Power distribution network self-healing capability evaluation method and device and storage medium

Abstract

The invention discloses a power distribution network self-healing capability evaluation method and device and a storage medium, and the method comprises the steps: building key indexes of the self-healing capability of a power distribution network, the key indexes comprising self-healing reliability, self-healing recovery speed, self-healing rate and self-healing benefit; defining a fuzzy set corresponding to each key index, and constructing a membership function corresponding to each fuzzy set; setting a fuzzy inference rule, and evaluating the self-healing capability of the power distribution network according to the fuzzy inference rule, the fuzzy set and the membership function to obtain membership degrees of a plurality of fuzzy intervals; and carrying out defuzzification processing on the membership degrees of the plurality of fuzzy intervals to obtain a self-healing capability evaluation result of the power distribution network. According to the method, various uncertain factors influencing the self-healing capability evaluation are comprehensively considered, so that the accuracy of the self-healing capability evaluation of the power distribution network can be effectively improved, the uncertainty in the fault recovery process of the power distribution network is effectively handled, and the overall stability and the operation efficiency of the power distribution network are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, a device and a storage medium for evaluating the self-healing capability of a distribution network. Background Art

[0002] With the rapid development of social economy and the continuous advancement of science and technology, the demand for electricity continues to grow, and users' requirements for power supply quality and reliability are also increasing. Traditional distribution networks mainly rely on manual intervention for repair when faults occur. This method not only has a long response time, but also has difficulty adapting to the complex and changing operating environment and high-reliability power supply requirements. In recent years, the rise of the concept of smart grids has brought new opportunities and challenges to the development of distribution networks. As one of the important characteristics of smart grids, the self-healing ability of distribution networks has become a hot area of ​​research. However, the evaluation of the self-healing ability of distribution networks is an extremely complex issue involving many factors and indicators.

[0003] Existing distribution network self-healing capability assessment methods are usually implemented based on deterministic models. However, the operating status of the distribution network is affected by many uncertain factors, such as the uncertainty of equipment aging, the randomness of environmental changes, the dynamics of load fluctuations, etc., resulting in low accuracy in the distribution network self-healing capability assessment. Summary of the invention

[0004] The present invention provides a distribution network self-healing capability evaluation method, device and storage medium to solve the technical problem that the existing distribution network self-healing capability evaluation method implements the distribution network self-healing capability evaluation based on a deterministic model, but does not fully consider various uncertain factors affecting the self-healing capability evaluation, resulting in low evaluation accuracy.

[0005] The present invention provides a method for evaluating the self-healing capability of a distribution network, comprising:

[0006] Establish key indicators of the self-healing capability of the distribution network, including self-healing reliability, self-healing recovery speed, self-healing rate and self-healing benefit;

[0007] Defining a fuzzy set corresponding to each of the key indicators, and constructing a membership function corresponding to each of the fuzzy sets;

[0008] Setting fuzzy inference rules, evaluating the self-healing capability of the distribution network according to the fuzzy inference rules, fuzzy sets and the membership function, and obtaining the membership of multiple fuzzy intervals; wherein the multiple fuzzy intervals include a high fuzzy interval, a medium fuzzy interval and a low fuzzy interval;

[0009] The membership degrees of the multiple fuzzy intervals are defuzzified to obtain a self-healing capacity evaluation result of the distribution network.

[0010] Furthermore, the key indicators for establishing the self-healing capability of the distribution network include:

[0011] Establishing the self-healing reliability based on the number of successful self-healing actions and the number of total self-healing actions;

[0012] Establishing the self-healing recovery speed according to the self-healing recovery time;

[0013] Establish the self-healing rate according to the self-healing strategy formulation time and self-healing remote control time;

[0014] The self-healing benefit is established based on the duration of the fault, the number of loads in the residential area, the number of loads in the commercial area, the number of loads in the industrial area, the equivalent electricity prices of the three areas, the self-healing credibility of the loads in the residential area, the load restored at time t in the residential area, the self-healing credibility of the loads in the commercial area, the load restored at time t in the commercial area, the self-healing credibility of the loads in the industrial area, and the load restored at time t in the industrial area.

[0015] Furthermore, the defining of the fuzzy sets corresponding to each of the key indicators and the construction of the membership functions corresponding to each of the fuzzy sets include:

[0016] The high fuzzy interval membership function for constructing self-healing reliability is:

[0017]

[0018] The medium fuzzy interval membership function for constructing self-healing reliability is:

[0019]

[0020] The low fuzzy interval membership function for constructing self-healing reliability is:

[0021]

[0022] In the formula, is the threshold parameter.

[0023] Furthermore, the step of defining a fuzzy set corresponding to each of the key indicators and constructing a membership function corresponding to each of the fuzzy sets further includes:

[0024] Construct the high fuzzy interval membership function of the self-healing recovery speed:

[0025]

[0026] Construct the medium fuzzy interval membership function of the self-healing recovery speed:

[0027]

[0028] Construct the low fuzzy interval membership function of the self-healing recovery speed:

[0029]

[0030] in, is the threshold parameter.

[0031] Furthermore, the step of defining a fuzzy set corresponding to each of the key indicators and constructing a membership function corresponding to each of the fuzzy sets further includes:

[0032] Construct the high fuzzy interval membership function of the self-healing rate:

[0033]

[0034] Construct the medium fuzzy interval membership function of the self-healing rate:

[0035]

[0036] Construct the low fuzzy interval membership function of the self-healing rate:

[0037]

[0038] in, parameter is the threshold parameter.

[0039] Furthermore, the step of defining a fuzzy set corresponding to each of the key indicators and constructing a membership function corresponding to each of the fuzzy sets further includes:

[0040] Construct the highly fuzzy interval membership function of the self-healing benefit:

[0041]

[0042] Construct the medium fuzzy interval membership function of the self-healing benefit:

[0043]

[0044] Construct the low fuzzy interval membership function of the self-healing benefit:

[0045]

[0046] in, d1, d2, d3, d4, d5, d6, d7 are threshold parameters.

[0047] Furthermore, the defuzzification of the membership degrees of the multiple fuzzy intervals is performed to obtain a self-healing capability evaluation result of the distribution network, including:

[0048] Set the score for each fuzzy interval;

[0049] Based on the score of each fuzzy interval, the membership of the multiple fuzzy intervals is defuzzified by using the centroid method, and the score of each fuzzy interval and the weighted average of the membership of each fuzzy interval are used as the score of each key indicator;

[0050] According to each of the key indicator scores and the corresponding adjustable weights, the self-healing capability evaluation result of the distribution network is determined.

[0051] The present invention also provides a distribution network self-healing capability evaluation device, comprising:

[0052] A key indicator establishment module is used to establish key indicators of the self-healing capability of the distribution network, wherein the key indicators include self-healing reliability, self-healing recovery speed, self-healing rate and self-healing benefit;

[0053] A membership function construction module, used to define a fuzzy set corresponding to each of the key indicators and to construct a membership function corresponding to each of the fuzzy sets;

[0054] A membership determination module is used to set fuzzy reasoning rules, evaluate the self-healing capability of the distribution network according to the fuzzy reasoning rules, fuzzy sets and the membership function, and obtain the membership of multiple fuzzy intervals; wherein the multiple fuzzy intervals include high fuzzy intervals, medium fuzzy intervals and low fuzzy intervals;

[0055] The self-healing capability evaluation module is used to perform defuzzification processing on the membership degrees of the multiple fuzzy intervals to obtain a self-healing capability evaluation result of the distribution network.

[0056] The present invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the distribution network self-healing capability evaluation method as described above is implemented.

[0057] The present invention also provides a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the distribution network self-healing capability evaluation method as described above.

[0058] The present invention constructs fuzzy sets and membership functions corresponding to each key indicator, and determines the membership of the self-healing capability of the distribution network in multiple fuzzy intervals based on the fuzzy sets, membership functions and fuzzy reasoning rules. The final self-healing capability evaluation result is obtained by defuzzifying the membership. The present invention can comprehensively consider various uncertain factors affecting the self-healing capability evaluation, thereby effectively improving the accuracy of the self-healing capability evaluation of the distribution network, effectively coping with the uncertainty in the distribution network fault recovery process, and improving the overall stability and operation efficiency of the distribution network.

[0059] Furthermore, the present invention obtains a score for each key indicator by defuzzifying the degree of membership, and determines a self-healing capacity evaluation result of the distribution network according to each key indicator score and the corresponding adjustable weight. The self-healing capacity evaluation result can be quantified by the scoring result, so that the self-healing capacity of the current distribution network can be accurately judged according to the size of the score, thereby further improving the accuracy and reliability of the self-healing capacity evaluation of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a flow chart of a method for evaluating the self-healing capability of a distribution network provided by an embodiment of the present invention;

[0061] Figure 2 is a schematic diagram of key indicator weights provided by an embodiment of the present invention;

[0062] Figure 3 It is a structural schematic diagram of a distribution network self-healing capability assessment device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0064] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0065] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0066] See also Figure 1 The present invention provides a method for evaluating the self-healing capability of a distribution network, comprising:

[0067] S1. Establish key indicators of the self-healing capability of the distribution network, including self-healing reliability, self-healing recovery speed, self-healing rate and self-healing benefit;

[0068] S2. Define the fuzzy set corresponding to each key indicator and construct the membership function corresponding to each fuzzy set;

[0069] In the embodiment of the present invention, the fuzzy set may include a high fuzzy interval fuzzy set, a medium fuzzy interval fuzzy set and a low fuzzy interval fuzzy set.

[0070] S3, setting fuzzy reasoning rules, evaluating the self-healing capability of the distribution network according to the fuzzy reasoning rules, fuzzy sets and membership functions, and obtaining the membership of multiple fuzzy intervals; wherein the multiple fuzzy intervals include a high fuzzy interval, a medium fuzzy interval and a low fuzzy interval;

[0071] In an embodiment of the present invention, line operation data and equipment information can be collected as basic data for evaluation. Based on these basic data, the self-healing capability of the distribution network is evaluated according to fuzzy inference rules, fuzzy sets and membership functions to obtain the membership degrees of multiple fuzzy intervals.

[0072] In the embodiment of the present invention, fuzzy inference rules may be set based on expert experience and historical data.

[0073] S4. Defuzzify the membership degrees of multiple fuzzy intervals to obtain the self-healing capacity evaluation result of the distribution network.

[0074] The embodiment of the present invention constructs fuzzy sets and membership functions corresponding to each key indicator, and determines the membership of the self-healing capability of the distribution network in multiple fuzzy intervals based on the fuzzy sets, membership functions and fuzzy reasoning rules. The final self-healing capability evaluation result is obtained by defuzzifying the membership. The embodiment of the present invention can comprehensively consider various uncertain factors affecting the self-healing capability evaluation, thereby effectively improving the accuracy of the self-healing capability evaluation of the distribution network, so as to effectively deal with the uncertainty in the distribution network fault recovery process and improve the overall stability and operation efficiency of the distribution network.

[0075] In one embodiment, step S1, establishing key indicators of the self-healing capability of the distribution network, includes:

[0076] S11. Establish self-healing reliability based on the number of successful self-healing actions and the number of total self-healing actions;

[0077] In the embodiment of the present invention, the self-healing reliability H st It indicates the stability and long-term effectiveness of the self-healing operation, that is, the ability of the system to operate stably after self-healing, and examines whether the self-healing operation will cause secondary failures or instability. It can be expressed as follows:

[0078]

[0079] Among them, K r Represents the number of successful self-healing actions, K a Indicates the total number of self-healing actions.

[0080] S12. Establishing a self-healing recovery speed according to the self-healing recovery time;

[0081] In the embodiment of the present invention, the self-healing recovery speed H sh Represented as the self-healing recovery time T sh The reciprocal of can be expressed as follows:

[0082]

[0083] Among them, the time T can be set according to the self-healing strategy st And self-healing remote control time T c Determine the self-healing recovery time, the expression is as follows:

[0084] T sh =T st +T c .

[0086] S13, establish the self-healing rate according to the self-healing strategy formulation time and self-healing remote control time;

[0087] In the embodiment of the present invention, the self-healing rate H sr Indicates the number of times the system successfully performs self-recovery operations when a failure occurs. r Total number of failures N a The ratio reflects the success rate of the self-healing system. A high self-healing rate means that the system has a high self-healing success rate when dealing with faults. The expression of the self-healing rate is as follows:

[0088]

[0089] S14. Establish the self-healing benefit based on the duration of the fault, the number of loads in the residential area, the number of loads in the commercial area, the number of loads in the industrial area, the equivalent electricity prices of the three areas, the self-healing credibility of the loads in the residential area, the loads restored at time t in the residential area, the self-healing credibility of the loads in the commercial area, the loads restored at time t in the commercial area, the self-healing credibility of the loads in the industrial area and the loads restored at time t in the industrial area.

[0090] In the embodiment of the present invention, the self-healing benefit H sb The socioeconomic losses reduced by the self-healing operation can be expressed as the loss S of the residential area. r , the loss of commercial areas is reduced c and the loss of industrial areas S i The sum of is shown below.

[0091]

[0092] Among them, H sb For self-healing benefits, T c Indicates the duration of the fault. r represents the number of loads in residential areas, M c Indicates the number of loads in the commercial area, M i represents the number of loads in the industrial area, θ r ,θ c and θ g represents the equivalent electricity price of the three regions, N be,ki is the self-healing credibility of residential area load, P rc is the load restored in the residential area at time t, N bc.ki is the self-healing reliability of the commercial area load, P ca is the load restored in the commercial area at time t, N be.ki is the self-healing reliability of the industrial load, P gi is the load restored in the industrial zone at time t.

[0093] The embodiments of the present invention can cover factors such as the scope, speed, operational difficulty, and duration of load recovery after a distribution network failure through key indicators such as self-healing reliability, self-healing recovery speed, self-healing rate, and self-healing benefit, as well as the effect of the self-healing process and the uncertainty of power grid components, thereby effectively improving the accuracy and reliability of the distribution network self-healing capability assessment.

[0094] In one embodiment, a fuzzy set corresponding to each key indicator is defined, and a membership function corresponding to each fuzzy set is constructed, including:

[0095] The high fuzzy interval membership function for constructing self-healing reliability is:

[0096]

[0097] The medium fuzzy interval membership function for constructing self-healing reliability is:

[0098]

[0099] The low fuzzy interval membership function for constructing self-healing reliability is:

[0100]

[0101] In the formula, is the threshold parameter.

[0102] In an embodiment of the present invention, It can be determined and adjusted according to actual needs. The medium fuzzy interval and the low fuzzy interval can be partially crossed within a certain range. Through different parameter intervals and function value changes, the fuzziness of self-healing reliability at different degrees can be accurately described, thereby effectively improving the accuracy and reliability of the distribution network self-healing capacity evaluation.

[0103] In one embodiment, defining a fuzzy set corresponding to each key indicator and constructing a membership function corresponding to each fuzzy set also includes:

[0104] Construct a highly fuzzy interval membership function for the self-healing recovery speed:

[0105]

[0106] Construct the medium fuzzy interval membership function of self-healing recovery speed:

[0107]

[0108] Construct a low fuzzy interval membership function for the self-healing recovery speed:

[0109]

[0110] in, is the threshold parameter.

[0111] In the embodiment of the present invention, the The value of .

[0112] In one embodiment, defining a fuzzy set corresponding to each key indicator and constructing a membership function corresponding to each fuzzy set also includes:

[0113] Construct a high fuzzy interval membership function for the self-healing rate:

[0114]

[0115] Construct the medium fuzzy interval membership function of the self-healing rate:

[0116]

[0117] Construct the low fuzzy interval membership function of the self-healing rate:

[0118]

[0119] in, parameter is the threshold parameter.

[0120] In the embodiment of the present invention, the The value of .

[0121] In one embodiment, defining a fuzzy set corresponding to each key indicator and constructing a membership function corresponding to each fuzzy set also includes:

[0122] Constructing a highly fuzzy interval membership function for self-healing benefits:

[0123]

[0124] Constructing the medium fuzzy interval membership function of self-healing benefit:

[0125]

[0126] Constructing low fuzzy interval membership function of self-healing benefit:

[0127]

[0128] in, d1, d2, d3, d4, d5, d6, d7 are threshold parameters.

[0129] In the embodiment of the present invention, the values ​​of d1, d2, d3, d4, d5, d6, and d7 can be determined and adjusted according to actual needs.

[0130] In one embodiment, step S4, defuzzifying the membership of multiple fuzzy intervals to obtain a self-healing capability evaluation result of the distribution network, includes:

[0131] S41, setting the score of each fuzzy interval;

[0132] In the embodiment of the present invention, the score of each fuzzy interval can be set as:

[0133]

[0134] Among them, v Hi is the score of the high fuzzy interval, v Mi is the score of the medium fuzzy interval, v Li is the score of the low fuzzy interval, and v1, v2, and v3 are the scores that can be set for each fuzzy interval.

[0135] In the embodiment of the present invention, the set of key indicators can be expressed as H si ={H st ,H sh ,H sr ,H sb}.

[0137] S42, based on the score of each fuzzy interval, the membership of multiple fuzzy intervals is defuzzified using the centroid method, and the score of each fuzzy interval and the weighted average of the membership of each fuzzy interval are used as the score of each key indicator;

[0138] In the embodiment of the present invention, the calculation formula for each key indicator score is:

[0139]

[0140] Among them, u Hi 、u Mi 、u Li are the membership degrees of high fuzzy interval, medium fuzzy interval and low fuzzy interval respectively.

[0141] S43. Determine the self-healing capability evaluation result of the distribution network according to each key indicator score and the corresponding adjustable weight.

[0142] In the embodiment of the present invention, the self-healing capability evaluation result of the distribution network can be quantified by a scoring method to obtain the self-healing capability evaluation result in a scoring form, as shown in the following formula:

[0143] M H =α1M(H st )+α2M(H sh )+α3M(H sr )+α3M(H sb )

[0144] Among them, M H is the self-healing ability scoring result, α1, α2, α3 and α4 are the adjustable weights of self-healing reliability, self-healing recovery speed, self-healing rate and self-healing benefit respectively. In the embodiment of the present invention, the higher the self-healing ability score, the stronger the self-healing ability.

[0145] See also Figure 2 , which is a schematic diagram of key indicator weights provided in an embodiment of the present invention.

[0146] In an embodiment of the present invention, the score of each key indicator is obtained by defuzzifying the membership degree, and the self-healing capacity evaluation result of the distribution network is determined according to each key indicator score and the corresponding adjustable weight. The self-healing capacity evaluation result can be quantified by the scoring result, so that the self-healing capacity of the current distribution network can be accurately judged according to the size of the score, thereby further improving the accuracy and reliability of the self-healing capacity evaluation of the distribution network.

[0147] The implementation of the embodiments of the present invention has the following beneficial effects:

[0148] The embodiment of the present invention constructs fuzzy sets and membership functions corresponding to each key indicator, and determines the membership of the self-healing capability of the distribution network in multiple fuzzy intervals based on the fuzzy sets, membership functions and fuzzy reasoning rules. The final self-healing capability evaluation result is obtained by defuzzifying the membership. The embodiment of the present invention can comprehensively consider various uncertain factors affecting the self-healing capability evaluation, thereby effectively improving the accuracy of the self-healing capability evaluation of the distribution network, so as to effectively deal with the uncertainty in the distribution network fault recovery process and improve the overall stability and operation efficiency of the distribution network.

[0149] Furthermore, the embodiment of the present invention obtains a score for each key indicator by defuzzifying the degree of membership, and determines a self-healing capability evaluation result of the distribution network according to each key indicator score and the corresponding adjustable weight. The self-healing capability evaluation result can be quantified by the scoring result, so that the self-healing capability of the current distribution network can be accurately judged according to the size of the score, thereby further improving the accuracy and reliability of the self-healing capability evaluation of the distribution network.

[0150] See also Figure 3 Based on the same inventive concept as the above embodiment, the present invention further provides a distribution network self-healing capability evaluation device, comprising:

[0151] A key indicator establishment module 10 is used to establish key indicators of the self-healing capability of the distribution network, the key indicators including self-healing reliability, self-healing recovery speed, self-healing rate and self-healing benefit;

[0152] A membership function construction module 20 is used to define the fuzzy set corresponding to each key indicator and construct the membership function corresponding to each fuzzy set;

[0153] The membership determination module 30 is used to set fuzzy reasoning rules, evaluate the self-healing capability of the distribution network according to the fuzzy reasoning rules, fuzzy sets and membership functions, and obtain the membership of multiple fuzzy intervals; wherein the multiple fuzzy intervals include a high fuzzy interval, a medium fuzzy interval and a low fuzzy interval;

[0154] The self-healing capability evaluation module 40 is used to perform defuzzification processing on the membership degrees of multiple fuzzy intervals to obtain a self-healing capability evaluation result of the distribution network.

[0155] In one embodiment, the key indicator establishment module 10 is further used to:

[0156] Establish self-healing reliability based on the number of successful self-healing actions and the number of total self-healing actions;

[0157] Establish self-healing recovery speed based on self-healing recovery time;

[0158] Establish the self-healing rate according to the self-healing strategy formulation time and self-healing remote control time;

[0159] The self-healing benefit is established based on the duration of the fault, the number of loads in the residential area, the number of loads in the commercial area, the number of loads in the industrial area, the equivalent electricity prices of the three areas, the self-healing credibility of the loads in the residential area, the load restored at time t in the residential area, the self-healing credibility of the loads in the commercial area, the load restored at time t in the commercial area, the self-healing credibility of the loads in the industrial area, and the load restored at time t in the industrial area.

[0160] In one embodiment, the membership function building module 20 is further used for:

[0161] The high fuzzy interval membership function for constructing self-healing reliability is:

[0162]

[0163] The medium fuzzy interval membership function for constructing self-healing reliability is:

[0164]

[0165] The low fuzzy interval membership function for constructing self-healing reliability is:

[0166]

[0167] In the formula, In one embodiment, the membership function construction module 20 is further used to: construct a high fuzzy interval membership function of the self-healing recovery speed:

[0168]

[0169] Construct the medium fuzzy interval membership function of self-healing recovery speed:

[0170]

[0171] Construct a low fuzzy interval membership function for the self-healing recovery speed:

[0172]

[0173] in, In one embodiment, the membership function construction module 20 is further used to: construct a high fuzzy interval membership function of the self-healing rate:

[0174]

[0175] Construct the medium fuzzy interval membership function of the self-healing rate:

[0176]

[0177] Construct the low fuzzy interval membership function of the self-healing rate:

[0178]

[0179] in, parameter is a threshold parameter. In one embodiment, the membership function building module 20 is also used for:

[0180] Constructing a highly fuzzy interval membership function for self-healing benefits:

[0181]

[0182] Constructing the medium fuzzy interval membership function of self-healing benefit:

[0183]

[0184] Constructing low fuzzy interval membership function of self-healing benefit:

[0185]

[0186] in, d1, d2, d3, d4, d5, d6, d7 are threshold parameters.

[0187] In one embodiment, the self-healing capability assessment module 40 is further configured to:

[0188] Set the score for each fuzzy interval;

[0189] Based on the score of each fuzzy interval, the membership of multiple fuzzy intervals is defuzzified using the centroid method, and the score of each fuzzy interval and the weighted average of the membership of each fuzzy interval are used as the score of each key indicator;

[0190] The self-healing capacity evaluation result of the distribution network is determined based on the score of each key indicator and the corresponding adjustable weight.

[0191] Accordingly, an embodiment of the present invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the distribution network self-healing capability evaluation method of any one of the above embodiments is implemented.

[0192] The terminal device of this embodiment includes: a processor, a memory, and a computer program and computer instructions stored in the memory and executable on the processor. When the processor executes the computer program, each step in the above embodiment 1 is implemented, for example Figure 1 Alternatively, when the processor executes the computer program, the functions of each module / unit in the above device embodiment, such as the membership determination module 30, are implemented.

[0193] Exemplarily, the computer program can be divided into one or more modules / units, one or more modules / units are stored in a memory and executed by a processor to complete the present invention. One or more modules / units can be a series of computer program instruction segments that can perform specific functions, and the instruction segments are used to describe the execution process of the computer program in a terminal device. For example, a membership determination module is used to set fuzzy reasoning rules, evaluate the self-healing capability of the distribution network according to the fuzzy reasoning rules, fuzzy sets and membership functions, and obtain the membership of multiple fuzzy intervals; wherein the multiple fuzzy intervals include high fuzzy intervals, medium fuzzy intervals and low fuzzy intervals.

[0194] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. The terminal device may include more or fewer components than shown in the diagram, or may combine certain components, or different components. For example, the terminal device may also include an input / output device, a network access device, a bus, etc.

[0195] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0196] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile terminal, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Med ia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (F l ash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0197] Among them, if the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electric carrier signals and telecommunication signals.

[0198] Accordingly, an embodiment of the present invention further provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the distribution network self-healing capability evaluation method as described in any one of the above embodiments.

[0199] The above specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for evaluating the self-healing capability of a distribution network, characterized in that: include: Establish key indicators of the self-healing capability of the distribution network, including self-healing reliability, self-healing recovery speed, self-healing rate and self-healing benefit; Defining a fuzzy set corresponding to each of the key indicators, and constructing a membership function corresponding to each of the fuzzy sets; Setting fuzzy inference rules, evaluating the self-healing capability of the distribution network according to the fuzzy inference rules, fuzzy sets and the membership function, and obtaining the membership of multiple fuzzy intervals; wherein the multiple fuzzy intervals include a high fuzzy interval, a medium fuzzy interval and a low fuzzy interval; The membership degrees of the multiple fuzzy intervals are defuzzified to obtain a self-healing capacity evaluation result of the distribution network.

2. The method for evaluating the self-healing capability of a distribution network according to claim 1, characterized in that: The key indicators for establishing the self-healing capability of the distribution network include: Establishing the self-healing reliability based on the number of successful self-healing actions and the number of total self-healing actions; Establishing the self-healing recovery speed according to the self-healing recovery time; Establish the self-healing rate according to the self-healing strategy formulation time and self-healing remote control time; The self-healing benefit is established based on the duration of the fault, the number of loads in the residential area, the number of loads in the commercial area, the number of loads in the industrial area, the equivalent electricity prices of the three areas, the self-healing credibility of the loads in the residential area, the load restored at time t in the residential area, the self-healing credibility of the loads in the commercial area, the load restored at time t in the commercial area, the self-healing credibility of the loads in the industrial area, and the load restored at time t in the industrial area.

3. The method for evaluating the self-healing capability of a distribution network according to claim 1, characterized in that: The step of defining a fuzzy set corresponding to each of the key indicators and constructing a membership function corresponding to each of the fuzzy sets includes: The high fuzzy interval membership function for constructing self-healing reliability is: The medium fuzzy interval membership function for constructing self-healing reliability is: The low fuzzy interval membership function for constructing self-healing reliability is: In the formula, is the threshold parameter.

4. The method for evaluating the self-healing capability of a distribution network according to claim 1, characterized in that: The step of defining a fuzzy set corresponding to each of the key indicators and constructing a membership function corresponding to each of the fuzzy sets further includes: Construct the high fuzzy interval membership function of the self-healing recovery speed: Construct the medium fuzzy interval membership function of the self-healing recovery speed: Construct the low fuzzy interval membership function of the self-healing recovery speed: in, is the threshold parameter.

5. The method for evaluating the self-healing capability of a distribution network according to claim 1, characterized in that: The step of defining a fuzzy set corresponding to each of the key indicators and constructing a membership function corresponding to each of the fuzzy sets further includes: Construct the high fuzzy interval membership function of the self-healing rate: Construct the medium fuzzy interval membership function of the self-healing rate: Construct the low fuzzy interval membership function of the self-healing rate: in, parameter is the threshold parameter.

6. The method for evaluating the self-healing capability of a distribution network according to claim 1, characterized in that: The step of defining a fuzzy set corresponding to each of the key indicators and constructing a membership function corresponding to each of the fuzzy sets further includes: Construct the highly fuzzy interval membership function of the self-healing benefit: Construct the medium fuzzy interval membership function of the self-healing benefit: Construct the low fuzzy interval membership function of the self-healing benefit: in, d1, d2, d3, d4, d5, d6, d7 are threshold parameters.

7. The method for evaluating the self-healing capability of a distribution network according to claim 1, characterized in that: The defuzzification process is performed on the membership degrees of the multiple fuzzy intervals to obtain the self-healing capability evaluation result of the distribution network, including: Set the score for each fuzzy interval; Based on the score of each fuzzy interval, the membership of the multiple fuzzy intervals is defuzzified by using the centroid method, and the score of each fuzzy interval and the weighted average of the membership of each fuzzy interval are used as the score of each key indicator; According to each of the key indicator scores and the corresponding adjustable weights, the self-healing capability evaluation result of the distribution network is determined.

8. A distribution network self-healing capability assessment device, characterized in that: include: A key indicator establishment module is used to establish key indicators of the self-healing capability of the distribution network, wherein the key indicators include self-healing reliability, self-healing recovery speed, self-healing rate and self-healing benefit; A membership function construction module, used to define a fuzzy set corresponding to each of the key indicators and to construct a membership function corresponding to each of the fuzzy sets; A membership determination module is used to set fuzzy reasoning rules, evaluate the self-healing capability of the distribution network according to the fuzzy reasoning rules, fuzzy sets and the membership function, and obtain the membership of multiple fuzzy intervals; wherein the multiple fuzzy intervals include high fuzzy intervals, medium fuzzy intervals and low fuzzy intervals; The self-healing capability evaluation module is used to perform defuzzification processing on the membership degrees of the multiple fuzzy intervals to obtain a self-healing capability evaluation result of the distribution network.

9. A terminal device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for evaluating the self-healing capability of a distribution network as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program; wherein, when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the distribution network self-healing capability evaluation method according to any one of claims 1 to 7.

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