Microseismic vibration synchronous monitoring system and method based on split deployment

Abstract

The application discloses a kind of micro wind vibration synchronous monitoring system and method based on split deployment, belong to electric power transmission line intelligent monitoring technical field.Through edge intelligent terminal, vibration sensor and meteorological sensing unit installed on A, B, C three-phase conductor form micro wind vibration synchronous monitoring system, remove adaptive integral error to the data collected in combination with data characteristics, respectively obtain the displacement signal corresponding to three-phase conductor;In combination with leakage compensation model, the power of displacement signal is compensated, the corresponding micro wind vibration amplitude is determined, to further guarantee its authenticity;At the same time, real-time wind speed data and real-time wind direction data are obtained, according to the real-time wind speed data and the real-time wind direction data and the preset micro wind vibration correlation model, the micro wind vibration amplitude is checked, and the accuracy of the data is verified.The problem that the accuracy of the existing micro wind vibration monitoring needs to be improved is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent monitoring of power transmission lines, in particular to a micro-wind vibration synchronous monitoring system and method based on split deployment. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute the prior art.

[0003] Micro-wind vibration monitoring of power transmission lines is one of the key technologies to ensure the safe operation of the power grid. Its core is to prevent potential risks such as conductor fatigue fracture and hardware wear caused by high-frequency small-amplitude vibration through real-time data acquisition and analysis.

[0004] Common micro-wind vibration monitoring methods include bending amplitude method and inverted vibration measurement method. These two methods measure the vibration amplitude and frequency at the 89mm cutout of the conductor clamp by using cantilever beam mechanical structures integrated with strain gauges. Since the monitoring point itself is in a high-frequency vibration environment, the mechanical structure will deform and fail after long-term operation, resulting in inaccurate monitoring data and monitoring equipment failure. It is impossible to achieve vibration synchronous sampling of multiple monitoring points and meteorological environment (wind speed, wind direction) ns, resulting in reduced measurement accuracy and inaccurate data. At the same time, the collected data is directly applied without considering the influence of noise, temperature change, sensor drift, and small changes in gravity on data acquisition.

[0005] Moreover, the existing technology monitors the power transmission line as a whole, without considering the vibration differences between the different phases of the power transmission line, resulting in the need to improve the accuracy of micro-wind vibration monitoring. SUMMARY

[0006] To solve the problems of the prior art, the present application provides a micro-wind vibration synchronous monitoring system and method based on split deployment, which uses split deployment of vibration sensors to reduce the impact of changes in the monitoring structure on monitoring accuracy. At the same time, considering the influence of environmental factors on sensor monitoring and the relevance of vibration monitoring and meteorological factors, conductor spatial distribution, etc., the accuracy of micro-wind vibration monitoring is improved.

[0007] In a first aspect, the present application provides a micro-wind vibration synchronous monitoring system based on split deployment;

[0008] A micro-wind vibration synchronous monitoring system based on split deployment comprises:

[0009] A vibration sensor, a plurality of vibration sensors are arranged on the three-phase conductors of the power line, the vibration sensor comprises a split vibration sensing unit arranged at both ends of the strain clamp outlet, for collecting acceleration data at both ends of the strain clamp outlet.

[0010] a meteorological sensing unit configured to collect real-time wind speed data and real-time wind direction data;

[0011] an edge intelligent terminal configured to obtain a plurality of groups of acceleration data and respectively perform differential operation to obtain corresponding acceleration continuous-time signals; based on the acceleration continuous-time signals, perform adaptive integral error removal in combination with data characteristics to respectively obtain corresponding displacement signals; perform power compensation on the displacement signals in combination with a leakage compensation model to determine corresponding micro-wind vibration amplitudes; obtain real-time wind speed data and real-time wind direction data, and calibrate the micro-wind vibration amplitudes in combination with a preset micro-wind vibration correlation model.

[0012] In some embodiments, based on the acceleration continuous-time signals, performing adaptive integral error removal in combination with data characteristics to respectively obtain corresponding displacement signals comprises:

[0013] calculating a standard deviation and a mean value of the acceleration continuous-time signals, and pre-processing the acceleration continuous-time signals according to the speed continuous-time signals, the standard deviation and the mean value;

[0014] performing a de-biasing operation and an integration operation on the pre-processed acceleration continuous-time signals in sequence to obtain a speed time sequence; performing a de-biasing operation and an integration operation on the speed time sequence in sequence to obtain an initial displacement signal;

[0015] eliminating a signal trend in the initial displacement signal by Gaussian row-elimination method fitting to obtain a displacement signal.

[0016] In some embodiments, eliminating a trend signal in the initial displacement signal by Gaussian row-elimination method fitting to obtain a displacement signal comprises:

[0017] constructing a polynomial trend model of the initial displacement signal, fitting the polynomial trend model by Gaussian row-elimination method to obtain a trend signal sequence;

[0018] determining the displacement signal according to a difference between the initial displacement signal and the trend signal sequence.

[0019] In some embodiments, the power compensation on the displacement signal in combination with the leakage compensation model to determine corresponding micro-wind vibration amplitudes comprises:

[0020] performing Fourier transform on the displacement signal to determine a frequency and an amplitude corresponding to the displacement signal;

[0021] compensating the amplitude based on the frequency and a preset leakage compensation model to determine corresponding micro-wind vibration amplitudes.

[0022] In some embodiments, the constructing the leakage compensation model comprises:

[0023] constructing a vibration amplitude attenuation coefficient polynomial model according to the frequency offset between the main frequency point frequency and the adjacent leakage point frequency;

[0024] constructing a leakage compensation model according to the energy distribution of the main frequency point and the leakage point; combining the leakage compensation model and the vibration amplitude attenuation coefficient polynomial model, determining the leakage compensation factor and updating the leakage compensation model by using the frequency offset.

[0025] In some embodiments, the leakage compensation model is expressed as:

[0026] A real =A s *P correced (f0) / P0;

[0027]

[0028] In the formula, A real represents the amplitude of the breeze vibration, A s represents the amplitude, f0 represents the main frequency point frequency, P0 represents the main frequency point power, Δf i represents the i-th frequency offset, k i represents the i-th leakage compensation factor.

[0029] In some embodiments, the constructing the breeze vibration correlation model specifically comprises: constructing the breeze vibration correlation model by using the wind speed, the angle between the wind direction and the power line, and the amplitude of the breeze vibration response.

[0030] In some embodiments, the edge intelligent terminal is also used to acquire the power information data of the separated vibration sensing unit, process the power information data, real-time wind direction data and real-time wind speed data through a preset wind speed and wind direction power-sampling model, and acquire and transmit the sampling frequency and waveform sampling duration parameters to the separated vibration sensing unit.

[0031] In some embodiments, the processing of the power information data, real-time wind direction data and real-time wind speed data through the preset wind speed and wind direction power-sampling model to acquire the sampling frequency and waveform sampling duration parameters specifically comprises: according to the comparison results of the battery power, the charging current and the preset threshold range, combining the comparison results of the wind speed data and the wind direction data with the preset threshold, and outputting the corresponding sampling frequency and waveform sampling duration parameters.

[0032] In a second aspect, the present application provides a breeze vibration synchronous monitoring method based on split deployment;

[0033] A breeze vibration synchronous monitoring method based on split deployment comprises:

[0034] A plurality of sets of acceleration data are obtained and differential operations are respectively performed to obtain corresponding acceleration continuous-time signals; wherein the acceleration data are collected by a separated vibration sensing unit arranged at both ends of the strain clamp outlet;

[0035] Based on the acceleration continuous-time signals, adaptive integral error removal is performed in combination with data characteristics to respectively obtain corresponding displacement signals; power compensation is performed on the displacement signals in combination with a leakage compensation model to determine corresponding micro-wind vibration amplitudes;

[0036] Real-time wind speed data and real-time wind direction data are obtained, and the micro-wind vibration amplitudes are calibrated in combination with a preset micro-wind vibration correlation model; wherein the real-time wind speed data and the real-time wind direction data are collected by a meteorological sensing unit.

[0037] In some embodiments, based on the acceleration continuous-time signals, adaptive integral error removal is performed in combination with data characteristics to respectively obtain corresponding displacement signals, including:

[0038] The standard deviation and the average value of the acceleration continuous-time signals are calculated, and the acceleration continuous-time signals are preprocessed according to the speed continuous-time signals, the standard deviation and the average value;

[0039] The preprocessed acceleration continuous-time signals are sequentially subjected to a de-biasing operation and an integration operation to obtain a speed time sequence; the speed time sequence is sequentially subjected to a de-biasing operation and an integration operation to obtain an initial displacement signal;

[0040] The initial displacement signal is fitted by a Gaussian row main elimination method to eliminate a signal trend in the initial displacement signal to obtain a displacement signal.

[0041] In some embodiments, the initial displacement signal is fitted by a Gaussian row main elimination method to eliminate a trend signal in the initial displacement signal to obtain a displacement signal, including:

[0042] A polynomial trend model of the initial displacement signal is constructed, and the polynomial trend model is fitted by a Gaussian row main elimination method to obtain a trend signal sequence;

[0043] The displacement signal is determined according to a difference between the initial displacement signal and the trend signal sequence.

[0044] In some embodiments, the power compensation is performed on the displacement signal in combination with a leakage compensation model to determine corresponding micro-wind vibration amplitudes, including:

[0045] The displacement signal is subjected to a Fourier transform to determine a frequency and an amplitude corresponding to the displacement signal;

[0046] The amplitude is compensated based on the frequency and a preset leakage compensation model to determine corresponding micro-wind vibration amplitudes.

[0047] In some embodiments, the constructing the leakage compensation model comprises:

[0048] constructing a vibration amplitude attenuation coefficient polynomial model according to the frequency offset between the main frequency point frequency and the adjacent leakage point frequency;

[0049] constructing a leakage compensation model according to the energy distribution of the main frequency point and the leakage point; combining the leakage compensation model and the vibration amplitude attenuation coefficient polynomial model, determining the leakage compensation factor and updating the leakage compensation model by using the frequency offset.

[0050] In some embodiments, the leakage compensation model is expressed as:

[0051]

[0052] wherein f0 represents the main frequency point frequency, P0 represents the main frequency point power, Δf represents the frequency offset, ki represents the i-th leakage compensation factor, and P represents the power of the i-th leakage point. i wherein f0 represents the main frequency point frequency, P0 represents the main frequency point power, Δf represents the frequency offset, ki represents the i-th leakage compensation factor, and P represents the power of the i-th leakage point. i wherein f0 represents the main frequency point frequency, P0 represents the main frequency point power, Δf represents the frequency offset, ki represents the i-th leakage compensation factor, and P represents the power of the i-th leakage point.

[0053] In some embodiments, the constructing the wind-induced vibration correlation model specifically comprises: constructing the wind-induced vibration correlation model by using the wind speed, the angle between the wind direction and the power line, and the amplitude of the wind-induced vibration response.

[0054] In some embodiments, the method further comprises: obtaining the electric quantity information data of the separated vibration sensing unit, processing the electric quantity information data, the real-time wind direction data and the real-time wind speed data by using the preset wind speed and wind direction electric quantity-sampling model, and obtaining and transmitting the sampling frequency and the waveform sampling duration parameter to the separated vibration sensing unit.

[0055] In some embodiments, the processing the electric quantity information data, the real-time wind direction data and the real-time wind speed data by using the preset wind speed and wind direction electric quantity-sampling model to obtain the sampling frequency and the waveform sampling duration parameter specifically comprises: according to the comparison results of the battery electric quantity, the charging current and the preset threshold range, combining the comparison results of the wind speed data and the wind direction data with the preset threshold, and outputting the corresponding sampling frequency and waveform sampling duration parameter.

[0056] Compared with the prior art, the present application has the following advantages:

[0057] 1. The technical scheme provided by the present application, the vibration sensing unit is deployed in a split type, and the vibration sensing units interact with each other through wireless communication without physical connection, so that installation and maintenance are more convenient, the problem of fracture of integral connection cables and structures caused by vibration of the monitoring point is avoided, the influence on the monitoring vibration amplitude frequency is reduced, the accuracy of the sampling data of the monitoring point is improved, the reliability of the equipment itself is ensured, and the vibration of the three-phase conductor is monitored, so that the comprehensiveness of the breeze vibration monitoring is ensured.

[0058] 2. The technical scheme provided by the present application, the data is processed by the trend removal method of removing heterogeneity, eliminating deviation and self-adaptation, and the compensation model of FFT spectrum leakage is established, the interference of external factors on the data collected by the vibration sensor is eliminated, the accuracy and authenticity of the collected data are improved, and system misjudgment is avoided.

[0059] 3. The technical scheme provided by the present application, a wind speed and direction electric quantity-sampling model is constructed, the sampling frequency and sampling waveform length are adaptively adjusted based on the instantaneous data of wind speed and direction and the current electric quantity and state of charge, so as to reduce the overall operation power consumption of the system as much as possible, thereby reducing the configuration of the power supply system capacity (battery + solar panel capacity configuration) of the device, greatly reducing the overall weight of the device, the equipment is more portable, and the installation of hardware at the vibration monitoring point position is reduced to the greatest extent, and the accuracy of the sampling of the monitoring point is affected. BRIEF DESCRIPTION OF DRAWINGS

[0060] The drawings accompanying the specification of the present application form part of the present application and serve to provide further understanding of the present application, the illustrative embodiments of the present application and their descriptions serve to explain the present application, and do not constitute improper limitation on the present application.

[0061] Fig. 1 The deployment schematic diagram of the vibration sensor provided for the embodiment of the present application is shown in the figure;

[0062] Fig. 2 The data transmission schematic diagram of the breeze vibration synchronous monitoring system based on split type deployment provided for the embodiment of the present application is shown in the figure;

[0063] Fig. 3 The data processing schematic diagram of the wind speed and direction electric quantity-sampling model provided for the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0064] It should be pointed out that the following detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in the present application have the same meaning as generally understood by those skilled in the art to which the present application belongs.

[0065] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. Furthermore, it should be understood that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0066] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0067] Example 1

[0068] Existing micro-wind vibration monitoring methods are limited by mechanical structure and external environmental factors, resulting in a need to improve the accuracy and reliability of monitoring results. Therefore, this invention provides a micro-wind vibration synchronous monitoring system based on a split-type deployment. By cooperating with a vibration sensor, a meteorological sensing unit, and an edge intelligent terminal set up separately, it can realize synchronous micro-wind vibration monitoring of three-phase conductors and improve the accuracy of monitoring.

[0069] Next, combined Figs. 1-3 This embodiment discloses a micro-wind vibration synchronous monitoring system based on a split-deployment configuration. The system includes vibration sensors, a meteorological sensing unit, and an edge intelligent terminal. The edge intelligent terminal communicates with the vibration sensors and the meteorological sensing unit via a LoRa communication channel. Three vibration sensors are deployed on the A, B, and C phase conductors. Each vibration sensor includes separate vibration sensing units deployed at both ends of the tension clamp outlet (the location with the largest bending amplitude). The separate vibration sensing units collect acceleration data at both ends of the tension clamp outlet. The meteorological sensing unit collects real-time wind speed and direction data. The edge intelligent terminal acquires multiple sets of acceleration data and performs differential calculations to obtain corresponding continuous-time acceleration signals. Based on the continuous-time acceleration signals, adaptive integral error removal is performed using data characteristics to obtain corresponding displacement signals. Power compensation is applied to the displacement signals using a leakage compensation model to determine the corresponding micro-wind vibration amplitude. Real-time wind speed and direction data are acquired, and the micro-wind vibration amplitude is calibrated using a preset micro-wind vibration correlation model.

[0070] Based on this, by processing the three sets of acceleration data collected by the three vibration sensors, the amplitude of the wind vibration corresponding to the three-phase conductors was obtained.

[0071] In this embodiment, the interval of the two separated vibration sensing units is 89mm, the separated vibration sensing unit includes a mems accelerometer and an MCU, the MCU is in communication connection with the mems accelerometer through a data line; the separated vibration sensing unit and the edge intelligent terminal are in wireless communication connection through a LoRa module.

[0072] Next, the working method of the micro-wind vibration synchronous monitoring system based on the split deployment described in this embodiment is combined to further illustrate the micro-wind vibration synchronous monitoring system based on the split deployment.

[0073] As an implementation, before the separated vibration sensing unit works, it further includes:

[0074] The edge intelligent terminal sends a timing instruction to the separated vibration sensing unit through the LoRa communication channel, and after the separated vibration sensing unit receives the timing instruction, it completes the timing with a precision of 100ns through a single Beidou timing chip; after the separated vibration sensing unit completes the timing, it sends a timing completion flag to the edge intelligent terminal; if the timing is not completed (the default is 3 minutes), the edge intelligent terminal sends an instruction to the separated vibration sensing unit that has not completed the timing, until the timing of the six separated vibration sensing units and itself is completed.

[0075] In order to save cost and realize low power consumption of the equipment, it is necessary to reduce the computing resources of the MCU and the consumption of the MCU as much as possible; therefore, it is necessary to achieve short sampling time and sampling data that just meet the analysis resource demand; therefore, in this embodiment, the sampling rate f sample is set to 2000HZ, the sampling time is 1.2 seconds, and there are a total of 2400 points.

[0076] During sampling, the edge intelligent terminal issues a command, and the LORA module of the air monitoring wake-up vibration sensor is used to wake up the MCU of the separated vibration sensing unit through the external IO port triggering mode; the low-power MCU starts the mems accelerometer of the two-end vibration sensing unit according to the indication command.

[0077] Based on this, the separated vibration sensing unit is triggered for sampling through LORA air paging to reduce power consumption.

[0078] Further, a plurality of groups of acceleration data are obtained and are subjected to differential operation respectively to obtain corresponding acceleration continuous time signals; based on the acceleration continuous time signals, adaptive integral error removal is performed in combination with data characteristics to obtain corresponding displacement signals, which specifically include:

[0079] Step 1, differential operation is performed on the acceleration data at both ends of the exit position of the strain clamp to obtain an acceleration continuous time signal a(t).

[0080] Step 2, calculate the standard deviation and mean value of the acceleration continuous-time signal, and pre-process the acceleration continuous-time signal according to the velocity continuous-time signal, the standard deviation and the mean value.

[0081] Specifically, first, calculate the standard deviation σa of the acceleration continuous-time signal a(t), and calculate the mean value of a(t) Then, determine whether a(t) satisfies If yes, the data sampling is normal and smoothing is not needed, and step 3 is performed; if not, smoothing processing is performed according to the data points of the current signal before and after the time, and the smoothing formula is as follows:

[0082] a(t) = (8a(t-1) + 5a(t+1)) / 13.

[0083] Through the smoothing processing in step 2, the obvious abnormal signals in the acceleration continuous-time signal can be removed.

[0084] Step 3, sequentially perform the de-biasing operation and the integration operation on the pre-processed acceleration continuous-time signal to obtain the velocity time series.

[0085] Remove the direct current component (de-bias) of the acceleration continuous-time signal to reduce the trend in the integration calculation; specifically, remove the average value of the time series from a(t) Thus, a new acceleration time series is obtained, denoted as:

[0086]

[0087] Integrate a(t) to obtain the velocity time series v(t), denoted as:

[0088] v(t) = ∫a(t)dt.

[0089] Step 4, sequentially perform the same de-biasing operation and integration operation as in step 3 on the velocity time series to obtain the initial displacement signal s(t).

[0090] Step 5, perform fitting by Gaussian row elimination method to eliminate the signal trend in the initial displacement signal and obtain the displacement signal.

[0091] Since s(t) has undergone double integration, the signal contains a large integral cumulative error, and the integral trend needs to be effectively removed to obtain a displacement time series that can more accurately reflect the phenomenon of wind vibration; therefore, an adaptive integral error removal method is provided in this embodiment, which automatically removes the trend according to the characteristics of the input data. The specific process is as follows:

[0092] First, a polynomial trend model fitting the initial displacement signal is designed, denoted as:

[0093] s trend (t) = c0 + c1t + c2t 2 +…+c n t n .

[0094] Here, the fitting target of the discrete data is: S*C=Y, where C is the coefficient vector to be solved C=[c0, c1, c2,..., c n ] T , Y is the signal vector Y=[s1, s2,..., s m ] T , S is the fitting matrix, which is represented as follows:

[0095]

[0096] Before removing the trend, the initial setting is carried out. According to the experimental experience and model analysis, in this embodiment, the maximum fitting order is defined as 8, and the minimum fitting order is defined as 2; an error threshold is defined, which is used to measure whether the residual of fitting meets the requirements. The order is gradually increased, starting from the minimum fitting order 2, and the fitting order is gradually increased. The trend signal s trend (t) and the residual residual=||Y-S*C||2 are calculated each time. If the residual is less than the error threshold, stop increasing the order; a constraint condition is set. If the residual still does not meet the requirements when the maximum fitting order 8 is reached, return the fitting result of the highest order.

[0097] Then, in order to solve S*C=Y, the coefficient C is obtained by Gaussian elimination method. Specifically, an augmented matrix [S|Y] is constructed, which is represented as:

[0098]

[0099] The main element is selected. In each column, the element with the largest absolute value is selected as the main element, and the rows are exchanged; in the elimination process, for the kth column, all elements after the k+1th row are set to 0; back substitution is used to solve C. The back substitution method is used to solve C from the last row; the fitting polynomial order n, the fitting coefficient C and s trend (t) are output, and the displacement signal sequence after removing the trend is represented as

[0100] s detrend (t) = s(t) - s trend (t).

[0101] Further, in combination with the leakage compensation model, the displacement signal is power compensated to determine the corresponding microseismic vibration amplitude, which includes:

[0102] S1, the fast Fourier transform is used to calculate the displacement signal s detrendFrequency and amplitude A of (t) s .

[0103] Due to the basic assumption of Discrete Fourier Transform (DFT) does not match the actual signal. Specifically, the essence of spectral leakage is that the periodicity assumption is no longer met after the signal is truncated, resulting in the spectrum "leaking" from one frequency point to other frequency points, for example, the frequency and frequency resolution are not aligned, if the real frequency of the signal is 5Hz, but the FFT resolution corresponding to the sampling point is 4.8Hz or 5.2Hz, which will cause leakage.

[0104] FFT is a fast Fourier transform with efficient calculation, and due to the factor that the resolution cannot be infinitely high, the value calculated by FFT will not be accurate after spectral leakage, and the amplitude A s is the initial wind vibration amplitude, which is obtained by FFT calculation. s Due to calculation reasons, different degrees of energy attenuation will be caused, so that a smaller amplitude than the actual value is obtained; therefore, in this embodiment, leakage compensation is performed through the following steps.

[0105] S2, compensating the wind vibration amplitude based on a preset leakage compensation model to determine the corresponding wind vibration amplitude; represented as:

[0106] A real = A s * P correced (f0) / P0;

[0107]

[0108] Δf i = |f i -f0|;

[0109] In the formula, f0 represents the main frequency point frequency, P0 represents the main frequency point power, and Δf i represents the i-th frequency offset, f i represents the i-th frequency point frequency, and k i represents the i-th leakage compensation factor.

[0110] Here, the main frequency point frequency and the main frequency point power are values obtained through experiments when constructing the leakage compensation model.

[0111] In this step, only the wind vibration amplitude A s needs to be compensated by the pre-constructed leakage compensation model, and the leaked power component is redistributed back to the main frequency point. After compensation, the amplitude of the main frequency point is improved, the leakage energy is effectively compensated back to the main frequency point, and the spectrum is closer to the real value.

[0112] Further, the construction of the leakage compensation model is specifically as follows: a vibration amplitude attenuation coefficient polynomial model is constructed according to the frequency offset between the main frequency point frequency and the adjacent leakage point frequency; a leakage compensation model is constructed according to the energy distribution of the main frequency point and the leakage point; the leakage compensation model and the vibration amplitude attenuation coefficient polynomial model are combined, the frequency offset is used to determine the leakage compensation factor and update the leakage compensation model.

[0113] Specifically, first, according to the monitoring requirement of the breeze vibration, different sine signals of 0-200HZ are simulated and input, the vibration amplitude is a fixed amplitude, and the amplitude is increased by 1HZ, and the calculated amplitude of each group of signals after FFT calculation is recorded by software. According to the FFT result obtained by the experiment, the main frequency point f0 and its adjacent leakage points f1, f2, … exist a certain offset, and the frequency offset is expressed as:

[0114] Δf i =|f i -f0|.

[0115] Then, the vibration amplitude attenuation coefficient polynomial model under the experimental environment is established

[0116] y=k0x+k1x 2 +k2x 3 +...;

[0117] Wherein, x is the frequency offset Δf i , y describes the attenuation ratio of the power amplitude with the offset, k0, k1, k2 need to be obtained by experiment fitting.

[0118] Further, in order to reduce the spectrum leakage, the signal is windowed before FFT, w[n] = 0.54-0.46cos(2πn / (N-1)), this step can reduce the influence of spectrum leakage phenomenon, but the phenomenon still exists.

[0119] According to the above simulation experiment, it is assumed that there is leakage at a certain frequency point f0, and the leakage spectrum power will be expanded to adjacent frequency points f1, f2, … Based on the leakage characteristics after windowing (such as the frequency domain response of window function), the leakage distribution ratio is estimated, in most cases, the leakage is mainly concentrated in 2-3 frequency points outside the main lobe, the main lobe range can be calculated by window function theory, and the weight ratio of leakage energy distribution is determined. The compensation formula of the power P1, P2, … of the main frequency point f0 and its adjacent leakage points f1, f2, … is

[0120] P corrected (f0)=P0+k1P1+k2P2+...;

[0121] Wherein, k n is a leakage weight factor, which is an empirical weight, used to adjust the contribution of adjacent leakage points.

[0122] The frequency offset is brought into the pre-set attenuation model under the experiment, and for each leakage point f i , its frequency offset is calculated, denoted as:

[0123] x i = Δf i = |f i -f0|;

[0124] Substitute x i into the vibration amplitude attenuation model to obtain the attenuation ratio y i of the corresponding leakage point, which is used to describe the leakage contribution weight of the point to the main frequency point, denoted as:

[0125] y i = k0x i +k1x i 2 +k2x i 3 +...;

[0126] The k i in the leakage compensation formula is directly related to the vibration attenuation ratio y i , and it is not difficult to directly set k i = y i , that is, the value of the leakage compensation factor k i is derived from the vibration amplitude attenuation model. Therefore, for each leakage point f i , the final compensation factor is:

[0127] k i = k0Δf i +k1(Δf i ) 2 +k2(Δf i ) 3 +...

[0128] Combine the power contribution and compensation factor of all leakage points to obtain the final leakage compensation model, denoted as:

[0129] A real = A s *P correced (f0) / P0;

[0130]

[0131] In summary, the leakage compensation model suitable for the wind monitoring scene can be obtained, which can be directly called in actual wind monitoring to ensure the real-time performance of data monitoring.

[0132] Further, the micro-wind vibration correlation model is constructed by using the wind speed, the angle between the wind direction and the power line, and the amplitude of the micro-wind vibration response.

[0133] The wind speed determines the energy of the air flow, thereby affecting the vibration intensity and excitation frequency of the structure or equipment. Micro-wind vibration is usually caused by low-speed wind. When the wind speed is low, the aerodynamic force generated is relatively small, and the vibration amplitude of the structure is also small. However, as the wind speed increases, the aerodynamic effect becomes more significant, which may induce stronger vibration, especially near the resonance frequency of the structure.

[0134] The wind pressure is proportional to the square of the wind speed. Therefore, the greater the wind speed, the more significant the aerodynamic load and excitation force generated by the wind on the object, thereby inducing stronger vibration. For micro-wind vibration, it can be assumed that the excitation force generated by the wind speed on the structure is the main factor affecting the vibration intensity. The relationship between the vibration intensity and the excitation force can be represented by a simplified linear model:

[0135] F wind = k v 2 ;

[0136] where k is a constant, which depends on the shape and surface characteristics of the vibration monitoring unit structure.

[0137] The influence of wind direction on vibration intensity is more complex, because the wind direction determines the direction of wind force and the distribution of airflow on the surface of the object. The wind direction affects the way the object is affected by the wind, leading to different vibration modes. We simplify the model and assume that the angle of the wind direction relative to the windward surface of the object is θ, then the effective pressure of the wind will be affected by the angle of the wind direction. The component of the wind force can be represented as:

[0138] F effective = F wind *cos(θ);

[0139] where F wind = k v 2 is the total wind excitation, and θ is the angle between the wind direction and the normal of the object surface. When the wind direction is directly opposite to the object, i.e., θ is 0 degrees, the influence of the wind force on the object is the largest. When the wind direction is parallel to the object surface, i.e., θ is 90 degrees, the effective action of the wind force is the smallest. Different wind directions change the point of action of the wind force on the object, thereby affecting the intensity and mode of vibration.

[0140] Specifically, in the actual situation of power line monitoring, the wind speed and wind direction jointly act on the micro-wind vibration intensity of the object. In consideration of the wind speed and wind direction, the micro-wind vibration correlation model is represented as:

[0141]

[0142] In the formula, X vibration represents the amplitude (intensity) of the wind vibration response, A represents a constant related to factors such as structural stiffness, mass, damping, etc., w n represents the natural frequency of the structure, θ represents the angle between the wind direction and the normal of the object surface, and v represents the wind speed.

[0143] For example, in the working process of the micro-vibration synchronous monitoring system based on the split deployment, the real-time wind direction data and the real-time wind speed data collected are brought into the micro-vibration correlation model to obtain the amplitude of the micro-vibration response, and it is judged whether the difference between the obtained micro-vibration amplitude and the micro-vibration amplitude is within a preset range. If yes, the collected micro-vibration amplitude is accurate.

[0144] As an implementation mode, the edge intelligent terminal is further used to acquire power information data of the split vibration sensing unit, process the power information data, real-time wind direction data and real-time wind speed data through a preset wind speed and wind direction-power sampling model, and acquire and transmit sampling frequency and waveform sampling duration parameters to the split vibration sensing unit.

[0145] Specifically, according to the comparison result of the battery power, the charging current and the preset threshold range, and the comparison result of the wind speed data and the wind direction data and the preset threshold, corresponding sampling frequency and waveform sampling duration parameters are output.

[0146] For example, when the wind speed is 0, the sampling call is not started; when the battery power is greater than 80% and the wind speed is greater than 0 m / s, sampling is performed according to the standard sampling frequency and the number of waveform sampling points; when the battery power is in the interval of 80%-50% and the charging current is greater than 100mA, the wind speed is greater than 0 m / s, and sampling is performed according to the standard sampling frequency and the number of waveform sampling points; when the battery power is in the interval of 80%-50% and the charging current is greater than 50mA and less than 100mA, the wind speed is greater than 0 m / s, the sampling frequency is reduced by 10%, and the waveform sampling length is reduced by 10%; when the battery power is in the interval of 80%-50% and the charging current is less than 50mA, the wind speed is greater than 0 m / s, the sampling frequency is reduced by 20%, and the waveform sampling length is reduced by 20%; when the battery power is in the interval of 50%-30% and the charging current is greater than 100mA, the wind speed is greater than 0 m / s, the sampling frequency is reduced by 10%, and the waveform sampling length is reduced by 10%; when the battery power is in the interval of 50%-30% and the charging current is greater than 50mA and less than 100mA, the wind speed is greater than 0 m / s, the sampling frequency is reduced by 30%, and the waveform sampling length is reduced by 30%; when the battery power is in the interval of 10%-30% and the charging current is greater than 100mA, the wind speed is greater than 0 m / s, the sampling frequency is reduced by 30%, and the waveform sampling length is reduced by 30%; when the battery power is in the interval of 10%-30% and the charging current is greater than 50mA and less than 100mA, the wind speed is greater than 0 m / s, the sampling frequency is reduced by 40%, and the waveform sampling length is reduced by 40%; when the battery power is in the interval of 10%-20% and the wind speed is greater than 5 m / s, sampling is triggered, the sampling frequency is reduced by 40%, and the waveform sampling length is reduced by 40%; when the battery power is less than 10%, the wind speed is greater than 5 m / s, and the wind direction is greater than 30° from the wire, sampling is triggered, the sampling frequency is reduced by 40%, and the waveform sampling length is reduced by 40%.

[0147] The weight of the vibration sensor is particularly important in the technical indicators of the system, and the standard specifies that the maximum weight does not exceed 1 kg; in fact, the lighter the weight, the smaller the impact on the monitoring point, and the more accurate the measurement data. The power supply system of the vibration sensor adopts a power supply scheme of a storage battery + solar panel, which accounts for a large proportion of the weight of the entire device. Through low-power design of the software and hardware of the equipment, the overall system power consumption of the device is reduced, the dependence on the capacity of the power supply system is reduced, and the structure is lightweight and small in size.

[0148] Embodiment Two

[0149] Based on the micro-wind vibration synchronous monitoring system based on split deployment described in Embodiment One, the present embodiment provides a micro-wind vibration synchronous monitoring method based on split deployment, which is deployed in an edge intelligent terminal; comprising the following steps:

[0150] A plurality of groups of acceleration data are acquired and differential operation is respectively performed to obtain corresponding acceleration continuous time signals; wherein the acceleration data is collected by the separated vibration sensing unit arranged at both ends of the strain clamp outlet;

[0151] Based on the acceleration continuous time signals, adaptive integral error removal is performed in combination with data characteristics to respectively obtain corresponding displacement signals; power compensation is performed on the displacement signals in combination with a leakage compensation model to determine the corresponding micro wind vibration amplitude;

[0152] Real-time wind speed data and real-time wind direction data are acquired, and the micro wind vibration amplitude is calibrated in combination with a preset micro wind vibration correlation model according to the real-time wind speed data and the real-time wind direction data; wherein the real-time wind speed data and the real-time wind direction data are collected by a meteorological sensing unit.

[0153] Further, a plurality of groups of acceleration data are acquired and differential operation is respectively performed to obtain corresponding acceleration continuous time signals; based on the acceleration continuous time signals, adaptive integral error removal is performed in combination with data characteristics to respectively obtain corresponding displacement signals, which specifically includes:

[0154] Step 1, differential operation is performed on the acceleration data at both ends of the strain clamp outlet to obtain the acceleration continuous time signal a(t).

[0155] Step 2, the standard deviation and the average value of the acceleration continuous time signal are calculated, and the acceleration continuous time signal is preprocessed according to the speed continuous time signal, the standard deviation and the average value.

[0156] Specifically, first, the standard deviation σa of the acceleration continuous time signal a(t) is calculated, and the average value of a(t) is calculated Then, it is judged whether a(t) satisfies If it is satisfied, the data sampling is normal and smoothing is not needed, and step 3 is executed; if it is not satisfied, smoothing processing is performed according to the data points of the current signal before and after the time, and the smoothing formula is as follows:

[0157] a(t) = (8a(t-1) + 5a(t+1)) / 13.

[0158] Through the smoothing processing in step 2, the obvious abnormal signals in the acceleration continuous time signal can be removed.

[0159] Step 3, the preprocessed acceleration continuous time signal is sequentially subjected to a bias removal operation and an integration operation to obtain a speed time sequence.

[0160] The direct current component of the acceleration continuous time signal is removed (bias removal) to reduce the trend in the integral calculation; specifically, the removal method is to subtract the average value of the time sequence from a(t) Thus a new acceleration time series is obtained, denoted as:

[0161]

[0162] Integrating a(t), a velocity time series v(t) is obtained, denoted as:

[0163] v(t) = ∫a(t)dt.

[0164] Step 4, the same de-biasing operation and integration operation as in step 3 are sequentially performed on the velocity time series to obtain an initial displacement signal s(t).

[0165] Step 5, fitting is performed by the Gaussian row-elimination method to eliminate the signal trend in the initial displacement signal to obtain a displacement signal.

[0166] Since s(t) has been subjected to double integration, there is a great integral cumulative error in the signal, and the integral trend needs to be effectively removed to obtain a displacement time series that can more accurately reflect the phenomenon of wind vibration; therefore, an adaptive integral error removal method that automatically removes the trend according to the characteristics of the input data is provided in the embodiment. The specific process is as follows:

[0167] First, a polynomial trend model fitting the initial displacement signal is designed, denoted as:

[0168] s trend (t) = c0 + c1t + c2t 2 + … + c n t n .

[0169] Here, the fitting target of the discrete data is: S*C = Y, where C is the coefficient vector to be solved C = [c0, c1, c2,..., c n ] T , Y is the signal vector Y = [s1, s2,..., s m ] T , and S is the fitting matrix, which is denoted as follows:

[0170]

[0171] Before removing the trend, initial settings are made. According to experimental experience and model analysis, in the embodiment, the maximum fitting order is defined as 8 and the minimum fitting order is defined as 2; an error threshold ∈ is defined to measure whether the fitting residual meets the requirements. The order is gradually increased, starting from the minimum fitting order 2, and the fitting order is gradually increased each time, and the trend signal s trend(t) and residual = ||Y-S*C||2, if the residual is less than the error threshold e, stop increasing the order; set a constraint, if the residual still does not meet the requirement when the maximum fitting order 8 is reached, return the fitting result of the highest order.

[0172] Then, in order to solve S*C=Y, the coefficient C is obtained by Gaussian row elimination method; specifically, an augmented matrix [S|Y] is constructed, which is expressed as:

[0173]

[0174] Select the pivot, select the element with the largest absolute value in each column as the pivot and exchange rows; in the elimination process, for the kth column, make all elements after the k+1th row to be 0; back substitution, solve C from the last row by back substitution method; output the fitting polynomial order n, fitting coefficient C and s trend (t), then the displacement signal sequence after trend removal is expressed as

[0175] s detrend (t) = s(t)-s trend (t).

[0176] Further, in combination with the leakage compensation model, the displacement signal is power compensated to determine the corresponding wind vibration amplitude, which includes:

[0177] S1, using fast Fourier transform to calculate the frequency and amplitude A detrend of the displacement signal s s .

[0178] Due to the basic assumption of discrete Fourier transform (DFT) does not match the actual signal. Specifically, the essence of spectral leakage is that after the signal is truncated, the periodicity assumption is no longer met, resulting in spectral "leakage" from one frequency point to other frequency points, for example, if the real frequency of the signal is 5Hz, but the FFT resolution corresponding to the sampling point is 4.8Hz or 5.2Hz, which will cause leakage.

[0179] FFT is a fast Fourier transform with high efficiency. Due to the factor that the resolution cannot be infinitely high, the value calculated by FFT will not be accurate after spectral leakage, and the amplitude A s is the initial wind vibration amplitude, which is a characteristic quantity of wind vibration. A s calculated by FFT will have different degrees of energy attenuation due to calculation, so that a smaller amplitude than the actual value is obtained; therefore, in this embodiment, leakage compensation is performed by the following steps.

[0180] S2, compensating the amplitude of the wind vibration based on a preset leakage compensation model to determine a corresponding amplitude of the wind vibration; denoted as:

[0181] A real = A s *P correced (f0) / P0;

[0182]

[0183] Δf i =|f i -f0|;

[0184] In the formula, f0 represents the main frequency point frequency, P0 represents the main frequency point power, Δf i represents the i-th frequency offset, f i represents the i-th frequency point frequency, k i represents the i-th leakage compensation factor.

[0185] Here, the main frequency point frequency and the main frequency point power are values obtained through experiments when constructing the leakage compensation model.

[0186] In this step, only the amplitude of the wind vibration A s needs to be compensated by the pre-constructed leakage compensation model to redistribute the leaked power component back to the main frequency point. After compensation, the amplitude of the main frequency point is improved, the leakage energy is effectively compensated back to the main frequency point, and the spectrum is closer to the true value.

[0187] Further, the leakage compensation model is constructed as follows: according to the frequency offset between the main frequency point frequency and the frequency of the adjacent leakage point, a vibration amplitude decay coefficient polynomial model is constructed; according to the energy distribution of the main frequency point and the leakage point, a leakage compensation model is constructed; the leakage compensation model and the vibration amplitude decay coefficient polynomial model are combined, the frequency offset is used to determine the leakage compensation factor and update the leakage compensation model.

[0188] Specifically, first, according to the monitoring requirements of the wind vibration, different sinusoidal signals of 0-200HZ are simulated and input, the vibration amplitude is a fixed amplitude, and it is increased by every 1HZ. The calculated amplitude of each group of signals after FFT calculation is recorded by software. According to the experimental FFT results, the main frequency point f0 and its adjacent leakage points f1, f2, … have a certain offset, and the frequency offset is denoted as:

[0189] Δf i =|f i -f0|.

[0190] Then, the vibration amplitude decay coefficient polynomial model

[0191] y=k0x+k1x 2+ k2x 3 +... ;

[0192] where x is the frequency offset Δf i , y describes the decay ratio of power amplitude with offset, k0, k1, k2 need to be obtained by experimental fitting.

[0193] Further, in order to reduce the spectrum leakage, the signal is windowed before FFT, w[n] = 0.54 - 0.46cos(2πn / (N-1)), this step can reduce the influence of spectrum leakage phenomenon, but the phenomenon still exists.

[0194] According to the above simulation experiment, assuming that there is leakage at a certain frequency point f0, the leaked spectrum power will spread to adjacent frequency points f1, f2, … Based on the leakage characteristics after windowing (such as the frequency domain response of the window function), the leakage distribution ratio is estimated, and in most cases, the leakage is mainly concentrated in 2-3 frequency points outside the main lobe, the main lobe range can be calculated by window function theory, and the weight ratio of leakage energy distribution is determined. The compensation formula of the power P1, P2, … of the main frequency point f0 and its adjacent leakage points f1, f2, … is

[0195] P corrected (f0) = P0 + k1P1 + k2P2 +... ;

[0196] where k n is the leakage weight factor, which is an empirical weight used to adjust the contribution of adjacent leakage points.

[0197] The frequency offset is brought into the pre-set decay model in the experiment, for each leakage point f i , its frequency offset is calculated and expressed as:

[0198] x i = Δf i = |f i -f0|;

[0199] Substitute x i into the vibration amplitude decay model to obtain the decay ratio yi of the corresponding leakage point, which is used to describe the leakage contribution weight of the point to the main frequency point; expressed as:

[0200] y i = k0x i +k1x i 2 +k2x i 3 +... ;

[0201] The k i in the leakage compensation formula is directly related to the vibration decay ratio y i , and it is not difficult to directly set ki = y i , i.e. the leakage compensation factor k i is derived from the vibration amplitude decay model. Therefore, for each leakage point f i , the final compensation factor is:

[0202] k i = k0Δf i + k1(Δf i ) 2 + k2(Δf i ) 3 +...

[0203] Combining the power contributions and compensation factors for all leakage points, the final leakage compensation model is obtained and expressed as:

[0204] A real = A s * P correced (f0) / P0;

[0205]

[0206] In summary, a leakage compensation model suitable for wind monitoring scenarios can be obtained, which can be directly called in actual wind monitoring to ensure the real-time performance of data monitoring.

[0207] Further, the wind vibration correlation model is constructed as follows: the wind speed, the angle between the wind direction and the power line, and the amplitude of the wind vibration response are used to construct the wind vibration correlation model.

[0208] Wind speed determines the energy of air flow, thereby affecting the vibration intensity and excitation frequency of structures or equipment. Wind vibration is usually caused by low-speed wind, and when the wind speed is low, the aerodynamic force generated is relatively small, and the vibration amplitude of the structure is also small. However, as the wind speed increases, the aerodynamic effect becomes more significant, which may cause stronger vibration, especially near the resonance frequency of the structure. Wind direction determines the distribution and influence of wind force on the structure. Different wind directions may cause uneven distribution of wind force on the structure, thereby affecting the vibration mode and intensity.

[0209] Wind pressure is proportional to the square of wind speed. Therefore, the greater the wind speed, the more significant the aerodynamic load and excitation force generated by the wind on the object, thereby causing stronger vibration. For wind vibration, it can be assumed that the excitation force generated by the wind speed on the structure is the main factor affecting the vibration intensity. The relationship between vibration intensity and excitation force can be represented by a simplified linear model:

[0210] F wind = kv 2 ;

[0211] where k is a constant depending on the shape and surface properties of the vibration monitoring unit structure.

[0212] The influence of wind direction on vibration intensity is more complex, because the wind direction determines the direction of wind force and the distribution of airflow on the surface of the object. The wind direction affects the way the object is subjected to the wind, resulting in different vibration modes. We simplify the model and assume that the angle of the wind direction relative to the windward surface of the object is θ, then the effective pressure of the wind will be affected by the angle of the wind direction. The component of the wind force can be expressed as:

[0213] F effective = F wind *cos(θ);

[0214] where F wind = kv 2 is the total wind excitation, and θ is the angle between the wind direction and the normal line of the surface of the object. When the wind direction is directly opposite to the object, that is, θ is 0 degrees, the influence of the wind force on the object is the largest. When the wind direction is parallel to the surface of the object, that is, θ is 90 degrees, the effective action of the wind force is the smallest. Different wind directions will change the point of action of the wind force on the object, thereby affecting the intensity and mode of vibration.

[0215] Specifically, in the actual situation of power transmission line monitoring, wind speed and wind direction jointly act on the intensity of the aeolian vibration of the object. In the case of considering wind speed and wind direction, the aeolian vibration correlation model is expressed as:

[0216]

[0217] where X vibration represents the amplitude (intensity) of the aeolian vibration response, A represents a constant related to factors such as the stiffness, mass, and damping of the structure, w n represents the natural frequency of the structure, θ represents the angle between the wind direction and the normal line of the surface of the object, and v represents the wind speed.

[0218] Illustratively, in the working process of the aeolian vibration synchronous monitoring system based on the split deployment, the real-time wind direction data and real-time wind speed data collected are brought into the aeolian vibration correlation model to obtain the amplitude of the aeolian vibration response, and it is judged whether the difference between the obtained aeolian vibration amplitude and the amplitude is within a preset range. If yes, the collected aeolian vibration amplitude is accurate.

[0219] As an implementation mode, the edge intelligent terminal is further used to acquire power information data of the separated vibration sensing unit, process the power information data, real-time wind direction data, and real-time wind speed data through a preset wind speed and wind direction-power sampling model, and acquire and transmit a sampling frequency and a waveform sampling time length parameter to the separated vibration sensing unit.

[0220] Specifically, according to the comparison result of the battery power, the charging current and the preset threshold range, in combination with the comparison result of the wind speed data and the wind direction data and the preset threshold, the corresponding sampling frequency and waveform sampling time length parameters are output.

[0221] For example, when the wind speed is 0, the sampling is not started; when the battery power is greater than 80%, and the wind speed is greater than 0 m / s, the sampling is performed according to the standard sampling frequency and the waveform sampling point number; when the battery power is in the interval of 80%-50%, and the charging current is greater than 100 mA, the wind speed is greater than 0 m / s, the sampling is performed according to the standard sampling frequency and the waveform sampling point number; when the battery power is in the interval of 80%-50%, and the charging current is greater than 50 mA and less than 100 mA, the wind speed is greater than 0 m / s, the sampling frequency is reduced by 10% and the waveform sampling length is reduced by 10%; when the battery power is in the interval of 80%-50%, and the charging current is less than 50 mA, the wind speed is greater than 0 m / s, the sampling frequency is reduced by 20% and the waveform sampling length is reduced by 20%; when the battery power is in the interval of 50%-30%, and the charging current is greater than 100 mA, the wind speed is greater than 0 m / s, the sampling frequency is reduced by 10% and the waveform sampling length is reduced by 10%; when the battery power is in the interval of 50%-30%, and the charging current is greater than 50 mA and less than 100 mA, the wind speed is greater than 0 m / s, the sampling frequency is reduced by 30% and the waveform sampling length is reduced by 30%; when the battery power is in the interval of 10%-30%, and the charging current is greater than 100 mA, the wind speed is greater than 0 m / s, the sampling frequency is reduced by 30% and the waveform sampling length is reduced by 30%; when the battery power is in the interval of 10%-30%, and the charging current is greater than 50 mA and less than 100 mA, the wind speed is greater than 0 m / s, the sampling frequency is reduced by 40% and the waveform sampling length is reduced by 40%; when the battery power is in the interval of 10%-20%, and the wind speed is greater than 5 m / s, the sampling is triggered, the sampling frequency is reduced by 40% and the waveform sampling length is reduced by 40%; when the battery power is less than 10%, and the wind speed is greater than 5 m / s, and the wind direction is greater than 30° with the wire, the sampling is triggered, the sampling frequency is reduced by 40% and the waveform sampling length is reduced by 40%.

[0222] The description of each of the above embodiments focuses on different aspects, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0223] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A micro-wind vibration synchronous monitoring system based on split-type deployment, characterized in that, include: Vibration sensors, wherein multiple vibration sensors are respectively installed on the three-phase conductors of the power line, and each vibration sensor includes a separate vibration sensing unit respectively installed at both ends of the tension clamp outlet, for collecting acceleration data at both ends of the tension clamp outlet; A weather sensing unit, which is used to collect real-time wind speed data and real-time wind direction data; An edge intelligent terminal is used to acquire multiple sets of acceleration data and perform differential operations on them to obtain corresponding continuous-time acceleration signals; based on the continuous-time acceleration signals, adaptive integral error removal is performed in combination with data characteristics to obtain corresponding displacement signals; combined with a leakage compensation model, power compensation is performed on the displacement signals to determine the corresponding aerodynamic vibration amplitude. Real-time wind speed and real-time wind direction data are acquired, and the amplitude of the wind vibration is checked and calibrated by combining the preset wind vibration correlation model. The process of combining the leakage compensation model to perform power compensation on the displacement signal and determine the corresponding aerobatic vibration amplitude includes: Perform a Fourier transform on the displacement signal to determine the frequency and amplitude of the displacement signal; The amplitude is compensated based on the frequency and a preset leakage compensation model. The corresponding aerodynamic vibration amplitude is determined according to the main frequency and the leakage point frequency. Furthermore, constructing the leakage compensation model includes: Based on the frequency offset between the main frequency and the frequencies of adjacent leakage points, a polynomial model of vibration amplitude attenuation coefficient is constructed. Based on the energy distribution at the dominant frequency and the leakage point, a leakage compensation model is constructed. The leakage compensation model is combined with the polynomial model of vibration amplitude attenuation coefficient. The leakage compensation factor is determined and the leakage compensation model is updated by using frequency shift. Furthermore, the leakage compensation model is expressed as: ； ； In the formula, Indicates the amplitude of wind vibration. Indicates amplitude. f 0 indicates the main frequency. P 0 indicates the power at the main frequency. Indicates the first i One frequency offset, k i Indicates the first i One leakage compensation factor.

2. The micro-wind vibration synchronous monitoring system based on split deployment as described in claim 1, characterized in that, Based on the continuous-time acceleration signal, adaptive integration error removal is performed using data characteristics to obtain the corresponding displacement signals, including: Calculate the standard deviation and average value of the acceleration continuous-time signal, and preprocess the acceleration continuous-time signal based on the velocity continuous-time signal, the standard deviation, and the average value; The preprocessed acceleration continuous-time signal is subjected to debiasing and integration operations in sequence to obtain a velocity time series; the velocity time series is subjected to debiasing and integration operations in sequence to obtain an initial displacement signal. The displacement signal is obtained by fitting the Gaussian column principal elimination method to eliminate the signal trend in the initial displacement signal. Furthermore, by fitting using Gaussian partial principal elimination, trend signals in the initial displacement signal are eliminated, and the resulting displacement signal includes: A polynomial trend model of the initial displacement signal is constructed, and the polynomial trend model is fitted by Gaussian column principal elimination method to obtain the trend signal sequence; The displacement signal is determined based on the difference between the initial displacement signal and the trend signal sequence.

3. The micro-wind vibration synchronous monitoring system based on split deployment as described in claim 1, characterized in that, The specific steps for constructing the micro-wind vibration correlation model are as follows: using the angle between wind speed, wind direction and power lines, as well as the amplitude of the micro-wind vibration response, to construct the micro-wind vibration correlation model.

4. The micro-wind vibration synchronous monitoring system based on split deployment as described in claim 1, characterized in that, The edge intelligent terminal is also used to acquire the power information data of the separate vibration sensing unit, and to process the power information data, real-time wind direction data and real-time wind speed data through a preset wind speed, wind direction and power-sampling model, to acquire the sampling frequency and waveform sampling duration parameters and transmit them to the separate vibration sensing unit. Furthermore, the battery information data, real-time wind direction data, and real-time wind speed data are processed through a preset wind speed, wind direction, and power consumption sampling model to obtain the sampling frequency and waveform sampling duration parameters. Specifically, based on the comparison results of battery power and charging current with preset threshold ranges, and combined with the comparison results of wind speed data and wind direction data with preset thresholds, the corresponding sampling frequency and waveform sampling duration parameters are output.

5. A method for synchronous monitoring of micro-wind vibration based on split-type deployment, characterized in that, include: Multiple sets of acceleration data are acquired and differential operations are performed on them to obtain the corresponding continuous-time acceleration signals; wherein, the acceleration data is acquired by separate vibration sensing units set at both ends of the tension clamp outlet; Based on the acceleration continuous-time signal, adaptive integral error removal is performed in combination with data characteristics to obtain the corresponding displacement signals; combined with the leakage compensation model, power compensation is performed on the displacement signals to determine the corresponding aerodynamic vibration amplitude. Real-time wind speed data and real-time wind direction data are acquired, and the amplitude of the wind vibration is checked and calibrated by combining the preset wind vibration correlation model; wherein, the real-time wind speed data and the real-time wind direction data are collected by a meteorological sensing unit; The process of combining the leakage compensation model to perform power compensation on the displacement signal and determine the corresponding aerobatic vibration amplitude includes: Perform a Fourier transform on the displacement signal to determine the frequency and amplitude of the displacement signal; The amplitude is compensated based on the frequency and a preset leakage compensation model to determine the corresponding aerodynamic vibration amplitude. Furthermore, constructing the leakage compensation model includes: Based on the frequency offset between the main frequency and the frequencies of adjacent leakage points, a polynomial model of vibration amplitude attenuation coefficient is constructed. Based on the energy distribution at the dominant frequency and the leakage point, a leakage compensation model is constructed. The leakage compensation model is combined with the polynomial model of vibration amplitude attenuation coefficient. The leakage compensation factor is determined and the leakage compensation model is updated by using frequency shift. Furthermore, the leakage compensation model is expressed as: ； ； In the formula, Indicates the amplitude of wind vibration. Indicates amplitude. f 0 indicates the main frequency. P 0 indicates the power at the main frequency. Indicates the first i One frequency offset, k i Indicates the first i One leakage compensation factor.

6. The method for synchronous monitoring of micro-wind vibration based on split deployment as described in claim 5, characterized in that, Based on the continuous-time acceleration signal, adaptive integration error removal is performed using data characteristics to obtain the corresponding displacement signals, including: Calculate the standard deviation and average value of the acceleration continuous-time signal, and preprocess the acceleration continuous-time signal based on the velocity continuous-time signal, the standard deviation, and the average value; The preprocessed acceleration continuous-time signal is subjected to debiasing and integration operations in sequence to obtain a velocity time series; the velocity time series is subjected to debiasing and integration operations in sequence to obtain an initial displacement signal. The displacement signal is obtained by fitting the Gaussian column principal elimination method to eliminate the signal trend in the initial displacement signal. Furthermore, by fitting using Gaussian partial principal elimination, trend signals in the initial displacement signal are eliminated, and the resulting displacement signal includes: A polynomial trend model of the initial displacement signal is constructed, and the polynomial trend model is fitted by Gaussian column principal elimination method to obtain the trend signal sequence; The displacement signal is determined based on the difference between the initial displacement signal and the trend signal sequence.

7. The method for synchronous monitoring of micro-wind vibration based on split deployment as described in claim 5, characterized in that, The specific steps for constructing the micro-wind vibration correlation model are as follows: using the angle between wind speed, wind direction and power lines, as well as the amplitude of the micro-wind vibration response, to construct the micro-wind vibration correlation model.

8. The method for synchronous monitoring of micro-wind vibration based on split deployment as described in claim 5, characterized in that, Also includes: The electrical information data of the separate vibration sensing unit is acquired. The electrical information data, real-time wind direction data and real-time wind speed data are processed by the preset wind speed, wind direction and electrical power sampling model to obtain the sampling frequency and waveform sampling duration parameters and transmit them to the separate vibration sensing unit. Furthermore, the battery information data, real-time wind direction data, and real-time wind speed data are processed through a preset wind speed, wind direction, and power consumption sampling model to obtain the sampling frequency and waveform sampling duration parameters. Specifically, based on the comparison results of battery power and charging current with preset threshold ranges, and combined with the comparison results of wind speed data and wind direction data with preset thresholds, the corresponding sampling frequency and waveform sampling duration parameters are output.

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