A monitoring system and method for overhead power line inspection

Abstract

The present application belongs to the technical field of overhead transmission line inspection, and provides a monitoring system and method for overhead transmission line inspection, specifically comprising the following steps: obtaining inclination data and wind speed data of an overhead transmission line tower, determining abnormal characteristics of the inclination data of the tower, if it is determined that there are abnormal characteristics, performing scene matching analysis on the wind speed data, if it is matched as a weak wind scene, triggering a confusion scene discrimination process; the confusion scene discrimination process specifically comprises: performing state analysis on the fluctuation amplitude of the inclination data through a short-term window. More accurate distinction between weak wind low-frequency vibration and two types of easily confused risks of basic real settlement, optimization of the misjudgment problem caused by frequency domain overlap, improvement of the inclination risk determination accuracy of the tower, and reduction of the dispersion of operation and maintenance resources, reduction of invalid inspection caused by vibration misjudgment settlement, and reduction of the overhead transmission line inspection and operation and maintenance cost.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of overhead transmission line inspection, and in particular relates to a monitoring system and method for overhead transmission line inspection. BACKGROUND

[0002] As an important part of the power system, the safe operation of overhead transmission lines is directly related to the stability and reliability of the power grid. Towers are the core support structure of overhead transmission lines, and tower tilt is one of the common structural safety problems of overhead transmission lines, which may be caused by instantaneous vibration (such as dynamic load caused by strong wind, earthquake, etc.) or static deformation (such as long-term cumulative problems such as foundation settlement and tower body corrosion, etc.).

[0003] Currently, tower tilt of overhead transmission lines is the focus of inspection and monitoring. Tower tilt monitoring is mainly through sensor monitoring, and then converting the monitoring data into frequency domain signals to determine the tilt cause through frequency components (such as high frequency corresponding to instantaneous vibration and low frequency corresponding to static deformation). Based on the preset fixed tilt threshold, the threshold is dynamically adjusted in combination with environmental parameters (such as wind speed and temperature) to reduce false positives.

[0004] However, in a continuous weak wind environment, continuous weak wind may cause low-frequency vibration of the tower, which is close to the frequency domain of the slow settlement of the foundation, and is easy to cause confusion. The sensor may misjudge the low-frequency vibration as static deformation, triggering false positives (such as no wind day to focus on checking the settlement, but weak wind day to cause misjudgment due to vibration interference), and if the vibration is misjudged as real settlement, it will increase the consumption of manpower and material resources for inspection and operation and maintenance, and if the real settlement is misjudged as vibration, it may cause the continuous accumulation of settlement, eventually leading to tower tilt exceeding the limit, tower collapse or line breakage.

[0005] Therefore, the present application provides a monitoring system and method for overhead transmission line inspection. SUMMARY

[0006] In order to make up for the deficiencies of the prior art and solve at least one technical problem raised in the background art.

[0007] The technical solution adopted by the present application to solve its technical problems is: a monitoring method for overhead transmission line inspection, specifically comprising the following steps:

[0008] Obtaining the tilt data and wind speed data of the tower of the overhead transmission line, determining the abnormal characteristics of the tilt data of the tower, if it is determined that there are abnormal characteristics, then performing scene matching analysis on the wind speed data, if it is matched as a weak wind scene, then triggering the confusion scene discrimination process;

[0009] The confusion scene discrimination process specifically includes:

[0010] The state analysis of fluctuation amplitude of the tilt data is performed through a short-term window, and linear trend analysis of the tilt data is performed through a long-term window to identify dynamic and static states and identify whether there is an accumulative trend, and the analysis results of the short-term and long-term windows are comprehensively output to output preliminary determination results, including: preliminary determination to be verified;

[0011] Based on the preliminary determination results, the correlation analysis of wind speed and tilt fluctuation is performed, and based on the correlation degree, the correlation verification results are output, including: boundary correlation to be supplemented;

[0012] For the case that the output result is preliminary determination to be verified or boundary correlation to be supplemented, the low-frequency tilt component in the tilt data is extracted, and wavelet packet decomposition is performed to analyze the energy distribution and output the frequency domain determination result;

[0013] The final determination result is obtained by combining the preliminary determination result, the correlation verification result and the frequency domain determination result, and the warning push of the overhead transmission line inspection and monitoring is performed according to the final determination result.

[0014] A monitoring system for overhead transmission line inspection, comprising:

[0015] A trigger condition analysis module, a time sequence trend judgment module, a correlation verification module, a frequency domain feature supplementary analysis module and a comprehensive decision output module.

[0016] The beneficial effects of the present application are as follows:

[0017] The present application constructs a time domain, correlation and frequency domain triple verification closed loop, first locks the weak wind confusion scene through the trigger condition analysis module, then preliminarily classifies the dynamic and static states and trend accumulation by the time sequence trend judgment module, verifies the driving relationship from the wind-caused cause and effect by the correlation verification module, and finally calibrates from the physical nature by the frequency domain feature supplementary analysis module, so as to more accurately distinguish the two types of easily confused risks of weak wind low-frequency vibration and basic real settlement, optimize the misjudgment problem caused by frequency domain overlap, improve the accuracy of tower tilt risk judgment, and can reduce the dispersion of operation and maintenance resources, reduce the invalid inspection caused by vibration misjudgment settlement, and reduce the overhead transmission line inspection and maintenance cost. BRIEF DESCRIPTION OF DRAWINGS

[0018] The present application will be further described below with reference to the accompanying drawings.

[0019] Figure 1 is an architecture diagram of a monitoring system for overhead transmission line inspection according to the present application;

[0020] Figure 2 is a step flowchart of a monitoring method for overhead transmission line inspection according to the present application. DETAILED DESCRIPTION

[0021] In order to make the technical means, creative features, purposes and effects of the present application easy to understand, the present application is further described below in conjunction with specific embodiments.

[0022] Embodiment 1:

[0023] Please refer to Figure 2 The monitoring method for overhead power transmission line inspection according to the embodiments of the present application specifically comprises the following steps:

[0024] Step S10: Obtain the inclination data and wind speed data of the overhead power transmission line tower, perform abnormal feature determination on the inclination data of the tower, if it is determined that there is an abnormal feature, perform scene matching analysis on the wind speed data, if it is matched as a weak wind scene, trigger the confusion scene discrimination process;

[0025] In this step, the inclination data of the tower is inclination angle data;

[0026] The monitoring data of the tower is obtained in real time by a dual-axis inclination sensor deployed at the top of the tower or at a key position of the tower body, and the sampling frequency of the dual-axis inclination sensor needs to be set to be not less than 1 Hz, so that the subtle changes in the low frequency band (0.1-1 Hz) can be captured.

[0027] The wind speed data is obtained after the real-time acquisition value of a plurality of wind speed sensors deployed around the tower is processed by mean value, and the sampling frequency of the wind speed sensor needs to be set to be not less than 0.5 Hz, and the data acquisition time stamp needs to be recorded, so that the wind speed data can be time-aligned with the inclination data in subsequent analysis and identification.

[0028] In this step, the process of performing abnormal feature determination on the inclination data of the tower is as follows:

[0029] Extract the inclination data in the low frequency band (0.1-1 Hz) and perform signal separation;

[0030] Specifically, a Butterworth low-pass filter (cutoff frequency 1 Hz) and a high-pass filter (cutoff frequency 0.1 Hz) are combined to perform band-pass filtering on the inclination data, completely filtering the direct current drift (such as baseline shift caused by slow temperature change) below 0.1 Hz and the high-frequency vibration (such as strong wind, equipment mechanical vibration) above 1 Hz, and only retaining the low-frequency signal of 0.1-1 Hz. The inclination data in the low frequency band is the core overlapping frequency band of weak wind vibration and real settlement, and is also the confusion interval that needs to be analyzed;

[0031] Perform band-pass filtering on the inclination angle data of the X-axis (in-line direction) and the Y-axis (perpendicular to the line direction) respectively to obtain the X-axis low-frequency inclination angle sequence and the Y-axis low-frequency inclination angle sequence;

[0032] Based on the X-axis low-frequency tilt angle sequence and the Y-axis low-frequency tilt angle sequence, a preset sliding time window (the size of the sliding time window can be set to 5 minutes) is set;

[0033] For each sliding time window, the difference between the maximum tilt angle and the minimum tilt angle corresponding to the X-axis and the Y-axis is calculated as the X-axis low-frequency tilt amplitude and the Y-axis low-frequency tilt amplitude, respectively.

[0034] If the X-axis low-frequency tilt amplitude is greater than the low-frequency tilt amplitude limit or the Y-axis low-frequency tilt amplitude is greater than the low-frequency tilt amplitude limit, the sliding time window is marked as an amplitude abnormal period.

[0035] If the X-axis low-frequency tilt amplitude is less than or equal to the corresponding low-frequency tilt amplitude limit and the Y-axis low-frequency tilt amplitude is less than or equal to the corresponding low-frequency tilt amplitude limit, the sliding time window is marked as an amplitude normal period.

[0036] The low-frequency tilt amplitude limit corresponding to the X-axis and the Y-axis is set by the skilled person in the art based on the core goal of distinguishing 0.1-1Hz low-frequency weak wind low-frequency vibration from the actual settlement, combined with the normal fluctuation characteristics of the same type of tower in a risk-free state, the structural stiffness and stress difference of the X-axis (in-line direction) and the Y-axis (perpendicular to the line direction), and combined with historical working experience summary.

[0037] The abnormal duration of the continuous sliding time window is counted, wherein when counting the abnormal duration, the time stamp when the X-axis or Y-axis low-frequency tilt amplitude > low-frequency tilt amplitude limit is counted as the start time, and when the low-frequency tilt amplitude of the X-axis and the Y-axis is ≤ low-frequency tilt amplitude limit in two consecutive sliding time windows, it is recorded as the abnormal termination time stamp.

[0038] If the abnormal duration is less than or equal to the abnormal duration limit, it means that the abnormal state is a short-term disturbance and does not have a lasting impact, and it is determined as a transient abnormal disturbance, and the abnormal determination process is terminated.

[0039] If the abnormal duration is greater than the abnormal duration limit, it means that the tilt abnormal state is not a transient disturbance, but an abnormal feature with stability, and it is determined that there is an abnormal feature, and the wind speed data is subjected to scene matching analysis.

[0040] The abnormal duration limit is set by the skilled person in the art according to the maximum duration of common transient disturbance of overhead transmission line towers, to ensure that transient disturbance and stable abnormality that needs to be concerned can be effectively distinguished.

[0041] In this step, the process of scene matching analysis of wind speed data is as follows:

[0042] The wind speed data is pre-processed, the wind speed data is aligned with the same time node of multiple groups of wind speed values according to the same time stamp as the tilt data, and the average wind speed sequence is obtained by taking the arithmetic average value of the same time node of multiple groups of wind speed values;

[0043] If the average wind speed is not in the preset weak wind interval, it is marked as a wind speed interval mismatch, and it is determined as a non-weak wind scene;

[0044] If the average wind speed is in the preset weak wind interval, it is marked as a wind speed interval match, and the matching duration of the wind speed interval match is counted;

[0045] The matching duration is calculated by ratio calculation with the sliding window duration, and the matching duration ratio is obtained;

[0046] The coefficient of variation of the average wind speed in the matching duration (i.e. the ratio of the standard deviation of the average wind speed to the average wind speed) is calculated; the coefficient of variation is converted into a stability coefficient, i.e. stability coefficient = 1-variation coefficient;

[0047] The matching duration ratio and the stability coefficient are multiplied to obtain the scene comprehensive judgment value;

[0048] It needs to be explained that the scene comprehensive judgment value is the product of the matching duration ratio and the stability coefficient, which is essentially a quantitative representation of the comprehensive degree of wind speed data satisfying the requirements of being continuously in the weak wind interval and the wind speed being stable without large fluctuations, providing a basis for determining the weak wind scene match;

[0049] For the matching duration ratio, its physical meaning is that in the set analysis period, the time ratio of the wind speed meeting the weak wind interval requirement is closer to 1, indicating that the wind speed is continuously in the weak wind range in the analysis period, and is more consistent with the physical characteristics of continuous weak wind; the ratio close to 0 indicates that the wind speed exceeds the weak wind interval most of the time, and does not have the continuity of the weak wind scene;

[0050] For the stability coefficient, its physical meaning is the smoothness ratio of the wind speed in the weak wind interval, and the closer the coefficient is to 1, the more stable the wind speed in the weak wind interval is, which is a continuous and stable weak wind; the coefficient close to 0 indicates that the wind speed in the weak wind interval fluctuates violently, which is an unstable weak wind and does not meet the wind condition characteristics of the weak wind vibration and real settlement confusion scene;

[0051] If the scene comprehensive judgment value is greater than the judgment threshold, it is determined as a weak wind scene, and the confusion scene discrimination process is triggered;

[0052] If the scene comprehensive judgment value is less than or equal to the judgment threshold, it is determined as a non-weak wind scene;

[0053] It should be noted that the preset weak wind range can be fine-tuned by a person skilled in the art according to the climate characteristics of the area where the tower is located, for example, it can be widened to 1.5-6 m / s in coastal areas, and it can be tightened to 0.8-4 m / s in inland windless areas; similarly, for the scene comprehensive judgment value corresponding to the area, there is a corresponding threshold value;

[0054] The judgment threshold is set by a person skilled in the art in combination with the climate characteristics of the area where the tower is located, the wind speed fluctuation law and the preset weak wind range, and historical working experience;

[0055] Step S10 is the entry of scene screening, and the core function is to accurately lock the weak wind confusion scene that needs to be analyzed, and to reduce the invalid analysis of non-key scenes. It is through the deployment of a dual-axis tilt sensor with a sampling frequency not less than 1 Hz on the top of the tower or a key position, capturing the subtle changes of the tilt in the 0.1-1 Hz low frequency band, and at the same time, the mean value processing and time stamp alignment of the wind speed data collected by the multiple wind speed sensors around the tower with a sampling frequency not less than 0.5 Hz, to ensure the high quality and time consistency of the input data. Then, Butterworth band-pass filtering is used to filter out temperature drift, high-frequency vibration and other interference, the X-axis (along the line) and Y-axis (perpendicular to the line) tilt amplitude are calculated through a sliding window, and the abnormal duration is determined (excluding short-term transient disturbance) to identify the tilt abnormality characteristics with stability. Finally, the scene comprehensive judgment value is used to quantitatively screen the "continuous and stable weak wind scene" (the weak wind range can be fine-tuned according to the regional climate), accurately locate the core scene of weak wind vibration and real subsidence confusion, and provide accurate analysis objects for the subsequent confusion scene identification process, reducing redundant calculation under non-weak wind scenes.

[0056] The confusion scene identification process specifically includes:

[0057] Step S20: Perform state analysis on the fluctuation amplitude of the tilt data through a short-term window to identify dynamic and static states, and then perform linear trend analysis on the tilt data through a long-term window to identify whether there is an accumulative trend. The analysis results of the short-term window and the long-term window are combined to output a preliminary determination result;

[0058] The preliminary determination result includes: suspected weak wind vibration, suspected real subsidence, and preliminary determination to be verified.

[0059] In this step, for the X-axis low-frequency tilt angle sequence and the Y-axis low-frequency tilt angle sequence, the process of short-term window analysis is as follows:

[0060] A preset short-term window length is used to calculate the difference between the maximum and minimum tilt angles of the X-axis and Y-axis respectively within the short-term window, as the X-axis low-frequency tilt fluctuation amplitude and the Y-axis low-frequency tilt fluctuation amplitude;

[0061] If the X-axis low-frequency tilt fluctuation amplitude is greater than the corresponding fluctuation amplitude limit value or the Y-axis low-frequency tilt fluctuation amplitude is greater than the corresponding fluctuation amplitude limit value, mark the current window as a potential dynamic fluctuation window;

[0062] If the X-axis low-frequency tilt fluctuation amplitude is less than or equal to the fluctuation amplitude limit value and the Y-axis low-frequency tilt fluctuation amplitude is less than or equal to the fluctuation amplitude limit value, mark the current window as a potential static stable window;

[0063] Wherein, the fluctuation amplitude limit value corresponding to the X-axis and Y-axis is set by the person skilled in the art in combination with the fluctuation characteristics of 0.1-1Hz low-frequency tilt data and the structural force difference of the X-axis (in-line direction) and Y-axis (perpendicular to the line direction) to distinguish between potential dynamic fluctuation windows and potential static stable windows;

[0064] In this step, for the X-axis low-frequency tilt angle sequence and the Y-axis low-frequency tilt angle sequence, the process of long-term window analysis is as follows:

[0065] The preset long-term window length (set according to the cumulative rate of true settlement);

[0066] The least square method is used to linearly fit the X-axis low-frequency tilt angle sequence and the Y-axis low-frequency tilt angle sequence respectively;

[0067] Taking time as the independent variable and the X-axis low-frequency tilt angle and the Y-axis low-frequency tilt angle as the dependent variable, the X-axis linear fitting equation and the Y-axis linear fitting equation are constructed, and the corresponding slope values are calculated as the X-axis tilt change rate and the Y-axis tilt change rate;

[0068] The determination coefficients of the linear fitting corresponding to the X-axis linear fitting equation and the Y-axis linear fitting equation are calculated respectively,

[0069] If the determination coefficient is less than the determination coefficient limit value, it means that the linear fitting is unreliable, and it is marked as fitting to be confirmed, which needs to be further verified in combination with the short-term window analysis result;

[0070] Wherein, the determination coefficient limit value is set to distinguish whether the fitting result is reliable to support the accumulation trend judgment, which is set by the person skilled in the art based on historical data;

[0071] If the determination coefficient is greater than or equal to the determination coefficient limit value, it means that the linear fitting is reliable, and the accumulation trend judgment is performed. Since the true settlement has accumulation and the weak wind vibration has no accumulation, the core basis is to determine whether there is an accumulation trend in the long-term window. The process of accumulation trend is as follows:

[0072] If the X-axis tilt change rate is greater than the corresponding change rate limit value or the Y-axis tilt change rate is greater than the corresponding change rate limit value, it means that the tilt angle of one direction changes unidirectionally with time, and it is marked as existing accumulation trend;

[0073] If the X-axis inclination rate of change is less than or equal to the rate of change limit value and the Y-axis inclination rate of change is less than or equal to the rate of change limit value, it indicates that the biaxial inclination angle has no significant unidirectional change over time, the fitted straight line is close to horizontal, and is marked as no cumulative trend;

[0074] wherein the rate of change limit value corresponding to the X-axis and the Y-axis is summarized and set by the skilled person in the art according to the structural stiffness and stress environment difference between the X-axis (in the direction of the line) and the Y-axis (perpendicular to the line of the line), and combined with historical working experience;

[0075] In this step, the process of outputting the preliminary determination result is as follows:

[0076] Since the preset long-term window includes multiple short-term windows, first, for any long-term window, the dominant state of the short-term window in the long-term window needs to be determined;

[0077] The number of short-term windows marked as potential dynamic fluctuations and the number of short-term windows marked as potential static stability in the long-term window are counted respectively;

[0078] If the number of short-term windows marked as potential dynamic fluctuations is greater than the number of short-term windows marked as potential static stability, the potential dynamic fluctuation is taken as the dominant state;

[0079] If the number of short-term windows marked as potential dynamic fluctuations is less than the number of short-term windows marked as potential static stability, the potential static stability is taken as the dominant state;

[0080] If the number of short-term windows marked as potential dynamic fluctuations is equal to the number of short-term windows marked as potential static stability, the state of the last short-term window in the long-term window is taken as the dominant state;

[0081] For the dominant state of the long-term window being potential dynamic fluctuation;

[0082] If the long-term window is marked as no cumulative trend, the preliminary determination result is output as suspected weak wind vibration;

[0083] If the long-term window is marked as having a cumulative trend or fitting to be confirmed, the preliminary determination result is output as preliminary determination to be verified;

[0084] For the dominant state of the long-term window being potential static stability;

[0085] If the biaxial long-term window is marked as no cumulative trend, the preliminary determination result is output as stable state;

[0086] If any axis of the long-term window is marked as no cumulative trend, the preliminary determination result is output as suspected real settlement;

[0087] If at least one axis of the long-term window is marked as fitting to be confirmed, the preliminary determination result is preliminarily determined to be verified.

[0088] It needs to be explained that there is no need to emphasize axis analysis in the dynamic fluctuation scenario, because the determination of dynamic fluctuation is based on the fluctuation characteristics of the X-axis or Y-axis (any axis fluctuation exceeding the limit is marked as dynamic), and the core of weak wind vibration is reciprocating fluctuation without accumulation. As long as the axis of the triggered dynamic meets the non-accumulation feature, whether it is single-axis or double-axis dynamic, it points to vibration; and the axis difference of vibration does not affect the conclusion of the existence of vibration, which is different from the positioning requirement of settlement that needs to determine which axis has accumulation, so there is no need for additional axis analysis.

[0089] The reason for the above determination result is that weak wind vibration is back and forth but the position does not change, real settlement is slowly biased and the position is more and more far away, stable state is neither shaking nor biased, weak wind vibration shows dynamic fluctuation and no accumulation offset, real settlement shows static stability and one-way cumulative offset, and stable state is static stability and no cumulative offset; when the long-term window is dominated by dynamic fluctuation, if there is no cumulative offset, it meets the vibration characteristics and is determined as suspected weak wind vibration; if there is accumulation or the fitting is unreliable, it is contradictory to the non-accumulation nature of vibration and needs to be verified; when the long-term window is dominated by static stability, if both axes have no accumulation, it is determined as stable state; if any axis has accumulation, it meets the settlement characteristics and is determined as suspected real settlement; if there is unreliable fitting, it needs to be excluded and confirmed because static data should be reliably fitted.

[0090] Step S20 classifies in time domain, provides preliminary determination basis for risk type from dynamic state and cumulative trend, which calculates the low-frequency tilt fluctuation amplitude of X-axis and Y-axis through a pre-set short-term window, distinguishes "potential dynamic fluctuation window" (pointing to weak wind vibration) and "potential static stability window" (pointing to real settlement) according to whether the fluctuation exceeds the limit, realizes dynamic state recognition; at the same time, a long-term window adaptive to real settlement accumulation rate is pre-set, least square method is used to linearly fit the low-frequency tilt sequence of double axes, and the tilt change rate (judging whether it is one-way accumulation) and the determination coefficient (verifying fitting reliability) are used to identify the cumulative trend; finally, the dominant dynamic state in the long-term window is counted, the preliminary determination result is output combined with the cumulative trend, and the time domain preliminary distinction of the two types of risks is effectively realized, providing direction guidance for subsequent correlation verification.

[0091] Step S30: Based on the preliminary determination result, the correlation analysis of wind speed and tilt fluctuation is carried out, and the correlation verification result is output based on the correlation degree.

[0092] Among them, the correlation verification result includes: weak wind vibration characteristic matching, real settlement characteristic matching, and boundary correlation to be supplemented.

[0093] In this step, the process of correlation analysis of wind speed and inclination fluctuation is as follows:

[0094] Based on the preliminary determination result output in step S20, the following analysis is performed according to the core logic that weak wind vibration is driven by wind load (strong correlation between wind speed and inclination fluctuation) and real settlement is caused by foundation deformation (no correlation between wind speed and inclination fluctuation);

[0095] Extract the time interval corresponding to the long-term window, and extract the wind speed sub-sequence in the time interval from the pre-processed average wind speed sequence;

[0096] At the same time, extract the X-axis low-frequency inclination fluctuation sub-sequence and Y-axis low-frequency inclination fluctuation sub-sequence in the same time interval;

[0097] Calculate the Pearson correlation coefficient of the wind speed sub-sequence and the X-axis low-frequency inclination fluctuation sub-sequence, and take the absolute value as the X-axis correlation coefficient;

[0098] Calculate the Pearson correlation coefficient of the wind speed sub-sequence and the Y-axis low-frequency inclination fluctuation sub-sequence, and take the absolute value as the Y-axis correlation coefficient;

[0099] Since weak wind acts on the tower to produce a small time delay (usually 1-3 seconds), i.e. 1-3 seconds after the wind speed changes, the tower inclination fluctuation is triggered;

[0100] Calculate the X-axis lag time by cross-correlation analysis of the wind speed sequence and the X-axis low-frequency inclination fluctuation sub-sequence, which is the time difference corresponding to the maximum X-axis correlation coefficient;

[0101] Calculate the Y-axis lag time by cross-correlation analysis of the wind speed sequence and the Y-axis low-frequency inclination fluctuation sub-sequence, which is the time difference corresponding to the maximum Y-axis correlation coefficient;

[0102] Identify whether the X-axis lag time and the Y-axis lag time are both within the lag time interval (1-3 seconds);

[0103] If either the X-axis lag time or the Y-axis lag time is not within the lag time interval, the time verification is not met, and the correlation verification result is real settlement feature matching;

[0104] If the X-axis lag time and the Y-axis lag time are both within the lag time interval, the time verification is met, and the correlation degree verification is performed;

[0105] If either the X-axis correlation coefficient or the Y-axis correlation coefficient is greater than the correlation coefficient threshold, it means that the wind speed and the inclination fluctuation are strongly correlated, which meets the driving logic of weak wind vibration, and the correlation verification result is weak wind vibration feature matching;

[0106] If there are X-axis correlation coefficients and Y-axis correlation coefficients less than or equal to the minimum value of the threshold critical interval, it indicates that the wind speed and the inclination fluctuation are related, and the correlation verification result is output as a real settlement feature matching;

[0107] If there are X-axis correlation coefficients and Y-axis correlation coefficients less than or equal to the correlation coefficient threshold, and there are X-axis correlation coefficients and Y-axis correlation coefficients in the threshold critical interval, the wind speed and the inclination fluctuation are not clear, and the correlation verification result is output as a boundary correlation to be supplemented;

[0108] The threshold critical interval is the interval range from the difference between the correlation coefficient threshold and 0.1 to the correlation coefficient threshold. The difference is calculated with 0.1 to set the critical interval, which is based on the reasonable fluctuation range of the correlation degree in engineering practice, to ensure that the critical interval can cover the atypical interference scenario.

[0109] The correlation coefficient threshold is set by the person skilled in the art according to the physical logic of the weak wind load driving rod tower inclination fluctuation combined with historical data;

[0110] It needs to be explained that the logic basis of the above result output is that the core is around the weak wind vibration wind-induced driving, which needs to meet the 1-3 second physical lag and strong correlation at the same time, the essential difference between the real settlement basic deformation driving and the wind speed without causality / weak correlation, combined with the actual situation that sensor noise in engineering can easily lead to a ±0.1 fluctuation of the correlation degree, the time verification does not meet, indicating that there is no wind-induced causality, which meets the settlement characteristics, the time verification meets and the correlation degree of any axis is greater than the threshold, which indicates that there is causality and strong correlation, which meets the vibration characteristics, and the time verification meets but the correlation degree is less than or equal to the correlation coefficient threshold; if there is a critical interval (disturbed correlation is ambiguous), it needs to be supplemented; if they are all less than the correlation coefficient threshold-0.1 (weak correlation), it meets the characteristics of no strong correlation of settlement.

[0111] Step S30 verifies the risk driving relationship from the "wind-induced causality logic" level, supplements the deficiency of the time trend judgment, excludes the interference of non-wind-induced factors (such as foundation settlement) through the essential logic of wind-induced driving, further narrows the risk judgment range, and improves the reliability of the preliminary judgment result.

[0112] Step S40: For the case that the output result is preliminary judgment to be verified or boundary correlation to be supplemented, extract the low-frequency inclination component in the inclination data, perform wavelet packet decomposition on the low-frequency inclination component, analyze the energy distribution law, and output the frequency domain judgment result based on the energy distribution judgment;

[0113] In this step, the process of supplementary analysis for the output result of preliminary judgment to be verified or boundary correlation to be supplemented is as follows:

[0114] The output result of preliminary judgment to be verified or boundary correlation to be supplemented is taken as the to-be-verified output result;

[0115] extract the long-term window time interval corresponding to the output result to be verified, and extract the X-axis tilt angle data and Y-axis tilt angle data in the corresponding time interval as the to-be-analyzed X-axis low-frequency sequence and the to-be-analyzed Y-axis low-frequency sequence;

[0116] Set the wavelet packet decomposition parameters, use the commonly used db4 wavelet basis for low-frequency signal analysis, set the decomposition layer number to 4, and after decomposition, the 0.1-1Hz frequency band can be subdivided into 16 sub-bands, which can accurately distinguish the frequency domain difference between weak wind vibration and real settlement;

[0117] Perform 4-layer wavelet packet decomposition on the to-be-analyzed X-axis low-frequency sequence and the to-be-analyzed Y-axis low-frequency sequence respectively to obtain the wavelet packet coefficients of each sub-band;

[0118] Calculate the energy value of each sub-band through the energy calculation formula, and then calculate the total energy (sum of energy of all sub-bands) of the X-axis and the Y-axis respectively;

[0119] The energy calculation formula is: the energy of a certain sub-band is the sum of squares of all wavelet packet coefficients in the frequency band;

[0120] Calculate the proportion of the energy of each sub-band of the X-axis in the total energy to obtain the X-axis frequency energy proportion distribution table;

[0121] Calculate the proportion of the energy of each sub-band of the Y-axis in the total energy to obtain the Y-axis frequency energy proportion distribution table;

[0122] Extract the maximum value in the X-axis frequency energy proportion distribution table and the Y-axis frequency energy proportion distribution table respectively as the X-axis maximum energy proportion and the Y-axis maximum energy proportion;

[0123] If the X-axis maximum energy proportion and the Y-axis maximum energy proportion are both greater than the high energy proportion threshold, it means that the energy of the two axes is highly concentrated, and the frequency domain determination result is real settlement frequency domain feature matching;

[0124] The high energy proportion threshold is set by a person skilled in the art based on historical data and the structure stress of the X-axis and the Y-axis;

[0125] Wherein, under the condition of fixed total energy, the higher the single-band proportion, the more energy is concentrated in that frequency band. When the maximum sub-band energy proportion of the X-axis and the Y-axis both exceeds the high energy proportion threshold, it means that the tilt data energy of the two axes is not dispersed in multiple sub-bands, but mainly concentrated in a few or even a single sub-band, so it is determined that the energy is highly concentrated;

[0126] If any one of the X-axis maximum energy proportion and the Y-axis maximum energy proportion is less than or equal to the high energy proportion threshold, it means that the energy of at least one axis is dispersed in multiple sub-bands, and the frequency domain determination result is weak wind vibration frequency domain feature matching.

[0127] Step S40 is a "physical essence calibrator", which is used to preliminarily determine the ambiguous scene to be verified or the boundary to be supplemented, to realize accurate distinction from the aspect of energy distribution law, to solve the ambiguity in time domain and correlation analysis by relying on the physical essence difference of energy distribution, and to provide final calibration basis for comprehensive decision-making.

[0128] Step S50: combining the preliminary judgment result, the correlation verification result and the frequency domain judgment result, obtaining the final judgment result, and executing the early warning push of the overhead transmission line inspection and monitoring according to the final judgment result, which is used to distinguish the weak wind low frequency vibration and the foundation real settlement of the overhead transmission line tower, reduce the invalid operation and maintenance resource consumption caused by the vibration misjudgment settlement, reduce the tower tilt overrun, tower collapse or wire breakage accident caused by the settlement misjudgment vibration, provide data-driven decision support for the structural safety inspection and monitoring of the overhead transmission line, and ensure the stability and reliability of the power grid operation;

[0129] In this step, if the output result is suspected weak wind vibration and weak wind vibration feature matching, the final judgment result is to confirm the weak wind low frequency vibration of the overhead transmission line tower;

[0130] If the output result is suspected real settlement and real settlement feature matching, the final judgment result is to confirm the foundation real settlement of the overhead transmission line tower;

[0131] If the output result is preliminary judgment to be verified or boundary correlation to be supplemented, the frequency domain judgment result is determined, if the output frequency domain judgment result is real settlement frequency domain feature matching, the final judgment result is to confirm the foundation real settlement of the overhead transmission line tower, and if the output frequency domain judgment result is weak wind vibration frequency domain feature matching, the final judgment result is to confirm the weak wind low frequency vibration of the overhead transmission line tower;

[0132] If the output result is one of the two contradictory scene results, suspected weak wind vibration and real settlement feature matching, suspected real settlement and weak wind vibration feature matching, return to step S40 for analysis;

[0133] Extract the X-axis low frequency tilt angle data and Y-axis low frequency tilt angle data in the long-term window corresponding to the contradictory scene, perform frequency domain judgment (the process of step S40), determine according to the frequency domain judgment result, if the output frequency domain judgment result is real settlement frequency domain feature matching, the final judgment result is to confirm the foundation real settlement of the overhead transmission line tower, and if the output frequency domain judgment result is weak wind vibration frequency domain feature matching, the final judgment result is to confirm the weak wind low frequency vibration of the overhead transmission line tower.

[0134] If the final judgment result is to confirm the weak wind low frequency vibration of the overhead transmission line tower, the early warning push strategy includes but is not limited to: low risk early warning;

[0135] Push timing: complete the push within 10 seconds after the final determination result is generated;

[0136] Push terminal: overhead transmission line operation and maintenance monitoring platform (popup reminder), operation and maintenance personnel mobile terminal APP (message notification);

[0137] If the final determination result is to confirm the real settlement of the overhead transmission line tower foundation, the early warning push strategy includes but is not limited to: high-risk early warning;

[0138] Push timing: complete the push within 5 seconds after the final determination result is generated, and trigger the mobile terminal APP voice reminder at the same time;

[0139] Push terminal: operation and maintenance monitoring platform (top popup and sound and light alarm), operation and maintenance personnel mobile terminal APP (voice and vibration reminder), operation and maintenance management responsible person WeChat / short message (emergency notification).

[0140] The complete closed loop formed by each step finally realizes three core comprehensive effects: first, accurately distinguish risks, solve the misjudgment problem caused by the frequency domain overlap of weak wind low frequency vibration and real foundation settlement through time domain, correlation and frequency domain triple verification, improve the risk judgment accuracy of tower tilt, and reduce "vibration misjudgment settlement" or "settlement misjudgment vibration"; Second, optimize operation and maintenance cost, reduce the consumption of manpower and material resources for invalid on-site inspection for low-risk weak wind vibration, and focus operation and maintenance resources on key hidden dangers to significantly reduce the operation and maintenance cost of overhead transmission line inspection; Third, ensure the safety of power grid, the high-risk early warning of real settlement realizes 5-second fast push, which provides key time for intervention, effectively reduces the tower tilt overrun, tower collapse or broken line accidents caused by settlement accumulation, and the dynamic monitoring of weak wind vibration can also timely respond to changes in wind conditions, providing data-driven decision support for the structural safety of overhead transmission line, and finally ensuring the stability and reliability of power grid operation.

[0141] Embodiment 2:

[0142] Based on the same inventive concept as the monitoring system for overhead transmission line inspection in the foregoing embodiment, as shown in Figure 1 , the present application provides a monitoring method for overhead transmission line inspection, wherein the system specifically comprises:

[0143] Trigger condition analysis module: obtain the inclination data and wind speed data of the overhead transmission line tower, determine the abnormal characteristics of the inclination data of the tower, if it is determined that there are abnormal characteristics, perform scene matching analysis on the wind speed data, if it is matched as a weak wind scene, trigger the confusion scene discrimination process;

[0144] The confusion scene discrimination process specifically includes:

[0145] The time trend judgment module: through the state analysis of fluctuation amplitude of the inclination data by a short-term window, the dynamic and static states are identified, and then the linear trend analysis of the inclination data is performed by a long-term window to identify whether there is an accumulated trend, and the analysis results of the short-term window and the long-term window are combined to output a preliminary determination result;

[0146] The correlation verification module: based on the preliminary determination result, the correlation analysis of the wind speed and the inclination fluctuation is performed, and based on the correlation degree, a correlation verification result is output;

[0147] The frequency domain feature supplementary analysis module: for the case that the output result is a preliminary determination to be verified or a boundary correlation to be supplemented, the low-frequency inclination component in the inclination data is extracted, the low-frequency inclination component is decomposed by a wavelet packet, the energy distribution law is analyzed, and based on the energy distribution determination, a frequency domain determination result is output;

[0148] The comprehensive decision output module: the final determination result is obtained by combining the preliminary determination result, the correlation verification result and the frequency domain determination result, and based on the final determination result, the early warning push of the overhead transmission line inspection and monitoring is performed.

[0149] The above shows and describes the basic principles, main features and advantages of the present application. It should be understood by those skilled in the art that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only to illustrate the principles of the present application, and various changes and improvements can be made without departing from the spirit and scope of the present application, and these changes and improvements all fall within the scope of the present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A monitoring method for overhead power line inspection, characterized in that: Specifically comprising the following steps: Obtaining the inclination data and wind speed data of the overhead transmission line tower, performing abnormal feature judgment on the inclination data of the tower, if it is judged that there is an abnormal feature, performing scene matching analysis on the wind speed data, if it is matched as a weak wind scene, triggering a confusion scene discrimination process; The confusion scene discrimination process specifically comprises: Through a short-term window, the state of the inclination data is analyzed in terms of fluctuation amplitude, and then through a long-term window, the inclination data is analyzed in terms of linear trend, to identify dynamic and static states and identify whether there is an accumulative trend, and the analysis results of the short-term and long-term windows are combined to output a preliminary determination result, including: preliminary determination to be verified; Based on the preliminary determination result, the correlation between wind speed and inclination fluctuation is analyzed, and based on the correlation degree, a correlation verification result is output, including: boundary correlation to be supplemented; For the case where the output result is preliminary determination to be verified or boundary correlation to be supplemented, the low-frequency inclination component in the inclination data is extracted and wavelet packet decomposition is performed, the energy distribution is analyzed, and a frequency domain determination result is output; The preliminary determination result, the correlation verification result and the frequency domain determination result are combined to obtain a final determination result, and the final determination result is used to perform early warning pushing of the overhead transmission line inspection and monitoring.

2. The monitoring method for overhead transmission line inspection according to claim 1, characterized in that: The process of performing abnormal feature judgment on the inclination data of the tower is: The low-frequency inclination data is extracted, signal separation is performed, and the X-axis low-frequency inclination angle sequence and the Y-axis low-frequency inclination angle sequence are obtained; For each sliding time window, the difference between the maximum inclination angle and the minimum inclination angle corresponding to the X-axis is calculated as the X-axis low-frequency inclination amplitude, and the Y-axis low-frequency inclination amplitude is calculated; If the X-axis low-frequency inclination amplitude or the Y-axis low-frequency inclination amplitude is out of limit, it is marked as an amplitude abnormal period; The abnormal duration of the continuous sliding time window is counted, and if the abnormal duration is out of limit, it is judged that there is an abnormal feature.

3. The monitoring method for overhead power transmission line inspection according to claim 1, characterized in that: The process of performing scene matching analysis on the wind speed data is: The wind speed data is preprocessed to obtain an average wind speed sequence; If the average wind speed is in a preset weak wind interval, it is marked as wind speed interval matching, the matching duration is counted, and the ratio of the matching duration to the sliding window duration is calculated to obtain a matching duration ratio; The coefficient of variation of the average wind speed in the matching duration is calculated and converted into a stability coefficient; The matching duration ratio and the stability coefficient are multiplied to obtain a scene comprehensive judgment value; If the scene comprehensive judgment value is out of limit, it is judged as a weak wind scene.

4. The monitoring method for overhead power transmission line inspection according to claim 1, characterized in that: The process of identifying dynamic and static states and identifying whether there is an accumulative trend is: In a preset short-term window, the difference between the maximum inclination angle and the minimum inclination angle corresponding to the X-axis is calculated as the X-axis low-frequency inclination fluctuation amplitude, and the Y-axis low-frequency inclination fluctuation amplitude is calculated; If the X-axis or Y-axis low-frequency inclination fluctuation amplitude is out of limit, it is marked as a potential dynamic fluctuation window, otherwise, it is marked as a potential static stable window; A preset long-term window length is used to perform linear fitting on the X-axis and Y-axis low-frequency inclination angle sequences by least squares method, and the corresponding slope values are calculated as the X-axis and Y-axis inclination change rates; The determination coefficients of the linear fitting of the X-axis and Y-axis linear fitting equations are calculated, If the determination coefficient is less than the determination coefficient limit value, it is marked as fitting to be confirmed; If the determination coefficient is greater than or equal to the determination coefficient limit value, the cumulative trend determination is performed; If the X-axis or Y-axis inclination change rate is out of limit, it is marked as existing cumulative trend, otherwise, it is marked as no cumulative trend.

5. The monitoring method for overhead power transmission line inspection according to claim 4, characterized in that: The process of outputting the preliminary determination result is: The number of windows marked as potential dynamic fluctuation and the number of windows marked as potential static stability in the long-term window are counted respectively, and the size is identified, and the larger one is taken as the dominant state; If the number is equal, the state of the last short-term window in the long-term window is taken as the dominant state; If the dominant state is potential dynamic fluctuation, if the long-term window is marked as no cumulative trend, the preliminary determination result is output as suspected weak wind vibration; if the long-term window is marked as existing cumulative trend or fitting to be confirmed, the preliminary determination result is output as preliminary determination to be verified; If the dominant state is potential static stability, if the long-term window is marked as no cumulative trend in both axes, the preliminary determination result is output as stable state; if the long-term window is marked as no cumulative trend in any axis, the preliminary determination result is output as suspected true settlement; If at least one axis of the long-term window is marked as fitting to be confirmed, the preliminary determination result is output as preliminary determination to be verified.

6. The monitoring method for overhead power transmission line inspection according to claim 1, wherein: The process of correlation analysis of wind speed and inclination fluctuation is: The time interval corresponding to the long-term window is extracted, and the wind speed subsequence, the X-axis low-frequency inclination fluctuation subsequence and the Y-axis low-frequency inclination fluctuation subsequence are obtained; The Pearson correlation coefficient of the wind speed subsequence and the X-axis low-frequency inclination fluctuation subsequence is calculated, and the absolute value is taken as the X-axis correlation coefficient, and the Y-axis correlation coefficient is calculated; The X-axis lag time is calculated by cross-correlation analysis, which is the time difference corresponding to the maximum value of the X-axis correlation coefficient of the wind speed sequence and the X-axis low-frequency inclination fluctuation subsequence, and the Y-axis lag time is calculated.

7. The monitoring method for overhead power transmission line inspection according to claim 6, characterized in that: The process of outputting the correlation verification result is: If any one of the X-axis and Y-axis lag time is not in the lag time interval, the true settlement feature matching is output; If the X-axis and Y-axis lag time are in the lag time interval, the time verification is correct, and the correlation degree verification is performed; If any one of the X-axis and Y-axis correlation coefficients is out of limit, the weak wind vibration feature matching is output; If the X-axis and Y-axis correlation coefficients are less than or equal to the minimum value of the threshold critical interval, the true settlement feature matching is output; If the X-axis and Y-axis correlation coefficients are all out of limit, and any one of the X-axis and Y-axis correlation coefficients is in the threshold critical interval, the boundary correlation to be supplemented is output.

8. The monitoring method for overhead power transmission line inspection according to claim 1, wherein: The process of outputting the frequency domain determination result is: The long-term window time interval corresponding to the to-be-verified output result is extracted, and the X-axis inclination angle data and Y-axis inclination angle data in the corresponding time interval are extracted as the to-be-analyzed X-axis low-frequency sequence and to-be-analyzed Y-axis low-frequency sequence; The wavelet packet decomposition parameters are set, and 4-layer wavelet packet decomposition is performed on the to-be-analyzed X-axis low-frequency sequence and to-be-analyzed Y-axis low-frequency sequence respectively to obtain the wavelet packet coefficients of each sub-band; The energy value of each sub-band is calculated by the energy calculation formula, and the total energy of the X-axis and Y-axis is calculated respectively. The proportions of the energy of each sub-band of the X-axis and the Y-axis in the total energy are calculated respectively, and the X-axis and Y-axis frequency energy proportion distribution tables are obtained respectively, and the maximum values of the X-axis and Y-axis frequency energy proportions are extracted respectively as the X-axis maximum energy proportion and the Y-axis maximum energy proportion; If the X-axis and Y-axis maximum energy proportions are both over the standard, the output is real settlement frequency domain feature matching, otherwise, the output is weak wind vibration frequency domain feature matching.

9. The monitoring method for overhead power transmission line inspection according to claim 1, wherein: The process of obtaining the final determination result is as follows: If the output result is suspected weak wind vibration and weak wind vibration feature matching, the final determination result is to confirm that the overhead transmission line tower is weak wind low frequency vibration; If the output result is suspected real settlement and real settlement feature matching, the final determination result is to confirm that the overhead transmission line tower foundation is real settlement; If the output result is one of the two contradictory scenarios of suspected weak wind vibration and real settlement feature matching, suspected real settlement and weak wind vibration feature matching, the frequency domain feature supplementary analysis module is used for analysis, and the frequency domain determination result is used for determination; If the output result is preliminary determination to be verified or boundary correlation to be supplemented, the frequency domain determination result is used for determination.

10. A monitoring system for inspection of overhead power lines, characterized in that The system is used to execute the method of any one of claims 1-9, and specifically comprises: a trigger condition analysis module, a time sequence trend judgment module, a correlation verification module, a frequency domain feature supplementary analysis module, and a comprehensive decision output module.

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