Electric power fitting clamping degree monitoring method and system

Abstract

The invention relates to the technical field of power equipment state monitoring, and particularly provides a power fitting clamping degree monitoring method and system, and the method comprises the steps: synchronously obtaining the comprehensive stress data and first environment temperature data of a power fitting, and the first load current data of a power transmission line; calculating a first thermally induced stress component caused by the environment temperature change and a second thermally induced stress component caused by the Joule heating effect of the load current according to the first environment temperature data, the first load current data and preset material characteristics corresponding to the electric power fitting; calculating a static stress component reflecting the clamping force of the electric power fitting according to the comprehensive stress data, the first thermally induced stress component and the second thermally induced stress component; analyzing and acquiring the current clamping degree of the electric power fitting according to the static stress component; according to the method, the signals generated by the Joule heating effect of the environment temperature and the load current in the mixed signals collected by the stress sensor can be effectively separated from the static stress component reflecting the real clamping force of the electric power fitting.

Description

Technical Field

[0001] The present application relates to the technical field of power equipment status monitoring, and in particular to a method and system for monitoring the clamping degree of power fittings. Background Art

[0002] Transmission lines operate in complex, outdoor environments. Power fittings, as key components, require monitoring the clamping force of these components to ensure grid security. Existing technologies integrate stress sensors on fittings or fasteners to collect real-time clamping force data, which is then transmitted wirelessly to a monitoring center for analysis. This represents the future of online monitoring technology, aiming to improve line reliability and prevent failures.

[0003] However, in actual operation, this stress sensor-based monitoring method faces severe interference from complex environmental factors. First, electrical fittings and the monitoring devices installed on them are exposed to the elements for long periods of time, and their operating temperature changes dynamically with changes in sunlight intensity, seasonal changes, and significant diurnal temperature differences. Metal materials generally have the physical property of thermal expansion and contraction. Temperature fluctuations directly cause small but significant changes in the dimensions of the fitting itself, fasteners, and even the stress sensor itself, leading to corresponding changes in their internal stress state. These temperature-induced thermal stress changes inevitably superimpose on the mechanical stress generated by the wire clamping force, resulting in a mixed signal in the raw data collected by the stress sensor that cannot directly and accurately reflect the actual clamping state. For example, on a hot summer afternoon, the fitting temperature rises significantly, and the material expansion can cause the stress sensor reading to drift or fluctuate. The monitoring system may mistakenly interpret this as a change in clamping degree, triggering unnecessary maintenance or false alarms.

[0004] Furthermore, the actual operating temperature of electrical fittings is significantly affected not only by ambient air temperature but also by the Joule heating effect generated by the load current flowing through the transmission lines. As the load on the transmission line increases, the current flowing through the conductors also increases. According to Joule's law, the temperature of the conductors and the electrical fittings in close contact with them will also rise. It is worth noting that the load current of transmission lines changes dynamically, and its changes are closely related to user electricity consumption behavior, exhibiting significant randomness and volatility.

[0005] To sum up, the stress signal collected by the stress sensor is a mixed signal generated by the combined effects of clamping force, ambient temperature and Joule heating effect of load current. Since the existing stress sensor-based monitoring method cannot effectively separate the signals generated by ambient temperature and Joule heating effect of load current in the mixed signal, the existing technology has the problem of reduced accuracy in clamping degree assessment due to the inability to extract the static stress component that can truly reflect the irreversible loosening trend of electrical hardware from the stress signal collected by the stress sensor, thereby causing misjudgment or false alarm.

[0006] There is no effective technical solution to the above problems. It should be noted that the above information disclosed in this section is only used to understand the background of the present invention, and therefore may contain information that does not constitute prior art. Summary of the Invention

[0007] The purpose of this application is to provide a method and system for monitoring the clamping degree of electric hardware, which can effectively separate the signal generated by the Joule heating effect of ambient temperature and load current in the mixed signal collected by the stress sensor from the static stress component reflecting the actual clamping force of the electric hardware.

[0008] In a first aspect, the present application provides a method for monitoring the clamping degree of an electric fitting, which comprises the following steps: S1. Synchronously obtain comprehensive stress data of power fittings, first ambient temperature data, and first load current data of transmission lines; S2. Calculating a first thermally induced stress component caused by ambient temperature change and a second thermally induced stress component caused by Joule heating effect of the load current based on the first ambient temperature data, the first load current data, and preset material properties corresponding to the electrical fittings; S3. Calculate the static stress component reflecting the clamping force of the power fitting based on the comprehensive stress data, the first thermally induced stress component, and the second thermally induced stress component; S4. Obtain the current clamping degree of the electrical fittings based on static stress component analysis.

[0009] In a second aspect, the present application further provides a power fitting clamping degree monitoring system, which comprises: A data acquisition module, configured to synchronously acquire comprehensive stress data of the power fittings, first ambient temperature data, and first load current data of the transmission conductor; a thermally induced stress component calculation module, configured to calculate a first thermally induced stress component caused by ambient temperature change and a second thermally induced stress component caused by Joule heating effect of the load current based on the first ambient temperature data, the first load current data, and preset material properties corresponding to the electrical fitting; A static stress component calculation module, used to calculate the static stress component reflecting the clamping force of the power fitting based on the comprehensive stress data, the first thermally induced stress component and the second thermally induced stress component; The clamping degree analysis module is used to obtain the current clamping degree of the power fittings based on static stress component analysis.

[0010] From the above, it can be seen that the present application provides a method and system for monitoring the clamping force of electric hardware, which simultaneously obtains the comprehensive stress data, ambient temperature data and transmission line load current data of the electric hardware, and calculates and strips off the thermally induced stress components caused by ambient temperature changes and the Joule heating effect of the load current based on the material properties of the electric hardware, thereby extracting the static stress component reflecting the true clamping force of the electric hardware from the comprehensive stress data. That is, the present application can effectively separate the signal generated by the Joule heating effect of the ambient temperature and load current in the mixed signal collected by the stress sensor from the static stress component reflecting the true clamping force of the electric hardware. Therefore, the present application can effectively solve the problem of decreased accuracy in clamping degree assessment due to the inability to extract the static stress component that can truly reflect the irreversible loosening trend of the electric hardware from the stress signal collected by the stress sensor, thereby effectively avoiding the occurrence of misjudgments or false alarms caused by the accuracy of clamping degree assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 This is a flow chart of a method for monitoring the clamping degree of electrical fittings provided in an embodiment of the present application.

[0012] Figure 2 This is a structural diagram of an electrical fitting clamping degree monitoring system provided in an embodiment of the present application.

[0013] Reference numerals: 1. Data acquisition module; 2. Thermal stress component calculation module; 3. Static stress component calculation module; 4. Clamping degree analysis module. DETAILED DESCRIPTION

[0014] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present application.

[0015] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0016] First, as Figure 1 As shown, the present application provides a method for monitoring the clamping degree of electric hardware, which includes the following steps: S1. Synchronously obtain comprehensive stress data of power fittings, first ambient temperature data, and first load current data of transmission lines; S2. Calculating a first thermally induced stress component caused by ambient temperature change and a second thermally induced stress component caused by Joule heating effect of the load current based on the first ambient temperature data, the first load current data, and preset material properties corresponding to the electrical fittings; S3. Calculate the static stress component reflecting the clamping force of the power fitting based on the comprehensive stress data, the first thermally induced stress component, and the second thermally induced stress component; S4. Obtain the current clamping degree of the electrical fittings based on static stress component analysis.

[0017] The comprehensive stress data of step S1 refers to the original signal collected by the stress sensor on the power fitting that reflects its overall stress state. The comprehensive stress data is a mixed signal containing multiple stress components such as mechanical stress and thermal stress that the power fitting is subjected to during actual operation. This embodiment can use a resistance strain gauge, fiber Bragg grating sensor or piezoelectric sensor installed on the power fitting to obtain the comprehensive stress data. This embodiment can obtain the total stress information containing multiple stress components by obtaining the comprehensive stress data. The first ambient temperature data of step S1 refers to the temperature measurement value of the environment in which the power fitting is located. This embodiment can use a thermal resistor, thermocouple or infrared temperature sensor to obtain the first ambient temperature data. This embodiment can quantify the impact of the ambient temperature on the stress state of the power fitting by obtaining the first ambient temperature data. The first load current data of the transmission line refers to the current measurement value of the transmission line connected to the power fitting. This embodiment can use a current transformer, Hall sensor or flexible current probe to obtain the first load current data. This embodiment can quantify the impact of the Joule heating effect of the load current on the stress state of the power fitting by obtaining the first load current data. Step S1 can make these different types of data consistent in time by synchronously acquiring the comprehensive stress data of the power fittings, the first ambient temperature data and the first load current data of the transmission line. This embodiment can use a unified timestamp, a synchronous sampling clock or a centralized data acquisition system to achieve the synchronous acquisition of the comprehensive stress data, the first ambient temperature data and the first load current data to ensure that all data points are recorded at the same time or within a very short time interval and provide time-aligned original input for subsequent data processing.

[0018] The preset material properties in step S2 refer to the inherent physical property parameters of the electrical fitting body and its fasteners. These preset material properties are preferably obtained by measuring the electrical fitting during its manufacture. That is, in this embodiment, the preset material properties can be obtained by consulting a material manual or laboratory test data. The preset material properties can include, for example, the coefficient of thermal expansion, Young's modulus, and resistivity. This embodiment can provide the basic physical parameters for calculating thermally induced stress components by obtaining the preset material properties. The first thermally induced stress component in step S2 refers to the stress component generated by the expansion or contraction of the electrical fitting material due to changes in ambient temperature. This embodiment can quantify the contribution of ambient temperature changes to the total stress by calculating the first thermally induced stress component. The second thermally induced stress component in step S2 refers to the stress component generated by the temperature increase of the electrical fitting due to the Joule heating effect of the load current in the transmission line. This embodiment can quantify the contribution of the load current change to the total stress by calculating the second thermally induced stress component. This embodiment can calculate the second thermally induced stress component based on Joule's law, the heat balance equation, and the thermal expansion coefficient and Young's modulus of the material. For example, the temperature rise caused by the current is first calculated and then converted into stress.

[0019] The static stress component in step S3 refers to the stress component that primarily reflects the clamping force of the electrical hardware after stripping the thermally induced stress component and other dynamic stress components from the comprehensive stress data. Because this embodiment calculates the static stress component based on the comprehensive stress data, the first thermally induced stress component, and the second thermally induced stress component, the static stress component in this embodiment is equivalent to the stress generated by the clamping force of the electrical hardware itself, unaffected by dynamic changes in ambient temperature and load current. Step S3 can be implemented by utilizing the first and second thermally induced stress components in the comprehensive stress data to calculate the static stress component based on the comprehensive stress data, the first and second thermally induced stress components. That is, the calculation formula for the static stress component is: static stress component = comprehensive stress data - first thermally induced stress component - second thermally induced stress component.

[0020] Step S4 determines the clamping state of the electrical fitting by analyzing the static stress component. This embodiment can first calculate the static stress change based on the static stress component and the preset initial clamping stress reference value, and then evaluate the current clamping degree based on the static stress change (which can be implemented by a table lookup method or a machine learning model). This embodiment can also obtain the current clamping degree of the electrical fitting based on the static stress component analysis by analyzing which stress component range the static stress component belongs to.

[0021] The core innovation of this application lies in that the static stress component reflecting the true clamping force of the power fittings is extracted from the comprehensive stress data by synchronously acquiring the comprehensive stress data, ambient temperature data and transmission line load current data of the power fittings, and calculating and stripping off the thermally induced stress components caused by ambient temperature changes and the Joule heating effect of the load current based on the material properties of the power fittings. That is, this application can effectively separate the signal generated by the Joule heating effect of the ambient temperature and load current in the mixed signal collected by the stress sensor from the static stress component reflecting the true clamping force of the power fittings. Therefore, this application can effectively solve the problem of decreased accuracy in clamping degree assessment due to the inability to extract the static stress component that can truly reflect the irreversible loosening trend of the power fittings from the stress signal collected by the stress sensor, thereby effectively avoiding the occurrence of misjudgments or false alarms caused by the accuracy of clamping degree assessment.

[0022] Specifically, this method first synchronously acquires the comprehensive stress data of the power fitting, first ambient temperature data, and first load current data of the transmission line, ensuring temporal consistency of the multi-source data required for subsequent analysis. Next, based on the acquired first ambient temperature data, first load current data, and the preset material properties of the power fitting, a first thermally induced stress component caused by ambient temperature changes and a second thermally induced stress component caused by the Joule heating effect of the load current are calculated. These calculated thermally induced stress components quantify the impact of environmental and load changes on the stress state of the power fitting. Subsequently, based on the synchronously acquired comprehensive stress data and the calculated first and second thermally induced stress components, the influence of the thermally induced stress components is removed from the comprehensive stress data to calculate a static stress component that primarily reflects the clamping force of the power fitting. Finally, based on the calculated static stress component, the current clamping state or change trend of the power fitting is analyzed and evaluated to determine the current clamping degree of the power fitting. The entire process, comprising data acquisition, thermally induced stress calculation and separation, static stress extraction, and clamping degree analysis, forms a complete clamping degree monitoring process that effectively addresses environmental and load interference.

[0023] As a preferred embodiment, the solution of this application is specifically implemented as follows: strain sensors and temperature sensors are installed on the power fittings, and current sensors are installed on the transmission conductors. These sensors, through a data acquisition unit, synchronously collect a comprehensive stress signal (comprehensive stress data), an ambient temperature signal (first ambient temperature data), and a transmission conductor current signal (first load current data). The collected signals undergo signal conditioning and digitization before being transmitted to a processing unit. The processing unit stores the material parameters of the power fittings (e.g., thermal expansion coefficient, Young's modulus, etc.). Based on the collected ambient temperature signal and material parameters, the processing unit calculates the thermal stress caused by ambient temperature changes (the first thermally induced stress component). Simultaneously, based on the collected current signal, material parameters, and a heat transfer model, the processing unit calculates the temperature rise caused by the Joule heating effect and the resulting thermal stress (the second thermally induced stress component). The processing unit subtracts these two calculated thermal stress components from the collected comprehensive stress signal to obtain static stress data. Finally, the processing unit analyzes the static stress data, for example, by comparing it with the initial static stress or monitoring its rate of change, to determine the current clamping degree of the power fittings. Through the above scheme, the present application can effectively separate the interference of ambient temperature and load current changes on the stress measurement of power fittings, extract the static stress component that truly reflects the clamping state, improve the accuracy and reliability of power fitting clamping degree monitoring, and avoid misjudgment or false alarms caused by environmental factors.

[0024] In some preferred embodiments, step S1 includes: S11, synchronously obtaining comprehensive stress data of the power fitting, first ambient temperature data, and first load current data of the transmission line; S12. Preprocessing the comprehensive stress data, the first ambient temperature data, and the first load current data. The preprocessing includes signal conditioning and drift compensation.

[0025] Preprocessing in step S12 refers to a series of data processing operations performed after the raw data (comprehensive stress data, first ambient temperature data, and first load current data) are collected to improve data quality and reliability. This preprocessing includes signal conditioning and drift compensation. Signal conditioning is the process of adjusting and optimizing the raw electrical signal, and can include operations such as filtering, amplification, and impedance matching. Drift compensation corrects for slow, unintended drift or changes in the sensor output signal over time or due to environmental changes. Drift compensation can be achieved through methods such as baseline correction, trend fitting and removal, or reference point-based calibration. Due to the influence of the characteristics of the sensor itself, external electromagnetic interference or environmental factors, the original comprehensive stress data, first ambient temperature data and first load current data may contain noise or drift over time. This embodiment can use signal conditioning to remove high-frequency noise or transient interference in the data to make the signal smoother and clearer. This embodiment can use drift compensation to correct the zero offset or sensitivity change of the sensor caused by long-term use or environmental changes to ensure the accuracy of the data. Therefore, this embodiment can improve the data quality and reliability of the comprehensive stress data, first ambient temperature data and first load current data by preprocessing the comprehensive stress data, first ambient temperature data and first load current data to improve the accuracy of subsequent calculations, thereby significantly improving the overall accuracy and reliability of the power hardware clamping degree monitoring and further reducing misjudgments and false alarms.

[0026] In some preferred embodiments, step S11 includes: S111, synchronously acquiring first ambient temperature data, first load current data of the transmission line, and first local stress data corresponding to different stress collection positions on the power fitting; S112 : Generate comprehensive stress data based on all the first local stress data according to the stress collection position corresponding to the first local stress data and the structural characteristics of the electrical hardware.

[0027] The different stress collection locations on the electrical fitting in this embodiment are preferably multiple discrete points or areas on the fitting where stress concentration or stress distribution is representative, such as bolt connections, wire crimping points, and stress-bearing areas of the fitting body. The first local stress data in this embodiment are stress measurements collected by stress sensors located at these different stress collection locations. The structural characteristics of the electrical fitting in this embodiment refer to the physical properties and geometric features of the fitting itself, such as its material type, geometry, dimensions, connection method, and the stiffness distribution of its various components. These structural characteristics determine the distribution pattern and transmission path of internal stress in the fitting when subjected to loads (including clamping force and thermal stress). In this embodiment, generating comprehensive stress data based on all first local stress data according to the stress collection position corresponding to the first local stress data and the structural characteristics of the electric hardware means integrating all first local stress data into a comprehensive value that can represent the overall stress state of the hardware by utilizing the specific collection position of the first local stress data and the structural characteristics of the electric hardware itself. This embodiment can use a physical model-based calculation (such as post-processing of finite element analysis results) to achieve the generation of comprehensive stress data. This embodiment can also use a data-driven method (such as integrating local stress data through a machine learning model) to achieve the generation of comprehensive stress data. This embodiment can also use an experience-based or theoretical weighted average (for example, based on expert experience, assigning corresponding weights to each first local stress data according to the stress collection position of the first local stress data and the structural characteristics of the electric hardware, and then performing weighted average based on all first local stress data and their corresponding weights) to achieve the generation of comprehensive stress data.

[0028] This solution first obtains first-local stress data corresponding to different stress collection locations on the electrical fitting to gain a more comprehensive understanding of the fitting's stress distribution from multiple perspectives. This provides multi-source information for the subsequent generation of comprehensive stress data that better represents the fitting's overall state. Subsequently, this solution generates comprehensive stress data based on all first-local stress data, based on the stress collection locations corresponding to the first-local stress data and the structural characteristics of the electrical fitting. This approach utilizes the location information of the first-local stress data and the structural characteristics of the electrical fitting to perform targeted processing and integration (e.g., weighted averaging, model calculation, or other forms of integration) of the first-local stress data from different stress collection locations to generate comprehensive stress data that more accurately reflects the fitting's overall stress state than simple superposition or single-point selection. This generation method, based on multiple local stress points and combined with structural characteristics, enhances the representativeness of the comprehensive stress data, laying a solid foundation for subsequent separation of thermally induced stresses, extraction of static stresses, and ultimately analysis of clamping tension. Therefore, by obtaining local stress at multiple points and integrating it with the structural characteristics of the hardware, we can obtain comprehensive stress data that more accurately reflects the overall stress state of the hardware, thereby solving the problem that directly obtaining comprehensive stress data may not be comprehensive or accurate enough, and providing more reliable basic data for subsequent more accurate calculation of static stress components and analysis of clamping degree.

[0029] In a specific embodiment, strain gauges (stress sensors) are attached to multiple predetermined key locations on the electrical fitting (e.g., bolt roots, below conductor grooves, and the maximum stress-bearing areas of the fitting body). Temperature sensors are installed near or on the fitting body, and current sensors are installed on the transmission line. During monitoring, a data acquisition unit can synchronously collect data from all strain gauges, temperature sensors, and current sensors to obtain first local stress data, first ambient temperature data, and first load current data corresponding to different stress acquisition locations. This data is transmitted to a processing unit. The processing unit stores structural model information of the electrical fitting (e.g., geometric data and material property parameters obtained through 3D scanning or design drawings). The processing unit can run an algorithm that, using, for example, finite element inverse analysis or a pre-trained model, integrates these discrete local stress data into a comprehensive stress value representing the overall stress state of the fitting based on the strain gauge location corresponding to each local stress data point and the structural model of the fitting. For example, the local stress data can be weighted summed according to the weight of each location's contribution to the overall stress state (this weight can be predetermined based on structural analysis) to obtain the comprehensive stress data.

[0030] In some preferred embodiments, step S112 includes: A1. Acquire second ambient temperature data, second load current data, and a second local stress data set of the electrical fitting during its initial operation phase, where the second local stress data set includes second local stress data corresponding to different stress collection positions on the electrical fitting; A2. Analyze and obtain response characteristics of each stress collection location based on the second local stress data set, the second ambient temperature data, and the second load current data; A3. Determine the weight corresponding to each first local stress data according to the response characteristics of each stress collection position and the structural characteristics of the power fittings; A4. Perform weighted averaging on all first local stress data and their corresponding weighted weights to obtain comprehensive stress data.

[0031] The collection method of the second ambient temperature data, the second load current data and the second local stress data set of this embodiment is preferably the same as the collection method of the first ambient temperature data, the first load current data and the first local stress data of the above-mentioned embodiment. The initial operation stage of this embodiment refers to a specific period of time after the installation of the electrical fitting is completed and put into operation for the first time, for example, it can be several days, weeks or months. This period of time is sufficient to cover the typical operating state of the fitting under different environmental and load conditions and allow its stress distribution to reach relative stability. The response characteristics of this embodiment refer to the sensitivity and pattern of the local stress data at a specific stress collection position on the electrical fitting to changes in external factors such as ambient temperature and load current. The response characteristics may include the sensitivity of stress to temperature changes, the sensitivity of stress to load current changes and the baseline stress values under different environmental conditions. This embodiment may adopt a linear regression model, nonlinear regression, polynomial fitting or machine learning algorithm to obtain the response characteristics of each stress collection position based on the analysis of the second local stress data set, the second ambient temperature data and the second load current data. Determining the weighted weight refers to calculating the contribution ratio of each local stress data based on the response characteristics of each stress collection position (for example, sensitivity to environmental interference, representativeness of overall stress) and the structural characteristics of the electrical hardware (for example, stress concentration area, force path, structural stiffness of the sensor installation position). This embodiment can adopt a method based on expert experience, hierarchical analysis, principal component analysis, or an inverse error variance method to determine the weighted weight corresponding to each first local stress data based on the response characteristics of each stress collection position and the structural characteristics of the electrical hardware. For example, a higher weight is assigned to a collection point that is less affected by ambient temperature and load current or is located in a critical stress area; a lower weight is assigned to a collection point that is more affected by ambient temperature and load current or is located in a non-critical stress area. That is, the weighted weight in the embodiment refers to a numerical factor assigned to each local stress data, and the weighted weight is used to measure the importance or contribution of the data in calculating the comprehensive stress data.

[0032] This solution overcomes the potential errors introduced by simply combining local stress data by incorporating refined analysis of individual differences in electrical hardware and the influence of external environments. Specifically, in step A1, basic operating data for the electrical hardware during its initial operation is first acquired. Next, in step A2, based on this basic operating data, the relationship between the local stress data at each stress collection location and the ambient temperature and load current is analyzed to quantify the response characteristics of each stress collection location to these external factors under the current installation state. This identifies locations whose stress data is most affected by temperature or load, and which locations more reliably reflect the clamping force. Subsequently, in step A3, the weighting of each currently collected first local stress data in calculating the comprehensive stress data is determined by combining the response characteristics obtained in step A2 with the inherent structural characteristics of the electrical hardware. For example, locations with strong response characteristics (i.e., those most affected by external factors) are assigned lower weights, while locations with weak response characteristics and a high correlation with the clamping force are assigned higher weights. Furthermore, locations that are more structurally critical and more representative of the overall stress state may also be assigned higher weights. Finally, in step A4, all the first local stress data currently collected are weighted averaged with their corresponding weighted weights to obtain comprehensive stress data. This weighted averaging method can effectively integrate information from different locations, highlight important and reliable data, and suppress interference or less relevant data, thereby generating a comprehensive stress data that can more accurately reflect the true overall stress state of the power fittings. Therefore, this solution can dynamically or pre-determine the optimal weight distribution according to the actual installation status and operating environment of each specific fitting, so that the generated comprehensive stress data can more accurately reflect the actual stress condition of the fittings and lay a solid foundation for the subsequent accurate calculation of static stress components.

[0033] For example, in a specific implementation, step A1 can involve synchronously collecting ambient temperature, load current flowing through the conductors, and readings from multiple stress sensors distributed at key stress-bearing locations (e.g., bolts and body connections) every hour through a sensor network deployed on the electrical fitting within the first month after installation. This data is then stored in a local storage unit or uploaded to a remote server. Step A2 can involve offline analysis of the stored initial operating data. For example, for each stress collection point, a multivariate linear regression model is established between the local stress, ambient temperature, and load current. The coefficients of the model can serve as the response characteristic parameters of the collection point, reflecting its sensitivity to temperature and current changes in the current installation state. Step A3 can involve, after completing the response characteristic analysis, determining a weight based on the coefficients reflecting the effects of temperature and current in the regression model and the location of the collection point in the fitting structure (e.g., whether it is located in the primary load-bearing area) based on expert experience or preset rules. For example, points with larger absolute values of the temperature coefficient or current coefficient can have a lower weight, while points located in the primary load-bearing area can have a higher weight. Step A4 can be that during the subsequent real-time monitoring process, whenever a new set of first local stress data is collected, a weighted average is performed based on all the first local stress data and their corresponding weighted weights to obtain comprehensive stress data. Through the above technical solution, the present application can dynamically or pre-determine the contribution weights of the data collected at different stress locations based on the individual differences of the power fittings and the actual operating environment, thereby generating comprehensive stress data that can more accurately reflect the true overall stress state of the power fittings. This effectively solves the problem of inaccurate comprehensive stress data in the prior art due to the failure to fully consider the individual differences of the fittings and the influence of the external environment, and improves the accuracy of subsequent clamping degree assessment.

[0034] In some preferred embodiments, the preset material properties include thermal expansion coefficient, Young's modulus, resistivity, effective heat transfer coefficient, effective heat dissipation perimeter, and effective conductive cross-sectional area, and step S2 includes: S21, calculating a temperature difference based on the first ambient temperature data and a preset reference temperature, and then calculating a first thermally induced stress component caused by the ambient temperature change based on the temperature difference, the thermal expansion coefficient, and the Young's modulus; S22. Calculate the temperature rise of the electrical fitting caused by the Joule heating effect of the load current based on the first load current data, resistivity, effective heat transfer coefficient, effective heat dissipation perimeter, and effective conductive cross-sectional area. Then, calculate the second thermally induced stress component caused by the Joule heating effect of the load current based on the temperature rise of the electrical fitting, the thermal expansion coefficient, and Young's modulus.

[0035] The thermal expansion coefficient of this embodiment refers to the relative change in length or volume of a material per unit temperature increase. This embodiment can use a dilatometer or other device to obtain the thermal expansion coefficient. The Young's modulus of this embodiment refers to the ratio of stress to strain within the elastic deformation range of the material. This Young's modulus can reflect the material's ability to resist elastic deformation. This embodiment can use a tensile test or other method to obtain the Young's modulus. The resistivity of this embodiment refers to the material's ability to resist the flow of current. This embodiment can use a four-terminal method or other electrical measurement method to obtain the resistivity. The effective heat transfer coefficient of this embodiment refers to the ability of the surface of the electrical fitting to exchange heat with the surrounding air through convection. This embodiment can use numerical simulation or other methods to obtain the effective heat transfer coefficient. The effective heat dissipation perimeter of this embodiment refers to the effective surface perimeter of the electrical fitting used for heat exchange with the environment. This embodiment can calculate or estimate the effective heat dissipation perimeter based on the geometric shape of the electrical fitting. The effective conductive cross-sectional area of this embodiment refers to the effective cross-sectional area through which current flows through the electrical fitting. This embodiment can calculate or estimate the effective conductive cross-sectional area based on the structure and current distribution characteristics of the electrical fitting. The preset reference temperature of this embodiment refers to the base temperature used to calculate the temperature difference. The preset reference temperature can be the ambient temperature when the electrical fitting is installed or a long-term average ambient temperature. Specifically, the calculation formula for the first thermally induced stress component of this embodiment is: ; Wherein, thermal_env represents the first thermal stress component, k_env represents the preset environmental thermal stress calibration factor, E represents Young's modulus, α represents the thermal expansion coefficient, T ref Indicates the preset reference temperature, T env The calculation formula of the temperature rise of the electric fittings in this embodiment is: ;in, represents the temperature rise of the electrical fitting, k_joule_temp represents the preset Joule heat temperature rise calibration factor, I_load represents the first load current data, ρ represents the resistivity, h_eff represents the effective heat transfer coefficient, P_eff represents the effective heat dissipation perimeter, and A_eff represents the effective conductive cross-sectional area. The calculation formula for the second thermally induced stress component of this embodiment is: ; Where thermal_joule represents the second thermally induced stress component, and k_joule_stress represents the preset Joule thermal stress calibration factor.

[0036] In some preferred embodiments, step S2 further includes the following steps performed before step S21: B1. Obtain historical operating data of electrical fittings; B2. Evaluate the material aging status of electrical fittings based on historical operating data; B3. Correct the thermal expansion coefficient, Young's modulus, resistivity, effective heat transfer coefficient, effective heat dissipation perimeter and effective conductive cross-sectional area according to the aging status of the material.

[0037] The historical operating data of this embodiment refers to relevant data recorded at different time points or under different operating conditions after the electrical fitting is put into use. This historical operating data may include information such as operating time, cumulative load current, ambient temperature range, and duration of exposure to specific environments (such as high humidity and corrosive gases). The material aging state of this embodiment refers to the degree of performance degradation of the electrical fitting material due to the combined effects of thermal, electrical, and mechanical stresses and environmental factors (such as oxidation, corrosion, and fatigue) during long-term operation. This material aging state can be characterized by changes in key performance parameters such as material strength, toughness, conductivity, and thermal expansion coefficient. Correction of the thermal expansion coefficient, Young's modulus, resistivity, effective convective heat transfer coefficient, effective heat dissipation perimeter, and effective conductive cross-sectional area in this embodiment refers to the process of adjusting the values of these material characteristic parameters used in subsequent calculations based on the assessed material aging state to more closely approximate the actual performance values of the electrical fitting. This embodiment can use methods such as aging models, empirical formulas, or table lookup methods to achieve correction of the thermal expansion coefficient, Young's modulus, resistivity, effective heat transfer coefficient, effective heat dissipation perimeter, and effective conductive cross-sectional area based on the material aging state.

[0038] This application effectively improves the accuracy of thermally induced stress component calculation by introducing an evaluation of the aging state of the electrical fitting material and correcting the material characteristic parameters according to the evaluation results before calculating the thermally induced stress component. Specifically, obtaining the historical operating data of the electrical fitting provides the necessary data basis for evaluating the changes that may occur in the material during long-term operation. The degree of aging that the electrical fitting material may experience is evaluated based on the historical operating data, for example, considering the impact of factors such as operating time and environmental conditions on material properties, thereby obtaining an evaluation result that reflects the current aging state of the material. According to the aging state of the material obtained by the evaluation, the thermal expansion coefficient, Young's modulus, resistivity, effective convection heat transfer coefficient, effective heat dissipation perimeter and effective conductive cross-sectional area are corrected. This means that when calculating the thermally induced stress component, fixed preset values are no longer used. Instead, material characteristic parameters that are closer to the current actual values after adjustment based on the actual operating conditions and aging state are used. By using the corrected material property parameters for subsequent calculations, the first thermally induced stress component caused by ambient temperature changes and the second thermally induced stress component caused by the Joule heating effect of the load current can be more accurately calculated, providing a more reliable basis for the subsequent calculation of the static stress component reflecting the clamping force of the power fitting, ultimately improving the accuracy of the clamping degree assessment. This method of dynamically correcting material parameters before calculation enables the entire monitoring method to adapt to changes in the performance of power fitting materials over time, improving the robustness and long-term effectiveness of the method.

[0039] In one embodiment, historical operating data for power fittings can be obtained through sensors installed on the fittings or transmission lines, data loggers, or data collected through a SCADA system. This data can include the fitting's cumulative operating time, the highest and lowest ambient temperatures experienced, the cumulative amount of charge passed (related to Joule heating), and information such as ambient humidity and salt spray. Evaluating the material aging state of the fitting based on historical operating data can be accomplished by establishing a material aging model. This model can be based on laboratory accelerated aging test data and field operating experience data, using historical operating data as input and outputting a quantitative aging index or directly providing predicted values for various material performance parameters. For example, for aluminum alloy fittings, the aging model can consider the effects of operating time, number of temperature cycles, and cumulative high-temperature exposure time on the material's yield strength and conductivity. Correcting material property parameters based on material aging can be accomplished by consulting a pre-established correction factor table. This table can provide correction ratios or amounts for various material property parameters relative to their initial values based on different aging indices or aging stages. For example, if the assessment results indicate that the material is in a moderate aging stage, the thermal expansion coefficient may need to be increased by a certain percentage, while the Young's modulus and conductivity may need to be decreased by a certain percentage.

[0040] In some preferred embodiments, step S22 includes: S221. Acquire current environmental parameters, including current wind speed, current humidity, and current rainfall; S222. Correct the effective heat transfer coefficient and the effective heat dissipation perimeter according to current environmental parameters; S223. Calculate the temperature rise of the electrical fitting caused by the Joule heating effect of the load current based on the first load current, resistivity, effective heat transfer coefficient, effective heat dissipation perimeter, and effective conductive cross-sectional area. Then, calculate the second thermally induced stress component caused by the Joule heating effect of the load current based on the temperature rise of the electrical fitting, thermal expansion coefficient, and Young's modulus.

[0041] The current environmental parameters of this embodiment refer to the real-time environmental status data of the location of the power fitting when monitoring the clamping degree of the power fitting. The current environmental parameters may include external factors that affect heat exchange, such as the current wind speed, current humidity, and current rainfall. This embodiment can obtain the current environmental parameters by deploying environmental sensors (such as anemometers, humidity sensors, and rain gauges) near the power fitting or by obtaining real-time data provided by local weather stations. This embodiment corrects the effective convection heat transfer coefficient and the effective heat dissipation perimeter based on the current environmental parameters, which refers to the process of adjusting the values of the effective convection heat transfer coefficient and the effective heat dissipation perimeter used in the temperature calculation model based on the current environmental parameters obtained in real time to make them more consistent with the current actual environmental conditions. This embodiment can use a preset empirical formula, a table lookup method, or a physical model to achieve the correction of the effective heat transfer coefficient and the effective heat dissipation perimeter based on the current environmental parameters.

[0042] The technical solution of the present application improves the accuracy of the calculation of the temperature rise and the second thermally induced stress component by introducing current environmental parameters to correct key parameters affecting heat exchange when calculating the second thermally induced stress component caused by the Joule heating effect of the load current. Specifically, step S221 obtains the current environmental parameters, which include the current wind speed, current humidity, and current rainfall. These environmental parameters are important factors affecting the efficiency of heat exchange between the surface of the electrical fitting and the surrounding environment. For example, wind speed can significantly affect the intensity of convective heat transfer, and rainfall can change the surface heat transfer conditions. Step S222 corrects the effective convective heat transfer coefficient and the effective heat dissipation perimeter based on the obtained current environmental parameters. Because the effective heat transfer coefficient and the effective heat dissipation perimeter are both affected by environmental factors such as wind speed, humidity, and rainfall and change dynamically, this embodiment can make the corrections to the effective heat transfer coefficient and the effective heat dissipation perimeter used to calculate the temperature rise closer to the current actual operating environment by correcting the effective heat transfer coefficient and the effective heat dissipation perimeter based on the current environmental parameters. Step S223 calculates the temperature rise of the electrical fitting due to the Joule heating effect of the load current based on the first load current, resistivity, the effective convection heat transfer coefficient corrected for environmental parameters, the corrected effective heat dissipation perimeter, and the effective conductive cross-sectional area. The second thermally induced stress component due to the Joule heating effect of the load current is then calculated based on the calculated temperature rise, thermal expansion coefficient, and Young's modulus of the electrical fitting. This step utilizes the more accurate effective convection heat transfer coefficient and effective heat dissipation perimeter corrected for environmental parameters, combined with other relevant material and current parameters, to more accurately calculate the temperature rise of the electrical fitting due to the Joule heating effect. Based on this more accurate temperature rise, combined with the thermal expansion coefficient and Young's modulus of the material, a more accurate second thermally induced stress component due to the Joule heating effect is then calculated. This allows for more precise separation of the stress component due to the thermal effect from the comprehensive stress, thereby improving the accuracy of the subsequent static stress component calculation and, in turn, effectively enhancing the accuracy and reliability of the clamping degree assessment.

[0043] In one embodiment, step S221 can be achieved by installing a small weather station near the electrical fittings. The weather station can integrate wind speed sensors, humidity sensors and rainfall sensors to collect current wind speed, humidity and rainfall data in real time, and transmit these data to the monitoring system wirelessly. Step S222 can pre-establish a correction model or lookup table based on environmental parameters. For example, for the effective convective heat transfer coefficient, a functional relationship can be established so that its value increases with the increase of wind speed and undergoes specific changes with the occurrence of rainfall; for the effective heat dissipation perimeter, the effect of the water film formed by rainfall on the heat dissipation area can be considered for correction. After receiving the real-time current environmental parameters, the monitoring system can call the model or lookup table to calculate the corrected effective convective heat transfer coefficient and effective heat dissipation perimeter based on the current wind speed, humidity and rainfall. Step S223 calculates the temperature rise of the electrical fitting based on the corrected effective convection heat transfer coefficient and the effective heat dissipation perimeter, combined with the first load current obtained in real time, the preset resistivity, and the effective conductive cross-sectional area, and then calculates the second thermally induced stress component based on the temperature rise of the electrical fitting, the preset thermal expansion coefficient, and the Young's modulus.

[0044] In some preferred embodiments, step S3 includes: S31. Perform frequency characteristic analysis on the comprehensive stress data to identify dynamic stress components with a specific frequency range caused by external mechanical vibration; S32. Calculate the static stress component reflecting the clamping force of the electrical hardware based on the comprehensive stress data, the first thermally induced stress component, the second thermally induced stress component, and the dynamic stress component.

[0045] Frequency characteristic analysis in this embodiment refers to the process of mathematically transforming a signal to convert it from a time domain representation to a frequency domain representation, thereby revealing the different frequency components contained in the signal and their intensities. This embodiment may employ techniques such as Fourier transform, wavelet transform, and power spectral density analysis to perform frequency characteristic analysis on the integrated stress data. Identifying dynamic stress components with a specific frequency range caused by external mechanical vibrations refers to identifying stress signals generated by external mechanical vibrations from the integrated stress data after frequency characteristic analysis based on known or estimated frequency characteristics of stress signals that may be generated by external mechanical vibrations (such as wind vibrations and dancing). This embodiment may employ methods such as bandpass filtering, spectrum analysis, and modal analysis to identify dynamic stress components with a specific frequency range caused by external mechanical vibrations. Calculating the static stress component reflecting the clamping force of the electric hardware based on the comprehensive stress data, the first thermally induced stress component, the second thermally induced stress component and the dynamic stress component refers to the process of subtracting the stress components caused by non-clamping forces that have been calculated or identified (including the first thermally induced stress component, the second thermally induced stress component and the dynamic stress component) from the comprehensive stress data to obtain a relatively stable stress component generated only by the clamping force of the electric hardware.

[0046] The comprehensive stress data of the present application is the result of the superposition of multiple factors. The comprehensive stress data includes the static stress generated by the clamping force, the thermal stress generated by the ambient temperature and the load current, and the dynamic stress generated by the external mechanical vibration. After obtaining the comprehensive stress data, the first ambient temperature data and the first load current data, the first thermal stress component and the second thermal stress component are first calculated based on the temperature and current data. The stress changes are usually slow or synchronized with the changes in the environment and the load. At the same time, since the stress caused by external mechanical vibration usually manifests as periodic or random fluctuations with a specific frequency range, this embodiment can distinguish this part of the rapidly changing dynamic component from other stress components and identify its size by performing frequency characteristic analysis on the comprehensive stress data. Finally, when calculating the static stress component reflecting the clamping force of the electrical hardware, not only the calculated thermal stress component is subtracted, but also the dynamic stress component identified by frequency analysis is further subtracted. In this way, the interference caused by thermal and vibration effects is stripped away from the original comprehensive stress signal, so that the static stress component finally calculated can more purely reflect the static stress level determined by the clamping state of the power fittings, thereby effectively improving the accuracy of the extraction of stress components related to the clamping force, and providing a more reliable data basis for subsequent clamping degree evaluation.

[0047] In one embodiment, frequency characteristics analysis of the integrated stress data can be performed using a fast Fourier transform (FFT) or wavelet transform. For example, a sequence of integrated stress data is collected over a period of time and processed using an FFT to obtain the energy distribution of the stress signal at different frequencies. Based on the structural characteristics and conductor type of the transmission line, as well as common wind vibration and galloping patterns, it is determined that the stress signals caused by these vibrations are primarily concentrated within a specific frequency range (for example, breeze vibration may range from a few hertz to tens of hertz, and galloping may range from a few tenths of hertz to a few hertz). Frequency-domain analysis can identify the energy components within these specific frequency ranges, and their corresponding time-domain signals can be reconstructed or quantized into dynamic stress components. For example, the FFT results can be integrated or summed within a specific frequency range, or a bandpass filter can be used to directly filter out the components within this frequency range from the time-domain signal as the dynamic stress components. After identifying the dynamic stress components, the static stress component reflecting the clamping force of the power fittings can be calculated according to the following formula: static stress component = integrated stress data - first thermally induced stress component - second thermally induced stress component - dynamic stress component. This embodiment can more accurately calculate the static stress component reflecting the clamping force of the power fitting by performing frequency characteristic analysis on the comprehensive stress data to identify and remove the dynamic stress component caused by external mechanical vibration. Therefore, this embodiment can significantly reduce the impact of vibration interference on the stress monitoring results and enable the extracted static stress component to more realistically reflect the actual clamping state of the power fitting, thereby making the clamping degree analysis and evaluation based on more accurate static stress components more reliable, which will help reduce misjudgments and false alarms, and effectively improve the accuracy and effectiveness of power fitting clamping degree monitoring.

[0048] In some preferred embodiments, step S31 includes: S311, obtaining current environment parameters; S312. Determine the frequency range and amplitude characteristics of the vibration mode based on environmental parameters, structural characteristics of the power fittings, and the type of transmission line; S313, performing time-frequency domain conversion on the comprehensive stress data to obtain a time-frequency domain conversion result, where the time-frequency domain conversion result includes frequency distribution and energy distribution of the comprehensive stress data at different time points; S314. Identify a dynamic stress component with a specific frequency range caused by external mechanical vibration from the time-frequency domain conversion result according to the frequency range and amplitude characteristics.

[0049] The type of transmission conductor refers to the properties of the transmission conductor connected to the power fittings, which may include but are not limited to the conductor material, cross-sectional area, diameter, mass per unit length, suspension method, etc. The frequency range and amplitude characteristics of the vibration mode refer to the main frequency range corresponding to the typical vibration forms (such as wind vibration and sub-span vibration) that may occur in the power fitting-conductor system under specific environmental conditions, as well as the stress amplitude level that these vibrations may reach. The time-frequency domain conversion result refers to the two-dimensional or three-dimensional data representation obtained after the time-frequency domain conversion, such as the spectrogram obtained by STFT or the scalogram obtained by wavelet transform. These results can show how the frequency components of the signal change over time and their corresponding energy or intensity.

[0050] This approach constructs a more accurate dynamic stress component identification process by considering multiple factors influencing external mechanical vibration characteristics and combining them with time-frequency domain analysis. First, current environmental parameters are acquired to provide real-time external condition information for subsequent analysis. Next, based on these environmental parameters and the inherent characteristics of electrical hardware and transmission lines, the possible vibration modes and their corresponding frequency and amplitude characteristics under the current operating conditions are predicted. This prediction process leverages the system's response to external excitation to provide a more targeted assessment of the vibration stress characteristics. Subsequently, the original integrated stress data is transformed into the time-frequency domain, decomposing the signal into a two-dimensional plane of time and frequency to meticulously display the signal's frequency composition and energy distribution at different moments. Finally, using the predetermined frequency range and amplitude characteristics of the vibration mode as the identification basis, the signal components that meet these characteristics are precisely located and extracted from the time-frequency domain conversion results to identify the dynamic stress components caused by external mechanical vibration. This method avoids the misjudgment or missed judgment that may result from filtering solely within a fixed frequency range, thereby improving the accuracy of dynamic stress component identification. Since this embodiment can more accurately identify and remove dynamic stress components, it can more accurately separate the static stress component reflecting the clamping force of the electrical hardware from the comprehensive stress data, thereby improving the reliability of overall clamping degree monitoring.

[0051] In a specific embodiment, obtaining current environmental parameters can be achieved through a sensor network installed on or near the transmission line, such as wind speed sensors and temperature sensors, which collect data such as wind speed and ambient temperature in real time. The frequency range and amplitude characteristics of the vibration mode are determined by querying a pre-established database or model based on historical monitoring data, field tests, or simulation analysis based on the environmental parameters, the structural characteristics of the power hardware, and the type of transmission line. This database or model stores the frequency range (for example, for a specific line and wind speed, the wind vibration frequency may be between 0.1 Hz and 3 Hz) and approximate amplitude levels of stress vibrations that may be caused by typical external mechanical vibrations (such as wind vibration) under different environmental conditions (such as different wind speed levels), different hardware structures, and different line types. The time-frequency domain conversion of the integrated stress data can be performed by using a short-time Fourier transform (STFT) to segment the stress data over a period of time into multiple overlapping short-time windows, and performing a Fourier transform on each window to obtain a series of time-varying frequency spectra, thereby forming a spectrum graph. Identifying dynamic stress components from the time-frequency domain conversion results based on frequency range and amplitude characteristics can be accomplished by searching for frequency components within a predetermined frequency range (e.g., 0.1Hz-3Hz) and with energy or amplitude exceeding a certain threshold (the threshold can be determined based on the predetermined amplitude characteristics) on the resulting spectrum, and identifying the stress changes corresponding to these components as dynamic stress components. This embodiment can more accurately identify dynamic stress components caused by external mechanical vibrations from complex integrated stress signals by comprehensively considering the impact of the environment, structure, and wire type on vibration characteristics and using refined time-frequency domain analysis methods. Therefore, this embodiment can significantly improve the accuracy of dynamic stress component separation, making the subsequently calculated static stress component reflecting the clamping force of the electrical hardware more accurate, thereby improving the reliability and accuracy of monitoring the clamping degree of the electrical hardware.

[0052] Second, as Figure 2 As shown, the present application also provides a power fitting clamping degree monitoring system, which includes: Data acquisition module 1, used for synchronously acquiring comprehensive stress data of power fittings, first ambient temperature data and first load current data of transmission conductors; Thermally induced stress component calculation module 2, used to calculate a first thermally induced stress component caused by ambient temperature change and a second thermally induced stress component caused by Joule heating effect of the load current based on the first ambient temperature data, the first load current data, and preset material properties corresponding to the electrical fittings; Static stress component calculation module 3, used to calculate the static stress component reflecting the clamping force of the power fitting based on the comprehensive stress data, the first thermally induced stress component and the second thermally induced stress component; The clamping degree analysis module 4 is used to obtain the current clamping degree of the power fitting based on static stress component analysis.

[0053] The present application provides an electric fitting clamping degree monitoring system, which includes a data acquisition module 1, a thermal stress component calculation module 2, a static stress component calculation module 3, and a clamping degree analysis module 4. The electric fitting clamping degree monitoring system provided in this embodiment is used to execute the steps in the electric fitting clamping degree monitoring method provided in the first aspect. The principle of the electric fitting clamping degree monitoring system provided in this embodiment is the same as the principle of the electric fitting clamping degree monitoring method provided in the first aspect, and will not be discussed in detail here.

[0054] From the above, it can be seen that the present application provides a method and system for monitoring the clamping force of electric hardware, which simultaneously obtains the comprehensive stress data, ambient temperature data and transmission line load current data of the electric hardware, and calculates and strips off the thermally induced stress components caused by ambient temperature changes and the Joule heating effect of the load current based on the material properties of the electric hardware, thereby extracting the static stress component reflecting the true clamping force of the electric hardware from the comprehensive stress data. That is, the present application can effectively separate the signal generated by the Joule heating effect of the ambient temperature and load current in the mixed signal collected by the stress sensor from the static stress component reflecting the true clamping force of the electric hardware. Therefore, the present application can effectively solve the problem of decreased accuracy in clamping degree assessment due to the inability to extract the static stress component that can truly reflect the irreversible loosening trend of the electric hardware from the stress signal collected by the stress sensor, thereby effectively avoiding the occurrence of misjudgments or false alarms caused by the accuracy of clamping degree assessment.

[0055] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another robot, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, the indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms.

[0056] In addition, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0057] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any actual relationship or order between these entities or operations.

[0058] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for monitoring the clamping degree of electric power fittings, characterized in that: The method for monitoring the clamping degree of electric fittings comprises the following steps: S1. Synchronously obtain comprehensive stress data of power fittings, first ambient temperature data, and first load current data of transmission lines; S2. Calculate a first thermally induced stress component caused by ambient temperature change and a second thermally induced stress component caused by Joule heating effect of the load current based on the first ambient temperature data, the first load current data, and preset material properties corresponding to the electrical fittings; S3. Calculating a static stress component reflecting the clamping force of the electrical hardware according to the comprehensive stress data, the first thermally induced stress component, and the second thermally induced stress component; S4. Obtain the current clamping degree of the electrical hardware according to the static stress component analysis.

2. The method for monitoring the clamping degree of electric power fittings according to claim 1, characterized in that: Step S1 includes: S11, synchronously obtaining comprehensive stress data of the power fitting, first ambient temperature data, and first load current data of the transmission line; S12. Preprocess the comprehensive stress data, the first ambient temperature data, and the first load current data, wherein the preprocessing includes signal conditioning and drift compensation.

3. The method for monitoring the clamping degree of electric power fittings according to claim 2, characterized in that: Step S11 includes: S111, synchronously acquiring first ambient temperature data, first load current data of the transmission line, and first local stress data corresponding to different stress collection positions on the power fitting; S112: Generate comprehensive stress data based on all of the first local stress data according to the stress collection position corresponding to the first local stress data and the structural characteristics of the electrical hardware.

4. The method for monitoring the clamping degree of electric power fittings according to claim 3, characterized in that: Step S112 includes: A1. Obtaining second ambient temperature data, second load current data, and a second local stress data set of the electrical hardware during an initial operation phase, wherein the second local stress data set includes second local stress data corresponding to different stress collection positions on the electrical hardware; A2. Analyze and obtain response characteristics of each stress collection location based on the second local stress data set, the second ambient temperature data, and the second load current data; A3. Determine the weight corresponding to each of the first local stress data according to the response characteristics of each of the stress collection positions and the structural characteristics of the electrical hardware; A4. Perform weighted averaging on all the first local stress data and their corresponding weights to obtain comprehensive stress data.

5. The method for monitoring the clamping degree of electric power fittings according to claim 1, characterized in that: The preset material properties include thermal expansion coefficient, Young's modulus, resistivity, effective heat transfer coefficient, effective heat dissipation perimeter and effective conductive cross-sectional area. Step S2 includes: S21, calculating a temperature difference based on the first ambient temperature data and a preset reference temperature, and then calculating a first thermally induced stress component caused by the ambient temperature change based on the temperature difference, the thermal expansion coefficient, and the Young's modulus; S22. Calculate the temperature rise of the electrical hardware caused by the Joule heating effect of the load current based on the first load current, the resistivity, the effective heat transfer coefficient, the effective heat dissipation perimeter, and the effective conductive cross-sectional area. Then, calculate the second thermally induced stress component caused by the Joule heating effect of the load current based on the temperature rise of the electrical hardware, the thermal expansion coefficient, and the Young's modulus.

6. The method for monitoring the clamping degree of electric power fittings according to claim 5, characterized in that: Step S2 also includes the following steps performed before step S21: B1. Obtaining historical operating data of the electrical fittings; B2. evaluating the material aging status of the electrical fittings based on the historical operating data; B3. Correct the thermal expansion coefficient, the Young's modulus, the resistivity, the effective heat transfer coefficient, the effective heat dissipation perimeter, and the effective conductive cross-sectional area according to the aging state of the material.

7. The method for monitoring the clamping degree of electric power fittings according to claim 6, characterized in that: Step S22 includes: S221. Acquire current environmental parameters, where the current environmental parameters include current wind speed, current humidity, and current rainfall; S222. Correct the effective heat transfer coefficient and the effective heat dissipation perimeter according to the current environmental parameters; S223. Calculate the temperature rise of the electrical hardware caused by the Joule heating effect of the load current based on the first load current data, the resistivity, the effective heat transfer coefficient, the effective heat dissipation perimeter, and the effective conductive cross-sectional area. Then, calculate the second thermally induced stress component caused by the Joule heating effect of the load current based on the temperature rise of the electrical hardware, the thermal expansion coefficient, and the Young's modulus.

8. The method for monitoring the clamping degree of electric power fittings according to claim 1, characterized in that: Step S3 includes: S31. Performing frequency characteristic analysis on the comprehensive stress data to identify dynamic stress components caused by external mechanical vibration and having a specific frequency range; S32. Calculate a static stress component reflecting the clamping force of the electrical hardware according to the comprehensive stress data, the first thermally induced stress component, the second thermally induced stress component, and the dynamic stress component.

9. The method for monitoring the clamping degree of electric power fittings according to claim 8, characterized in that: Step S31 includes: S311, obtaining current environment parameters; S312. Determine the frequency range and amplitude characteristics of the vibration mode according to the environmental parameters, the structural characteristics of the power fittings, and the type of the transmission line; S313, performing time-frequency domain conversion on the comprehensive stress data to obtain a time-frequency domain conversion result, wherein the time-frequency domain conversion result includes frequency distribution and energy distribution of the comprehensive stress data at different time points; S314 . Identify a dynamic stress component with a specific frequency range caused by external mechanical vibration from the time-frequency domain conversion result according to the frequency range and the amplitude characteristics.

10. A power fitting clamping degree monitoring system, characterized in that: The electric power fitting clamping degree monitoring system comprises: A data acquisition module, configured to synchronously acquire comprehensive stress data of the power fittings, first ambient temperature data, and first load current data of the transmission conductor; a thermally induced stress component calculation module, configured to calculate a first thermally induced stress component caused by ambient temperature change and a second thermally induced stress component caused by Joule heating effect of the load current based on the first ambient temperature data, the first load current data, and preset material properties corresponding to the electrical fitting; a static stress component calculation module, configured to calculate a static stress component reflecting the clamping force of the electrical fitting based on the comprehensive stress data, the first thermally induced stress component, and the second thermally induced stress component; The clamping degree analysis module is used to obtain the current clamping degree of the power fitting based on the static stress component analysis.

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