Methods, devices, equipment, media, and products for assessing thermal failures in switchgear.

Abstract

This application provides a method, apparatus, device, medium, and product for thermal fault assessment of switchgear. In this method, after acquiring temperature sampling data at multiple consecutive assessment times, a target temperature is determined for each assessment time based on the temperature sampling data at each assessment time and a preset finite element thermal simulation model of the switchgear. The target temperature data includes the temperatures of multiple preset assessment areas within the switchgear. Then, based on the target temperature data at each assessment time, assessment parameters are calculated for each assessment time and input into the preset assessment model to obtain the thermal fault assessment result for each preset assessment area. This solution determines the temperatures of multiple preset assessment areas within the switchgear using temperature sampling data and a preset finite element thermal simulation model of the switchgear, and uses the temperatures of these preset assessment areas for thermal fault assessment, thus improving the accuracy of the assessment.

Description

Technical Field

[0001] This application relates to the field of power grid technology, and in particular to a method, apparatus, equipment, medium and product for assessing thermal faults in switchgear. Background Technology

[0002] Switchgear, as a core power distribution device in urban power grids, industrial parks, public buildings, and new energy access systems, directly impacts the reliability and security of power supply due to its operational stability. With the trend towards compactness, miniaturization, and intelligence in power distribution equipment, the density of equipment within the cabinet has significantly increased, limiting airflow space and making it prone to thermal failures. Therefore, thermal failure assessment is necessary to detect potential thermal failures in switchgear in advance.

[0003] In existing technologies, temperature sensors are typically installed in the switchgear to collect the temperature data and input it into a prediction model to obtain the predicted temperature of the switchgear. Based on the predicted temperature, a thermal fault prediction and assessment score is calculated.

[0004] However, the locations for temperature sensors in switchgear are limited, and some of the collected temperatures cannot be used for thermal fault assessment, resulting in low assessment accuracy. Summary of the Invention

[0005] The thermal fault assessment method, apparatus, equipment, medium, and product for switchgear provided in this application are intended to solve the problem in the prior art where the limited number of locations for temperature sensors in switchgear prevents some collected temperatures from being used for thermal fault assessment, resulting in low assessment accuracy.

[0006] In a first aspect, embodiments of this application provide a method for assessing thermal failures in switchgear, including:

[0007] Acquire temperature sampling data at multiple consecutive evaluation times, the temperature sampling data including the temperature of multiple sampling points in the switch cabinet;

[0008] For each evaluation time, based on the temperature sampling data at the evaluation time and the preset switch cabinet finite element thermal simulation model, the target temperature data at the evaluation time is determined. The target temperature data includes the temperature of multiple preset evaluation areas in the switch cabinet.

[0009] Based on the target temperature data at each evaluation time, calculate the evaluation parameters for each evaluation time.

[0010] The evaluation parameters at each evaluation time are input into the preset evaluation model to obtain the thermal fault evaluation results for each preset evaluation region. The evaluation results include the thermal fault prediction evaluation score. The preset evaluation model is a pre-trained attention-temporal convolutional network model that determines the thermal fault evaluation results for each preset evaluation region based on the evaluation parameters at multiple times.

[0011] In one possible implementation, determining the target temperature data for the evaluation time based on the temperature sampling data at the evaluation time and a preset finite element thermal simulation model of the switchgear includes:

[0012] Based on the temperature sampling data at the evaluation time and the preset switch cabinet finite element thermal simulation model, the sparrow search algorithm is used to process the data to obtain the target simulation model's operating parameters.

[0013] Based on the target simulation model's operating parameters, the preset switchgear finite element thermal simulation model is run to obtain the target temperature data at the evaluation time.

[0014] In one possible implementation, the temperature sampling data at the evaluation time and the preset switchgear finite element thermal simulation model are used to calculate the fitness in the sparrow search algorithm.

[0015] In one possible implementation, the evaluation parameters include: the maximum temperature of each preset evaluation area, the average temperature of each preset evaluation area, and the difference between the average temperature of each preset evaluation area and the current ambient temperature.

[0016] In one possible implementation, the evaluation results also include a predicted temperature, with the current time included among all evaluation times;

[0017] The method further includes:

[0018] Based on the evaluation parameters at the current moment, calculate the current thermal fault evaluation score for each preset evaluation area;

[0019] For each preset assessment area, the warning level of the preset assessment area is determined based on the thermal fault prediction assessment score, the current thermal fault assessment score, and the predicted temperature of the preset assessment area;

[0020] For each preset assessment area, an early warning is issued based on the warning level of the preset assessment area.

[0021] In one possible implementation, determining the warning level of the preset assessment area based on the thermal fault prediction assessment score, the current thermal fault assessment score, and the predicted temperature of the preset assessment area includes:

[0022] The warning score is calculated based on the thermal fault prediction assessment score, the current thermal fault assessment score, and the predicted temperature of the preset assessment area, as well as the preset weights.

[0023] The warning level corresponding to the warning score is determined based on the preset correspondence between the score range and the level.

[0024] Secondly, embodiments of this application provide a thermal fault assessment device for switchgear, comprising:

[0025] The acquisition module is used to acquire temperature sampling data at multiple consecutive evaluation times, and the temperature sampling data includes the temperature of multiple sampling points in the switch cabinet;

[0026] Processing module, used for:

[0027] For each evaluation time, based on the temperature sampling data at the evaluation time and the preset switch cabinet finite element thermal simulation model, the target temperature data at the evaluation time is determined. The target temperature data includes the temperature of multiple preset evaluation areas in the switch cabinet.

[0028] Based on the target temperature data at each evaluation time, calculate the evaluation parameters for each evaluation time.

[0029] The evaluation module is used to input the evaluation parameters at each evaluation time into the preset evaluation model to obtain the thermal fault evaluation results for each preset evaluation region. The evaluation results include the thermal fault prediction evaluation score. The preset evaluation model is a pre-trained attention-temporal convolutional network model that determines the thermal fault evaluation results for each preset evaluation region based on the evaluation parameters at multiple times.

[0030] Thirdly, embodiments of this application provide an electronic device, including:

[0031] Processor, memory, communication interface;

[0032] The memory is used to store the executable instructions of the processor;

[0033] The processor is configured to execute the thermal fault assessment method for the switchgear according to any one of the first aspects by executing the executable instructions.

[0034] Fourthly, embodiments of this application provide a readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the thermal fault assessment method for switchgear as described in any one of the first aspects.

[0035] Fifthly, embodiments of this application provide a computer program product, including a computer program, which, when executed by a processor, is used to implement the thermal fault assessment method for switchgear according to any one of the first aspects.

[0036] The thermal fault assessment method, apparatus, equipment, medium, and product for switchgear provided in this application acquire temperature sampling data at multiple consecutive assessment times. Based on the temperature sampling data at each assessment time and a preset finite element thermal simulation model of the switchgear, the target temperature data for each assessment time is determined. The target temperature data includes the temperatures of multiple preset assessment areas within the switchgear. Then, based on the target temperature data at each assessment time, assessment parameters are calculated and input into the preset assessment model to obtain the thermal fault assessment results for each preset assessment area. This solution determines the temperatures of multiple preset assessment areas within the switchgear using temperature sampling data and a preset finite element thermal simulation model of the switchgear, and uses the temperatures of these preset assessment areas for thermal fault assessment, thus improving the accuracy of the assessment. Attached Figure Description

[0037] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0038] Figure 1 A flowchart illustrating an embodiment of the thermal fault assessment method for switchgear provided in this application;

[0039] Figure 2 This is a schematic diagram of the sampling points provided in this application;

[0040] Figure 3 A schematic diagram of the temperature field distribution of the preset switchgear finite element thermal simulation model provided in this application;

[0041] Figure 4 A flowchart illustrating Embodiment 2 of the thermal fault assessment method for switchgear provided in this application;

[0042] Figure 5 A schematic diagram of the structure of an embodiment of the thermal fault assessment device for switchgear provided in this application;

[0043] Figure 6 This is a schematic diagram of the structure of an electronic device provided in this application.

[0044] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0045] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0046] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0047] First, let me explain the terms used in this application:

[0048] Switchgear, as a core power distribution device in urban power grids, industrial parks, public buildings, and new energy access systems, directly impacts the reliability and security of power supply due to its operational stability. With the trend towards compactness, miniaturization, and intelligence in power distribution equipment, the density of equipment within the cabinet has significantly increased, limiting airflow space and making it prone to thermal failures. Therefore, thermal failure assessment is necessary to detect potential thermal failures in switchgear in advance.

[0049] In existing technologies, temperature sensors are typically installed in the switchgear to collect the temperature data and input it into a prediction model to obtain the predicted temperature of the switchgear. Based on the predicted temperature, a thermal fault prediction and assessment score is calculated.

[0050] However, the locations for temperature sensors in switchgear are limited, and the temperature of areas where thermal faults occur cannot be collected. Some collected temperatures cannot be used for thermal fault assessment, leading to low assessment accuracy.

[0051] To address the problems existing in the prior art, the inventors, during their research on thermal fault assessment methods for switchgear, discovered that to improve assessment accuracy, the temperature of a preset assessment area within the switchgear can be determined. This preset assessment area is a region prone to thermal faults. Therefore, a preset finite element thermal simulation model of the switchgear is used to determine the model's operating results, closely approximating the temperatures of multiple sampling points within the switchgear, thereby determining the temperature of the preset assessment area. Based on the target temperature data, assessment parameters are calculated and input into the preset assessment model to obtain the thermal fault assessment results for each preset assessment area. The assessment results include a thermal fault prediction assessment score. Based on the above inventive concept, the thermal fault assessment scheme for switchgear described in this application was designed.

[0052] The subject executing the thermal fault assessment method for switchgear in this application can be a computer, or a server, terminal equipment, etc. This application does not limit it. The following explanation uses a computer as an example.

[0053] The following provides an example illustrating the application scenarios of the thermal fault assessment method for switchgear provided in this application.

[0054] For example, in this application scenario, staff set up temperature sensors at sampling points in the switch cabinet. The sensors transmit the temperature data at multiple consecutive evaluation times to the computer, which can then obtain the temperature sampling data at multiple consecutive evaluation times.

[0055] For each evaluation moment, the computer determines the target temperature data based on the temperature sampling data at that evaluation moment and the preset finite element thermal simulation model of the switchgear. The target temperature data includes the temperature of multiple preset evaluation areas in the switchgear.

[0056] Based on the target temperature data at each evaluation time, the evaluation parameters for each evaluation time are calculated.

[0057] The evaluation parameters at each evaluation time are input into the preset evaluation model to obtain the thermal fault evaluation results for each preset evaluation region. The evaluation results include the thermal fault prediction evaluation score. The preset evaluation model is a pre-trained attention-temporal convolutional network model that determines the thermal fault evaluation results for each preset evaluation region based on the evaluation parameters at multiple times.

[0058] The computer will then display the thermal fault assessment results for each preset assessment area, and staff can then inspect the preset assessment areas with the predicted thermal fault assessment scores.

[0059] It should be noted that the above scenario is only an example of an application scenario provided by the embodiments of this application. The embodiments of this application do not limit the actual form of the various devices included in the scenario, nor do they limit the interaction method between devices. In the specific application of the solution, it can be set according to actual needs.

[0060] The technical solution of this application will now be described in detail through specific embodiments. It should be noted that the following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0061] Figure 1 This is a flowchart illustrating an embodiment of the thermal fault assessment method for switchgear provided in this application. This embodiment describes how a computer determines the temperature of multiple preset assessment areas in the switchgear based on temperature sampling data and a preset finite element thermal simulation model of the switchgear, and then, in conjunction with the preset assessment model, obtains the thermal fault assessment result for each preset assessment area. The method in this embodiment can be implemented through software, hardware, or a combination of both. Figure 1 As shown, the thermal fault assessment method for this switchgear specifically includes the following steps:

[0062] S101: Acquire temperature sampling data at multiple consecutive evaluation times.

[0063] In this step, in order to perform thermal fault assessment on the switchgear, it is necessary to obtain temperature sampling data at multiple consecutive assessment times. The temperature sampling data includes the temperature of multiple sampling points in the switchgear.

[0064] Temperature sampling data serves as the raw input for subsequent thermal state inference. Temperature values ​​at multiple sampling points within the switchgear are recorded chronologically at various evaluation times. These sampling points are located inside the switchgear or at positions corresponding to internal heat-conducting components.

[0065] For example, Figure 2 This is a schematic diagram of the sampling points provided in this application, such as... Figure 2 As shown in the figure, the black circles indicate the sampling points.

[0066] Sampling points can be busbar joints, circuit breaker contacts or load switch contacts, cable terminal crimping points, disconnector switch connections, etc. This application does not limit the sampling points, and they can be determined according to the actual situation.

[0067] It should be noted that the temperature of the sampling point can be collected through wireless temperature measurement, fiber optic temperature measurement, patch temperature sensor temperature measurement, or infrared thermal imaging temperature measurement. This application does not limit the method of acquiring the temperature of the sampling point; it can be determined according to the actual situation.

[0068] It should be noted that the intervals between evaluation times can be the same or different.

[0069] By acquiring temperature sampling data from multiple sampling points at consecutive evaluation times, basic time-series information reflecting the thermal evolution of the switchgear can be formed, providing input for subsequently determining the temperature data of multiple preset areas within the cabinet.

[0070] S102: For each evaluation time, the target temperature data for that evaluation time is determined based on the temperature sampling data for that evaluation time and the preset switchgear finite element thermal simulation model.

[0071] In this step, after the computer obtains the temperature of the sampling point, in order to improve the accuracy of the evaluation, for each evaluation time, based on the temperature sampling data of that evaluation time and the preset finite element thermal simulation model of the switch cabinet, the target temperature data for that evaluation time is determined. The target temperature data includes the temperature of multiple preset evaluation areas in the switch cabinet.

[0072] The preset finite element thermal simulation model of the switchgear can run according to the operating parameters and obtain the temperature field distribution.

[0073] The preset evaluation area is a spatial unit divided according to the needs of thermal fault assessment, which can correspond to different areas in the switchgear. The preset evaluation area is an area prone to thermal faults, so the temperature of the preset evaluation area needs to be obtained. The preset evaluation area can be the cabinet area, busbar area, circuit breaker contact area, load switch contact area, disconnector switch connection point area, cable terminal area, transformer area, secondary intelligent equipment area, partition area, ventilation hole area, etc. The embodiments of this application do not limit the preset evaluation area, which can be determined according to the actual situation.

[0074] Specifically, based on the temperature sampling data at the evaluation time and the preset finite element thermal simulation model of the switchgear, the sparrow search algorithm is used to process the data and obtain the operating parameters of the target simulation model.

[0075] The operating parameters of the preset switchgear finite element thermal simulation model are used as individuals in the sparrow search algorithm. First, multiple individuals are generated, and then the fitness of each individual is calculated. Then, the individuals are updated according to the fitness, and the fitness of the new individuals is calculated. This process is repeated until the preset number of iterations or solution accuracy is reached to obtain the optimal individual, which is the target simulation model operating parameters.

[0076] Temperature sampling data at the evaluation time and a preset finite element thermal simulation model of the switchgear are used to calculate the fitness in the sparrow search algorithm.

[0077] In other words, by running a pre-defined finite element thermal simulation model of the switchgear based on individual (operating parameters), the temperature field distribution can be obtained, thus yielding the simulated temperature at each sampling point. Furthermore, based on the fitness function... The fitness is calculated.

[0078] Where J represents fitness and N represents the number of sampling points. This represents the temperature at the i-th sampling point. This represents the simulated temperature at the i-th sampling point.

[0079] It should be noted that the fitness function can also be: Where J represents fitness and N represents the number of sampling points. This represents the temperature at the i-th sampling point. Let represent the simulated temperature at the i-th sampling point, and M represent the number of operating parameters for the preset finite element thermal simulation model of the switchgear. This represents the j-th operating parameter of the preset switchgear finite element thermal simulation model. This represents the j-th preset initial operating parameter of the preset switchgear finite element thermal simulation model. This indicates the preset weight.

[0080] The preset weight can be 0.1, 0.2, 0.4, 0.6, 0.8, etc. This application embodiment does not limit the preset weight, and it can be determined according to the actual situation.

[0081] Based on the target simulation model's operating parameters, the preset switchgear finite element thermal simulation model is run to obtain the target temperature data at the evaluation time.

[0082] The Sparrow Search algorithm aims to minimize the fitness function. Therefore, based on the running parameters of the target simulation model, the preset finite element thermal simulation model of the switchgear is run. The predicted temperature of the sampling points in the obtained temperature field distribution is close to the collected temperature, thus making the temperature of the preset evaluation area in the temperature field distribution more accurate.

[0083] It should be noted that the operating parameters of the preset switchgear finite element thermal simulation model may include boundary heat transfer coefficient, heat source input coefficient, contact thermal resistance parameter, ventilation and heat dissipation parameter, etc. This application embodiment does not limit the operating parameters, which can be determined according to the actual situation.

[0084] It should be noted that the sparrow search algorithm can reduce the probability of parameter calibration getting stuck in local optima by initializing the population through chaotic mapping, introducing an adaptive discoverer ratio or step size factor, and using reverse learning or perturbation update mechanisms to update the positions of individual sparrows.

[0085] By using the sparrow search algorithm to optimize the operating parameters of the target simulation model, and then using the optimized parameters to drive the operation of the preset switchgear finite element thermal simulation model, the output target temperature data can be made closer to the actual thermal state at the evaluation time, and provide a more consistent temperature basis for subsequent evaluation parameter calculation and thermal fault assessment result determination.

[0086] For example, Figure 3 A schematic diagram of the temperature field distribution of the preset switchgear finite element thermal simulation model provided in this application is shown below. Figure 3 As shown, the temperature at each point in the switch cabinet can be obtained from the temperature field distribution.

[0087] It should be noted that the Sparrow Search algorithm can be replaced with an improved Sparrow Search algorithm.

[0088] S103: Calculate the evaluation parameters for each evaluation time based on the target temperature data at each evaluation time.

[0089] In this step, after the computer obtains the target temperature data, it calculates the evaluation parameters for each evaluation time based on the target temperature data at each evaluation time.

[0090] The evaluation parameters include: the maximum temperature of each preset evaluation area, the average temperature of each preset evaluation area, and the difference between the average temperature of each preset evaluation area and the current ambient temperature.

[0091] The temperature of the preset evaluation area refers to the temperature of each point in that area.

[0092] For each evaluation time and each preset evaluation region, the maximum value among all temperatures in the preset evaluation region at that evaluation time is the maximum temperature of the preset evaluation region at that evaluation time.

[0093] The average temperature of all temperatures in the preset evaluation area at the evaluation time is the average temperature of the preset evaluation area at that evaluation time.

[0094] It should be noted that the current ambient temperature refers to the temperature of the environment in which the switchgear is located. This temperature can be collected by sensors and transmitted to a computer, input by staff, or retrieved from a database. This application does not limit the method by which the computer obtains the current ambient temperature; it can be determined according to the actual situation.

[0095] The maximum temperature is used to capture peak thermal anomalies within the region, the average temperature is used to describe the overall thermal level of the region, and the difference correlates the regional temperature with the external environmental benchmark, enabling the preset assessment model to simultaneously identify local hotspots, overall temperature rise, and relative overheating under environmental influences. Inputting these three types of parameters together enhances the ability to characterize thermal fault symptoms in different preset regions, making the thermal fault assessment results output by the preset assessment model more closely match actual operating conditions.

[0096] S104: Input the evaluation parameters at each evaluation time into the preset evaluation model to obtain the thermal fault evaluation results for each preset evaluation area.

[0097] In this step, after the computer obtains the evaluation parameters, it inputs the evaluation parameters at each evaluation time into the preset evaluation model to obtain the thermal fault evaluation results for each preset evaluation area.

[0098] The evaluation results include thermal fault prediction evaluation scores. The preset evaluation model is a pre-trained attention-temporal convolutional network model that determines the thermal fault evaluation results for each preset evaluation region based on evaluation parameters at multiple time points.

[0099] The pre-set evaluation model is a pre-trained and deployed temporal inference model, whose core structure is a combination of a temporal convolutional network and an attention mechanism. The temporal convolutional network part of the attention-based temporal convolutional network model processes the evaluation parameter sequence across multiple consecutive evaluation times, extracting thermal change patterns at different time scales. The attention mechanism part assigns different weights to different evaluation times and feature dimensions, thereby highlighting key regions and key time segments related to thermal faults. The thermal fault evaluation result is a risk quantification result output for each pre-set region, where the thermal fault prediction evaluation score represents the probability of a future thermal fault occurring in the corresponding region.

[0100] It should be noted that the preset evaluation model may include a one-dimensional causal convolutional layer, dilated convolutional residual blocks, channel attention or temporal attention modules, and a fully connected output layer.

[0101] Regarding model training, the pre-set evaluation model is trained on historical sample data before deployment. This historical sample data includes evaluation parameter sequences at multiple time points and corresponding regional thermal fault labels or fault risk scores. During training, the loss function between the predicted output and the labeled results is used as the optimization objective, and backpropagation is employed to update the network parameters until the model's output on the validation set stabilizes. During deployment, the model parameters are fixed, and only forward inference is performed. In the online evaluation phase, each time evaluation parameters at a new evaluation time point are acquired, the input window is updated and model inference is performed once, outputting the current thermal fault evaluation results for each pre-set region. Based on the above analysis, this step uses an attention-based temporal convolutional network model to jointly model evaluation parameters at multiple time points, combining continuous temperature evolution characteristics to output thermal fault prediction evaluation scores for each pre-set region. This enables continuous evaluation and predictive judgment of switchgear thermal faults under complex operating conditions.

[0102] The thermal fault assessment method for switchgear provided in this embodiment acquires temperature sampling data at multiple consecutive assessment times. Based on the temperature sampling data at each assessment time and a preset finite element thermal simulation model of the switchgear, it determines the target temperature data for each assessment time. The target temperature data includes the temperatures of multiple preset assessment areas within the switchgear. Then, based on the target temperature data at each assessment time, the assessment parameters for each assessment time are calculated and input into the preset assessment model to obtain the thermal fault assessment results for each preset assessment area. This scheme determines the temperatures of multiple preset assessment areas within the switchgear using temperature sampling data and a preset finite element thermal simulation model of the switchgear, enabling a quantitative expression of the thermal state of areas inside the switchgear that cannot be directly observed. Using the temperatures of the preset assessment areas for thermal fault assessment improves the accuracy of the assessment.

[0103] In addition, compared to existing technologies that only use data from the current moment for evaluation, this method uses evaluation parameters from multiple evaluation moments and a preset evaluation model for evaluation, which improves the accuracy of the evaluation.

[0104] Figure 4 This is a flowchart illustrating a second embodiment of the thermal fault assessment method for switchgear provided in this application. Based on the above embodiments, this application describes the tiered early warning system implemented after the computer obtains the thermal fault assessment results. Figure 4 As shown, the thermal fault assessment method for this switchgear specifically includes the following steps:

[0105] S401: Calculate the current thermal fault assessment score for each preset assessment area based on the assessment parameters at the current moment.

[0106] In this step, after the computer obtains the thermal fault assessment results, it can issue graded warnings. Since all assessment times include the current time, the current assessment score of the thermal fault in each preset assessment area can be calculated based on the assessment parameters at the current time.

[0107] The maximum temperature, average temperature, and the difference between the average temperature and the current ambient temperature of each preset evaluation area at the current moment are normalized to obtain the normalized maximum temperature, normalized average temperature, and normalized temperature difference of each preset evaluation area.

[0108] For each preset evaluation region, the normalized maximum temperature, normalized average temperature, and normalized temperature difference of the preset evaluation region are weighted and summed with the preset maximum temperature weight, preset average temperature weight, and preset temperature difference weight to obtain the current thermal fault evaluation score of the preset evaluation region.

[0109] It should be noted that the preset maximum temperature weight, preset average temperature weight, and preset temperature difference weight can be 0.1, 0.2, 0.4, 0.6, 0.7, 0.9, etc. This application embodiment does not limit the preset maximum temperature weight, preset average temperature weight, and preset temperature difference weight, and can be determined according to the actual situation.

[0110] S402: For each preset assessment area, determine the warning level of the preset assessment area based on the thermal fault prediction assessment score, the current thermal fault assessment score, and the predicted temperature of the preset assessment area.

[0111] In this step, after the computer obtains the current thermal fault assessment score, since the assessment results also include the predicted temperature, it can determine the warning level of each preset assessment area based on the predicted thermal fault assessment score, the current thermal fault assessment score, and the predicted temperature of that preset assessment area.

[0112] Specifically, an early warning score is calculated based on the thermal fault prediction assessment score of the preset assessment area, the current thermal fault assessment score and the predicted temperature, as well as preset weights.

[0113] The thermal fault prediction assessment score, the current thermal fault assessment score, and the predicted temperature are normalized, and then weighted and summed with the preset prediction assessment score weight, the preset current assessment score weight, and the preset temperature weight to obtain the warning score.

[0114] It should be noted that the preset predicted evaluation score weight, the preset current evaluation score weight, and the preset temperature weight can be 0.1, 0.2, 0.4, 0.6, 0.7, 0.9, etc. This application embodiment does not limit the preset predicted evaluation score weight, the preset current evaluation score weight, and the preset temperature weight, and can be determined according to the actual situation.

[0115] Based on the preset correspondence between score ranges and levels, the warning level corresponding to the warning score is determined.

[0116] In other words, the level corresponding to the score range to which the warning score belongs is used as the warning level.

[0117] By unifying the predicted assessment score, current assessment score, and temperature into a single scoring framework, early warning judgments can simultaneously reflect the current thermal state and future thermal evolution trends. Mapping early warning levels to score intervals ensures consistent judgment criteria for regional risk outputs and improves the stability and interpretability of thermal fault early warning results.

[0118] S403: For each preset assessment area, issue an early warning based on the early warning level of that preset assessment area.

[0119] In this step, after the computer obtains the warning level, it issues a warning for each preset assessment area based on the warning level of that preset assessment area.

[0120] In other words, based on the preset correspondence between warning levels and warning methods, the warning method corresponding to the warning level is determined, and then the warning is issued according to that warning method. The warning content may include maintenance measures, which can be determined based on the correspondence between the warning level and the measures.

[0121] The thermal fault assessment method for switchgear provided in this embodiment not only includes predictive assessment information in the thermal fault assessment results, but also introduces the predicted temperature to participate in the level determination, so that different preset assessment areas can obtain more detailed early warning output based on current risks and future trends, thereby improving the distinguishability and pertinence of switchgear thermal fault early warning.

[0122] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0123] Figure 5 This is a schematic diagram of the structure of an embodiment of the thermal fault assessment device for switchgear provided in this application; as shown below. Figure 5 As shown, the thermal fault assessment device 50 of the switchgear includes:

[0124] The acquisition module 51 is used to acquire temperature sampling data at multiple consecutive evaluation times. The temperature sampling data includes the temperature of multiple sampling points in the switch cabinet.

[0125] Processing module 52 is used for:

[0126] For each evaluation time, the target temperature data for the evaluation time is determined based on the temperature sampling data at the evaluation time and the preset finite element thermal simulation model of the switchgear. The target temperature data includes the temperature of multiple preset evaluation areas in the switchgear.

[0127] Based on the target temperature data at each evaluation time, calculate the evaluation parameters for each evaluation time.

[0128] Evaluation module 53 is used to input the evaluation parameters at each evaluation time into the preset evaluation model to obtain the thermal fault evaluation results for each preset evaluation region. The evaluation results include the thermal fault prediction evaluation score. The preset evaluation model is a pre-trained attention-temporal convolutional network model that determines the thermal fault evaluation results for each preset evaluation region based on the evaluation parameters at multiple times.

[0129] Furthermore, the processing module 52 is specifically used for:

[0130] Based on the temperature sampling data at the evaluation time and the preset finite element thermal simulation model of the switchgear, the sparrow search algorithm is used to process the data and obtain the operating parameters of the target simulation model.

[0131] Based on the target simulation model's operating parameters, the preset switchgear finite element thermal simulation model is run to obtain the target temperature data at the evaluation time.

[0132] Furthermore, the temperature sampling data at the evaluation time and the preset finite element thermal simulation model of the switchgear are used to calculate the fitness in the sparrow search algorithm.

[0133] Furthermore, the processing module 52 evaluates parameters including: the maximum temperature of each preset evaluation area, the average temperature of each preset evaluation area, and the difference between the average temperature of each preset evaluation area and the current ambient temperature.

[0134] Furthermore, the evaluation results also include predicted temperature, and the current time is included in all evaluation times. Processing module 52 is also used for:

[0135] Calculate the current thermal fault assessment score for each preset assessment area based on the assessment parameters at the current moment;

[0136] For each preset assessment area, the warning level of the preset assessment area is determined based on the thermal fault prediction assessment score, the current thermal fault assessment score, and the predicted temperature of the preset assessment area.

[0137] For each preset assessment area, an early warning is issued based on the warning level of the preset assessment area.

[0138] Furthermore, the processing module 52 is specifically used for:

[0139] The warning score is calculated based on the thermal fault prediction assessment score, the current thermal fault assessment score and the predicted temperature of the preset assessment area, as well as the preset weights.

[0140] Based on the preset correspondence between score ranges and levels, the warning level corresponding to the warning score is determined.

[0141] The thermal fault assessment device for switchgear provided in this embodiment is used to execute the technical solutions in any of the aforementioned method embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0142] Figure 6 This is a schematic diagram of the structure of an electronic device provided in this application. Figure 6 As shown, the electronic device 60 includes:

[0143] Processor 61, memory 62, and communication interface 63;

[0144] Memory 62 is used to store executable instructions of processor 61;

[0145] The processor 61 is configured to execute the technical solutions in any of the foregoing method embodiments by executing executable instructions.

[0146] Optionally, the memory 62 can be either standalone or integrated with the processor 61.

[0147] Optionally, when the memory 62 is a device independent of the processor 61, the electronic device 60 may further include:

[0148] Bus 64, memory 62 and communication interface 63 are connected to processor 61 through bus 64 and complete communication with each other. Communication interface 63 is used to communicate with other devices.

[0149] Optionally, the communication interface 63 can be implemented using a transceiver. The communication interface is used to enable communication between the database access device and other devices (e.g., clients, read-write databases, and read-only databases). The memory may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk drive.

[0150] Bus 64 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0151] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0152] The electronic device is used to execute the technical solutions in any of the foregoing method embodiments. Its implementation principle and technical effect are similar, and will not be repeated here.

[0153] This application also provides a readable storage medium storing a computer program thereon, which, when executed by a processor, implements the technical solutions provided in any of the foregoing method embodiments.

[0154] This application also provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the technical solutions provided in any of the foregoing method embodiments.

[0155] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0156] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for assessing thermal failures in switchgear, characterized in that, include: Acquire temperature sampling data at multiple consecutive evaluation times, the temperature sampling data including the temperature of multiple sampling points in the switch cabinet; For each evaluation time, based on the temperature sampling data at the evaluation time and the preset switch cabinet finite element thermal simulation model, the target temperature data at the evaluation time is determined. The target temperature data includes the temperature of multiple preset evaluation areas in the switch cabinet. Based on the target temperature data at each evaluation time, calculate the evaluation parameters for each evaluation time. The evaluation parameters at each evaluation time are input into the preset evaluation model to obtain the thermal fault evaluation results for each preset evaluation region. The evaluation results include the thermal fault prediction evaluation score. The preset evaluation model is a pre-trained attention-temporal convolutional network model that determines the thermal fault evaluation results for each preset evaluation region based on the evaluation parameters at multiple times.

2. The method according to claim 1, characterized in that, The step of determining the target temperature data at the evaluation time based on the temperature sampling data at the evaluation time and the preset finite element thermal simulation model of the switchgear includes: Based on the temperature sampling data at the evaluation time and the preset switch cabinet finite element thermal simulation model, the sparrow search algorithm is used to process the data to obtain the target simulation model's operating parameters. Based on the target simulation model's operating parameters, the preset switchgear finite element thermal simulation model is run to obtain the target temperature data at the evaluation time.

3. The method according to claim 2, characterized in that, The temperature sampling data at the evaluation time and the preset switchgear finite element thermal simulation model are used to calculate the fitness in the sparrow search algorithm.

4. The method according to claim 1, characterized in that, The evaluation parameters include: the maximum temperature of each preset evaluation area, the average temperature of each preset evaluation area, and the difference between the average temperature of each preset evaluation area and the current ambient temperature.

5. The method according to any one of claims 1 to 4, characterized in that, The assessment results also include predicted temperatures, and all assessment times include the current time; The method further includes: Based on the evaluation parameters at the current moment, calculate the current thermal fault evaluation score for each preset evaluation area; For each preset assessment area, the warning level of the preset assessment area is determined based on the thermal fault prediction assessment score, the current thermal fault assessment score, and the predicted temperature of the preset assessment area; For each preset assessment area, an early warning is issued based on the warning level of the preset assessment area.

6. The method according to claim 5, characterized in that, The step of determining the warning level of the preset assessment area based on the thermal fault prediction assessment score, the current thermal fault assessment score, and the predicted temperature of the preset assessment area includes: The warning score is calculated based on the thermal fault prediction assessment score, the current thermal fault assessment score, and the predicted temperature of the preset assessment area, as well as the preset weights. The warning level corresponding to the warning score is determined based on the preset correspondence between the score range and the level.

7. A thermal fault assessment device for switchgear, characterized in that, include: The acquisition module is used to acquire temperature sampling data at multiple consecutive evaluation times, and the temperature sampling data includes the temperature of multiple sampling points in the switch cabinet; Processing module, used for: For each evaluation time, based on the temperature sampling data at the evaluation time and the preset switch cabinet finite element thermal simulation model, the target temperature data at the evaluation time is determined. The target temperature data includes the temperature of multiple preset evaluation areas in the switch cabinet. Based on the target temperature data at each evaluation time, calculate the evaluation parameters for each evaluation time. The evaluation module is used to input the evaluation parameters at each evaluation time into the preset evaluation model to obtain the thermal fault evaluation results for each preset evaluation region. The evaluation results include the thermal fault prediction evaluation score. The preset evaluation model is a pre-trained attention-temporal convolutional network model that determines the thermal fault evaluation results for each preset evaluation region based on the evaluation parameters at multiple times.

8. An electronic device, characterized in that, include: Processor, memory, communication interface; The memory is used to store the executable instructions of the processor; The processor is configured to execute the thermal fault assessment method for switchgear according to any one of claims 1 to 6 by executing the executable instructions.

9. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the thermal fault assessment method for switchgear according to any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, is used to implement the thermal fault assessment method for switchgear according to any one of claims 1 to 6.

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