Data transmission method and system for power grid dispatching data network

Abstract

The present invention relates to the field of digital information transmission, and more specifically, to a data transmission method and system for a power grid dispatching data network. The method comprises: obtaining current and voltage from power grid dispatching data and determining the local range of each data point; using the least squares method to curve fit the current data and voltage data within the local range, respectively, and obtaining the noise performance level of each data point in the power grid dispatching data based on the fitting results; calculating the area enclosed by adjacent data points within the local range, and calculating the credibility of the data points using an exponential function combining the area standard deviation and the noise performance level; clustering the power grid dispatching data based on a credibility-corrected Fisher optimal solution algorithm, and transmitting the power grid dispatching data based on the clustering results. By introducing the credibility of each data point, the present invention reduces the impact of noise on the clustering results and improves the accuracy and reliability of data transmission.

Description

Technical Field

[0001] The present invention relates to the field of digital information transmission, and more particularly to a data transmission method and system for a power grid dispatching data network. Background Art

[0002] Grid dispatch data primarily relies on dedicated communication lines or private networks for transmission, such as telephone lines and power carrier communications. These traditional transmission methods suffer from slow transmission speeds, limited transmission capacity, high costs, and low security, making them difficult to meet the modern power system's demand for efficient and secure transmission. With the development of information technology and the demand for intelligent power systems, modern grid dispatch data network transmission technologies, such as wireless communication, have emerged. These technologies offer advantages such as fast transmission speeds, large capacity, low costs, and high security. These transmission technologies require dividing grid dispatch data into multiple data packets to improve data transmission efficiency.

[0003] The existing Chinese patent application document with publication number CN117596273A discloses a method and system for data transmission between power grid dispatching systems. The method sends a power grid public information model and a power grid measurement information point table respectively through the sending end, and performs inter-group data redundancy grouping processing on the data point numbers based on the plant station, forming a grouped power grid measurement information point table and sending it to the receiving end, so that the data point numbers of each plant station have redundant mutual backup between different groups, avoiding the easy omission of data during data communication, and improving the reliability of data transmission between the receiving end and the sending end. The sending end groups the power grid measurement data to be transmitted based on the plant station, and forms multiple data blocks with multiple groups.

[0004] The application document parses multiple data blocks in parallel at the receiving end, matches the measurement point information in the information index table, and updates the measurement data of the measurement point, thereby improving the efficiency of data transmission and matching. Currently, the Fisher optimal solution is a method for clustering ordered samples. It does not allow the order of samples to be broken during the classification process. That is, the power grid data can be divided into multiple segments with high similarity. Each segment is transmitted as an independent data packet, reducing redundant information between data packets and improving the compactness and efficiency of data transmission. However, the working environment of the power grid is complex, and the data is more affected by noise. This noise will affect the clustering process of the Fisher optimal solution. Because this method finds the optimal segmentation point by minimizing the loss function, noise may cause the minimum loss function to increase, thereby affecting the division and transmission efficiency of the data packet. Summary of the Invention

[0005] To address the problem that the power grid operating environment is complex and data is greatly affected by noise, resulting in noise affecting the clustering process of the Fisher optimal solution, thereby affecting the division and transmission efficiency of data packets, the present invention provides solutions in the following aspects.

[0006] In a first aspect, a data transmission method for a power grid dispatching data network includes: obtaining power grid dispatching data, wherein the power grid dispatching data includes: current and voltage, with each data point in the power grid dispatching data as the center point, and a preset number of data points are set on both sides to determine the local range; using the least squares method to perform curve fitting on the current data and voltage data in the local range respectively, and obtaining the noise performance level of each data point in the power grid dispatching data based on the difference between the fitting result and the actual value; calculating the area enclosed by adjacent data points in the local range, using an exponential function to calculate the product of the standard deviation of the area set between two adjacent data points in the local range and the noise performance level of the corresponding data point to obtain the credibility of each data point; correcting the Fisher optimal solution algorithm based on the credibility, clustering the power grid dispatching data according to the corrected Fisher optimal solution algorithm, and transmitting the power grid dispatching data according to the clustering result.

[0007] The effect is that by performing least squares curve fitting on the current and voltage data in a local area, the noise level of each data point can be quantified. This method can identify data points that deviate significantly from the fitted curve, thereby assessing their degree of noise influence. By calculating the area enclosed by adjacent data points in the local area and combining it with the noise level, an exponential function is used to calculate the credibility of each data point, which helps to distinguish high-quality and low-quality data points in subsequent processing. Based on the credibility of the data points, the Fisher optimal solution algorithm is modified. This method reduces the impact of noisy data points on clustering results and improves the accuracy and efficiency of clustering.

[0008] Preferably, performing curve fitting on the current data and voltage data within the local range respectively includes:

[0009] The time sequence number of the data point in the local range is used as the horizontal axis, and the current or voltage value of the data point is used as the vertical axis to fit the current curve and voltage curve respectively.

[0010] Preferably, obtaining the noise performance level includes:

[0011] The average values ​​of the absolute differences between the fitted values ​​and the actual values ​​of the current and voltage in the local range are calculated for normalization, and the summed average is taken as the noise performance of each data point in the local range.

[0012] The effect is that by identifying data points with high levels of noise, these data points can be further analyzed or cleaned, thereby improving the quality of the overall data set. During data transmission, the impact of noisy data points is reduced, and the efficiency and reliability of data transmission are improved. This helps reduce the number of data packets and lower the transmission error rate.

[0013] Preferably, obtaining the noise performance level further includes:

[0014] The difference between the actual value of the data point in the local range and the moving average is calculated and divided by the moving average, and then squared to obtain the square of the deviation of each data point. The sum of the square of the deviation of all data points is divided by the number of data points in the local range to obtain the noise performance of each data point.

[0015] The effect is that by dividing the difference by the moving average, data from different ranges can be compared in a standardized manner, and the squaring process amplifies the impact of larger deviations, making these deviations more significant in subsequent analysis and helping to identify outliers or noisy data points.

[0016] Preferably, calculating the area enclosed by adjacent data points within a local range includes:

[0017] Based on any data point in the local range as the marked midpoint, the vertical distance between the current fitting curve and the voltage fitting curve at the marked point is calculated to obtain the area enclosed by the current fitting curve and the voltage fitting curve between all adjacent data points in the local range.

[0018] The effect is that by calculating the vertical distance between the current and voltage fitting curves, the consistency of the data points in the local range can be evaluated. If the current and voltage change trends are consistent, then the distance between the two curves should be small, indicating that there is a high consistency between the data points. If the distance between the current and voltage fitting curves is abnormally large at some data points, this may indicate that these points are outliers or are affected by noise.

[0019] Preferably, the calculating the area enclosed by adjacent data points within the local range further includes:

[0020] The differences between the current and voltage fitting values ​​between adjacent data points in the local range are calculated and added to obtain the sum of the current and voltage differences between two adjacent data points. The area between adjacent data points is obtained using the trapezoidal rule formula.

[0021] The effect is that the trapezoidal rule can be used to approximately calculate the area under the current and voltage fitting curves between two adjacent data points. Compared with integration, it is easier to calculate, especially for complex functions. It simplifies the calculation process, making the calculation faster and more practical. Especially when dealing with a large number of data points, it helps to improve the efficiency of the entire data analysis process.

[0022] Preferably, the correction process satisfies the following relationship:

[0023] ;

[0024] Where, Indicates that in the process of Fisher optimal solution, The ordered samples are divided into Classification, Indicates the The number of data points in a cluster, Indicates the The mean coordinates of the clusters, Indicates the The first data points, express The first The credibility of a data point.

[0025] In a second aspect, a data transmission system for a power grid dispatching data network includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned data transmission method for the power grid dispatching data network is implemented.

[0026] The present invention has the following effects:

[0027] 1. By introducing the credibility of each data point, this paper improves the loss function of the Fisher optimal solver when processing power grid dispatch data, reduces the impact of noise on clustering results, and makes the clustering results more accurate. Accurate clustering results help to better understand the operating status of the power grid, optimize the power grid dispatch strategy, and improve the power grid's operating efficiency.

[0028] 2. The present invention improves the quality of the overall power grid dispatching data by identifying and processing noise data points. During the data transmission process, low-quality data points may lead to erroneous or inaccurate decisions. By reducing the impact of noise data points, the accuracy and reliability of the data can be improved, thereby improving the efficiency and safety of power grid dispatching. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an illustrative and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0030] Figure 1 It is a method flow chart of steps S1 to S4 in a data transmission method for a power grid dispatching data network according to an embodiment of the present invention.

[0031] Figure 2 It is a structural block diagram of a data transmission system for a power grid dispatching data network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0033] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0034] Reference Figure 1 The data transmission method for the power grid dispatching data network includes steps S1 to S4, which are specifically as follows:

[0035] It should be noted that, in combination with specific scenarios, because the current sensor and voltage sensor will be disturbed by the surrounding environment during the process of collecting current and voltage data, noise will exist in the power grid dispatching data sequence. Such noise is manifested as the data points breaking their regular changes. Therefore, the regularity can be used to calculate the noise performance degree of the power grid dispatching data points. The better the regularity, the lower the noise performance degree of the data point.

[0036] S1: Obtaining grid dispatching data, wherein the grid dispatching data includes: current and voltage. Taking each data point in the grid dispatching data as the center point, a preset number of data points are set on both sides to determine the local range.

[0037] Exemplarily, the grid dispatching data includes but is not limited to the current data and voltage data of each node. The current data and voltage data of each node of the grid (such as substations, power plants, etc.) are collected using current sensors and voltage sensors respectively. The collection frequency is 50Hz to obtain the collected current data and voltage data of any grid node. The data format is the current data and voltage data collected each time, and the grid dispatching data sequence S is analyzed.

[0038] A grid dispatch data point contains a current data point and a voltage data point. Therefore, the regularity of a grid dispatch data point includes the regularity of both the current and voltage data. The higher the regularity of the current and voltage, the higher the regularity of the data point. The regularity of a data point can be expressed as the average fitting error when performing curve fitting on a local range of the data point. A larger average fitting error indicates a worse regularity, as a more regular data point set is more conducive to curve fitting, i.e., the smaller the fitting error.

[0039] For example, The first data points, 、 Represent the power grid dispatch data points The local range of the corresponding current data points and voltage data points is 2m+1 data point lengths centered on the current point (edge ​​data points are discarded accordingly).

[0040] S2: Use the least squares method to perform curve fitting on the current data and voltage data in the local range respectively, and use the difference between the fitting results and the actual values ​​to obtain the noise performance level of each data point in the grid dispatching data.

[0041] The time sequence number of the data point in the local range is used as the horizontal axis, and the current or voltage value of the data point is used as the vertical axis to fit the current curve and voltage curve respectively.

[0042] Get the noise performance level, including:

[0043] The average values ​​of the absolute differences between the fitted values ​​and the actual values ​​of the current and voltage in the local range are calculated for normalization, and the summed average is taken as the noise performance of each data point in the local range.

[0044] Specifically, the noise performance level satisfies the following relationship:

[0045] ;

[0046] Where, Indicates the The noise level of each data point, Indicates the data points corresponds to the number of data points in the local range, Indicates the The data point corresponds to the The current fitting value of the data point, Indicates the The data point corresponds to the The actual current value of the data point, Indicates the The data point corresponds to the The voltage fitting value of the data point, Indicates the The data point corresponds to the The actual voltage value of the data point, Represents the normalization function.

[0047] That is to say, represents the fitting error at this point, then Represents a grid dispatch data point The average fitting error of the current data fitting curve in the local range indicates the regularity of the current data and is negatively correlated with the regularity;

[0048] Similarly Represents a grid dispatch data point The average fitting error of the voltage data fitting curve in a local range indicates the regularity of the voltage data; while the regularity is negatively correlated with the degree of noise expression, the average fitting error is positively correlated with the degree of noise expression;

[0049] use The function is normalized to eliminate the dimension between current data and voltage data. It is also a normalization operation for the degree of noise performance.

[0050] Furthermore, since there may be some normal data changes (such as load changes) in the power grid dispatching data that cause changes in current or voltage data, such changes cause the sample's variation characteristics in the local range to be similar to noise. Therefore, it is necessary to adjust the noise performance of the sample with load changes to make the credibility calculation more accurate. In the power grid, voltage and current are positively correlated, that is, if it is a normal data change, the change of voltage and current in the local range must be positively correlated, while the data change caused by noise is random; therefore, the correlation between the voltage and current in the local range of the power grid dispatching data point can be used to correct the noise performance. The logical relationship is: the stronger the correlation, the lower the noise performance and the higher the credibility.

[0051] It should be noted that the correlation between the current data and the voltage data within the local range of the power grid dispatching data point can be directly expressed by the consistency of the area enclosed by the two adjacent points in the above-mentioned fitting curves of the two. Because if the current and voltage data in the local range are more positively correlated, the change trends of the fitting curves of the two are consistent, and the areas enclosed by the two adjacent points of the fitting curves are more consistent.

[0052] In addition, another embodiment also includes: calculating the difference between the actual value of the data point in the local range and the moving average value, dividing it by the moving average value, and squaring it to obtain the square of the deviation of each data point, summing the square of the deviation of all data points and dividing it by the number of data points in the local range to obtain the noise performance level of each data point.

[0053] Specifically, the noise performance level satisfies the following relationship:

[0054] ;

[0055] Where, Indicates the The noise level of each data point, Indicates the data points corresponds to the number of data points in the local range, Indicates the The actual value of the data point, Indicates the The moving average of the data points.

[0056] For example, an odd number is selected as the window size. data points, calculated from arrive The sum of all data points in the window is divided by the window size. Different weights are assigned to different data points in the local range through weighted moving average. Usually, the most recent data point has a higher weight. The weighted average is calculated to get the moving average. The moving average reflects the local average behavior of the data point, which helps to identify and smooth short-term fluctuations. The noise performance of different data points can be standardized by dividing the deviation by the moving average and squaring it.

[0057] S3: Calculate the area enclosed by adjacent data points in the local range, and use the exponential function to calculate the product of the standard deviation of the area set between two adjacent data points in the local range and the noise performance level of the corresponding data point to obtain the credibility of each data point.

[0058] Based on any data point in the local range as the marked midpoint, the vertical distance between the current fitting curve and the voltage fitting curve at the marked point is calculated to obtain the area enclosed by the current fitting curve and the voltage fitting curve between all adjacent data points in the local range.

[0059] Specifically, the area satisfies the following relationship:

[0060] ;

[0061] Where, Represents a grid dispatch data point The adjacent Point and The area enclosed by the current fitting curve and the voltage fitting curve between the points, represents the integral function, Indicates the The data point corresponds to the The current fitting value of the data point, Indicates the The data point corresponds to the The voltage fitting value of each data point.

[0062] Furthermore, the area set between all adjacent points in the local range of the power grid dispatching data point is , in which, in this paper It represents the number of samples in the local range of the power grid dispatching data point, that is, the number of current data points or voltage data points involved in the fitting.

[0063] It should be noted that in the power grid, current and voltage are usually positively correlated, that is, their changing trends should be consistent. If the changing trends of current and voltage are inconsistent, it may indicate that there is noise or anomaly in the data; the area reflects the difference between the current and voltage fitting curves in a local range. If this area is large, it means that the changing trends of current and voltage are inconsistent, and there may be noise or anomaly. Conversely, if this area is small, it means that the changing trends of current and voltage are relatively consistent and the data is more reliable.

[0064] The above area calculation method is suitable for fitting complex curves or when high-precision area calculation is required. However, the calculation process may be complex and time-consuming. It can provide accurate area calculation results and is suitable for continuously changing curves.

[0065] In addition, another embodiment also includes:

[0066] The differences between the current and voltage fitting values ​​between adjacent data points in the local range are calculated and added to obtain the sum of the current and voltage differences between two adjacent data points. The area between adjacent data points is obtained using the trapezoidal rule formula.

[0067] Specifically, the area satisfies the following relationship:

[0068] ;

[0069] Where, Indicates the adjacent first Point and The area enclosed by the current fitting curve and the voltage fitting curve between the points, represents the vertical distance between adjacent data points, Indicates the The data point corresponds to the The current fitting value of the data point, Indicates the The data point corresponds to the The voltage fitting value of the data point, Indicates the The data point corresponds to the The current fitting value of the data point, Indicates the The data point corresponds to the The voltage fitting value of each data point.

[0070] The trapezoidal rule is a numerical integration method that improves computational efficiency by approximating the area under a curve as a trapezoid. This area reflects the energy loss or power difference between two adjacent measurement points due to inconsistencies in current and voltage. For example, if the current and voltage are out of phase, this can lead to a decrease in power factor, affecting grid efficiency. It helps identify abnormal areas in the grid, such as equipment failure, load imbalance, or data acquisition errors.

[0071] It should also be noted that this method is suitable for when the data points are distributed relatively evenly and the fitting curve changes relatively slowly. It is calculated by approximating the area under the curve to a trapezoid, which is suitable for scenarios where the area needs to be estimated quickly.

[0072] In practical applications, the choice of method can be based on specific data characteristics, computing resources, and accuracy requirements. For grid dispatch data, if the data is collected frequently and changes gradually, the trapezoidal rule may be a more practical choice. Conversely, if precise analysis of the grid's operating status is required, an integral method may be used to obtain more accurate results. During the calculation process, the current and voltage data should be limited to a certain range to avoid extreme cases.

[0073] Specifically, the credibility satisfies the following relationship:

[0074] ;

[0075] Where, Represents a grid dispatch data point credibility, Represents a grid dispatch data point The noise performance level, Represents a grid dispatch data point The area set between all adjacent points in the local range, It represents the standard deviation function. Indicates An exponential function with base .

[0076] That is to say, Represents a grid dispatch data point The consistency of the area enclosed by two adjacent points of the current fitting curve and the voltage fitting curve in a local range is positively correlated with the degree of noise performance and negatively correlated with the credibility.

[0077] S4: The Fisher optimal solution algorithm is modified based on the credibility, the power grid dispatching data is clustered according to the modified Fisher optimal solution algorithm, and the power grid dispatching data is transmitted according to the clustering result.

[0078] It should be noted that the Fisher optimal solution algorithm is a well-known technology in this field and will not be described in detail.

[0079] Specifically, the correction process satisfies the following relationship:

[0080] ;

[0081] Where, Indicates that in the process of Fisher optimal solution, The ordered samples are divided into Classification, Indicates the The number of data points in a cluster, Indicates the The mean coordinates of the clusters, Indicates the The first data points, express The first The credibility of a data point.

[0082] It should also be noted that the number of clusters can be directly determined using existing technologies such as the elbow method, and each cluster in the clustering result is a segment; each segment can be encapsulated into a standardized data packet, and the encapsulated data packet can be encrypted to ensure confidentiality and integrity during transmission. The encrypted data packet can be transmitted between nodes through the power dispatching data network.

[0083] The present invention also provides a data transmission system for a power grid dispatching data network. Figure 2 As shown, the system includes a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, the data transmission method for the power grid dispatching data network according to the first aspect of the present invention is implemented.

[0084] The system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and therefore will not be described in detail here.

[0085] In the present invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, the computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium can be part of, accessible to, or connectable to a device. Any application or module described in the present invention can be implemented by computer-readable / executable instructions stored or otherwise retained by such a computer-readable medium.

[0086] In the description of this specification, "multiple" and "several" mean at least two, such as two, three or more, etc., unless otherwise clearly defined.

[0087] While several embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous modifications, variations, and alternatives will occur to those skilled in the art without departing from the concept and spirit of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in practicing the present invention.

Claims

1. A data transmission method for a power grid dispatching data network, characterized in that: include: Obtaining grid dispatch data, wherein the grid dispatch data includes current and voltage, with each data point in the grid dispatch data as the center point, and a preset number of data points are set on both sides to determine the local range; The least squares method is used to perform curve fitting on the current data and voltage data in the local range respectively. The difference between the fitting results and the actual values ​​is used to obtain the noise performance of each data point in the power grid dispatching data. Calculating the area enclosed by adjacent data points within a local range, including: using any data point within the local range as a marked midpoint, calculating the vertical distance between the current fitting curve and the voltage fitting curve of the marked point, and obtaining the area enclosed by the current fitting curve and the voltage fitting curve between all adjacent data points within the local range; using an exponential function to calculate the product of the standard deviation of the area set between two adjacent data points in the local range and the noise performance level of the corresponding data point, to obtain the credibility of each data point; The Fisher optimal solution algorithm is modified based on the credibility, the power grid dispatching data is clustered according to the modified Fisher optimal solution algorithm, and the power grid dispatching data is transmitted according to the clustering result.

2. The data transmission method for a power grid dispatching data network according to claim 1, characterized in that: The curve fitting of the current data and the voltage data within the local range respectively includes: The time sequence number of the data point in the local range is used as the horizontal axis, and the current or voltage value of the data point is used as the vertical axis to fit the current curve and voltage curve respectively.

3. The data transmission method for a power grid dispatching data network according to claim 1, characterized in that: Obtaining the noise performance level includes: The average values ​​of the absolute differences between the fitted values ​​and the actual values ​​of the current and voltage in the local range are calculated for normalization, and the summed average is taken as the noise performance of each data point in the local range.

4. The data transmission method for a power grid dispatching data network according to claim 1, characterized in that: Obtaining the noise performance level further includes: The difference between the actual value of the data point in the local range and the moving average is calculated and divided by the moving average, and then squared to obtain the square of the deviation of each data point. The sum of the square of the deviation of all data points is divided by the number of data points in the local range to obtain the noise performance of each data point.

5. The data transmission method for a power grid dispatching data network according to claim 1, characterized in that: The calculation of the area enclosed by adjacent data points within the local range further includes: The differences between the current and voltage fitting values ​​between adjacent data points in the local range are calculated and added to obtain the sum of the current and voltage differences between two adjacent data points. The area between adjacent data points is obtained using the trapezoidal rule formula.

6. The data transmission method for a power grid dispatching data network according to claim 1, characterized in that: The correction process satisfies the following relationship: ； Where, Indicates that in the process of Fisher optimal solution, The ordered samples are divided into Classification, Indicates the The number of data points in a cluster, Indicates the The mean coordinates of the clusters, Indicates the The first data points, express The first The credibility of a data point.

7. A data transmission system for a power grid dispatching data network, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the data transmission method for a power grid dispatching data network according to any one of claims 1 to 6 is implemented.

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