and managing wind power generation can help virtual power plants better predict and adjust the operation of the power grid, ensuring the stability and reliability of power supply. However, when predicting wind power generation, existing technologies usually use long short-term neural network (LSTW) algorithms to predict current data based on historical power generation data. Although this method has certain effects, its prediction accuracy still has room for improvement, especially when dealing with changes in wind power, the model cannot fully pay attention to the real time point.

[0004] Therefore, there is an urgent need for a method that can effectively improve the efficiency of virtual power plant data utilization to better continuously track and detect abnormal data. Summary of the Invention

[0005] In order to solve the above technical problems, the purpose of the present invention is to provide a virtual power plant data tracking and detection method and system to solve the above problems. In order to achieve the above purpose, the technical solutions adopted by the present invention are as follows:

[0006] In a first aspect, the present application provides a virtual power plant data tracking and detection method, the method comprising:

[0007] Real-time collection of wind speed and power generation to obtain wind speed time series and power time series;

[0008] Analyzing and confirming the wind speed time series and the power time series to obtain a wind speed-generated power function model;

[0009] Confirming the authenticity evaluation value of each power data according to the wind speed-generation power function model, the wind speed time series and the power time series;

[0010] Dividing the wind speed time series into a training set and a prediction set;

[0011] Assigning different weights to each power data in the training set according to a vector weighting algorithm and an authenticity evaluation value of each power data, and training a preset neural network model to obtain a prediction model;

[0012] Assigning different weights to each power data in the prediction set according to a vector weighting algorithm and an authenticity evaluation value of each power data and inputting the weights into the prediction model for prediction to obtain predicted power data;

[0013] The predicted power data is compared with the power data corresponding to the predicted time, and the virtual power plant is tracked and detected based on the comparison result. The predicted time is the time corresponding to the predicted power data.

[0014] In conjunction with the first aspect, in a possible implementation, analyzing and confirming the wind speed time series and the power time series to obtain a wind speed-power generation function model includes:

[0015] Analyze the wind speed time series based on Weibull distribution to confirm the authenticity assessment value of each wind speed data;

[0016] A wind speed-generation power function model is constructed based on the authenticity evaluation value of each wind speed data and the power time series.

[0017] In conjunction with the first aspect, in one possible implementation, analyzing the wind speed time series based on the Weibull distribution to confirm the authenticity evaluation value of each wind speed data includes:

[0018] Calculating the probability that the moment corresponding to each wind speed data is the Weber center based on the wind speed time series;

[0019] The total number of Weber centers is obtained by judging the probability corresponding to each moment according to a preset probability threshold, and the probability corresponding to the Weber center is greater than the preset probability threshold;

[0020] A wind speed function to be solved is constructed based on the total number of Weber centers and a preset Weber function, wherein the wind speed function to be solved includes at least one wind speed parameter;

[0021] Performing wind speed parameter optimization processing on all the wind speed parameters based on the least squares method to obtain a wind speed function;

[0022] Calculating an effective wind speed time series based on the wind speed function;

[0023] The authenticity evaluation value of each wind speed is calculated based on the effective wind speed time series and the wind speed time series.

[0024] In conjunction with the first aspect, in a possible implementation, the wind speed parameter optimization process includes:

[0025] An error desirability evaluation function is constructed based on each wind speed data in the wind speed time series and the data on both sides of each wind speed data;

[0026] A wind speed error sum of squares function is constructed based on the error desirability evaluation function and the wind speed function to be solved;

[0027] The wind speed time series and power time series are fitted using the wind speed error sum square function as the fitting function of the least squares method to obtain the solution for each wind speed parameter.

[0028] In combination with the first aspect, in a possible implementation, the preset probability threshold is 0.7.

[0029] In conjunction with the first aspect, in one possible implementation, a wind speed-generation power function model is constructed based on the authenticity evaluation value of each wind speed data and the power time series, including:

[0030] Obtaining a wind speed-power sequence based on the authenticity evaluation value of each wind speed data, the wind speed time series, and the power time series;

[0031] Obtaining the wind speed corresponding to the maximum power generation based on the wind speed-power sequence analysis;

[0032] A wind speed-generated power function model to be solved is constructed based on the wind speed corresponding to the maximum generated power, wherein the wind speed-generated power function model to be solved includes at least one power parameter;

[0033] All the power parameters are optimized based on the least square method to obtain a wind speed-power generation function model.

[0034] With reference to the first aspect, in one possible implementation, the power parameter optimization process includes:

[0035] Constructing an error contribution function based on the authenticity evaluation value of each wind speed data;

[0036] A power error square sum function is constructed based on the wind speed-generation power function model to be solved and the error contribution function;

[0037] The wind speed-power sequence is fitted using the power error sum square function as the fitting function of the least square method to obtain a solution for the power parameter.

[0038] In conjunction with the first aspect, in one possible implementation, obtaining the wind speed corresponding to the maximum power generation based on the wind speed-power sequence analysis includes:

[0039] Calculate the standard deviations on both sides of each wind speed in the wind speed-power sequence respectively to obtain a first standard deviation and a second standard deviation corresponding to each wind speed, wherein the first standard deviation is obtained by calculating the standard deviation of the power generation corresponding to a plurality of successively increasing first wind speeds, and the second standard deviation is obtained by calculating the standard deviation of the power generation corresponding to a plurality of successively increasing second wind speeds, wherein the first wind speed is less than the second wind speed;

[0040] The maximum effective wind speed probability corresponding to each wind speed is calculated based on the first standard deviation and the second standard deviation corresponding to each wind speed, and the maximum effective wind speed probability is used to represent the probability that the wind speed has just reached the maximum power generation power;

[0041] The wind speed corresponding to the maximum power generation is selected based on the maximum effective wind speed probability.

[0042] In a second aspect, the present application also provides a virtual power plant data tracking and detection system, comprising:

[0043] Data acquisition module, used to collect wind speed and power generation in real time to obtain wind speed time series and power time series;

[0044] A model confirmation module is used to analyze and confirm the wind speed time series and the power time series to obtain a wind speed-generation power function model;

[0045] A power evaluation module is used to confirm the authenticity evaluation value of each power data based on the wind speed-generation power function model, the wind speed time series and the power time series;

[0046] A division module, configured to divide the wind speed time series into a training set and a prediction set;

[0047] A training module, configured to assign different weights to each power data in the training set according to a vector weighting algorithm and an authenticity evaluation value of each power data, and to train a preset neural network model to obtain a prediction model;

[0048] A prediction module, configured to assign different weights to each power data in the prediction set according to a vector weighting algorithm and an authenticity evaluation value of each power data, and input the weights into the prediction model for prediction to obtain predicted power data;

[0049] A comparison module is used to compare the predicted power data with the power data corresponding to the predicted time, and to track and detect the virtual power plant based on the comparison result, where the predicted time is the time corresponding to the predicted power data.

[0050] In conjunction with the second aspect, in one possible implementation, the model confirmation module includes:

[0051] Wind speed module, used to analyze wind speed time series based on Weibull distribution to confirm the authenticity evaluation value of each wind speed data;

[0052] The model building module is used to build a wind speed-generation power function model based on the authenticity evaluation value of each wind speed data and the power time series.

[0053] The present invention has the following beneficial effects:

[0054] In the present invention, based on the correlation between wind speed and power generation, a wind speed-power generation function model is confirmed; and based on the wind speed-power generation function model, the authenticity evaluation of each power data is confirmed, and different weights are assigned to each power data using the authenticity evaluation value, so that the prediction model can better focus on more important time points, thereby improving the prediction accuracy, and based on the more accurate prediction results, the non-steady-state changes of the wind turbine can be directly confirmed by comparison, which can effectively improve the data utilization efficiency of the virtual power plant and more conveniently track and detect abnormalities of the wind turbines in the virtual power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 A flowchart of a virtual power plant data tracking and detection method provided in Example 1 of the present invention;

[0057] Figure 2 The wind speed diagram described in Example 1 of the present invention;

[0058] Figure 3 This is a flow chart of step S2 provided in Example 1 of the present invention;

[0059] Figure 4 This is a structural diagram of the virtual power plant data tracking and detection system described in Example 2 of the present invention;

[0060] Figure 5 Schematic diagram of the structure of the model confirmation module described in Example 2 of the present invention. DETAILED DESCRIPTION

[0061] To further illustrate the technical means and effectiveness of the present invention in achieving its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a virtual power plant data tracking and detection method and system according to the present invention, including its specific implementation, structure, features, and effectiveness. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0062] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0063] Example 1:

[0064] The specific scheme of the virtual power plant data tracking and detection method provided by the present invention is described in detail below with reference to the accompanying drawings.

[0065] See also Figure 1 , which shows a flow chart of a virtual power plant data tracking and detection method provided by an embodiment of the present invention. Specifically, the method includes step S1, step S2, step S3, step S4, step S5, step S6, step S7 and step S8.

[0066] S1. Real-time collection of wind speed and power generation to obtain wind speed time series and power time series.

[0067] It should be noted that in this embodiment, the wind speed data is collected by anemometers installed on wind turbines (collected once per second), and each anemometer is connected to a data acquisition system for real-time data transmission. At the same time, the power generation mentioned in the embodiment is collected on the monitoring system of each wind turbine (collected once per second). And it is divided into a wind speed time series and a power time series according to the number of days. Through the above two data sources, in this embodiment, a wind speed data set in time series and a power generation data set in time series can be obtained. In order to facilitate subsequent explanations, in this embodiment, the wind speed data set in time series is referred to as a wind speed time series, and the power generation data set in time series is referred to as a power time series.

[0068] In this embodiment, considering that the wind speed data collected on the wind turbine may be the result of the combined action of multiple wind forces, see Figure 2The wind speed graph shows how wind speed changes over time, indicating that a single wind speed data point may be the result of the combined effects of multiple winds. Correspondingly, the power generated by a wind turbine is also generated by the combined effects of multiple winds. Therefore, in this embodiment, a wind speed-power generation function model is first determined based on the wind speed time series and the power time series. The wind speed-power generation function model is then used to determine the authenticity of the power generation at each time point. Finally, this authenticity is weighted and combined with the long-short-term neural network model to accurately predict the power generation at the next moment. Therefore, see steps S2-S6 for details.

[0069] S2. Analyze and confirm the wind speed time series and the power time series to obtain a wind speed-generated power function model.

[0070] In this embodiment, we consider that a piece of wind speed data may be the result of the combined effects of multiple winds, and combine the characteristics of the wind speed showing a Weibull distribution to determine the authenticity of the wind speed data at each time in the time dimension. We then use the authenticity of the wind speed data to determine the unique power generation data corresponding to each wind speed data, and use this to construct a wind speed-power generation function model. Based on this, to facilitate understanding by those skilled in the art, the wind speed data mentioned in this embodiment refers to the wind speed corresponding to a time, and similarly, the power generation data refers to the power generation data corresponding to a time; the wind speed refers to the wind speed itself, and the power generation refers to the power generation itself.

[0071] Specifically, see Figure 3 , Figure 3 FIG. 4 shows that step S2 includes step S21 and step S22.

[0072] S21. Analyze the wind speed time series based on the Weibull distribution to confirm the authenticity evaluation value of each wind speed data.

[0073] In this embodiment, the authenticity of wind speed is first determined in the time dimension based on the characteristics of the wind speed exhibiting a Weber distribution. For time series wind speed data collected from the same wind turbine, each wind speed data point is the result of the combined forces of multiple winds. Specifically, in this embodiment, the number of Weber centers is first determined, and then a wind speed function is constructed using the number of Weber centers. The desirability of each error is then determined by comparing it with surrounding spatiotemporal data. The wind speed function is then determined by minimizing the constructed error sum function, and the authenticity of the wind speed is determined using the wind speed function. For more details, see steps S211-S2116.

[0074] S211 . Calculate, based on the wind speed time series, the probability that the moment corresponding to each wind speed data is the Weber center.

[0075] In this embodiment, the probability corresponding to each moment is determined by constructing a probability calculation formula. The probability calculation formula is:

[0076]

[0077] Among them, W A It represents the probability that the moment corresponding to the A-th wind speed data in the wind speed time series is the Weber center; exp represents the exponential function with the natural constant e as the base, e +[A,A ′ 1...A′q] Indicates the error of using the optimal straight line with a positive slope to fit the Ath wind speed data and the q wind speed data on the left side of its time series; A ′ 1 means the first wind speed data on the left side of the A-th wind speed data time series; A ′ q represents the qth wind speed data on the left side of the Ath wind speed data time series; e -[A,A ′ 1 ′ ...A′q′] Indicates the error of using the optimal straight line with a negative slope to fit the Ath wind speed data and the q wind speed data on the right side of its time series; A ′ 1 ′ Indicates the first wind speed data on the right side of the Ath wind speed data time series; A ′ q ′ Indicates the qth wind speed data on the right side of the Ath wind speed data time series; q=600.

[0078] In this embodiment, considering that the Weber center is the moment corresponding to the highest point in the Weber distribution of wind, the slope of the straight line formed by fitting the data on the right should be less than 0, and the slope of the straight line formed by fitting the data on the left should be greater than 0. Furthermore, because the wind speed changes continuously over time, the fitted straight line has high smoothness (small error). Therefore, this technique constructs the above equation to represent the probability of each data point being the Weber center by fitting the q data points on the left with a straight line with a slope greater than 0, and the q data points on the right with a straight line with a slope less than 0, with the smaller the fitting error, the better. Furthermore, considering that the shortest interval between each gust of wind can be between several minutes and several tens of minutes, the value of q in this invention is 600. However, those skilled in the art may also choose other values ​​of q, and this embodiment does not impose any specific restrictions.

[0079] S212. Determine the total number of Weber centers by judging the probability corresponding to each moment according to a preset probability threshold, wherein the probability corresponding to the Weber center is greater than the preset probability threshold.

[0080] Specifically, in this embodiment, the preset probability threshold is 0.7. If the probability corresponding to each moment is greater than 0.7, it proves that the wind generated at that moment is a Weber center of wind.

[0081] S213 . Constructing a wind speed function to be solved based on the total number of Weber centers and a preset Weber function, wherein the wind speed function to be solved includes at least one wind speed parameter.

[0082] That is, according to the above analysis results, it can be seen that the wind speed function is the result of the cumulative expression of a series of Weibull distributions. Therefore, in this embodiment, the following functional expression is constructed to represent the wind speed function in the time series:

[0083]

[0084] Where Z(t) represents the wind speed function of the relationship between wind speed and time; m represents the total number of Weber centers; x represents wind speed; e represents a natural constant; a i Represents the i-th shape parameter. Since the shape parameter is used to describe the radian state of Weber, a i Not zero; b i represents the i-th scale parameter; T i represents the time when the i-th wind speed corresponds to the Weber center, T i Not zero.

[0085] It should be noted that the calculation method of the shape parameter and the scale parameter is an existing technology and will not be repeated in this embodiment; in addition, one Weber center represents one wind, which corresponds to one shape parameter and one scale parameter, so the number of shape parameters and scale parameters is equal to the number of Weber centers.

[0086] In the above calculation formula, The desired wind speed function for this embodiment is obtained by accumulating and summing the Weber functions corresponding to individual wind speeds. Each Weber function has only one unknown parameter. Therefore, in this embodiment, assuming that 20 Weber centers are confirmed within an hour, the wind speed function has a total of m = 20 unknown parameters.

[0087] S214. Perform wind speed parameter optimization processing on all the wind speed parameters based on the least square method to obtain a wind speed function.

[0088] To clarify the process of optimizing the wind speed parameters, please refer to steps S2141 to S2143 for details.

[0089] S2141. Construct an error desirability evaluation function based on each wind speed data in the wind speed time series and the data located on both sides of each wind speed data.

[0090] In this embodiment, for unknown wind speed parameters, the least squares method is used to minimize the error sum to solve the wind speed parameters. However, for some points with excessively large differences, the impact on the minimized error sum is significant. Therefore, in this embodiment, the following error desirability evaluation function formula is first constructed to determine the error desirability evaluation value of each wind speed data in the wind speed time series:

[0091]

[0092] Among them, P A represents the error desirability evaluation value of the A-th wind speed data in the wind speed time series; exp represents the exponential function with the natural constant e as the base; n represents the total number of wind speed data in the wind speed time series. For example, if the wind speed time series is one day's data, then n = 86400; Z A represents the A-th wind speed data in the wind speed time series, Z A′ Indicates the wind speed data to the left of the A-th wind speed data in the wind speed time series, Z A″ represents the wind speed data on the right side of the A-th wind speed data in the wind speed time series; Z p represents the pth wind speed data in the wind speed time series; Z p+1 represents the p+1th wind speed data in the wind speed time series; || represents the absolute value function.

[0093] In the above calculation formula, |Z A -Z A′ | represents the volatility of the wind speed data of the Ath time series and the wind speed on the left side of the time series; |Z A -Z A″ | represents the volatility of the wind speed data of the Ath time series and the wind speed on the right side of the time series;

[0094] Indicates the volatility of the A-th wind speed data in the wind speed time series; |Z p -Z p+1 | represents the volatility between the i-th and i+1-th wind speed data in the wind speed time series; It represents the sum of the differences between the volatility of the A-th wind speed data and the volatility of the entire wind speed time series.

[0095] In this embodiment, the wind speed change caused by the wind is a continuous change without sudden changes. Compared with the overall fluctuations of all adjacent moments, the smaller the error, the more desirable it is. Therefore, the above error desirability evaluation function formula can better measure the error desirability of each wind speed data.

[0096] S2142. Construct a wind speed error sum of squares function based on the error desirability evaluation function and the wind speed function to be solved.

[0097] Specifically, in this embodiment, the wind speed error sum square function is:

[0098]

[0099] Among them, S w represents the sum of squared errors of all wind speed data points; n represents the total number of wind speed data points; P p represents the error desirability evaluation value of the p-th wind speed data in the time series, Z(p) represents the wind speed value calculated by substituting the wind speed function into the corresponding moment of the p-th wind speed data, and Z p represents the actual wind speed value collected at the corresponding moment of the p-th wind speed data; || represents the absolute value function.

[0100] In this embodiment, the method of minimizing the sum of squares of data point errors is used to determine the m wind speed parameters of the wind speed function. However, if an undesirable point with a large error occurs, the error in the selection of the wind speed parameter will be extremely large. This step makes the acquisition of the wind speed function more accurate by reducing the contribution of data points with low error desirability evaluation values.

[0101] S2143. Fit the wind speed time series and the power time series using the wind speed error sum square function as the fitting function of the least squares method to obtain a solution for each wind speed parameter.

[0102] The process of how to perform fitting using the wind speed error square sum function using the least squares method is prior art and will not be described in detail in this embodiment.

[0103] S215: Obtain an effective wind speed time series based on the wind speed function. That is, in this step, each wind speed data in the effective wind speed time series is obtained by calculating the wind speed function.

[0104] S216: Calculate the authenticity evaluation value of each wind speed based on the effective wind speed time series and the wind speed time series. Specifically, in this embodiment, the authenticity evaluation value calculation formula of the wind speed is:

[0105] V A =exp(-|Z(A)-Z A |)

[0106] Among them, V A represents the authenticity evaluation value of the A-th wind speed data in the wind speed time series; exp represents the exponential function with the natural constant e as the base; Z(A) represents the wind speed obtained by substituting the wind speed function into the A-th wind speed data at the corresponding moment; Z Arepresents the actual wind speed value collected at the corresponding moment of the A-th wind speed data; || represents the absolute value function.

[0107] S22. Constructing a wind speed-generation power function model based on the authenticity evaluation value of each wind speed data and the power time series.

[0108] In this embodiment, considering that the power generation of the wind turbine is linearly proportional to the cube of the wind speed, a wind speed-power generation function model is constructed in this embodiment, and then the authenticity of the power generation is obtained through the wind speed-power generation function model.

[0109] At the same time, this embodiment also takes into account the construction of this wind speed-generation power function model. Because the wind speed-generation power function model requires that the independent variable can only correspond to one dependent variable, and the same wind speed in the time series can correspond to multiple power generation data at different times, here it is necessary to make the power generation value corresponding to the more realistic wind speed data contribute more to it, obtain all the data point pairs required by the wind speed-generation power function model, and thus construct the wind speed-generation power function model. Then, through the desirability of these data point pairs, the optimal model parameters are obtained, and the authenticity of the power generation data can be obtained using the model error. Specifically, see steps S221-step S224.

[0110] S221 . Obtain a wind speed-power sequence based on the authenticity evaluation value of each wind speed data, the wind speed time series, and the power time series.

[0111] In this embodiment, the same wind speed data may correspond to multiple power generation data at different times. This makes it difficult to confirm the wind speed-power generation function model in the subsequent steps. Therefore, in this step, the power generation corresponding to each wind speed is determined by the following calculation formula:

[0112]

[0113] Among them, G M Indicates the power generation capacity that wind speed M should have; l represents the total number of times corresponding to the same wind speed M. Assuming that wind speed M occurs 30 times in a day, then l = 30; G represents the authenticity evaluation value of the wind speed data corresponding to the jth occurrence of wind speed M. j Indicates the power generation data at the time corresponding to the j-th wind speed data.

[0114] The above calculation formula can uniquely identify a power generation rate for each wind speed, overcoming the problem that the same wind speed value can correspond to multiple power generation rates at different times in the historical time series. Furthermore, the above method can be used to adjust the contribution of each wind speed data point based on the authenticity assessment value, making the unique power generation rate determined by wind speed more realistic. This results in a wind speed-power sequence.

[0115] S222. Obtain the wind speed corresponding to the maximum power generation power based on the wind speed-power sequence analysis.

[0116] Since the power generated by a wind turbine does not actually increase indefinitely with increasing wind speed, but is subject to the upper limit of the wind turbine's energy conversion efficiency, this embodiment further includes steps S2221 to S2223 for analyzing the wind speed corresponding to the maximum power generation.

[0117] S2221. Calculate the standard deviations on both sides of each wind speed in the wind speed-power sequence respectively to obtain a first standard deviation and a second standard deviation corresponding to each wind speed, wherein the first standard deviation is obtained by calculating the standard deviation of the power generation corresponding to several successively increasing first wind speeds, and the second standard deviation is obtained by calculating the standard deviation of the power generation corresponding to several successively increasing second wind speeds, and the first wind speed is less than the second wind speed.

[0118] S2222. Calculate the maximum effective wind speed probability corresponding to each wind speed based on the first standard deviation and the second standard deviation corresponding to each wind speed, where the maximum effective wind speed probability is used to represent the probability that the wind speed has just reached the maximum power generation power.

[0119] The maximum effective wind speed probability calculation formula is as follows:

[0120]

[0121] Among them, H A It indicates the probability that wind speed A is the wind speed that just reaches the maximum power generation; Represents the standard deviation of wind speed A and its generated power at the m wind speeds on the left side of the wind speed-power sequence; S1 ′ Indicates the power generation capacity of the first wind speed on the left side of wind speed A; S ′ m Indicates the power generation capacity of the mth wind speed on the left side of wind speed A; Represents the standard deviation of wind speed A and its generated power at the m wind speeds on the right side of the wind speed-power sequence; S1 ″ Indicates the power generation capacity of the first wind speed on the right side of wind speed A; S ′ m ′It represents the power generation capacity of the mth wind speed to the right of wind speed A; exp represents the exponential function with the natural constant e as the base.

[0122] In the above calculation formula, [S,S1 ′ ...S ′ m ] means the first standard deviation of the power generation corresponding to the first wind speed; [S,S1 ″ ...S ′ m ′ ] represents the second standard deviation of the power generated corresponding to the second wind speed. In this embodiment, it is taken into account that the power generated by the wind turbine does not increase infinitely with the increase of wind speed in practice, but is subject to the upper limit of the energy conversion efficiency of the wind turbine. Therefore, for the wind speed that just reaches the maximum power, the left side is stable, so The smaller the better. On this basis, the right side changes more, so The bigger the better.

[0123] S2223. Filter the wind speed corresponding to the maximum power generation based on the maximum effective wind speed probability.

[0124] S223. Constructing a wind speed-generated power function model to be solved based on the wind speed corresponding to the maximum generated power, wherein the wind speed-generated power function model to be solved includes at least one power parameter;

[0125] The wind speed-generation power function model to be solved in this embodiment is:

[0126]

[0127] Where G(Z) represents the wind speed-generation power function model to be solved; k represents the power parameter; Z represents the wind speed, that is, the collected time series wind speed; Indicates the maximum power generation capacity; S indicates the wind speed that just reaches the maximum power generation capacity.

[0128] The above-mentioned wind speed-power generation function model to be solved can well show that the maximum power generation of a wind turbine has a limit. Before this limit, the cube of the wind speed is linearly proportional to the power generation. After the limit, the power generation is constant.

[0129] S224. Perform power parameter optimization processing on all the power parameters based on the least square method to obtain a wind speed-power generation function model.

[0130] To clarify the process of optimizing the wind speed parameters, please refer to steps S2241 to S2243 for details.

[0131] S2241. Construct an error contribution function based on the authenticity evaluation value of each wind speed data.

[0132] Among them, the error contribution function in this step is:

[0133]

[0134] Among them, D M represents the error contribution of wind speed M to power generation; l represents the total number of moments corresponding to the same wind speed M; It represents the authenticity evaluation value corresponding to the wind speed data when the wind speed M appears for the jth time.

[0135] S2242. Construct a power error square sum function based on the wind speed-generation power function model to be solved and the error contribution function.

[0136] Specifically, in this embodiment, the power error square sum function is:

[0137]

[0138] Among them, S P represents the sum of squared errors of all power generation data points; n represents the total number of wind speed data points; D p represents the error contribution of the p-th wind speed data corresponding to the power generation; G(p) represents the power generation calculated by substituting the wind speed collected at the time corresponding to the p-th wind speed data into the wind speed-power generation function model to be solved, G p Indicates the power generation data collected at the time corresponding to the p-th wind speed data in the time series.

[0139] In this embodiment, the method of minimizing the sum of squares of data point errors is used to determine the power parameter k of the wind speed-generation power function model to be solved. However, if an undesirable point with a large error occurs, the error in the selection of the power parameter k will be extremely large. In this step, the error contribution balances the data, so that the confirmation of the power parameter is more in line with the actual situation.

[0140] S2243. Fit the wind speed-power sequence using the power error square sum function as the fitting function of the least squares method to obtain a solution for the power parameter.

[0141] S3. Confirm and obtain the authenticity evaluation value of each power data according to the wind speed-power generation function model, the wind speed time series and the power time series.

[0142] Specifically, in this embodiment, the authenticity evaluation value calculation formula of power data is:

[0143] F p =exp(-|G(Zp )-G p |)

[0144] Formula explanation: F p Indicates the power generation G at the time corresponding to the i-th wind speed data in the time series p The authenticity evaluation value of exp represents the exponential function with the natural constant e as the base; Z(p) represents the wind speed value calculated by substituting the wind speed function into the p-th wind speed data at the corresponding moment; G(Z p ) represents the wind speed value calculated by substituting the p-th wind speed data into the wind speed function at the corresponding moment, and then substituting it into the wind speed-power generation function model to obtain the power generation value, G p represents the power generation data collected at the time corresponding to the p-th wind speed data in the time series; || represents the absolute value function.

[0145] S4. Divide the wind speed time series into a training set and a prediction set.

[0146] It should be noted that in this embodiment, the above-mentioned wind speed time series is further divided, where the prediction set consists of ten wind speed data, and the corresponding training set consists of several groups of samples consisting of ten wind speed data, and each sample in the training set corresponds to a power generation data for training the model.

[0147] S5. Assign different weights to each power data in the training set according to a vector weighting algorithm and the authenticity evaluation value of each power data, and train a preset neural network model to obtain a prediction model.

[0148] S6. Assign different weights to each power data in the prediction set according to a vector weighting algorithm and the authenticity evaluation value of each power data, and input the weights into the prediction model for prediction to obtain predicted power data.

[0149] It should be noted that the vector weighting algorithm mentioned in step S5 and step S6 in this embodiment is a prior art and will not be described in detail in this embodiment.

[0150] S7. Compare the predicted power data with the power data corresponding to the predicted time, and track and detect the virtual power plant based on the comparison result, where the predicted time is the time corresponding to the predicted power data.

[0151] In this embodiment, it is determined whether the predicted power data matches the actual power data corresponding to the predicted moment. If the predicted power data matches the actual power data, the generated power is directly output; if the predicted result does not match the actual generated power result, the data is continuously tracked to determine whether the wind turbine has an abnormal situation, such as whether there is a continuous mismatch of data.

[0152] It should also be noted that the matching mentioned in the above steps refers to whether the absolute value of the difference between the predicted power data and the actual power data is within an error range, for example, an error range can be 0 to 50 kW.

[0153] Example 2:

[0154] like Figure 4 As shown, this embodiment provides a virtual power plant data tracking and detection system, the system comprising:

[0155] Data acquisition module, used to collect wind speed and power generation in real time to obtain wind speed time series and power time series;

[0156] A model confirmation module is used to analyze and confirm the wind speed time series and the power time series to obtain a wind speed-power generation function model;

[0157] A power evaluation module is used to confirm the authenticity evaluation value of each power data based on the wind speed-generation power function model, the wind speed time series and the power time series;

[0158] A division module, configured to divide the wind speed time series into a training set and a prediction set;

[0159] A training module, configured to assign different weights to each power data in the training set according to a vector weighting algorithm and an authenticity evaluation value of each power data, and to train a preset neural network model to obtain a prediction model;

[0160] A prediction module, configured to assign different weights to each power data in the prediction set according to a vector weighting algorithm and an authenticity evaluation value of each power data, and input the weights into the prediction model for prediction to obtain predicted power data;

[0161] A comparison module is used to compare the predicted power data with the power data corresponding to the predicted time, and to track and detect the virtual power plant based on the comparison result, where the predicted time is the time corresponding to the predicted power data.

[0162] In some specific embodiments, such as Figure 5 The model confirmation module shown includes:

[0163] Wind speed module, used to analyze wind speed time series based on Weibull distribution to confirm the authenticity evaluation value of each wind speed data;

[0164] The model building module is used to build a wind speed-generation power function model based on the authenticity evaluation value of each wind speed data and the power time series.

[0165] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated on here.

[0166] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0167] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A virtual power plant data tracking and detection method, characterized in that: The method comprises: Real-time collection of wind speed and power generation to obtain wind speed time series and power time series; Analyzing and confirming the wind speed time series and the power time series to obtain a wind speed-generated power function model; Confirming the authenticity evaluation value of each power data according to the wind speed-generation power function model, the wind speed time series and the power time series; Dividing the wind speed time series into a training set and a prediction set; Assigning different weights to each power data in the training set according to a vector weighting algorithm and an authenticity evaluation value of each power data, and training a preset neural network model to obtain a prediction model; Assigning different weights to each power data in the prediction set according to a vector weighting algorithm and an authenticity evaluation value of each power data and inputting the weights into the prediction model for prediction to obtain predicted power data; Comparing the predicted power data with power data corresponding to a predicted time, and tracking and detecting the virtual power plant based on the comparison result, wherein the predicted time is the time corresponding to the predicted power data; The wind speed time series and the power time series are analyzed and confirmed to obtain a wind speed-power generation function model, including: Analyze the wind speed time series based on Weibull distribution to confirm the authenticity assessment value of each wind speed data; A wind speed-power generation function model is constructed based on the authenticity evaluation value of each wind speed data and the power time series; The wind speed time series is analyzed based on the Weibull distribution to confirm the authenticity assessment value of each wind speed data, including: Calculating the probability that the moment corresponding to each wind speed data is the Weber center based on the wind speed time series; The total number of Weber centers is obtained by judging the probability corresponding to each moment according to a preset probability threshold, and the probability corresponding to the Weber center is greater than the preset probability threshold; A wind speed function to be solved is constructed based on the total number of Weber centers and a preset Weber function, wherein the wind speed function to be solved includes at least one wind speed parameter; Performing wind speed parameter optimization processing on all the wind speed parameters based on the least squares method to obtain a wind speed function; Calculating an effective wind speed time series based on the wind speed function; The authenticity evaluation value of each wind speed is calculated based on the effective wind speed time series and the wind speed time series.

2. The virtual power plant data tracking and detection method according to claim 1, characterized in that: The wind speed parameter optimization process includes: An error desirability evaluation function is constructed based on each wind speed data in the wind speed time series and the data on both sides of each wind speed data; A wind speed error sum of squares function is constructed based on the error desirability evaluation function and the wind speed function to be solved; The wind speed time series and power time series are fitted using the wind speed error sum square function as the fitting function of the least squares method to obtain the solution for each wind speed parameter.

3. The virtual power plant data tracking and detection method according to claim 1, characterized in that: The preset probability threshold is 0.

7.

4. The virtual power plant data tracking and detection method according to claim 1, characterized in that: A wind speed-generation power function model is constructed based on the authenticity evaluation value of each wind speed data and the power time series, including: Obtaining a wind speed-power sequence based on the authenticity evaluation value of each wind speed data, the wind speed time series, and the power time series; Obtaining the wind speed corresponding to the maximum power generation based on the wind speed-power sequence analysis; A wind speed-generated power function model to be solved is constructed based on the wind speed corresponding to the maximum generated power, wherein the wind speed-generated power function model to be solved includes at least one power parameter; All the power parameters are optimized based on the least square method to obtain a wind speed-power generation function model.

5. The virtual power plant data tracking and detection method according to claim 4, characterized in that: The power parameter optimization process includes: Constructing an error contribution function based on the authenticity evaluation value of each wind speed data; A power error square sum function is constructed based on the wind speed-generation power function model to be solved and the error contribution function; The wind speed-power sequence is fitted using the power error sum square function as the fitting function of the least square method to obtain a solution for the power parameter.

6. The virtual power plant data tracking and detection method according to claim 4, characterized in that: The wind speed corresponding to the maximum power generation is obtained based on the wind speed-power sequence analysis, including: Calculate the standard deviations on both sides of each wind speed in the wind speed-power sequence respectively to obtain a first standard deviation and a second standard deviation corresponding to each wind speed, wherein the first standard deviation is obtained by calculating the standard deviation of the power generation corresponding to a plurality of successively increasing first wind speeds, and the second standard deviation is obtained by calculating the standard deviation of the power generation corresponding to a plurality of successively increasing second wind speeds, wherein the first wind speed is less than the second wind speed; The maximum effective wind speed probability corresponding to each wind speed is calculated based on the first standard deviation and the second standard deviation corresponding to each wind speed, and the maximum effective wind speed probability is used to represent the probability that the wind speed has just reached the maximum power generation power; The wind speed corresponding to the maximum power generation is selected based on the maximum effective wind speed probability.

7. A virtual power plant data tracking and detection system, characterized in that: include: Data acquisition module, used to collect wind speed and power generation in real time to obtain wind speed time series and power time series; A model confirmation module is used to analyze and confirm the wind speed time series and the power time series to obtain a wind speed-power generation function model; A power evaluation module is used to confirm the authenticity evaluation value of each power data based on the wind speed-generation power function model, the wind speed time series and the power time series; A division module, configured to divide the wind speed time series into a training set and a prediction set; A training module, configured to assign different weights to each power data in the training set according to a vector weighting algorithm and an authenticity evaluation value of each power data, and to train a preset neural network model to obtain a prediction model; A prediction module, configured to assign different weights to each power data in the prediction set according to a vector weighting algorithm and an authenticity evaluation value of each power data, and input the weights into the prediction model for prediction to obtain predicted power data; a comparison module, configured to compare the predicted power data with power data corresponding to a predicted time, and to track and detect the virtual power plant based on the comparison result, wherein the predicted time is a time corresponding to the predicted power data; The wind speed time series and the power time series are analyzed and confirmed to obtain a wind speed-power generation function model, including: Analyze the wind speed time series based on Weibull distribution to confirm the authenticity assessment value of each wind speed data; A wind speed-power generation function model is constructed based on the authenticity evaluation value of each wind speed data and the power time series; The wind speed time series is analyzed based on the Weibull distribution to confirm the authenticity assessment value of each wind speed data, including: Calculating the probability that the moment corresponding to each wind speed data is the Weber center based on the wind speed time series; The total number of Weber centers is obtained by judging the probability corresponding to each moment according to a preset probability threshold, and the probability corresponding to the Weber center is greater than the preset probability threshold; A wind speed function to be solved is constructed based on the total number of Weber centers and a preset Weber function, wherein the wind speed function to be solved includes at least one wind speed parameter; Performing wind speed parameter optimization processing on all the wind speed parameters based on the least squares method to obtain a wind speed function; Calculating an effective wind speed time series based on the wind speed function; The authenticity evaluation value of each wind speed is calculated based on the effective wind speed time series and the wind speed time series.

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