Distribution network cable dielectric loss withstand voltage partial discharge intelligent detection and evaluation method and system

Abstract

The invention discloses a distribution network cable dielectric loss withstand voltage partial discharge intelligent detection and evaluation method and system, and relates to the technical field of power equipment state monitoring, and the method comprises the steps: synchronously collecting a dielectric loss spectrum, a withstand voltage leakage current curve and a partial discharge spectrum of a distribution network cable through a phase resolution measurement device; processing the partial discharge spectrum by using a frequency domain adaptive filter to generate a denoised partial discharge characteristic spectrum; constructing a cable multi-dimensional parameter normal domain hyperspherical model, and calculating a deviation distance value; and inputting the deviation distance value into a fuzzy inference engine, performing nonlinear conversion according to a preset fuzzy rule base, generating an insulation state evaluation index and a fault risk probability distribution diagram of the distribution network cable, and realizing accurate evaluation of the cable insulation state. According to the invention, the accurate evaluation of the cable insulation state and the accurate positioning of the fault position are realized, and the operation reliability and maintenance efficiency of the power distribution network are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment status monitoring, and in particular to a method and system for intelligent detection and evaluation of dielectric loss, withstand voltage and partial discharge of distribution network cables. Background Art

[0002] Distribution cables are an important part of the urban power system, and their insulation status is directly related to the safe and reliable operation of the distribution network. With the continuous expansion of the scale of the distribution network and the accumulation of cable usage time, cable insulation aging and fault risk assessment has become an important topic in the operation and maintenance of the power system. Traditional cable insulation evaluation mainly relies on single parameter tests, such as dielectric loss test, voltage withstand test or partial discharge test. These methods are carried out independently and it is difficult to fully reflect the comprehensive insulation status of the cable. The dielectric loss test can reflect the degree of macroscopic insulation degradation, but it is not sensitive to early defects; the voltage withstand test can verify the short-term voltage withstand capability of the cable, but it cannot reflect the long-term operation performance of the insulation; the partial discharge test is sensitive to local defects, but it is seriously interfered by environmental noise, and the data interpretation is complicated. This single parameter evaluation method is one-sided and limited in practical applications, and it is difficult to accurately judge the actual status of the cable, especially under complex environments and multiple factors, which is easy to lead to misjudgment or missed judgment.

[0003] Existing cable status assessment systems generally adopt a simple threshold judgment method, compare the measurement results with a fixed threshold, and determine the cable status according to the degree of exceeding the threshold. This method ignores the interrelationship between cable parameters, cannot adapt to the characteristic differences of cables of different types, different operating environments, and different aging stages, and the assessment results are highly dependent on the rationality of the threshold setting. In addition, the existing technology lacks the ability to accurately locate the spatial distribution of cable fault risks. Most assessments only give the overall status level and cannot indicate the specific risk point location, which is not conducive to precise maintenance. At the same time, the traditional system is not intelligent enough in signal processing and data analysis, and has limited noise suppression capabilities for partial discharge signals, making it difficult to extract effective features from complex backgrounds. However, the intelligent detection and evaluation method and system for partial discharge of distribution network cable dielectric loss withstand voltage provided by the present invention solve the problems existing in the above-mentioned technologies. The present invention belongs to the field of power equipment status monitoring and evaluation technology. Through multi-parameter collaborative detection, intelligent signal processing, data-driven modeling and fuzzy reasoning evaluation, accurate evaluation of cable insulation status and accurate positioning of fault locations are achieved, thereby improving the operation reliability and maintenance efficiency of the distribution network. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the present invention provides a distribution network cable dielectric loss withstand voltage partial discharge intelligent detection and evaluation method and system, which can solve the problems mentioned in the background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a distribution network cable dielectric loss withstand voltage partial discharge intelligent detection and evaluation method, comprising: synchronously collecting the dielectric loss spectrum, withstand voltage leakage current curve and partial discharge spectrum of the distribution network cable through a phase resolution measurement device, and applying a frequency domain adaptive filter to process the partial discharge spectrum to generate a denoised partial discharge characteristic spectrum; the frequency domain adaptive filter includes a fast Fourier transform module, a spectrum analysis module and an adaptive bandpass filter module; A support vector data description algorithm is used to construct a normal domain hypersphere model of cable multidimensional parameters based on historical normal sample data, and the deviation distance values ​​of the dielectric loss spectrum, the withstand voltage leakage current curve and the denoised partial discharge characteristic spectrum relative to the normal domain hypersphere are calculated; the normal domain hypersphere model includes kernel function mapping parameters, boundary relaxation parameters and a model update mechanism; The deviation distance value is input into the fuzzy reasoning engine, and nonlinear conversion is performed according to the preset fuzzy rule library to generate the insulation status evaluation index and fault risk probability distribution diagram of the distribution network cable, so as to realize the accurate evaluation of the insulation status of the cable; the fuzzy reasoning engine includes a fuzzification processing module, a rule reasoning module and a defuzzification processing module.

[0007] As a preferred solution of the distribution network cable dielectric loss withstand voltage partial discharge intelligent detection and evaluation method described in the present invention, wherein: applying a frequency domain adaptive filter to process the partial discharge spectrum includes: Converting the partial discharge spectrum into the frequency domain through the fast Fourier transform module; Using the spectrum analysis module to identify characteristic frequency bands of signals and noise, and calculate signal-to-noise ratio distribution; The adaptive bandpass filtering module sets the filtering bandwidth according to the signal-to-noise ratio distribution; when the signal-to-noise ratio distribution presents a unimodal characteristic, a narrowband filtering parameter is set; when the signal-to-noise ratio distribution presents a multimodal characteristic, a multi-channel filtering parameter is set to complete the enhancement of the partial discharge spectrum and the noise suppression.

[0008] As a preferred solution of the intelligent detection and evaluation method for dielectric loss, withstand voltage and partial discharge of distribution network cables described in the present invention, a support vector data description algorithm is used to construct a normal domain hypersphere model of cable multi-dimensional parameters based on historical normal sample data, including: Using radial basis function as the kernel function mapping parameter, the multi-dimensional parameters of the distribution network cable are mapped to a high-dimensional feature space; The sensitivity of the normal domain hypersphere model to abnormal points is controlled by the boundary relaxation parameter; wherein the boundary relaxation parameter is related to the characteristic distribution range of the historical normal sample data; According to the model update mechanism, the normal domain hypersphere model update is triggered when one of the following conditions is met: the number of newly added normal samples reaches a predetermined ratio, and the statistical characteristics of the deviation distance value exceed a preset reference range.

[0009] As a preferred solution of the intelligent detection and evaluation method for dielectric loss, withstand voltage and partial discharge of distribution network cables described in the present invention, wherein: the fuzzification processing module converts the deviation distance value into a fuzzy set; wherein the fuzzy set includes four levels: normal, slightly abnormal, moderately abnormal and severely abnormal; The rule inference module performs inference calculation according to the fuzzy rule base to obtain a fuzzy inference result; the fuzzy rule base contains fuzzy rules based on the deviation distance value of the dielectric loss spectrum and the deviation distance value of the partial discharge characteristic spectrum; The defuzzification processing module converts the fuzzy reasoning result into the insulation state evaluation index and the fault risk probability distribution diagram by using the centroid method.

[0010] As a preferred solution of the intelligent detection and evaluation method for partial discharge of distribution network cable dielectric loss withstand voltage described in the present invention, the fuzzy reasoning result includes: when the deviation distance value of the dielectric loss spectrum is greater than the first threshold and the deviation distance value of the partial discharge characteristic spectrum is greater than the second threshold, it is judged as a serious abnormality; when the deviation distance value of the dielectric loss spectrum is greater than the first threshold and the deviation distance value of the partial discharge characteristic spectrum is not greater than the second threshold, it is judged as a moderate abnormality; when the deviation distance value of the dielectric loss spectrum is not greater than the first threshold and the deviation distance value of the partial discharge characteristic spectrum is greater than the second threshold, it is judged as a slight abnormality; when the deviation distance value of the dielectric loss spectrum is not greater than the first threshold and the deviation distance value of the partial discharge characteristic spectrum is not greater than the second threshold, it is judged as normal.

[0011] As a preferred solution of the intelligent detection and evaluation method for dielectric loss, withstand voltage and partial discharge of distribution network cables described in the present invention, the calculation of the deviation distance value includes: Taking the center of the normal domain hypersphere as the origin, establishing a kernel function mapping coordinate system; Calculating the Euclidean distances of the dielectric loss spectrum, the withstand voltage leakage current curve, and the denoised partial discharge characteristic spectrum in the kernel function mapping coordinate system; The deviation distance value is determined according to the ratio of the Euclidean distance to the radius of the normal domain hypersphere.

[0012] As a preferred solution of the intelligent detection and evaluation method for dielectric loss, withstand voltage and partial discharge of distribution network cables described in the present invention, the generation process of the fault risk probability distribution map includes: Dividing the distribution network cable into a plurality of monitoring sections along the length direction; calculating the local discharge density, discharge amplitude and discharge frequency for the plurality of monitoring sections respectively; Based on the local discharge density, the discharge amplitude and the discharge frequency, a risk coefficient of each monitoring section is calculated; wherein, when the local discharge density and the discharge amplitude increase or decrease simultaneously, the risk coefficient is proportional to the product of the local discharge density and the discharge amplitude; when the local discharge density increases and the discharge amplitude decreases or the local discharge density decreases and the discharge amplitude increases, the risk coefficient remains within a preset range; Combined with the insulation status evaluation index, the failure risk probability of the several monitoring sections is visualized in the form of a heat map.

[0013] To further solve the above technical problems, the present invention provides the following technical solutions: A distribution network cable dielectric loss withstand voltage partial discharge intelligent detection and evaluation system, comprising: a signal processing unit, used to synchronously collect the dielectric loss spectrum, withstand voltage leakage current curve and partial discharge spectrum of the distribution network cable through a phase resolution measurement device, and apply a frequency domain adaptive filter to process the partial discharge spectrum to generate a denoised partial discharge characteristic spectrum; A model building unit, used to build a normal domain hypersphere model of cable multi-dimensional parameters based on historical normal sample data by using a support vector data description algorithm, and calculate the deviation distance value of the dielectric loss spectrum, the withstand voltage leakage current curve and the denoised partial discharge characteristic spectrum relative to the normal domain hypersphere; The risk assessment unit is used to input the deviation distance value into a fuzzy inference engine, perform nonlinear conversion according to a preset fuzzy rule library, generate an insulation status assessment index and a fault risk probability distribution diagram of the distribution network cable, and realize accurate assessment of the cable insulation status.

[0014] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned intelligent detection and evaluation method for dielectric loss, withstand voltage and partial discharge of distribution network cables are implemented.

[0015] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned intelligent detection and evaluation method for dielectric loss, withstand voltage and partial discharge of distribution network cables.

[0016] Beneficial effects of the present invention: the intelligent detection and evaluation method and system for partial discharge of distribution network cable dielectric loss withstand voltage provided by the present invention have significant technical advantages: first, three key parameters are synchronously collected by a phase resolution measurement device, and multi-dimensional cable state characterization is realized, overcoming the one-sidedness of traditional single parameter detection; secondly, the frequency domain adaptive filter is applied to process the partial discharge spectrum, and the filtering strategy is dynamically adjusted according to the single peak or multi-peak characteristics of the signal-to-noise ratio distribution, which significantly improves the signal extraction capability under complex environments; thirdly, the support vector data description algorithm is used to construct a normal domain hypersphere model of cable multi-dimensional parameters, and radial basis function mapping parameters, boundary relaxation parameters and model update mechanism are introduced to achieve accurate modeling of nonlinear data distribution and objective quantification of abnormal states; finally, the deviation distance value is input into the fuzzy inference engine for nonlinear conversion, and the fault risk probability distribution heat map is generated in combination with the partial discharge density, discharge amplitude and discharge frequency, which not only realizes the accurate evaluation of the insulation state, but also can accurately locate the specific fault location. In addition, the system also has the ability of adaptive learning and dynamic update, and can automatically adjust the evaluation parameters as the cable aging process, providing a scientific basis for preventive maintenance decisions, effectively reducing the failure rate of the distribution network, and improving the reliability and economic benefits of the power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 It is a schematic diagram of the overall process of the intelligent detection and evaluation method for dielectric loss, withstand voltage and partial discharge of distribution network cables proposed by the present invention; Figure 2 This is a diagram of the computer equipment in the intelligent detection and evaluation method for dielectric loss, withstand voltage and partial discharge of distribution network cables proposed by the present invention. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0020] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0021] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides an intelligent detection and evaluation method for dielectric loss, withstand voltage and partial discharge of distribution network cables.

[0022] Figure 1 The overall process diagram of the intelligent detection and evaluation method for dielectric loss, withstand voltage and partial discharge of distribution network cables is shown, including the following steps: S1: synchronously collect the dielectric loss spectrum, withstand voltage leakage current curve and partial discharge spectrum of the distribution network cable through a phase resolution measurement device, and process the partial discharge spectrum with a frequency domain adaptive filter to generate a denoised partial discharge characteristic spectrum.

[0023] Specifically, the frequency domain adaptive filter includes a fast Fourier transform module, a spectrum analysis module and an adaptive bandpass filter module.

[0024] Furthermore, the process of processing the partial discharge spectrum by the frequency domain adaptive filter includes: Converting the partial discharge spectrum into the frequency domain through the fast Fourier transform module; Using the spectrum analysis module to identify characteristic frequency bands of signals and noise, and calculate signal-to-noise ratio distribution; The adaptive bandpass filtering module sets the filtering bandwidth according to the signal-to-noise ratio distribution; when the signal-to-noise ratio distribution presents a unimodal characteristic, a narrowband filtering parameter is set; when the signal-to-noise ratio distribution presents a multimodal characteristic, a multi-channel filtering parameter is set to complete the enhancement of the partial discharge spectrum and the noise suppression.

[0025] When implementing the intelligent detection and evaluation method for dielectric loss, withstand voltage and partial discharge of distribution network cables of the present invention, a phase-resolved measurement device is first set. The phase-resolved measurement device is specifically an integrated cable testing device, which is composed of an AC high-voltage source, a precision measurement unit, a data acquisition system and a control host. The AC high-voltage source provides a sine wave test voltage with an adjustable frequency of 0.1Hz-1Hz and an amplitude range of 1kV-10kV, which is suitable for testing distribution network cables of different voltage levels. The precision measurement unit includes a dielectric loss measurement circuit, a withstand voltage test circuit and a partial discharge detection circuit, and each circuit has an independent sensor and signal conditioning module. The data acquisition system adopts a 16-bit high-precision acquisition card with a sampling rate of up to 10MS / s, has a phase locking function, and can synchronously collect various parameters within a phase accuracy range of 0.1°. The phase-resolved measurement device can realize the synchronous acquisition of the three key parameters of the distribution network cable-the dielectric loss spectrum, the withstand voltage leakage current curve and the partial discharge spectrum, ensuring that all data are acquired under the same phase reference, providing a basis for subsequent comprehensive analysis.

[0026] In the actual measurement process, the phase-resolved measurement device first applies a standard test voltage to the distribution network cable, and the voltage waveform is used as a phase reference signal, and the three measurement channels of dielectric loss, withstand voltage leakage current and partial discharge are triggered to start data collection. The dielectric loss spectrum is obtained by frequency domain scanning of the voltage and current phase difference, with a frequency range of 0.01Hz-100Hz; the withstand voltage leakage current curve records the leakage current value that changes with time under the standard test voltage; the partial discharge spectrum collects the partial discharge pulse signal generated by the cable during the test, and records its amplitude, phase, occurrence frequency and other information.

[0027] The acquired partial discharge spectrum usually contains various interference noises, such as electromagnetic interference, high-frequency switching power supply interference and background white noise, etc. These noises will seriously affect the extraction and identification of partial discharge features. Therefore, the present invention uses the frequency domain adaptive filter to process the partial discharge spectrum to generate the denoised partial discharge feature spectrum. The frequency domain adaptive filter includes the fast Fourier transform module, the spectrum analysis module and the adaptive bandpass filter module, and each module works together to achieve effective extraction of partial discharge signals.

[0028] In an optional embodiment, the specific process of applying the frequency domain adaptive filter to process the partial discharge spectrum is as follows: First, the partial discharge spectrum is converted to the frequency domain through the fast Fourier transform module. The fast Fourier transform module uses a segmented FFT algorithm to perform a 1024-point transformation on the signal, and the optional windowing function is a Hanning window to reduce the spectrum leakage effect. After the transformation is completed, a complete frequency domain representation can be obtained, including an amplitude spectrum and a phase spectrum.

[0029] Next, the transformed frequency domain signal is processed using the spectrum analysis module, which uses an adaptive threshold algorithm to identify the characteristic frequency bands of the signal and noise. Specifically, by calculating the spectrum energy distribution curve, the frequency band with concentrated energy is identified as a possible signal frequency band; at the same time, the stable area of ​​the spectrum is analyzed and identified as a noise frequency band. Based on these two parts of information, the signal-to-noise ratio distribution of each frequency band is calculated to generate a signal-to-noise ratio spectrum diagram, which provides a basis for the subsequent setting of filtering parameters.

[0030] In another optional embodiment, the spectrum analysis module can also combine wavelet analysis technology to extract signal features at different scales and enhance the recognition ability of non-stationary discharge signals. This method is particularly suitable for signal processing in complex cable environments and can effectively distinguish signal components with similar spectrum characteristics but different time-frequency distributions.

[0031] In the present invention, the unimodal and multimodal characteristics of the signal-to-noise ratio distribution are identified by frequency domain signal feature analysis. The unimodal characteristic means that the signal-to-noise ratio distribution curve shows a dominant peak region in the entire frequency domain, and the energy of this region occupies the main part of the total energy of the spectrum (such as a preset range of more than 50%), and the energy of other frequency bands outside this region is lower than the main peak region. This distribution morphology indicates that the partial discharge signal presents a concentrated distribution characteristic in the spectrum, which is usually associated with a single type of insulation defect.

[0032] In contrast, the multi-peak characteristic means that the signal-to-noise ratio distribution curve is expressed as multiple independent peak regions in the frequency domain, the energy distribution in each region is uniform, and there are non-overlapping frequency intervals between the peaks. This distribution pattern indicates that the partial discharge signal presents a dispersed characteristic in the frequency spectrum, which usually corresponds to multiple insulation defect sources or complex defects.

[0033] The signal-to-noise ratio distribution characteristics are determined by using a spectrum morphology recognition algorithm, which identifies the number of frequency domain energy cluster centers, the energy proportion of each cluster center, and the frequency interval between clusters, and completes the characteristic classification in combination with the discharge physical model. The system selects the judgment parameters according to the test environment and cable type, and optimizes the classification process through an iterative algorithm.

[0034] Finally, the adaptive bandpass filter module automatically sets the filter bandwidth according to the signal-to-noise ratio distribution. When the signal-to-noise ratio distribution presents a single peak characteristic, the narrowband filter parameters are set, the center frequency is positioned at the main peak point, and the bandwidth is determined based on the half-peak full width value of the energy density curve.

[0035] When the signal-to-noise ratio distribution shows a multi-peak characteristic, it indicates that the partial discharge signal is distributed in multiple discontinuous frequency bands. At this time, the multi-channel filtering parameters are set, and an independent bandpass filter is set for each peak area. The filter parameters are dynamically adjusted according to the characteristics of each peak area. The multi-channel filtering results are fused through the optimal ratio synthesis algorithm to enhance the partial discharge spectrum and suppress noise. In practical applications, the setting of the multi-channel filtering parameters will also take into account the spectrum characteristics of different types of discharge sources, and optimize and adjust for different cable fault types.

[0036] Exemplary, specific numerical values ​​are used as follows: When testing a 10kV cross-linked polyethylene insulated distribution network cable that has been in operation for 5 years, the phase-resolved measurement device applies a test voltage of 8kV, the sampling rate is set to 5MS / s, and the acquisition time is 60 seconds. The original partial discharge spectrum obtained shows that there are obvious discharge clusters in the 0°-90° and 180°-270° phase intervals, but the signal is seriously polluted by 50Hz power frequency interference and random noise, and the signal-to-noise ratio is only 6.8dB. After being processed by the frequency domain adaptive filter, the signal-to-noise ratio is increased to 23.5dB. Specifically, the fast Fourier transform module performs a 1024-point FFT transform on the discharge signal, and the obtained spectrum shows that the discharge signal is mainly distributed in the two frequency bands of 0.5MHz-1.8MHz and 2.3MHz-4.5MHz, forming a typical multi-peak characteristic distribution, and the signal-to-noise ratios of each peak area are 25.3dB and 21.7dB, respectively. The adaptive bandpass filter module sets two bandpass filter channels accordingly, with center frequencies of 1.2MHz and 3.6MHz, and bandwidths of ±0.6MHz and ±1.0MHz, respectively. The filtered signals are fused through a weighted synthesis algorithm with a weight ratio of 0.6:0.4, and finally the denoised partial discharge characteristic spectrum is obtained, which clearly shows two discharge sources in the internal insulation structure, with discharge amplitudes of 25pC and 42pC, respectively.

[0037] It should be noted that the phase-resolved measurement device synchronously collects the dielectric loss spectrum, the withstand voltage leakage current curve and the partial discharge spectrum of the distribution network cable, and applies the frequency domain adaptive filter to process the partial discharge spectrum, which has many technical advantages and practical application value. First, the synchronous acquisition of three key parameters can fully reflect the various characteristics of cable insulation, avoiding the one-sidedness and limitations brought by the traditional single parameter test method. Secondly, the frequency domain adaptive filter can dynamically adjust the filtering strategy according to the actual characteristics of the signal, and has stronger adaptability and higher signal-to-noise ratio improvement effect than the traditional fixed parameter filtering method. In particular, for the complex situation where the signal-to-noise ratio distribution presents multi-peak characteristics, the setting of the multi-channel filtering parameters can effectively retain the effective signal components distributed in different frequency bands, avoiding the signal distortion and information loss that may be caused by simple bandpass filtering. In addition, the denoised partial discharge characteristic spectrum is combined with the dielectric loss spectrum and the withstand voltage leakage current curve for analysis, which can realize the multi-dimensional characterization of the cable insulation state, improve the accuracy and reliability of fault diagnosis, and provide a scientific basis for preventive maintenance decisions, thereby extending the service life of the cable, reducing operation and maintenance costs, and improving the reliability of distribution network operation.

[0038] S2: Using the support vector data description algorithm, a normal domain hypersphere model of the cable multi-dimensional parameters is constructed based on the historical normal sample data, and the deviation distance values ​​of the dielectric loss spectrum, the withstand voltage leakage current curve and the denoised partial discharge characteristic spectrum relative to the normal domain hypersphere are calculated.

[0039] Specifically, the normal domain hypersphere model includes kernel function mapping parameters, boundary relaxation parameters and a model update mechanism.

[0040] Furthermore, the support vector data description algorithm is used to construct a normal domain hypersphere model of cable multi-dimensional parameters based on historical normal sample data, including: Using radial basis function as the kernel function mapping parameter, the multi-dimensional parameters of the distribution network cable are mapped to a high-dimensional feature space; The sensitivity of the normal domain hypersphere model to abnormal points is controlled by the boundary relaxation parameter; wherein the boundary relaxation parameter is related to the characteristic distribution range of the historical normal sample data; According to the model update mechanism, the normal domain hypersphere model update is triggered when one of the following conditions is met: the number of newly added normal samples reaches a predetermined ratio, and the statistical characteristics of the deviation distance value exceed a preset reference range.

[0041] The calculation of the deviation distance value includes: Taking the center of the normal domain hypersphere as the origin, establishing a kernel function mapping coordinate system; Calculating the Euclidean distances of the dielectric loss spectrum, the withstand voltage leakage current curve, and the denoised partial discharge characteristic spectrum in the kernel function mapping coordinate system; The deviation distance value is determined according to the ratio of the Euclidean distance to the radius of the normal domain hypersphere.

[0042] In the distribution network cable dielectric loss withstand voltage partial discharge intelligent detection and evaluation method of the present invention, after obtaining the denoised partial discharge characteristic spectrum, it is necessary to quantitatively evaluate the cable insulation state. Therefore, the present invention uses the support vector data description SVDD algorithm to construct a cable multi-dimensional parameter normal domain hypersphere model based on historical normal sample data, and calculates the deviation degree of the current detection cable parameter relative to the normal domain, so as to achieve an objective evaluation of the cable insulation state.

[0043] The support vector data description algorithm is a single-class classification method. Its core idea is to find a hypersphere with the smallest volume in the feature space so that it contains most of the normal sample data points and excludes abnormal points. The algorithm determines the center and radius of the hypersphere by solving the quadratic programming problem to form a boundary description of normal data. In cable status assessment, the normal domain hypersphere model includes three key components: kernel function mapping parameters, boundary relaxation parameters, and model update mechanism.

[0044] The specific implementation method of constructing the normal domain hypersphere model using the support vector data description algorithm includes the following steps: First, the detection records of healthy cables are selected from the historical detection database as a normal sample set. Each sample contains three types of feature data: the frequency response feature vector of the dielectric loss spectrum, the time domain feature vector of the withstand voltage leakage current curve, and the statistical feature vector of the denoised partial discharge feature map. These three types of feature vectors together constitute a multidimensional parameter space that characterizes the insulation state of the cable.

[0045] Next, the radial basis function is used as the kernel function mapping parameter to map the multidimensional parameters of the distribution network cable to the high-dimensional feature space. The radial basis function is defined as K(x,y)=exp(-||xy||² / σ²), where σ is the kernel width parameter, which controls the feature resolution of the mapping space. The choice of radial basis function is based on its excellent performance in processing nonlinear data distribution, and it can map the complex distribution in the original feature space to a simple structure in the high-dimensional space. In practical applications, the kernel width parameter σ is determined by a cross-validation method to obtain the best classification effect.

[0046] Then, the sensitivity of the normal domain hypersphere model to outliers is controlled by the boundary relaxation parameter. The boundary relaxation parameter is represented by C, and its value range is (0,1], and its value is related to the characteristic distribution range of the historical normal sample data. When the normal sample distribution is concentrated, a smaller C value is set to make the model boundary more compact; when the normal sample distribution is dispersed, a larger C value is set to allow the model boundary to be looser. The introduction of the boundary relaxation parameter enhances the robustness of the model to noise and outliers and avoids overfitting problems.

[0047] In order to adapt to the dynamic changes in the operating status of the cable, the present invention designs the model update mechanism. According to the model update mechanism, the update of the normal domain hypersphere model is triggered when one of the following conditions is met: the number of newly added normal samples reaches a predetermined proportion, or the statistical characteristics of the deviation distance value exceed the preset reference range. Specifically, when the number of newly added normal samples exceeds 20% of the total number of samples in the current model training, or when the mean of the deviation distance value measured for 30 consecutive times shows an obvious trend of change, the system will trigger the model retraining process and update the parameters of the normal domain hypersphere model. This dynamic update mechanism ensures that the model can be adaptively adjusted as the cable ages and the environment changes to maintain the accuracy of the evaluation.

[0048] In the actual detection process, it is necessary to calculate the deviation distance value between the current cable parameters and the normal domain hypersphere as a quantitative indicator for evaluating the insulation state of the cable. The calculation method of the deviation distance value includes three steps: First, a kernel function mapping coordinate system is established with the center of the normal domain hypersphere as the origin. The center of the hypersphere is obtained by solving the dual problem of the support vector data description algorithm and is expressed as the weighted sum of the support vectors.

[0049] Secondly, the Euclidean distance of the dielectric loss spectrum, the withstand voltage leakage current curve and the denoised partial discharge characteristic spectrum is calculated in the kernel function mapping coordinate system. For the currently measured cable parameters, they are mapped to the high-dimensional feature space through the kernel function K, and then the Euclidean distance from the mapping point to the center of the hypersphere is calculated. This distance characterizes the degree of difference between the current cable parameters and the normal state parameter set.

[0050] Finally, the deviation distance value is determined according to the ratio of the Euclidean distance to the normal domain hypersphere radius. The specific calculation formula is: deviation distance value = Euclidean distance / hypersphere radius. When the deviation distance value is less than 1, it means that the current cable parameters fall within the normal domain; when the deviation distance value is greater than 1, it means that the current cable parameters fall outside the normal domain, and the larger the value, the higher the degree of abnormality.

[0051] The deviation distance value calculated by the above method provides an objective quantitative indicator for subsequent fuzzy reasoning and cable status evaluation. Compared with the traditional threshold judgment method, the support vector data description algorithm can handle high-dimensional nonlinear data distribution and automatically adapt to the statistical characteristics of the data, achieving a more accurate evaluation of the cable insulation status.

[0052] For example, in the detection of 10kV cross-linked polyethylene cables in a distribution station, 50 groups of historical normal samples were collected to construct the normal domain hypersphere model. Each group of samples contains 15 frequency point data of the dielectric loss spectrum, 10 characteristic points of the withstand voltage leakage current curve and 8 statistical features of the partial discharge characteristic spectrum, forming a 33-dimensional parameter space. These parameters are mapped to the high-dimensional feature space through the radial basis function, and the kernel width parameter is set to the square root of the average distance between samples. The normal domain hypersphere model is obtained by training with the support vector data description algorithm, in which the number of support vectors is 12, accounting for 24% of the total samples, indicating that the model has good generalization ability.

[0053] A cable that has been in operation for 5 years was tested, and the dielectric loss spectrum obtained showed a slight increase in the high frequency band, the withstand voltage leakage current curve remained stable, and the partial discharge characteristic spectrum showed weak discharge activity in a specific phase interval. These parameters were input into the normal domain hypersphere model, and the deviation distance value of the dielectric loss spectrum was calculated to be 1.08, the deviation distance value of the withstand voltage leakage current curve was 0.85, and the deviation distance value of the partial discharge characteristic spectrum was 1.12. These results show that the insulation state of the cable has begun to show slight abnormalities, but has not yet reached a serious level, which is consistent with the service life of the cable and actual operation.

[0054] It should be noted that the method of constructing a normal domain hypersphere model of cable multi-dimensional parameters using the support vector data description algorithm and calculating the parameter deviation distance value has broken through the limitations of traditional fixed threshold judgment and can adapt to the characteristic differences and operating environment changes of different types of cables. The radial basis function is used as a kernel function mapping parameter to effectively solve the problem of nonlinear distribution of cable parameters; the boundary relaxation parameter improves the robustness of the model to abnormal points; and the model update mechanism ensures that the evaluation system can be dynamically adjusted as the cable ages. This data-driven state assessment method provides objective and accurate decision-making basis for power system operation and maintenance personnel, helps to achieve predictive maintenance of distribution network cables, reduce failure rates, and improve the reliability of power grid operation.

[0055] S3: Input the deviation distance value into the fuzzy inference engine, perform nonlinear conversion according to the preset fuzzy rule library, generate the insulation status evaluation index and fault risk probability distribution diagram of the distribution network cable, and realize accurate evaluation of the cable insulation status.

[0056] Specifically, the fuzzy reasoning engine includes a fuzzification processing module, a rule reasoning module and a defuzzification processing module.

[0057] Furthermore, the fuzzy processing module converts the deviation distance value into a fuzzy set, wherein the fuzzy set includes four levels: normal, slightly abnormal, moderately abnormal, and severely abnormal.

[0058] The rule reasoning module performs reasoning calculation according to the fuzzy rule base to obtain a fuzzy reasoning result.

[0059] Specifically, the fuzzy rule base includes fuzzy rules based on the deviation distance value of the dielectric loss spectrum and the deviation distance value of the partial discharge characteristic spectrum.

[0060] The fuzzy reasoning results include: when the deviation distance value of the dielectric loss spectrum is greater than a first threshold and the deviation distance value of the partial discharge characteristic spectrum is greater than a second threshold, it is judged as a serious abnormality; when the deviation distance value of the dielectric loss spectrum is greater than the first threshold and the deviation distance value of the partial discharge characteristic spectrum is not greater than the second threshold, it is judged as a moderate abnormality; when the deviation distance value of the dielectric loss spectrum is not greater than the first threshold and the deviation distance value of the partial discharge characteristic spectrum is greater than the second threshold, it is judged as a slight abnormality; when the deviation distance value of the dielectric loss spectrum is not greater than the first threshold and the deviation distance value of the partial discharge characteristic spectrum is not greater than the second threshold, it is judged as normal.

[0061] The defuzzification processing module converts the fuzzy reasoning result into the insulation state evaluation index and the fault risk probability distribution diagram by using the centroid method.

[0062] Furthermore, the generation process of the fault risk probability distribution diagram includes: Dividing the distribution network cable into a plurality of monitoring sections along the length direction; calculating the local discharge density, discharge amplitude and discharge frequency for the plurality of monitoring sections respectively; Based on the local discharge density, the discharge amplitude and the discharge frequency, a risk coefficient of each monitoring section is calculated; wherein, when the local discharge density and the discharge amplitude increase or decrease simultaneously, the risk coefficient is proportional to the product of the local discharge density and the discharge amplitude; when the local discharge density increases and the discharge amplitude decreases or the local discharge density decreases and the discharge amplitude increases, the risk coefficient remains within a preset range; Combined with the insulation status evaluation index, the failure risk probability of the several monitoring sections is visualized in the form of a heat map.

[0063] In the intelligent detection and evaluation method for dielectric loss, withstand voltage and partial discharge of distribution network cables of the present invention, after obtaining the deviation distance values ​​of the dielectric loss spectrum, the withstand voltage leakage current curve and the denoised partial discharge characteristic spectrum relative to the normal domain hypersphere, it is necessary to comprehensively analyze the abnormality of various parameters to obtain an overall evaluation of the insulation state of the cable. Due to the complexity and fuzziness of cable insulation state evaluation, a single threshold judgment is difficult to accurately reflect the actual situation of insulation degradation. Therefore, the present invention inputs the deviation distance value into a fuzzy inference engine, performs nonlinear conversion according to a preset fuzzy rule base, generates an insulation state evaluation index and a fault risk probability distribution diagram of the distribution network cable, and realizes an accurate evaluation of the cable insulation state.

[0064] The fuzzy inference engine is an intelligent processing system based on fuzzy set theory, which includes three main components: fuzzification processing module, rule inference module and defuzzification processing module. This structural design follows the classic fuzzy control system framework and can effectively handle the uncertainty and nonlinear relationship in cable parameter evaluation.

[0065] First, the fuzzification processing module converts the deviation distance value into a fuzzy set. In specific implementation, corresponding fuzzy membership functions are established for the deviation distance value of the dielectric loss spectrum, the deviation distance value of the withstand voltage leakage current curve, and the deviation distance value of the denoised partial discharge characteristic spectrum. The fuzzy set includes four levels: normal, slightly abnormal, moderately abnormal, and severely abnormal, and each level corresponds to a fuzzy membership function.

[0066] For the deviation distance value of the dielectric loss spectrum, its fuzzy membership function adopts the form of a combination of trapezoids and triangles. When the deviation distance value is less than 0.8, the membership of the normal level is 1; when the deviation distance value is between 0.8 and 1.0, the membership of the normal level decreases linearly, and the membership of the slightly abnormal level increases linearly; and so on, a complete membership function set is established. The same method is also applied to the deviation distance value of the withstand voltage leakage current curve and the deviation distance value of the denoised partial discharge characteristic spectrum, but considering the difference in sensitivity of different parameters to cable status assessment, the boundary point settings of the membership function of each parameter are different.

[0067] Next, the rule inference module performs inference calculation according to the fuzzy rule base to obtain a fuzzy inference result. The fuzzy rule base contains fuzzy rules based on the deviation distance value of the dielectric loss spectrum and the deviation distance value of the partial discharge characteristic spectrum, because these two parameters are most sensitive to changes in the cable insulation state. The fuzzy rules are expressed in the classic IF-THEN format, for example, IF the deviation distance value of the dielectric loss spectrum is moderately abnormal AND the deviation distance value of the partial discharge characteristic spectrum is slightly abnormal THEN the insulation state is moderately abnormal.

[0068] The fuzzy inference result specifically includes: when the deviation distance value of the dielectric loss spectrum is greater than the first threshold and the deviation distance value of the partial discharge characteristic spectrum is greater than the second threshold, it is determined to be a serious abnormality; when the deviation distance value of the dielectric loss spectrum is greater than the first threshold and the deviation distance value of the partial discharge characteristic spectrum is not greater than the second threshold, it is determined to be a moderate abnormality; when the deviation distance value of the dielectric loss spectrum is not greater than the first threshold and the deviation distance value of the partial discharge characteristic spectrum is greater than the second threshold, it is determined to be a slight abnormality; when the deviation distance value of the dielectric loss spectrum is not greater than the first threshold and the deviation distance value of the partial discharge characteristic spectrum is not greater than the second threshold, it is determined to be normal. The first threshold and the second threshold are determined according to historical data statistics and expert experience, respectively, and are used to distinguish the boundaries of different abnormal levels.

[0069] In the actual reasoning process, the rule reasoning module adopts the Mamdani reasoning method, which realizes the synthesis of the condition part by taking the small operator and the synthesis of the conclusion part by taking the large operator to form the final fuzzy reasoning result. This reasoning method retains the fuzzy characteristics of the input variables and can better handle the uncertainty in cable status assessment.

[0070] Finally, the defuzzification processing module uses the centroid method to convert the fuzzy reasoning results into the insulation state evaluation index and the fault risk probability distribution diagram. The centroid method is a commonly used defuzzification method, which obtains accurate numerical output by calculating the centroid position of the fuzzy set graph. Specifically, the insulation state evaluation index ranges from 0 to 100, where 0 represents the healthiest state and 100 represents the most serious abnormal state. By calculating the centroid position of the fuzzy reasoning result and mapping it to the evaluation index range of 0-100, the quantitative evaluation result of the cable insulation state is obtained.

[0071] In order to accurately locate the cable fault position, the present invention also generates the fault risk probability distribution map. The generation process of the fault risk probability distribution map first divides the distribution network cable into a plurality of monitoring sections along the length direction. For cables of different lengths, the granularity of the division can be different. Generally, the length of each monitoring section is 5-10 meters to ensure sufficient spatial resolution.

[0072] Next, the partial discharge density, discharge amplitude and discharge frequency are calculated for the several monitoring sections. The partial discharge density refers to the number of discharge pulses detected in a specific section per unit time; the discharge amplitude refers to the average energy or amplitude of the discharge pulse; and the discharge frequency refers to the repetition rate or frequency characteristics of the discharge activity. These three parameters comprehensively reflect the discharge activity characteristics of each section of the cable.

[0073] Then, based on the local discharge density, the discharge amplitude and the discharge frequency, the risk factor of each monitoring section is calculated. During the calculation process, when the local discharge density and the discharge amplitude increase or decrease simultaneously, the risk factor is proportional to the product of the local discharge density and the discharge amplitude. This is because the simultaneous increase in discharge density and amplitude usually indicates that the severity of insulation defects is increasing. When the local discharge density increases and the discharge amplitude decreases or the local discharge density decreases and the discharge amplitude increases, the risk factor remains within a preset range. This situation usually indicates a change in the discharge mode rather than a unidirectional deterioration of the insulation state.

[0074] Finally, combined with the insulation status assessment index, the failure risk probability of the several monitoring sections is visualized in the form of a heat map. The heat map uses a color gradient to indicate the degree of risk, usually red indicates a high-risk area and green indicates a low-risk area. Through this intuitive visual expression, maintenance personnel can quickly identify potential fault locations in the cable, providing an accurate basis for subsequent maintenance or replacement decisions.

[0075] In an optional embodiment, the construction of the fuzzy rule base combines the cable insulation aging theory and historical failure case analysis. In addition to the above basic rules, parameter change trends and historical test results are also considered. For example, when the deviation distance value of the dielectric loss spectrum shows a continuous growth trend in multiple consecutive measurements, even if the current value does not exceed the threshold, a higher level of abnormal warning may be triggered. This dynamic evaluation mechanism significantly improves the accuracy and timeliness of fault warning.

[0076] In another optional embodiment, the calculation of the fault risk probability distribution map also takes into account the installation environment and operating conditions of the cable. For example, for cable segments buried in high temperature areas or frequently subjected to mechanical vibration, the weight coefficient will be appropriately increased in the risk assessment to reflect the role of these external factors in promoting insulation aging. This comprehensive evaluation method can more comprehensively reflect the actual operating status of the cable.

[0077] For example, when evaluating the outgoing cable of a 110kV substation, the deviation distance value of the dielectric loss spectrum obtained is 1.25, the deviation distance value of the withstand voltage leakage current curve is 0.95, and the deviation distance value of the denoised partial discharge characteristic spectrum is 1.35. After conversion by the fuzzy processing module, the membership of the deviation distance value of the dielectric loss spectrum in the moderate abnormal category is 0.65, and the membership of the slight abnormal category is 0.35; the membership of the deviation distance value of the partial discharge characteristic spectrum in the moderate abnormal category is 0.70, and the membership of the slight abnormal category is 0.30. Calculated by the rule reasoning module, the activation degree of the comprehensive fuzzy reasoning result in the moderate abnormal category is 0.65. Calculated by the centroid method of the defuzzification processing module, the insulation state evaluation index is finally obtained as 65 points, indicating that the cable insulation has been significantly deteriorated and regular inspections need to be arranged.

[0078] At the same time, the cable was divided into 20 monitoring sections along the length direction, and the local discharge characteristic parameters were calculated for each section. The results showed that the discharge density in the section about 100 meters away from the substation exit was 25 times / minute, the discharge amplitude was 55pC, and the discharge frequency distribution was concentrated in 300-500kHz. According to the risk calculation model, the risk coefficient of this section is 0.78, which is significantly higher than other sections. The resulting fault risk probability distribution heat map clearly shows this high-risk area. Based on this, the technicians conducted an on-site inspection and found that the outer sheath of the cable was slightly mechanically damaged, which may cause moisture to penetrate and affect the insulation performance.

[0079] It should be noted that the method of inputting the deviation distance value into the fuzzy reasoning engine, performing nonlinear conversion according to the preset fuzzy rule base, and generating the insulation status evaluation index and fault risk probability distribution map of the distribution network cable has many technical advantages compared with the traditional hard threshold judgment. First, fuzzy reasoning technology can handle the uncertainty and ambiguity in cable insulation evaluation, avoiding misjudgment or missed judgment that may be caused by simple threshold judgment. Secondly, by comprehensively considering multiple parameters and their interactions, a comprehensive evaluation of the cable status is achieved, and the accuracy of the judgment is improved. In particular, the generation of the fault risk probability distribution map breaks through the limitation that traditional cable testing can only give an overall status evaluation but cannot locate the specific fault location, and provides a powerful tool for precise maintenance. In addition, the rule system of the fuzzy reasoning engine combines the dual advantages of expert experience and data-driven, so that the evaluation results are more in line with actual engineering needs.

[0080] In summary, the distribution network cable dielectric loss withstand voltage partial discharge intelligent detection and evaluation method and system provided by the present invention have significant technical advantages: first, three key parameters are synchronously collected by a phase resolution measurement device, and multi-dimensional cable state characterization is realized, overcoming the one-sidedness of traditional single parameter detection; secondly, the frequency domain adaptive filter is applied to process the partial discharge spectrum, and the filtering strategy is dynamically adjusted according to the single peak or multi-peak characteristics of the signal-to-noise ratio distribution, which significantly improves the signal extraction capability under complex environments; thirdly, the support vector data description algorithm is used to construct a normal domain hypersphere model of the cable multi-dimensional parameters, and radial basis function mapping parameters, boundary relaxation parameters and model update mechanism are introduced to achieve accurate modeling of nonlinear data distribution and objective quantification of abnormal states; finally, the deviation distance value is input into the fuzzy inference engine for nonlinear conversion, and the partial discharge density, discharge amplitude and discharge frequency are combined to generate a heat map of the probability distribution of fault risk, which not only realizes the accurate evaluation of the insulation state, but also can accurately locate the specific fault location. In addition, the system also has the ability of adaptive learning and dynamic updating, and can automatically adjust the evaluation parameters as the cable aging process, providing a scientific basis for preventive maintenance decisions, effectively reducing the failure rate of the distribution network, and improving the reliability and economic benefits of the power grid operation.

[0081] Embodiment 2 is an embodiment of the present invention, which provides a distribution network cable dielectric loss withstand voltage partial discharge intelligent detection and evaluation system, including: A signal processing unit is used to synchronously collect the dielectric loss spectrum, withstand voltage leakage current curve and partial discharge spectrum of the distribution network cable through a phase resolution measurement device, and process the partial discharge spectrum using a frequency domain adaptive filter to generate a denoised partial discharge characteristic spectrum; A model building unit is used to build a normal domain hypersphere model of cable multi-dimensional parameters based on historical normal sample data by using a support vector data description algorithm, and calculate the deviation distance value of the dielectric loss spectrum, the withstand voltage leakage current curve and the denoised partial discharge characteristic spectrum relative to the normal domain hypersphere; The risk assessment unit is used to input the deviation distance value into the fuzzy inference engine, perform nonlinear conversion according to the preset fuzzy rule library, generate the insulation status assessment index and fault risk probability distribution diagram of the distribution network cable, and realize accurate assessment of the cable insulation status.

[0082] Example 3, reference Figure 2, is an embodiment of the present invention, which is different from the previous embodiment in that: if the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0083] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0084] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0085] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0086] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. An intelligent detection and evaluation method for dielectric loss, withstand voltage and partial discharge of distribution network cables, characterized in that: include: The dielectric loss spectrum, withstand voltage leakage current curve and partial discharge spectrum of the distribution network cable are synchronously collected by a phase resolution measurement device, and the partial discharge spectrum is processed by a frequency domain adaptive filter to generate a denoised partial discharge characteristic spectrum; the frequency domain adaptive filter includes a fast Fourier transform module, a spectrum analysis module and an adaptive bandpass filter module; Using a support vector data description algorithm, a normal domain hypersphere model of a cable multi-dimensional parameter is constructed based on historical normal sample data, and the deviation distance values ​​of the dielectric loss spectrum, the withstand voltage leakage current curve, and the denoised partial discharge characteristic spectrum relative to the normal domain hypersphere are calculated; The normal domain hypersphere model includes kernel function mapping parameters, boundary relaxation parameters and model update mechanism; The deviation distance value is input into the fuzzy reasoning engine, and nonlinear conversion is performed according to the preset fuzzy rule library to generate the insulation status evaluation index and fault risk probability distribution diagram of the distribution network cable, so as to realize the accurate evaluation of the insulation status of the cable; the fuzzy reasoning engine includes a fuzzification processing module, a rule reasoning module and a defuzzification processing module.

2. The intelligent detection and evaluation method for dielectric loss, withstand voltage and partial discharge of distribution network cables according to claim 1, characterized in that: Applying a frequency domain adaptive filter to process the partial discharge spectrum includes: Converting the partial discharge spectrum into the frequency domain through the fast Fourier transform module; Using the spectrum analysis module to identify characteristic frequency bands of signals and noise, and calculate signal-to-noise ratio distribution; The adaptive bandpass filtering module sets the filtering bandwidth according to the signal-to-noise ratio distribution; when the signal-to-noise ratio distribution presents a unimodal characteristic, a narrowband filtering parameter is set; when the signal-to-noise ratio distribution presents a multimodal characteristic, a multi-channel filtering parameter is set to complete the enhancement of the partial discharge spectrum and the noise suppression.

3. The intelligent detection and evaluation method for dielectric loss, withstand voltage and partial discharge of distribution network cables according to claim 2 is characterized in that: Using the support vector data description algorithm, a normal domain hypersphere model of cable multidimensional parameters is constructed based on historical normal sample data, including: Using radial basis function as the kernel function mapping parameter, the multi-dimensional parameters of the distribution network cable are mapped to a high-dimensional feature space; The sensitivity of the normal domain hypersphere model to abnormal points is controlled by the boundary relaxation parameter; wherein the boundary relaxation parameter is related to the characteristic distribution range of the historical normal sample data; According to the model update mechanism, the normal domain hypersphere model update is triggered when one of the following conditions is met: the number of newly added normal samples reaches a predetermined ratio, and the statistical characteristics of the deviation distance value exceed a preset reference range.

4. The intelligent detection and evaluation method for dielectric loss, withstand voltage and partial discharge of distribution network cables according to claim 3 is characterized in that: The fuzzy processing module converts the deviation distance value into a fuzzy set; wherein the fuzzy set includes four levels: normal, slightly abnormal, moderately abnormal and severely abnormal; The rule inference module performs inference calculation according to the fuzzy rule base to obtain a fuzzy inference result; the fuzzy rule base contains fuzzy rules based on the deviation distance value of the dielectric loss spectrum and the deviation distance value of the partial discharge characteristic spectrum; The defuzzification processing module converts the fuzzy reasoning result into the insulation state evaluation index and the fault risk probability distribution diagram by using the centroid method.

5. The intelligent detection and evaluation method for dielectric loss, withstand voltage and partial discharge of distribution network cables according to claim 4, characterized in that: The fuzzy inference results include: when the deviation distance value of the dielectric loss spectrum is greater than a first threshold and the deviation distance value of the partial discharge characteristic spectrum is greater than a second threshold, it is determined to be a serious abnormality; when the deviation distance value of the dielectric loss spectrum is greater than the first threshold and the deviation distance value of the partial discharge characteristic spectrum is not greater than the second threshold, it is determined to be a moderate abnormality; when the deviation distance value of the dielectric loss spectrum is not greater than the first threshold and the deviation distance value of the partial discharge characteristic spectrum is greater than the second threshold, it is determined to be a slight abnormality; when the deviation distance value of the dielectric loss spectrum is not greater than the first threshold and the deviation distance value of the partial discharge characteristic spectrum is not greater than the second threshold, it is determined to be normal.

6. The intelligent detection and evaluation method for dielectric loss, withstand voltage and partial discharge of distribution network cables according to claim 5, characterized in that: The calculation of the deviation distance value includes: Taking the center of the normal domain hypersphere as the origin, establishing a kernel function mapping coordinate system; Calculating the Euclidean distances of the dielectric loss spectrum, the withstand voltage leakage current curve, and the denoised partial discharge characteristic spectrum in the kernel function mapping coordinate system; The deviation distance value is determined according to the ratio of the Euclidean distance to the radius of the normal domain hypersphere.

7. The intelligent detection and evaluation method for dielectric loss, withstand voltage and partial discharge of distribution network cables according to claim 6, characterized in that: The generation process of the fault risk probability distribution diagram includes: Dividing the distribution network cable into a plurality of monitoring sections along the length direction; calculating the local discharge density, discharge amplitude and discharge frequency for the plurality of monitoring sections respectively; Based on the local discharge density, the discharge amplitude and the discharge frequency, a risk coefficient of each monitoring section is calculated; wherein, when the local discharge density and the discharge amplitude increase or decrease simultaneously, the risk coefficient is proportional to the product of the local discharge density and the discharge amplitude; when the local discharge density increases and the discharge amplitude decreases or the local discharge density decreases and the discharge amplitude increases, the risk coefficient remains within a preset range; Combined with the insulation status evaluation index, the failure risk probability of the several monitoring sections is visualized in the form of a heat map.

8. A distribution network cable dielectric loss, withstand voltage and partial discharge intelligent detection and evaluation system, based on the distribution network cable dielectric loss, withstand voltage and partial discharge intelligent detection and evaluation method according to any one of claims 1 to 7, characterized in that: include, A signal processing unit, used to synchronously collect the dielectric loss spectrum, withstand voltage leakage current curve and partial discharge spectrum of the distribution network cable through a phase resolution measurement device, and process the partial discharge spectrum by applying a frequency domain adaptive filter to generate a denoised partial discharge characteristic spectrum; A model building unit, used to build a normal domain hypersphere model of cable multi-dimensional parameters based on historical normal sample data by using a support vector data description algorithm, and calculate the deviation distance value of the dielectric loss spectrum, the withstand voltage leakage current curve and the denoised partial discharge characteristic spectrum relative to the normal domain hypersphere; The risk assessment unit is used to input the deviation distance value into a fuzzy inference engine, perform nonlinear conversion according to a preset fuzzy rule library, generate an insulation status assessment index and a fault risk probability distribution diagram of the distribution network cable, and realize accurate assessment of the cable insulation status.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent detection and evaluation method for dielectric loss, withstand voltage and partial discharge of distribution network cables according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent detection and evaluation method for dielectric loss, withstand voltage and partial discharge of distribution network cables according to any one of claims 1 to 7 are implemented.

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