Wind power plant power analysis method and apparatus, electronic device, and storage medium

Abstract

This application provides a method, apparatus, electronic device, and storage medium for power analysis of wind power equipment. The method includes: acquiring multiple candidate output powers of the wind power equipment and the predicted probability of each candidate output power; calculating the predicted output power of the wind power equipment based on the multiple candidate output powers and the predicted probabilities of each candidate output power; determining whether the wind power equipment is a power-sensitive node; acquiring the transmission energy consumption of the wind power equipment when it is a power-sensitive node; and calculating the comprehensive power of the wind power equipment based on the predicted output power and transmission energy consumption. In this application embodiment, the predicted output power of the wind power equipment is calculated based on multiple candidate output powers and their corresponding predicted probabilities, making the obtained predicted output power more accurate; for wind power equipment belonging to power-sensitive nodes, the comprehensive power is further calculated, thereby reducing the computational load; the calculation of the comprehensive power combines the predicted output power and transmission energy consumption, making the obtained comprehensive power more accurate.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a power analysis method, apparatus, electronic device and storage medium for wind power equipment. Background Technology

[0002] With societal development, human demand for energy is increasing daily. The extensive use of fossil fuels has not only led to the depletion of traditional energy sources but also caused severe environmental pollution. Against this backdrop, renewable distributed power generation has attracted significant attention due to its advantages such as sustainability, environmental friendliness, and flexible installation. In recent years, the protection and development of islands has become a hot topic in marine affairs. Islands and their surrounding waters possess abundant fishery, oil, tourism, port, and mineral resources, making island development of great economic and strategic importance. Increasingly sophisticated renewable energy power generation technologies such as wind, solar, and tidal power can effectively reduce dependence on conventional energy sources and diesel generators.

[0003] With the emergence of microgrids, island microgrid systems, primarily powered by new energy sources, have come into being. Various countries are actively researching and constructing island microgrids, with their technological equipment and installed capacity continuously improving. However, simply connecting diverse distributed power sources in parallel cannot constitute a stable power supply network, resulting in poor power quality, protection, and operation. The power output of wind turbines in a microgrid has a significant impact on the layout of distributed power sources; therefore, power analysis of wind turbines plays a crucial role in constructing a stable power supply network and ensuring energy supply to islands. Summary of the Invention

[0004] In view of the above problems, this application proposes a method, apparatus, electronic device and storage medium for power analysis of wind power equipment, which can perform power analysis on wind power equipment and provide a reference for building a stable power supply network.

[0005] According to one aspect of an embodiment of this application, a method for power analysis of wind power equipment is provided, the method comprising:

[0006] Obtain multiple candidate output powers of the wind power equipment and the predicted probability of each candidate output power;

[0007] Based on the plurality of candidate output powers and the predicted probability of each candidate output power, the predicted output power of the wind power equipment is calculated;

[0008] Determine whether the wind power equipment is a power-sensitive node;

[0009] When the wind turbine is a power-sensitive node, the transmission energy consumption of the wind turbine is obtained;

[0010] The comprehensive power of the wind power equipment is calculated based on the predicted output power and the transmission energy consumption.

[0011] Optionally, obtaining multiple candidate output powers of the wind turbine and the predicted probability of each candidate output power includes: obtaining the predicted probability of zero output power and the predicted probability of rated output power of the wind turbine; determining multiple estimated output powers and the predicted probability of each estimated output power using a point estimation method based on the predicted probability of zero output power and the predicted probability of rated output power; obtaining the current probability of a set output power of the wind turbine; calculating the predicted probability of the set output power using a Markov transition probability matrix based on the current probability; and using the zero output power, the rated output power, the estimated output power, and the set output power as candidate output powers to obtain the multiple candidate output powers and the predicted probability of each candidate output power.

[0012] Optionally, the prediction probability includes the normal prediction probability. Calculating the predicted output power of the wind power equipment based on the plurality of candidate output powers and the probability of each candidate output power includes: calculating the product of the candidate output power and the normal prediction probability of the candidate output power; and performing a weighted summation of the products corresponding to the plurality of candidate output powers to obtain the predicted output power of the wind power equipment.

[0013] Optionally, the prediction probability includes a normal prediction probability. Calculating the predicted output power of the wind power equipment based on the plurality of candidate output powers and the probability of each candidate output power includes: selecting the candidate output power with the highest normal prediction probability from the plurality of candidate output powers; and using the product of the selected candidate output power and the normal prediction probability of the selected candidate output power as the predicted output power of the wind power equipment.

[0014] Optionally, determining whether the wind power equipment is a power-sensitive node includes: obtaining the current output power of the wind power equipment; calculating the difference between the current output power and the predicted output power; and determining the wind power equipment as a power-sensitive node if the difference meets a preset condition.

[0015] Optionally, obtaining the transmission energy consumption of the wind power equipment includes: obtaining at least one of the following energy consumptions of the wind power equipment as the transmission energy consumption of the wind power equipment: wireless transmission energy consumption, wide area network transmission energy consumption, edge computing node energy consumption, and cloud data center energy consumption.

[0016] Optionally, obtaining the comprehensive power of the wind power equipment based on the predicted output power and the transmission energy consumption includes: performing a weighted summation of the predicted output power and the transmission energy consumption to obtain the comprehensive power of the wind power equipment.

[0017] According to another aspect of the embodiments of this application, a wind power analysis device is provided, the device comprising:

[0018] The first acquisition module is used to acquire multiple candidate output powers of the wind power equipment and the predicted probability of each candidate output power;

[0019] The first calculation module is used to calculate the predicted output power of the wind power equipment based on the plurality of candidate output powers and the predicted probability of each candidate output power;

[0020] The judgment module is used to determine whether the wind power equipment is a power-sensitive node;

[0021] The second acquisition module is used to acquire the transmission energy consumption of the wind power equipment when the wind power equipment is a power sensitive node.

[0022] The second calculation module is used to calculate the comprehensive power of the wind power equipment based on the predicted output power and the transmission energy consumption.

[0023] Optionally, the first acquisition module includes: a first information acquisition unit, configured to acquire the predicted probability of zero output power and the predicted probability of rated output power of the wind power equipment, and based on the predicted probability of zero output power and the predicted probability of rated output power, determine multiple estimated output powers of the wind power equipment and the predicted probability of each estimated output power using a point estimation method; a second information acquisition unit, configured to acquire the current probability of the set output power of the wind power equipment, and calculate the predicted probability of the set output power using a Markov transition probability matrix based on the current probability; and an information determination unit, configured to use the zero output power, the rated output power, the estimated output power, and the set output power as candidate output powers to obtain the multiple candidate output powers and the predicted probability of each candidate output power.

[0024] Optionally, the predicted probability includes the normal prediction probability, and the first calculation module includes: a product calculation unit, used to calculate the product of the candidate output power and the normal prediction probability of the candidate output power; and a first power calculation unit, used to perform a weighted summation calculation on the products corresponding to the plurality of candidate output powers to obtain the predicted output power of the wind power equipment.

[0025] Optionally, the prediction probability includes a normal prediction probability, and the first calculation module includes: a power selection unit, used to select the candidate output power with the highest normal prediction probability from the plurality of candidate output powers; and a second power calculation unit, used to take the product of the selected candidate output power and the normal prediction probability of the selected candidate output power as the predicted output power of the wind power equipment.

[0026] Optionally, the judgment module includes: a power acquisition unit for acquiring the current output power of the wind power equipment; a difference calculation unit for calculating the difference between the current output power and the predicted output power; and a node determination unit for determining the wind power equipment as a power sensitive node when the difference meets a preset condition.

[0027] Optionally, the second acquisition module is specifically used to acquire at least one of the following energy consumptions of the wind power equipment as the transmission energy consumption of the wind power equipment: wireless transmission energy consumption, wide area network transmission energy consumption, edge computing node energy consumption, and cloud data center energy consumption.

[0028] Optionally, the second calculation module is specifically used to perform a weighted summation of the predicted output power and the transmission energy consumption to obtain the comprehensive power of the wind power equipment.

[0029] According to another aspect of the embodiments of this application, an electronic device is provided, comprising: one or more processors; and one or more computer-readable storage media having instructions stored thereon; wherein, when the instructions are executed by the one or more processors, the processors cause the processors to perform the wind power analysis method as described in any of the preceding claims.

[0030] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, causes the processor to perform the wind power analysis method as described in any of the preceding claims.

[0031] In this embodiment, multiple candidate output powers of a wind turbine and their predicted probabilities are obtained; based on the multiple candidate output powers and their predicted probabilities, the predicted output power of the wind turbine is calculated; it is determined whether the wind turbine is a power-sensitive node; when the wind turbine is a power-sensitive node, its transmission energy consumption is obtained; and based on the predicted output power and the transmission energy consumption, the comprehensive power of the wind turbine is obtained. Therefore, in this embodiment, the predicted output power of the wind turbine is calculated based on multiple candidate output powers and their corresponding predicted probabilities, making the obtained predicted output power more accurate; the determination of power-sensitive nodes for wind turbines, and the further calculation of comprehensive power for wind turbines belonging to power-sensitive nodes, reduces the computational load and saves resources; the calculation of comprehensive power combines predicted output power and transmission energy consumption, making the obtained comprehensive power more comprehensive and accurate. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some drawings of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart illustrating the steps of a wind power analysis method according to an embodiment of this application.

[0034] Figure 2 This is a network model of a 5-node fully interconnected power system according to an embodiment of this application.

[0035] Figure 3 This is a structural block diagram of a wind power analysis device according to an embodiment of this application.

[0036] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0037] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0038] The embodiments in this application address the power generation problems of islands with abundant renewable energy resources such as wave energy, wind power, and photovoltaic power, combined with supercapacitors and large-scale deployment. The application also considers the integration of distributed generation (DG) into the power grid: An existing regional power grid (with a certain number of wind turbines) is considering constructing a large or medium-sized wind power REGs (registered storage groups) within its area, exploring how to scientifically plan the REGs and energy storage devices. In this case, the existing wind turbines in the grid need to be retained. The evaluation criteria for scientific planning should be to minimize the operating costs of the new system (including the operating costs of the existing wind turbines, and the depreciated operating costs of the REGs and energy storage devices (initial installation, maintenance, and replacement)) while ensuring reliable and stable power supply. For the above scenario, power analysis of the wind turbines in the grid is required to provide a reference for the integration of distributed generation into the grid.

[0039] The following section provides a detailed introduction to the power analysis methods for wind power equipment.

[0040] Reference Figure 1 The diagram illustrates a flowchart of a wind power analysis method according to an embodiment of this application.

[0041] like Figure 1 As shown, the power analysis method for wind power equipment may include the following steps:

[0042] Step 101: Obtain multiple candidate output powers of the wind power equipment and the predicted probability of each candidate output power.

[0043] In this embodiment of the application, in order to improve the accuracy of the predicted output power of wind power equipment, for each wind power equipment, multiple candidate output powers of the wind power equipment and the prediction probability of each candidate output power can be predicted first.

[0044] For example, point estimation, Markov transition probability matrix model, neural network model, and other methods can be used to predict multiple candidate output powers of wind power equipment and the prediction probability of each candidate output power.

[0045] In one optional implementation, to improve the comprehensiveness and accuracy of the predicted candidate output power and the predicted probability of the candidate output power, a combination of point estimation prediction and Markov transition probability matrix model prediction can be used. Therefore, the process of obtaining multiple candidate output powers of wind power equipment and the predicted probability of each candidate output power may include the following steps A1 to A3:

[0046] Step A1: Obtain the predicted probability of zero output power and the predicted probability of rated output power of the wind power equipment. Based on the predicted probability of zero output power and the predicted probability of rated output power, determine multiple estimated output powers of the wind power equipment and the predicted probability of each estimated output power using a point estimation method.

[0047] In this embodiment, a discrete wind speed distribution based on point estimation is constructed to predict the power distribution of wind turbines. Considering the large computational load and slow solution speed of the Monte Carlo method, the basic idea of ​​the discrete estimation using point estimation in this embodiment is to divide the values ​​of continuous random variables into multiple finite groups. In order to consider the power distribution of the entire wind turbine during the optimization process, the uncertainty of wind speed is represented by point estimation, and probabilistic optimization is performed by discreteizing the output power of the wind turbine.

[0048] For example, the five-point method of point estimation will be used as an example for illustration.

[0049] The predicted probability of zero output power for wind turbines is calculated using the following formula:

[0050] P1 = {Y = 0} = (V ≤ V) a )+(V>V ∞ Formula 1

[0051] Where P1 represents the predicted probability of zero output power of the wind turbine, P represents the probability, Y represents the output power of the wind turbine, and V represents the wind speed. a This indicates the cut-in wind speed.

[0052] The predicted probability of the rated output power of wind power equipment is calculated using the following formula:

[0053] P5=P{Y=P wtr}=P(V r ≤V≤V ∞ Formula 2

[0054] Where P5 represents the predicted probability of the rated output power of the wind turbine, P represents the probability, and Y represents the output power of the wind turbine. wtr The rated output power of the wind turbine is represented by V, and the wind speed is represented by V. r This indicates the rated wind speed.

[0055] The output power Y of the wind turbine is a random variable, and the probability density function of this random variable can be expressed as follows: Formula 3:

[0056]

[0057] Among them, g Y(y|k,c)Let α and β represent the probability density function of the output power, α and β represent the linear parameters in the mathematical model of the wind power output, and k and c represent the Weibull parameters of the wind speed distribution.

[0058] It should be noted that the following formula four must be satisfied:

[0059]

[0060] The discretization process of continuous random variables will be handled using the following formulas: Formula 5, Formula 6, and Formula 7.

[0061]

[0062]

[0063]

[0064] in, Let Y be the mean of a continuous random variable. M is the standard deviation of the continuous random variable Y; j Let be the j-th order central moment of the continuous random variable Y.

[0065] make It represents the standard value of the continuous random variable Y, and its moment equation can be expressed as the following formula:

[0066]

[0067] Where, p i For the corresponding z i The probability of.

[0068] Solving Formula 8 above, we can obtain Formulas 9 and 10 as follows:

[0069]

[0070]

[0071] g can be determined using formulas nine and ten. Y The remaining three points are discretely distributed with probabilities p2, p3, p4 and corresponding positions z2, z3, z4.

[0072] Then we can estimate the remaining three points Y for estimating the output power of the continuous random variable Y. i (i = 2, 3, 4) and their corresponding predicted probabilities are shown in Formulas 11 and 12:

[0073]

[0074]

[0075] It should be noted that the predicted probability calculated above can represent the normal prediction probability.

[0076] Step A2: Obtain the current probability of the set output power of the wind power equipment, and calculate the predicted probability of the set output power based on the current probability using the Markov transition probability matrix.

[0077] In this embodiment, a Markov transition probability matrix model is used to accurately predict anomalies in wind power distribution under the background of discrete wind speed distribution based on the point estimation method of the five-point method.

[0078] The Markov transition probability matrix model is expressed as follows: X(k+1) = X(k) × P. Where X(k) represents the state vector of the trend analysis and prediction object at time t = k, P represents the one-step transition probability matrix, and X(k+1) represents the state vector of the trend analysis and prediction object at time t = k+1.

[0079] In this embodiment of the application, at least one preset output power of the wind power equipment is set in advance. The preset output power can be the current output power or it can be set according to actual experience.

[0080] Obtain the current probability of the set output power of the wind turbine, which is X(k) in the Markov transition probability matrix model described above. For example, an initial probability of the set output power can be set, and then the current probability can be calculated based on the initial probability.

[0081] Obtain the one-step transition probability matrix of the wind power equipment, which is P in the Markov transition probability matrix model mentioned above. For example, the one-step transition probability matrix indicates the probability of the wind power distribution changing from abnormal to normal in the current time period and the probability of the wind power distribution changing from normal to abnormal in the current time period. The one-step transition probability matrix can be set according to the actual situation.

[0082] Based on the current probability of the set output power of the wind turbine and the one-step transition probability matrix of the wind turbine, the predicted probability of the set output power of the wind turbine is calculated using the Markov transition probability matrix. This predicted probability is X(k+1) in the Markov transition probability matrix model mentioned above.

[0083] It should be noted that the predicted probability of the output power set above can include normal prediction probability and abnormal prediction probability.

[0084] The following are examples:

[0085] The initial probabilities of wind power distribution in the previous period are [0.3, 0.7], where 0.3 represents the probability of an anomaly and 0.7 represents the probability of normality.

[0086] The probability of the wind power distribution changing from abnormal to normal during the current period is [0.6, 0.4], where 0.6 represents the probability of changing from abnormal to abnormal, and 0.4 represents the probability of changing from abnormal to normal.

[0087] The probability of the wind power distribution changing from normal to abnormal during the current period is [0.3, 0.7], where 0.3 represents the probability of changing from normal to abnormal and 0.7 represents the probability of changing from normal to normal.

[0088] The probability of anomalies in wind power distribution in the next period is: 0.3×0.6+0.3×0.7=0.39;

[0089] The probability of normal wind power distribution in the next period is: 0.3×0.4+0.7×0.7=0.61.

[0090] Step A3: Using the zero output power, the rated output power, the estimated output power, and the set output power as candidate output powers, obtain the plurality of candidate output powers and the predicted probability of each candidate output power.

[0091] Step 102: Calculate the predicted output power of the wind power equipment based on the plurality of candidate output powers and the predicted probability of each candidate output power.

[0092] In one optional implementation, the process of calculating the predicted output power of the wind turbine based on the plurality of candidate output powers and the probability of each candidate output power includes: calculating the product of the candidate output power and the normal prediction probability of the candidate output power; and performing a weighted summation of the products corresponding to the plurality of candidate output powers to obtain the predicted output power of the wind turbine. The weight values ​​corresponding to each candidate output power can be set according to actual conditions, and this application embodiment does not impose any restrictions on this.

[0093] In one optional implementation, the process of calculating the predicted output power of the wind power equipment based on the plurality of candidate output powers and the probability of each candidate output power includes: selecting the candidate output power with the highest normal prediction probability from the plurality of candidate output powers; and taking the product of the selected candidate output power and the normal prediction probability of the selected candidate output power as the predicted output power of the wind power equipment.

[0094] Step 103: Determine whether the wind power equipment is a power-sensitive node.

[0095] In this embodiment, considering the presence of power-sensitive nodes in the power grid, the installation location of distributed power sources should avoid these power-sensitive nodes as much as possible to ensure reliable connection of distributed power sources and stable operation of the power grid. Therefore, for each wind turbine in the power grid, it can be determined whether the wind turbine is a power-sensitive node.

[0096] In one optional implementation, for each wind turbine, it can be determined whether the wind turbine is a power-sensitive node based on the predicted output power of the wind turbine calculated above. For example, the process of determining whether the wind turbine is a power-sensitive node includes: obtaining the current output power of the wind turbine; calculating the difference between the current output power and the predicted output power; and determining the wind turbine as a power-sensitive node if the difference meets a preset condition.

[0097] The difference between the current output power and the predicted output power indicates the degree of change in the wind turbine's output power. The greater the change in output power, the more sensitive the wind turbine is to power fluctuations. The preset condition can be that the difference between the current output power and the predicted output power is greater than a preset threshold, or that the difference between the current output power and the predicted output power is greater than a preset multiple of the current output power, etc. This application embodiment does not impose any limitations on this.

[0098] In one alternative implementation, in order to improve the computation speed of the algorithm and effectively avoid voltage collapse in the power grid, the power sensitive nodes in the power grid can be calculated based on the small-world theory.

[0099] To simplify the calculation, we can assume:

[0100] ① All transmission lines have the same topological characteristics. We ignore the differences in transmission voltage, physical performance and power parameters of the transmission lines and assume that all edges are undirected.

[0101] ② The nodes considered include generator nodes, load nodes, and connection nodes. Grounding nodes are not considered, and all nodes are assumed to be identical. In this embodiment, the wind power equipment is considered a node.

[0102] ③ Power data are the raw data for power flow calculation; they are variables in the model.

[0103] For example, in a power system network, consider two interconnected nodes. The apparent power of node 1 is denoted as S1, and the apparent power of node 2 is denoted as S2. A change in S1 will cause a change in S2, and vice versa. If S1 >> S2, then a small change in S1 will lead to a large change in S2; conversely, a change in S2 will have a small change in S1. Therefore, the influence coefficient can be defined using the following formula:

[0104] The influence coefficient γ of node 1 on node 2 is expressed as follows: Formula Thirteen:

[0105]

[0106] The influence coefficient δ of node 2 on node 1 is expressed as follows: Formula XIV:

[0107]

[0108] If S1>S2, then γ>δ, meaning that the influence of node 1 on node 2 is greater than the influence of node 2 on node 1.

[0109] To further explain how to apply the above principles and the small-world model of complex networks to find sensitive nodes in complex networks, we will now use... Figure 2 The network model of the 5-node fully interconnected power system is shown below. Figure 2 It contains 5 nodes i, j, k, f, and e. If the apparent power of one of the nodes changes, it will affect the apparent power of the other nodes.

[0110] Taking components i and j as an example, if the apparent power of node i changes, the power changes of node j and other nodes can be calculated using small-world theory. Nodes with significant power changes are considered sensitive nodes.

[0111] There are n paths between nodes i and j. Let ij0, ij1, ..., ij be the path between nodes i and j. m-1 ,ij m For example, ij represents the starting and ending nodes of this path as i and j respectively, ij0 indicates that this node is the first node in the path, which is node i in this case. m This indicates that this node is the last node in the path, which is node j in this case. m represents the number of all components traversed from node i to node j on the path, ij1, ..., ij m-1 For all other nodes on this path. The rated apparent power of each component on this path is S. ij0 S ij1 , ..., S ijm-1 S ijm .

[0112] When the apparent power change of node ij0 (i.e. node i) is ΔS:

[0113] The power change ΔS of the neighboring node ij1 of node ij0 ij1 This is expressed as Formula Fifteen:

[0114]

[0115] Power variation of node ij1's neighboring node ij2 This is represented by the following formula sixteen:

[0116]

[0117] And so on, node ij m (i.e., the power change ΔS at node j) ijm This is expressed as Formula Seventeen:

[0118]

[0119] Similarly, the power changes from node i to node j in the other n-1 paths between nodes i and j can be calculated as Δ2, Δ3, ..., Δ n Here, n represents the total number of paths between node i and node j. The final power change of node j is Δ. ij This is expressed as Formula 18:

[0120]

[0121] Similarly, the impact of a node's power changes on other nodes can be calculated, with the node exhibiting the largest power change being the most sensitive node.

[0122] In this embodiment of the application, at least one node with the largest power change can be identified as a power-sensitive node, wherein the node is a wind power device.

[0123] Step 104: When the wind power equipment is a power-sensitive node, obtain the transmission energy consumption of the wind power equipment.

[0124] In one alternative implementation, at least one of the following energy consumptions of the wind power equipment can be obtained as the transmission energy consumption of the wind power equipment: wireless transmission energy consumption, wide area network transmission energy consumption, edge computing node energy consumption, and cloud data center energy consumption.

[0125] Wireless transmission energy consumption refers to the energy consumed during the transmission of data from wind power equipment via a wireless link. This is expressed as Formula Nineteen:

[0126]

[0127] Wide area network (WAN) transmission energy consumption refers to the energy consumption generated during the transmission of data from wind power equipment through WAN links. This can be represented by the following formula:

[0128]

[0129] Edge computing node energy consumption refers to the energy consumption generated during the transmission of data from wind power equipment to edge computing nodes. This is expressed as Formula Twenty-One:

[0130]

[0131] Cloud data center energy consumption refers to the energy consumed during the transmission of data from wind power equipment to cloud data center servers. This can be expressed as formula twenty-two:

[0132]

[0133] Among them, P W P represents the energy consumed (J / bit) to transmit one bit of data over a wireless link. 1 η represents the energy consumed (J / bit) to transmit one bit of data over a wide area network link. e η represents the energy consumption factor (J / CPU cycle) per unit CPU revolutions of an edge computing node. c This represents the energy consumption factor (J / CPU cycle) per unit CPU revolutions of a cloud data center server. These parameters are constants and are related to the user equipment and the server's hardware. i Indicates the amount of data transmitted, C i Indicates CPU revolutions, λ i This indicates the preset weight value.

[0134] Step 105: Calculate the comprehensive power of the wind power equipment based on the predicted output power and the transmission energy consumption.

[0135] In one optional implementation, the predicted output power and transmission energy consumption of the wind turbine can be weighted and summed to obtain the comprehensive power of the wind turbine. The weight values ​​corresponding to the predicted output power and each transmission energy consumption level can be set according to actual conditions; this application embodiment does not impose any restrictions on this.

[0136] In one alternative implementation, the predicted output power of the wind power equipment and the transmission energy consumption of the wind power equipment can be summed to obtain the comprehensive power of the wind power equipment.

[0137] In this embodiment, the predicted output power of wind turbines is calculated based on multiple candidate output powers and their corresponding prediction probabilities, making the predicted output power more accurate. Power-sensitive nodes are identified for each wind turbine, and the comprehensive power of those nodes is further calculated, reducing computational load and saving resources. The comprehensive power calculation combines the predicted output power and transmission energy consumption, resulting in a more comprehensive and accurate comprehensive power. By identifying power-sensitive nodes in the power grid and predicting their comprehensive power, a basis can be provided for the installation of distributed power sources, minimizing the need to install power-sensitive nodes.

[0138] Reference Figure 3The diagram shows a structural block diagram of a wind power analysis device according to an embodiment of this application.

[0139] like Figure 3 As shown, the wind power analysis device may include the following modules:

[0140] The first acquisition module 301 is used to acquire multiple candidate output powers of the wind power equipment and the predicted probability of each candidate output power;

[0141] The first calculation module 302 is used to calculate the predicted output power of the wind power equipment based on the plurality of candidate output powers and the predicted probability of each candidate output power;

[0142] The judgment module 303 is used to determine whether the wind power equipment is a power sensitive node;

[0143] The second acquisition module 304 is used to acquire the transmission energy consumption of the wind power equipment when the wind power equipment is a power sensitive node.

[0144] The second calculation module 305 is used to calculate the comprehensive power of the wind power equipment based on the predicted output power and the transmission energy consumption.

[0145] Optionally, the first acquisition module 301 includes: a first information acquisition unit, configured to acquire the predicted probability of zero output power and the predicted probability of rated output power of the wind power equipment, and determine multiple estimated output powers of the wind power equipment and the predicted probability of each estimated output power based on the predicted probability of zero output power and the predicted probability of rated output power using a point estimation method; a second information acquisition unit, configured to acquire the current probability of the set output power of the wind power equipment, and calculate the predicted probability of the set output power based on the current probability using a Markov transition probability matrix; and an information determination unit, configured to use the zero output power, the rated output power, the estimated output power, and the set output power as candidate output powers to obtain the multiple candidate output powers and the predicted probability of each candidate output power.

[0146] Optionally, the predicted probability includes the normal prediction probability, and the first calculation module 302 includes: a product calculation unit, used to calculate the product of the candidate output power and the normal prediction probability of the candidate output power; and a first power calculation unit, used to perform a weighted summation calculation on the products corresponding to the plurality of candidate output powers to obtain the predicted output power of the wind power equipment.

[0147] Optionally, the prediction probability includes a normal prediction probability. The first calculation module 302 includes: a power selection unit, used to select the candidate output power with the highest normal prediction probability from the plurality of candidate output powers; and a second power calculation unit, used to take the product of the selected candidate output power and the normal prediction probability of the selected candidate output power as the predicted output power of the wind power equipment.

[0148] Optionally, the judgment module 303 includes: a power acquisition unit for acquiring the current output power of the wind power equipment; a difference calculation unit for calculating the difference between the current output power and the predicted output power; and a node determination unit for determining the wind power equipment as a power sensitive node when the difference meets a preset condition.

[0149] Optionally, the second acquisition module 304 is specifically used to acquire at least one of the following energy consumptions of the wind power equipment as the transmission energy consumption of the wind power equipment: wireless transmission energy consumption, wide area network transmission energy consumption, edge computing node energy consumption, and cloud data center energy consumption.

[0150] Optionally, the second calculation module 305 is specifically used to perform a weighted summation calculation of the predicted output power and the transmission energy consumption to obtain the comprehensive power of the wind power equipment.

[0151] In this embodiment, the predicted output power of the wind turbine is calculated based on multiple candidate output powers and their corresponding prediction probabilities, making the predicted output power more accurate. Power-sensitive nodes are identified for the wind turbine, and the comprehensive power is further calculated for wind turbines that are power-sensitive nodes, thereby reducing the amount of calculation and saving resources. The calculation of the comprehensive power combines the predicted output power and transmission energy consumption, making the comprehensive power more comprehensive and accurate.

[0152] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0153] In embodiments of this application, an electronic device is also provided. This electronic device may include one or more processors and one or more computer-readable storage media storing instructions thereon, such as application programs. When the instructions are executed by the one or more processors, the processors cause the processors to perform the wind power analysis method of any of the above embodiments.

[0154] Reference Figure 4 The diagram illustrates a schematic representation of an electronic device structure according to an embodiment of this application. Figure 4As shown, the electronic device includes a processor 401, a communication interface 402, a memory 403, and a communication bus 404. The processor 401, communication interface 402, and memory 403 communicate with each other via the communication bus 404.

[0155] Memory 403 is used to store computer programs.

[0156] When the processor 401 executes the program stored in the memory 403, it implements the wind power analysis method of any of the above embodiments.

[0157] Communication interface 402 is used for communication between the above-mentioned electronic device and other devices.

[0158] The aforementioned communication bus 404 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, it is represented by only one thick line in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0159] The processor 401 mentioned above may include, but is not limited to: a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0160] The aforementioned memory 403 may include, but is not limited to: Read Only Memory (ROM), Random Access Memory (RAM), Compact Disc Read Only Memory (CD-ROM), Electronic Erasable Programmable Read Only Memory (EEPROM), Hard Disk, Floppy Disk, Flash Memory, etc.

[0161] In embodiments of this application, a computer-readable storage medium is also provided, on which a computer program is stored, which can be executed by a processor of an electronic device, and when the computer program is executed by the processor, the processor performs the wind power analysis method as described in any of the above embodiments.

[0162] The various embodiments in this specification are related to each other and are described in a progressive manner. Each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other.

[0163] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0164] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this 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 is stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0165] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0166] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0167] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0168] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0169] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0170] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0171] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0172] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. In summary, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for power analysis of wind power equipment, characterized in that, The method includes: Obtain multiple candidate output powers of the wind power equipment and the predicted probability of each candidate output power; Based on the plurality of candidate output powers and the predicted probability of each candidate output power, the predicted output power of the wind power equipment is calculated; Determine whether the wind power equipment is a power-sensitive node; When the wind turbine is a power-sensitive node, the transmission energy consumption of the wind turbine is obtained; Based on the predicted output power and the transmission energy consumption, the comprehensive power of the wind power equipment is calculated; The acquisition of multiple candidate output powers of the wind power equipment and the predicted probability of each candidate output power includes: The predicted probability of zero output power and the predicted probability of rated output power of the wind power equipment are obtained. Based on the predicted probability of zero output power and the predicted probability of rated output power, multiple estimated output powers of the wind power equipment and the predicted probability of each estimated output power are determined by point estimation. Obtain the current probability of the set output power of the wind power equipment, and calculate the predicted probability of the set output power based on the current probability using the Markov transition probability matrix; The zero output power, the rated output power, the estimated output power, and the set output power are used as candidate output powers to obtain the plurality of candidate output powers and the predicted probability of each candidate output power.

2. The method according to claim 1, characterized in that, The predicted probability includes the normal predicted probability. The calculation of the predicted output power of the wind turbine based on the plurality of candidate output powers and the probability of each candidate output power includes: Calculate the product of the candidate output power and the normal prediction probability of the candidate output power; The predicted output power of the wind power equipment is obtained by weighted summation of the products corresponding to the multiple candidate output powers.

3. The method according to claim 1, characterized in that, The predicted probability includes the normal predicted probability. The calculation of the predicted output power of the wind turbine based on the plurality of candidate output powers and the probability of each candidate output power includes: From the plurality of candidate output powers, select the candidate output power with the highest probability of normal prediction; The product of the selected candidate output power and the normal prediction probability of the selected candidate output power is taken as the predicted output power of the wind power equipment.

4. The method according to claim 1, characterized in that, The determination of whether the wind power equipment is a power-sensitive node includes: Obtain the current output power of the wind power equipment; Calculate the difference between the current output power and the predicted output power; If the difference meets the preset conditions, the wind power equipment is determined to be a power sensitive node.

5. The method according to claim 1, characterized in that, The process of obtaining the transmission energy consumption of the wind power equipment includes: The energy consumption of the wind power equipment is obtained as the transmission energy consumption of the wind power equipment from at least one of the following: wireless transmission energy consumption, wide area network transmission energy consumption, edge computing node energy consumption, and cloud data center energy consumption.

6. The method according to claim 1, characterized in that, The calculation of the comprehensive power of the wind power equipment based on the predicted output power and the transmission energy consumption includes: The predicted output power and the transmission energy consumption are weighted and summed to obtain the comprehensive power of the wind power equipment.

7. A power analysis device for wind power equipment, characterized in that, The device includes: The first acquisition module is used to acquire multiple candidate output powers of the wind power equipment and the predicted probability of each candidate output power; The first calculation module is used to calculate the predicted output power of the wind power equipment based on the plurality of candidate output powers and the predicted probability of each candidate output power; The judgment module is used to determine whether the wind power equipment is a power-sensitive node; The second acquisition module is used to acquire the transmission energy consumption of the wind power equipment when the wind power equipment is a power sensitive node. The second calculation module is used to calculate the comprehensive power of the wind power equipment based on the predicted output power and the transmission energy consumption. The first acquisition module includes: a first information acquisition unit, configured to acquire the predicted probability of zero output power and the predicted probability of rated output power of the wind power equipment, and determine multiple estimated output powers and the predicted probability of each estimated output power using a point estimation method based on the predicted probability of zero output power and the predicted probability of rated output power; a second information acquisition unit, configured to acquire the current probability of the set output power of the wind power equipment, and calculate the predicted probability of the set output power using a Markov transition probability matrix based on the current probability; and an information determination unit, configured to use the zero output power, the rated output power, the estimated output power, and the set output power as candidate output powers to obtain the multiple candidate output powers and the predicted probability of each candidate output power.

8. An electronic device, characterized in that, include: One or more processors; and One or more computer-readable storage media on which instructions are stored; When the instruction is executed by the one or more processors, the processors perform the wind power analysis method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, causes the processor to perform the wind power analysis method as described in any one of claims 1 to 6.

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