Multi-level fuzzy comprehensive evaluation method based on security situation of power distribution network

Abstract

The invention provides a multilevel fuzzy comprehensive evaluation method based on a power distribution network security situation, and relates to the technical field of power distribution network security situation assessment, and the method comprises the steps: collecting distributed power supply output, EV charging and discharging amount, load data and meteorological information of a power distribution network in real time, and building a power supply capability model of a power distribution network system; constructing a multi-level early warning index system based on the power supply capacity model of the power distribution network system; performing two-level fuzzy comprehensive evaluation on the multi-level early warning index system; performing early warning grade dynamic correction based on a two-stage fuzzy comprehensive evaluation result; the method can improve the power supply capability and flexibility, achieves the precise evaluation of the safety situation of the power distribution network, improves the operation reliability of the system, and remarkably improves the early warning capability of the safety risk of the power distribution network.

Description

Technical Field

[0001] This invention relates to the field of power distribution network security status assessment technology, and more specifically, to a multi-level fuzzy comprehensive evaluation method based on the security status of power distribution networks. Background Technology

[0002] With the integration of distributed power sources, diversified loads, and frequent extreme weather events, the operating environment of power distribution networks is becoming increasingly complex, posing dynamic and uncertain challenges to their safety status assessment. Existing technologies often rely on fixed safety thresholds or simple statistical analysis, making it difficult to adapt to real-time changes in operating parameters and resulting in delayed identification of risks such as overvoltage and equipment failure. Furthermore, data processing frequently ignores the periodicity and non-stationarity of measurement data, failing to effectively uncover the correlations between multi-source data and leading to insufficient assessment accuracy. In addition, some methods suffer from problems such as ambiguous hierarchical division of indicator systems and static weight allocation, making it difficult to quantify the coupled impact of different levels of factors (such as equipment status, network topology, and environmental interference) on the overall safety status, thus restricting the systematic assessment and early warning capabilities for power distribution network safety risks. Summary of the Invention

[0003] The purpose of this invention is to provide a multi-level fuzzy comprehensive evaluation method based on the security status of distribution networks, which can improve power supply capacity and flexibility, achieve accurate assessment of the security status of distribution networks, enhance the operational reliability of the system, and significantly improve the early warning capability for security risks of distribution networks.

[0004] The technical solution of this invention is as follows:

[0005] Firstly, this application provides a multi-level fuzzy comprehensive evaluation method based on the security status of a power distribution network, which includes the following steps:

[0006] S1. Collect real-time data on the output and load of distributed power sources in the distribution network and establish a power supply capacity model for the distribution network system.

[0007] S2. Construct a multi-level early warning indicator system based on the power supply capacity model of the power distribution network system;

[0008] S3. Conduct two-level fuzzy comprehensive evaluation of the multi-level early warning indicator system;

[0009] S4. Dynamically correct the early warning level based on the two-level fuzzy comprehensive evaluation results.

[0010] Furthermore, in step S1, the calculation formula for establishing the power supply capacity model of the distribution network system includes: S DDC (t)=S DSC (t)+S DG (t), SDG (t)=S qv (t)+S wind (t)+S out.put (t),

[0011] In the formula, S DDC (t) represents the power supply capacity of the distribution network system at time t, S DSC (t) represents the power supply capacity of a traditional distribution network system at time t, S DG (t) represents the total transferable capacity of distributed generation in the distribution network at time t, S qv (t) represents the photovoltaic power transfer capacity at time t, S wind (t) represents the wind power supply capacity at time t, S out.put (t) represents the output of DG at time t.

[0012] Further, step S2 includes: dividing the early warning indicators into a load characteristic subset, a system power supply characteristic subset, and a climate factor subset, wherein:

[0013] The aforementioned load characteristic subset includes the proportion of important loads, the proportion of relatively heavy loads, the proportion of general loads, the proportion of flexible loads, the rate of load increase, and the rate of load decrease.

[0014] The aforementioned subset of system power supply characteristics includes maximum power supply capacity, power supply margin, real-time output of distributed power sources, average load rate of main transformer, rate of increase of power supply capacity, and rate of decrease of power supply capacity.

[0015] The aforementioned subset of climate factors includes the influence coefficient of light intensity, the influence coefficient of wind speed, the main transformer failure rate, and the line failure rate.

[0016] Further, step S3 includes:

[0017] Each subset of indicators is evaluated using fuzzy criteria, and the membership degree of the indicators is calculated using the triangular membership function. A fuzzy evaluation matrix is ​​constructed based on the membership degree of the indicators, and the weight vector is determined by combining the expert method to obtain the subset evaluation results.

[0018] The expression for the above subset evaluation result is as follows: Bi = Ai ⋅ Ji (i = 1, 2, 3),

[0019] In the formula, Bi is the subset evaluation result, Ai is the weight vector determined by the expert method, and Ji is the fuzzy evaluation matrix;

[0020] Using the subset evaluation results as input, a first-level evaluation matrix is ​​constructed. The weighted average method is used to calculate the final warning level vector, and the system security status warning level is determined in combination with the warning level classification standard.

[0021] Furthermore, the aforementioned early warning level classification criteria include:

[0022] When F ≤ 1, the early warning level is safe; when 1 < F < 2, the early warning level is approaching danger; when 2 ≤ F < 3, the early warning level is risky; when F ≥ 3, the early warning level is severe risk; where F is the final early warning level vector, and its calculation formula is: F = A⋅Bi (i = 1, 2, 3),

[0023] In the formula, F is the final early warning level vector, Ai is the weight vector determined by the expert method, and Bi is the subset evaluation result.

[0024] Furthermore, step S4 includes: adjusting the early warning level in real time through the power supply capacity margin and the distributed power output, and triggering an early warning upgrade when the system approaches the preset overload value; where the calculation formula of the above power supply capacity margin includes: ,

[0025] In the formula, η DDC is the power supply capacity margin, S DDC (t) is the power supply capacity of the distribution network system at time t, and L total (t) is the load of the distribution network system at time t.

[0026] In a second aspect, the present application provides an electronic device, including:

[0027] A memory for storing one or more programs;

[0028] A processor;

[0029] When the above one or more programs are executed by the above processor, a multi-level fuzzy comprehensive evaluation method based on the distribution network security situation as described in any one of the above first aspects is implemented.

[0030] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, a multi-level fuzzy comprehensive evaluation method based on the distribution network security situation as described in any one of the above first aspects is implemented.

[0031] Compared with the prior art, the present invention has at least the following advantages or beneficial effects:

[0032] The multi-level fuzzy comprehensive evaluation method based on the distribution network security situation provided by the present invention significantly improves the power supply capacity and flexibility of the distribution network by integrating distributed energy; and conducts dynamic early warning through a multi-layer index system, significantly improving the accurate evaluation ability of the distribution network security situation, enhancing the reliability of the distribution network system operation, and providing an extensible framework for the future situation early warning of intelligent distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a flowchart illustrating the steps of a multi-level fuzzy comprehensive evaluation method for the security status of a power distribution network according to the present invention.

[0035] Figure 2 This is a schematic structural block diagram of an electronic device according to an embodiment of the present invention.

[0036] Icons: 101, memory; 102, processor; 103, communication interface. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0038] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0039] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0040] It should be noted that, in this document, the term "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0041] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the various embodiments and features described below can be combined with each other.

[0042] Example 1

[0043] Please see Figure 1 , Figure 1 The diagram shows the steps of a multi-level fuzzy comprehensive evaluation method based on the security status of a power distribution network provided in an embodiment of this application.

[0044] Firstly, this application provides a multi-level fuzzy comprehensive evaluation method based on the security status of a power distribution network, which includes the following steps:

[0045] S1. Collect real-time data on the output and load of distributed power sources in the distribution network and establish a power supply capacity model for the distribution network system.

[0046] S2. Construct a multi-level early warning indicator system based on the power supply capacity model of the power distribution network system;

[0047] S3. Conduct two-level fuzzy comprehensive evaluation of the multi-level early warning indicator system;

[0048] S4. Dynamically correct the early warning level based on the two-level fuzzy comprehensive evaluation results.

[0049] In a preferred implementation, step S1 includes the following calculation formula for establishing the power supply capacity model of the distribution network system: S DDC (t)=S DSC (t)+S DG (t), S DG (t)=S qv (t)+S wind (t)+S out.put (t),

[0050] In the formula, S DDC (t) represents the power supply capacity of the distribution network system at time t, S DSC (t) represents the power supply capacity of a traditional distribution network system at time t, S DG (t) represents the total transferable capacity of distributed generation in the distribution network at time t, S qv (t) represents the photovoltaic power transfer capacity at time t, S wind (t) represents the wind power supply capacity at time t, S out.put (t) represents the output of DG at time t.

[0051] As a preferred implementation, step S2 includes: dividing the early warning indicators into a load characteristic subset, a system power supply characteristic subset, and a climate factor subset, wherein:

[0052] The load characteristic subset includes the proportion of important loads, the proportion of heavy loads, the proportion of general loads, the proportion of flexible loads, the load rising rate, and the load falling rate;

[0053] The system power supply characteristic subset includes the maximum power supply capacity, the power supply margin, the real-time output of distributed power sources, the average load rate of main transformers, the power supply capacity rising rate, and the power supply capacity falling rate;

[0054] The climate factor subset includes the illumination intensity influence coefficient, the wind speed influence coefficient, the failure rate of main transformers, and the failure rate of lines.

[0055] As a preferred implementation, step S3 includes:

[0056] Perform fuzzy evaluation on each index subset respectively, calculate the index membership degree through the triangular membership function, construct a fuzzy evaluation matrix according to the index membership degree, and determine the weight vector in combination with the expert method to obtain the subset evaluation result;

[0057] Among them, the expression of the subset evaluation result is: Bi = Ai⋅Ji (i = 1, 2, 3),

[0058] In the formula, Bi is the subset evaluation result, Ai is the weight vector determined in combination with the expert method, and Ji is the fuzzy evaluation matrix;

[0059] Take the subset evaluation result as the input, construct a first-level evaluation matrix, calculate the final early warning level vector using the weighted average method, and determine the system security situation early warning level in combination with the early warning level division standard.

[0060] As a preferred implementation, the early warning level division standard includes:

[0061] When F ≤ 1, the early warning level is safe; when 1 < F < 2, the early warning level is approaching danger; when 2 ≤ F < 3, the early warning level is risky; when F ≥ 3, the early warning level is severe risk; where F is the final early warning level vector, and its calculation formula is: F = A⋅Bi (i = 1, 2, 3),

[0062] In the formula, F is the final early warning level vector, Ai is the weight vector determined in combination with the expert method, and Bi is the subset evaluation result.

[0063] As a preferred implementation, step S4 includes: adjusting the early warning level in real time through the power supply capacity margin and the output of distributed power sources, and triggering early warning upgrade when the system approaches the preset overload value; among them, the calculation formula of the power supply capacity margin includes: ,

[0064] In the formula, ηDDC For power supply margin, S DDC (t) represents the power supply capacity of the distribution network system at time t, L total (t) represents the load of the distribution network system at time t.

[0065] Example 2

[0066] Please see Figure 2 , Figure 2 This is a schematic structural block diagram of an electronic device provided in an embodiment of this application.

[0067] An electronic device includes a memory 101, a processor 102, and a communication interface 103. The memory 101, processor 102, and communication interface 103 are electrically connected directly or indirectly to enable data transmission or interaction. For example, these components can be electrically connected to each other via one or more communication buses or signal lines. The memory 101 can be used to store software programs and modules. The processor 102 executes the software programs and modules stored in the memory 101 to perform various functional applications and data processing. The communication interface 103 can be used for signaling or data communication with other node devices.

[0068] The memory 101 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0069] The processor 102 can be an integrated circuit chip with signal processing capabilities. The processor 102 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0070] It is understood that the structure shown in the figure is for illustrative purposes only. A multi-level fuzzy comprehensive evaluation method based on the security status of a distribution network may include more or fewer components than those shown in the figure, or have a different configuration. The components shown in the figure can be implemented using hardware, software, or a combination thereof.

[0071] In the embodiments provided in this application, it should be understood that the disclosed methods can also be implemented in other ways. The embodiments described above are merely illustrative. For example, the flowcharts or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0072] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0073] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0074] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0075] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within this application. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A multi-level fuzzy comprehensive evaluation method based on the security status of a power distribution network, characterized in that, It includes the following steps: S1. Collect the output of distributed power sources and load data of the distribution network in real time, and establish a power supply capacity model of the distribution network system; S2. Build a multi-level warning index system based on the power supply capacity model of the distribution network system; S3. Conduct a two-level fuzzy comprehensive evaluation on the multi-level warning index system; S4. Dynamically correct the warning level based on the results of the two-level fuzzy comprehensive evaluation.

2. The multi-level fuzzy comprehensive evaluation method based on the security status of a distribution network as described in claim 1, characterized in that, In step S1, the calculation formula for establishing the power supply capacity model of the distribution network system includes: S DDC (t)=S DSC (t)+S DG (t); S DG (t)=S qv (t)+S wind (t)+S out.put (t); In the formula, S DDC (t) represents the power supply capacity of the distribution network system at time t, S DSC (t) represents the power supply capacity of a traditional distribution network system at time t, S DG (t) represents the total transferable capacity of distributed generation in the distribution network at time t, S qv (t) represents the photovoltaic power transfer capacity at time t, S wind (t) represents the wind power supply capacity at time t, S out.put (t) represents the output of DG at time t.

3. The multi-level fuzzy comprehensive evaluation method based on the security status of a distribution network as described in claim 1, characterized in that, Step S2 includes: classifying the warning indicators into a load characteristic subset, a system power supply characteristic subset, and a climate factor subset, where: The load characteristic subset includes the proportion of important loads, the proportion of heavier loads, the proportion of general loads, the proportion of flexible loads, the load rising rate, and the load falling rate; The system power supply characteristic subset includes the maximum power supply capacity, the power supply margin, the real-time output of distributed power sources, the average load rate of main transformers, the power supply capacity rising rate, and the power supply capacity falling rate; The climate factor subset includes the light intensity influence coefficient, the wind speed influence coefficient, the failure rate of main transformers, and the failure rate of lines.

4. The multi-level fuzzy comprehensive evaluation method based on the security status of a distribution network as described in claim 1, characterized in that, Step S3 includes: Conduct a fuzzy evaluation on each index subset respectively, calculate the index membership degree through the triangular membership function, construct a fuzzy evaluation matrix based on the index membership degree, and determine the weight vector in combination with the expert method to obtain the subset evaluation result; Among them, the expression of the subset evaluation result is: Bi = Ai ⋅ Ji (i = 1, 2, 3), In the formula, Bi is the subset evaluation result, Ai is the weight vector determined in combination with the expert method, and Ji is the fuzzy evaluation matrix; Take the subset evaluation result as the input, construct a first-level evaluation matrix, use the weighted average method to calculate the final warning level vector, and determine the system security situation warning level in combination with the warning level division standard.

5. The multi-level fuzzy comprehensive evaluation method based on the security status of a distribution network as described in claim 4, characterized in that, The warning level division standard includes: When F ≤ 1, the warning level is safe; when 1 < F < 2, the warning level is approaching danger; when 2 ≤ F < 3, the warning level is risky; when F ≥ 3, the warning level is serious risk; where F is the final warning level vector, and its calculation formula is: F = A ⋅ Bi (i = 1, 2, 3), In the formula, F is the final warning level vector, Ai is the weight vector determined in combination with the expert method, and Bi is the subset evaluation result.

6. The multi-level fuzzy comprehensive evaluation method based on the security status of a distribution network as described in claim 1, characterized in that, Step S4 includes: adjusting the warning level in real time through the power supply capacity margin and the output of distributed power sources, and triggering a warning upgrade when the system approaches the preset overload value; among them, the calculation formula for the power supply capacity margin includes: , In the formula, η DDC For power supply margin, S DDC (t) represents the power supply capacity of the distribution network system at time t, L total (t) represents the load of the distribution network system at time t.

7. An electronic device, characterized in that, includes: A memory for storing one or more programs; A processor; 8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the one or more programs are executed by the processor, a multi-level fuzzy comprehensive evaluation method based on the security situation of the distribution network as described in any one of claims 1-6 is implemented. When the computer program is executed by the processor, a multi-level fuzzy comprehensive evaluation method based on the security situation of the distribution network as described in any one of claims 1-6 is implemented.

Images