A multi-scenario integrated power distribution equipment testing method and device

Abstract

The present invention discloses a multi-scenario fusion test method and device for power distribution equipment, and relates to the technical field of equipment testing. The method comprises: determining a burst scenario sample of the power distribution equipment; setting scenario parameters according to the burst scenario sample, and outputting the test parameters of each burst scenario sample; performing scenario fusion on the burst scenario sample, and outputting a binary scenario group, a ternary scenario group, and a multi-scenario group; testing the power distribution equipment based on the test parameter groups corresponding to the binary scenario group, the ternary scenario group, and the multi-scenario group, and obtaining a scenario group test data set; performing performance evaluation according to the scenario group test data set, and outputting equipment performance indicators. The technical problem of insufficient performance evaluation of power distribution equipment in complex and changeable scenarios in the prior art is solved, and the performance of the power distribution equipment is comprehensively evaluated through a multi-scenario fusion test method, achieving the technical effect of improving the comprehensiveness and accuracy of equipment testing.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment testing, and in particular to a multi-scenario integrated power distribution equipment testing method and device. Background Art

[0002] With the continuous growth of global energy demand and the rapid development of power technology, the complexity and scale of power systems are constantly expanding, which puts forward higher requirements for the safe and stable operation of distribution equipment. As a key link between the power grid and users, the stability and reliability of the performance of distribution equipment are directly related to the continuity and safety of power supply. However, in practical applications, distribution equipment often faces various emergency scenarios, such as short circuit faults, equipment overload, extreme temperature conditions, and power supply terminal failures. These emergencies pose severe challenges to the performance of distribution equipment. Traditional distribution equipment testing methods are often limited to the simulation and evaluation of a single or a few scenarios, and cannot fully reflect the comprehensive performance of the equipment in a complex and changing environment. The limitation of this test method is that it ignores the possible interactions and influences between different emergency scenarios, as well as the potential impact of these interactions on equipment performance. Therefore, it is difficult for traditional test methods to accurately evaluate the stability and reliability of distribution equipment in a real operating environment. Summary of the invention

[0003] The present application provides a multi-scenario integrated power distribution equipment testing method and device, which solves the technical problem of insufficient performance evaluation of power distribution equipment in complex and changeable scenarios in the prior art.

[0004] In view of the above problems, the present application provides a multi-scenario integrated power distribution equipment testing method and device.

[0005] In a first aspect of the present application, a multi-scenario integrated power distribution equipment testing method is provided, the method comprising:

[0006] Determine burst scenario samples of power distribution equipment, wherein the burst scenario samples include at least short circuit fault, equipment overload, temperature anomaly and power supply terminal fault; set scenario parameters according to the burst scenario samples, and output test parameters of each burst scenario sample, wherein the test parameters include test duration, test impact range and test frequency; perform scenario fusion on the burst scenario samples, and output binary scenario groups, ternary scenario groups and multivariate scenario groups; test the power distribution equipment based on the test parameter groups corresponding to the binary scenario groups, the ternary scenario groups and the multivariate scenario groups, and obtain a scenario group test data set, wherein the test items include signal stability, fault response time, overload handling capability and recovery performance; perform performance evaluation according to the scenario group test data set, and output equipment performance indicators.

[0007] A second aspect of the present application provides a multi-scenario integrated power distribution equipment testing device, the device comprising:

[0008] A sample determination module, the sample determination module is used to determine the sudden scenario samples of the distribution equipment, wherein the sudden scenario samples include at least short circuit fault, equipment overload, temperature anomaly and power supply terminal fault; a parameter setting module, the parameter setting module is used to set the scenario parameters according to the sudden scenario samples, and output the test parameters of each sudden scenario sample, wherein the test parameters include test duration, test impact range and test frequency; a scenario fusion module, the scenario fusion module is used to perform scenario fusion on the sudden scenario samples, and output binary scenario groups, ternary scenario groups and multivariate scenario groups; a testing module, the testing module is used to test the distribution equipment based on the test parameter groups corresponding to the binary scenario group, the ternary scenario group and the multivariate scenario group, respectively, to obtain a scenario group test data set, wherein the test items include signal stability, fault response time, overload handling capability and recovery performance; a performance evaluation module, the performance evaluation module is used to perform performance evaluation according to the scenario group test data set, and output equipment performance indicators.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] First, the burst scenario samples of the power distribution equipment are determined, wherein the burst scenario samples include at least short circuit fault, equipment overload, temperature anomaly and power supply terminal fault. Then, the scenario parameters are set according to the burst scenario samples, and the test parameters of each burst scenario sample are output, wherein the test parameters include test duration, test impact range and test frequency. Further, the burst scenario samples are subjected to scenario fusion, and binary scenario groups, ternary scenario groups and multivariate scenario groups are output. Then, the power distribution equipment is tested based on the test parameter groups corresponding to the binary scenario groups, ternary scenario groups and multivariate scenario groups, respectively, to obtain the scenario group test data set, wherein the test items include signal stability, fault response time, overload handling capability and recovery performance; finally, the performance is evaluated according to the scenario group test data set, and the equipment performance indicators are output. The technical problem of insufficient performance evaluation of the power distribution equipment in complex and changeable scenarios in the prior art is solved, and the performance of the power distribution equipment is comprehensively evaluated through the multi-scenario fusion test method, so as to achieve the technical effect of improving the comprehensiveness and accuracy of equipment testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A schematic diagram of a flow chart of a multi-scenario integrated power distribution equipment testing method provided in an embodiment of the present application;

[0013] Figure 2 A schematic diagram of the structure of a multi-scenario integrated power distribution equipment testing device provided in an embodiment of the present application.

[0014] Explanation of the reference numerals: sample determination module 11 , parameter setting module 12 , scene fusion module 13 , testing module 14 , performance evaluation module 15 . DETAILED DESCRIPTION

[0015] The present application solves the technical problem of insufficient performance evaluation of power distribution equipment in complex and changeable scenarios in the prior art by providing a multi-scenario integrated power distribution equipment testing method and device.

[0016] 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, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0017] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides a multi-scenario integrated power distribution equipment testing method, wherein the method includes:

[0019] Determine emergency scenario samples of the power distribution equipment, wherein the emergency scenario samples at least include short circuit fault, equipment overload, temperature abnormality and power supply terminal fault.

[0020] Based on the historical operating data of the distribution equipment, we can determine the emergency scenario samples that the distribution equipment may encounter in actual operation. Among them, the emergency scenario samples include at least short circuit failure, equipment overload, temperature abnormality and power supply terminal failure. Each emergency scenario represents a specific challenge that the equipment may face and has different requirements for the stability and reliability of the equipment.

[0021] Scenario parameters are set according to the burst scenario samples, and test parameters of each burst scenario sample are output, wherein the test parameters include test duration, test impact range and test frequency.

[0022] For each emergency scenario sample, detailed scenario parameter settings are carried out, including determining the test duration (i.e. how long each scenario needs to last to observe the device response), the test impact range (assessing which parts or functions of the device are affected by the scenario) and the test frequency (simulating the frequency of scenario occurrence to examine the device's continuous response capabilities).

[0023] The burst scene samples are subjected to scene fusion, and binary scene groups, ternary scene groups and multi-element scene groups are output.

[0024] By combining the burst scenario samples, more complex test scenario groups are formed, including binary scenario groups (i.e., a combination of two scenarios occurring simultaneously), ternary scenario groups (a combination of three scenarios), and multivariate scenario groups (a combination of multiple scenarios). By combining multiple burst scenario samples, scenario groups of different complexity are formed to evaluate the performance and adaptability of the power distribution equipment.

[0025] Furthermore, after performing scene fusion on the burst scene samples and outputting binary scene groups, ternary scene groups and multi-element scene groups, the method includes:

[0026] Identify the burst frequency of each scene sample in the burst scene sample; calculate the occurrence probability of each scene sample according to the burst frequency of each scene sample, and output the occurrence probability set corresponding to the burst scene sample; perform scene fusion on the burst scene samples according to the occurrence probability set, and output binary scene groups, ternary scene groups and multivariate scene groups.

[0027] By collecting and analyzing historical data, fault records, etc., the burst frequency of each emergency scenario sample (such as short circuit fault, equipment overload, temperature anomaly, power supply terminal fault, etc.) in actual operation can be identified. The burst frequency can be expressed as the number of times the scenario occurs in a unit time (such as year, month, day). Based on the burst frequency of each scenario sample, its occurrence probability can be further calculated, and the occurrence probability set corresponding to the burst scenario sample can be output; where the occurrence probability is the possibility of the scenario occurring in a specific time period. When calculating the occurrence probability, the historical data of the distribution equipment can be used and normalized to ensure that the sum of the occurrence probabilities of all scenarios is 1. The burst scenario samples are subjected to scene fusion according to the occurrence probability set, and binary scenario groups, ternary scenario groups, and multivariate scenario groups are output. For example, scenes with higher occurrence probabilities can be preferentially combined to form binary scenario groups, ternary scenario groups, and the like.

[0028] Furthermore, the method of performing scene fusion on the burst scene samples according to the occurrence probability set includes:

[0029] Perform joint probability analysis on binary scene groups, ternary scene groups and multivariate scene groups according to the occurrence probability set to obtain the binary joint probability of each scene group in the binary scene group, the ternary joint probability of each scene group in the ternary scene group, and the multivariate joint probability of each scene group in the multivariate scene group; judge between the binary joint probability, ternary joint probability and multivariate joint probability and the preset occurrence probability, and screen out binary scene groups, ternary scene groups and multivariate scene groups with occurrence probabilities greater than the preset probability.

[0030] Specifically, joint probability analysis is performed on binary scenario groups, ternary scenario groups and multivariate scenario groups according to the occurrence probability set, and the binary joint probability of each scenario group in the binary scenario group, the ternary joint probability of each scenario group in the ternary scenario group, and the multivariate joint probability of each scenario group in the multivariate scenario group are obtained through calculation; the calculated binary joint probability, ternary joint probability and multivariate joint probability are compared with the preset occurrence probability, wherein the preset occurrence probability is a threshold used to screen out scenario groups that are more likely to occur simultaneously in actual operation; after comparison, binary scenario groups whose binary joint probability is greater than the preset occurrence probability, ternary scenario groups whose ternary joint probability is greater than the preset occurrence probability, and multivariate scenario groups whose multivariate joint probability is greater than the preset occurrence probability are screened out respectively.

[0031] Furthermore, the method further comprises:

[0032] A conflict detection mechanism is established, and conflicts between scenes in each group of the output binary scene groups, ternary scene groups and multi-scene groups are monitored according to the conflict detection mechanism, and conflict indicators of each scene group in the binary scene groups, conflict indicators of each scene group in the ternary scene groups and conflict indicators of each scene group in the multi-scene groups are obtained; and binary scene groups, ternary scene groups and multi-scene groups whose conflict indicators are less than preset ones are screened.

[0033] By establishing a conflict detection mechanism, the conflict of scenarios in each scenario group can be monitored, so as to exclude scenario groups containing conflicting scenarios. In the emergency scenario of power distribution equipment, conflict may refer to the physical, logical or operational interference or contradiction between different scenarios. For example, short circuit fault and power supply terminal fault may cause equipment shutdown at the same time, but they are not directly conflicting physically; while equipment overload and temperature abnormality may aggravate each other due to the heat caused by overload, forming a logical conflict. The conflict detection mechanism can be established based on the conflict matrix, which can be constructed based on expert knowledge, historical data, equipment specifications or simulation results. The conflict matrix is ​​a two-dimensional array used to represent the degree of conflict between different scenarios; in this matrix, each row and each column represents a scenario, and each element in the matrix represents the conflict value between the two scenarios represented by the corresponding row and column. According to the conflict detection mechanism, the conflicts between the scenes in each group of the output binary scene group, ternary scene group and multi-scene group are monitored. Specifically, for each scene group, the conflict index calculation formula can be used to calculate its overall conflict index, and the conflict index of each scene group in the binary scene group, the conflict index of each scene group in the ternary scene group, and the conflict index of each scene group in the multi-scene group are obtained. Among them, the conflict index calculation formula , Represents a scene group, represents the conflict value between scene i and scene j, Indicates the number of scenarios in the scenario group; based on the calculated conflict index and the preset conflict index threshold, binary scenario groups, ternary scenario groups and multi-scenario groups with a conflict index less than the preset conflict index are screened out. These scenario groups represent emergencies that occur simultaneously in actual operation and have little mutual interference.

[0034] Furthermore, a joint probability analysis is performed on the binary scenario group, the ternary scenario group and the multivariate scenario group according to the occurrence probability set, and the method includes:

[0035] Obtain the correlation between the scenes in each group of the binary scene group, the ternary scene group and the multivariate scene group; if the correlation is returned as 0, the corresponding scene group will be independently identified; if the correlation is returned as 1, the corresponding scene group will be associated and identified; output independent-binary scene group, independent-ternary scene group, independent-multivariate scene group, associated-binary scene group, associated-ternary scene group, and associated-multivariate scene group; perform probability analysis on the independent-binary scene group, independent-ternary scene group, and independent-multivariate scene group according to independent probability, perform probability analysis on the associated-binary scene group, associated-ternary scene group, and associated-multivariate scene group according to conditional probability, and output the binary joint probability, ternary joint probability, and multivariate joint probability.

[0036] Specifically, the correlation between two or more scenes is determined by using similarity metrics (such as cosine similarity, Pearson correlation coefficient, etc.); for each binary, ternary and multivariate scene group, the correlation between all scene pairs within it is calculated, and the correlation can be 0 (indicating complete irrelevance) or 1 (indicating correlation); if the correlation between all scenes in a scene group is 0, the scene group is identified as an independent scene group, including an independent-binary scene group, an independent-ternary scene group, and an independent-multivariate scene group; if the correlation between at least one scene pair is 1 (that is, there is a correlation), the scene group is identified as an associated scene group, including an associated-binary scene group, an associated-ternary scene group, and an associated-multivariate scene group. Use independent probability (that is, the product of the probabilities of occurrence of each scene event) to analyze independent-binary scene groups, independent-ternary scene groups, and independent-multivariate scene groups. For example, for independent-binary scene groups , and its joint probability is ; According to the conditional probability, the probability analysis is performed on the association-binary scenario group, the association-ternary scenario group, and the association-multivariate scenario group, and the binary joint probability, the ternary joint probability, and the multivariate joint probability are output.

[0037] Furthermore, the expression for probability analysis of the association-binary scene group according to conditional probability is: ; The expression for probability analysis of the association-ternary scene group according to conditional probability is: ; The expression for probability analysis of the association-multivariate scenario group according to conditional probability includes: ;

[0038] in, is the probability of scenario A occurring, is the probability of scenario B occurring under the condition that scenario A occurs, is the probability of scenario C occurring when both scenario A and scenario B occur, is the probability of scene N occurring under the condition that scenes A, B, and M occur.

[0039] Specifically, the expression for probability analysis of the association-binary scenario group according to conditional probability is: ; The expression for probability analysis of the association-ternary scene group according to conditional probability is: ; The expression for probability analysis of the association-multivariate scenario group according to conditional probability includes: ;in, is the probability of scenario A occurring, is the probability of scenario B occurring under the condition that scenario A occurs, is the probability of scenario C occurring when both scenario A and scenario B occur, is the probability of scene N occurring under the condition that scenes A, B, and M occur.

[0040] The power distribution equipment is tested based on the test parameter groups corresponding to the binary scenario group, the ternary scenario group and the multivariate scenario group respectively to obtain a scenario group test data set, wherein the test items include signal stability, fault response time, overload handling capability and recovery performance.

[0041] Based on scenario groups of different sizes (binary, ternary and multivariate scenario groups), the corresponding test parameter groups are used to test the power distribution equipment. The test items include signal stability, fault response time, overload handling capability and recovery performance. Signal stability is used to evaluate the signal transmission quality of the equipment in various scenarios, including signal distortion, noise level, signal strength, etc. Fault response time refers to the response speed of the simulated equipment when encountering a fault (such as short circuit, overload, undervoltage, etc.); overload handling capability refers to the performance of the test equipment when it exceeds its rated load capacity, including evaluating whether the overload protection mechanism of the equipment is effective, whether it can run stably for a period of time under overload conditions, and its recovery capability after overload, etc. Recovery performance is used to evaluate the recovery speed and degree of the equipment after the fault or abnormal situation is eliminated. For each scenario group, a series of test data will be generated by executing the above test items, which constitute the scenario group test data set.

[0042] A performance evaluation is performed based on the scenario group test data set, and device performance indicators are output.

[0043] Based on the collected scenario group test data set, the performance of the power distribution equipment is comprehensively evaluated, covering aspects such as equipment stability, response speed, processing capability, and recovery capability. Through data analysis, the equipment performance indicators in different scenarios are obtained and output in the form of reports or charts.

[0044] Furthermore, the method further comprises:

[0045] Detect the real-time connection relationship of the power distribution equipment; wherein the real-time connection relationship includes the internal connection relationship inside the power distribution equipment and the external connection relationship outside the power distribution equipment; when it is detected that the real-time connection relationship of the power distribution equipment has changed, locate the changed connection relationship, and update the emergency scenario sample of the power distribution equipment according to the changed connection relationship.

[0046] Preferably, the real-time connection relationship of the power distribution equipment is detected, including the internal connection relationship inside the power distribution equipment and the external connection relationship outside the power distribution equipment; specifically, sensors such as current sensors, voltage sensors, temperature sensors, etc. are installed inside the power distribution equipment, and the connection status between the components (i.e., the internal connection relationship) is identified by analyzing the data collected by the sensors, such as the closed state of the switch, the on-off state of the line, etc.; the communication interface (such as Ethernet interface, serial port, etc.) on the power distribution equipment is used to communicate with the external power grid or other equipment to obtain real-time connection status information (i.e., the external connection relationship); when it is detected that the real-time connection relationship of the power distribution equipment has changed, that is, when the real-time connection relationship does not match the preset benchmark connection relationship, the current real-time connection relationship is compared and analyzed with the connection relationship recorded the most recently, and the changed connection relationship is found; according to the detected changed connection relationship, its impact on the stable operation of the equipment is evaluated, and the possible emergency scenarios are predicted, and the corresponding scenario samples are added or updated in the emergency scenario sample library. Through the above steps, the effective monitoring of the real-time connection relationship of the power distribution equipment and the timely positioning of the changed connection relationship can be achieved, and then the emergency scenario samples can be updated to provide a strong guarantee for the stable operation of the power grid.

[0047] In summary, the embodiments of the present application have at least the following technical effects:

[0048] First, the burst scenario samples of the power distribution equipment are determined, wherein the burst scenario samples include at least short circuit fault, equipment overload, temperature anomaly and power supply terminal fault. Then, the scenario parameters are set according to the burst scenario samples, and the test parameters of each burst scenario sample are output, wherein the test parameters include test duration, test impact range and test frequency. Further, the burst scenario samples are subjected to scenario fusion, and binary scenario groups, ternary scenario groups and multivariate scenario groups are output. Then, the power distribution equipment is tested based on the test parameter groups corresponding to the binary scenario groups, ternary scenario groups and multivariate scenario groups, respectively, to obtain the scenario group test data set, wherein the test items include signal stability, fault response time, overload handling capability and recovery performance; finally, the performance is evaluated according to the scenario group test data set, and the equipment performance indicators are output. The technical problem of insufficient performance evaluation of the power distribution equipment in complex and changeable scenarios in the prior art is solved, and the performance of the power distribution equipment is comprehensively evaluated through the multi-scenario fusion test method, so as to achieve the technical effect of improving the comprehensiveness and accuracy of equipment testing.

[0049] Embodiment 2 is based on the same inventive concept as a multi-scenario integrated power distribution equipment testing method in the above embodiment. Figure 2 As shown, the present application provides a multi-scenario integrated power distribution equipment testing device, wherein the device includes:

[0050] A sample determination module 11, the sample determination module 11 is used to determine the sudden scenario samples of the distribution equipment, wherein the sudden scenario samples include at least short circuit fault, equipment overload, temperature anomaly and power supply terminal fault; a parameter setting module 12, the parameter setting module 12 is used to set the scenario parameters according to the sudden scenario samples, and output the test parameters of each sudden scenario sample, wherein the test parameters include test duration, test impact range and test frequency; a scene fusion module 13, the scene fusion module 13 is used to perform scene fusion on the sudden scenario samples, and output a binary scene group, a ternary scene group and a multivariate scene group; a test module 14, the test module 14 is used to test the distribution equipment based on the test parameter groups corresponding to the binary scene group, the ternary scene group and the multivariate scene group, and obtain a scene group test data set, wherein the test items include signal stability, fault response time, overload handling capability and recovery performance; a performance evaluation module 15, the performance evaluation module 15 is used to perform performance evaluation according to the scene group test data set, and output equipment performance indicators.

[0051] Furthermore, the scene fusion module 13 is used to execute the following method:

[0052] Identify the burst frequency of each scene sample in the burst scene sample; calculate the occurrence probability of each scene sample according to the burst frequency of each scene sample, and output the occurrence probability set corresponding to the burst scene sample; perform scene fusion on the burst scene samples according to the occurrence probability set, and output binary scene groups, ternary scene groups and multivariate scene groups.

[0053] Furthermore, the scene fusion module 13 is used to execute the following method:

[0054] Perform joint probability analysis on binary scene groups, ternary scene groups and multivariate scene groups according to the occurrence probability set to obtain the binary joint probability of each scene group in the binary scene group, the ternary joint probability of each scene group in the ternary scene group, and the multivariate joint probability of each scene group in the multivariate scene group; judge between the binary joint probability, ternary joint probability and multivariate joint probability and the preset occurrence probability, and screen out binary scene groups, ternary scene groups and multivariate scene groups with occurrence probabilities greater than the preset probability.

[0055] Furthermore, the scene fusion module 13 is used to execute the following method:

[0056] A conflict detection mechanism is established, and conflicts between scenes in each group of the output binary scene groups, ternary scene groups and multi-scene groups are monitored according to the conflict detection mechanism, and conflict indicators of each scene group in the binary scene groups, conflict indicators of each scene group in the ternary scene groups and conflict indicators of each scene group in the multi-scene groups are obtained; and binary scene groups, ternary scene groups and multi-scene groups whose conflict indicators are less than preset ones are screened.

[0057] Furthermore, the scene fusion module 13 is used to execute the following method:

[0058] Obtain the correlation between the scenes in each group of the binary scene group, the ternary scene group and the multivariate scene group; if the correlation is returned as 0, the corresponding scene group will be independently identified; if the correlation is returned as 1, the corresponding scene group will be associated and identified; output independent-binary scene group, independent-ternary scene group, independent-multivariate scene group, associated-binary scene group, associated-ternary scene group, and associated-multivariate scene group; perform probability analysis on the independent-binary scene group, independent-ternary scene group, and independent-multivariate scene group according to independent probability, perform probability analysis on the associated-binary scene group, associated-ternary scene group, and associated-multivariate scene group according to conditional probability, and output the binary joint probability, ternary joint probability, and multivariate joint probability.

[0059] Furthermore, the scene fusion module 13 is used to execute the following method:

[0060] The expression for probability analysis of the association-binary scenario group according to conditional probability is: ; The expression for probability analysis of the association-ternary scene group according to conditional probability is: The expressions for performing probability analysis on the association-multivariate scenario group according to conditional probability include: ;in, is the probability of scenario A occurring, is the probability of scenario B occurring under the condition that scenario A occurs, is the probability of scenario C occurring when both scenario A and scenario B occur, is the probability of scene N occurring under the condition that scenes A, B, and M occur.

[0061] Furthermore, the sample determination module 11 is used to perform the following method:

[0062] Detect the real-time connection relationship of the power distribution equipment; wherein the real-time connection relationship includes the internal connection relationship inside the power distribution equipment and the external connection relationship outside the power distribution equipment; when it is detected that the real-time connection relationship of the power distribution equipment has changed, locate the changed connection relationship, and update the emergency scenario sample of the power distribution equipment according to the changed connection relationship.

[0063] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0064] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0065] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A multi-scenario integrated power distribution equipment testing method, characterized in that: The method comprises: Determine emergency scenario samples of power distribution equipment, wherein the emergency scenario samples at least include short circuit fault, equipment overload, temperature abnormality and power supply terminal fault; Setting scene parameters according to the burst scene samples, and outputting test parameters of each burst scene sample, wherein the test parameters include test duration, test impact range, and test frequency; Performing scene fusion on the burst scene samples, and outputting a binary scene group, a ternary scene group, and a multi-element scene group; The power distribution equipment is tested based on the test parameter groups respectively corresponding to the binary scenario group, the ternary scenario group and the multivariate scenario group to obtain a scenario group test data set, wherein the test items include signal stability, fault response time, overload handling capability and recovery performance; Perform performance evaluation based on the scenario group test data set and output device performance indicators; Wherein, after performing scene fusion on the burst scene samples and outputting binary scene groups, ternary scene groups and multi-element scene groups, it includes: Identifying the burst frequency of each scene sample in the burst scene samples; Calculate the occurrence probability of each scene sample according to the burst frequency of each scene sample, and output the occurrence probability set corresponding to the burst scene sample; Perform scene fusion on the burst scene samples according to the occurrence probability set, and output a binary scene group, a ternary scene group and a multi-element scene group; The step of performing scene fusion on the burst scene samples according to the occurrence probability set includes: Performing joint probability analysis on the binary scene group, the ternary scene group and the multivariate scene group according to the occurrence probability set, obtaining the binary joint probability of each scene group in the binary scene group, the ternary joint probability of each scene group in the ternary scene group, and the multivariate joint probability of each scene group in the multivariate scene group; The binary joint probability, ternary joint probability and multivariate joint probability are judged with the preset occurrence probability to screen the binary scene group, ternary scene group and multivariate scene group with a probability greater than the preset occurrence probability.

2. A multi-scenario integrated power distribution equipment testing method as claimed in claim 1, characterized in that: The method further comprises: Establishing a conflict detection mechanism, monitoring the conflict between the scenes in each of the binary scene groups, the ternary scene groups and the multi-scene groups outputted according to the conflict detection mechanism, and obtaining the conflict index of each scene group in the binary scene group, the conflict index of each scene group in the ternary scene group, and the conflict index of each scene group in the multi-scene group; Filter binary scenario groups, ternary scenario groups, and multi-element scenario groups that are less than the preset conflict index.

3. A multi-scenario fusion distribution equipment testing method as claimed in claim 1, characterized in that: According to the occurrence probability set, a joint probability analysis is performed on the binary scenario group, the ternary scenario group and the multivariate scenario group, the method comprising: Obtain the correlation between the scenes in each group of the binary scene group, the ternary scene group and the multi-scene group, if the correlation is returned as 0, the corresponding scene group is independently identified, if the correlation is returned as 1, the corresponding scene group is associated, and outputs an independent-binary scene group, an independent-ternary scene group, an independent-multi-scene group, an associated-binary scene group, an associated-ternary scene group, and an associated-multi-scene group; Perform probability analysis on the independent-binary scenario group, independent-ternary scenario group, and independent-multivariate scenario group according to independent probability, perform probability analysis on the associated-binary scenario group, associated-ternary scenario group, and associated-multivariate scenario group according to conditional probability, and output the binary joint probability, ternary joint probability, and multivariate joint probability.

4. A multi-scenario integrated power distribution equipment testing method as claimed in claim 3, characterized in that: The expression for probability analysis of the association-binary scenario group according to conditional probability is: ; The expression for probability analysis of the association-ternary scene group according to conditional probability is: ; The expressions for performing probability analysis on the association-multivariate scenario group according to conditional probability include: ; in, is the probability of scenario A occurring, is the probability of scenario B occurring under the condition that scenario A occurs, is the probability of scenario C occurring when both scenario A and scenario B occur, is the probability of scene N occurring under the condition that scenes A, B, and M occur.

5. A multi-scenario integrated power distribution equipment testing method as claimed in claim 1, characterized in that: The method further comprises: Detecting the real-time connection relationship of the power distribution equipment; Wherein, the real-time connection relationship includes an internal connection relationship within the power distribution equipment and an external connection relationship outside the power distribution equipment; When it is detected that the real-time connection relationship of the power distribution equipment has changed, the changed connection relationship is located, and the emergency scenario samples of the power distribution equipment are updated according to the changed connection relationship.

6. A multi-scenario integrated power distribution equipment testing device, characterized in that: A method for testing a power distribution equipment with multi-scenario integration according to any one of claims 1 to 5, the device comprising: A sample determination module, the sample determination module is used to determine the emergency scenario samples of the power distribution equipment, wherein the emergency scenario samples at least include short circuit fault, equipment overload, temperature abnormality and power supply terminal fault; A parameter setting module, the parameter setting module is used to set scene parameters according to the burst scene samples, and output test parameters of each burst scene sample, wherein the test parameters include test duration, test impact range and test frequency; A scene fusion module, which is used to perform scene fusion on the burst scene samples and output a binary scene group, a ternary scene group and a multi-element scene group; A test module, the test module is used to test the power distribution equipment based on the test parameter groups corresponding to the binary scenario group, the ternary scenario group and the multivariate scenario group, respectively, to obtain a scenario group test data set, wherein the test items include signal stability, fault response time, overload handling capability and recovery performance; A performance evaluation module is used to perform performance evaluation based on the scenario group test data set and output device performance indicators.

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