A power distribution network topology optimization method and system

Abstract

The application discloses a power distribution network topology optimization method and system, and relates to the technical field of power distribution network topology optimization.The system comprises a dynamic health degree acquisition module, an operation risk cost module, a topology optimization module and a maintenance scheme generation module.The dynamic health degree index and the operation risk cost of key electrical equipment are brought into a topology optimization objective function, and the weight is dynamically adjusted according to the equipment health state, so that the problem that the optimization scheme may accelerate equipment deterioration due to the fact that the actual health condition of equipment is ignored in the prior art is effectively solved, the real-time health condition evaluation information of key equipment can be effectively integrated, the short-term operation economic index and the long-term operation reliability of equipment are dynamically balanced in the optimization process, the risk of unexpected equipment failure is reduced, and the overall reliability and safety of power distribution network operation are improved.

Description

Technical Field

[0001] This invention relates to the field of distribution network topology optimization technology, and in particular to a distribution network topology optimization method and system. Background Technology

[0002] The underground working environment in coal mines typically involves high humidity, large amounts of coal dust, dripping water in the tunnels, mechanical vibration, and the potential presence of methane or corrosive gases in some areas. These harsh environmental factors continuously accelerate the degradation of the physical properties of underground electrical equipment, such as circuit breakers and disconnectors in high and low voltage switchgear, power transformers, power cables and their joints, including insulation aging, corrosion of metal parts, increased contact resistance, and reduced heat dissipation efficiency. This gradual degradation of equipment performance may not cause equipment tripping or obvious operational anomalies in its early stages, and may not be directly captured by conventional telemetry parameters.

[0003] Based on the above, the unique nature of coal mine production necessitates extremely high requirements for power supply continuity. Any unplanned power outage caused by a critical equipment failure can directly lead to serious problems such as the shutdown of mining faces, and may even threaten the safety of underground personnel. Therefore, in the specific context of the harsh underground environment in coal mines, where electrical equipment ages rapidly and its actual operating status dynamically evolves over time, how to enable distribution network topology optimization methods to effectively integrate real-time health status assessment information of critical equipment, and dynamically balance short-term operational economic indicators with long-term equipment reliability during the optimization process, has become an urgent problem to be solved. Summary of the Invention

[0004] The present invention aims to solve the technical problems mentioned in the background art. The purpose of the present invention is to provide a distribution network topology optimization method and system that can integrate real-time health status assessment information of key equipment, and dynamically balance short-term operating economic indicators with long-term operating reliability of equipment during the optimization process, reduce the risk of unexpected equipment failures, and improve the overall reliability and safety of distribution network operation.

[0005] To achieve the above objectives, the technical solutions of the present invention are as follows:

[0006] As one aspect of this application, a power distribution network topology optimization method is provided, which is applied in the application scenario of key electrical equipment in underground coal mines, and includes the following steps:

[0007] S1. Establish a calculation model for obtaining the dynamic health index of key electrical equipment, wherein the dynamic health index is used to characterize the aging degree or operating status of key equipment in the underground coal mine environment.

[0008] S2. Based on the dynamic health index of key electrical equipment, establish an operation risk cost model for the key electrical equipment. The operation risk cost model is used to quantify the operation mode of each key electrical equipment and determine the operation risk cost of the key electrical equipment.

[0009] S3. Construct an objective function for topology optimization. The objective function for topology optimization determines the total operating risk cost of the key electrical equipment by using the economic cost of distribution network operation and the operating risk cost. The objective function for topology optimization assigns weight coefficients to the economic cost of distribution network operation and the operating risk cost of key electrical equipment, and the weight coefficients are adjusted according to the health status of the key electrical equipment reflected by the dynamic health index.

[0010] S4. Based on the obtained operating costs of key electrical equipment and the dynamic health index of key electrical equipment, the optimal application results of key electrical equipment are obtained, and recommended operation suggestions for distribution network topology are generated.

[0011] Compared with existing technologies, the distribution network topology optimization method of this application incorporates the dynamic health index and operating risk cost of key electrical equipment into the topology optimization objective function and dynamically adjusts the weights according to the health status of the equipment. This effectively solves the problem in existing technologies that ignore the actual health status of equipment, which may lead to accelerated equipment degradation due to the optimization scheme. Thus, it can effectively integrate the real-time health status assessment information of key equipment and dynamically balance short-term operating economic indicators with long-term operating reliability of equipment during the optimization process, reduce the risk of unexpected equipment failures, and improve the overall reliability and safety of distribution network operation.

[0012] In this application, the step of establishing a calculation model for obtaining the dynamic health index of key electrical equipment, wherein the dynamic health index is used to characterize the aging degree or operating status of key equipment in the underground coal mine environment, specifically includes:

[0013] S11. Based on the acquired online monitoring data and offline information data, the equipment aging mode of key electrical equipment is obtained by matching from the preset aging information database;

[0014] S12. Based on the aging patterns of key electrical equipment and the online and offline monitoring data of key electrical equipment, establish a calculation function model for calculating the dynamic health index. Calculate the dynamic health index for each key electrical equipment using the calculation model. The dynamic health index for each key electrical equipment is periodically updated based on the corresponding online monitoring data.

[0015] Furthermore, the calculation function model for the dynamic health index is as follows:

[0016] C=w1*R1+w2*R2+…+wm*Rm, i=1,2,3,…,m;

[0017] Wherein, C indicates the dynamic health index of the critical electrical equipment, which is one of the following: transformer, switch cabinet, and cable; Ri indicates the health level of the critical electrical equipment in the i-th monitoring data; and wi indicates the health level weight of the critical electrical equipment in the i-th monitoring data.

[0018] In this application, the step of establishing an operational risk cost model for key electrical equipment based on its dynamic health index, wherein the operational risk cost model is used to quantify the operational mode of each key electrical equipment and determine its operational risk cost, specifically includes:

[0019] S21. Based on the dynamic health index of key electrical equipment, combined with the current load of key electrical equipment and the environmental index of underground coal mine, construct an operation risk cost model, wherein the operation risk cost model includes an accelerated aging cost sub-model and an expected failure loss sub-model.

[0020] S22. Based on the operational risk cost model, perform the following operations:

[0021] The accelerated aging cost sub-model is applied to calculate the equipment life loss rate caused by the current load on critical electrical equipment, and the equipment loss rate is converted into an economic cost value in combination with the current equipment cost of critical electrical equipment. This is used as an additional consumption of the remaining life of the equipment by the current operating mode of critical electrical equipment.

[0022] By applying the expected failure loss sub-model, the failure probability of the current critical electrical equipment is obtained by calculating the dynamic health index and load of the current critical electrical equipment. After obtaining the failure probability of the current critical electrical equipment, the potential economic loss of the current critical electrical equipment is calculated based on the obtained failure probability and the comprehensive loss of the critical electrical equipment under the failure probability.

[0023] S23. Based on the additional consumption of the remaining lifespan of the key equipment and the potential economic loss of the key equipment obtained under the accelerated aging cost sub-model and the expected failure loss sub-model, the operating risk cost of the key electrical equipment is comprehensively determined.

[0024] In this application, in the step of constructing an objective function for topology optimization, wherein the objective function for topology optimization determines the total operating risk cost of the key electrical equipment through the economic cost of distribution network operation and the operating risk cost, and wherein the objective function for topology optimization assigns weight coefficients to the economic cost of distribution network operation and the operating risk cost of key electrical equipment, and the weight coefficients are adjusted according to the health status of the key electrical equipment reflected by the dynamic health index, the adjustment of the weight coefficients includes:

[0025] A1. Divide the power distribution network into multiple electrical equipment groups, obtain the dynamic health index of each key electrical equipment in each electrical equipment group, and calculate the group health risk value of each electrical equipment group based on the obtained dynamic health index and the degree of influence of each key electrical equipment on the power supply reliability of the electrical equipment group.

[0026] A2. Based on the calculated group health risk value of each group of electrical equipment, determine the group risk weight coefficient corresponding to each group of electrical equipment, wherein the group risk weight coefficient of the electrical equipment group with the high group health risk value is set to high.

[0027] A3. When constructing the objective function for topology optimization, the total operating risk cost is obtained by weighting the operating risk costs of each key electrical device in each electrical equipment group using the corresponding group risk weight coefficient, and then summing the weight coefficients according to the health status of the key electrical devices reflected by the dynamic health index.

[0028] Furthermore, the step of dividing the power distribution network into multiple electrical equipment groups, obtaining the dynamic health index of each key electrical equipment within each electrical equipment group, and calculating the group health risk value of each electrical equipment group based on the obtained dynamic health index and the impact of each key electrical equipment on the power supply reliability of the electrical equipment group specifically includes:

[0029] A11. Divide the power distribution network into multiple electrical equipment groups, obtain the dynamic health index of each key electrical equipment in each electrical equipment group, and the reliability impact parameter used to characterize the degree of influence of each key electrical equipment on the power supply reliability of the electrical equipment group to which it belongs;

[0030] A12. Based on the obtained dynamic health index of each key electrical device and its corresponding reliability impact parameter, identify the bottleneck electrical device in the electrical device group, wherein the bottleneck electrical device is indicated as the key electrical device whose dynamic health index is lower than a preset bottleneck state threshold and whose corresponding reliability impact parameter is higher than a preset impact weight threshold.

[0031] A13. Extract the dynamic health index of the bottleneck electrical equipment as the dominant risk indication parameter of the group health risk value, and obtain the dynamic health index of other key electrical equipment in the electrical equipment group other than the bottleneck electrical equipment, and calculate the overall health benchmark value of the electrical equipment group based on the obtained dynamic health index of these other key electrical equipment.

[0032] A14. Based on the dominant risk indication parameter of the bottleneck electrical equipment and the overall health benchmark value of the electrical equipment group, and combined with the dispersion index used to characterize the distribution differences of the dynamic health index of each key electrical equipment in the electrical equipment group, the dominant risk indication parameter is corrected to obtain the health risk value of the group. The dispersion index is used to quantify the risk of the electrical equipment group that may be caused by the heterogeneous distribution of the dynamic health index.

[0033] Furthermore, the step of using the dispersion index to characterize the differences in the dynamic health index distribution of each of the key electrical devices within the electrical equipment group specifically includes:

[0034] Based on the dynamic health index of each key electrical device within the electrical equipment group, the statistical dispersion of the dynamic health index of each key electrical device within the electrical equipment group is calculated to obtain the dispersion index.

[0035] Furthermore, the step of correcting the dominant risk indication parameter based on the dominant risk indication parameter of the bottleneck electrical equipment and the overall health benchmark value of the electrical equipment group, and in conjunction with the dispersion index used to characterize the differences in the dynamic health index distribution of each key electrical equipment within the electrical equipment group, to obtain the group health risk value, includes:

[0036] A141. Obtain monitoring data of environmental parameters in a specific area underground in the coal mine, and based on the obtained monitoring data of the environmental parameters, identify significant short-term changes in the environmental parameters.

[0037] A142. In response to a short-term significant change in the identified environmental parameter, an environmental impact adjustment parameter is determined based on the type and degree of change of the environmental parameter and the characteristics of the critical electrical equipment within the electrical equipment group.

[0038] A143. Based on the dominant risk indicator parameter and the overall health baseline value, and in conjunction with the dispersion index and the environmental impact adjustment parameter, the dominant risk indicator parameter is corrected to obtain the group health risk value that has incorporated the impact of short-term significant changes in the environmental parameters.

[0039] In this application, the step of obtaining the optimal application result of key electrical equipment based on the obtained operating cost and dynamic health index of key electrical equipment, and generating recommended operation suggestions for the distribution network topology, specifically includes:

[0040] S41. Preset maintenance early warning thresholds and operational risk cost warning values;

[0041] S42. Construct an optimization algorithm model and perform the following operations:

[0042] When it is determined that the operating cost of critical electrical equipment is consistently higher than the warning value of operating risk cost, the optimal result for the application of critical electrical equipment is obtained by solving the topology optimization objective function under the current operating cost of critical electrical equipment through the optimization algorithm model, and the distribution network topology operation suggestions are output accordingly.

[0043] When the dynamic health index of critical electrical equipment is determined to be lower than the preset maintenance warning threshold, the optimal application result of critical electrical equipment is obtained by solving the topology optimization objective function under the current dynamic health index of critical electrical equipment through the optimization algorithm model, and the distribution network topology operation suggestion is output accordingly.

[0044] Meanwhile, when outputting distribution network topology operation suggestions, the application of optimization algorithm models must meet the basic operating constraints of the distribution network, wherein the basic operating constraints are one or more of the following: voltage qualification, current not exceeding limits, network connectivity, and radial operation.

[0045] As a second aspect of this application, a power distribution network topology optimization system is provided, which is applied in the application scenario of key electrical equipment in underground coal mines, including:

[0046] A dynamic health acquisition module is used to establish a calculation model for acquiring the dynamic health index of key electrical equipment. The dynamic health index is used to characterize the aging degree or operating status of key equipment in the underground coal mine environment.

[0047] The module for calculating the operational risk cost is used to establish an operational risk cost model for the key electrical equipment based on the dynamic health index of the key electrical equipment. The operational risk cost model is used to quantify the operational mode of each key electrical equipment and determine the operational risk cost of the key electrical equipment.

[0048] A topology optimization decision module is used to construct an objective function for topology optimization. The objective function for topology optimization determines the total operating risk cost of the key electrical equipment through the economic cost of distribution network operation and the operating risk cost. The objective function for topology optimization assigns weight coefficients to the economic cost of distribution network operation and the operating risk cost of key electrical equipment, and the weight coefficients are adjusted according to the health status of the key electrical equipment reflected by the dynamic health index.

[0049] The maintenance plan generation module is used to obtain the optimal application result of key electrical equipment based on the obtained operating cost and dynamic health index of key electrical equipment, and generate recommended operation suggestions for distribution network topology.

[0050] This application discloses a distribution network topology optimization system, which includes a dynamic health status acquisition module, an operational risk cost module, a topology optimization module, and a maintenance scheme generation module. By incorporating the dynamic health status index and operational risk cost of key electrical equipment into the topology optimization objective function and dynamically adjusting the weights according to the equipment health status, it effectively solves the problem in the prior art that neglecting the actual health status of equipment may lead to accelerated equipment deterioration due to the optimization scheme. Thus, it can effectively integrate the real-time health status assessment information of key equipment and dynamically balance short-term operating economic indicators with long-term operating reliability of equipment during the optimization process, reduce the risk of unexpected equipment failures, and improve the overall reliability and safety of distribution network operation.

[0051] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating a power distribution network topology optimization method in this embodiment;

[0053] Figure 2 This is a flowchart illustrating step S1 in a power distribution network topology optimization method of this embodiment;

[0054] Figure 3 This is a flowchart illustrating step S2 in a power distribution network topology optimization method of this embodiment;

[0055] Figure 4This is a flowchart illustrating the adjustment of weight coefficients in step S3 of a power distribution network topology optimization method in this embodiment.

[0056] Figure 5 This is a flowchart illustrating step S4 in a power distribution network topology optimization method of this embodiment;

[0057] Figure 6 This is a system structure block diagram of a power distribution network topology optimization system in this embodiment;

[0058] Attached reference numerals: 101, Dynamic health status acquisition module; 102, Operational risk cost calculation module; 103, Topology optimization decision module; 104, Maintenance plan generation module. Detailed Implementation

[0059] To better illustrate the present invention, the invention will now be described in further detail with reference to the accompanying drawings.

[0060] It should be understood that, in order to make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0061] When the power distribution network topology optimization system is applied in underground coal mines, the optimization calculation is mainly based on the nameplate rated parameters or ideal operating state parameters of the equipment. In pursuing immediate economic indicators such as minimizing network losses or load balancing, it is necessary to fully consider the actual and dynamically changing health status or aging degree of each key device in the network.

[0062] For example, suppose a power distribution network in a coal mine face requires topology reconfiguration to adapt to load changes and reduce line losses. An existing topology optimization system calculates an optimal solution that requires transferring the load from one feeder to another. This target feeder is connected in series to a switchgear that has been in operation for a long time and has been in a high-humidity environment. Although the switchgear has not yet reached the fault alarm threshold, its internal contacts may already be in a sub-healthy state with increased contact resistance.

[0063] Faced with the aforementioned problems, this application initially considered strengthening the health status monitoring of critical electrical equipment in coal mines and setting strict operational limits to prevent sub-optimal equipment from being overloaded. However, simple monitoring and static limitations cannot achieve global optimization and may lead to underutilization of some network resources, affecting overall operational economy.

[0064] In this regard, this application aims to explore whether the health status information of equipment can be integrated into the decision-making process of distribution network topology optimization. By comprehensively considering the health status and operational risks of the optimization model, more reasonable topology operation suggestions can be generated, enabling a dynamic balance between economic benefits and equipment risks in the proposed strategy.

[0065] The following is a specific embodiment for illustration. In this embodiment:

[0066] Firstly, such as Figure 1 As shown, a power distribution network topology optimization method is provided, which is applied to the application scenario of key electrical equipment in underground coal mines, and includes the following steps:

[0067] S1. Establish a calculation model for obtaining the dynamic health index of key electrical equipment, wherein the dynamic health index is used to characterize the aging degree or operating status of key equipment in the underground coal mine environment.

[0068] S2. Based on the dynamic health index of key electrical equipment, establish an operation risk cost model for the key electrical equipment. The operation risk cost model is used to quantify the operation mode of each key electrical equipment and determine the operation risk cost of the key electrical equipment.

[0069] S3. Construct an objective function for topology optimization. The objective function for topology optimization determines the total operating risk cost of the key electrical equipment by using the economic cost of distribution network operation and the operating risk cost. The objective function for topology optimization assigns weight coefficients to the economic cost of distribution network operation and the operating risk cost of key electrical equipment, and the weight coefficients are adjusted according to the health status of the key electrical equipment reflected by the dynamic health index.

[0070] S4. Based on the obtained operating costs of key electrical equipment and the dynamic health index of key electrical equipment, the optimal application results of key electrical equipment are obtained, and recommended operation suggestions for distribution network topology are generated.

[0071] The dynamic health index is a quantitative indicator used to characterize the aging degree or operating status of key equipment in the underground coal mine environment. It can be calculated using a technology based on multiple data sources such as sensor monitoring data, historical operating records, and maintenance information.

[0072] The operational risk cost model is a mathematical model used to quantify the operational risk costs of key electrical equipment based on its operating mode. It can be implemented using various risk assessment models, such as those including accelerated aging cost sub-models and expected failure loss sub-models. For example, risk costs can be calculated by analyzing the impact of different load levels and environmental factors on equipment lifespan loss and failure probability. Its main purpose is to transform the operating status and health condition of equipment into quantifiable economic costs, providing a basis for optimization decisions.

[0073] The objective function used for topology optimization is a mathematical expression that guides the topology optimization process of a distribution network. It determines the total operating risk cost of key electrical equipment by considering the economic cost and risk cost of the distribution network operation. It can be implemented using a comprehensive function that includes multiple cost items such as line loss cost, operation and maintenance cost, and equipment risk cost.

[0074] Using an exemplary example, firstly, online monitoring data (such as temperature, current, voltage, partial discharge, etc.) and offline information data (such as service life, maintenance records, environmental parameters, etc.) of key electrical equipment (e.g., transformers, switchgear, cables, etc.) are collected. Based on this data, a dynamic health index for each key electrical device is calculated using a pre-defined calculation model (e.g., a health assessment model trained on historical data). This index can be a value between 0 and 1, with lower values ​​indicating poorer health. Next, based on the device's dynamic health index, current load, and environmental conditions, an operational risk cost model is established. This model can calculate the economic cost corresponding to the additional wear and tear on the device's lifespan under the current operating mode, as well as the probability of device failure and the resulting loss costs based on the health index and load prediction. These costs are summed to obtain the operational risk cost of the device. Then, a topology optimization objective function is constructed, incorporating the total distribution network loss cost and the total operational risk cost of all key electrical equipment. The weight of the operational risk cost in the objective function is dynamically adjusted according to the device's dynamic health index; for example, when the health index of a device falls below a certain threshold, the weight of its associated operational risk cost can be significantly increased. Finally, the objective function is solved to find the distribution network topology that minimizes the objective function, and specific switching operation suggestions are generated based on the optimal topology.

[0075] By establishing a computational model to obtain the dynamic health index of critical electrical equipment, the actual aging degree or operating status of critical equipment in the underground coal mine environment can be accurately grasped. Based on the obtained dynamic health index, an operational risk cost model for critical electrical equipment is further established, quantifying the potential risks of different operating modes to the equipment and converting them into economic costs. Subsequently, an objective function for topology optimization is constructed. This function comprehensively considers the economic cost of distribution network operation and the operational risk cost of critical electrical equipment. Crucially, the weight coefficients of each cost item in the objective function are dynamically adjusted according to the equipment health status reflected by the dynamic health index. Finally, based on the obtained equipment operating costs and the judgment results of the dynamic health index, the optimal result for the application of critical electrical equipment is obtained by solving this dynamically adjusted objective function, and recommended distribution network topology operation suggestions are generated.

[0076] In summary, by incorporating the dynamic health index of key electrical equipment into the distribution network topology optimization decision-making process, the optimization results not only consider traditional operational economics but also fully take into account the actual health status and potential operational risks of the equipment. This helps avoid placing excessive operational pressure on equipment in a sub-healthy state, thereby slowing down equipment aging and reducing the risk of unplanned power outages.

[0077] In the process of obtaining the dynamic health index of critical electrical equipment, only the calculation model for the dynamic health index was proposed, without providing a specific implementation plan. This resulted in the inability to accurately and efficiently assess the health status of critical electrical equipment, which in turn affected the reliability of subsequent topology optimization decisions.

[0078] In this regard, such as Figure 2 As shown in this embodiment, the step of establishing a calculation model for obtaining the dynamic health index of key electrical equipment, wherein the dynamic health index is used to characterize the aging degree or operating status of key equipment in the underground coal mine environment, specifically includes:

[0079] S11. Based on the acquired online monitoring data and offline information data, the equipment aging mode of key electrical equipment is obtained by matching from the preset aging information database;

[0080] S12. Based on the aging patterns of key electrical equipment and the online and offline monitoring data of key electrical equipment, establish a calculation function model for calculating the dynamic health index. Calculate the dynamic health index for each key electrical equipment using the calculation model. The dynamic health index for each key electrical equipment is periodically updated based on the corresponding online monitoring data.

[0081] Online monitoring data refers to various operating parameters collected in real time during equipment operation, including voltage, current, temperature, vibration, partial discharge, etc. Its purpose is to reflect the current real-time operating status of the equipment. Offline information data refers to historical or static information related to the equipment that is not acquired in real time, including the equipment's manufacturing date, installation location, maintenance records, fault history, factory test reports, environmental parameter history records, etc. Its purpose is to provide background information and long-term operating status of the equipment.

[0082] The pre-set aging information database refers to a collection of typical aging mode characteristics of different types of key electrical equipment under various operating and environmental conditions. Its purpose is to provide a reference benchmark for identifying the actual aging state of the equipment. The corresponding equipment aging mode refers to a specific set of laws or characteristics of the performance degradation of key electrical equipment over time under specific stress. Specifically, it can manifest as decreased insulation performance, mechanical wear, contact deterioration, etc. Its purpose is to characterize the current degradation stage and trend of the equipment.

[0083] The calculation function model for the dynamic health index refers to a mathematical model or algorithm used to transform online monitoring data, offline information data, and identified aging patterns of equipment into a quantitative health index. Specifically, it can employ rule-based expert systems, statistical models, machine learning models, etc. Its purpose is to comprehensively assess the health status of the equipment and output numerical indicators.

[0084] Specifically, for example, in a fuzzy algorithm model based on expert decision-making, the calculation function model for indicative of the dynamic health index is as follows:

[0085] C=w1*R1+w2*R2+…+wm*Rm, i=1,2,3,…,m;

[0086] Wherein, C indicates the dynamic health index of the critical electrical equipment, which is one of the following: transformer, switch cabinet, and cable; Ri indicates the health level of the critical electrical equipment in the i-th monitoring data; and wi indicates the health level weight of the critical electrical equipment in the i-th monitoring data.

[0087] Ri indicates the health level of critical electrical equipment based on the i-th monitoring data point. It refers to the value or state of a specific monitoring data point (such as temperature, humidity, current, voltage, partial discharge, oil gas content, etc.) of the critical electrical equipment. Specifically, the monitoring data can be divided into multiple intervals or thresholds, with each interval corresponding to a health level. For example, it can be set as an integer level from 1 to 5, with higher levels indicating better health. wi indicates the health level weight of critical electrical equipment based on the i-th monitoring data point. It is a relative importance coefficient used to measure the degree of influence of the i-th monitoring data point on the overall health status of the critical electrical equipment. Specifically, the importance of different monitoring data points can be determined based on expert experience, historical fault data analysis, or research on equipment aging mechanisms, and corresponding weight values ​​can be assigned to them.

[0088] For example, let's take a power transformer used in a coal mine as an example to illustrate how to calculate its dynamic health index. Assume the transformer has three key monitoring data points: oil temperature, winding temperature, and total dissolved hydrocarbon content in the oil. First, we need to establish health level classification rules for these three monitoring data points. For example, oil temperature can be divided into 5 levels, winding temperature into 5 levels, and total hydrocarbon content into 5 levels, with higher levels indicating better health. Then, based on the degree of influence of these three monitoring data points on the transformer's health status, we determine their weights. For example, we can set the weight of oil temperature w1 to 0.3, the weight of winding temperature w2 to 0.4, and the weight of total hydrocarbon content w3 to 0.3. At a certain moment, the transformer's oil temperature is monitored at level R1=4, the winding temperature at level R2=3, and the total hydrocarbon content at level R3=5. At this point, according to the calculation function model, the dynamic health index C of the transformer can be calculated as: C = w1*R1 + w2*R2 + w3*R3 = 0.3*4 + 0.4*3 + 0.3*5 = 1.2 + 1.2 + 1.5 = 3.9. This calculated index of 3.9 quantifies the health status of the transformer at that moment.

[0089] The proposed solution first obtains the aging patterns of key electrical equipment by matching acquired online monitoring data and offline information data from a pre-set aging information database. This is because different aging patterns correspond to different health states and degradation paths, and identifying specific patterns provides a targeted basis for subsequent health assessments. Then, based on the identified equipment aging patterns and the online and offline information data of the key electrical equipment, a calculation function model for calculating the dynamic health index is established, and the dynamic health index is calculated using this model. Simultaneously, the dynamic health index for each key electrical device is periodically updated based on the corresponding online monitoring data, ensuring that the health index can promptly capture subtle changes and dynamic evolutions in the equipment's condition, thereby providing real-time and accurate health assessment results.

[0090] To illustrate with a specific example, for an underground transformer in a coal mine, the system first acquires its online monitoring data, such as winding temperature, oil temperature, load current, and partial discharge signals, while simultaneously acquiring its offline information data, such as installation date, last overhaul record, oil chromatography analysis report, and historical fault records. The system then matches this data with transformer aging patterns in a pre-defined aging information database. For instance, if oil chromatography analysis shows an abnormally high acetylene content and active partial discharge signals, the system might match a pattern of "accelerated aging of oil-paper insulation partial discharge." Next, based on the identified "accelerated aging of oil-paper insulation partial discharge" pattern, the system establishes or calls a specific calculation function model. This model might focus more on parameters such as the characteristics of partial discharge signals, the rate of change of gas components in the oil, and the impact of temperature on insulation life. The system inputs the current online monitoring data and relevant offline information data into this model to calculate the transformer's current dynamic health index, constraining it to a value between 0 and 100. Subsequently, the system will periodically recalculate the dynamic health index of the transformer based on new online monitoring data (e.g., collected once per hour) to reflect the latest changes in its health status.

[0091] In providing risk assessments based on equipment health status for distribution network topology optimization, effectively converting a dynamic health index into quantifiable operational risk costs and using this to guide distribution network topology optimization is a problem that needs to be solved. Especially in complex and variable environments such as underground coal mines, it is insufficient to consider only the health status of the equipment itself; it is also necessary to comprehensively assess its operational risks by combining the load borne by the equipment and the environmental factors in which it is located.

[0092] In this regard, such as Figure 3 As shown, the step of establishing an operational risk cost model for key electrical equipment based on the dynamic health index of the key electrical equipment, wherein the operational risk cost model is used to quantify the operational mode of each key electrical equipment and determine the operational risk cost of the key electrical equipment, specifically includes:

[0093] S21. Based on the dynamic health index of key electrical equipment, combined with the current load of key electrical equipment and the environmental index of underground coal mine, construct an operation risk cost model, wherein the operation risk cost model includes an accelerated aging cost sub-model and an expected failure loss sub-model.

[0094] S22. Based on the operational risk cost model, perform the following operations:

[0095] The accelerated aging cost sub-model is applied to calculate the equipment life loss rate caused by the current load on critical electrical equipment, and the equipment loss rate is converted into an economic cost value in combination with the current equipment cost of critical electrical equipment. This is used as an additional consumption of the remaining life of the equipment by the current operating mode of critical electrical equipment.

[0096] By applying the expected failure loss sub-model, the failure probability of the current critical electrical equipment is obtained by calculating the dynamic health index and load of the current critical electrical equipment. After obtaining the failure probability of the current critical electrical equipment, the potential economic loss of the current critical electrical equipment is calculated based on the obtained failure probability and the comprehensive loss of the critical electrical equipment under the failure probability.

[0097] S23. Based on the additional consumption of the remaining lifespan of the key equipment and the potential economic loss of the key equipment obtained under the accelerated aging cost sub-model and the expected failure loss sub-model, the operating risk cost of the key electrical equipment is comprehensively determined.

[0098] Among them, the economic cost value refers to the quantitative value of converting the equipment life wear rate or potential failure loss into monetary units; the load and the environmental index of the coal mine refers to the comprehensive or single index of the electrical quantities such as current and power that the equipment bears at a specific moment or period and the quantitative impact of environmental factors in the coal mine (such as temperature, humidity, dust, corrosive gases, etc.) on the equipment; the potential economic loss refers to the product of the probability of equipment failure and the comprehensive loss after failure; the additional consumption of remaining life refers to the additional economic cost caused by the current operating mode to the remaining life of the equipment, calculated by the accelerated aging cost sub-model; the potential economic loss refers to the economic loss that the equipment faces due to possible failure, calculated by the expected failure loss sub-model; and the operating risk cost refers to the economic indicator that quantitatively represents the current operating risk of the equipment after comprehensively considering the accelerated aging cost and the expected failure loss.

[0099] By leveraging the dynamic health index of key electrical equipment, combined with the current load on this equipment and the environmental index in the coal mine, an operational risk cost model is constructed. This model includes an accelerated aging cost sub-model and an expected failure loss sub-model. This model construction approach allows the solution to comprehensively consider the impact of the equipment's own health status, external operating load, and environmental factors on equipment operational risks. By applying the accelerated aging cost sub-model, the solution can calculate the rate of equipment lifespan degradation caused by load and convert it into an economic cost value, thereby quantifying the additional consumption of the equipment's remaining lifespan by the current operating mode. Simultaneously, by applying the expected failure loss sub-model, the solution can calculate the equipment failure probability based on the dynamic health index and load, and combine this with comprehensive losses to calculate potential economic losses.

[0100] For example, suppose a transformer in a coal mine has a dynamic health index calculated using the aforementioned method, for example, 75 (out of 100). Simultaneously, the transformer's current load data is obtained, such as a current load rate of 80%, and the environmental indices of the area in the coal mine, such as high ambient temperature and high humidity. Based on this input data, an operational risk cost model is constructed. The accelerated aging cost sub-model in this model can calculate the transformer's lifespan loss rate under the current operating condition, for example, 0.01% loss per day, based on factors such as the transformer's load rate and ambient temperature, using a preset calculation formula or lookup table. Combining the transformer's equipment cost and design life, this lifespan loss rate can be converted into a daily economic cost value, for example, 100 yuan per day. This 100 yuan represents the additional consumption caused by the current operating mode to the transformer's remaining lifespan. Meanwhile, the expected failure loss sub-model in the model can calculate the probability of the transformer failing within a future period (e.g., one year), for example, 2%, based on the transformer's dynamic health index (75) and load rate (80%), using another preset probability model. Assume the total losses (including repairs and downtime) after a transformer failure are 500,000 yuan. Then, the potential economic loss is the failure probability multiplied by the total loss, i.e., 2% * 500,000 yuan = 10,000 yuan. Finally, combining the economic costs of accelerated aging (e.g., 100 yuan per day, equivalent to 36,500 yuan per year) with the potential economic loss (10,000 yuan) yields the operating risk cost of the transformer under its current operating condition. This comprehensively determined operating risk cost, for example, 46,500 yuan / year, quantitatively reflects the current operating risk of the transformer.

[0101] In constructing the objective function for topology optimization, which determines the total operating risk cost of the key electrical equipment through the economic cost of distribution network operation and the operating risk cost, the weighting coefficients are adjusted solely based on the dynamic health index of the key electrical equipment itself, ignoring the differences in the impact of different electrical equipment on the overall power supply reliability of the distribution network. This may result in the optimized topology failing to fully consider the global risks that may be caused by the failure of key equipment.

[0102] In this regard, such as Figure 4 As shown, in the step of constructing an objective function for topology optimization, whereby the objective function for topology optimization determines the total operating risk cost of the key electrical equipment through the economic operating cost of the distribution network and the operating risk cost, and the objective function for topology optimization assigns weight coefficients to the economic operating cost of the distribution network and the operating risk cost of the key electrical equipment, and the weight coefficients are adjusted according to the health status of the key electrical equipment reflected by the dynamic health index, the adjustment of the weight coefficients includes:

[0103] A1. Divide the power distribution network into multiple electrical equipment groups, obtain the dynamic health index of each key electrical equipment in each electrical equipment group, and calculate the group health risk value of each electrical equipment group based on the obtained dynamic health index and the degree of influence of each key electrical equipment on the power supply reliability of the electrical equipment group.

[0104] A2. Based on the calculated group health risk value of each group of electrical equipment, determine the group risk weight coefficient corresponding to each group of electrical equipment, wherein the group risk weight coefficient of the electrical equipment group with the high group health risk value is set to high.

[0105] A3. When constructing the objective function for topology optimization, the total operating risk cost is obtained by weighting the operating risk costs of each key electrical device in each electrical equipment group using the corresponding group risk weight coefficient, and then summing the weight coefficients according to the health status of the key electrical devices reflected by the dynamic health index.

[0106] Dividing the power distribution network into multiple electrical equipment groups refers to grouping the key electrical equipment in the power distribution network according to certain rules or logic, such as geographical location, power supply area, load importance level, voltage level, or functional type.

[0107] The degree of impact on the power supply reliability of a group of electrical equipment refers to the extent to which a failure of a critical electrical device affects the continuity or quality of power supply to the loads within the group of electrical equipment to which the device belongs. It can be represented by a reliability impact parameter.

[0108] By dividing the power distribution network into multiple electrical equipment groups, and based on the dynamic health index of each key electrical equipment within each group and the impact of each key electrical equipment on the power supply reliability of its group, a group health risk value is calculated for each electrical equipment group. Based on the calculated group health risk value, a group risk weight coefficient is determined for each electrical equipment group; electrical equipment groups with higher risk values ​​have higher corresponding group risk weight coefficients. When constructing the topology optimization objective function, the total operating risk cost is obtained by weighting the operating risk costs of each key electrical equipment within each group using the corresponding group risk weight coefficient. This group risk-based weight adjustment method ensures that weight adjustment no longer relies solely on the health of individual devices, but considers the importance of devices in the system and the overall risk status of the group.

[0109] For example, the underground power distribution network in a coal mine can be divided into multiple electrical equipment groups according to different mining areas or important load areas. For instance, the power supply area of ​​the main haulage roadway can be divided into Group 1, and the power supply area of ​​a certain mining face can be divided into Group 2. For Group 2, the dynamic health index of key electrical equipment (such as coal mining machine transformers, switchgear, and cables) is obtained. Simultaneously, the impact of these devices on the power supply reliability of the mining face is assessed. For example, a coal mining machine transformer failure will cause the entire working face to shut down, indicating a high impact; a branch switch failure only affects some equipment, indicating a medium impact. Based on the dynamic health index of these devices and their impact on the group's power supply reliability, the group health risk value of Group 2 is calculated. For example, bottleneck electrical equipment within the group can be identified, and its dynamic health index can be extracted as the dominant risk indicator parameter. Combined with the health of other equipment within the group, an overall health benchmark value is calculated, and then corrected considering factors such as the dispersion of the health distribution to obtain the group health risk value. Based on the calculated group health risk value, the group risk weight coefficient corresponding to Group 2 is determined. If the risk value of Group 2 is high, the corresponding weight coefficient is set high. When constructing the topology optimization objective function, the operating risk cost of each key electrical device in Group 2 is multiplied by the risk weight coefficient of Group 2, and then summed together with the weighted risk costs of other groups to obtain the total operating risk cost.

[0110] Based on the above explanation of the adjustment of weight coefficients, step A1 will be further explained.

[0111] The steps of dividing the power distribution network into multiple electrical equipment groups, obtaining the dynamic health index of each key electrical equipment within each electrical equipment group, and calculating the group health risk value of each electrical equipment group based on the obtained dynamic health index and the impact of each key electrical equipment on the power supply reliability of the electrical equipment group, specifically include:

[0112] A11. Divide the power distribution network into multiple electrical equipment groups, obtain the dynamic health index of each key electrical equipment in each electrical equipment group, and the reliability impact parameter used to characterize the degree of influence of each key electrical equipment on the power supply reliability of the electrical equipment group to which it belongs;

[0113] A12. Based on the obtained dynamic health index of each key electrical device and its corresponding reliability impact parameter, identify the bottleneck electrical device in the electrical device group, wherein the bottleneck electrical device is indicated as the key electrical device whose dynamic health index is lower than a preset bottleneck state threshold and whose corresponding reliability impact parameter is higher than a preset impact weight threshold.

[0114] A13. Extract the dynamic health index of the bottleneck electrical equipment as the dominant risk indication parameter of the group health risk value, and obtain the dynamic health index of other key electrical equipment in the electrical equipment group other than the bottleneck electrical equipment, and calculate the overall health benchmark value of the electrical equipment group based on the obtained dynamic health index of these other key electrical equipment.

[0115] A14. Based on the dominant risk indication parameter of the bottleneck electrical equipment and the overall health benchmark value of the electrical equipment group, and combined with the dispersion index used to characterize the distribution differences of the dynamic health index of each key electrical equipment in the electrical equipment group, the dominant risk indication parameter is corrected to obtain the health risk value of the group. The dispersion index is used to quantify the risk of the electrical equipment group that may be caused by the heterogeneous distribution of the dynamic health index.

[0116] Among them, the reliability impact parameter refers to the numerical value that quantifies the degree of impact on the power supply reliability of the group of electrical equipment when a critical electrical device fails. Its purpose is to identify the equipment in the group that is critical to the continuity of power supply.

[0117] Bottleneck electrical equipment refers to critical electrical equipment within an electrical equipment group that has a poor health status (low dynamic health index) and a significant impact on the reliability of the group's power supply (high reliability impact parameter). It is identified by setting preset bottleneck status thresholds and preset impact weight thresholds. Its purpose is to focus risk assessment on those critical equipment that are most likely to cause overall group risk.

[0118] The preset bottleneck state threshold refers to the lower limit of the dynamic health index used to determine whether the health status of critical electrical equipment is in a bottleneck state. Its purpose is to screen out equipment with a health status below a certain level.

[0119] The preset impact weight threshold is a lower limit value of the reliability impact parameter used to determine whether the impact of critical electrical equipment on the reliability of group power supply reaches the importance level. Its purpose is to screen out equipment that has a greater impact on the group's function.

[0120] The dominant risk indicator parameter refers to the direct use of the dynamic health index of the identified bottleneck electrical equipment as the core indicator for assessing the health risk of the group, with the aim of highlighting the risk level of the most vulnerable link in the group.

[0121] The overall health benchmark refers to the comprehensive reflection of the dynamic health index of other key electrical equipment in an electrical equipment group, excluding bottleneck electrical equipment. Its purpose is to provide a reference for the overall health level of the group, which can be used to correct risk assessments based on bottleneck equipment.

[0122] The dispersion index is a numerical value used to quantify the degree of difference in the distribution of dynamic health index of key electrical equipment within a group of electrical equipment. Its purpose is to capture the additional risks that may be caused by inconsistencies in the health status of equipment.

[0123] The power distribution network is divided into electrical equipment groups, and a refined risk assessment is conducted for each group. First, the dynamic health index and reliability impact parameters of each key device within the group are obtained; these data form the basis of the assessment. Based on this data, bottleneck electrical equipment within the group is identified—these are the key points with poor health and significant impact. By using the dynamic health index of the bottleneck equipment as the dominant risk indicator parameter, and simultaneously calculating the overall health benchmark value of other equipment within the group, a dispersion index is introduced to quantify the differences in equipment health status within the group. Finally, through correction calculations, information from the three dimensions of bottleneck risk, overall health level, and health heterogeneity is organically combined to adjust the dominant risk indicator parameter, thereby obtaining a more comprehensive and accurate group health risk value.

[0124] For example, a distribution network can be divided into multiple electrical equipment groups based on feeder or substation areas. For each group, the dynamic health index of key equipment such as transformers, switchgear, and cables is obtained through online monitoring systems and offline detection data. Simultaneously, based on the group's network structure and downstream load type, the reliability impact parameter corresponding to each key equipment is determined; for example, equipment connected to important loads or located on critical paths has a higher impact parameter. Next, a preset bottleneck state threshold, such as 0.6, and a preset impact weight threshold, such as 0.8, are set. All key equipment within the group is iterated over; if a device's dynamic health index is below 0.6 and its reliability impact parameter is above 0.8, it is identified as a bottleneck electrical equipment. If a bottleneck device is identified, its dynamic health index is extracted as the dominant risk indicator parameter for the group's health risk value. Simultaneously, the dynamic health indices of all non-bottleneck equipment within the group are collected, and their average is calculated as the overall health benchmark value for the group. Then, the standard deviation of the dynamic health indices of all key equipment (both bottleneck and non-bottleneck) within the group is calculated as the dispersion index. Finally, based on the dominant risk indicator parameter of the bottleneck device, the overall health benchmark value, and the dispersion index, the dominant risk indicator parameter is corrected using a preset correction formula to obtain the final group health risk value.

[0125] In addition, the step of using the dispersion index to characterize the distribution difference of the dynamic health index of each key electrical device in the electrical equipment group specifically involves: calculating the statistical dispersion of the dynamic health index of each key electrical device in the electrical equipment group based on the dynamic health index of each key electrical device in the electrical equipment group, and obtaining the dispersion index.

[0126] By calculating the statistical dispersion of the dynamic health index of each key electrical device within the electrical equipment group based on the dynamic health index of each key electrical device within the electrical equipment group, a dispersion index is obtained, thereby quantifying the differences in the distribution of the dynamic health index of each key electrical device within the electrical equipment group.

[0127] Specifically, the dispersion index, obtained by calculating statistical dispersion, reflects the uniformity of the health status distribution of devices within a group. When the health status of devices within a group varies significantly (high dispersion), even if the bottleneck device's health status is not extremely low, or the average health status of other devices is relatively high, the overall risk of the group may increase due to this imbalance. Incorporating this dispersion index into the calculation of the group's health risk value allows for an assessment of the group's overall risk. High dispersion leads to a higher adjusted group health risk value, thus assigning the group a higher weight when subsequently determining the group's risk weight coefficient. Ultimately, in the topology optimization objective function, the operational risk cost of devices within the group will receive a higher weighting.

[0128] Based on the above explanation of the adjustment of weight coefficients, step A14 will be further explained.

[0129] The step of correcting the dominant risk indication parameter based on the dominant risk indication parameter of the bottleneck electrical equipment and the overall health benchmark value of the electrical equipment group, and in conjunction with the dispersion index used to characterize the differences in the dynamic health index distribution of each key electrical equipment within the electrical equipment group, to obtain the group health risk value, includes:

[0130] A141. Obtain monitoring data of environmental parameters in a specific area underground in the coal mine, and based on the obtained monitoring data of the environmental parameters, identify significant short-term changes in the environmental parameters.

[0131] A142. In response to a short-term significant change in the identified environmental parameter, an environmental impact adjustment parameter is determined based on the type and degree of change of the environmental parameter and the characteristics of the critical electrical equipment within the electrical equipment group.

[0132] A143. Based on the dominant risk indicator parameter and the overall health baseline value, and in conjunction with the dispersion index and the environmental impact adjustment parameter, the dominant risk indicator parameter is corrected to obtain the group health risk value that has incorporated the impact of short-term significant changes in the environmental parameters.

[0133] Among them, a short-term significant change in environmental parameters refers to a change in the value or state of environmental parameters that exceeds the normal fluctuation range or a preset threshold within a relatively short period of time. Such a change may have an adverse impact on the operation or lifespan of electrical equipment. An environmental impact adjustment parameter is a quantified value or function used to characterize the degree of impact of short-term significant changes in environmental parameters on the risk of electrical equipment groups.

[0134] By acquiring monitoring data of environmental parameters in specific areas of underground coal mines and identifying significant short-term changes in these parameters, crucial environmental information input is provided for risk assessment. In response to the identified significant short-term changes in environmental parameters, an environmental impact adjustment parameter is determined based on the type and degree of change of the environmental parameters and the characteristics of key electrical equipment within the electrical equipment group. This step transforms abstract environmental changes into quantitative indicators that can be used for risk assessment. Finally, based on the original dominant risk indicator parameter, overall health baseline value, and dispersion index, and in conjunction with the determined environmental impact adjustment parameter, the dominant risk indicator parameter is corrected to obtain the group health risk value that incorporates the impact of significant short-term changes in environmental parameters.

[0135] For example, suppose an electrical equipment group is located in a specific area underground in a coal mine, where temperature, humidity, and methane concentration sensors are installed. First, the system periodically acquires monitoring data from these sensors. At a certain moment, the system detects a significant simultaneous increase in both temperature and humidity within a short period, for example, a 5-degree Celsius increase in temperature and a 15% increase in humidity. Based on preset thresholds or rate-of-change rules, the system identifies this as a short-term significant change event in environmental parameters. Second, in response to the short-term significant increase in temperature and humidity, the system determines an environmental impact adjustment parameter based on the types of these two environmental parameters, their respective degrees of change (5 degrees Celsius and 15%), and the characteristics of critical electrical equipment within the group (e.g., transformers are temperature-sensitive, cable joints are humidity-sensitive), through a lookup table or computational model. Third, the system performs a correction calculation on the dominant risk indication parameter based on previously calculated dominant risk indication parameters for the group (e.g., determined by the health of the weakest transformer in the group), the overall health baseline, and the dispersion index, combined with the determined environmental impact adjustment parameter (e.g., a risk increase coefficient). The correction calculation can be to multiply the dominant risk indicator parameter by the risk increase coefficient, or to add an additional risk value determined by the environmental impact adjustment parameter to the original correction result. The final group health risk value includes the additional risk impact brought about by the significant short-term changes in environmental parameters.

[0136] In this embodiment, as Figure 5As shown, the steps for obtaining the optimal application result of key electrical equipment based on the obtained operating costs and dynamic health index of key electrical equipment, and generating recommended distribution network topology operation suggestions, specifically include:

[0137] S41. Preset maintenance early warning thresholds and operational risk cost warning values;

[0138] S42. Construct an optimization algorithm model and perform the following operations:

[0139] When it is determined that the operating cost of critical electrical equipment is consistently higher than the warning value of operating risk cost, the optimal result for the application of critical electrical equipment is obtained by solving the topology optimization objective function under the current operating cost of critical electrical equipment through the optimization algorithm model, and the distribution network topology operation suggestions are output accordingly.

[0140] When the dynamic health index of critical electrical equipment is determined to be lower than the preset maintenance warning threshold, the optimal application result of critical electrical equipment is obtained by solving the topology optimization objective function under the current dynamic health index of critical electrical equipment through the optimization algorithm model, and the distribution network topology operation suggestion is output accordingly.

[0141] Meanwhile, when outputting distribution network topology operation suggestions, the application of optimization algorithm models must meet the basic operating constraints of the distribution network, wherein the basic operating constraints are one or more of the following: voltage qualification, current not exceeding limits, network connectivity, and radial operation.

[0142] By setting preset maintenance warning thresholds and operational risk cost warning values, a clear benchmark is provided for subsequent judgments and decisions.

[0143] When the system determines that the operating cost of critical electrical equipment consistently exceeds the warning value for operational risk costs, or when it determines that the dynamic health index of critical electrical equipment is below the preset maintenance warning threshold, the optimization algorithm model will be invoked. This optimization algorithm model will solve for the topology optimization objective function based on the current equipment state (high operating cost or low health). This objective function comprehensively considers both the economic cost of distribution network operation and the operational risk cost of critical electrical equipment, and its weighting coefficients are adjusted according to the health status of the critical electrical equipment reflected by the dynamic health index. By solving this objective function, the system can find the distribution network topology that achieves the optimal application results for critical electrical equipment under the current equipment state. Subsequently, the system will generate recommended distribution network topology operation suggestions based on this optimal result.

[0144] Based on the real-time status of the operating costs and dynamic health index of key electrical equipment, the distribution network topology optimization process is triggered in a timely manner. The generated topology operation suggestions can comprehensively consider the economics and health status of the equipment and enforce the basic operating constraints of the distribution network, thereby improving the intelligence level and risk response capability of the distribution network operation and ensuring the continuity and security of power supply in underground coal mines.

[0145] Secondly, such as Figure 6 As shown, a power distribution network topology optimization system 100 is provided, which is applied in the application scenario of key electrical equipment in underground coal mines, including:

[0146] The dynamic health acquisition module 101 is used to establish a calculation model for acquiring the dynamic health index of key electrical equipment. The dynamic health index is used to characterize the aging degree or operating status of key equipment in the underground coal mine environment.

[0147] The operation risk cost calculation module 102 is used to establish an operation risk cost model for the key electrical equipment based on the dynamic health index of the key electrical equipment. The operation risk cost model is used to quantify the operation mode of each key electrical equipment and determine the operation risk cost of the key electrical equipment.

[0148] The topology optimization decision module 103 is used to construct an objective function for topology optimization. The objective function for topology optimization determines the total operating risk cost of the key electrical equipment through the economic cost of distribution network operation and the operating risk cost. The objective function for topology optimization assigns weight coefficients to the economic cost of distribution network operation and the operating risk cost of key electrical equipment, and the weight coefficients are adjusted according to the health status of the key electrical equipment reflected by the dynamic health index.

[0149] The maintenance scheme generation module 104 is used to obtain the optimal application result of the key electrical equipment based on the obtained operating cost of the key electrical equipment and the dynamic health index of the key electrical equipment, and generate recommended operation suggestions for the distribution network topology.

[0150] This embodiment of a distribution network topology optimization system includes a dynamic health acquisition module, an operational risk cost module, a topology optimization module, and a maintenance plan generation module. By incorporating the dynamic health index and operational risk cost of key electrical equipment into the topology optimization objective function and dynamically adjusting the weights according to the equipment health status, it effectively solves the problem in the prior art that neglecting the actual health status of equipment may lead to accelerated equipment degradation due to the optimization plan. This allows for the effective integration of real-time health status assessment information of key equipment and the dynamic balancing of short-term operational economic indicators and long-term operational reliability during the optimization process, reducing the risk of unexpected equipment failures and improving the overall reliability and safety of the distribution network operation.

[0151] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit them. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure.

Claims

1. A power distribution network topology optimization method, applied to the application scenario of key electrical equipment in underground coal mines, characterized in that, Includes the following steps: S1. Establish a calculation model for obtaining the dynamic health index of key electrical equipment, wherein the dynamic health index is used to characterize the aging degree or operating status of key equipment in the underground coal mine environment. S2. Based on the dynamic health index of key electrical equipment, establish an operation risk cost model for the key electrical equipment. The operation risk cost model is used to quantify the operation mode of each key electrical equipment and determine the operation risk cost of the key electrical equipment. S3. Construct an objective function for topology optimization. The objective function for topology optimization determines the total operating risk cost of the key electrical equipment through the economic cost of distribution network operation and the operating risk cost. The economic cost of distribution network operation includes line loss cost. The objective function for topology optimization assigns weight coefficients to the economic cost of distribution network operation and the operating risk cost of key electrical equipment. The weight coefficients are adjusted according to the health status of the key electrical equipment reflected by the dynamic health index. S4. Based on the obtained operating costs of key electrical equipment and the dynamic health index of key electrical equipment, the optimal application results of key electrical equipment are obtained, and recommended operation suggestions for distribution network topology are generated. In the step of constructing an objective function for topology optimization, where the objective function for topology optimization determines the total operating risk cost of the key electrical equipment through the economic operating cost of the distribution network and the operating risk cost, and the objective function for topology optimization assigns weight coefficients to the economic operating cost of the distribution network and the operating risk cost of the key electrical equipment, and the weight coefficients are adjusted according to the health status of the key electrical equipment reflected by the dynamic health index, the adjustment of the weight coefficients includes: A1. Divide the power distribution network into multiple electrical equipment groups, obtain the dynamic health index of each key electrical equipment in each electrical equipment group, and calculate the group health risk value of each electrical equipment group based on the obtained dynamic health index and the degree of influence of each key electrical equipment on the power supply reliability of the electrical equipment group. A2. Based on the calculated group health risk value of each group of electrical equipment, determine the group risk weight coefficient corresponding to each group of electrical equipment, wherein the group risk weight coefficient of the electrical equipment group with the high group health risk value is set to high. A3. When constructing the objective function for topology optimization, the total operating risk cost is still determined jointly by the distribution network operating economic cost and the operating risk cost of each of the key electrical equipment. Specifically, the operating risk cost of key electrical equipment within each group of electrical equipment is weighted and summarized using the corresponding group risk weight coefficient, and then combined with the distribution network operating economic cost to obtain the total operating risk cost. The weight coefficient is adjusted according to the health status of the key electrical equipment reflected by the dynamic health index.

2. The distribution network topology optimization method according to claim 1, characterized in that, The step of establishing a calculation model for obtaining the dynamic health index of key electrical equipment, wherein the dynamic health index is used to characterize the aging degree or operating status of key equipment in the underground coal mine environment, specifically includes: S11. Based on the acquired online monitoring data and offline information data, the equipment aging mode of key electrical equipment is obtained by matching from the preset aging information database; S12. Based on the aging patterns of key electrical equipment and the online and offline monitoring data of key electrical equipment, establish a calculation function model for calculating the dynamic health index. Calculate the dynamic health index for each key electrical equipment using the calculation model. The dynamic health index for each key electrical equipment is periodically updated based on the corresponding online monitoring data.

3. The distribution network topology optimization method according to claim 2, characterized in that, The calculation function model for the dynamic health index is as follows: C=w1*R1+w2*R2+…+wi*Ri+…+wm*Rm, i=1,2,3,…,m; C indicates the dynamic health index of critical electrical equipment, which includes one of transformers, switchgear, and cables. Ri indicates the health level of critical electrical equipment at the i-th monitoring data point; wi indicates the health level weight of critical electrical equipment in the i-th monitoring data.

4. The distribution network topology optimization method according to claim 1, characterized in that, The step of establishing an operational risk cost model for key electrical equipment based on its dynamic health index, wherein the operational risk cost model is used to quantify the operational mode of each key electrical equipment and determine its operational risk cost, specifically includes: S21. Based on the dynamic health index of key electrical equipment, combined with the current load of key electrical equipment and the environmental index of underground coal mine, construct an operation risk cost model, wherein the operation risk cost model includes an accelerated aging cost sub-model and an expected failure loss sub-model. S22. Based on the operational risk cost model, perform the following operations: The accelerated aging cost sub-model is applied to calculate the equipment life loss rate caused by the current load on critical electrical equipment, and the equipment loss rate is converted into an economic cost value in combination with the current equipment cost of critical electrical equipment. This is used as an additional consumption of the remaining life of the equipment by the current operating mode of critical electrical equipment. By applying the expected failure loss sub-model, the failure probability of the current critical electrical equipment is obtained by calculating the dynamic health index and load of the current critical electrical equipment. After obtaining the failure probability of the current critical electrical equipment, the potential economic loss of the current critical electrical equipment is calculated based on the obtained failure probability and the comprehensive loss of the critical electrical equipment under the failure probability. S23. Based on the additional consumption of the remaining lifespan of the key equipment and the potential economic loss of the key equipment obtained under the accelerated aging cost sub-model and the expected failure loss sub-model, the operating risk cost of the key electrical equipment is comprehensively determined.

5. The distribution network topology optimization method according to claim 1, characterized in that, The steps of dividing the power distribution network into multiple electrical equipment groups, obtaining the dynamic health index of each key electrical equipment within each electrical equipment group, and calculating the group health risk value of each electrical equipment group based on the obtained dynamic health index and the impact of each key electrical equipment on the power supply reliability of the electrical equipment group, specifically include: A11. Divide the power distribution network into multiple electrical equipment groups, obtain the dynamic health index of each key electrical equipment in each electrical equipment group, and the reliability impact parameter used to characterize the degree of influence of each key electrical equipment on the power supply reliability of the electrical equipment group to which it belongs; A12. Based on the obtained dynamic health index of each key electrical device and its corresponding reliability impact parameter, identify the bottleneck electrical device in the electrical device group, wherein the bottleneck electrical device is indicated as the key electrical device whose dynamic health index is lower than a preset bottleneck state threshold and whose corresponding reliability impact parameter is higher than a preset impact weight threshold. A13. Extract the dynamic health index of the bottleneck electrical equipment as the dominant risk indication parameter of the group health risk value, and obtain the dynamic health index of other key electrical equipment in the electrical equipment group other than the bottleneck electrical equipment, and calculate the overall health benchmark value of the electrical equipment group based on the obtained dynamic health index of these other key electrical equipment. A14. Based on the dominant risk indication parameter of the bottleneck electrical equipment and the overall health benchmark value of the electrical equipment group, and combined with the dispersion index used to characterize the distribution differences of the dynamic health index of each key electrical equipment in the electrical equipment group, the dominant risk indication parameter is corrected to obtain the health risk value of the group. The dispersion index is used to quantify the risk of the electrical equipment group that may be caused by the heterogeneous distribution of the dynamic health index.

6. The distribution network topology optimization method according to claim 5, characterized in that, The step of using the dispersion index to characterize the differences in the dynamic health index distribution of each key electrical device within the electrical equipment group specifically includes: Based on the dynamic health index of each key electrical device within the electrical equipment group, the statistical dispersion of the dynamic health index of each key electrical device within the electrical equipment group is calculated to obtain the dispersion index.

7. The distribution network topology optimization method according to claim 5, characterized in that, The step of correcting the dominant risk indication parameter based on the dominant risk indication parameter of the bottleneck electrical equipment and the overall health benchmark value of the electrical equipment group, and in conjunction with the dispersion index used to characterize the differences in the dynamic health index distribution of each key electrical equipment within the electrical equipment group, to obtain the group health risk value, includes: A141. Obtain monitoring data of environmental parameters in the power supply area of ​​the electrical equipment group in the underground coal mine, and based on the obtained monitoring data of the environmental parameters, identify short-term significant changes in the environmental parameters. A142. In response to a short-term significant change in the identified environmental parameter, an environmental impact adjustment parameter is determined based on the type and degree of change of the environmental parameter and the characteristics of the critical electrical equipment within the electrical equipment group. A143. Based on the dominant risk indicator parameter and the overall health baseline value, and in conjunction with the dispersion index and the environmental impact adjustment parameter, the dominant risk indicator parameter is corrected to obtain the group health risk value that has incorporated the impact of short-term significant changes in the environmental parameters.

8. The distribution network topology optimization method according to claim 1, characterized in that, The application of key electrical equipment is determined based on the obtained operating costs and dynamic health index of the key electrical equipment. The steps for achieving the optimal result and generating recommended distribution network topology operation suggestions specifically include: S41. Preset maintenance early warning thresholds and operational risk cost warning values; S42. Construct an optimization algorithm model and perform the following operations: When it is determined that the operating cost of critical electrical equipment is consistently higher than the warning value of operating risk cost, the optimal result for the application of critical electrical equipment is obtained by solving the topology optimization objective function under the current operating cost of critical electrical equipment through the optimization algorithm model, and the distribution network topology operation suggestions are output accordingly. When the dynamic health index of critical electrical equipment is determined to be lower than the preset maintenance warning threshold, the optimal application result of critical electrical equipment is obtained by solving the topology optimization objective function under the current dynamic health index of critical electrical equipment through the optimization algorithm model, and the distribution network topology operation suggestion is output accordingly. Meanwhile, when outputting distribution network topology operation suggestions, the optimized algorithm model is based on meeting the basic operating constraints of the distribution network, wherein the basic operating constraints are one or more of the following: voltage qualification, current not exceeding limits, network connectivity, and radial operation.

9. A power distribution network topology optimization system, applied in the application scenario of key electrical equipment in underground coal mines, characterized in that, include: A dynamic health acquisition module is used to establish a calculation model for acquiring the dynamic health index of key electrical equipment. The dynamic health index is used to characterize the aging degree or operating status of key equipment in the underground coal mine environment. The module for calculating the operational risk cost is used to establish an operational risk cost model for the key electrical equipment based on the dynamic health index of the key electrical equipment. The operational risk cost model is used to quantify the operational mode of each key electrical equipment and determine the operational risk cost of the key electrical equipment. The topology optimization decision module is used to construct an objective function for topology optimization. The objective function for topology optimization determines the total operating risk cost of the key electrical equipment through the economic cost of distribution network operation and the operating risk cost. The economic cost of distribution network operation includes line loss cost. The objective function for topology optimization assigns weight coefficients to the economic cost of distribution network operation and the operating risk cost of key electrical equipment, and the weight coefficients are adjusted according to the health status of key electrical equipment reflected by the dynamic health index. It is also used to divide the power distribution network into multiple electrical equipment groups, obtain the dynamic health index of each key electrical equipment in each electrical equipment group, and calculate the group health risk value of each electrical equipment group based on the obtained dynamic health index and the degree of influence of each key electrical equipment on the power supply reliability of the electrical equipment group. Based on the calculated group health risk value of each group of electrical equipment, a group risk weight coefficient corresponding to each group of electrical equipment is determined, wherein the group risk weight coefficient of the electrical equipment group with a high group health risk value is set to a high level. When constructing the objective function for topology optimization, the total operating risk cost is still determined jointly by the distribution network operating economic cost and the operating risk cost of each of the key electrical equipment. Specifically, the operating risk cost of key electrical equipment within each group of electrical equipment is weighted and summarized using the corresponding group risk weight coefficient, and then combined with the distribution network operating economic cost to obtain the total operating risk cost. The weight coefficient is adjusted according to the health status of the key electrical equipment reflected by the dynamic health index. The maintenance plan generation module is used to obtain the optimal application result of key electrical equipment based on the obtained operating cost and dynamic health index of key electrical equipment, and generate recommended operation suggestions for distribution network topology.

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