Intelligent power grid monitoring system

Abstract

The invention discloses an intelligent power grid monitoring system, which relates to the technical field of power grid monitoring and comprises a data acquisition module, a reference condensation degree acquisition module, a reference separation degree acquisition module, a comparison profile tolerance acquisition module, a real-time data monitoring module, a profile judgment interval acquisition module and an abnormal node judgment module. The technical problems that a traditional power grid monitoring system generally can only monitor some basic electrical parameters, the operation state of a power grid is difficult to comprehensively reflect, fine monitoring of all nodes in the power grid is lacked, and local node abnormity cannot be found in time are solved. Abnormal nodes are accurately judged and output; according to the invention, fine monitoring of each node in the power grid is realized, abnormal conditions of local nodes can be found in time, operation and maintenance personnel can find and process abnormal nodes in the power grid in time, and comprehensive, real-time and accurate monitoring of states of each node in the power grid is realized.

Description

Technical Field

[0001] The invention belongs to the technical field of power grid monitoring, and in particular relates to an intelligent power grid monitoring system. Background Art

[0002] With the continuous growth of energy demand and the increasing requirements for the reliability and efficiency of power systems, smart grids have become the main direction of future power grid development. Smart grids need to achieve real-time monitoring, precise control and optimized management of the power grid operation status to improve the safety, stability and economy of the power grid. During the operation of the power grid, power grid monitoring mainly monitors and controls it, which can better judge the power grid operation failures and operating conditions, and ensure that problems are handled and solved in a timely manner. With the rapid development of information technology, the development of power grid operation presents intelligent characteristics. As the main part of the power system, smart grids are directly related to the stability and safety of the power system. They can not only ensure that the monitoring data has a high degree of accuracy, but also avoid energy loss in the transmission process.

[0003] Grid monitoring mainly uses advanced sensing and control technologies. Under the premise of information network and communication network, it can realize effective monitoring of grid operation, making the grid operation process more environmentally friendly, reliable and economical. In short, grid monitoring can better meet people's daily electricity needs and ensure more reasonable use of electric energy resources.

[0004] The patent publication number CN113241848A discloses a comprehensive monitoring system for distribution network, which collects distribution network power consumption data through the power consumption information collection module and sends the distribution network power consumption data to the intelligent gateway; the distribution equipment status monitoring module collects the distribution equipment operation status data, and sends the distribution equipment operation status data to the intelligent gateway through the communication module; the intelligent gateway sends the distribution network power consumption data and the distribution equipment operation status data to the edge computing module and manages them; edge computing is performed on the distribution network power consumption data and the distribution equipment operation status data to obtain power consumption data monitoring result data. Through the functions of power consumption information collection, equipment operation status monitoring, etc., the data information in the distribution network is comprehensively processed, and based on the edge technology, the distribution network operation status is monitored according to the collected power consumption information and the operation status of the distribution equipment in the distribution network environment, and the power consumption data monitoring result data is obtained, which can effectively and accurately monitor the data of the distribution system.

[0005] However, traditional power grid monitoring systems may have the problem of incomplete data collection, resulting in blind spots in the monitoring of power grid status. Traditional power grid monitoring systems can usually only monitor some basic electrical parameters, such as voltage and current, which makes it difficult to fully reflect the operating status of the power grid. Some nodes may have slight abnormalities in the early stage, but these abnormalities may not be enough to trigger the traditional fault alarm mechanism. Traditional power grid monitoring systems lack refined monitoring of each node in the power grid and cannot detect abnormal conditions of local nodes in time, which affects the normal operation of the power grid. Based on this, a smart grid monitoring system is proposed. Summary of the invention

[0006] The purpose of the present invention is to provide an information interaction method for power field operations, which solves the technical problems that traditional power grid monitoring systems can usually only monitor some basic electrical parameters, are difficult to fully reflect the operating status of the power grid, lack refined monitoring of each node in the power grid, and cannot promptly detect abnormal conditions of local nodes.

[0007] A smart grid monitoring system, comprising:

[0008] The data acquisition module collects the voltage corresponding to each node in the power grid at a preset number of times n;

[0009] The reference cohesion acquisition module analyzes the voltage data of each node in the power grid in a preset number of times n to obtain the reference cohesion corresponding to each node;

[0010] The reference separation acquisition module analyzes the voltage data of each node in the power grid in a preset number of times n to obtain the reference separation corresponding to each node;

[0011] The contrast contour acquisition module comprehensively analyzes the cohesion and separation of each node to obtain the contrast contour corresponding to each node;

[0012] Real-time data monitoring module, obtains the real-time voltage value of each node for analysis, and obtains the real-time contour corresponding to each node;

[0013] The contour determination interval acquisition module analyzes the comparative contour degrees corresponding to each node and generates the contour determination intervals corresponding to each node;

[0014] The abnormal node determination module compares and analyzes the real-time contour degree corresponding to each node with its corresponding contour determination interval to determine the generation of abnormal nodes.

[0015] As a further solution of the present invention: the specific method for obtaining the benchmark cohesion corresponding to each node is:

[0016] The voltage values ​​corresponding to each node in the preset number of times n are marked as vi n, where i refers to different nodes in the power grid, i is a positive integer, i>1; through the formula, Calculate and obtain the benchmark cohesion Li corresponding to each node in the power grid;

[0017] Wherein, vij refers to the voltage value of the i-th node measured at the jth time, i≥j≥1, and vpi is the average voltage of all measured values ​​corresponding to each node in the preset number of times n.

[0018] As a further solution of the present invention: the specific method of obtaining the reference separation degree corresponding to each node is:

[0019] S1: Randomly select a node from all nodes without replacement as the analysis node;

[0020] S2: randomly select one time from the preset times n without replacement as the target time, obtain the voltage value corresponding to the analysis node in the target time, and the average of the absolute values ​​of the difference between the voltage value and other nodes, and use it as the node deviation value C1 corresponding to the analysis node in the target time;

[0021] S3: Repeat the above steps S1-S2 to obtain the node deviation values ​​Cn corresponding to the analysis nodes in the preset number of times n, obtain the discrete value U1 of the node deviation value Cn, analyze the discrete value U1 of the node deviation value Cn, and obtain the separation degree corresponding to the analysis node;

[0022] S4: Repeat the above steps S1-S3 to obtain the degree of separation Di corresponding to each node in the power grid.

[0023] As a further solution of the present invention: the discrete values ​​of the node deviation values ​​are analyzed to obtain the separation degree corresponding to the analysis node in the following specific manner:

[0024] When the discrete value U1 is less than or equal to the preset threshold Y1, the mean of the node deviation values ​​Cn is used as the separation degree corresponding to the analysis node. When the discrete value U1 is greater than the preset threshold Y1, the mean of the maximum and minimum values ​​of the node deviation values ​​Cn is used as the separation degree corresponding to the analysis node.

[0025] As a further solution of the present invention: the specific method of obtaining the contrast contour corresponding to each node is:

[0026] Through the formula, Ki = |Li-Di| / |Lmax-Dmax|, the contrast contour Ki corresponding to each node is calculated, where Lmax and Dmax are the maximum values ​​of the cohesion Li and separation Di respectively.

[0027] As a further solution of the present invention: the specific method of generating the contour determination interval corresponding to each node is:

[0028] Obtain the value Kb of the comparison profile Ki corresponding to each node in the power grid that meets the preset condition E1, where b is the number of Ki that meets the preset condition E1, i≥b≥1, and compare and analyze b with the preset value Y2 to obtain the reference profile H;

[0029] The absolute values ​​of the differences between the contrast contour Ki corresponding to each node in the power grid and the reference contour H are obtained, and used as the domain values ​​Zi corresponding to each node in the power grid. The difference between the contrast contour Ki of each node and its corresponding domain value Zi is used as the lower limit value of the contour judgment interval of each node. The sum of the contrast contour Ki of each node and its corresponding domain value Zi is used as the upper limit value of the contour judgment interval of each node, thereby obtaining the contour judgment interval Qi[Ki-Zi, Ki+Zi] corresponding to each node.

[0030] As a further solution of the present invention: the specific method of obtaining the reference profile is:

[0031] b is compared and analyzed with the preset value Y2. When b≥Y2, the mean Kp of Ki is used as the reference contour H. When b<Y2, the mean of the maximum and minimum values ​​in Ki is used as the reference contour H, and Y2 is the preset value.

[0032] As a further solution of the present invention: the preset condition E1 is |Ki-Kp|>Y3, Kp is the average value of Ki, and Y3 is the preset value.

[0033] As a further solution of the present invention: the specific method of obtaining the real-time contour corresponding to each node is:

[0034] Get the real-time voltage value Ri corresponding to each node in the power grid; through the formula, The real-time cohesion EAi corresponding to each node in the power grid is calculated; wherein Re is any value in Ri, and Rp is the mean of Ri, and the mean of the absolute value of the difference between the voltage value of each node and the voltage values ​​of other nodes is obtained, and it is used as the real-time separation degree EBi corresponding to each node in the power grid, and the real-time cohesion EAi and real-time separation EBi corresponding to each node in the power grid are respectively multiplied by the preset fixed coefficients β1 and β2 as the real-time contour degree Gi corresponding to each node, wherein the preset fixed coefficients β1 and β2 are formulated by relevant personnel according to actual needs, satisfying 1=β1+β2 and β1>β2.

[0035] As a further solution of the present invention: the specific method of determining the generation of abnormal nodes is:

[0036] The real-time contour degree Gi corresponding to each node in the power grid is compared with the contour judgment interval Qi[Ki-Zi, Ki+Zi] corresponding to each node. When the real-time contour degree Gi belongs to the corresponding contour judgment interval, no processing is performed. When the real-time contour degree Gi does not belong to the corresponding contour judgment interval, the corresponding node is marked as an abnormal node.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention compares and analyzes the real-time contour degree with the contour judgment interval to accurately judge and output abnormal nodes; it realizes refined monitoring of each node in the power grid, accurately judges and outputs abnormal nodes, which is conducive to timely discovering abnormal conditions of local nodes, taking measures in advance for maintenance and repair, avoiding further deterioration of the problem, and realizing comprehensive, real-time and accurate monitoring of the status of each node in the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the system framework structure of the present invention;

[0040] Figure 2 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0041] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] Embodiment 1

[0043] See also Figure 1-Figure 2 ,This application provides a smart grid monitoring system, including;

[0044] The data acquisition module is used to acquire the voltage corresponding to each node in the power grid for multiple times and record the voltage acquired each time. The acquisition times are n, where n is a preset number and n>1;

[0045] By collecting voltage data from various nodes in the power grid multiple times and monitoring them in real time, the comprehensiveness and timeliness of the data are ensured, which provides accurate basic data for subsequent analysis.

[0046] The reference cohesion acquisition module is used to analyze the voltage of each node in the power grid in the preset number of times n, and obtain the reference cohesion corresponding to each node in the power grid according to the analysis results. The specific method is as follows:

[0047] The voltage values ​​corresponding to each node in the preset number of times n are marked as vi n, where i refers to different nodes in the power grid, i is a positive integer, i>1;

[0048] By formula, Calculate and obtain the benchmark cohesion Li corresponding to each node in the power grid;

[0049] Wherein, vij refers to the voltage value of the i-th node measured at the jth time, i≥j≥1, and vpi is the average voltage of all the measured values ​​corresponding to each node in the preset number of times n;

[0050] Through the reference cohesion acquisition module and the reference separation acquisition module, the cohesion and separation of each node can be accurately calculated. The reference cohesion acquisition module and the reference separation reflect the similarities and differences between nodes, providing an important reference for fault detection.

[0051] The reference separation acquisition module is used to analyze the voltage of each node in the power grid in a preset number of times n, and obtain the reference separation corresponding to each node in the power grid according to the analysis results. The specific method is:

[0052] Obtain the average of the absolute values ​​of the differences between the voltage values ​​of each node and other nodes in each preset number of times n, and mark them as node deviation values, respectively, and analyze the node deviation values ​​corresponding to each node in each preset number of times n, thereby obtaining the separation degrees corresponding to each node;

[0053] S1: Randomly select a node from all nodes without replacement as the analysis node;

[0054] S2: Randomly select one time from the preset number of times n without replacement as the target time;

[0055] Obtain the voltage value corresponding to the analysis node in the target time and the average of the absolute values ​​of the differences between the voltage value and other nodes, and use it as the node deviation value C1 corresponding to the analysis node in the target time;

[0056] S3: Repeat the above steps S1-S2 to obtain the node deviation values ​​Cn corresponding to the analysis nodes in the preset number of times n;

[0057] Obtain the discrete value U1 of the node deviation value Cn, analyze and compare the discrete value U1 with the preset threshold value Y1, when the discrete value U1 is less than or equal to the preset threshold value Y1, the mean value of the node deviation value Cn is used as the separation degree D1 corresponding to the analysis node, when the discrete value U1 is greater than the preset threshold value Y1, the mean value of the maximum and minimum values ​​in the node deviation value Cn is used as the separation degree D1 corresponding to the analysis node, that is, D1 = (Cmin + Cmax) / 2, where Cmin is the minimum value in Cn, Cmax is the maximum value in Cn, and the specific value of the preset threshold value Y1 is formulated by relevant personnel according to actual needs;

[0058] S4: Repeat the above steps S1-S3 to obtain the separation degree Di corresponding to each node in the power grid;

[0059] The cohesion and separation of nodes are comprehensively analyzed to generate a contrast profile, which further quantifies the characteristics of nodes in the power grid and helps to identify abnormal nodes more intuitively.

[0060] The contrast profile acquisition module is used to comprehensively analyze the cohesion and separation corresponding to each node in the power grid, and obtain the contrast profile corresponding to each node in the power grid according to the analysis results. The specific method is as follows:

[0061] Obtain the maximum values ​​Lmax and Dmax of the cohesion Li and separation Di corresponding to each node respectively;

[0062] Through the formula, Ki = |Li-Di| / |Lmax-Dmax|; calculate and obtain the contrast contour Ki corresponding to each node;

[0063] The contour determination interval acquisition module obtains the domain value corresponding to each node in the power grid according to the comparative contour degree analysis corresponding to each node in the power grid, and generates the contour determination interval corresponding to each node according to the comparative contour degree and domain value analysis corresponding to each node. The specific method is as follows:

[0064] Obtain the values ​​of the contrast profile Ki corresponding to each node in the power grid that meet the preset condition E1, and mark them as Kb, where b is the number of Ki that meets the preset condition E1, i≥b≥1;

[0065] Compare b with the preset value Y2. When b≥Y2, the mean value Kp of Ki is taken as the reference profile H. When b<Y2, the mean value of the maximum and minimum values ​​in Ki is taken as the reference profile H, that is, H1=(Kmin+Kmax) / 2, where Kmin is the minimum value in Ki and Kmax is the maximum value in Ki.

[0066] The preset condition E1 is: |Ki-Kp|>Y3, Y2 and Y3 are both preset values, and the specific values ​​of Y2 and Y3 can be formulated by relevant personnel according to actual needs and application scenarios;

[0067] Obtain the absolute value of the difference between the comparison contour Ki corresponding to each node in the power grid and the reference contour H, and use it as the domain value Zi corresponding to each node in the power grid;

[0068] The difference between the contrast contour degree Ki of each node and its corresponding domain degree value Zi is taken as the lower limit value of the contour judgment interval of each node, and the sum of the contrast contour degree Ki of each node and its corresponding domain degree value Zi is taken as the upper limit value of the contour judgment interval of each node, thereby obtaining the contour judgment interval Qi[KiZi, Ki+Zi] corresponding to each node;

[0069] Perform personalized evaluation based on the characteristics of different nodes. Due to the different positions and load conditions of each node in the power grid, the contour degree will also be different. By determining a specific contour judgment interval for each node, the evaluation of the node status is more objective and accurate. Obtaining the contour judgment interval corresponding to each node is of great significance to the smart grid monitoring system. It can improve the accuracy of monitoring, realize refined management, and ensure the reliability and stability of the power grid. It can better adapt to the actual conditions of different nodes and realize refined monitoring.

[0070] Embodiment 2

[0071] As the second embodiment of the present invention, when the present application is specifically implemented, compared with the first embodiment, the technical solution of the present embodiment is different from the first embodiment only in that in the present embodiment, the real-time data monitoring module and the abnormal node determination module;

[0072] The real-time data monitoring module is used to obtain the real-time voltage value corresponding to each node in the power grid, analyze it, and then obtain the real-time profile corresponding to each node. The specific method is as follows:

[0073] Obtain the real-time voltage value Ri corresponding to each node in the power grid;

[0074] By formula, Calculate and obtain the real-time cohesion EAi corresponding to each node in the power grid; where Re is any value in Ri, Rp is the mean value of Ri, i≥e≥1;

[0075] Obtain the average of the absolute values ​​of the differences between the voltage values ​​of each node and the voltage values ​​of other nodes, and use it as the real-time separation degree EBi corresponding to each node in the power grid;

[0076] The real-time cohesion EA i and the real-time separation EB i corresponding to each node in the power grid are respectively the sum of the products of the preset fixed coefficients β1 and β2 as the real-time contour Gi corresponding to each node, wherein the preset fixed coefficients β1 and β2 are both formulated by relevant personnel according to actual needs, satisfying 1=β1+β2 and β1>β2;

[0077] The abnormal node determination module is used to compare and analyze the real-time contour degrees corresponding to each node in the power grid with the contour determination intervals corresponding to each node, and determine the generation of abnormal nodes according to the analysis results. The specific method is as follows:

[0078] The real-time contour Gi corresponding to each node in the power grid is compared with the contour judgment interval Qi[Ki-Zi, Ki+Zi] corresponding to each node. When the real-time contour Gi belongs to the corresponding contour judgment interval, no processing is performed. When the real-time contour Gi does not belong to the corresponding contour judgment interval, the corresponding node is marked as an abnormal node and output;

[0079] According to the comparative contour analysis, a contour determination interval is generated for each node. This interval is used as a standard to determine whether the node is abnormal, which improves the accuracy of fault detection. The real-time contour is compared and analyzed with the contour determination interval to accurately determine and output abnormal nodes. By judging abnormal nodes, potential problem nodes can be detected more accurately, and measures can be taken in advance for maintenance and repair to avoid further deterioration of the problem, so that operation and maintenance personnel can promptly discover and deal with fault problems in the power grid to avoid the expansion of faults. Accurate fault detection and real-time early warning functions reduce the workload of operation and maintenance personnel, timely discover and deal with abnormal nodes in the power grid, and ensure the safe operation of the power grid. It solves the problem that the traditional monitoring system cannot promptly detect abnormal conditions of local nodes, and realizes comprehensive, real-time and accurate monitoring of the status of each node in the power grid.

[0080] Embodiment 3

[0081] As the third embodiment of the present invention, when the present application is specifically implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments mentioned above for implementation.

[0082] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0083] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A smart grid monitoring system, characterized in that: include: The data acquisition module collects the voltage corresponding to each node in the power grid at a preset number of times n; The reference cohesion acquisition module analyzes the voltage data of each node in the power grid in a preset number of times n to obtain the reference cohesion corresponding to each node; The reference separation acquisition module analyzes the voltage data of each node in the power grid in a preset number of times n to obtain the reference separation corresponding to each node; The contrast contour acquisition module comprehensively analyzes the cohesion and separation of each node to obtain the contrast contour corresponding to each node; Real-time data monitoring module, obtains the real-time voltage value of each node for analysis, and obtains the real-time contour corresponding to each node; The contour determination interval acquisition module analyzes the comparative contour degrees corresponding to each node and generates the contour determination intervals corresponding to each node; The abnormal node determination module compares and analyzes the real-time contour degree corresponding to each node with its corresponding contour determination interval to determine the generation of abnormal nodes.

2. A smart grid monitoring system according to claim 1, characterized in that: The specific method for obtaining the benchmark cohesion corresponding to each node is: The voltage values ​​corresponding to each node in the preset number of times n are marked as vin, where i refers to different nodes in the power grid, i is a positive integer, i>1; through the formula, Calculate and obtain the benchmark cohesion Li corresponding to each node in the power grid; Wherein, vij refers to the voltage value of the i-th node measured at the j-th time, i≥j≥1, and vpi is the average voltage of all measured values ​​corresponding to each node in the preset number of times n.

3. A smart grid monitoring system according to claim 2, characterized in that: The specific method of obtaining the benchmark separation corresponding to each node is: S1: Randomly select a node from all nodes without replacement as the analysis node; S2: randomly select one time from the preset times n without replacement as the target time, obtain the voltage value corresponding to the analysis node in the target time, and the average of the absolute values ​​of the difference between the voltage value and other nodes, and use it as the node deviation value C1 corresponding to the analysis node in the target time; S3: Repeat the above steps S1-S2 to obtain the node deviation values ​​Cn corresponding to the analysis nodes in the preset number of times n, obtain the discrete value U1 of the node deviation value Cn, analyze the discrete value U1 of the node deviation value Cn, and obtain the separation degree corresponding to the analysis node; S4: Repeat the above steps S1-S3 to obtain the degree of separation Di corresponding to each node in the power grid.

4. A smart grid monitoring system according to claim 3, characterized in that: The specific method of analyzing the discrete values ​​of the node deviation values ​​and obtaining the separation degree corresponding to the analysis node is as follows: When the discrete value U1 is less than or equal to the preset threshold Y1, the mean of the node deviation values ​​Cn is used as the separation degree corresponding to the analysis node. When the discrete value U1 is greater than the preset threshold Y1, the mean of the maximum and minimum values ​​of the node deviation values ​​Cn is used as the separation degree corresponding to the analysis node.

5. A smart grid monitoring system according to claim 3, characterized in that: The specific method of obtaining the contrast contour corresponding to each node is: Through the formula, Ki = |Li-Di| / |Lmax-Dmax|, the contrast contour Ki corresponding to each node is calculated, where Lmax and Dmax are the maximum values ​​of the cohesion Li and separation Di respectively.

6. A smart grid monitoring system according to claim 5, characterized in that: The specific method of generating the contour determination interval corresponding to each node is: Obtain the value Kb of the comparison profile Ki corresponding to each node in the power grid that meets the preset condition E1, where b is the number of Ki that meets the preset condition E1, i≥b≥1, and compare and analyze b with the preset value Y2 to obtain the reference profile H; The absolute values ​​of the differences between the contrast contour Ki corresponding to each node in the power grid and the reference contour H are obtained, and used as the domain values ​​Zi corresponding to each node in the power grid. The difference between the contrast contour Ki of each node and its corresponding domain value Zi is used as the lower limit value of the contour judgment interval of each node. The sum of the contrast contour Ki of each node and its corresponding domain value Zi is used as the upper limit value of the contour judgment interval of each node, thereby obtaining the contour judgment interval Qi[Ki-Zi, Ki+Zi] corresponding to each node.

7. A smart grid monitoring system according to claim 6, characterized in that: The specific method of obtaining the reference profile is: b is compared and analyzed with the preset value Y2. When b≥Y2, the mean Kp of Ki is used as the reference contour H. When b<Y2, the mean of the maximum and minimum values ​​in Ki is used as the reference contour H, and Y2 is the preset value.

8. The smart grid monitoring system according to claim 6, characterized in that: The preset condition E1 is |Ki-Kp|>Y3, Kp is the average value of Ki, and Y3 is the preset value.

9. The smart grid monitoring system according to claim 1, characterized in that: The specific method of obtaining the real-time contour corresponding to each node is: Get the real-time voltage value Ri corresponding to each node in the power grid; through the formula, The real-time cohesion EAi corresponding to each node in the power grid is calculated; wherein Re is any value in Ri, and Rp is the mean of Ri. The mean of the absolute value of the difference between the voltage value of each node and the voltage values ​​of other nodes is obtained, and used as the real-time separation EBi corresponding to each node in the power grid. The real-time cohesion EAi and the real-time separation EBi corresponding to each node in the power grid are respectively multiplied by the preset fixed coefficients β1 and β2 as the real-time contour Gi corresponding to each node, wherein the preset fixed coefficients β1 and β2 are formulated by relevant personnel according to actual needs, satisfying 1=β1+β2 and β1>β2.

10. The smart grid monitoring system according to claim 1, characterized in that: The specific method for determining the generation of abnormal nodes is as follows: The real-time contour degree Gi corresponding to each node in the power grid is compared with the contour judgment interval Qi[Ki-Zi, Ki+Zi] corresponding to each node. When the real-time contour degree Gi belongs to the corresponding contour judgment interval, no processing is performed. When the real-time contour degree Gi does not belong to the corresponding contour judgment interval, the corresponding node is marked as an abnormal node.

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