Power cable laying tension degree adjusting method and adjusting device capable of preventing sagging

Abstract

The invention relates to the technical field of power cable laying tensity adjustment, in particular to a sagging-preventing power cable laying tensity adjustment method and device, and the method comprises the steps: obtaining a current tension data sequence on a cable laying path; multi-scale fluctuation analysis is carried out on the current tension data sequence to obtain a current fluctuation energy unbalance degree, and the fluctuation energy unbalance degree is used for representing the dynamic energy instability degree of the cable system; based on the fluctuation energy unbalance degree, determining a current sagging risk degree of the cable, the current sagging risk degree being used for representing a comprehensive risk level of sagging of the cable; and adjusting the tension of the cable based on the current sagging risk degree of the cable. According to the method, the possible sagging phenomenon of the cable can be predicted in advance, line faults caused by stress concentration and insulation abrasion are effectively avoided, and long-term stable operation of a cable line is guaranteed.

Description

Technical Field

[0001] This application relates to the field of tension adjustment technology for power cable laying, specifically to a method and device for adjusting the tension of power cable laying to prevent sagging. Background Technology

[0002] Maintaining stable cable tension is crucial for ensuring power transmission safety and long-term reliable operation of power lines during power cable laying. Cables encounter complex conditions such as vertical drops, bends, and conduit runs during installation, leading to drastic changes in contact conditions with pulleys or trench walls and causing sudden tension fluctuations.

[0003] In related technologies, the tension is usually adjusted based on the comparison between the instantaneous tension value collected in real time by the tension sensor and a preset threshold.

[0004] However, the geometry and friction coefficient of the actual laying path have significant time-varying and nonlinear characteristics. The above methods are too simple and cannot accurately adapt to complex on-site conditions. They also have a lag in response to sudden tension changes and insufficient control precision, and cannot effectively prevent the risk of cable sagging caused by dynamic energy imbalance. Summary of the Invention

[0005] To address the technical problems of delayed response and insufficient control precision in response to sudden tension changes in related technologies, this application aims to provide a method and device for adjusting the tension of power cable laying to prevent sagging. The specific technical solution adopted is as follows: This application provides a method for adjusting the tension of power cables to prevent sag, comprising: acquiring a current tension data sequence along the cable laying path; performing multi-scale fluctuation analysis on the current tension data sequence to obtain a current fluctuation energy imbalance, wherein the fluctuation energy imbalance is used to characterize the degree of dynamic energy instability of the cable system; determining the current sag risk level of the cable based on the fluctuation energy imbalance, wherein the current sag risk level is used to characterize the comprehensive risk level of cable sag; and adjusting the cable tension based on the current sag risk level of the cable.

[0006] Optionally, the above-mentioned multi-scale fluctuation analysis of the current tension data sequence to obtain the current fluctuation energy imbalance includes: performing multi-scale fluctuation analysis on the current tension data sequence to obtain the energy and center frequency of multiple fluctuation components; inputting the energy and center frequency of the multiple fluctuation components into a target mechanical potential energy loss prediction model to obtain a current loss prediction value, the target mechanical potential energy loss prediction model being used to predict mechanical energy dissipation value based on the energy and center frequency of the fluctuation components; and determining the current fluctuation energy imbalance based on the current loss prediction value and the energy of the multiple fluctuation components.

[0007] Optionally, determining the current fluctuation energy imbalance based on the current loss prediction value and the energy of the plurality of fluctuation components includes: determining the current total energy of the plurality of fluctuation components and the energy change rate of each fluctuation component; determining a first ratio, the first ratio being the first ratio of the current loss prediction value to the current total energy; and determining the current fluctuation energy imbalance based on the first ratio and the maximum energy change rate among the energy change rates of the plurality of fluctuation components.

[0008] Optionally, determining the current sag risk of the cable based on the fluctuation energy imbalance includes: inputting the current fluctuation energy imbalance into a target risk assessment model to obtain the current sag risk probability of the cable, wherein the target risk assessment model is used to assess the current sag risk probability of the cable; and determining the current sag risk of the cable based on the current sag risk probability and the current fluctuation energy imbalance.

[0009] Optionally, adjusting cable tension based on the current sag risk of the cable includes: determining the change in current sag risk based on the difference between the current sag risk of the cable and the sag risk of the cable at the previous moment; adjusting the original proportional gain based on the change in current sag risk to obtain the adjusted proportional gain; and adjusting the cable tension based on the adjusted proportional gain.

[0010] Optionally, adjusting cable tension based on the adjusted proportional gain includes: determining a tension setting deviation based on the tension value at the current moment and a preset tension value; generating a tension control signal based on the adjusted proportional gain and the tension setting deviation; and adjusting cable tension based on the tension control signal.

[0011] Optionally, the method further includes: acquiring a historical dataset, which includes tension data sequences and historical mechanical potential energy loss sequences at multiple historical moments; performing multi-scale fluctuation analysis on the tension data sequence at each historical moment to obtain the energy and center frequency of the fluctuation component at each historical moment; and training an initial mechanical potential energy loss prediction model based on the energy and center frequency of the historical fluctuation component and the historical mechanical potential energy loss sequence to obtain the target mechanical potential energy loss prediction model.

[0012] Optionally, the method further includes: acquiring historical sag event labels and historical fluctuation energy imbalance sequences, wherein the historical sag event labels are used to characterize whether the cable sags at the corresponding historical acquisition time; and constructing the target risk assessment model based on the historical fluctuation energy imbalance sequence, the historical sag event labels, and the fuzzy logic system.

[0013] Optionally, the above-mentioned construction of the target risk assessment model based on the historical fluctuation energy imbalance sequence, the historical drooping event label, and the fuzzy logic system includes: optimizing the membership function parameters of the fuzzy logic system based on the historical fluctuation energy imbalance sequence and the historical drooping event label; and constructing the target risk assessment model based on the optimized membership function parameters.

[0014] This application also provides a tension adjustment device for power cable laying to prevent sag, including a data acquisition unit and a central control unit; the data acquisition unit is used to acquire the current tension data sequence on the cable laying path; the central control unit is used to perform multi-scale fluctuation analysis on the tension data sequence to obtain the current fluctuation energy imbalance degree, which is used to characterize the degree of dynamic energy instability of the cable system; the central control unit is also used to determine the current sag risk degree of the cable based on the fluctuation energy imbalance degree, which is used to characterize the comprehensive risk level of cable sag; the central control unit is also used to adjust the cable tension based on the current sag risk degree of the cable.

[0015] This application has the following beneficial effects: In this embodiment, by performing multi-scale fluctuation analysis on the tension data sequence, the multi-frequency information contained in the tension fluctuation is analyzed from a physical perspective, and the degree of dynamic instability of the internal energy of the cable is assessed accordingly. Then, the degree of fluctuation energy instability is mapped to the current sag risk of the cable. Based on the current sag risk, the cable tension is adjusted, which can predict the possible sag phenomenon of the cable in advance and take intervention measures in advance before the sag phenomenon occurs. This significantly improves the intelligence level and safety margin of the cable laying process, effectively avoids line faults caused by stress concentration and insulation wear, and ensures the long-term stable operation of the cable line. Attached Figure Description

[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a structural diagram of a power cable tension adjustment device for preventing sagging, provided in one embodiment of this application. Figure 2 A flowchart illustrating a method for adjusting the tension of power cable laying to prevent sagging, provided as an embodiment of this application; Figure 3A flowchart illustrating another method for adjusting the tension of power cable laying to prevent sagging, provided in one embodiment of this application; Figure 4 A flowchart illustrating another method for adjusting the tension of power cable laying to prevent sagging, provided in one embodiment of this application; Figure 5 This is a flowchart illustrating another method for adjusting the tension of power cable laying to prevent sagging, provided as an embodiment of this application. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method and device for adjusting the tension of power cable laying to prevent sagging, as proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0020] In power cable laying projects, when power cables are laid along vertical drops, bends, and inflections, there are sliding or fixed points due to the use of pulleys or conduits. As a result, the cable comes into contact with the pulleys or conduits and then comes into contact with them. This causes the frictional force to change abruptly and rapidly, resulting in multi-scale oscillations of high and low frequency tension and low frequency drift. If this cannot be suppressed in time, it will cause local stress concentration in the cable and may even cause the cable to lose effective support, resulting in cable sagging, damage or destruction of the insulation layer, and short circuit accidents.

[0021] In the early stages, the technology relied primarily on the experience of construction workers and simple mechanical tools for rudimentary control. With the development of ultra-high voltage and long-distance power transmission, this technology has gradually evolved into an intelligent system integrating tension sensors, automatic control systems, and precision traction equipment, enabling real-time monitoring and precise feedback adjustment of tension during the laying process. This development has not only significantly improved the mechanical safety and service life of cable lines, effectively preventing power outages caused by sagging, but also holds significant strategic importance for optimizing the power grid structure and supporting cross-regional energy allocation. It is an indispensable technological component in building a highly reliable modern smart grid.

[0022] However, since most control system models are pre-set idealized paths, they cannot adapt to changes in on-site geometry and friction coefficients, and will still produce certain adjustment lag and accuracy errors.

[0023] The following description, in conjunction with the accompanying drawings, details the specific scheme of the tension adjustment method and device for preventing sagging during power cable laying provided in this application.

[0024] Please see Figure 1 The diagram shows a structural diagram of a power cable tension adjustment device for preventing sagging, provided in one embodiment of this application.

[0025] like Figure 1 As shown, the anti-sagging power cable laying tension adjustment device 10 includes a data acquisition unit 101 and a central control unit 102.

[0026] The data acquisition unit 101 is used to acquire the current tension data sequence on the cable laying path.

[0027] The data acquisition unit consists of a signal conditioning circuit, an analog-to-digital converter (ADC), and an embedded microprocessor.

[0028] The data acquisition unit is also used to filter, amplify, and digitally quantize the acquired tension data and output a pre-processed tension data sequence.

[0029] The central control unit 102 is used to perform multi-scale fluctuation analysis on the tension data sequence to obtain the current fluctuation energy imbalance degree, which is used to characterize the degree of dynamic energy instability of the cable system.

[0030] Optionally, the central control unit consists of an industrial programmable logic controller (PLC) or an embedded industrial computer.

[0031] Optionally, the central control unit 102 can combine variational mode decomposition (VMD), random forest regression, particle swarm optimization, and fuzzy logic system to complete the calculation work and output control commands based on real-time acquisition of subsequent information.

[0032] The central control unit 102 is also used to determine the current sag risk level of the cable based on the fluctuation energy imbalance, which is used to characterize the overall risk level of cable sag.

[0033] The central control unit 102 is also used to adjust the cable tension based on the current sag risk of the cable.

[0034] In one alternative implementation, the anti-sagging power cable laying tension adjustment device 10 further includes an actuator, a guide pulley block, a human-machine interface, and a communication module.

[0035] The actuators include electrically driven winches, hydraulic tensioners, or traction devices driven by servo motors.

[0036] This actuator is used to automatically change the cable tension based on control signals.

[0037] The guide pulley block adopts an anti-skid design to reduce contact friction between the cable and the pulley and sudden changes in the wrap angle. The pulley surface is coated with polyurethane to reduce wear.

[0038] The human-machine interface includes a touch screen that can display the tension curve, system operating status, and alarm information in real time, and allows users to set parameters and intervene manually.

[0039] The communication module enables remote data transmission between Ethernet, RS485 and 4G / 5G (or other wireless methods), and can also enable remote monitoring.

[0040] Please see Figure 2 The diagram illustrates a flowchart of a method for adjusting the tension of power cable laying to prevent sagging, according to an embodiment of this application.

[0041] like Figure 2 As shown, the method for adjusting the tension of power cable laying to prevent sagging includes S201-S204.

[0042] S201. Obtain the current tension data sequence along the cable laying path.

[0043] It should be understood that the current tension data sequence is the tension data sequence corresponding to the current moment. This tension data sequence is the tension value of multiple consecutive moments within a time window, which includes information on the tension change trend of the cable within that time window.

[0044] In one alternative implementation, a high-precision resistance strain gauge tension sensor can be installed at the tension wheel of the cable laying system. The tension sensor collects the original tension value in real time based on a preset frequency. At each collection moment, the original tension values ​​within a preset sliding time window (i.e., the current moment and several consecutive collection moments prior to it) are arranged in ascending order of time to form the tension data sequence corresponding to the current moment.

[0045] For example, the preset frequency can be 1 Hz, and the preset sliding time window can be 5 seconds.

[0046] It should be understood that obtaining a tension data sequence rather than a tension value at a single moment facilitates the subsequent analysis of multi-scale tension fluctuations using continuous data in the time domain.

[0047] Optionally, the acquired raw tension values ​​can be preprocessed. First, a statistical method based on a sliding window is used to detect and correct outliers in the raw tension values. For example, for a raw tension value at a given acquisition time, it is compared with the raw tension values ​​at several adjacent times (e.g., three points before and after). If the value exceeds three times the standard deviation of the mean of these seven points, it is identified as an outlier and replaced with the median or mean of the sliding window to eliminate interference from instantaneous mechanical impact or sensor noise.

[0048] Then, the data after outlier detection and correction are subjected to Z-score standardization. Specifically, for the raw tension value at any given time, the raw tension value is Z-score standardized based on the mean and standard deviation of the data in the previous fixed-length sliding window (e.g., the past 60 seconds) to eliminate the influence of dimensional differences on subsequent analysis. Then, the current tension data sequence is constructed based on the preprocessed tension value.

[0049] Understandably, the preprocessed data retains the distribution characteristics of the original data while improving numerical stability.

[0050] S202. Perform multi-scale fluctuation analysis on the current tension data sequence to obtain the current fluctuation energy imbalance.

[0051] Among them, the fluctuation energy imbalance degree is used to characterize the degree of dynamic energy instability of the cable system.

[0052] It is understandable that energy instability may occur during cable laying due to factors such as friction, vibration, and path changes. In this embodiment, the degree of instability is quantified by measuring the energy imbalance of fluctuations.

[0053] It should be understood that this fluctuation energy imbalance is used to characterize the degree of dynamic energy instability of a cable system under the combined influence of multiple physical factors, and can reflect the imbalance between the total fluctuation energy and the energy loss trend in the cable. Multi-scale fluctuation analysis of the current tension data sequence aims to extract deep energy characteristics from the tension data sequence, providing crucial input for subsequent sag risk assessment.

[0054] Optionally, the current tension data sequence can be decomposed into multiple wave components of different frequency scales by empirical mode decomposition or wavelet transform. Then, the wave energy imbalance can be determined by analyzing the time-varying characteristics of energy and its correlation with system energy loss.

[0055] It should be understood that multi-scale decomposition can separate complex cable tension fluctuations into components dominated by different physical mechanisms, such as mechanical vibration, triboelectric mutation, and thermostriction.

[0056] S203. Determine the current sag risk of the cable based on the fluctuation energy imbalance.

[0057] The current sag risk level is used to characterize the overall risk level of cable sag.

[0058] Specifically, physical indicators reflecting the dynamic energy imbalance of the cable system (i.e., fluctuating energy imbalance degree) will be mapped to a comprehensive risk indicator (i.e., current sag risk degree) to guide control decisions. This mapping relationship aims to quantify the probability and severity of cable sag.

[0059] Fluctuation energy imbalance measures the degree of energy instability in a cable system caused by factors such as friction, vibration, and path changes during installation. The greater the fluctuation energy imbalance, the more severe the energy imbalance in the system, and the more prone the cable is to stress concentration, sag, insulation wear, or even short-circuit faults, i.e., the greater the risk of sag.

[0060] In one alternative implementation, the fluctuation energy imbalance can be input into a probability prediction model to predict the current droop risk level, resulting in a probability value representing the probability of droop occurring. This probability value can then be used as the current droop risk level.

[0061] Optionally, the preset probability prediction model can be a logistic regression model or a Bayesian network. This probability prediction model predicts the relationship between a fluctuation energy imbalance and the current probability of sag by learning the relationship between the fluctuation energy imbalance and the actual sag state of the cable in historical data.

[0062] S204. Adjust cable tension based on the current sag risk level of the cable.

[0063] It should be understood that a higher current risk of cable sag indicates a greater risk of the cable sag due to energy imbalance. In this case, the pulling or tensioning force on the cable should be increased, i.e., by increasing the cable tension through traction devices or brakes, to counteract the gravitational components and energy losses that cause sag, thereby keeping the cable within the designed safe sag range.

[0064] The lower the current sag risk of the cable, the more stable the cable operation and the lower the sag risk. At this time, the tension can be maintained or reduced to avoid excessive tension causing damage to the cable insulation layer or wasting energy.

[0065] In this embodiment, by performing multi-scale fluctuation analysis on the tension data sequence, the multi-frequency information contained in the tension fluctuation is analyzed from a physical perspective, and the degree of dynamic instability of the internal energy of the cable is assessed accordingly. Then, the degree of fluctuation energy instability is mapped to the current sag risk of the cable. Based on the current sag risk, the cable tension is adjusted, which can predict the possible sag phenomenon of the cable in advance and take intervention measures in advance before the sag phenomenon occurs. This significantly improves the intelligence level and safety margin of the cable laying process, effectively avoids line faults caused by stress concentration and insulation wear, and ensures the long-term stable operation of the cable line.

[0066] Combination Figure 2 ,like Figure 3 As shown, in one implementation of this application embodiment, the above-mentioned S102 can be specifically implemented by S301-S303.

[0067] S301. Perform multi-scale fluctuation analysis on the current tension data sequence to obtain the energy and center frequency of multiple fluctuation components.

[0068] Optionally, the fluctuation component can be an intrinsic mode function (IMF). The VMD algorithm can be used to process the current tension data sequence and decompose it into K IMFs.

[0069] One of the IMFs reflects the tension fluctuation components in different frequency ranges of the cable excited by different physical mechanisms (such as sudden changes in pulley friction, structural vibration, thermal expansion and contraction, etc.), where K is a positive integer preset according to the physical characteristics of the cable.

[0070] In this VMD algorithm, the number of modes M and the balance parameter SV can be determined through pre-experimentation or simulation optimization based on the cable type and laying environment.

[0071] Optionally, in order to ensure that the model covers the wave components generated by the main physical factors such as mechanical vibration and thermal expansion of the cable, the number of modes M can be set to 5; in order to ensure that each modal component is fully separated in the frequency domain, the balance parameter SV can be set to 2000.

[0072] It should be understood that an energy value reflects the intensity of the corresponding wave component throughout the entire analysis period; the center frequency reflects the distribution characteristics of tension waves excited by different physical mechanisms in the frequency domain and the dominant frequency characteristics.

[0073] Optionally, after determining the K IMFs, the sum of the squares of the amplitudes of an IMF at multiple times can be used to determine the energy of an IMF.

[0074] Optionally, the frequency region with the most concentrated energy in the power spectral density curve of each IMF can be identified, and the center position of this region can be taken as the center frequency of the IMF.

[0075] Alternatively, the Hilbert transform can be used to obtain the average value of the instantaneous frequency, and the average value of the instantaneous frequency can be determined as the center frequency of the IMF.

[0076] S302. Input the energy and center frequency of multiple fluctuation components into the target mechanical potential energy loss prediction model to obtain the current loss prediction value.

[0077] The target mechanical potential energy loss prediction model is used to predict the mechanical energy dissipation value based on the energy of the wave component and the center frequency.

[0078] It should be understood that the target mechanical potential energy loss prediction model is a trained model with high accuracy.

[0079] Before executing S302, the target mechanical potential energy loss prediction model can be trained first.

[0080] In one implementation of this application, a historical dataset can be acquired first, and a multi-scale fluctuation analysis can be performed on the tension data sequence at each historical moment to obtain the energy and center frequency of the fluctuation component at each historical moment. Based on the energy and center frequency of the fluctuation component at each historical moment, as well as the historical mechanical potential energy loss sequence, an initial mechanical potential energy loss prediction model can be trained to obtain the target mechanical potential energy loss prediction model.

[0081] The historical dataset includes tension data sequences and historical mechanical potential energy loss sequences from multiple historical moments.

[0082] It should be understood that the method for obtaining the tension data sequence at this historical moment is similar to the method for obtaining the current tension data sequence, and the method for performing multi-scale fluctuation analysis on the tension data sequence at this historical moment is also similar to the method for performing multi-scale fluctuation analysis on the current tension data sequence, so it will not be elaborated here.

[0083] It is understandable that the historical mechanical potential energy loss sequence includes mechanical energy dissipation values ​​at multiple historical moments. Mechanical energy dissipation values ​​are the energy lost by the cable during operation, including the dissipated work done by the cable to overcome frictional resistance and the energy loss caused by vibration.

[0084] Optionally, the mechanical energy dissipation value recorded by the cable laying system at historical moments can be obtained, or it can be calculated using the vibration acceleration data of the pulley block in three orthogonal directions collected by the triaxial accelerometer installed on the pulley block, and the rotation angle or angular velocity data of the pulley block collected synchronously by the rotary encoder.

[0085] Optionally, the work done to overcome friction is determined based on the angular velocity and rotation angle, and the energy consumed by vibration is determined based on the vibration acceleration and the equivalent vibration mass of the pulley system. Then, the sum of the two is determined as the mechanical energy dissipation value at that moment.

[0086] Optionally, all historical data can be preprocessed using the max-min normalization method to transform them to the numerical range of [0,1].

[0087] Optionally, in the process of training an initial mechanical potential energy loss prediction model based on the energy, center frequency, and historical mechanical potential energy loss sequence of historical fluctuation components, to obtain the target mechanical potential energy loss prediction model, the energy and center frequency of the IMF corresponding to each historical moment can be used as input features, and the mechanical energy dissipation value at the same historical moment can be used as the prediction target to construct a training sample set. The first 70% of the training sample set is used as the training set, and the last 30% is used as the test set, and the random forest algorithm is used for model training.

[0088] Optionally, the number of decision trees can be set to 100, and the maximum tree depth can be set to 10. The model parameters can be optimized by minimizing the mean square error between the predicted and the true values.

[0089] Understandably, training the mechanical potential energy loss prediction model based on historical data ensures that the model can learn and capture the complex mapping relationship between tension fluctuations and energy loss in a specific laying environment.

[0090] It should be understood that after the target mechanical potential energy loss prediction model is trained, the energy and center frequency of the multiple fluctuation components can be input into the target mechanical potential energy loss prediction model to obtain the current loss prediction value corresponding to the current tension data sequence.

[0091] S303. Based on the current loss prediction value and the energy of multiple fluctuation components, determine the current fluctuation energy imbalance.

[0092] In one implementation of this application, the current total energy of multiple fluctuation components and the energy change rate of each fluctuation component can be determined first; then a first ratio can be determined, which is the first ratio of the current loss prediction value to the current total energy; finally, the current fluctuation energy imbalance can be determined based on the first ratio and the maximum energy change rate among the multiple fluctuation components.

[0093] It should be understood that the current total energy is the sum of the energy of the fluctuation components in the current tension data sequence. The current total energy reflects the total energy level of the cable throughout the multi-scale fluctuations, and the first ratio reflects the relative intensity of energy loss. When the first ratio is high, it indicates that energy loss dominates the total fluctuations and the degree of energy instability is high; when the first ratio is low, it indicates that the current loss is low and the degree of energy instability is low.

[0094] Understandably, the rate of change of energy for a fluctuation component reflects the rate at which energy changes over time, i.e., the intensity of the fluctuation. The maximum value of this rate of change represents the most intense instantaneous energy fluctuation, highlighting the part of the fluctuation component where energy changes the fastest, and reflecting the system's risk of sudden change.

[0095] Optionally, the current fluctuation energy imbalance satisfies the following formula: in, Indicates the current degree of energy imbalance in fluctuations. This represents the current predicted loss value. This represents the fluctuation component in the current tension data sequence. energy, This represents the total number of fluctuation components corresponding to the current tension data sequence. This represents the sum of the energy of the fluctuation components in the current tension data sequence. Indicates the first The change in energy of each fluctuation component between the current moment and the previous moment. This indicates taking the absolute value. This represents the time difference between the current moment and the previous moment. Indicates the first The rate of change of energy of each fluctuation component This represents the maximum energy change rate among the multiple fluctuation components corresponding to the current tension data sequence. Indicates the first ratio. This is a parameter tuning factor used to prevent the denominator from being 0. This parameter tuning factor is a very small positive number. For example, the value of this parameter tuning factor can be 0.01.

[0096] In this formula, The unit is joule (J). The unit is also joule (J). The unit is joules per second (J / s), therefore The unit is J / s.

[0097] Based on this formula, it should be understood that The larger the value, the greater the overall fluctuation intensity of the system, and the more unstable the system. The larger, The larger the value, the more severe the system's energy loss. However, this is not a manifestation of volatility, but rather a reflection of the damping effect. Appropriate energy dissipation (damping) contributes to system stability. Therefore, The smaller, the more... To amplify the impact of the most violent fluctuations. The result is... The value not only focuses on the long-term trend of energy loss, but is also sensitive to instantaneous fluctuations, thus comprehensively characterizing the dynamic energy instability of the cable system.

[0098] Understandably, the fluctuation energy imbalance index is obtained by combining the current loss prediction with the ratio of the total fluctuating energy and the extreme value of the energy change rate. This index not only reflects the relative intensity of energy loss but also captures the most severe instantaneous energy fluctuations in the system, thus sensitively characterizing the risk level of the cable transitioning from a stable to an unbalanced state.

[0099] Based on the methods provided in S301-S303 above, the target mechanical potential energy loss prediction model is introduced to achieve accurate prediction of energy loss trend. Based on the current loss prediction value and the energy of the fluctuation component, the current fluctuation energy imbalance is accurately assessed.

[0100] Combination Figure 2 ,like Figure 4 As shown, in one implementation of this application embodiment, the above-mentioned S203 can be specifically implemented by S401-S402.

[0101] S401. Input the current fluctuation energy imbalance into the target risk assessment model to obtain the current sag risk probability of the cable.

[0102] The target risk assessment model is used to evaluate the current probability of cable sagging.

[0103] It should be understood that the target risk assessment model is a model trained based on the historical data of the cable. The target risk assessment model can output a relatively accurate risk probability based on the input fluctuation energy imbalance.

[0104] In this embodiment of the application, the target risk assessment model can be constructed before executing S401.

[0105] In one alternative implementation, historical drooping event labels and historical volatility energy imbalance sequences can be obtained first. Then, based on the historical volatility energy imbalance sequence, the historical drooping event labels, and the fuzzy logic system, the target risk assessment model can be constructed.

[0106] Among them, the historical sag event label is used to indicate whether the cable sags at the corresponding historical acquisition time.

[0107] It should be understood that the historical fluctuation energy imbalance sequence includes the fluctuation energy imbalance of multiple historical moments arranged in chronological order. The fluctuation energy imbalance of each historical moment can be obtained by calculating the tension data sequence of each historical moment.

[0108] Optionally, the cable sag status can be monitored in real time using high-definition cameras and laser rangefinders deployed on the top of the tower or on inspection drones. When the cable sag exceeds a preset warning value, a sag event tag is generated. Each sag event tag corresponds to a historical acquisition time, used to characterize whether the cable has sagged at that time: 1 is marked when sag occurs, and 0 is marked when there is no sag.

[0109] For example, the permissible sag value of a cable is typically 4.8 meters, and the preset warning value can be 85% of the permissible sag value, i.e., 4.08 meters.

[0110] When constructing the target risk assessment model, a fuzzy logic system is adopted as the core reasoning framework. It should be understood that a fuzzy logic system is an intelligent system that handles uncertainty and nonlinear problems. Its basic structure includes four main components: a fuzzification interface, a fuzzy rule base, a fuzzy inference engine, and a defuzzification interface.

[0111] The fuzzification interface is responsible for converting the precise input (fluctuation energy imbalance degree) into a fuzzy set, and using a membership function to describe the degree to which the input belongs to each fuzzy set. The fuzzy rule base contains a series of "if-then" fuzzy rules to describe the logical relationship between the fluctuation energy imbalance degree and the droop risk. The fuzzy inference engine performs the inference process according to the fuzzy rules, mapping the input fuzzy set to the output fuzzy set. The defuzzification interface converts the fuzzy output obtained from the inference into a precise droop risk probability value.

[0112] For example, the input variable of the fuzzy logic system is set as the fluctuation energy imbalance degree, and the output variable is the droop risk probability. This fuzzy system is used to establish a nonlinear mapping relationship between the input and the output.

[0113] In this embodiment, based on the historical fluctuation energy imbalance sequence and historical droop event labels, the fuzzy logic system is configured and optimized to construct the final target risk assessment model. This target risk assessment model can map the fluctuation energy imbalance (numerical value) to an accurate droop risk probability (percentage).

[0114] In one alternative implementation, the membership function parameters of the fuzzy logic system can be optimized based on the historical fluctuation energy imbalance sequence and the historical drooping event label; the target risk assessment model can then be constructed based on the optimized membership function parameters.

[0115] Optionally, a particle swarm optimization algorithm can be used to automatically optimize the membership function parameters. Specifically, each combination of parameters for a membership function is defined as a particle, containing the center value and width of the Gaussian membership function. The particle swarm size is set to 50 particles, and the maximum number of iterations is 100. The optimization process uses minimizing the mean square error between the output value of the fuzzy logic system and the historical drooping event labels as the objective function. During the optimization process, each particle updates its velocity and position based on its individual optimal position and the group optimal position, and iteratively searches for the optimal parameter combination that minimizes the objective function. After a preset number of iterations, the algorithm outputs the optimized membership function parameter set, including the center value and width of the Gaussian membership function corresponding to each fuzzy set.

[0116] Optionally, the objective function satisfies the following formula: in, This represents the mean square error. Indicates the number of historical data samples. Indicates the first The output value of the fuzzy logic system for each sample. Indicates the first The true value of the historical sagging event label corresponding to each sample. Indicates the first The squared prediction error for each sample.

[0117] Optionally, the optimized parameters can be configured into a fuzzy logic system to form a target risk assessment model that can accurately map the relationship between the degree of energy imbalance and the probability of droop risk.

[0118] The method described above for constructing a target risk assessment model optimizes the parameters of the fuzzy logic system using historical drooping event data. This enables the risk assessment model to accurately reflect the intrinsic relationship between fluctuation energy imbalance and drooping risk in specific application scenarios. Simultaneously, the training process utilizes optimization algorithms to automatically find the optimal membership function parameters, significantly improving the model's risk identification accuracy and adaptability, and reducing reliance on manual parameter tuning.

[0119] It should be understood that after obtaining the target risk assessment model, since the input of the target risk assessment model is the fluctuation energy imbalance degree and the output is the sag risk probability, the current fluctuation energy imbalance degree can be directly input into the target risk assessment model to obtain the current sag risk probability of the cable.

[0120] S402. Based on the current sag risk probability and the current fluctuation energy imbalance, determine the current sag risk level of the cable.

[0121] Optionally, the current droop risk level satisfies the following formula: in, Indicates the current risk level of drooping. Indicates the current degree of energy imbalance in fluctuations. This indicates the current probability of downward pressure. This represents a normalization function, such as a max-min normalization function, used to normalize... Normalization is performed.

[0122] It should be understood that the normalization function in the embodiments of this application adopts local normalization based on a sliding time window, or is calculated based on a globally preset maximum / minimum value obtained from historical dataset statistics, in order to ensure uniformity of units.

[0123] As can be seen from the description of the above embodiments, The dimension of is J / s, which becomes a dimensionless value after being normalized. P represents the droop risk probability, which ranges from [0, 1] and has no dimension. Therefore, B also has no dimension.

[0124] Based on this formula, it should be understood that the normalized fluctuation energy imbalance... This reflects the severity of the current energy imbalance in the cable. The larger the value, the more severe the energy imbalance, and the greater the abnormal stress on the cable, leading to... The larger; conversely, when The smaller the value, the more the energy state tends to be in equilibrium. The smaller. When An increase in the value indicates an increased probability of sagging under the current energy imbalance state. Increase accordingly; when A decrease in the value indicates a lower probability of drooping. The corresponding decrease.

[0125] The methods provided in S401-S402 above employ a fuzzy logic system to map the imbalance of fluctuating energy, thereby achieving a probabilistic assessment of cable sag risk. This method effectively addresses the nonlinear relationship between tension fluctuations and sag risk, and based on the target risk assessment model, it can output a sag risk probability that better reflects actual working conditions, making the risk assessment results more reliable.

[0126] Combination Figure 2 ,like Figure 5 As shown, in one implementation of this application embodiment, the above-mentioned S204 can be specifically implemented by S501-S503.

[0127] S501. Determine the change in current sag risk based on the difference between the current sag risk of the cable and the sag risk of the cable at the previous moment.

[0128] Optionally, the sag risk level at the previous moment can be determined based on the tension data sequence at the previous moment, and then the difference between the current sag risk level and the sag risk level of the cable at the previous moment can be determined.

[0129] It should be understood that the current downward risk change is used to characterize the trend of risk over time; a positive value indicates an increase in risk, and a negative value indicates a decrease in risk.

[0130] S502. Adjust the original proportional gain based on the current change in droop risk to obtain the adjusted proportional gain.

[0131] In this embodiment, an adaptive proportional-integral-derivative (PID) controller adjusts the original proportional gain. Specifically, the adaptive PID controller dynamically adjusts the proportional gain by monitoring the change in droop risk in real time. When the change in current droop risk is greater than 0, it indicates that the droop risk is increasing. At this time, the adaptive PID controller automatically increases the proportional gain to enhance the control effect and quickly suppress the development of risk. When the change in current droop risk is less than 0, it indicates that the droop risk is decreasing. At this time, the adaptive PID controller automatically decreases the proportional gain to avoid over-tuning and oscillation, and maintain system stability. When the change in current droop risk is equal to 0, the proportional gain remains unchanged.

[0132] Optionally, the adjusted proportional gain satisfies the following formula: in, This indicates the adjusted proportional gain. Indicates the original proportional gain. Indicates the current risk level of drooping. This indicates the change in the current risk level of drooping. and For example, the weighting coefficients are... and The value of can be 1.

[0133] S503. Adjust cable tension based on the adjusted proportional gain.

[0134] It should be understood that when the proportional gain increases, the cable tension increases, and when the proportional gain decreases, the cable tension decreases.

[0135] In one alternative implementation, the tension setting deviation can be determined based on the current tension value and the preset tension value; a tension control signal can be generated based on the adjusted proportional gain and the tension setting deviation; and the cable tension can be adjusted based on the tension control signal.

[0136] Optionally, the difference between the preset tension value and the tension value at the current moment can be defined as the tension setting deviation.

[0137] It should be understood that the preset tension value is the tension value set according to the cable specifications and laying requirements, while the tension value at the current moment is the actual tension value measured by the tension sensor at the current moment.

[0138] Understandably, the proportional gain is used to control the response speed to tension deviations.

[0139] It should be understood that the input to the adaptive PID controller is the tension setting deviation, and then the tension control signal is output based on the adjusted proportional gain, original integral gain and original derivative gain in the adaptive PID controller.

[0140] Finally, the adaptive PID controller outputs the generated tension control signal to the actuator, so that the actuator drives tensioning devices such as servo motors, hydraulic tensioners or electric winches to achieve precise adjustment of cable tension.

[0141] The methods provided in S501-S503 above achieve adaptive optimization of the adaptive PID controller parameters by using the change in the current sag risk of the cable as the direct basis for adjusting the control parameters. This allows the response strength of the adaptive PID controller to match the sag risk faced by the cable in real time: automatically strengthening the control action to quickly compensate for tension loss when the risk increases, and appropriately weakening the control to prevent overshoot when the risk decreases. This dynamic adjustment mechanism significantly improves the adaptability of the control system to time-varying and nonlinear objects.

[0142] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0143] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for adjusting the tension of power cables to prevent sagging, characterized in that, include: Obtain the current tension data sequence along the cable laying path; Multi-scale fluctuation analysis is performed on the current tension data sequence to obtain the current fluctuation energy imbalance, which is used to characterize the degree of dynamic energy instability of the cable system. Based on the fluctuation energy imbalance, the current sag risk level of the cable is determined, and the current sag risk level is used to characterize the overall risk level of cable sag. Adjust the cable tension based on the current sag risk of the cable.

2. The method for adjusting the tension of power cable laying to prevent sagging according to claim 1, characterized in that, The step of performing multi-scale fluctuation analysis on the current tension data sequence to obtain the current fluctuation energy imbalance includes: Multi-scale wave analysis was performed on the current tension data sequence to obtain the energy and center frequency of multiple wave components; The energy and center frequency of the multiple wave components are input into the target mechanical potential energy loss prediction model to obtain the current loss prediction value. The target mechanical potential energy loss prediction model is used to predict the mechanical energy dissipation value based on the energy and center frequency of the wave components. Based on the current loss prediction value and the energy of the multiple fluctuation components, the current fluctuation energy imbalance is determined.

3. The method for adjusting the tension of power cable laying to prevent sagging according to claim 2, characterized in that, The determination of the current fluctuation energy imbalance based on the current loss prediction value and the energy of the multiple fluctuation components includes: Determine the current total energy of multiple fluctuation components and the rate of energy change of each fluctuation component; Determine a first ratio, which is the ratio of the current predicted loss value to the current total energy. The current fluctuation energy imbalance is determined based on the first ratio and the maximum energy change rate among the energy change rates of multiple fluctuation components.

4. The method for adjusting the tension of power cable laying to prevent sagging according to claim 1, characterized in that, The determination of the current sag risk of the cable based on the fluctuation energy imbalance includes: The current fluctuation energy imbalance is input into the target risk assessment model to obtain the current sag risk probability of the cable. The target risk assessment model is used to assess the current sag risk probability of the cable. The current sag risk level of the cable is determined based on the current sag risk probability and the current fluctuation energy imbalance.

5. The method for adjusting the tension of power cable laying to prevent sagging according to claim 1, characterized in that, Adjusting cable tension based on the current sag risk of the cable includes: The change in the current sag risk level is determined based on the difference between the current sag risk level of the cable and the sag risk level of the cable at the previous moment. The original proportional gain is adjusted based on the current change in droop risk to obtain the adjusted proportional gain. Based on the adjusted proportional gain, the cable tension is adjusted.

6. The method for adjusting the tension of power cable laying to prevent sagging according to claim 5, characterized in that, Adjusting the cable tension based on the adjusted proportional gain includes: Based on the current tension value and the preset tension value, determine the tension setting deviation; Based on the adjusted proportional gain and the tension setting deviation, a tension control signal is generated; The cable tension is adjusted based on the tension control signal.

7. The method for adjusting the tension of power cable laying to prevent sagging according to claim 2, characterized in that, The method further includes: Obtain historical datasets, which include tension data sequences and historical mechanical potential energy loss sequences from multiple historical moments; Multi-scale fluctuation analysis was performed on the tension data sequence at each historical moment to obtain the energy and center frequency of the fluctuation component at each historical moment; Based on the energy and center frequency of the fluctuation component at each historical moment, and the historical mechanical potential energy loss sequence, an initial mechanical potential energy loss prediction model is trained to obtain the target mechanical potential energy loss prediction model.

8. The method for adjusting the tension of power cable laying to prevent sagging according to claim 4, characterized in that, The method further includes: Obtain historical sag event tags and historical fluctuation energy imbalance sequence. The historical sag event tags are used to characterize whether the cable sags at the corresponding historical acquisition time. Based on the historical fluctuation energy imbalance sequence, the historical drooping event labels, and the fuzzy logic system, the target risk assessment model is constructed.

9. The method for adjusting the tension of power cable laying to prevent sagging according to claim 8, characterized in that, The construction of the target risk assessment model based on the historical fluctuation energy imbalance sequence, the historical droop event labels, and the fuzzy logic system includes: Based on the historical fluctuation energy imbalance sequence and the historical drooping event labels, optimize the membership function parameters of the fuzzy logic system; The target risk assessment model is constructed based on the optimized membership function parameters.

10. A tension adjustment device for power cable laying to prevent sagging, characterized in that, Includes a data acquisition unit and a central control unit; The data acquisition unit is used to acquire the current tension data sequence along the cable laying path; The central control unit is used to perform multi-scale fluctuation analysis on the tension data sequence to obtain the current fluctuation energy imbalance, which is used to characterize the degree of dynamic energy instability of the cable system. The central control unit is also used to determine the current sag risk level of the cable based on the fluctuation energy imbalance degree. The current sag risk level is used to characterize the comprehensive risk level of cable sag. The central control unit is also used to adjust the cable tension based on the current sag risk of the cable.

Images