Novel power system section correlation analysis method and system

Abstract

The invention discloses a novel power system section correlation analysis method and system, and the method comprises the steps: collecting the operation data of a plurality of power flow sections in a power system in a preset period based on a preset time interval; performing normalization processing on the operation data, and determining required power and time sequences of a plurality of power flow sections based on the operation data after normalization processing; and performing correlation analysis on the plurality of power flow sections based on the required power of the power flow sections and the time sequence, and determining the correlation among the power flow sections. According to the technical scheme, section limit distribution is optimized, abandoned electricity is reduced, the operation efficiency of a power grid is improved, and the method has important significance on reliable and stable power supply and sustainable energy development.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation, and more specifically, to a novel power system section correlation analysis method and system. Background Art

[0002] In traditional power systems, each section in the power grid is usually regarded as independent, and the interaction relationship between sections is ignored or simplified. However, in a novel power system with a high proportion of new energy, the power that different sections can transmit is restricted by the security and stability of the power grid, and there are complex coupling relationships between different sections. The research on the coupling relationship between multi-level sections of the power grid is one of the key issues for understanding the power grid operation mode and optimizing the power system. The research on the section coupling relationship aims to reveal the interaction and interdependence between different power grid sections. By studying the coupling relationship between different sections, we can understand the mutual influence and mutual regulation mechanism between the power of the power grid sections. This helps us optimize the allocation of section limits to make it more in line with actual needs and reduce the occurrence of power abandonment.

[0003] Therefore, studying the coupling relationship between multi-level sections of the power grid is crucial for optimizing the planning, operation, and management of the power system. Summary of the Invention

[0004] The technical solution of the present invention provides a novel power system section correlation analysis method and system to solve the problem of how to perform correlation analysis on the power flow sections of a novel power system.

[0005] To solve the above problems, the present invention provides a novel power system section correlation analysis method, and the method includes:

[0006] Collect the operation data of multiple power flow sections in the power system within a preset period based on a preset time interval;

[0007] Perform normalization processing on the operation data, and based on the normalized operation data, determine the demand power and time series of multiple power flow sections;

[0008] Perform correlation analysis on multiple power flow sections based on the demand power and time series of the power flow sections to determine the correlation between each power flow section.

[0009] Preferably, the operation data includes: the actual active power and the abandoned power of the power flow section at the acquisition moment.

[0010] Preferably, the performing normalization processing on the operation data and establishing the demand power and time series of multiple power flow sections includes:

[0011] Sum the actual active power and the curtailment power of the power flow section to obtain the demand power of the power flow section, and normalize the demand power:

[0012]

[0013] Among them, represents the normalized value of the demand power of the position power flow section, p max represents the maximum demand power among all power flow sections, p min represents the minimum demand power among all power flow sections, represents the demand power of the position power flow section.

[0014] Preferably, the correlation analysis of multiple power flow sections based on the demand power and time series of the power flow section to determine the correlation between each power flow section includes:

[0015] Perform correlation analysis on multiple power flow sections based on the Pearson correlation coefficient:

[0016]

[0017] Among them, cov is the covariance of variable X and variable Y, σ X and σ Y are the standard deviations of variable X and variable Y respectively, X and Y represent the values of two variables respectively, E(X) and E(Y) are the means of variable X and variable Y, ρ X，Y is the Pearson correlation coefficient of variable X and variable Y, and variable X and variable Y represent the variables on the curve with the demand power as the ordinate and the time series as the abscissa respectively.

[0018] Preferably, the correlation analysis of the demand power and time series of multiple power flow sections to determine the correlation between each power flow section includes:

[0019] The value range of the Pearson correlation coefficient r is between -1 and 1:

[0020] When r = 1, it indicates a perfect positive linear correlation, that is, variable X and variable Y are completely positively correlated;

[0021] When r = -1, it indicates a perfect negative linear correlation, that is, variable X and variable Y are completely negatively correlated;

[0022] When r = 0, it indicates no linear correlation, that is, there is no linear relationship between variable X and variable Y;

[0023] Determine the correlation between each power flow section based on the correlation between variable X and variable Y.

[0024] Based on another aspect of the present invention, the present invention provides a novel power system section correlation analysis system, and the system includes:

[0025] An operating mode data acquisition module, configured to acquire the operating data of multiple power flow sections in the power system within a preset period based on a preset time interval;

[0026] A data normalization processing module, configured to perform normalization processing on the operating data, and determine the demand power and time series of multiple power flow sections based on the normalized operating data;

[0027] A correlation calculation and analysis module, configured to perform correlation analysis on multiple power flow sections based on the demand power and time series of the power flow sections, and determine the correlation between each power flow section.

[0028] Preferably, the operating data includes: the actual active power and the discarded power of the power flow section at the acquisition moment.

[0029] Preferably, the processing unit is configured to perform normalization processing on the operating data, establish the demand power and time series of multiple power flow sections, and is further configured to:

[0030] Sum the actual active power and the discarded power of the power flow section to obtain the demand power of the power flow section, and perform normalization processing on the demand power:

[0031]

[0032] Wherein, represents the normalized value of the demand power of the position power flow section, p max represents the maximum demand power among all power flow sections, p min represents the minimum demand power among all power flow sections, represents the demand power of the position power flow section.

[0033] Preferably, the analysis unit is configured to perform correlation analysis on multiple power flow sections based on the demand power and time series of the power flow sections, determine the correlation between each power flow section, and is further configured to:

[0034] Perform correlation analysis on multiple power flow sections based on the Pearson correlation coefficient:

[0035]

[0036] Wherein, cov is the covariance of variable X and variable Y, σ X and σ YThey are the standard deviations of variable X and variable Y respectively. X and Y represent the values of two variables respectively, E(X) and E(Y) are the means of variable X and variable Y, and ρ X，Y is the Pearson correlation coefficient of variable X and variable Y. Variable X and variable Y represent the variables on the curve with the demand power as the vertical coordinate and the time series as the horizontal coordinate respectively.

[0037] Preferably, the analysis unit is used to perform a correlation analysis on the demand power and time series of multiple power flow sections to determine the correlation between each power flow section, and is also used for:

[0038] The value range of the value r of the Pearson correlation coefficient is between -1 and 1:

[0039] When r = 1, it indicates a perfect positive linear correlation, that is, variable X and variable Y are completely positively correlated;

[0040] When r = -1, it indicates a perfect negative linear correlation, that is, variable X and variable Y are completely negatively correlated;

[0041] When r = 0, it indicates no linear correlation, that is, there is no linear relationship between variable X and variable Y;

[0042] Based on the correlation between variable X and variable Y, determine the correlation between each power flow section.

[0043] Based on another aspect of the present invention, the present invention provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, and the computer program is used to execute a new method for analyzing the correlation of power system sections.

[0044] Based on another aspect of the present invention, the present invention provides an electronic device, and the electronic device includes: a processor and a memory; wherein,

[0045] The memory is used to store the executable instructions of the processor;

[0046] The processor is used to read the executable instructions from the memory and execute the instructions to implement a new method for analyzing the correlation of power system sections.

[0047] The technical solution of the present invention provides a new method and system for analyzing the correlation of power system sections. The method includes: collecting the operation data of multiple power flow sections in the power system within a preset period based on a preset time interval; performing normalization processing on the operation data, and determining the demand power and time series of multiple power flow sections based on the normalized operation data; performing correlation analysis on multiple power flow sections based on the demand power and time series of the power flow sections to determine the correlation between each power flow section. The technical solution of the present invention realizes the optimization of the allocation of section limits, reduces the abandonment of electricity, and improves the operation efficiency of the power grid by understanding the interaction and interdependence relationships between sections. The technical solution of the present invention is of great significance for reliable and stable power supply and sustainable energy development. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The exemplary embodiments of the present invention can be more fully understood by referring to the following drawings:

[0049] Figure 1 FIG. is a flowchart of a new method for analyzing the correlation of power system sections according to a preferred embodiment of the present invention;

[0050] Figure 2 FIG. is a structural diagram of a new system for analyzing the correlation of power system sections according to a preferred embodiment of the present invention;

[0051] Figure 3 FIG. is a schematic diagram of the actual active power curve of a section according to a preferred embodiment of the present invention;

[0052] Figure 4 FIG. is a heat map of the correlation coefficient of the demand power of a section according to a preferred embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] Now, the exemplary embodiments of the present invention will be described with reference to the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely, and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the drawings are not intended to limit the present invention. In the drawings, the same unit / element is denoted by the same reference numeral.

[0054] Unless otherwise specified, the terms (including scientific and technical terms) used herein have the ordinary meaning understood by those skilled in the art. In addition, it can be understood that the terms defined in the commonly used dictionary should be understood as having a meaning consistent with the context of the relevant field, and should not be understood as an idealized or overly formal meaning.

[0055] Figure 1A flowchart of a new method for analyzing the relevance of power system sections according to a preferred embodiment of the present invention.

[0056] The research on the relevance of section power aims to reveal the energy transfer and distribution between different power grid sections, as well as its impact on the performance and reliability of the power grid. By deeply studying and understanding the relevance between the powers of different power grid sections, the design of the power grid can be better optimized and the power transmission efficiency can be improved. In addition, by deeply studying the coupling relationship between section powers, potential synergy effects and optimization strategies can be discovered. For example, when the power of a certain section approaches the limit, the power balance can be achieved by adjusting the power distribution of other sections, thereby reducing the load pressure. This mutual adjustment mechanism helps to improve the stability and reliability of the power system.

[0057] As Figure 1 shown, the present invention provides a new method for analyzing the relevance of power system sections, and the method includes:

[0058] Step 101: Based on a preset time interval, collect the operation data of multiple power flow sections in the power system within a preset period;

[0059] Preferably, the operation data includes: the actual active power and the discarded power at the collection moment.

[0060] The present invention is responsible for collecting the operation mode data of the actual power system, specifically referring to the operation data of the power flow sections in the power system. The power flow section refers to a specific part of the cross-section of the power transmission channel in the power system. During the power transmission and distribution process, the power flow section of the power grid is used to describe the path and capacity of the current flow. It determines the current transmission capacity and affects the safety and stability of the power grid, which is crucial for the design and operation of the power system. The operation data refers to monitoring the actual active power and the discarded power of the power flow section on a certain time scale. The actual active powers and discarded powers of multiple power flow sections at each moment are respectively combined to form a set of sample points to be classified, and the data dimension of the sample points is the number of power flow sections.

[0061] Step 102: Perform normalization processing on the operation data, and based on the normalized operation data, determine the demand power and time series of multiple power flow sections;

[0062] Preferably, performing normalization processing on the operation data and determining the demand power and time series of multiple power flow sections based on the normalized operation data includes:

[0063] Sum the actual active power and the discarded power of the power flow section to obtain the demand power of the power flow section, and perform normalization processing on the demand power:

[0064]

[0065] Among them, represents the normalized value of the demand power of the position power flow section, p max represents the maximum demand power among all power flow sections, p min represents the minimum demand power among all power flow sections, represents the demand power of the position power flow section.

[0066] The present invention is responsible for normalizing the collected section power data.

[0067] First, add the actually collected power and the discarded power to obtain the demand power of each section, and normalize the demand power data of all sections:

[0068]

[0069] Among them, represents the normalized value of the demand power of the position power flow section, p max represents the maximum value of the demand power of all power flow sections, p min represents the minimum value of the demand power of all power flow sections.

[0070] Step 103: Perform a correlation analysis on multiple power flow sections based on the demand power and time series of the power flow sections to determine the correlation between each power flow section.

[0071] Preferably, performing a correlation analysis on multiple power flow sections based on the demand power and time series of the power flow sections to determine the correlation between each power flow section includes:

[0072] Performing a correlation analysis on the demand power and time series of multiple power flow sections based on the Pearson correlation coefficient:

[0073]

[0074] Among them, cov is the covariance of variable X and variable Y, σ X and σ Y are the standard deviations of variable X and variable Y respectively, X and Y represent the values of two variables respectively, E(X) and E(Y) are the means of variable X and variable Y, ρ X，Y is the Pearson correlation coefficient of variable X and variable Y, and variable X and variable Y represent the variables on the curve with the demand power as the ordinate and the time series as the abscissa respectively.

[0075] Preferably, performing a correlation analysis on the demand power and time series of multiple power flow sections to determine the correlation between each power flow section includes:

[0076] The value range of the Pearson correlation coefficient r is between -1 and 1:

[0077] When r = 1, it indicates a perfect positive linear correlation, that is, variable X and variable Y are completely positively correlated;

[0078] When r = -1, it indicates a perfect negative linear correlation, that is, variable X and variable Y are completely negatively correlated;

[0079] When r = 0, it indicates no linear correlation, that is, there is no linear relationship between variable X and variable Y;

[0080] Based on the correlation between variable X and variable Y, the correlation between each power flow section is determined.

[0081] In order to further explore the relationship between each section in the present invention, we use the Pearson correlation coefficient to calculate the correlation between the power generation time series of each section.

[0082] The Pearson correlation coefficient is a statistic used to measure the degree of linear correlation between two variables, and its expression is as follows:

[0083]

[0084] Among them, cov is the covariance, and σ is the standard deviation. X and Y represent the values of the two variables respectively, and E(X) and E(Y) represent the means of the two variables respectively.

[0085] The value range of the Pearson correlation coefficient is as follows:

[0086] When r = 1, it indicates a perfect positive linear correlation, that is, the two variables are completely positively correlated.

[0087] When r = -1, it indicates a perfect negative linear correlation, that is, the two variables are completely negatively correlated.

[0088] When r = 0, it indicates no linear correlation, that is, there is no linear relationship between the two variables.

[0089] The Pearson correlation coefficient measures the strength and direction of the linear relationship between two variables, and its value range is between -1 and 1.

[0090] The present invention is responsible for visualizing the correlation coefficient matrix output by the previous module to realize the analysis of the time distribution characteristics and similarity of the operation mode data.

[0091] The present invention proposes a new method for analyzing the correlation of power system sections. Through data collection, normalization processing, and correlation calculation, it reveals the power coupling situation between different sections of the new power system and its impact on grid performance and reliability. By deeply studying and understanding the correlation between the powers of different grid sections, the grid design can be better optimized and the power transmission efficiency can be improved. By understanding the interaction and interdependence between sections, we can optimize the allocation of section limits, reduce power curtailment, and improve the operating efficiency of the grid. This is of great significance for achieving reliable and stable power supply and sustainable energy development.

[0092] A specific embodiment of the present invention is the operation data of the power flow sections of an actual power system in a certain area within one year. The power flow sections include the HX power flow section, the XJ power flow section, the DY power flow section, the GY power flow section, the QY power flow section, and the HY power flow section.

[0093] Sample the actual power and curtailed power of six sections, namely HX, XJ, DY, GY, QY, and HY, in the power grid within one year. Obtain data every 15 minutes, with a total of 35042 pieces of data. Take the sum of the actual power and the curtailed power of the power flow section as the demand power of the section. Analyzing the data finds that the demand powers of different sections vary greatly. Therefore, first perform normalization processing on all the data:

[0094]

[0095] Among them, represents the normalized value of the demand power of the position power flow section, where position represents the HX, XJ, DY, GY, QY, and HY power flow sections, and p max represents the maximum value of the demand powers of all power flow sections, and p min represents the minimum value of the demand powers of all power flow sections.

[0096] After normalization, the demand powers of the 6 power flow sections change with time as Figure 3 shown.

[0097] Analyze Figure 3 the curves in it. It can be seen that the demand powers of the 6 sections show a certain time correlation with time, and there is also a certain complementary relationship between the power flow sections. To further explore the relationship between each section, we use the Pearson correlation coefficient to calculate the correlation between the power generation time series of each power flow section.

[0098] The Pearson correlation coefficient is a statistic used to measure the linear correlation degree between two variables, and its expression is as follows:

[0099]

[0100] Among them, cov is the covariance, and σ is the standard deviation. X and Y represent the values of two variables respectively, and E(X) and E(Y) represent the means of the two variables respectively. The value range of the Pearson correlation coefficient is as follows:

[0101] When r = 1, it indicates a perfect positive linear correlation, that is, the two variables are completely positively correlated.

[0102] When r = -1, it indicates a perfect negative linear correlation, that is, the two variables are completely negatively correlated.

[0103] When r = 0, it indicates no linear correlation, that is, there is no linear relationship between the two variables.

[0104] The Pearson correlation coefficient measures the strength and direction of the linear relationship between two variables, and its value range is between -1 and 1. The correlation coefficient results of the demand power of each power flow section are as Figure 4 shown.

[0105] According to the data analysis in the heat map, the correlation coefficient between GY and QY is 0.8383, and the correlation coefficient between QY and HY is 0.9026, both showing a relatively high correlation. In addition, the correlation coefficients between DY, GY, QY, and HY pairwise are all between 0.54 and 0.65. Generally speaking, the correlation of the demand power of the DY, GY, QY, and HY power flow sections is relatively high. The correlation coefficient between HX and the demand power of the power flow sections other than HY does not exceed 0.3, and the correlation coefficient between XJ and the demand power of the power flow sections other than DY does not exceed 0.2, indicating that the correlation between the demand power of the HX power flow section and the XJ power flow section and most of the power flow sections is weak.

[0106] The present invention proposes a new power system power flow section correlation analysis system, which includes four modules: an operation mode data acquisition module, a data normalization processing module, a correlation calculation and analysis module, and a result output module.

[0107] As Figure 2 shown, the present invention provides a new power system section correlation analysis system, and the system includes:

[0108] An operation mode data acquisition module, configured to collect the operation data of multiple power flow sections in the power system within a preset period based on a preset time interval;

[0109] Preferably, the operation data includes: the actual active power and the abandoned power of the power flow section at the acquisition moment.

[0110] A data normalization processing module is used to perform normalization processing on operation data, and based on the operation data after normalization processing, determine the demand power and time series of multiple power flow sections.

[0111] Preferably, the data normalization processing module is used to perform normalization processing on operation data, determine the demand power and time series of multiple power flow sections based on the operation data after normalization processing, and is further used for:

[0112] Sum the actual active power and the discarded power of the power flow section to obtain the demand power of the power flow section, and perform normalization processing on the demand power:

[0113]

[0114] Among them, represents the normalized value of the demand power of the position power flow section, p max represents the maximum demand power among all power flow sections, p min represents the minimum demand power among all power flow sections, represents the demand power of the position power flow section.

[0115] A correlation calculation and analysis module is used to perform correlation analysis on the demand power and time series of multiple power flow sections to determine the correlation between each power flow section.

[0116] Preferably, the correlation calculation and analysis module is used to perform correlation analysis on multiple power flow sections based on the demand power and time series of the power flow section to determine the correlation between each power flow section, and is further used for:

[0117] Perform correlation analysis on multiple power flow sections based on the Pearson correlation coefficient:

[0118]

[0119] Among them, cov is the covariance of variable X and variable Y, σ X and σ Y are the standard deviations of variable X and variable Y respectively, X and Y represent the values of two variables respectively, E(X) and E(Y) are the means of variable X and variable Y, ρ X，Y is the Pearson correlation coefficient of variable X and variable Y, and variable X and variable Y represent the variables on the curve with the demand power as the ordinate and the time series as the abscissa respectively.

[0120] Preferably, the correlation calculation and analysis module is used to perform correlation analysis on the demand power and time series of multiple power flow sections to determine the correlation between each power flow section, and is further used for:

[0121] The value range of the Pearson correlation coefficient r is between -1 and 1:

[0122] When r = 1, it indicates a perfect positive linear correlation, that is, variable X and variable Y are completely positively correlated;

[0123] When r = -1, it indicates a perfect negative linear correlation, that is, variable X and variable Y are completely negatively correlated;

[0124] When r = 0, it indicates no linear correlation, that is, there is no linear relationship between variable X and variable Y;

[0125] Based on the correlation between variable X and variable Y, the correlation between each power flow section is determined.

[0126] The present invention provides a computer-readable storage medium storing a computer program for executing a method for analyzing the correlation of sections of a new type of power system.

[0127] The present invention provides an electronic device including: a processor and a memory; wherein,

[0128] The memory is used to store the instructions executable by the processor;

[0129] The processor is used to read the executable instructions from the memory and execute the instructions to implement a method for analyzing the correlation of sections of a new type of power system.

[0130] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes. The solutions in the embodiments of the present invention can be implemented in various computer languages, for example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0131] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementation in the processFigure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0132] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device that implements the functions in the process Figure 1 one process or multiple processes and / or blocks Figure 1 specified in one block or multiple blocks.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide for implementing the functions specified in the process Figure 1 one process or multiple processes and / or blocks Figure 1 steps for the functions specified in one block or multiple blocks.

[0134] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0135] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

[0136] The present invention has been described by referring to a few embodiments. However, as is known to those skilled in the art, as defined by the appended patent claims, other embodiments equivalent to those disclosed above of the present invention equally fall within the scope of the present invention.

[0137] Generally, all terms used in the claims are construed according to their ordinary meanings in the technical field, unless otherwise clearly defined therein. All references to "a / the [device, component, etc.]" are construed broadly as at least one instance of the device, component, etc., unless otherwise clearly stated. The steps of any method disclosed herein need not be run in the exact order disclosed, unless clearly stated.

Claims

1. A novel method for analyzing the correlation of power system sections, the method comprising: Collecting the operation data of multiple power flow sections in the power system within a preset period based on a preset time interval; Performing normalization processing on the operation data, and determining the demand power and time series of multiple power flow sections based on the normalized operation data; Performing correlation analysis on multiple power flow sections based on the demand power and time series of the power flow sections, and determining the correlation between each power flow section.

2. The method according to claim 1, wherein the operation data includes: The actual active power and curtailment power of the power flow section at the collection moment.

3. According to the method described in claim 2, the performing normalization processing on the operation data, and determining the demand power and time series of multiple power flow sections based on the normalized operation data includes: Summing the actual active power and curtailment power of the power flow section to obtain the demand power of the power flow section, and performing normalization processing on the demand power: Among them, represents the normalized value of the demand power of the position power flow section, p max represents the maximum demand power among all power flow sections, p min represents the minimum demand power among all power flow sections, represents the demand power of the position power flow section.

4. According to the method described in claim 1, the performing correlation analysis on multiple power flow sections based on the demand power and time series of the power flow sections, and determining the correlation between each power flow section includes: Performing correlation analysis on multiple power flow sections based on the Pearson correlation coefficient: where cov is the covariance of variable X and variable Y, σ X and σ Y are the standard deviations of variable X and variable Y respectively, X and Y represent the values of the two variables respectively, E(X) and E(Y) are the means of variable X and variable Y, ρ X，Y is the Pearson correlation coefficient of variable X and variable Y, and variable X and variable Y represent the variables on the curve with the demand power as the ordinate and the time series as the abscissa respectively.

5. According to the method described in claim 4, the performing correlation analysis on the demand power and time series of multiple power flow sections, and determining the correlation between each power flow section includes: The value range of the Pearson correlation coefficient r is between -1 and 1: When r = 1, it indicates a completely positive linear correlation, that is, variable X and variable Y are completely positively correlated; When r = -1, it indicates a completely negative linear correlation, that is, variable X and variable Y are completely negatively correlated; When r = 0, it indicates no linear correlation, that is, there is no linear relationship between variable X and variable Y; Determining the correlation between each power flow section based on the correlation between variable X and variable Y.

6. A novel power system section correlation analysis system, the system comprising: An operation mode data collection module, configured to collect the operation data of multiple power flow sections in the power system within a preset period based on a preset time interval; A data normalization processing module, configured to perform normalization processing on the operation data, and determine the demand power and time series of multiple power flow sections based on the normalized operation data; A correlation calculation and analysis module, configured to perform correlation analysis on multiple power flow sections based on the demand power and time series of the power flow sections, and determine the correlation between each power flow section.

7. The system according to claim 6, wherein the operation data includes: The actual active power and curtailment power of the power flow section at the collection moment.

8. According to the system described in claim 7, the processing unit is configured to perform normalization processing on the operation data, establish the demand power and time series of multiple power flow sections, and is further configured to: Sum the actual active power and curtailment power of the power flow section to obtain the demand power of the power flow section, and perform normalization processing on the demand power: Among them, Represents the normalized value of the demand power of the position power flow section, p max Represents the maximum demand power among all power flow sections, p min Represents the minimum demand power among all power flow sections Represents the demand power of the position power flow section 9. According to the system described in claim 6, the analysis unit is configured to perform correlation analysis on multiple power flow sections based on the demand power and time series of the power flow sections, and determine the correlation between each power flow section, and is further configured to: Conduct correlation analysis on multiple tidal current sections based on the Pearson correlation coefficient: Among them, cov is the covariance of variable X and variable Y, σ X and σ Y are the standard deviations of variable X and variable Y respectively. X and Y represent the values of the two variables respectively, E(X) and E(Y) are the means of variable X and variable Y, ρ X，Y is the Pearson correlation coefficient of variable X and variable Y. Variable X and variable Y represent the variables on the curve with demand power as the vertical coordinate and time series as the horizontal coordinate respectively.

10. The system according to claim 9, wherein the analysis unit is configured to conduct correlation analysis on the demand power and time series of multiple tidal current sections to determine the correlation between each tidal current section, and is further configured to: The value r of the Pearson correlation coefficient ranges from -1 to 1: When r = 1, it indicates a perfect positive linear correlation, that is, variable X and variable Y are completely positively correlated; When r = -1, it indicates a perfect negative linear correlation, that is, variable X and variable Y are completely negatively correlated; When r = 0, it indicates no linear correlation, that is, there is no linear relationship between variable X and variable Y; Determine the correlation between each tidal current section based on the correlation between variable X and variable Y.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the computer program is used to execute the method described in any one of claims 1-5.

12. An electronic device, characterized in that, The electronic device includes: a processor and a memory; wherein, The memory is used to store the executable instructions of the processor; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1-5.

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