Power transmission and transformation equipment operation state detection method, system and equipment and storage medium

Abstract

The invention discloses a power transmission and transformation equipment operation state detection method, system and device and a storage medium, and belongs to the technical field of equipment state detection.The method comprises the steps that multi-mode operation state data of power transmission and transformation equipment are collected in real time, and time sequence state data are generated based on the multi-mode operation state data; performing data cleaning on the time sequence state data based on the time sequence to obtain target multi-mode operation state data; performing integrated analysis on the target multi-mode operation state data through a multi-physics field coupling model corresponding to the power transmission and transformation equipment to obtain operation state characteristic data of the power transmission and transformation equipment; and determining an operation state detection result corresponding to the power transmission and transformation equipment according to the operation state feature data. By applying the technical scheme, the technical problem of low detection precision caused by the fact that the operation state detection of the power transmission and transformation equipment by adopting a periodic inspection mode in the prior art is difficult to accurately accord with the actual degradation rate of the power transmission and transformation equipment is solved.

Description

Technical Field

[0001] This invention relates to the field of equipment condition monitoring technology, specifically to a method, system, equipment, and storage medium for monitoring the operating status of power transmission and transformation equipment. Background Technology

[0002] Power transmission and transformation equipment is exposed to complex natural environments and high-intensity operating conditions for extended periods, facing multiple threats of failure. Extreme weather events such as torrential rains, floods, strong winds, blizzards, and lightning strikes can cause direct physical damage to the equipment, leading to decreased insulation performance and mechanical deformation. Furthermore, chronic "ailments" such as insulation aging, overheating wear, and mechanical fatigue caused by long-term operation also subtly erode the equipment's health, creating potential for future failures. A sudden equipment failure can not only cause localized power outages, affecting industrial production, commercial operations, and the normal lives of residents, but may also trigger a chain reaction in the power grid, inducing widespread blackouts and causing incalculable losses to the social economy. Therefore, monitoring the operating status of power transmission and transformation equipment is crucial to ensuring the safe and stable operation of the power grid.

[0003] Currently, the monitoring of the operating status of power transmission and transformation equipment usually relies on periodic manual inspections. However, periodic inspections are often difficult to accurately match the actual deterioration rate of the power transmission and transformation equipment, resulting in low detection accuracy. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is to address the issue that the existing technology of using periodic inspections to detect the operating status of power transmission and transformation equipment is difficult to accurately match the actual deterioration rate of the power transmission and transformation equipment, resulting in low detection accuracy.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for detecting the operating status of power transmission and transformation equipment, comprising,

[0007] Real-time acquisition of multimodal operating status data of power transmission and transformation equipment, and generation of time-series status data based on the multimodal operating status data; data cleaning of the time-series status data to obtain target multimodal operating status data; integrated analysis of the target multimodal operating status data through the multiphysics coupling model corresponding to the power transmission and transformation equipment to obtain operating status characteristic data of the power transmission and transformation equipment; and determination of the operating status detection result corresponding to the power transmission and transformation equipment based on the operating status characteristic data.

[0008] As a preferred embodiment of the method for detecting the operating status of power transmission and transformation equipment according to the present invention, the real-time acquisition of multi-modal operating status data of power transmission and transformation equipment includes determining the electromagnetic characteristic parameters, thermal characteristic parameters and mechanical characteristic parameters of power transmission and transformation equipment before real-time acquisition of multi-modal operating status data of power transmission and transformation equipment.

[0009] An electromagnetic model corresponding to the power transmission and transformation equipment is constructed based on the electromagnetic characteristic parameters.

[0010] A thermal model corresponding to the power transmission and transformation equipment is constructed based on the aforementioned thermal characteristic parameters;

[0011] A mechanical model of the power transmission and transformation equipment is constructed based on the aforementioned mechanical characteristic parameters.

[0012] The electromagnetic model, the thermal model, and the mechanical model are fused to form a multiphysics coupling model corresponding to the power transmission and transformation equipment.

[0013] As a preferred embodiment of the method for detecting the operating status of power transmission and transformation equipment according to the present invention, the data cleaning includes analyzing the time series status data to determine the time series characteristics corresponding to the time series status data.

[0014] Based on the time series characteristics, determine the abnormal state data in the time series state data;

[0015] The abnormal state data is classified, and the abnormal state data to be cleaned is determined from the abnormal state data based on the classification results.

[0016] Data cleaning is performed on the time series state data based on the abnormal state data to be cleaned to obtain target multimodal operating state data.

[0017] As a preferred embodiment of the method for detecting the operating status of power transmission and transformation equipment according to the present invention, the operating status characteristic data includes: performing structural analysis on the power transmission and transformation equipment to obtain the vibration modes and compressive degrees of freedom corresponding to the power transmission and transformation equipment;

[0018] The stress distribution characteristics of the power transmission and transformation equipment are determined based on the vibration modes and the compressive degrees of freedom using a multiphysics coupling model corresponding to the power transmission and transformation equipment.

[0019] The multi-physics coupling model is used to integrate and analyze the multi-modal operating state data of the target to determine the temperature distribution characteristics and electromagnetic field distribution characteristics of the power transmission and transformation equipment.

[0020] The operating status characteristic data of the power transmission and transformation equipment are generated based on the stress distribution characteristics, temperature distribution characteristics, and electromagnetic field distribution characteristics.

[0021] As a preferred embodiment of the method for detecting the operating status of power transmission and transformation equipment according to the present invention, the step of determining the temperature distribution characteristics and electromagnetic field distribution characteristics corresponding to the power transmission and transformation equipment includes: performing integrated analysis on the target multimodal operating status data through the multiphysics coupling model to determine the magnetic vector potential corresponding to the electromagnetic equipment in the power transmission and transformation equipment;

[0022] The electromagnetic field distribution in the electromagnetic device is determined based on the magnetic vector potential and the electric scalar potential, and the electromagnetic field distribution characteristics are obtained.

[0023] A temperature gradient grid is constructed using the multiphysics coupling model based on the target multimodal operating state data and the multigrid method.

[0024] The temperature distribution characteristics of the power transmission and transformation equipment are obtained based on the temperature gradient grid.

[0025] As a preferred embodiment of the method for detecting the operating status of power transmission and transformation equipment according to the present invention, the operating status detection result includes determining key feature parameters based on the operating status feature data, wherein the key feature parameters include the maximum electric field strength, the highest temperature gradient, and the dominant frequency of the vibration spectrum.

[0026] Obtain the threshold values ​​of key feature parameters corresponding to the normal operation state of the power transmission and transformation equipment;

[0027] The key feature parameters are compared with the key feature parameter thresholds, and the operating status detection result of the power transmission and transformation equipment is determined based on the comparison result.

[0028] In a preferred embodiment of the power transmission and transformation equipment operation status detection method of the present invention, the operation status detection result further includes:

[0029] The electromagnetic model, the thermal model, and the mechanical model are reduced in order using the cross-Gram matrix low-rank decomposition method to obtain the reduced electromagnetic model, the reduced thermal model, and the reduced mechanical model.

[0030] A lightweight physical field coupling model is constructed based on the reduced electromagnetic model, the reduced thermal model, and the reduced mechanical model.

[0031] The lightweight physical field coupling model is used to update the operating status data of power transmission and transformation equipment in real time.

[0032] Another objective of this invention is to provide a system for detecting the operating status of power transmission and transformation equipment.

[0033] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a power transmission and transformation equipment operation status detection system, comprising: a data acquisition module, a data cleaning module, a data analysis module, and an operation status detection module;

[0034] The data acquisition module is used to collect multimodal operating status data of power transmission and transformation equipment in real time, and generate time series status data based on the multimodal operating status data;

[0035] The data cleaning module is used to clean the time series state data based on the time series to obtain target multimodal operating state data;

[0036] The data analysis module is used to integrate and analyze the target multimodal operating status data through the multiphysics coupling model corresponding to the power transmission and transformation equipment, and obtain the operating status characteristic data of the power transmission and transformation equipment.

[0037] The operation status detection module is used to determine the operation status detection result corresponding to the power transmission and transformation equipment based on the operation status feature data.

[0038] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method for detecting the operating status of power transmission and transformation equipment.

[0039] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the steps of the method for detecting the operating status of power transmission and transformation equipment.

[0040] The beneficial effects of this invention are as follows: This invention discloses a method for real-time acquisition of multimodal operating status data of power transmission and transformation equipment, and the generation of time-series status data based on the multimodal operating status data; data cleaning of the time-series status data to obtain target multimodal operating status data; integrated analysis of the target multimodal operating status data through a multiphysics coupling model corresponding to the power transmission and transformation equipment to obtain operating status characteristic data of the power transmission and transformation equipment; and determination of the corresponding operating status detection result of the power transmission and transformation equipment based on the operating status characteristic data. Because this invention can integrate and analyze the target multimodal operating status data of the power transmission and transformation equipment through a multiphysics coupling model corresponding to the power transmission and transformation equipment to obtain operating status characteristic data, and determine the corresponding operating status detection result of the power transmission and transformation equipment based on the operating status characteristic data, it solves the technical problem in the prior art where periodic inspections of power transmission and transformation equipment for operating status detection are difficult to accurately match the actual degradation rate of the power transmission and transformation equipment, resulting in low detection accuracy. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating the overall process of a method for detecting the operating status of power transmission and transformation equipment, as provided in one embodiment of the present invention.

[0043] Figure 2 This is an integrated analysis flowchart of a method for detecting the operating status of power transmission and transformation equipment, provided as an embodiment of the present invention.

[0044] Figure 3 This is a preliminary flowchart of a method for detecting the operating status of power transmission and transformation equipment according to an embodiment of the present invention.

[0045] Figure 4 The present invention provides an overall flowchart of a power transmission and transformation equipment operation status detection system according to an embodiment of the present invention.

[0046] Figure 5 This is a schematic diagram of the hardware operating environment involved in a method for detecting the operating status of power transmission and transformation equipment according to an embodiment of the present invention.

[0047] In the diagram, 10 is the data acquisition module; 20 is the data cleaning module; 30 is the data analysis module; 40 is the operation status detection module; 1001 is the processing device; 1002 is the read-only memory; 1003 is the storage device; 1004 is the random access memory; 1005 is the bus; 1006 is the input / output interface; 1007 is the input device; 1008 is the output device; and 1009 is the communication device. Detailed Implementation

[0048] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0049] Example 1, referring to Figures 1-3 This is one embodiment of the present invention, which provides a method for detecting the operating status of power transmission and transformation equipment, including:

[0050] This invention performs data cleaning on the operating data of multimodal power transmission and transformation equipment based on time series analysis to remove abnormal data and obtain cleaned operating data. It then constructs different types of mechanistic models for the power transmission and transformation equipment. The cleaned operating data is input into these mechanistic models, and the cleaned data is integrated and analyzed using a multiphysics coupling solution integration method to obtain the corresponding operating status detection results for the power transmission and transformation equipment.

[0051] This addresses the problem that existing periodic inspections often fail to accurately reflect the actual degradation rate of power transmission and transformation equipment, resulting in low detection accuracy.

[0052] In this embodiment, the method for detecting the operating status of power transmission and transformation equipment includes steps S10 to S40:

[0053] Step S10: Collect multi-modal operating status data of power transmission and transformation equipment in real time, and generate time series status data based on the multi-modal operating status data.

[0054] It should be understood that the aforementioned power transmission and transformation equipment can be any equipment in the power grid used to realize power transmission and voltage transformation, such as transformers, switchgear, power cables, etc., and this embodiment does not impose any restrictions on this.

[0055] It should be noted that the above-mentioned multimodal operating status data can be data reflecting the operating status of power transmission and transformation equipment from different physical characteristic dimensions, including: temperature data, mechanical data, and magnetic field data, etc.

[0056] In this embodiment, by comprehensively analyzing multi-modal operating status data such as temperature, mechanics, and magnetic field, the operating status of power transmission and transformation equipment can be assessed more comprehensively and accurately, potential faults can be detected in advance, and the safe and stable operation of the power grid can be ensured.

[0057] It should be noted that temperature data reflects the thermal state of power transmission and transformation equipment during operation. During operation, equipment generates heat due to current flow and resistance loss. Temperature changes in different parts of the equipment reflect its operating status. For example, excessively high transformer winding temperature indicates potential problems such as partial short circuits or overloads; abnormally high contact temperatures in switchgear suggest poor contact and increased contact resistance. If not addressed promptly, this could lead to equipment burnout or even power outages. In this embodiment, temperature data can be obtained using devices such as infrared thermometers and fiber optic temperature sensors. Specifically, infrared thermometers can quickly and accurately measure the temperature distribution on the equipment surface, while fiber optic temperature measurement technology enables real-time monitoring of the internal temperature of the equipment.

[0058] Mechanical data can be used to measure the forces exerted on power transmission and transformation equipment during operation and the changes in the equipment's mechanical state, such as vibration and stress. Taking the conductors of transmission lines as an example, conductors will vibrate under the influence of wind force and their own weight. Long-term vibration may lead to fatigue damage to the conductors. By monitoring the vibration frequency and amplitude of the conductors, the structural integrity of the conductors can be assessed, and potential risks of strand breakage and wire breakage can be detected in advance.

[0059] Furthermore, for equipment such as circuit breakers, vibrations and stress changes occur during opening and closing. By monitoring vibration and stress data, it is possible to determine whether there are problems such as loosening or wear in mechanical components. For example, if abnormal stress occurs in the operating mechanism of a circuit breaker, it may indicate a potential fault in the mechanical transmission part. In this embodiment, mechanical data of power transmission and transformation equipment can be obtained through vibration sensors, stress monitoring devices, etc., and compared and analyzed with equipment design parameters to assess the structural health status of the equipment. Magnetic field data can reflect the changes in the magnetic field generated by power transmission and transformation equipment during operation. For example, abnormal magnetic field distribution in the transformer core indicates a possible fault in the core; changes in the magnetic field strength around the busbars in the switchgear can reflect the current magnitude and equipment load.

[0060] In this embodiment, the magnetic field changes of key parts of the equipment can be monitored in real time by a magnetic field sensor, and the equipment status can be comprehensively analyzed by combining temperature and mechanical data. For example, when a transformer experiences partial discharge, it may be accompanied by an abnormal magnetic field. Combined with the increase in temperature, the fault can be accurately located.

[0061] In an optional embodiment, the comprehensive analysis of the equipment status can be achieved by inputting the cleaned temperature, mechanical, and magnetic field data into their respective mechanistic models (thermal model, mechanical stress model, and electromagnetic field model). After each model runs, it outputs key status indicators or feature values ​​(e.g., the thermal model outputs predicted hotspot temperatures and heat distribution uniformity index; the mechanical model outputs vibration dominant frequency offset and stress concentration factor; the electromagnetic field model outputs local magnetic field distortion and eddy current loss anomaly coefficient). A predefined expert rule base is constructed, where each rule is associated with a specific fault mode or status level, and its triggering condition is composed of a combination of feature values ​​output by multiple modal models or specific thresholds of the original data. All extracted model feature values ​​are input into a decision tree (or rule engine). The decision tree, based on the predefined rule base, performs condition judgments from top to bottom, traversing branches until it reaches a leaf node, which corresponds to a specific equipment operating status conclusion and possible fault type.

[0062] In another optional embodiment, the comprehensive analysis of equipment status can also involve constructing a feature template library using historical normal operation data and typical failure mode data. Multimodal data (raw data or key features output by the model) within the current time window is converted into feature vectors, and their similarity (or distance) to feature vectors of various states (normal, various types of failures) in the historical template library is calculated. The current equipment status is determined based on the most similar template status.

[0063] It is understandable that the aforementioned time-series state data can be the operating status data of power transmission and transformation equipment recorded in chronological order, reflecting the dynamic changes in the equipment's operating status data at different points in time. In practical applications, temperature, mechanical, and magnetic field data corresponding to the operation of power transmission and transformation equipment can be collected in real time by pre-installed sensors to obtain multimodal operating status data. This multimodal operating status data is then uploaded to a database for storage. The stored multimodal operating status data can be considered as feature data arranged in a time series, and its corresponding storage format can be "time.feature quantity". Ultimately, all the data collected by the sensors form a multivariate, continuous, and complete time series, which is the aforementioned time-series state data.

[0064] Please refer to Figure 3 In this embodiment, before step S10, the method further includes steps S01 to S05:

[0065] Step S01: Determine the electromagnetic characteristic parameters, thermal characteristic parameters, and mechanical characteristic parameters of the power transmission and transformation equipment.

[0066] It should be understood that the above-mentioned electromagnetic characteristic parameters can be used to describe the characteristic parameters exhibited by power transmission and transformation equipment under the action of electromagnetic fields, such as electric field strength, magnetic field strength, conductivity, dielectric constant, and permeability.

[0067] The aforementioned thermal characteristic parameters can be used to describe the characteristics exhibited by power transmission and transformation equipment under thermal effects, such as thermal conductivity, specific heat capacity, coefficient of thermal expansion, and thermal resistance.

[0068] The aforementioned mechanical characteristic parameters can be used to describe the characteristics exhibited by power transmission and transformation equipment under mechanical force, such as elastic modulus, yield strength, tensile strength, hardness, etc.

[0069] Step S02: Construct the electromagnetic model corresponding to the power transmission and transformation equipment based on the electromagnetic characteristic parameters.

[0070] It should be noted that the above electromagnetic model can be used to simulate the electromagnetic field distribution and related parameters (such as voltage, current, magnetic flux, etc.) in power transmission and transformation equipment.

[0071] In practical applications, taking the electromagnetic model of a transformer as an example, a model can be constructed based on Maxwell's equations and parameters such as the number of winding turns and the permeability of the iron core. This model can accurately show the magnetic flux direction, induced electromotive force generation, and leakage reactance under no-load and load conditions. It is of great significance for analyzing transformer inrush current and short-circuit reactance changes, and helps to assess the electrical stress on the insulation and the stress on the winding, and guides short-circuit protection design and relay protection setting.

[0072] In the electromagnetic model of transmission lines, factors such as conductor radius, phase-to-phase distance, and earth resistivity are considered. Distributed parameter line theory is used to accurately simulate power frequency and high frequency (such as those caused by lightning strikes and switching overvoltages) electromagnetic transients, control the electromagnetic environment and electromagnetic compatibility characteristics of the line, and ensure reliable power transmission and the safety of surrounding equipment.

[0073] Step S03: Construct a thermal model corresponding to the power transmission and transformation equipment based on the thermal characteristic parameters.

[0074] It should be noted that the above-mentioned thermal model can be used to describe the generation, transfer and dissipation of heat in power transmission and transformation equipment during operation. For example, when a transformer is running, it will generate heat due to resistance loss and other reasons, which will cause the internal temperature to rise. The thermal model can simulate the temperature distribution inside the transformer, thereby helping to determine the hot spot location and the highest temperature, and providing a basis for heat dissipation design and operation and maintenance.

[0075] In practical applications, thermal models are constructed around the heat generation, conduction, and heat dissipation mechanisms of power transmission and transformation equipment. These models are crucial for controlling equipment operating temperature, preventing overheating faults, and extending service life. For example, the thermal model of an oil-immersed transformer can comprehensively consider the heat generation from core and winding losses, as well as the natural convection and forced air cooling paths. Using the thermal circuit method, it simplifies complex heat transfer into a network of thermal resistance and thermal capacity, simulating oil temperature rise and hot spot temperature distribution, providing support for cooling system optimization and load capacity assessment. For high-voltage switchgear, the contact arc thermal effect is critical. Thermal models are constructed by combining arc energy input, contact and arc-extinguishing chamber heat dissipation characteristics to analyze temperature changes during the breaking process. This assists in contact material selection and arc-extinguishing structure improvement, ensuring equipment operational reliability and insulation stability.

[0076] Step S04: Construct the mechanical model corresponding to the power transmission and transformation equipment based on the mechanical characteristic parameters.

[0077] It should be understood that the aforementioned mechanical model can be a model used to describe the mechanical behavior (such as stress, strain, vibration, etc.) of power transmission and transformation equipment during operation. For example, the mechanical model can simulate the deformation and vibration of transformer windings under electromagnetic force, so that its impact on equipment performance and lifespan can be evaluated.

[0078] In this embodiment, the mechanical model focuses on the mechanical structure and dynamic characteristics of power transmission and transformation equipment to address the risk of failure caused by mechanical vibration and stress-strain.

[0079] In transformer mechanical models, the models cover the vibration modes, axial and radial displacements of windings under electromagnetic forces and short-circuit impacts, and the stress and deformation of the core clamping structure. The finite element method is used to discretize the structure, and vibration response is simulated based on material mechanics and dynamics equations to detect potential winding deformation and core loosening in advance. Transmission line mechanical models focus on conductor vibration in light winds and icing, considering factors such as wind load, conductor weight, and tension. Multi-degree-of-freedom vibration dynamics models are constructed using dynamics theory to predict vibration amplitude, frequency, and galloping trajectory, guiding the installation and maintenance of vibration and galloping prevention devices and safeguarding the mechanical integrity of the lines and the stability of power supply.

[0080] Step S05: The electromagnetic model, the thermal model, and the mechanical model are fused to form a multiphysics coupling model corresponding to the power transmission and transformation equipment.

[0081] In this embodiment, a multi-physics coupling model can be obtained by integrating various single-physics field models, including electromagnetic, thermal, and mechanical models. This multi-physics coupling model can analyze the interaction mechanism between electromagnetic, thermal, and mechanical physical fields, clarify the coupling relationship, and iteratively solve multiple physical quantities (displacement, temperature, electric field strength, magnetic field strength, etc.) at each node, thus realistically reproducing the complex physical process of equipment operation and improving the detection accuracy of the operating status of power transmission and transformation equipment.

[0082] In an optional embodiment, obtaining a multiphysics coupling model can involve analyzing the most critical physical interaction chains in the operation of a specific power transmission and transformation equipment. Based on the identified dominant path, the output of one model is used as the input boundary condition or load for the next model. The electromagnetic model, thermal model, and mechanical model are solved sequentially according to the chain order. If strong coupling exists, iterative calculations are required after each sequential calculation: the deformation calculated by the mechanical model or the final temperature of the thermal model is fed back to the electromagnetic model for a new round of calculations until the results converge, obtaining comprehensive state quantities reflecting the electromagnetic-thermal-mechanical coupling effect, such as hot spot temperatures at key locations, maximum thermal stress values, deformation of key components, or vibration spectrum characteristics.

[0083] In another alternative embodiment, obtaining the multiphysics coupling model can also be achieved by:

[0084] A high-precision 3D mesh covering the complete geometry of the equipment is constructed to ensure that the electromagnetic, thermal, and mechanical models share the same set of spatial discretized data; key interaction variables across models are defined (such as electromagnetic loss → heat source, temperature → material property update, thermal stress → structural deformation), and data is transmitted in real time through the platform; a multiphysics solver is used to iterate the equations of each field alternately or synchronously, updating the coupling variables at each step (such as temperature change feedback to the electromagnetic model to adjust resistivity); results that integrate the interactions of multiple fields (such as temperature-stress joint distribution, electromagnetic-mechanical vibration spectrum) are directly generated to support accurate condition assessment.

[0085] Step S20: Clean the time series state data based on the time series to obtain the target multimodal operating state data.

[0086] It should be understood that the aforementioned target multimodal operating status data can be obtained by cleaning up abnormal data in time-series state data. In this embodiment, state quantity data such as temperature, mechanical, and magnetic fields arranged in chronological order can be processed based on time series data to identify and process data that does not meet requirements, such as errors, missing data, duplication, and anomalies, to obtain target multimodal operating status data. This improves data quality and allows subsequent operation status monitoring of power transmission and transformation equipment based on the target multimodal operating status data, ensuring the accuracy and reliability of the monitoring results.

[0087] Further, step S20 includes:

[0088] Step S201: Analyze the time series state data to determine the time series characteristics corresponding to the time series state data.

[0089] It should be noted that the aforementioned time series features can be used to reflect the changing patterns, trends, and characteristics of time series state data.

[0090] Step S202: Determine the abnormal state data in the time series state data based on the time series characteristics.

[0091] It is understood that the above-mentioned abnormal state data can be data that is abnormal in time series state data, such as data with errors, missing data, duplicates, or other abnormalities. This embodiment does not limit this.

[0092] In practical applications, the status data of power transmission and transformation equipment under normal operating conditions generally exhibit the following patterns:

[0093] State variables with small amplitude changes, such as conductor tension, grounding current, and gas in oil, are all stationary series and can be directly fitted using the autoregressive moving average function ARMA(p,q).

[0094] State variables exhibit a slow upward trend, such as CO and CO2 in oil gases, which can be transformed into stationary sequences using the difference method and fitted with the autoregressive summation moving average function ARIMA(p,d,q).

[0095] State variables exhibit periodic changes, which manifest as similarities among observation points after s time intervals in a time series, such as oil temperature and conductor temperature. These can be fitted using ARIMA(p, ds, q). Therefore, this embodiment can determine whether there are abnormal state data in the time series state data based on the time series characteristics corresponding to the time series state data. If so, these abnormal data are identified as abnormal state data.

[0096] Step S203: Classify the abnormal state data and determine the abnormal state data to be cleaned in the abnormal state data according to the classification results.

[0097] It should be noted that the aforementioned abnormal state data to be cleaned can be abnormal state data that needs to be cleaned, such as noise points and missing values. In practical applications, anomalies in the state data can be divided into two categories based on the operating characteristics of power transmission and transformation equipment: 1) anomalies that can be used for data cleaning, i.e., abnormal state data to be cleaned, including noise points and missing values; 2) abnormal data caused by interference with the equipment's operating state. In practical applications, noise points refer to data that deviates significantly from the expected value due to instrument malfunctions or disturbances in the equipment system. These data not only affect the accuracy of model fitting but also lead to deviations in subsequent state assessments, causing misdiagnosis. Missing values ​​can be data interruptions caused by factors such as short-term sensor failures, communication port malfunctions, and recording errors. Missing values ​​in the state data will disrupt the continuity of system operation and are detrimental to subsequent state assessments and trend verification.

[0098] In addition, equipment may experience sudden failures or insulation deterioration during operation. These situations often cause abnormal horizontal migration and trend changes in data. Such abnormal data reflects abnormal operating conditions of the equipment and does not fall under the scope of cleaning.

[0099] In this embodiment, the time series of equipment status data often contains multiple abnormal data. The goal of cleaning equipment status data is to repair all noise points and missing values. Therefore, noise points and missing values ​​in abnormal status data can be identified as abnormal status data to be cleaned. At the same time, it is also necessary to effectively obtain information on sudden faults, rather than removing them as abnormal data.

[0100] In an optional embodiment, abnormal data identification can be achieved by using a sliding window to calculate local statistical features (such as mean and standard deviation) for time series data such as temperature and vibration of power transmission and transformation equipment, and combining them with historical statistical thresholds (such as ±3σ) during normal operation of the equipment to dynamically identify abnormal points.

[0101] In another optional embodiment, abnormal data determination can also involve dividing historical data into different operating condition clusters using unsupervised clustering (such as K-means) based on parameters such as equipment operating load rate and ambient temperature; establishing a normal fluctuation range model for the multimodal data within each operating condition cluster; matching real-time data to its cluster according to the current operating condition; if a data point deviates from the baseline model within the cluster (e.g., abnormal temperature-vibration relationship), it is determined to be an abnormality to be cleaned; if the overall modal relationship conforms to the fault characteristics, it is retained as real fault data. Step S204: Based on the abnormal state data to be cleaned, perform data cleaning on the time series state data to obtain target multimodal operating state data.

[0102] In this embodiment, after identifying the abnormal state data to be cleaned from the abnormal state data, the abnormal state data to be cleaned can be corrected or deleted, thereby obtaining the target multimodal operating state data.

[0103] In an optional embodiment, noise points can be replaced with predictions from a time series model;

[0104] In another alternative embodiment, the noise points are replaced with the average of adjacent data points.

[0105] Step S30: Integrate and analyze the target multimodal operating status data through the multiphysics coupling model corresponding to the power transmission and transformation equipment to obtain the operating status characteristic data of the power transmission and transformation equipment.

[0106] It should be noted that the aforementioned multiphysics coupling model can be used to comprehensively consider the interaction and influence of multiple physical fields such as thermal, mechanical, and electromagnetic fields during equipment operation. It can more accurately simulate the actual operating state of the equipment, providing a basis for condition assessment, fault diagnosis, and predictive maintenance. In this embodiment, the interaction mechanism between electromagnetic, thermal, and mechanical physical fields can be analyzed first to clarify the coupling relationship. For example, changes in the electromagnetic field generate heat, affecting the temperature field distribution; changes in temperature, in turn, lead to changes in the mechanical properties of materials, affecting the response of mechanical modules. Then, based on relevant physical laws and theories, partial differential equations describing each physical field and their boundary conditions can be established. For example, the electromagnetic field is based on Maxwell's equations, the thermal field on the heat conduction equation, and the mechanical field on the elasticity equation, etc. Finally, these equations are combined to construct a multiphysics coupling model.

[0107] It should be understood that the aforementioned operational status characteristic data can be used to characterize features such as temperature distribution, electromagnetic field distribution, stress-strain, and vibration data of power transmission and transformation equipment. In practical applications, multi-physics coupling models integrate various single-physics models. By establishing physical quantity interaction interfaces and coupling equations, simultaneous solutions for multiple fields can be achieved. For example, under the finite element framework, based on principles such as energy conservation and momentum conservation, the control equations of different physical fields are discretized to ensure that the mesh of each field element is adapted in terms of spatiotemporal scale. The multiple physical quantities (displacement, temperature, electric field strength, magnetic field strength, etc.) of each node are iteratively solved to obtain the operational status characteristic data of the power transmission and transformation equipment. This realistically reproduces the complex physical processes of equipment operation, thereby laying a solid foundation for comprehensive performance evaluation, fault prediction, and health management.

[0108] Step S40: Determine the operating status detection result corresponding to the power transmission and transformation equipment based on the operating status characteristic data.

[0109] Specifically, step S40 includes: determining key feature parameters based on the operating status feature data, the key feature parameters including maximum electric field strength, maximum temperature gradient and vibration spectrum dominant frequency; obtaining the key feature parameter threshold corresponding to the normal operating state of the power transmission and transformation equipment; comparing the key feature parameters with the key feature parameter threshold, and determining the operating status detection result corresponding to the power transmission and transformation equipment based on the comparison result.

[0110] It is understandable that the aforementioned maximum electric field strength can be the maximum value in the electric field strength distribution inside or around the power transmission and transformation equipment. For example, on the surface of insulators in high-voltage transmission lines, the electric field strength will vary at different locations due to factors such as structure and voltage distribution, and the largest value is the maximum electric field strength. The aforementioned maximum temperature gradient can be the maximum value of the rate of temperature change at the location where the temperature change is most drastic within the power transmission and transformation equipment. For example, in transformer windings, the current flowing through the windings generates heat, and different heat dissipation conditions at different parts of the windings lead to uneven temperature distribution and different rates of temperature change between adjacent locations, with the largest rate of temperature change being the maximum temperature gradient. The aforementioned dominant frequency of the vibration spectrum can be the frequency component with the largest amplitude in the vibration spectrum of the power transmission and transformation equipment. For example, when there is a mechanical fault in the equipment, such as bearing wear or rotor imbalance, vibrations at specific frequencies will occur. These frequencies will appear as peak values ​​with large amplitudes in the vibration spectrum, and the frequency with the largest amplitude is the dominant frequency of the vibration spectrum.

[0111] It should be noted that the aforementioned key characteristic parameter thresholds can be parameters such as the maximum electric field strength, the highest temperature gradient, and the dominant frequency of the vibration spectrum when the power transmission and transformation equipment is operating normally. In this embodiment, the maximum electric field strength, the highest temperature gradient, and the dominant frequency of the vibration spectrum when the power transmission and transformation equipment is operating can be compared in real time with the maximum electric field strength, the highest temperature gradient, and the dominant frequency of the vibration spectrum when the power transmission and transformation equipment is operating normally. It can be determined whether the current maximum electric field strength, the highest temperature gradient, and the dominant frequency of the vibration spectrum of the power transmission and transformation equipment exceed the corresponding thresholds. If they exceed the thresholds, it is determined that the operating state of the power transmission and transformation equipment is abnormal; otherwise, it indicates that the operating state of the power transmission and transformation equipment is not abnormal.

[0112] This embodiment provides a method for detecting the operating status of power transmission and transformation equipment. The method discloses real-time acquisition of multimodal operating status data of the power transmission and transformation equipment, and generation of time-series status data based on the multimodal operating status data. Data cleaning is performed on the time-series status data to obtain target multimodal operating status data. The target multimodal operating status data is integrated and analyzed using a multiphysics coupling model corresponding to the power transmission and transformation equipment to obtain operating status characteristic data of the power transmission and transformation equipment. The operating status detection result corresponding to the power transmission and transformation equipment is determined based on the operating status characteristic data. Because this embodiment can integrate and analyze the target multimodal operating status data of the power transmission and transformation equipment using a multiphysics coupling model corresponding to the power transmission and transformation equipment to obtain operating status characteristic data, and determine the operating status detection result corresponding to the power transmission and transformation equipment based on the operating status characteristic data, it solves the technical problem in the prior art where periodic inspections of power transmission and transformation equipment are difficult to accurately match the actual degradation rate of the power transmission and transformation equipment, resulting in low detection accuracy.

[0113] Please refer to Figure 2 Step S30 includes steps S301 to S304:

[0114] Step S301: Perform structural analysis on the power transmission and transformation equipment to obtain the vibration modes and compressive degrees of freedom corresponding to the power transmission and transformation equipment.

[0115] It should be noted that the aforementioned vibration modes can be the inherent vibration characteristics of the structure in the power transmission and transformation equipment. Each mode has a specific natural frequency, damping ratio, and mode shape. For example, when a transformer in a power transmission and transformation equipment is excited, its structure will vibrate at a specific frequency and mode shape; these specific vibration modes are called vibration modes. In practical applications, by analyzing the vibration modes corresponding to the power transmission and transformation equipment, we can understand the vibration characteristics of the equipment at different frequencies, providing a basis for vibration monitoring and fault diagnosis of the equipment. In this embodiment, the finite element method can be used to obtain the vibration modes corresponding to the power transmission and transformation equipment. Specifically, the structure of the power transmission and transformation equipment can be discretized into a finite number of elements. By establishing a mechanical model of each element and then assembling them into a mechanical model of the entire structure, the vibration equation of the structure can be solved to obtain the vibration mode parameters. For example, by using finite element analysis software such as ANSYS or ABAQUS to establish a three-dimensional model of the power transmission and transformation equipment, setting the mechanical parameters of the materials and boundary conditions, and performing modal analysis, the natural frequency, mode shape, and other vibration mode information of the power transmission and transformation equipment can be obtained.

[0116] It should be noted that the aforementioned compressible degrees of freedom can be the degrees of freedom after reasonable reduction or approximation of the power transmission and transformation equipment. In this embodiment, the compressible degrees of freedom corresponding to the power transmission and transformation equipment can be obtained using the substructure method. Specifically, the complex power transmission and transformation equipment structure can be divided into several substructures, and each substructure can be analyzed to obtain the vibration characteristics and degree of freedom information of the substructure. Then, the substructures can be combined through reasonable coupling conditions, and the degrees of freedom can be appropriately compressed during the combination process.

[0117] Step S302: Determine the stress distribution characteristics of the power transmission and transformation equipment based on the vibration modes and the compressive degrees of freedom using the multiphysics coupling model corresponding to the power transmission and transformation equipment.

[0118] It should be understood that the above-mentioned stress distribution characteristics can be used to reflect the stress and strain of various components of power transmission and transformation equipment under the action of electromagnetic forces, such as the degree of deformation of transformer windings under the action of electromagnetic forces.

[0119] In practical applications, vibration modes reflect the vibration characteristics of equipment at different frequencies, including mode shapes and natural frequencies. Therefore, by introducing vibration mode information into a multiphysics coupling model, the dynamic stress distribution of the equipment during vibration can be analyzed. Simultaneously, since compressive degrees of freedom alter the computational complexity and accuracy of the model, and also affect stress distribution, stress analysis of the model after compressing degrees of freedom yields stress distribution characteristics that more closely reflect reality. This embodiment allows the vibration modes and compressive degrees of freedom corresponding to the power transmission and transformation equipment to be input into a multiphysics coupling model. This enables the multiphysics coupling model to comprehensively consider the interactions of electromagnetic, thermal, and mechanical physical fields, as well as the influence of vibration modes and compressive degrees of freedom, obtaining stress distribution cloud maps of the equipment under different operating conditions. Based on these stress distribution cloud maps, the stress distribution characteristics of the power transmission and transformation equipment can be determined, thus providing a direct understanding of the equipment's stress distribution characteristics.

[0120] Step S303: Integrate and analyze the target multimodal operating state data through the multiphysics coupling model to determine the temperature distribution characteristics and electromagnetic field distribution characteristics of the power transmission and transformation equipment.

[0121] Understandably, the aforementioned temperature distribution characteristics can reflect the temperature conditions of various parts inside power transmission and transformation equipment, such as the temperature distribution of transformer windings and cores. These characteristics can be used to assess the equipment's heat dissipation performance and overheating risk. Similarly, the aforementioned electromagnetic field distribution characteristics can reflect parameters such as electromagnetic field strength and direction within power transmission and transformation equipment, such as the voltage, current, and magnetic flux distribution in transformers. These characteristics can be used to analyze the electromagnetic performance of the equipment and optimize its design.

[0122] Specifically, step S303 includes: performing integrated analysis on the target multimodal operating state data using the multiphysics coupling model to determine the magnetic vector potential corresponding to the electromagnetic equipment in the power transmission and transformation equipment; determining the electromagnetic field distribution in the electromagnetic equipment based on the magnetic vector potential and the electric scalar potential to obtain electromagnetic field distribution characteristics; constructing a temperature gradient grid based on the target multimodal operating state data and the multigrid method using the multiphysics coupling model; and obtaining the temperature distribution characteristics corresponding to the power transmission and transformation equipment based on the temperature gradient grid.

[0123] It should be noted that the aforementioned magnetic vector potential can be a vector field satisfying a specific magnetic induction intensity. In this embodiment, the multi-mode operating state data of the target can be integrated and analyzed using a multi-physics coupling model to obtain the magnetic vector potential corresponding to the power transmission and transformation equipment. This embodiment can use the magnetic vector potential and the electric scalar potential to describe the electromagnetic field, and introduce the generalized Lagrange multiplier method to handle the Coulomb gauge constraint, ensuring the uniqueness of the magnetic vector potential solution. Then, the distribution of the magnetic vector potential and the electric scalar potential is solved to obtain the electromagnetic field distribution.

[0124] It is understandable that the aforementioned temperature gradient grid can be a spatial grid used to accurately describe the temperature changes inside power transmission and transformation equipment. It discretizes the equipment in the spatial domain, records temperature information at each grid node, and uses this information to calculate the temperature change rate (i.e., temperature gradient) between adjacent nodes, thereby meticulously reflecting the spatial distribution and trend of temperature changes inside the equipment.

[0125] In this embodiment, the multigrid method improves computational efficiency by generating grids of different levels (such as fine grids, coarse grids, etc.). The fine grid is used to accurately capture the detailed temperature changes inside the device, while the coarse grid is used to quickly eliminate low-frequency errors. For example, when solving the temperature field of a transformer, the fine grid can describe the temperature distribution of the windings and core in detail, while the coarse grid can make overall adjustments to the temperature field on a larger spatial scale.

[0126] Step S304: Generate the operating status characteristic data of the power transmission and transformation equipment based on the stress distribution characteristics, the temperature distribution characteristics, and the electromagnetic field distribution characteristics.

[0127] It should be understood that after obtaining the stress distribution characteristics, temperature distribution characteristics, and electromagnetic field distribution characteristics of the power transmission and transformation equipment, these characteristics can be integrated to generate the operating status characteristic data of the power transmission and transformation equipment.

[0128] This embodiment discloses a method for structural analysis of power transmission and transformation equipment to obtain the corresponding vibration modes and compressive degrees of freedom; the method for determining the stress distribution characteristics of the power transmission and transformation equipment based on the vibration modes and compressive degrees of freedom using a multi-physics coupling model; the method for integrating and analyzing the target multi-mode operating state data using the multi-physics coupling model to determine the temperature distribution characteristics and electromagnetic field distribution characteristics of the power transmission and transformation equipment; and the method for generating operating state characteristic data of the power transmission and transformation equipment based on the stress distribution characteristics, temperature distribution characteristics, and electromagnetic field distribution characteristics. This allows for the accurate acquisition of characteristic data to characterize the current operating state of the power transmission and transformation equipment, thereby improving the accuracy of state detection of the power transmission and transformation equipment.

[0129] Furthermore, after step S40, the method further includes: performing model reduction processing on the electromagnetic model, the thermal model, and the mechanical model using the cross-Gram matrix low-rank decomposition method to obtain the reduced-order electromagnetic model, the reduced-order thermal model, and the reduced-order mechanical model; constructing a lightweight physical field coupling model based on the reduced-order electromagnetic model, the reduced-order thermal model, and the reduced-order mechanical model; and updating the operating status data of the power transmission and transformation equipment in real time through the lightweight physical field coupling model.

[0130] It should be noted that the low-rank decomposition method of the cross-Gram matrix can be a technique for reducing the order of asymmetric linear system models. Its core is to reduce the complexity of the model by approximating the low-rank decomposition of the cross-Gram matrix, while preserving the main dynamic characteristics of the model.

[0131] In practical applications, for the electromagnetic, thermal, and mechanical models of power transmission and transformation equipment, the impulse responses of these models can first be expanded on a Legendre polynomial basis. Specifically, based on the orthogonality of Legendre polynomials, an approximate low-rank decomposition of the cross-Gram matrix of the asymmetric linear system can be given. The cross-Gram matrix reflects the energy relationship between the model's input and output, and the main features of the model can be extracted through low-rank decomposition. Then, the original high-dimensional model can be mapped to a low-dimensional space through projection transformation to obtain an approximate equilibrium model of the original model, thereby reducing the model's dimensionality and achieving order reduction. This yields the reduced-order electromagnetic model, reduced-order thermal model, and reduced-order mechanical model.

[0132] It should be noted that the aforementioned lightweight physics coupling model can be a simplified and optimized multiphysics coupling model. In this embodiment, since the lightweight physics coupling model is constructed based on a reduced-order electromagnetic model, a reduced-order thermal model, and a reduced-order mechanical model, the complexity and computational load of the model can be reduced, thereby enabling more efficient simulation and analysis of the operating state of power transmission and transformation equipment under the influence of electromagnetic, thermal, and mechanical multiphysics fields. In practical applications, since the lightweight physics coupling model has undergone order reduction processing and has low computational complexity, it can quickly solve for the response of power transmission and transformation equipment under the influence of multiphysics fields, such as electromagnetic field distribution, temperature distribution, stress and strain, so as to update the operating state data of power transmission and transformation equipment in real time.

[0133] This embodiment discloses the determination of electromagnetic, thermal, and mechanical characteristic parameters of power transmission and transformation equipment; the construction of an electromagnetic model corresponding to the power transmission and transformation equipment based on the electromagnetic characteristic parameters; the construction of a thermal model corresponding to the power transmission and transformation equipment based on the thermal characteristic parameters; and the construction of a mechanical model corresponding to the power transmission and transformation equipment based on the mechanical characteristic parameters. The electromagnetic, thermal, and mechanical models are then fused to form a multi-physics coupling model corresponding to the power transmission and transformation equipment. This allows for accurate analysis of the coupling relationships between the electromagnetic, thermal, and mechanical physical fields of the power transmission and transformation equipment, thereby improving the detection accuracy of the operating status of the power transmission and transformation equipment.

[0134] Example 2, refer to Figure 4 According to one embodiment of the present invention, this embodiment provides a power transmission and transformation equipment operation status detection system, including: a data acquisition module 10, a data cleaning module 20, a data analysis module 30, and an operation status detection module 40;

[0135] The data acquisition module 10 is used to acquire multimodal operating status data of power transmission and transformation equipment in real time, and generate time series status data based on the multimodal operating status data;

[0136] Data cleaning module 20 is used to clean the time series state data based on the time series to obtain target multimodal operating state data;

[0137] Data analysis module 30 is used to integrate and analyze the target multimodal operating status data through the multiphysics coupling model corresponding to the power transmission and transformation equipment, and obtain the operating status characteristic data of the power transmission and transformation equipment;

[0138] The operation status detection module 40 is used to determine the operation status detection result corresponding to the power transmission and transformation equipment based on the operation status feature data.

[0139] This embodiment also provides an electronic device applicable to a method for detecting the operating status of power transmission and transformation equipment, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for detecting the operating status of power transmission and transformation equipment as proposed in the above embodiment.

[0140] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements a method for detecting the operating status of power transmission and transformation equipment as proposed in the above embodiments.

[0141] The storage medium proposed in this embodiment belongs to the same inventive concept as the method for detecting the operating status of power transmission and transformation equipment proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0142] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0143] like Figure 5 As shown, the power transmission and transformation equipment operation status monitoring device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory 1002 or the program loaded from the storage device 1003 into the random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the power transmission and transformation equipment operation status monitoring device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, touch screens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the power transmission and transformation equipment operation status monitoring device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows power transmission and transformation equipment operation status monitoring devices with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented alternatively.

[0144] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for detecting the operating status of power transmission and transformation equipment, characterized in that: include, Real-time acquisition of multimodal operating status data of power transmission and transformation equipment, and generation of time-series status data based on the multimodal operating status data; Data cleaning is performed on the time series state data based on the time series to obtain target multimodal operating state data; By integrating and analyzing the multi-physics coupling model corresponding to the power transmission and transformation equipment, the operating status characteristic data of the target multi-modal operation status are obtained. The operating status detection result of the power transmission and transformation equipment is determined based on the operating status characteristic data.

2. The method for detecting the operating status of power transmission and transformation equipment as described in claim 1, characterized in that: The real-time acquisition of multi-mode operating status data of power transmission and transformation equipment includes determining the electromagnetic characteristic parameters, thermal characteristic parameters, and mechanical characteristic parameters of the power transmission and transformation equipment before acquiring the multi-mode operating status data of the power transmission and transformation equipment in real time. An electromagnetic model corresponding to the power transmission and transformation equipment is constructed based on the electromagnetic characteristic parameters. A thermal model corresponding to the power transmission and transformation equipment is constructed based on the aforementioned thermal characteristic parameters; A mechanical model of the power transmission and transformation equipment is constructed based on the aforementioned mechanical characteristic parameters. The electromagnetic model, the thermal model, and the mechanical model are fused to form a multiphysics coupling model corresponding to the power transmission and transformation equipment.

3. The method for detecting the operating status of power transmission and transformation equipment as described in claim 2, characterized in that: The data cleaning includes analyzing the time series state data to determine the time series characteristics corresponding to the time series state data; Based on the time series characteristics, determine the abnormal state data in the time series state data; The abnormal state data is classified, and the abnormal state data to be cleaned is determined from the abnormal state data based on the classification results. Data cleaning is performed on the time series state data based on the abnormal state data to be cleaned to obtain target multimodal operating state data.

4. The method for detecting the operating status of power transmission and transformation equipment as described in claim 3, characterized in that: The operational status characteristic data includes structural analysis of the power transmission and transformation equipment to obtain the vibration modes and compressive degrees of freedom corresponding to the power transmission and transformation equipment; The stress distribution characteristics of the power transmission and transformation equipment are determined based on the vibration modes and the compressive degrees of freedom using a multiphysics coupling model corresponding to the power transmission and transformation equipment. The multi-physics coupling model is used to integrate and analyze the multi-modal operating state data of the target to determine the temperature distribution characteristics and electromagnetic field distribution characteristics of the power transmission and transformation equipment. The operating status characteristic data of the power transmission and transformation equipment are generated based on the stress distribution characteristics, temperature distribution characteristics, and electromagnetic field distribution characteristics.

5. The method for detecting the operating status of power transmission and transformation equipment as described in claim 4, characterized in that: The determination of the temperature distribution characteristics and electromagnetic field distribution characteristics of the power transmission and transformation equipment includes, through the multi-physics coupling model, performing integrated analysis on the target multi-modal operating state data, and determining the magnetic vector potential of the electromagnetic equipment in the power transmission and transformation equipment; The electromagnetic field distribution in the electromagnetic device is determined based on the magnetic vector potential and the electric scalar potential, and the electromagnetic field distribution characteristics are obtained. A temperature gradient grid is constructed using the multiphysics coupling model based on the target multimodal operating state data and the multigrid method. The temperature distribution characteristics of the power transmission and transformation equipment are obtained based on the temperature gradient grid.

6. The method for detecting the operating status of power transmission and transformation equipment as described in claim 5, characterized in that: The operational status detection results include determining key characteristic parameters based on the operational status characteristic data. The key characteristic parameters include the maximum electric field strength, the highest temperature gradient, and the dominant frequency of the vibration spectrum. Obtain the threshold values ​​of key feature parameters corresponding to the normal operation state of the power transmission and transformation equipment; The key feature parameters are compared with the key feature parameter thresholds, and the operating status detection result of the power transmission and transformation equipment is determined based on the comparison result.

7. The method for detecting the operating status of power transmission and transformation equipment as described in claim 6, characterized in that: The operational status detection results also include: The electromagnetic model, the thermal model, and the mechanical model are reduced in order using the cross-Gram matrix low-rank decomposition method to obtain the reduced electromagnetic model, the reduced thermal model, and the reduced mechanical model. A lightweight physical field coupling model is constructed based on the reduced electromagnetic model, the reduced thermal model, and the reduced mechanical model. The lightweight physical field coupling model is used to update the operating status data of power transmission and transformation equipment in real time.

8. A power transmission and transformation equipment operation status detection system, employing the power transmission and transformation equipment operation status detection method as described in any one of claims 1 to 7, characterized in that, include: Data acquisition module, data cleaning module, data analysis module, and operation status monitoring module; The data acquisition module is used to collect multimodal operating status data of power transmission and transformation equipment in real time, and generate time series status data based on the multimodal operating status data; The data cleaning module is used to clean the time series state data based on the time series to obtain target multimodal operating state data; The data analysis module is used to integrate and analyze the target multimodal operating status data through the multiphysics coupling model corresponding to the power transmission and transformation equipment, and obtain the operating status characteristic data of the power transmission and transformation equipment. The operation status detection module is used to determine the operation status detection result corresponding to the power transmission and transformation equipment based on the operation status feature data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for detecting the operating status of power transmission and transformation equipment according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for detecting the operating status of power transmission and transformation equipment as described in any one of claims 1 to 7.

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