Offshore wind power energy consumption distribution calculation and visualization method

Abstract

The invention relates to an offshore wind power energy consumption distribution calculation and visualization method. The method is suitable for the field of offshore wind power energy consumption analysis. According to the technical scheme, the offshore wind power energy consumption distribution calculation and visualization method is characterized by comprising the steps that wind power plant operation data are acquired, and energy consumption related data of all equipment nodes of a wind power plant are extracted; based on the branch condition of the previous-stage node of the equipment node, the equipment node is partitioned, and the equipment node is divided into a single power flow feeder region or a multi-power flow collection feeder region; respectively determining the energy consumption of each equipment node based on the energy consumption related data of the equipment node and the previous-level node and the partition condition of each equipment node; and according to the energy consumption of each equipment node, determining an RGB color system value corresponding to each equipment node by adopting an energy consumption gradient representation method.

Description

Technical Field

[0001] The present invention relates to a method for calculating and visualizing offshore wind power energy consumption distribution, and is applicable to the field of offshore wind power energy consumption analysis. Background Art

[0002] With the proposal of the "carbon peak and carbon neutrality" goals, wind power has received high attention as an important part of new energy power generation. Wind farms will have a large amount of power loss during operation, which restricts power generation efficiency. Existing technical means can only periodically evaluate the overall energy consumption of the whole farm based on total power generation and grid-connected power data. There is a lack of specific analysis of the energy consumption of equipment at all levels, and it is impossible to achieve real-time evaluation of energy consumption. Summary of the invention

[0003] The technical problem to be solved by the present invention is: in view of the above-mentioned problems, a method for calculating and visualizing the energy consumption distribution of offshore wind power is provided.

[0004] The technical solution adopted by the present invention is: a method for calculating and visualizing energy consumption distribution of offshore wind power, characterized by comprising:

[0005] Obtain wind farm operation data and extract energy consumption related data of each equipment node in the wind farm;

[0006] Based on the branching situation of the node at the previous level of the device node, the device node is partitioned, including dividing the device node into a single-flow feeder area or a multi-flow aggregation feeder area;

[0007] Based on the energy consumption related data of the device node and the previous level node, as well as the partitioning status of each device node, the energy consumption of each device node is determined respectively;

[0008] According to the energy consumption of each device node, the energy consumption gradient representation method is used to determine the RGB color value corresponding to each device node.

[0009] The step of obtaining wind farm operation data and extracting energy consumption related data of each equipment node in the wind farm includes:

[0010] Obtain wind farm operation data;

[0011] Data processing of wind farm operation data, including inserting the calculated values ​​into blank areas using Lagrange interpolation method, eliminating abnormal data using the normal distribution 3σ principle, and replacing the corresponding blank areas using Lagrange interpolation method;

[0012] The energy consumption related data of each equipment node in the wind farm is extracted from the data processed wind farm operation data.

[0013] Partitioning the device node based on the branching situation of the node at the previous level of the device node to determine whether the device node belongs to a single-flow feeder area or a multi-flow aggregation feeder area includes:

[0014] When the node before the device node has no branch, the device node belongs to the single-flow feeder area; when the node before the device node has a branch, the device node belongs to the multi-flow aggregation feeder area.

[0015] The energy consumption of each device node is determined based on the energy consumption related data of the device node and the previous level node, and the partitioning status of each device node, including:

[0016] If the device node belongs to the single-flow feeder area, the energy loss ratio k of the (x, y) node is determined by the following formula: (x,y) ;

[0017] ΔP (x,y) =P (x-1,y) -P (x,y)

[0018]

[0019] Among them, the horizontal axis in (x, y) is the power flow level, and the vertical axis is the number of the branch between the node and the previous node; P (x-1,y) is the real-time power of the node before the (x,y) node; P (x,y) is the real-time power of the (x,y) node; ΔP (x,y) is the power difference.

[0020] The energy consumption of each device node is determined based on the energy consumption related data of the device node and the previous level node, and the partitioning status of each device node, including:

[0021] If the device node belongs to the multi-flow feeder area, the energy loss ratio k of the branch between the (x, y) node and its previous node is determined by the following formula: (x,y) ;

[0022]

[0023]

[0024]

[0025]

[0026] Among them, the horizontal axis in (x, y) is the power flow level, and the vertical axis is the number of the branch between the node and the previous node; P (x-1,∑g) is the real-time total power of all g branches of the node before (x, y); P(x-1,i) is the real-time power of the i-th branch in the node one level before (x,y), i=1,2,...g; P (x-1,i) is the real-time power of the i-th branch in the node one level before (x,y), i=1,2,...g; P (x-1,k) is the real-time power of branch k between node (x, y) and its previous node; P (x,y) is the real-time power of the (x,y) node.

[0027] The method of determining the RGB color value corresponding to each device node by using the energy consumption gradient representation method according to the energy consumption of each device node includes:

[0028] Convert the energy loss ratio of each device node into an absolute percentage value of energy consumption;

[0029] Based on the absolute percentage value of energy consumption of each device node, the RGB color coefficient value of the device node is determined.

[0030] A system for calculating and visualizing energy consumption distribution of offshore wind power, characterized by comprising:

[0031] The data acquisition module is used to obtain the wind farm operation data and extract the energy consumption related data of each equipment node in the wind farm;

[0032] A node partitioning module is used to partition the device node based on the branching situation of the node at the previous level of the device node, including dividing the device node into a single-flow feeder area or a multi-flow aggregation feeder area;

[0033] A partition calculation module is used to determine the energy consumption of each device node based on the energy consumption related data of the device node and the previous level node, as well as the partition status of each device node;

[0034] The color determination module is used to determine the RGB color value corresponding to each device node according to the energy consumption of each device node by using the energy consumption gradient representation method.

[0035] A storage medium stores a computer program that can be executed by a processor, characterized in that when the computer program is executed, the steps of the method for calculating and visualizing energy consumption distribution of offshore wind power are implemented.

[0036] A device for calculating and visualizing energy consumption distribution of offshore wind power, comprising a memory and a processor, wherein the memory stores a computer program that can be executed by the processor, and wherein the computer program, when executed, implements the steps of the method for calculating and visualizing energy consumption distribution of offshore wind power.

[0037] A human-computer interaction method, characterized by comprising:

[0038] A human-computer interaction interface is displayed to the user, on which each device node of the wind farm is displayed, and the color of each device node is determined based on the calculation and visualization method for offshore wind power energy consumption distribution.

[0039] The beneficial effects of the present invention are as follows: the present invention uses power data to perform real-time energy consumption calculations, thereby solving the problem that in the past, only periodic energy consumption evaluation could be performed based on wind farm electricity data, and real-time energy consumption evaluation could not be achieved; the present invention calculates the energy consumption of each node device to achieve a refined evaluation of energy consumption at each level, and visualizes the energy consumption of each node through an energy consumption gradient representation method, thereby solving the problem that in the past, only the energy consumption of the entire wind farm could be evaluated, and the energy consumption distribution of the wind farm could not be intuitively reflected.

[0040] The present invention helps wind farm operation managers to timely identify abnormal energy consumption nodes through refined operation analysis of wind farms and real-time evaluation of energy consumption by classification and segmentation, so as to take measures to deal with them and improve the overall operation efficiency of the wind farm. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a flow chart of Example 1.

[0042] Figure 2 This is an example of the human-computer interaction interface in Example 5. DETAILED DESCRIPTION

[0043] Embodiment 1: This embodiment is a method for calculating and visualizing energy consumption distribution of offshore wind power, which specifically includes the following steps:

[0044] S1. Obtain wind farm operation data and extract energy consumption data of each equipment node in the wind farm. Use the data acquisition system to collect data such as rated power and actual power of different node equipment to form a wind farm data set, monitor and process data anomalies, and classify data at different nodes and different time periods.

[0045] S1-1. Obtain wind farm operation data through the data acquisition terminal, including equipment rated power, equipment real-time power, and check the data format, data dimension, and data row and column labels.

[0046] S1-2. Perform interpolation operation on blank values. Use Lagrange interpolation method to insert the calculated values ​​into the blank area. Data processing uses the normal distribution 3σ principle to eliminate abnormal data, and then use Lagrange interpolation method to replace the corresponding blank area.

[0047] S1-3. Extract the data set related to energy consumption according to the preset labels and conditions.

[0048] S1-4, extract the data of each device node respectively and sort and classify them.

[0049] S2. Based on the branching of the power flow of the device node to the previous node in the hierarchy, the device node is partitioned, including dividing the device node into a single flow feeder area or a multi-flow aggregation feeder area.

[0050] In this embodiment, the feeder area of ​​the wind farm energy consumption system needs to be divided into single-flow feeder and multi-flow aggregation feeder, and it is confirmed whether the required calculation node belongs to the single-flow feeder area or the multi-flow aggregation feeder area.

[0051] When distinguishing between single-flow feeders and multi-flow convergence feeders, it is necessary to observe whether the node before the node has multiple branches. If there are no branches, it is a single-flow feeder, and if there are multiple branches, it is a multi-flow convergence feeder. The purpose of distinguishing partitions is to ensure that the loss ratio from one node to the next is a positive value, and to ensure that the color distribution is reasonable when the final visualization is performed.

[0052] S3. Based on the energy consumption related data of the device node and the previous level node, and the partitioning status of each device node, the energy consumption of each device node is determined respectively.

[0053] In this embodiment, if the (x, y) node belongs to the single-flow feeder area, the energy loss ratio k of the (x, y) node is determined by the following formula: (x,y) ;

[0054] ΔP (x,y) =P (x-1,y) -P (x,y)

[0055]

[0056] Among them, the horizontal axis in (x, y) is the power flow level, and the vertical axis is the number of the branch between the node and the previous node; P (x-1,y) is the real-time power of the node before the (x,y) node; P (x,y) is the real-time power of the (x,y) node; ΔP (x,y) is the power difference.

[0057] In this example, if the (x, y) node belongs to the multi-flow feeder area, the following formula is used to determine the energy loss ratio k of the branch between the (x, y) node and its previous node: (x,y) ;

[0058]

[0059]

[0060]

[0061]

[0062] Among them, the horizontal axis in (x, y) is the power flow level, and the vertical axis is the number of the branch between the node and the previous node; P (x-1,∑g) is the real-time total power of all g branches of the node before (x, y); P (x-1,i) is the real-time power of the i-th branch in the node one level before (x,y), i=1,2,...g; P (x-1,i) is the real-time power of the i-th branch in the node one level before (x,y), i=1,2,...g; P (x-1,k) is the real-time power of branch k between node (x, y) and its previous node; P (x,y) is the real-time power of the (x,y) node.

[0063] S4. According to the energy consumption of each device node, an energy consumption gradient representation method is used to determine the RGB color value corresponding to each device node.

[0064] In this example, the energy consumption gradient representation method corresponds the energy consumption calculated by the above formula to the RGB color value, thereby realizing the conversion between numerical value and color.

[0065] In this embodiment, the energy loss ratio calculated according to S3 represents a relative percentage value of loss. The relative percentage value of loss is converted into an absolute percentage value, and then the energy consumption gradient representation method is used to perform numerical color conversion, using the following formula:

[0066]

[0067] Among them, k (x,y) represents the energy loss ratio of the (x,y) node, and k represents the absolute percentage of the (x,y) node loss to the total loss;

[0068]

[0069]

[0070] B=0

[0071] The calculated R, G, and B color values ​​are converted into hexadecimal values, and the corresponding values ​​of R are shifted left by 16 bits, and the corresponding values ​​of G are shifted left by 8 bits. The range of the values ​​is 0x00ff00 to 0xff0000. Among them, 0x00ff00 means that the energy consumption ratio is 0%, which is represented by green; 0xff0000 means that the energy consumption ratio is 100%, which is represented by red; the color values ​​corresponding to the remaining values ​​correspond to each other according to the value color correspondence table.

[0072] Embodiment 2: This embodiment is a system for calculating and visualizing energy consumption distribution of offshore wind power, including: a data acquisition module, a node partitioning module, a partition calculation module, a color determination module, etc.

[0073] In this example, the data acquisition module is used to obtain the wind farm operation data and extract the energy consumption related data of each equipment node in the wind farm.

[0074] The node partitioning module in this embodiment is used to partition the device node based on the branching situation of the node at the previous level of the device node, including dividing the device node into a single-flow feeder area or a multi-flow aggregation feeder area.

[0075] In this example, the partition calculation module is used to determine the energy consumption of each device node based on the energy consumption related data of the device node and the previous level node, as well as the partition status of each device node.

[0076] In this embodiment, the color determination module is used to determine the RGB color value corresponding to each device node according to the energy consumption of each device node by using the energy consumption gradient representation method.

[0077] Embodiment 3: This embodiment is a storage medium on which a computer program that can be executed by a processor is stored. When the computer program is executed, the steps of the method for calculating and visualizing offshore wind power energy consumption distribution applied in Embodiment 1 are implemented.

[0078] Embodiment 4: This embodiment is a device for calculating and visualizing energy consumption distribution of offshore wind power, comprising a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, the steps of the method for calculating and visualizing energy consumption distribution of offshore wind power applied in Embodiment 1 are implemented.

[0079] Embodiment 5: This embodiment is a human-computer interaction method, which includes displaying a human-computer interaction interface to a user, on which are displayed various device nodes of a wind farm, each device node being distributed hierarchically based on a power flow direction, and the color of each device node being determined based on the method for calculating and visualizing offshore wind power energy consumption distribution applied in Embodiment 1.

Claims

1. A method for calculating and visualizing offshore wind power energy consumption distribution, characterized in that: include: Obtain wind farm operation data and extract energy consumption related data of each equipment node in the wind farm; Based on the branching situation of the node at the previous level of the device node, the device node is partitioned, including dividing the device node into a single-flow feeder area or a multi-flow aggregation feeder area; Based on the energy consumption related data of the device node and the previous level node, as well as the partitioning status of each device node, the energy consumption of each device node is determined respectively; According to the energy consumption of each device node, the energy consumption gradient representation method is used to determine the RGB color value corresponding to each device node.

2. The method for calculating and visualizing offshore wind power energy consumption distribution according to claim 1, characterized in that: The step of obtaining wind farm operation data and extracting energy consumption related data of each equipment node in the wind farm includes: Obtain wind farm operation data; Data processing of wind farm operation data, including inserting the calculated values ​​into blank areas using Lagrange interpolation method, eliminating abnormal data using the normal distribution 3σ principle, and replacing the corresponding blank areas using Lagrange interpolation method; The energy consumption related data of each equipment node in the wind farm is extracted from the data processed wind farm operation data.

3. The method for calculating and visualizing offshore wind power energy consumption distribution according to claim 1 is characterized in that: Partitioning the device node based on the branching situation of the node at the previous level of the device node to determine whether the device node belongs to a single-flow feeder area or a multi-flow aggregation feeder area includes: When the node before the device node has no branch, the device node belongs to the single-flow feeder area; when the node before the device node has a branch, the device node belongs to the multi-flow aggregation feeder area.

4. The method for calculating and visualizing offshore wind power energy consumption distribution according to claim 1, characterized in that: The energy consumption of each device node is determined based on the energy consumption related data of the device node and the previous level node, and the partitioning status of each device node, including: If the device node belongs to the single-flow feeder area, the energy loss ratio k of the (x, y) node is determined by the following formula: (x,y) ; ΔP (x,y) =P (x-1,y) -P (x,y) Among them, the horizontal axis in (x, y) is the power flow level, and the vertical axis is the number of the branch between the node and the previous node; P (x-1,y) is the real-time power of the node before the (x,y) node; P (x,y) is the real-time power of the (x,y) node; ΔP (x,y) is the power difference.

5. The method for calculating and visualizing offshore wind power energy consumption distribution according to claim 1, characterized in that: The energy consumption of each device node is determined based on the energy consumption related data of the device node and the previous level node, and the partitioning status of each device node, including: If the device node belongs to the multi-flow feeder area, the energy loss ratio k of the branch between the (x, y) node and its previous node is determined by the following formula: (x,y) ; Among them, the horizontal axis in (x, y) is the power flow level, and the vertical axis is the number of the branch between the node and the previous node; P (x-1,∑g) is the real-time total power of all g branches of the node before (x, y); P (x-1,i) is the real-time power of the i-th branch in the node one level before (x,y), i=1,2,...g; P (x-1,i) is the real-time power of the i-th branch in the node one level before (x,y), i=1,2,...g; P (x-1,k) is the real-time power of branch k between node (x, y) and its previous node; P (x,y) is the real-time power of the (x,y) node.

6. The method for calculating and visualizing offshore wind power energy consumption distribution according to claim 1, characterized in that: The method of determining the RGB color value corresponding to each device node by using the energy consumption gradient representation method according to the energy consumption of each device node includes: Convert the energy loss ratio of each device node into an absolute percentage value of energy consumption; Based on the absolute percentage value of energy consumption of each device node, the RGB color coefficient value of the device node is determined.

7. A system for calculating and visualizing energy consumption distribution of offshore wind power, characterized in that: include: The data acquisition module is used to obtain the wind farm operation data and extract the energy consumption related data of each equipment node in the wind farm; A node partitioning module is used to partition the device node based on the branching situation of the node at the previous level of the device node, including dividing the device node into a single-flow feeder area or a multi-flow aggregation feeder area; A partition calculation module is used to determine the energy consumption of each device node based on the energy consumption related data of the device node and the previous level node, as well as the partition status of each device node; The color determination module is used to determine the RGB color value corresponding to each device node according to the energy consumption of each device node by using the energy consumption gradient representation method.

8. A storage medium having stored thereon a computer program executable by a processor, characterized in that: When the computer program is executed, the steps of the method for calculating and visualizing offshore wind power energy consumption distribution as described in any one of claims 1 to 6 are implemented.

9. A device for calculating and visualizing offshore wind power energy consumption distribution, comprising a memory and a processor, wherein the memory stores a computer program executable by the processor, characterized in that: When the computer program is executed, the steps of the method for calculating and visualizing offshore wind power energy consumption distribution as described in any one of claims 1 to 6 are implemented.

10. A human-computer interaction method, characterized in that: include: A human-computer interaction interface is presented to the user, on which each device node of the wind farm is displayed, and the color of each device node is determined based on the method for calculating and visualizing energy consumption distribution of offshore wind power as described in any one of claims 1 to 6.

Images