A distribution network area monitoring system and control method based on digital twin

Abstract

The present invention discloses a distribution network substation monitoring system based on digital twins and a control method thereof, which relate to the technical field of power equipment monitoring; the system comprises: a twin simulation module, a comparison module and a parameter processing module, wherein the twin simulation module is used to establish a twin simulation model, and simulate the scenario in the distribution network substation through the twin simulation model to realize comprehensive simulation and monitoring of the distribution network substation, and the comparison module is used to compare whether the internal temperature t1 and the external temperature t2 of the Nth power equipment are within a preset safe temperature range; the parameter processing module is used to collect the equipment failure-free time parameter u1 and the comparison failure-free time parameter u2 of the Nth power equipment, and evaluate them according to preset state evaluation rules; the failure probability of the power equipment is more accurately calculated by a formula with temperature as a variable, which is conducive to timely maintenance of the power equipment with excessive internal and external temperature difference and high failure probability, thereby ensuring that the distribution network substation in a low temperature environment can operate normally.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment monitoring, and specifically to a distribution network substation monitoring system based on digital twins and a control method thereof. Background Art

[0002] The distribution network substation monitoring system is a system used to monitor and manage the substations in the distribution network. It is usually composed of various sensors, monitoring equipment, data acquisition units, data processing and analysis software, and user interfaces. It aims to monitor the power parameters, equipment status, and environmental conditions of the substation in real time to ensure the safe and stable operation of the power grid. There are many distribution network substations and they are geographically dispersed. It is worth noting that some distribution network substations are located in a low-temperature environment for a long time, and the internal temperature of the power equipment is high during use, which leads to a large temperature difference between the inside and outside of the power equipment. The large temperature difference between the inside and outside of the power equipment will affect the normal operation of the distribution network substation. First, the large temperature difference between the inside and outside is likely to accelerate the aging of the materials in the power equipment, reduce the insulation performance, and increase the risk of equipment failure. Secondly, the large temperature difference between the inside and outside is likely to cause thermal damage or electrical failure of the components in the equipment, thereby increasing the risk of equipment downtime and affecting the stability of power supply. In addition, the large temperature difference between the inside and outside may cause thermal stress inside the power equipment. Due to the different thermal expansion coefficients of the materials, it is easy to cause thermal expansion or contraction of the internal components of the equipment, which may cause the material to rupture, deform or damage. Therefore, the technology based on temperature detection is of great significance for the maintenance of power equipment.

[0003] In the prior art, the monitoring system of the distribution network substation is mainly aimed at the distribution network substation in the normal temperature environment, and it is difficult to effectively monitor the power equipment in the distribution network substation in the low temperature environment for a long time. The characteristics of the power equipment in the low temperature environment for a long time are that the temperature difference between the inside and outside of the power equipment is large, and the internal temperature of different types of power equipment is not equal during use, which leads to different internal and external temperature differences of each power equipment. The general distribution network substation monitoring system does not have the technology to calculate the failure probability based on the temperature difference. Therefore, there is a large deviation in the calculation of the failure probability of different types of power equipment in the low temperature environment for a long time, which is not conducive to the management personnel to timely maintain the power equipment with too large internal and external temperature difference and high failure rate, and it is also difficult to ensure the normal operation of the distribution network substation in the low temperature environment for a long time. Summary of the invention

[0004] In view of the above technical deficiencies, the present invention provides a distribution network substation monitoring system based on digital twin and a control method thereof to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention is implemented through the following technical solutions:

[0006] A distribution network substation monitoring system based on digital twins includes: a twin simulation module, a comparison module, a parameter processing module and an early warning module, wherein the twin simulation module is used to establish a twin simulation model of the distribution network substation according to the holographic replication model of the holographic replication module, and simulate the scene in the distribution network substation through the twin simulation model to realize comprehensive simulation and monitoring of the distribution network substation; the comparison module is used to traverse n power equipment in the twin simulation model and obtain the internal temperature and external temperature data of each power equipment, and then compare whether the internal temperature t1 and the external temperature t2 of the Nth power equipment are within the preset safe temperature range (T1, T2); the parameter processing module is used to collect the equipment failure-free time of the Nth power equipment Parameter u1 and reference fault-free time parameter u2, and evaluate the equipment fault-free time parameter u1 and reference fault-free time parameter u2 of the Nth power equipment according to the preset state evaluation rule, and then judge the failure probability of the Nth power equipment by the evaluation result; the early warning module is used to receive the fault information sent by the comparison module and the parameter processing module and generate the corresponding early warning result, and then send it to the management end by wireless transmission to remind the management personnel; wherein, when at least one of the internal temperature t1 and the external temperature t2 is not in the safe temperature range (T1, T2), the internal temperature t1 is subtracted from the external temperature t2 and compared with the preset threshold temperature t0. If it is greater than the preset threshold temperature t0, the formula P is used. N =(Y=1|t)=exp(bo+b1t1+b2t2) / [1+exp(bo+b1t1+b2t2)] Calculate the failure probability P of the Nth power equipment under this temperature condition N ; If the failure probability of the Nth power equipment is P N ≥P0, the comparison module matches the corresponding simulation position information in the twin simulation model according to the real-scene position information of the holographic replica model, and marks the matched simulation position information in the twin simulation model to determine the fault probability P N The location of the Nth power device.

[0007] Furthermore, it also includes: a data acquisition module, a 3D modeling module and a holographic replication module, the data acquisition module is used to obtain the internal temperature and external temperature data of n power equipment in the distribution network substation, and then process the collected temperature data and send them to the 3D modeling module and the database respectively, the 3D modeling module creates a three-dimensional model of the distribution network substation based on the collected temperature data using 3D modeling software, and the holographic replication module establishes a holographic replication model of the distribution network substation based on the results of the 3D modeling module to achieve a high degree of simulation of the physical entity of the distribution network substation.

[0008] Furthermore, the database is connected to a data updating module, and the data updating module is used to update the temperature data collected in real time into the database into the holographic replication model, so as to update the temperature information in the holographic replication model.

[0009] Furthermore, the parameter processing module includes: a parameter collection unit and a parameter processing unit, the parameter collection unit is used to collect the equipment failure-free time parameter u1 and the comparison failure-free time parameter u2 of the Nth power equipment, the parameter processing unit is used to evaluate the equipment failure-free time parameter u1 and the comparison failure-free time parameter u2 based on a preset status evaluation rule, and judge the failure probability of the power equipment according to the evaluation result, if the failure probability of the power equipment is judged to be high, the early warning module will send the second fault information of the corresponding power equipment and the corresponding generated early warning result to the management end.

[0010] Furthermore, it also includes: a visualization interface module, which is connected to the 3D modeling module, the holographic replication module and the management end respectively, and the visualization interface module is used to present the information of the three-dimensional model and the holographic replication model of the distribution network substation in a graphical manner.

[0011] Furthermore, it also includes: a virtual-reality interaction module, which is connected to the twin simulation module and the management end respectively, and is used to receive instructions input by the management end and pass them to the twin simulation model, and at the same time visualize or present the feedback information of the twin simulation model to the management end in other ways.

[0012] The present invention also discloses a control method for a distribution network area monitoring system based on digital twins, which is used to control the above-mentioned distribution network area monitoring system. The method comprises the following steps:

[0013] S1, the data acquisition unit collects temperature data of n power equipment in the distribution network area, and then processes the collected temperature data and sends them to the 3D modeling module and the database respectively;

[0014] S2, the 3D modeling unit creates a three-dimensional model of the distribution network substation area using 3D modeling software based on the collected temperature data, the holographic replication unit establishes a holographic replication model of the distribution network substation area based on the results of the 3D modeling module to achieve a high degree of simulation of the physical entity, and the data update module updates the temperature data collected in real time to the database into the holographic replication model to update the temperature information in the holographic replication model;

[0015] S3, the twin simulation module establishes a twin simulation model of the distribution network area based on the holographic replication model and the temperature data updated by the data update module, and simulates the scene in the distribution network area through the twin simulation model to realize comprehensive simulation and monitoring of the distribution network area, wherein the twin simulation model includes: simulation location information of n power equipment in the distribution network area;

[0016] S4, the comparison module traverses the n power devices in the twin simulation model and obtains the internal temperature and external temperature data of each power device, and then compares whether the internal temperature t1 and the external temperature t2 of the Nth power device are within the preset safe temperature range (T1, T2). When at least one of the internal temperature t1 and the external temperature t2 is not within the safe temperature range (T1, T2), the internal temperature t1 is subtracted from the external temperature t2 and compared with the preset threshold temperature t0. If it is greater than the preset threshold temperature t0, the formula P is used. N =(Y=1|t)=exp(bo+b1t1+b2t2) / [1+exp(bo+b1t1+b2t2)] calculates the failure probability of the Nth power device under this temperature condition, where Y is the fault state, when Y=1 it indicates a fault, when Y=0 it indicates no fault, t1 is the internal temperature of the Nth device, t2 is the external temperature of the Nth device, b0, b1, b2 are model parameters, 1≤N≤n;

[0017] S5, if the failure probability of the Nth power equipment is P N ≥P0, the comparison module matches the corresponding simulation position information in the twin simulation model according to the real-scene position information of the holographic replica model, and marks the matched simulation position information in the twin simulation model to determine the fault probability P N The position of the Nth power equipment, wherein the real-scene position information and the simulated position information are in a mapping relationship, and P0 is a preset fault probability;

[0018] S6, the early warning module sends the first fault information of the Nth power equipment and the corresponding generated early warning result to the management end, wherein the first fault information includes: the simulated position information of the Nth power equipment and the fault probability P N .

[0019] Further, in S4, it also includes: S4.1, when the internal temperature t1 and the external temperature t2 are both within the safe temperature range (T1, T2), the comparison module controls the parameter processing module to collect the equipment failure-free time parameter u1 and the reference failure-free time parameter u2 of the Nth power equipment, wherein the equipment failure-free time parameter u1 is the actual failure-free time of the power equipment, and the reference failure-free time parameter u2 is the failure-free operation time of the power equipment with the same model as the Nth power equipment;

[0020] S4.2, the parameter processing module evaluates the equipment failure-free time parameter u1 and the control failure-free time parameter u2 of the Nth power equipment according to the preset status evaluation rules, and determines the failure probability of the Nth power equipment according to the evaluation results. If the failure probability of the Nth power equipment is judged to be high, the early warning module sends the second fault information of the Nth power equipment and the corresponding generated early warning result to the management end, and the second fault information includes: the simulation position information of the Nth power equipment and the evaluation result based on the failure-free time parameter.

[0021] The beneficial effects of the present invention are:

[0022] By establishing a twin simulation model to simulate the scenario within the distribution network substation, comprehensive simulation and monitoring of the distribution network substation can be achieved. Further, the failure probability of power equipment can be more accurately calculated through a formula with temperature as a variable. This is conducive to timely maintenance of power equipment with a large temperature difference between inside and outside and a high probability of failure. It can avoid failures of power equipment with a large temperature difference between inside and outside, thereby ensuring the normal operation of distribution network substations in a low temperature environment. In addition, marking power equipment with a high probability of failure in the twin simulation model enables managers to quickly determine the location of power equipment for maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 The schematic diagram of the monitoring system.

[0024] Figure 2 It is a structural diagram of the parameter processing module.

[0025] Figure 3 Flow chart of the control method of the monitoring system.

[0026] Figure 4 This is a flow chart of S4 in the control method of the monitoring system.

[0027] In the figure, 1. Twin simulation module; 2. Comparison module; 3. Parameter processing module; 4. Early warning module; 5. Holographic replication module; 6. Management end; 7. Data acquisition module; 8. 3D modeling module; 9. Database; 10. Data update module; 11. Parameter acquisition unit; 12. Parameter processing unit; 13. Visual interface module; 14. Virtual-reality interaction module. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] The present invention provides Figure 1 and Figure 2 In combination with the digital twin-based distribution network substation monitoring system shown, it includes: a twin simulation module 1, a comparison module 2, a parameter processing module 3 and an early warning module 4, wherein the twin simulation module 1 is used to establish a twin simulation model of the distribution network substation according to the holographic replication model of the holographic replication module 5, and simulate the scenario in the distribution network substation through the twin simulation model to achieve comprehensive simulation and monitoring of the distribution network substation; the comparison module 2 is used to traverse the n power equipment in the twin simulation model and obtain the internal temperature and external temperature data of each power equipment, and then compare whether the internal temperature t1 and the external temperature t2 of the Nth power equipment are within the preset safety temperature range (T1, T2); the parameter processing module 3 is used to collect the equipment wireless The fault time parameter u1 and the comparison fault-free time parameter u2 are compared, and the equipment fault-free time parameter u1 and the comparison fault-free time parameter u2 of the Nth power equipment are evaluated according to the preset state evaluation rules, and then the failure probability of the Nth power equipment is judged by the evaluation results; the early warning module 4 is used to receive the fault information sent by the comparison module 2 and the parameter processing module 3 and generate the corresponding early warning result, and then send it to the management terminal 6 by wireless transmission to remind the management personnel; wherein, when at least one of the internal temperature t1 and the external temperature t2 is not in the safe temperature range (T1, T2), the internal temperature t1 is subtracted from the external temperature t2 and compared with the preset threshold temperature t0. If it is greater than the preset threshold temperature t0, the formula P is used. N =(Y=1|t)=exp(bo+b1t1+b2t2) / [1+exp(bo+b1t1+b2t2)] Calculate the failure probability P of the Nth power equipment under this temperature condition N ; If the failure probability of the Nth power equipment is P N ≥P0, the comparison module 2 matches the corresponding simulation position information in the twin simulation model according to the real-scene position information of the holographic replica model, and marks the matched simulation position information in the twin simulation model to determine the fault probability P N The location of the Nth power device.

[0030] Among them, the twin simulation model of the distribution network substation established by the twin simulation module 1 is a digital simulation model constructed based on the holographic replication model and the temperature data updated by the data update module 10. The twin simulation model not only includes the simulation of the geometric shape and appearance of objects in the distribution network substation, but also includes the simulation of the operating status, behavior and performance of each power equipment in the distribution network substation, so as to realize the comprehensive simulation and monitoring of the distribution network substation, and also calculates the failure probability of the power equipment through the Logistic regression model, which is conducive to the timely maintenance of the power equipment with excessive internal and external temperature difference and high failure probability, and can avoid the failure of the power equipment with excessive internal and external temperature difference, thereby ensuring the normal operation of the distribution network substation in the low temperature environment.

[0031] At the same time, the comparison module 2 compares whether the internal temperature t1 and the external temperature t2 of the Nth power equipment are within the preset safe temperature range (T1, T2). If at least one of the internal temperature t1 and the external temperature t2 is not within the safe temperature range (T1, T2), it is preliminarily determined that the internal and external temperature difference of the Nth power equipment is too large, and then the internal temperature t1 is subtracted from the external temperature t2 and compared with the preset threshold temperature t0. If the difference is less than the preset threshold temperature t0, no processing is performed. If the difference is greater than the preset threshold temperature t0, it is further determined that the internal and external temperature difference of the Nth power equipment is too large. Therefore, at this time, a formula with temperature as a variable is used to calculate the failure probability of the Nth power equipment. The formula P for calculating the failure probability of the power equipment N =(Y=1|t)=exp(bo+b1t1+b2t2) / [1+exp(bo+b1t1+b2t2)] is obtained based on the Logistic regression model and the Sigmoid function, and can more accurately calculate the probability of failure of the power equipment based on the internal and external temperature difference of the power equipment; specifically, the Logistic regression model is a statistical learning model, and its output result can be interpreted as a probability, so the formula based on the Logistic regression model can be used to calculate the probability of failure of the power equipment, by inputting the result of the linear function (bo+b1t1+b2t2) into the Sigmoid function, and then obtaining a probability value, that is, the conditional probability P of the Nth power equipment failure (Y=1) occurring. N ; The characteristic of the Sigmoid function is to map the input value to an interval between 0 and 1. When the result of the linear function (bo+b1t1+b2t2) approaches positive infinity, P N When it is close to 1, it means that the failure probability of the Nth power equipment is very high. When the result of the linear function (bo+b1t1+b2t2) approaches negative infinity, P N is close to 0, indicating that the failure probability of the Nth power device is very low, so P NIt can be interpreted as the probability of failure of the Nth power device.

[0032] In addition, P0 is the preset failure probability. The failure probability P of the Nth power equipment is N ≥P0, the comparison module 2 matches the corresponding simulation position information in the twin simulation model according to the real-scene position information of the holographic replica model, and marks the matched simulation position information in the twin simulation model, so that the management personnel can quickly determine the fault probability as P N The location of the Nth power equipment is convenient for management personnel to maintain the power equipment with a higher probability of failure in the distribution network station area; it should be noted that only the Nth power equipment is explained here, and the remaining power equipment is the same as the Nth power equipment, which will not be repeated here.

[0033] In an optional embodiment, if Figure 1 As shown, it also includes: a data acquisition module 7, a 3D modeling module 8 and a holographic replication module 5, the data acquisition module 7 is used to obtain the internal temperature and external temperature data of n power equipment in the distribution network substation, and then process the collected temperature data and send them to the 3D modeling module 8 and the database 9 respectively, the 3D modeling module 8 creates a three-dimensional model of the distribution network substation based on the collected temperature data using 3D modeling software, and the holographic replication module 5 establishes a holographic replication model of the distribution network substation based on the result of the 3D modeling module 8, so as to achieve a high degree of simulation of the physical entity of the distribution network substation.

[0034] Among them, the data acquisition module 7 includes: a multi-eye 3D stereo camera and several temperature sensors. After the temperature sensor collects the internal temperature and external temperature data of the power equipment, it performs preliminary processing. The preliminary processing of the temperature data includes: data cleaning and data correction processes, thereby ensuring the accuracy and consistency of the temperature data, and then sends it to the database 9 for storage or sends it to the 3D modeling module 8 to create a three-dimensional model of the distribution network substation; the holographic replication model can be used as an advanced form of the three-dimensional model, and the twin simulation model is based on the holographic replication model. Through the integration and analysis of the temperature data of the power equipment, the dynamic simulation and prediction of each power equipment in the distribution network substation are realized, which is conducive to the management personnel to timely maintain the power equipment with excessive internal and external temperature difference and high failure probability, thereby ensuring the normal operation of the distribution network substation in a low temperature environment.

[0035] In an optional embodiment, if Figure 1 As shown, the database 9 is connected to a data updating module 10, and the data updating module 10 is used to update the temperature data collected in real time from the database 9 into the holographic replication model, so as to update the temperature information in the holographic replication model.

[0036] Among them, the data update module 10 obtains real-time temperature data by establishing a connection with the database 9, and updates it to the holographic replication model. It is also used to establish a twin simulation model of the distribution network substation; the update frequency of the data update module 10 will be configured according to the monitoring requirements and system performance to ensure the timeliness and accuracy of the temperature data update, which is conducive to more accurate calculation of the probability of power equipment failure by formulas with temperature data as variables.

[0037] In an optional embodiment, if Figure 1 and Figure 2 As shown in combination, the parameter processing module 3 includes: a parameter collection unit 11 and a parameter processing unit 12, the parameter collection unit 11 is used to collect the equipment failure-free time parameter u1 and the comparison failure-free time parameter u2 of the Nth power equipment, and the parameter processing unit 12 is used to evaluate the equipment failure-free time parameter u1 and the comparison failure-free time parameter u2 based on a preset status evaluation rule, and judge the failure probability of the power equipment according to the evaluation result, if the failure probability of the power equipment is judged to be high, the early warning module 4 will send the second fault information of the corresponding power equipment and the corresponding generated early warning result to the management terminal 6.

[0038] Among them, the comparison module 2 compares whether the internal temperature t1 and the external temperature t2 of the Nth power equipment are within the preset safety temperature range (T1, T2). If the internal temperature t1 and the external temperature t2 are both within the safety temperature range (T1, T2), it is preliminarily judged that the internal and external temperature difference of the Nth power equipment is small. At this time, the comparison module 2 controls the parameter acquisition unit 11 to collect the equipment failure-free time parameter u1 and the reference failure-free time parameter u2 of the Nth power equipment, and then the parameter processing unit 12 evaluates the equipment failure-free time parameter u1 and the reference failure-free time parameter u2 based on the preset state evaluation rules. When the internal and external temperature difference of the Nth power equipment is small, the state evaluation rules are used to predict the failure probability of the Nth power equipment, so that the failure probability can be quickly calculated while ensuring accuracy.

[0039] In an optional embodiment, if Figure 1 As shown, it also includes: a visualization interface module 13, which is respectively connected to the 3D modeling module 8, the holographic replication module 5 and the management terminal 6, and the visualization interface module 13 is used to present the information of the three-dimensional model and the holographic replication model of the distribution network substation in a graphical manner.

[0040] Among them, the visualization interface module 13 is used to present the information of the three-dimensional model and holographic replica model of the distribution network substation, so that managers can intuitively understand the real-time status of the distribution network substation. Managers can also observe the operation status of the distribution network substation, analyze data and view reports through the visualization interface module 13.

[0041] In an optional embodiment, if Figure 1 As shown, it also includes: a virtual-reality interaction module 14, which is connected to the twin simulation module 1 and the management terminal 6 respectively. The virtual-reality interaction module 14 is used to receive the instructions input by the management personnel at the management terminal 6 and pass it to the twin simulation model, and at the same time visualize or present the feedback information of the twin simulation model to the management personnel in other ways.

[0042] Among them, the virtual-reality interaction module 14 is used to interact with the twin simulation model. Managers can operate the twin simulation model and adjust parameters through the virtual-reality interaction module 14, thereby realizing the simulation of the distribution network substation in the twin simulation model; in actual use, managers can first understand the information of the holographic replication model through the visualization interface module 13, and then control and operate the twin simulation model through the virtual-reality interaction module 14, so that managers can use the distribution network substation monitoring system more conveniently, which improves the interactivity and operability of the distribution network substation monitoring system, and is conducive to managers to check the power equipment with excessive internal and external temperature difference and high failure probability.

[0043] The present invention also discloses a control method for a distribution network area monitoring system based on digital twins, which is used to control the above-mentioned distribution network area monitoring system, such as Figure 3 As shown, the method comprises the following steps:

[0044] S1, the data acquisition unit collects temperature data of n power equipment in the distribution network area, and then processes the collected temperature data and sends them to the 3D modeling module and the database respectively;

[0045] S2, the 3D modeling unit creates a three-dimensional model of the distribution network substation area using 3D modeling software based on the collected temperature data, the holographic replication unit establishes a holographic replication model of the distribution network substation area based on the results of the 3D modeling module to achieve a high degree of simulation of the physical entity, and the data update module updates the temperature data collected in real time to the database into the holographic replication model to update the temperature information in the holographic replication model;

[0046] S3, the twin simulation module establishes a twin simulation model of the distribution network area based on the holographic replication model and the temperature data updated by the data update module, and simulates the scene in the distribution network area through the twin simulation model to realize comprehensive simulation and monitoring of the distribution network area, wherein the twin simulation model includes: simulation location information of n power equipment in the distribution network area;

[0047] S4, the comparison module traverses the n power devices in the twin simulation model and obtains the internal temperature and external temperature data of each power device, and then compares whether the internal temperature t1 and the external temperature t2 of the Nth power device are within the preset safe temperature range (T1, T2). When at least one of the internal temperature t1 and the external temperature t2 is not within the safe temperature range (T1, T2), the internal temperature t1 is subtracted from the external temperature t2 and compared with the preset threshold temperature t0. If it is greater than the preset threshold temperature t0, the formula P is used. N =(Y=1|t)=exp(bo+b1t1+b2t2) / [1+exp(bo+b1t1+b2t2)] calculates the failure probability of the Nth power device under this temperature condition, where Y is the fault state, when Y=1 it indicates a fault, when Y=0 it indicates no fault, t1 is the internal temperature of the Nth device, t2 is the external temperature of the Nth device, b0, b1, b2 are model parameters, 1≤N≤n;

[0048] S5, if the failure probability of the Nth power equipment is P N ≥P0, the comparison module matches the corresponding simulation position information in the twin simulation model according to the real-scene position information of the holographic replica model, and marks the matched simulation position information in the twin simulation model to determine the fault probability P N The position of the Nth power equipment, wherein the real-scene position information and the simulated position information are in a mapping relationship, and P0 is a preset fault probability;

[0049] S6, the early warning module sends the first fault information of the Nth power equipment and the corresponding generated early warning result to the management end, wherein the first fault information includes: the simulated position information of the Nth power equipment and the fault probability P N .

[0050] In an optional embodiment, if Figure 4 As shown, in S4, it also includes: S4.1, when the internal temperature t1 and the external temperature t2 are both within the safe temperature range (T1, T2), the comparison module controls the parameter processing module to collect the equipment failure-free time parameter u1 and the reference failure-free time parameter u2 of the Nth power equipment, wherein the equipment failure-free time parameter u1 is the actual failure-free time of the power equipment, and the reference failure-free time parameter u2 is the failure-free operation time of the power equipment with the same model as the Nth power equipment;

[0051] S4.2, the parameter processing module evaluates the equipment failure-free time parameter u1 and the control failure-free time parameter u2 of the Nth power equipment according to the preset status evaluation rules, and determines the failure probability of the Nth power equipment according to the evaluation results. If the failure probability of the Nth power equipment is judged to be high, the early warning module sends the second fault information of the Nth power equipment and the corresponding generated early warning result to the management end, and the second fault information includes: the simulation position information of the Nth power equipment and the evaluation result based on the failure-free time parameter.

[0052] Among them, when the internal temperature t1 and the external temperature t2 are both within the safe temperature range (T1, T2), it means that the internal and external temperature difference of the Nth power equipment is small at this time. Therefore, the state evaluation rule can be used to predict the failure probability of the Nth power equipment at this time, while ensuring accuracy, the failure probability can be quickly calculated. The state evaluation rule is: if the failure-free time parameter u1 of the Nth power equipment is greater than or equal to the reference failure-free time parameter u2, it is judged that the failure probability of the Nth power equipment is high. At this time, the early warning module sends the second fault information of the Nth power equipment and the corresponding generated early warning result to the management end; if the failure-free time parameter u1 of the Nth power equipment is less than the reference failure-free time parameter u2, it is judged that the failure probability of the Nth power equipment is low, and the early warning module does not process it at this time.

[0053] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A distribution network area monitoring system based on digital twins, characterized in that: include: A twin simulation module is used to establish a twin simulation model of the distribution network substation according to the holographic replication model of the holographic replication module, and simulate the scenario in the distribution network substation through the twin simulation model to achieve comprehensive simulation and monitoring of the distribution network substation; A comparison module is used to traverse the n power devices in the twin simulation model and obtain the internal temperature and external temperature data of each power device, and then compare whether the internal temperature t1 and the external temperature t2 of the Nth power device are within the preset safe temperature range (T1, T2); A parameter processing module is used to collect the equipment failure-free time parameter u1 and the reference failure-free time parameter u2 of the Nth power equipment, and evaluate the equipment failure-free time parameter u1 and the reference failure-free time parameter u2 of the Nth power equipment according to a preset state evaluation rule, and then determine the failure probability of the Nth power equipment according to the evaluation result; The early warning module is used to receive the fault information sent by the comparison module and the parameter processing module and generate the corresponding early warning result, and then send it to the management end through wireless transmission to remind the management personnel; Among them, when at least one of the internal temperature t1 and the external temperature t2 is not within the safe temperature range (T1, T2), the internal temperature t1 is subtracted from the external temperature t2 and compared with the preset threshold temperature t0. If it is greater than the preset threshold temperature t0, the formula P is used. N =(Y=1|t)=exp(bo+b1t1+b2t2) / [1+exp(bo+b1t1+b2t2)] Calculate the failure probability P of the Nth power equipment under this temperature condition N ; If the failure probability of the Nth power equipment is P N ≥P0, the comparison module matches the corresponding simulation position information in the twin simulation model according to the real-scene position information of the holographic replica model, and marks the matched simulation position information in the twin simulation model to determine the fault probability P N The position of the Nth power equipment, Y is the fault status, when Y=1 it means fault, when Y=0 it means no fault, b0, b1, b2 are model parameters, 1≤N≤n.

2. According to a digital twin-based distribution network area monitoring system according to claim 1, it is characterized in that: Also includes: A data acquisition module, a 3D modeling module and a holographic replication module, wherein the data acquisition module is used to obtain the internal temperature and external temperature data of n power equipment in the distribution network substation, and then process the collected temperature data and send them to the 3D modeling module and the database respectively; the 3D modeling module creates a three-dimensional model of the distribution network substation based on the collected temperature data using 3D modeling software; the holographic replication module establishes a holographic replication model of the distribution network substation based on the results of the 3D modeling module, so as to achieve a high degree of simulation of the physical entity of the distribution network substation.

3. A distribution network area monitoring system based on digital twin according to claim 2, characterized in that: The database is connected to a data updating module, and the data updating module is used to update the temperature data collected in real time into the database into the holographic replication model, so as to update the temperature information in the holographic replication model.

4. According to a digital twin-based distribution network area monitoring system according to claim 1, it is characterized in that: The parameter processing module includes: a parameter collection unit and a parameter processing unit. The parameter collection unit is used to collect the equipment failure-free time parameter u1 and the comparison failure-free time parameter u2 of the Nth power equipment. The parameter processing unit is used to evaluate the equipment failure-free time parameter u1 and the comparison failure-free time parameter u2 based on a preset status evaluation rule, and judge the failure probability of the power equipment according to the evaluation result. If the failure probability of the power equipment is judged to be high, the early warning module will send the second fault information of the corresponding power equipment and the corresponding generated early warning result to the management end.

5. According to a digital twin-based distribution network area monitoring system according to claim 1, it is characterized in that: Also includes: A visualization interface module, which is connected to the 3D modeling module, the holographic replication module and the management end respectively, and is used to present the information of the three-dimensional model and the holographic replication model of the distribution network area in a graphical manner.

6. A distribution network area monitoring system based on digital twin according to claim 1, characterized in that: Also includes: A virtual-reality interaction module, which is connected to the twin simulation module and the management end respectively. The virtual-reality interaction module is used to receive instructions input by the management end and pass them to the twin simulation model, and at the same time visualize or present the feedback information of the twin simulation model to the management end in other ways.

7. A control method for a distribution network substation monitoring system based on digital twins, the control method being used to control the distribution network substation monitoring system according to any one of claims 1 to 6, characterized in that: The steps include: S1, the data acquisition unit collects temperature data of n power equipment in the distribution network area, and then processes the collected temperature data and sends them to the 3D modeling module and the database respectively; S2, the 3D modeling unit creates a three-dimensional model of the distribution network substation area using 3D modeling software based on the collected temperature data, the holographic replication unit establishes a holographic replication model of the distribution network substation area based on the results of the 3D modeling module to achieve a high degree of simulation of the physical entity, and the data update module updates the temperature data collected in real time to the database into the holographic replication model to update the temperature information in the holographic replication model; S3, the twin simulation module establishes a twin simulation model of the distribution network area based on the holographic replication model and the temperature data updated by the data update module, and simulates the scene in the distribution network area through the twin simulation model to realize comprehensive simulation and monitoring of the distribution network area, wherein the twin simulation model includes: simulation location information of n power equipment in the distribution network area; S4, the comparison module traverses the n power devices in the twin simulation model and obtains the internal temperature and external temperature data of each power device, and then compares whether the internal temperature t1 and the external temperature t2 of the Nth power device are within the preset safe temperature range (T1, T2). When at least one of the internal temperature t1 and the external temperature t2 is not within the safe temperature range (T1, T2), the internal temperature t1 is subtracted from the external temperature t2 and compared with the preset threshold temperature t0. If it is greater than the preset threshold temperature t0, the formula P is used. N =(Y=1|t)=exp(bo+b1t1+b2t2) / [1+exp(bo+b1t1+b2t2)] calculates the failure probability of the Nth power device under this temperature condition, where Y is the fault state, when Y=1 it indicates a fault, when Y=0 it indicates no fault, t1 is the internal temperature of the Nth device, t2 is the external temperature of the Nth device, b0, b1, b2 are model parameters, 1≤N≤n; S5, if the failure probability of the Nth power equipment is P N ≥P0, the comparison module matches the corresponding simulation position information in the twin simulation model according to the real-scene position information of the holographic replica model, and marks the matched simulation position information in the twin simulation model to determine the fault probability P N The position of the Nth power equipment, wherein the real-scene position information and the simulated position information are in a mapping relationship, and P0 is a preset fault probability; S6, the early warning module sends the first fault information of the Nth power equipment and the corresponding generated early warning result to the management end, wherein the first fault information includes: the simulated position information of the Nth power equipment and the fault probability P N .

8. The control method of the distribution network area monitoring system based on digital twin according to claim 7 is characterized in that: In S4, it also includes: S4.1, when the internal temperature t1 and the external temperature t2 are both within the safe temperature range (T1, T2), the comparison module controls the parameter processing module to collect the equipment failure-free time parameter u1 and the reference failure-free time parameter u2 of the Nth power equipment, wherein the equipment failure-free time parameter u1 is the actual failure-free time of the power equipment, and the reference failure-free time parameter u2 is the failure-free operation time of the power equipment with the same model as the Nth power equipment; S4.2, the parameter processing module evaluates the equipment failure-free time parameter u1 and the control failure-free time parameter u2 of the Nth power equipment according to the preset status evaluation rules, and determines the failure probability of the Nth power equipment according to the evaluation results. If the failure probability of the Nth power equipment is judged to be high, the early warning module sends the second fault information of the Nth power equipment and the corresponding generated early warning result to the management end, and the second fault information includes: the simulation position information of the Nth power equipment and the evaluation result based on the failure-free time parameter.

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