A method and system for monitoring insulation aging of high-voltage transmission and distribution line equipment

Abstract

The application discloses a kind of high-voltage transmission and distribution line equipment insulation aging monitoring method and system, it is related to equipment insulation state monitoring technical field, including, extracting voltage time-frequency feature matrix in voltage time series data set voltage time-frequency feature generation;Through the current mode component of separating current signal, output double-channel feature pair;Call high-voltage equipment standard parameter, according to double-channel feature pair calculation dielectric loss factor, through quantum tunneling effect eliminates measurement deviation and obtains correction dielectric loss factor;The frequency response characteristics of correction dielectric loss factor are analyzed, the aging type is judged by identifying offset mode, and the equipment aging degree coefficient is calculated to divide state grade;Based on aging type generation processing scheme, and according to state grade to obtain execution priority, through binding time stamp to generate maintenance instruction set.The application is combined with dynamic maintenance decision optimization response time by quantum tunneling effect, realizes the closed-loop management of insulation aging from accurate monitoring to active protection.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of equipment insulation state monitoring, in particular to a high-voltage power transmission and transformation line equipment insulation aging monitoring method and system. BACKGROUND

[0002] High-voltage power transmission and transformation line equipment insulation aging monitoring technology is a core field for guaranteeing reliable operation of a power grid, and has experienced evolution from power frequency dielectric loss alarm to broadband dielectric spectrum analysis. Early methods realize insulation state evaluation by measuring a power frequency dielectric loss factor, but are limited by insufficient information quantity of a single frequency point. With the maturity of broadband dielectric spectrum technology, the monitoring frequency band is expanded to a range from millihertz to kilohertz, and more abundant polarization relaxation characteristics can be captured. In recent years, multi-physical quantity fusion monitoring has become a trend, and a multi-dimensional evaluation system for insulation aging is constructed by combining local discharge signals, infrared thermal imaging and vibration spectrum analysis. The development of artificial intelligence technology further promotes the development of intelligent diagnosis, and a deep learning algorithm realizes aging mode classification and state grading by extracting dielectric spectrum time-frequency characteristics.

[0003] The field of high-voltage power transmission and transformation equipment insulation aging monitoring still faces core challenges of environmental interference and mechanism recognition. Under complex working conditions, traditional dielectric response analysis is easily affected by high-humidity environments, and the measurement deviation of dielectric parameters caused by water penetration is difficult to eliminate, and the main reason is that existing compensation methods fail to fully consider the influence of quantum effects under nanoscale. At the same time, thermal aging and damp aging show similar relaxation peak shift characteristics in the frequency domain response, and existing methods rely on artificial experience for type discrimination, lack of automatic distinguishing mechanism based on physical mechanism, and lead to the risk of aging type misjudgment, which restricts the accuracy of insulation state evaluation. SUMMARY

[0004] In view of the above existing problems, the application is proposed.

[0005] Therefore, the application provides a high-voltage power transmission and transformation line equipment insulation aging monitoring method to solve the problems of high-humidity environment dielectric loss measurement deviation and insufficient accuracy of insulation state evaluation.

[0006] To solve the above technical problems, the application provides the following technical solutions.

[0007] In a first aspect, the present application provides a high-voltage transmission line equipment insulation aging monitoring method, which comprises: collecting high-voltage equipment standard parameters and original response signals, and obtaining a high-voltage time series data set through preprocessing; extracting voltage time-frequency features in the high-voltage time series data set to generate a voltage time-frequency feature matrix, separating current modal components of a current signal to output a double-channel feature pair; calling the high-voltage equipment standard parameters, calculating a dielectric loss factor according to the double-channel feature pair, eliminating measurement deviation through quantum tunneling effect to obtain a corrected dielectric loss factor; analyzing the frequency response characteristics of the corrected dielectric loss factor, judging the aging type by identifying the offset mode, and calculating an equipment aging degree coefficient to divide a state level; generating a processing scheme based on the aging type, obtaining an execution priority according to the state level, and generating a maintenance instruction set by binding a time stamp.

[0008] As a preferred scheme of the high-voltage transmission line equipment insulation aging monitoring method, the high-voltage equipment standard parameters comprise environmental temperature and humidity, insulation layer thickness, material dielectric constant, equipment position coordinates and meteorological data.

[0009] The original response signals comprise a current signal, an equipment vibration signal and an excitation voltage signal.

[0010] The preprocessing comprises band-pass filtering, clock synchronization alignment and environmental temperature and humidity compensation calibration.

[0011] As a preferred scheme of the high-voltage transmission line equipment insulation aging monitoring method, the extraction of the voltage time-frequency features in the high-voltage time series data set to generate the voltage time-frequency feature matrix comprises the following specific steps:

[0012] The voltage time-frequency features in the high-voltage time series data set are extracted, and the energy region intensity distribution is obtained through curvature space mapping.

[0013] Based on the energy region intensity distribution, the transient detail features are focused in the high-energy region, and the noise suppression capability is enhanced in the low-energy region to generate the time-frequency feature matrix.

[0014] As a preferred scheme of the high-voltage transmission line equipment insulation aging monitoring method, the separation of the current modal components of the current signal to output the double-channel feature pair comprises the following specific steps:

[0015] The current signal is subjected to empirical mode decomposition to generate a set of intrinsic mode function components.

[0016] The kurtosis coefficients are calculated according to the set of intrinsic mode function components, and the high-kurtosis intrinsic mode function components are screened.

[0017] The feature frequencies of the high-kurtosis intrinsic mode function components are extracted, and are matched and verified with the equipment vibration signal to obtain the current modal components.

[0018] The time-frequency feature matrix and the current modal component are aligned by timestamps, and a double-channel feature pair is output.

[0019] As a preferred scheme of the high-voltage transmission line equipment insulation aging monitoring method, the standard parameters of the high-voltage equipment are called, the dielectric loss factor is calculated according to the double-channel feature pair, the measurement deviation is eliminated through the quantum tunneling effect to obtain the corrected dielectric loss factor, and the specific steps are as follows,

[0020] The standard parameters of the high-voltage equipment are called, and the parameterized feature set is constructed in combination with the environmental temperature and humidity parameters.

[0021] Based on the parameterized feature set and the double-channel feature pair, the dielectric loss factor is calculated through the quantum state superposition algorithm.

[0022] Based on the insulation thickness and the environmental temperature and humidity, the water molecule tunneling probability is calculated, and the measurement deviation is eliminated through the temperature and humidity compensation algorithm to obtain the corrected dielectric loss factor.

[0023] As a preferred scheme of the high-voltage transmission line equipment insulation aging monitoring method, the standard parameters of the high-voltage equipment are called, the standard parameters of the high-voltage equipment are called, the frequency response characteristics of the corrected dielectric loss factor are analyzed, the aging type is judged by identifying the shift mode, and the device aging degree coefficient is calculated to divide the state level, and the specific steps are as follows,

[0024] The frequency response characteristics of the corrected dielectric loss factor are subjected to variable frequency excitation signals, and the dielectric loss-frequency relationship spectrum line is generated by scanning;

[0025] The amplitude increase and decrease, position shift and spectrum line broadening degree of the characteristic frequency points of the dielectric loss frequency spectrum line are identified to judge the aging type;

[0026] The number of position shifts of the characteristic frequency points of the dielectric loss frequency spectrum line is counted to calculate the shift frequency point proportion, and the device aging degree coefficient is calculated in combination with the spectrum line broadening degree;

[0027] Based on the aging type and the device aging degree coefficient, the state level is divided through the differentiated grading rule.

[0028] As a preferred scheme of the high-voltage transmission line equipment insulation aging monitoring method, the aging type includes moisture aging and thermal aging.

[0029] The processing scheme is generated based on the aging type, the execution priority is obtained according to the state level, the maintenance instruction set is generated by binding the timestamp, and the specific steps are as follows,

[0030] Based on the aging type, the gradient hot air drying process is performed on the moisture aged equipment through the molecular mechanism mapping, and the micro-channel phase change cooling process is performed on the thermal aged equipment to generate the processing scheme.

[0031] The basis aging rule is generated according to the state level, real-time meteorological data is fused to dynamically adjust the aging parameter, and an execution priority is output;

[0032] The basis instruction is generated by fusing the processing scheme and the execution priority, and the maintenance instruction set is generated by binding the time stamp.

[0033] In a second aspect, the present application provides a high-voltage transmission line equipment insulation aging monitoring system, comprising a data acquisition module, a double-channel verification module, a quantum correction module, a state grading module and a maintenance decision module; the data acquisition module is used for acquiring high-voltage equipment standard parameters and original response signals, and obtaining a high-voltage time series data set through preprocessing; the double-channel verification module is used for extracting voltage time-frequency features in the high-voltage time series data set to generate a voltage time-frequency feature matrix, separating current modal components of a current signal, and outputting a double-channel feature pair; the quantum correction module is used for calling high-voltage equipment standard parameters, calculating a dielectric loss factor according to the double-channel feature pair, eliminating measurement deviation through quantum tunneling effect to obtain a corrected dielectric loss factor; the state grading module is used for analyzing frequency response characteristics of the corrected dielectric loss factor, judging an aging type by identifying a deviation mode, and calculating a device aging degree coefficient to divide a state level; the maintenance decision module is used for generating a processing scheme based on the aging type, obtaining an execution priority according to the state level, and generating a maintenance instruction set by binding a time stamp.

[0034] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, any step of the high-voltage transmission line equipment insulation aging monitoring method according to the first aspect of the present application is implemented.

[0035] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and when the computer program is executed by a processor, any step of the high-voltage transmission line equipment insulation aging monitoring method according to the first aspect of the present application is implemented.

[0036] The present application has the following beneficial effects: the quantum tunneling effect is used to correct the dielectric loss parameter, the quantum probability of moisture penetration in a high-humidity environment is quantified, the double-channel feature extraction is combined to ensure the purity of the input signal; the dynamic maintenance decision is combined with the fused meteorological data to optimize the response aging, and the closed-loop management from precise monitoring to active protection of insulation aging is realized, and the reliability of the power grid is improved. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0038] Fig. 1 A flow chart of a high-voltage power transmission line equipment insulation aging monitoring method.

[0039] Fig. 2 A schematic diagram of a high-voltage power transmission line equipment insulation aging monitoring system.

[0040] Fig. 3 A flow chart of generating a dual-channel feature pair.

[0041] Fig. 4 A flow chart of generating a maintenance instruction set. DETAILED DESCRIPTION

[0042] In order to make the above objectives, features and advantages of the present application more apparent and comprehensible, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0043] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced in other ways different from those described herein without departing from the scope of the present application, and those skilled in the art can make similar extensions without departing from the scope of the present application, so the present application is not limited to the specific embodiments disclosed below.

[0044] Secondly, the "one embodiment" or "embodiment" referred to herein can include specific features, structures or characteristics included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or alternative embodiment that excludes other embodiments.

[0045] Reference Figs. 1-4 For one embodiment of the present application, the embodiment provides a high-voltage power transmission line equipment insulation aging monitoring method, comprising the following steps:

[0046] S1, collecting high-voltage equipment standard parameters and original response signals, and obtaining high-voltage time series data sets through preprocessing;

[0047] S1.1 The high-voltage equipment standard parameters include environmental temperature and humidity, insulation layer thickness, material dielectric constant, equipment position coordinates and meteorological data;

[0048] It should be noted that the standard parameters of high-voltage equipment constitute the data cornerstone of insulation state monitoring, and the real-time quantification of environmental temperature and humidity penetrates the risk of moisture and the level of thermal stress, providing an environmental benchmark for dielectric correction; the thickness of the insulation layer is a core physical quantity for electric field distribution and water molecule tunneling, which directly determines the accuracy of quantum correction; the dielectric constant of the material constructs a polarization response calculation framework to analyze the aging behavior of material specificity; the device location coordinates drive the geographic fence resource scheduling to compress the emergency response path; meteorological data dynamically modulate the processing scheme strength, such as rainstorm environment to strengthen the drying cycle, to ensure the adaptability of extreme working conditions. The fusion of multiple parameters forms a physical and environmental dual-dimensional data chain, providing holographic decision support for accurate diagnosis to active protection of insulation aging.

[0049] S1.2 The original response signal includes current signal, device vibration signal and excitation voltage signal;

[0050] It should be noted that the original response signal constitutes the direct perception source of insulation state monitoring, in which the current signal captures the current-carrying characteristics of the insulation medium, reflecting defect characteristics such as partial discharge and electrical tree development; the device vibration signal verifies the authenticity of the current modal component through mechanical oscillation spectrum analysis and identifies hidden mechanical damage such as core rod creep; the excitation voltage signal as a synchronous reference benchmark ensures the phase alignment of time-frequency analysis, and provides excitation source calibration for dielectric response calculation.

[0051] S1.3 The preprocessing includes band-pass filtering, clock synchronization alignment and environmental temperature and humidity compensation calibration;

[0052] It should be noted that preprocessing eliminates high-frequency electromagnetic interference and power frequency harmonic noise through band-pass filtering, retaining signals sensitive to insulation aging; clock synchronization alignment uses high-precision GPS taming technology to achieve strict matching of current and voltage signal phases, avoiding time domain analysis deviation; environmental temperature and humidity compensation calibration dynamically corrects zero drift based on real-time sensing data, resisting the interference of humid environment on dielectric measurement.

[0053] S1.4 The high-voltage time series data set includes band-pass filtered current signal, voltage time-frequency characteristics, environmental temperature and humidity parameters, and time stamp.

[0054] It should be noted that the high-frequency interference and power frequency harmonics in the current signal are purified by band-pass filtering, and the current characteristics of the sensitive frequency band of the insulation defect are retained; the phases of the current, voltage and vibration signals are synchronized and aligned using a high-precision GPS clock, and the time domain analysis deviation is eliminated; the sensor zero drift is corrected in real time by combining environmental temperature and humidity compensation, resisting the influence of hot and humid environment, and finally integrating to generate high-voltage time sequence data set. The current signal filtered by band-pass filtering is used as the core carrier for insulation state analysis, and by suppressing high-frequency interference and power frequency harmonic distortion, the current characteristics of the sensitive frequency band of the insulation defect are retained; the voltage time-frequency characteristics are extracted by time-frequency transformation to extract the polarization relaxation response, and the dynamic evolution law of dielectric loss is revealed; the environmental temperature and humidity parameters are quantified in real time to quantify the risk of moisture penetration and thermal stress, and provide environmental benchmarks for quantum correction; the time stamp is synchronized and aligned with the current and voltage phases through a high-precision clock, ensuring the consistency of time-frequency analysis data.

[0055] S2, extracting voltage time-frequency characteristics in the high-voltage time sequence data set to generate a voltage time-frequency characteristic matrix, separating the current modal components of the current signal, and outputting a double-channel feature pair;

[0056] S2.1 Extracting voltage time-frequency characteristics in the high-voltage time sequence data set, and obtaining energy zone intensity distribution through curvature space mapping;

[0057] Specifically, the voltage time-frequency characteristics in the high-voltage time sequence data set are extracted to construct a time-frequency surface, and the time direction tangent vector and the frequency direction tangent vector of the time-frequency surface are extracted; the time direction tangent vector itself dot product value, the time direction tangent vector and the frequency direction tangent vector dot product value and the frequency direction tangent vector itself dot product value are obtained by dot product operation of the tangent vector, and output as the first basic form coefficient. The second order vector of the time-frequency surface is extracted and a unit normal vector perpendicular to the surface is constructed; the second order vector and the unit normal vector are dot product operated; and the dot product value is output as the second basic form coefficient.

[0058] The time direction tangent vector itself dot product value, the time direction tangent vector and the frequency direction tangent vector dot product value, and the frequency direction tangent vector itself dot product value in the first basic form coefficient, and the second order vector and the unit normal vector dot product value in the second basic form coefficient are substituted into the Gaussian curvature calculation formula, and mapped into a two-dimensional matrix according to the time axis and the frequency axis position, and the Gaussian curvature distribution map is output. In the Gaussian curvature distribution map, identify the relatively high value area of curvature, and mark the continuous area with high curvature amplitude as the energy aggregation core area;

[0059] Based on the spatial distribution characteristics of the energy aggregation core area, the curvature amplitude variation trend of the energy aggregation core area is extracted as the basic intensity factor, the curvature attenuation characteristics of the adjacent area are extracted as the diffusion attenuation factor, and the regional spatial correlation characteristics are extracted as the morphological stability factor to construct the energy zone intensity distribution function, and finally output the energy zone intensity distribution.

[0060] S2.2 Based on the energy region intensity distribution, the transient detail features are focused in the high energy region, and the noise suppression ability is enhanced in the low energy region, to generate a time-frequency feature matrix.

[0061] Specifically, the overall mean of the energy region intensity distribution is calculated, and the continuous region with intensity value higher than the overall mean is defined as the high energy region, and the continuous region with intensity value lower than the overall mean is defined as the low energy region.

[0062] The voltage signal is windowed, the high energy region uses a narrow window width Gaussian window function (the window width decreases with the increase of the intensity value), and the local discharge pulse and other transient details are captured; the low energy region uses a wide window width Gaussian window function (the window width expands with the decrease of the intensity value), to suppress the power frequency harmonic and random noise.

[0063] The windowed voltage signal segment is input into the fast Fourier transform algorithm, to calculate the complex spectrum of each frequency component in the signal segment; the complex spectrum contains the real part amplitude and the imaginary part frequency; the frequency spectrum graph is drawn with the imaginary part frequency as the horizontal axis and the real part amplitude as the vertical axis; the time-frequency spectrum segment of the output signal segment is output, and the time-frequency feature matrix is formed by integrating the time-frequency spectrum segments.

[0064] In the time-frequency feature matrix, the time axis sampling point sequence corresponding to the row is derived from the sampling time point sequence of the voltage signal in the high voltage time sequence data set, and the time accuracy is ensured by GPS clock synchronization alignment; the frequency axis spectrum line sequence corresponding to the column is determined by the frequency spectrum resolution of the short-time Fourier transform; the complex amplitude and phase information stored in the time-frequency feature matrix element directly comes from the short-time Fourier transform output.

[0065] S2.3 Perform empirical mode decomposition on the current signal to generate a set of intrinsic mode function components.

[0066] It should be noted that first, the discrete sampling point sequence of the current signal is traversed, and for each discrete sampling point in the discrete sampling point sequence, it is judged whether the current amplitude is greater than the current amplitudes of the left and right adjacent points at the same time, and if the condition is met, it is marked as a local maximum point; the discrete sampling point sequence of the current signal is traversed, and for each discrete sampling point, it is judged whether the current amplitude is less than the current amplitudes of the left and right adjacent points at the same time, and if the condition is met, it is marked as a local minimum point; the upper envelope line is generated by spline interpolation fitting of the local maximum points, and the lower envelope line is generated by spline interpolation fitting of the local minimum points; the mean envelope line of the upper envelope line and the lower envelope line is calculated; the difference between the current signal and the mean envelope line is taken as the intermediate signal.

[0067] The process of identifying extreme points of the intermediate signal, fitting envelope lines, calculating mean envelope lines, and generating new intermediate signals is repeated and iterated until the intermediate signal meets the two conditions of the intrinsic mode function component (the number of local maximum points and local minimum points differs by no more than one, and the mean of the upper envelope line and the lower envelope line at any time is zero).

[0068] outputting the intermediate signal satisfying the condition as an eigenmode function component; and repeating the above process until the residual eigenmode function component has regular zero points on the time axis; and finally outputting a set of eigenmode function components.

[0069] S2.4 calculating the kurtosis coefficient of the set of eigenmode function components, and screening high-kurtosis eigenmode function components;

[0070] It should be noted that the expression for calculating the kurtosis coefficient of the eigenmode function component is:

[0071] ;

[0072] wherein, the kurtosis coefficient of the eigenmode function component (for example, a positive real number with a value range of > 0, the kurtosis coefficient value of a normal distribution is 3, a kurtosis coefficient > 3 indicates that the signal has a sharp pulse characteristic, and a kurtosis coefficient < 3 indicates that the signal distribution is flat), is a mathematical expectation operator, is an eigenmode function component amplitude sequence, is the mean of the eigenmode function component amplitude sequence, is the standard deviation of the eigenmode function component amplitude sequence;

[0073] Then, the ratio of the sum of the kurtosis coefficients of all eigenmode function components to the total number of eigenmode function components in the set of eigenmode function components is taken as the average kurtosis coefficient, and the eigenmode function components with a kurtosis coefficient higher than the average kurtosis coefficient are retained, and the output is the high-kurtosis eigenmode function component.

[0074] S2.5 extracting the characteristic frequency of the high-kurtosis eigenmode function component, and matching and verifying with the device vibration signal to obtain a current modal component;

[0075] It should be noted that the discrete sampling point sequence of the high-kurtosis eigenmode function component is input into the discrete Fourier transform algorithm to obtain the complex spectrum value of each frequency component; the complex spectrum value includes real and imaginary parts; the real part of the complex spectrum value is extracted as the characteristic frequency, and the imaginary part is extracted as the frequency component amplitude; and the current component spectrum graph is plotted with the characteristic frequency as the horizontal coordinate axis and the frequency component amplitude as the vertical coordinate axis. In the current component spectrum graph, identify the points where the eigenmode function component amplitude exceeds the local maximum value, extract the characteristic frequency corresponding to the local maximum value point to form a current characteristic frequency set;

[0076] Synchronization acquisition device vibration signal, through the piezoelectric acceleration sensor collected mechanical vibration into an electric charge signal, through the charge amplifier converts the charge signal into a voltage signal; voltage signal data acquisition generates discrete vibration sampling sequence; the discrete vibration sampling sequence is calculated by fast Fourier transform to obtain the frequency component complex amplitude; the real part of the complex amplitude is extracted as the vibration frequency, and the imaginary part is extracted as the vibration energy amplitude; the vibration frequency is taken as the abscissa axis, and the vibration energy amplitude is taken as the ordinate axis to draw the device vibration signal spectrum diagram; the peak frequency point with the highest amplitude in the device vibration signal spectrum diagram is marked as the vibration spectrum peak frequency point;

[0077] The full width at half maximum value of the vibration spectrum peak in the device vibration signal spectrum diagram is taken as the tolerance range, and the current characteristic frequency set is compared with the vibration spectrum peak frequency point. If the difference between the points in the current characteristic frequency set and the vibration spectrum peak frequency point is within the tolerance range, it is determined that the frequencies coincide. The current component corresponding to the frequency coincidence is retained and marked as the current modal component.

[0078] S2.6 Align the time-frequency feature matrix and the current modal component by the timestamp, and output the double-channel feature pair.

[0079] It should be noted that the row index time point sequence of the time-frequency feature matrix and the time point sequence of the current modal component are extracted; the time point sequences are aligned by the GPS clock synchronization, so that the row vectors of the time-frequency feature matrix and the current modal component correspond to the same time; the time stamp is taken as the key to create an association mapping, and the row vector (complex amplitude and phase information) of the time-frequency feature matrix and the instantaneous amplitude of the current modal component are associated with the time stamp key; the time stamp key, the time-frequency feature row vector and the current modal amplitude are packaged as a structured data unit; all time stamp data units are integrated to form a double-channel feature pair set output.

[0080] S3, call high-voltage equipment standard parameters, calculate the dielectric loss factor according to the double-channel feature pair, and eliminate the measurement deviation by quantum tunneling effect to obtain the corrected dielectric loss factor;

[0081] S3.1 Call high-voltage equipment standard parameters, and combine environmental temperature and humidity parameters to construct a parameterized feature set;

[0082] It should be noted that the insulation layer thickness parameter and the material dielectric constant parameter in the high-voltage equipment standard parameter are extracted; the real-time temperature value and the relative humidity value in the environmental temperature and humidity parameter are read; the insulation layer thickness parameter, the material dielectric constant parameter, the real-time temperature value and the relative humidity value are combined into a four-dimensional feature vector; and the four-dimensional feature vector is taken as the parameterized feature set.

[0083] S3.2 Based on the parameterized feature set and the double-channel feature pair, the dielectric loss factor is calculated by the quantum state superposition algorithm;

[0084] It should be noted that the row vectors of the time-frequency feature matrix and the instantaneous amplitude of the current mode component in the dual-channel feature pair are extracted, and the insulation layer thickness, material dielectric constant, real-time temperature value and relative humidity value in the parameterized feature set are called.

[0085] The row vectors of the time-frequency characteristic matrix are mapped to dielectric response states in Hilbert space, the amplitudes of current mode components are mapped to carrier motion states, and the tensor product state of dielectric response state and carrier motion state is obtained; the expected value of the tensor product state is output as the dielectric loss factor.

[0086] Furthermore, Hilbert space is a complete inner product space, with its core characteristic being the definition of vector dot product operations. Hilbert space originates from the axiomatic requirements of quantum mechanics, providing a mathematical framework for describing the superposition of quantum states and the probability of measurement. For example, when the wave function is used as a spatial vector, the square of the modulus represents the probability density. Physically, Hilbert space describes quantum states, observables, and characterizes evolutionary mechanisms. Hilbert space is used to construct tensor product states of dielectric response states and charge carrier motion states, and to capture the quantum entanglement effect between dielectric response and charge carrier motion.

[0087] S3.3 calculates the water molecule tunneling probability based on insulation thickness and ambient temperature and humidity, and eliminates measurement bias through a temperature and humidity compensation algorithm to obtain the corrected dielectric loss factor.

[0088] It should be noted that the water molecule tunneling probability is calculated based on the insulation thickness parameter and the ambient temperature and humidity parameters: the insulation thickness parameter is extracted as the physical quantity of the barrier width; the relative humidity value in the ambient temperature and humidity parameters is read as the barrier modulation factor; the tunneling probability is calculated using the quantum tunneling probability formula, which is an exponential function formula for calculating the water molecule tunneling probability, and the expression is:

[0089] ;

[0090] in, Let be the probability of water molecule tunneling. This is the relative humidity value. For material characteristics, penetration depth For insulation layer thickness parameters, is the base of the natural logarithm function;

[0091] The temperature and humidity compensation algorithm is executed based on the real-time temperature value and the relative humidity value in the environmental temperature and humidity parameters; in a constant humidity environment, the linear slope of the dielectric loss factor changing with temperature is measured as the temperature drift coefficient (for example, the value range in the standard temperature rise test is 0.001-0.005 / °C); in a constant temperature environment, the linear slope of the dielectric loss factor changing with humidity is measured as the humidity drift coefficient (for example, the value range in the humidity cycle test is 0.003-0.008 / %); the temperature and humidity compensation algorithm is executed to generate a temperature and humidity compensation amount, and the temperature and humidity compensation amount is superimposed to generate a total compensation amount; and the total compensation amount is superimposed to the dielectric loss factor output to correct the dielectric loss factor, and the expression is:

[0092] ;

[0093] wherein, is the corrected dielectric loss factor, is the dielectric loss factor, is the water molecule tunneling probability, is the temperature drift coefficient, is the humidity drift coefficient, is the real-time temperature value, is the reference temperature, is the relative humidity value, is the reference relative humidity.

[0094] S4, analyze the frequency response characteristics of the corrected dielectric loss factor, identify the aging type by recognizing the shift mode, and calculate the device aging degree coefficient to divide the state level;

[0095] S4.1 applies a variable frequency excitation signal to the frequency response characteristics of the corrected dielectric loss factor, and scans to generate a dielectric loss-frequency relationship spectrum line;

[0096] It should be noted that the variable frequency excitation signal is generated by a signal generator, and the signal generator outputs a sinusoidal waveform voltage excitation, covering a low frequency band to a high frequency band, the starting point frequency value of the low frequency band is low, the terminal point frequency value of the high frequency band is high, and the frequency point distribution density is high in the low frequency band and low in the high frequency band;

[0097] The frequency response characteristics of the corrected dielectric loss factor are scanned, taking the frequency value as the horizontal coordinate axis and the corrected dielectric loss factor as the vertical coordinate axis, the horizontal coordinate axis adopts logarithmic scale, and the vertical coordinate axis adopts linear scale, to draw a continuous smooth curve; and a dielectric loss-frequency relationship spectrum line is generated.

[0098] S4.2 identifies the amplitude increase and decrease, position migration and spectrum line broadening degree of the characteristic frequency point of the dielectric loss frequency spectrum line to determine the aging type;

[0099] It should be noted that the position of the power frequency peak in the dielectric loss-frequency relationship spectrum line is located with the position of the interface polarization peak; the amplitude variation degree of the power frequency peak is analyzed, and the amplitude increase of the power frequency peak is determined as moisture aging; the frequency position migration direction of the power frequency peak is detected, and the frequency reduction of the power frequency peak is determined as moisture aging; the change ratio of the half-height width of the power frequency peak is measured, and the half-height width expansion is determined as moisture aging;

[0100] The amplitude variation degree of the interface polarization peak is analyzed, and the amplitude reduction of the interface polarization peak is determined as thermal aging; the frequency position migration direction of the interface polarization peak is detected, and the frequency increase of the interface polarization peak is determined as thermal aging; the change ratio of the half-height width of the interface polarization peak is measured, and the half-height width expansion is determined as thermal aging.

[0101] S4.3 Statistics of the number of characteristic frequency points in the dielectric loss-frequency spectrum line The offset frequency point ratio is calculated, and the device aging degree coefficient is calculated combined with the spectrum line broadening degree;

[0102] It should be noted that all characteristic frequency points in the dielectric loss-frequency relationship spectrum line are identified; the number of characteristic frequency points whose frequency position offset exceeds the average characteristic frequency point frequency position offset is calculated by calculating the characteristic frequency point frequency position offset, and the ratio to the total number of characteristic frequency points is defined as the offset frequency point ratio; The half-height width of the current power frequency peak and the interface polarization peak in the dielectric loss-frequency relationship spectrum line is measured, and the device aging degree coefficient is calculated, and the expression is:

[0103] ;

[0104] Wherein, The device aging degree coefficient (for example, the normal state coefficient is less than 0.3, the pre-warning state coefficient is [0.3, 0.7], and the serious state coefficient is greater than 0.7), The number of characteristic frequency points exceeding the average characteristic frequency point frequency position offset, The total number of characteristic frequency points, The average half-height width of the current power frequency peak and the interface polarization peak, The baseline half-height width at the initial stage of device operation.

[0105] S4.4 Based on the aging type and the device aging degree coefficient, the state level is divided by differentiated grading rules.

[0106] It should be noted that the differentiated grading rules are executed based on the aging type and the device aging degree coefficient. When the aging type is moisture aging, moisture penetration will cause sudden failures such as insulation resistance drop and surface flashover, and the risk response time requirement is high, so strict grading is adopted. When the aging type is thermal aging, thermal aging belongs to the process of gradual molecular chain rupture, and the failure development speed is slow, so relaxed grading is adopted.

[0107] For example, under the strict classification of moisture aging type: the device aging degree coefficient <0.25 outputs the normal state identifier, 0.25<device aging degree coefficient <0.7 outputs the early warning state identifier, and the device aging degree coefficient value >0.7 outputs the serious state identifier;

[0108] Under the loose classification of thermal aging type: the device aging degree coefficient <0.3 outputs the normal state identifier, 0.3<device aging degree coefficient <0.75 outputs the early warning state identifier, and the device aging degree coefficient value >0.75 outputs the serious state identifier.

[0109] S5, generating a processing scheme based on the aging type, and obtaining an execution priority according to the state level, and generating a maintenance instruction set by binding a time stamp.

[0110] S5.1 The aging type includes moisture aging and thermal aging.

[0111] Specifically, moisture aging refers to the deterioration process caused by the penetration of water molecules into the insulating medium. Water molecules invade the material pores to form conductive channels, resulting in a sharp increase in dielectric loss and a sudden drop in insulation resistance. The specific performance is that the power frequency dielectric loss peak moves to the left and the leakage current doubles. The core hazard is to induce surface flashover or internal breakdown, which requires a gradient hot air drying scheme to force moisture removal. Thermal aging refers to the molecular chain rupture caused by long-term high temperature operation. High temperature accelerates the oxidation reaction to make the material brittle and carbonized. The characteristics are that the interface polarization peak moves to the right and the mechanical strength decays. The hazard is the gradual insulation failure, which requires a micro-channel phase change cooling scheme to suppress temperature rise.

[0112] S5.2 Based on the aging type, the gradient hot air drying process is performed on the moisture aged device, and the micro-channel phase change cooling process is performed on the thermal aged device, to generate a processing scheme;

[0113] It should be noted that the gradient hot air drying process is performed based on the conclusion of moisture aging type. The molecular mechanism mapping divides the water molecule penetration process into three stages: adsorption stage, diffusion stage and capillary condensation stage.

[0114] The gradient hot air drying process performs a gradient temperature rising process for the three stages: low temperature gas flow is used in the adsorption stage to break the adsorption force of water molecules; medium temperature gas flow is used in the diffusion stage to enhance the diffusion kinetic energy and accelerate the escape of water; high temperature gas flow is used in the capillary condensation stage to break the capillary condensation water film; the hot air flow rate is dynamically adjusted according to the drying process, the low wind speed in the adsorption stage reduces the surface disturbance, and the high wind speed in the capillary condensation stage enhances the convective heat transfer; the total drying time is set according to the state level identifier, for example, the drying time for the normal state identifier is 2 hours, the drying time for the early warning state identifier is 4 hours, and the drying time for the serious state identifier is 6 hours; and the gradient hot air drying processing scheme is output.

[0115] Based on the aging type conclusion, micro-channel phase change cooling treatment is performed when thermal aging occurs; molecular mechanism mapping decomposes the thermal aging process into three stages: molecular chain rupture, oxidative crosslinking, and local hotspot formation; micro-channel phase change cooling treatment performs dynamic cooling process for the three stages: cooling medium flows through the micro-channel structure to absorb the heat energy released by molecular chain rupture; oxidative crosslinking occurs in the micro-channel; the strong turbulent structure in the micro-channel destroys the thermal boundary layer and forms local hot spots; the cooling medium flow in the micro-channel is dynamically adjusted according to the state level, for example, the flow is increased by 50% in the warning state and by 100% in the serious state; the cooling time is set according to the real-time temperature value of the device, for example, the basic cooling time is 24 hours, and the cooling time is extended by 2 hours for every 5℃ increase in temperature; and a micro-channel phase change cooling treatment scheme is output. The final treatment scheme is generated by integrating the gradient hot air drying treatment scheme and the micro-channel phase change cooling treatment scheme.

[0116] S5.3. Generate a basic aging rule according to the state level, dynamically adjust the aging parameters by integrating real-time weather data, and output the execution priority;

[0117] It should be noted that the basic aging rule is generated based on the state level, the state level is normal, and the aging rule is generated at the device maintenance time point; the state level is identified as a warning state, and a countdown execution aging rule is generated, for example, a 72-hour countdown execution window is set; the state level is identified as a serious state, and an immediate execution aging rule is generated, and a device self-lock protection linkage mechanism is activated; and the basic aging rule is output.

[0118] Read the real-time temperature value and relative humidity value in the real-time weather data and calculate the average temperature parameter and average relative humidity value; higher than the average temperature parameter is defined as a high temperature environment and a time efficiency acceleration coefficient is generated (for example, when the reference temperature is 25℃, the value range is: 0.8~1.2); higher than the average relative humidity value is defined as a high humidity environment and a time efficiency delay coefficient is generated (for example, when the reference humidity is 60%, the value range is: 0.6~1.8); the time efficiency acceleration coefficient and the time efficiency delay coefficient are superimposed with the basic aging rule to generate a dynamic aging parameter; for example, the dynamic aging parameter <1 indicates that the execution window is compressed, and the dynamic aging parameter >1 indicates that the execution window is expanded; and the dynamic aging parameter is output as the execution priority.

[0119] S5.4. Merge the treatment scheme and the execution priority to generate a basic instruction, and generate a maintenance instruction set by binding the time stamp.

[0120] It should be noted that the fusion processing scheme is generated based on the execution priority of the basic instruction; the basic instruction includes the type of processing scheme (gradient hot air drying and micro-channel phase change cooling), the execution priority (dynamic aging parameter value); the timestamp binding basic instruction is called to generate the timestamp key-value pair; the timestamp key-value pair is associated with the device location coordinates; the timestamp key-value pair, the basic instruction unit and the location coordinate parameter are packaged into a structured data unit; and all structured data units are integrated to form a maintenance instruction set.

[0121] The embodiment also provides a high-voltage transmission line equipment insulation aging monitoring system, comprising: a data acquisition module, configured to acquire high-voltage equipment standard parameters and original response signals, and obtain high-voltage time series data sets through preprocessing; a dual-channel verification module, configured to extract voltage time-frequency features in the high-voltage time series data sets to generate a voltage time-frequency feature matrix, separate current modal components of a current signal, and output a dual-channel feature pair; a quantum correction module, configured to call high-voltage equipment standard parameters, calculate a dielectric loss factor according to the dual-channel feature pair, eliminate measurement deviation through quantum tunneling effect, and obtain a corrected dielectric loss factor; a state grading module, configured to analyze frequency response characteristics of the corrected dielectric loss factor, judge an aging type by identifying a drift mode, and calculate a device aging degree coefficient to divide a state grade; and a maintenance decision module, configured to generate a processing scheme based on the aging type, obtain an execution priority according to the state grade, and generate a maintenance instruction set by binding a timestamp.

[0122] The embodiment also provides a computer device suitable for the high-voltage transmission line equipment insulation aging monitoring method, comprising: a memory and a processor; the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions to implement the high-voltage transmission line equipment insulation aging monitoring method proposed in the above embodiment.

[0123] The computer device can be a terminal, and the computer device comprises a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The communication interface of the computer device is configured to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device. In addition, the input device can be an external keyboard, touchpad or mouse, etc.

[0124] The embodiment also provides a storage medium on which a computer program is stored, the program being executed by a processor to implement the method for monitoring insulation aging of high-voltage transmission and distribution line equipment proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk, or an optical disk.

[0125] To sum up, the application realizes the closed-loop management of insulation aging from precise monitoring to active protection by correcting the dielectric loss parameter through the quantum tunneling effect, quantifying the quantum probability of moisture penetration in a high-humidity environment, ensuring the purity of the input signal by combining double-channel feature extraction, and optimizing the response timeliness by combining dynamic maintenance decision fusion meteorological data, thereby improving the reliability of the power grid.

[0126] It should be noted that the above embodiments are only used to illustrate the technical solutions of the application rather than limit the application. Although the application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the application, and all of them should be covered in the scope of the claims of the application.

Claims

1. A method for monitoring insulation aging in high-voltage transmission and transformation line equipment, characterized in that: include, Collect standard parameters and raw response signals of high-voltage equipment, and obtain high-voltage time-series datasets through preprocessing; Voltage time-frequency features are extracted from high-voltage time-series datasets to generate a voltage time-frequency feature matrix. By separating the current mode components of the current signal, dual-channel feature pairs are output. The standard parameters of the high-voltage equipment are used to calculate the dielectric loss factor based on the dual-channel characteristics. The corrected dielectric loss factor is obtained by eliminating measurement bias through quantum tunneling effect. The specific steps are as follows: The standard parameters of high-voltage equipment are called up, and a parameterized feature set is constructed by combining the ambient temperature and humidity. Based on parameterized feature sets and dual-channel feature pairs, the dielectric loss factor is calculated using a quantum state superposition algorithm. The probability of water molecule tunneling is calculated based on insulation thickness and ambient temperature and humidity, and the measurement deviation is eliminated by temperature and humidity compensation algorithm to obtain the corrected dielectric loss factor. The frequency response characteristics of the corrected dielectric loss factor are analyzed, the aging type is determined by identifying the offset mode, and the equipment aging degree coefficient is calculated to classify the status level. A processing scheme is generated based on the aging type, and the execution priority is obtained according to the status level. A maintenance instruction set is generated by binding timestamps.

2. The method for monitoring insulation aging of high-voltage transmission and transformation line equipment as described in claim 1, characterized in that: The standard parameters of the high-voltage equipment include ambient temperature and humidity, insulation layer thickness, material dielectric constant, equipment location coordinates, and meteorological data. The original response signal includes a current signal, a device vibration signal, and an excitation voltage signal; The preprocessing includes bandpass filtering, clock synchronization alignment, and environmental temperature and humidity compensation calibration.

3. The method for monitoring insulation aging of high-voltage transmission and transformation line equipment as described in claim 2, characterized in that: The specific steps for extracting voltage time-frequency features from the high-voltage time-series dataset to generate a voltage time-frequency feature matrix are as follows. Voltage time-frequency features are extracted from high-voltage time-series datasets, and energy region intensity distribution is obtained through curvature space mapping. Calculate the overall mean of the intensity distribution of the energy region, define the continuous region with an intensity value higher than the overall mean as the high energy region, and define the continuous region with an intensity value lower than the overall mean as the low energy region; Based on the intensity distribution in the energy region, a time-frequency feature matrix is ​​generated by focusing on transient detail features in the high-energy region and enhancing noise suppression capability in the low-energy region.

4. The method for monitoring insulation aging of high-voltage transmission and transformation line equipment as described in claim 3, characterized in that: The process of separating the current mode components of the current signal and outputting dual-channel feature pairs involves the following steps: Perform empirical mode decomposition on the current signal to generate a set of eigenmode function components; The kurtosis coefficient is calculated based on the set of intrinsic mode function components, and high-kurtosis intrinsic mode function components are selected. The characteristic frequencies of the high-kurtosis intrinsic mode function components are extracted and matched with the equipment vibration signal for verification to obtain the current mode components. The time-frequency feature matrix and the current mode components are aligned with timestamps to output a dual-channel feature pair.

5. The method for monitoring insulation aging of high-voltage transmission and transformation line equipment as described in claim 4, characterized in that: The analysis corrects the frequency response characteristics of the dielectric loss factor, identifies the aging type by recognizing the offset mode, and calculates the equipment aging degree coefficient to classify the condition level. The specific steps are as follows. A frequency conversion excitation signal is applied to the frequency response characteristics of the corrected dielectric loss factor, and the dielectric loss-frequency relationship spectrum is generated by scanning. The type of aging can be determined by identifying the amplitude increase or decrease, position shift, and spectral broadening of characteristic frequency points of dielectric loss frequency lines. The proportion of shifted frequency points is calculated by counting the number of characteristic frequency points of the statistical dielectric loss frequency spectrum that have shifted their positions, and the equipment aging coefficient is calculated by combining the degree of spectral broadening. Based on the aging type and equipment aging degree coefficient, the status level is divided through differentiated grading rules.

6. The method for monitoring insulation aging of high-voltage transmission and transformation line equipment as described in claim 5, characterized in that: The aging types include moisture aging and heat aging; The process involves generating a processing scheme based on aging type, obtaining execution priority according to status level, and generating a maintenance instruction set by binding timestamps. The specific steps are as follows: Based on the aging type and molecular mechanism mapping, a gradient hot air drying process is performed on the moisture-aged equipment, and a microchannel phase change cooling process is performed on the thermally aged equipment to generate a treatment scheme. Based on the status level, basic timeliness rules are generated, and timeliness parameters are dynamically adjusted by integrating real-time meteorological data, and the execution priority is output. The processing scheme and execution priority are integrated to generate basic instructions, and a maintenance instruction set is generated by binding timestamps.

7. A high-voltage transmission and transformation line equipment insulation aging monitoring system, based on the high-voltage transmission and transformation line equipment insulation aging monitoring method according to any one of claims 1 to 6, characterized in that: include, The data acquisition module is used to collect standard parameters and raw response signals of high-voltage equipment, and obtain high-voltage time-series datasets through preprocessing. The dual-channel verification module is used to extract voltage time-frequency features from the high-voltage time-series dataset to generate a voltage time-frequency feature matrix. By separating the current mode components of the current signal, it outputs dual-channel feature pairs. The quantum correction module is used to call the standard parameters of the high-voltage equipment, calculate the dielectric loss factor based on the dual-channel characteristics, and obtain the corrected dielectric loss factor by eliminating measurement deviation through the quantum tunneling effect. The status classification module is used to analyze the frequency response characteristics of the corrected dielectric loss factor, determine the aging type by identifying the offset mode, and calculate the equipment aging degree coefficient to classify the status level. The maintenance decision module is used to generate processing solutions based on aging type, obtain execution priority according to status level, and generate maintenance instruction sets by binding timestamps.

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