An integrated test method and system for cable oscillating ultra-low frequency withstand voltage and partial discharge

Abstract

The present invention discloses a method and system for integrated testing of oscillating ultra-low frequency withstand voltage and partial discharge of cables, which relates to the technical field of distribution network cable detection, and includes: applying an oscillating ultra-low frequency voltage signal to the cable under test; the oscillating ultra-low frequency voltage signal is optimized and adjusted through a manifold learning algorithm and used as a common excitation source for withstand voltage test, dielectric loss detection and partial discharge detection; obtaining the voltage signal and current signal of the cable under test under the excitation of the oscillating ultra-low frequency voltage signal, analyzing the deviation degree of the voltage signal and current signal from the pre-built feature manifold, and extracting the dielectric loss feature signal and partial discharge pulse signal; calculating the dielectric loss parameter based on the dielectric loss feature signal, determining the partial discharge position based on the partial discharge pulse signal, and generating a cable insulation status diagnosis report according to the dielectric loss parameter and partial discharge position. The present invention improves the cable testing efficiency, reduces the equipment burden and testing time, realizes the comprehensive evaluation of the insulation status and accurate fault location, and provides a scientific basis for the preventive maintenance of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network cable detection, and particularly to a method and system for integrated testing of cable oscillating ultra-low frequency withstand voltage and partial discharge. Background Art

[0002] The assessment of the insulation state of cables in the power distribution network system has always been a key link in the reliable operation of the power system. With the continuous advancement of the urbanization process, the underground distribution network has become increasingly complex, and cable insulation faults have become one of the main types of faults in the distribution network system. Traditional cable detection technologies mainly include three independent methods: dielectric loss testing, withstand voltage testing, and partial discharge detection. Dielectric loss testing usually uses power frequency or very low frequency signals to evaluate the overall degradation state of the insulation; withstand voltage testing uses high-voltage DC or 0.1Hz ultra-low frequency signals to verify the electrical strength of the insulation; partial discharge detection requires high-frequency sampling technology to capture weak discharge pulse signals. These three detection methods target different insulation characteristics and require different excitation sources and measurement devices, resulting in a complex on-site detection process, low efficiency, redundant equipment, and difficult comprehensive correlation analysis of detection results.

[0003] The existing technologies have deficiencies in the assessment of cable insulation state in many aspects. First, traditional detection technologies need to conduct multiple tests on the same cable, which not only prolongs the power outage time, increases the operation and maintenance costs, but also may accelerate the insulation degradation due to repeated application of high voltage. Second, the test results of each item are independent of each other, lacking an effective correlation analysis mechanism and unable to comprehensively reflect the complexity of the insulation state. For example, dielectric loss testing may show overall insulation aging, but it cannot locate the specific defect position; while partial discharge detection can locate the defect, but it is difficult to evaluate the overall insulation degradation degree. In addition, traditional detection methods often use test signals with fixed parameters and lack the adaptive ability to different cable characteristics, resulting in limited test sensitivity and accuracy. Finally, the existing technologies usually adopt empirical rules or simple threshold judgments in the signal processing and diagnosis process, and do not fully utilize advanced signal processing and artificial intelligence technologies, making it difficult to cope with the diverse fault modes in complex cable systems. Summary of the Invention

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

[0005] Therefore, the present invention provides a method and system for integrated testing of cable oscillating ultra-low frequency withstand voltage and partial discharge, which can solve the problems mentioned in the background art.

[0006] To solve the above technical problems, the present invention provides the following technical solution: A method for integrated testing of oscillating ultra-low frequency withstand voltage and partial discharge of a cable, comprising: applying an oscillating ultra-low frequency voltage signal to the cable under test; the oscillating ultra-low frequency voltage signal is optimized and adjusted by a manifold learning algorithm and used as a common excitation source for withstand voltage test, dielectric loss detection and partial discharge detection; obtaining the voltage signal and current signal of the cable under test under the excitation of the oscillating ultra-low frequency voltage signal, analyzing the deviation degree of the voltage signal and the current signal from a pre-established feature manifold, and extracting dielectric loss characteristic signals and partial discharge pulse signals; calculating dielectric loss parameters based on the dielectric loss characteristic signals, determining the partial discharge position based on the partial discharge pulse signals, and generating a cable insulation status diagnosis report according to the dielectric loss parameters and the partial discharge position.

[0007] As a preferred embodiment of the method for integrated testing of oscillating ultra-low frequency withstand voltage and partial discharge of the cable according to the present invention, wherein: the optimization and adjustment of the manifold learning algorithm includes: establishing a parameter space according to the type of the cable under test; generating a test parameter matrix in the parameter space; collecting test response characteristics to construct a feature manifold; optimizing the frequency, amplitude and waveform parameters of the oscillating ultra-low frequency voltage signal based on the feature manifold.

[0008] As a preferred embodiment of the method for integrated testing of oscillating ultra-low frequency withstand voltage and partial discharge of the cable according to the present invention, wherein: obtaining the voltage signal and current signal of the cable under test under the excitation of the oscillating ultra-low frequency voltage signal includes: collecting voltage sampling data and current sampling data; performing signal preprocessing on the voltage sampling data and the current sampling data; converting the preprocessed sampling data into the voltage signal and the current signal, and mapping them to a preset feature space.

[0009] As a preferred embodiment of the method for integrated testing of oscillating ultra-low frequency withstand voltage and partial discharge of the cable according to the present invention, wherein: the extraction of the dielectric loss characteristic signals and partial discharge pulse signals includes: performing orthogonal decomposition on the voltage signal and the current signal to obtain orthogonal components; performing time-frequency analysis on the orthogonal components to obtain energy distribution characteristics; classifying the orthogonal components into dielectric loss-related components and partial discharge-related components based on the energy distribution characteristics; extracting partial discharge characteristics from the partial discharge-related components to form the partial discharge pulse signals; extracting phase difference and amplitude ratio parameters from the dielectric loss-related components to form the dielectric loss characteristic signals.

[0010] As a preferred embodiment of the integrated test method for cable oscillating ultra-low frequency withstand voltage and partial discharge of the present invention, it includes: analyzing the deviation degrees of the voltage signal and the current signal from a pre-established characteristic manifold, including: calculating the distance parameters between the characteristic parameters of the voltage signal and the current signal and the standard characteristic manifold; determining the deviation state according to the distance parameters; if the deviation state exceeds the reference range, adjusting the frequency and amplitude of the oscillating ultra-low frequency voltage signal to make the deviation state return to the reference range; otherwise, maintaining the current oscillating ultra-low frequency voltage signal parameters and continuing the test process.

[0011] As a preferred embodiment of the integrated test method for cable oscillating ultra-low frequency withstand voltage and partial discharge of the present invention, it includes: determining the partial discharge position based on the partial discharge pulse signal, including: extracting the time characteristics of the partial discharge pulse signal; calculating the pulse propagation time difference; calculating the distance of the partial discharge source according to the propagation time difference and the cable propagation speed; calibrating the physical position of the cable corresponding to the distance of the partial discharge source.

[0012] As a preferred embodiment of the integrated test method for cable oscillating ultra-low frequency withstand voltage and partial discharge of the present invention, it includes: determining the partial discharge position based on the partial discharge pulse signal, including: extracting the time characteristics of the partial discharge pulse signal; calculating the pulse propagation time difference; calculating the distance of the partial discharge source according to the propagation time difference and the cable propagation speed; calibrating the physical position of the cable corresponding to the distance of the partial discharge source.

[0013] To further solve the above technical problems, the present invention provides the following technical solution: an integrated test system for cable oscillating ultra-low frequency withstand voltage and partial discharge, including: a signal excitation module for applying an oscillating ultra-low frequency voltage signal to the cable under test, and the oscillating ultra-low frequency voltage signal is optimized and adjusted by a manifold learning algorithm and serves as a common excitation source for withstand voltage test, dielectric loss detection and partial discharge detection; a signal processing module for acquiring the voltage signal and the current signal of the cable under test under the excitation of the oscillating ultra-low frequency voltage signal, analyzing the deviation degrees of the voltage signal and the current signal from a pre-established characteristic manifold, and extracting dielectric loss characteristic signals and partial discharge pulse signals; a state diagnosis module for calculating dielectric loss parameters based on the dielectric loss characteristic signals, determining the partial discharge position based on the partial discharge pulse signals, and generating a cable insulation state diagnosis report according to the dielectric loss parameters and the partial discharge position.

[0014] A computer device includes a memory and a processor, the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the integrated test method for cable oscillating ultra-low frequency withstand voltage and partial discharge as described above are implemented.

[0015] A computer-readable storage medium stores a computer program thereon, characterized in that when the computer program is executed by a processor, the steps of the cable oscillating ultra-low frequency withstand voltage and partial discharge integrated test method as described above are implemented.

[0016] Advantages of the present invention: By applying the manifold learning algorithm to optimize the oscillating ultra-low frequency voltage signal, the present invention successfully integrates the three tests of dielectric loss test, withstand voltage test, and partial discharge detection, which were traditionally carried out separately, into a combined test completed at one time, effectively solving the problems of extended power outage time, equipment redundancy, and accelerated insulation degradation caused by multiple tests in the prior art. The modified sine wave oscillating ultra-low frequency voltage signal adopted by the signal excitation module, through special waveform design, significantly enhances the dielectric loss characteristics and the sensitivity to partial discharge pulses while meeting the requirements of cable withstand voltage test, improving the measurement accuracy and detection sensitivity. The signal processing module combines the multi-resolution analysis method with the ensemble empirical mode decomposition algorithm, overcomes the mode mixing problem of the traditional EMD method, realizes the efficient separation of the dielectric loss characteristic signal and the partial discharge pulse signal, and effectively improves the signal-to-noise ratio. The state diagnosis module establishes an accurate identification mechanism for composite faults, insulation degradation, and local defects by comprehensively analyzing the dielectric loss parameters and the partial discharge position distribution characteristics, improves the partial discharge source location accuracy, and breaks through the technical bottleneck that is difficult to comprehensively diagnose by traditional methods. Overall, the present invention realizes the comprehensive evaluation of the cable insulation state and accurate fault location, provides a scientific basis for the preventive maintenance of the power system, and effectively improves the reliability and safety of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0018] Figure 1 It is a schematic diagram of the overall process of the cable oscillating ultra-low frequency withstand voltage and partial discharge integrated test method proposed by the present invention;

[0019] Figure 2 It is a diagram of a computer device in the cable oscillating ultra-low frequency withstand voltage and partial discharge integrated test method proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following provides a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the scope of protection of the present invention.

[0021] In the following description, many specific details are set forth to facilitate a thorough understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0022] Example 1, referring to Figure 1 , which is an embodiment of the present invention, provides an integrated test method for cable oscillating ultra-low frequency withstand voltage and partial discharge.

[0023] Figure 1 FIG. shows a schematic overall flow diagram of an integrated test method for cable oscillating ultra-low frequency withstand voltage and partial discharge, including the following steps:

[0024] S1: Apply an oscillating ultra-low frequency voltage signal to the cable under test.

[0025] Among them, the oscillating ultra-low frequency voltage signal is optimized and adjusted through a manifold learning algorithm and serves as a common excitation source for withstand voltage test, dielectric loss detection, and partial discharge detection.

[0026] Specifically, the optimization and adjustment of the manifold learning algorithm include: establishing a parameter space according to the type of the cable under test; generating a test parameter matrix within the parameter space; collecting test response characteristics to construct a feature manifold; and optimizing the frequency, amplitude, and waveform parameters of the oscillating ultra-low frequency voltage signal based on the feature manifold.

[0027] In the embodiments of the present application, the specific implementation process of step S1 may be:

[0028] First, apply an oscillating ultra-low frequency voltage signal to the cable under test. The oscillating ultra-low frequency voltage signal adopts a frequency range of 0.01 Hz - 0.1 Hz, and the specific frequency value is initially selected according to the type and length of the cable. In actual implementation, the oscillating ultra-low frequency voltage signal is generated through a half-bridge inverter circuit and applied to the cable under test through a high-voltage step-up transformer. The initial peak voltage is set to 1.5 - 1.7 times the rated voltage of the cable to ensure that the requirements for withstand voltage test can be met simultaneously.

[0029] In an alternative embodiment, the oscillating ultra-low frequency signal can be generated by a full-bridge inverter circuit. Compared with a half-bridge inverter circuit, the full-bridge structure can provide higher voltage output efficiency and lower harmonic distortion, and is particularly suitable for test scenarios of high-voltage cables of 35 kV and above.

[0030] Among them, the oscillating ultra-low frequency voltage signal is optimized and adjusted through a manifold learning algorithm to be a common excitation source for withstand voltage tests, dielectric loss detection, and partial discharge detection. The specific adjustment steps are as follows:

[0031] (1) Establish a parameter space according to the type of cable under test: The system first constructs a three-dimensional parameter space including a frequency range, an amplitude range, and a waveform type based on basic parameters such as the insulation type (such as XLPE, EPR, etc.), rated voltage level (such as 10 kV, 35 kV, etc.), cable cross-sectional area, and length of the cable under test. For example, for a 10 kV, 300 mm² XLPE cable, the frequency range is set to 0.01 Hz - 0.05 Hz, the amplitude range is set to 1.5U0 - 1.7U0 (U0 is the rated phase voltage), and the waveform types include sine wave, square wave correction wave, and trapezoidal wave.

[0032] In an alternative embodiment, the parameter space can be extended to a four-dimensional space by adding the voltage rise rate as the fourth-dimensional parameter, enabling the present invention to more precisely control the process of cable insulation under pressure, especially suitable for test scenarios of severely aged cables, and effectively reducing the breakdown risk during the test.

[0033] (2) Generate a test parameter matrix within the parameter space: The parameter space is discretized into multiple test points to form a test parameter matrix. Specifically, values are taken at intervals of 0.005 Hz in the frequency dimension, at intervals of 0.05U0 in the amplitude dimension, and three typical waveforms are selected in the waveform type dimension, thereby constructing a parameter matrix containing approximately 100 - 200 test points. Each test point represents a set of alternative oscillating ultra-low frequency voltage signal parameter combinations.

[0034] In an alternative embodiment, an adaptive grid division method can be used to generate the parameter matrix, with a smaller grid interval (such as a frequency interval of 0.002 Hz) used in the cable response sensitive area and a larger grid interval (such as 0.01 Hz) used in the response flat area. This non-uniform sampling method can improve the detection accuracy of the key parameter area while keeping the computational effort basically unchanged.

[0035] (3) Collect test response features to construct a feature manifold: Conduct a low-voltage pre-test (usually 30% of the rated voltage) on each parameter combination point in the parameter matrix, and collect the characteristic parameters of the response of the cable under test, including: voltage-current phase difference, harmonic content, current density distribution, transient response characteristics, etc. These characteristic parameters form a high-dimensional feature vector, and through dimensionality reduction methods such as principal component analysis (PCA) and locally linear embedding (LLE), the high-dimensional feature vector is mapped into a three-dimensional space to form a visualizable feature manifold. This feature manifold reflects the distribution of the response characteristics of the cable under test under different excitation parameters.

[0036] In an optional embodiment, more advanced algorithms such as t-SNE (t-distributed Stochastic Neighbor Embedding) or UMAP (Uniform Manifold Approximation and Projection) can be used for feature dimensionality reduction. These algorithms can better preserve the global topological relationship while maintaining the local structure, and are particularly suitable for feature analysis of complex cable systems. In addition, the pre-test voltage can be dynamically adjusted according to the cable condition. For newly laid cables, a 50% rated voltage can be used for pre-test to obtain richer feature information.

[0037] (4) Optimize the parameters of the oscillating ultra-low-frequency voltage signal based on the feature manifold: According to the pre-established standard cable feature manifold library, analyze the topological structure of the current cable under test feature manifold, and identify the optimal excitation region. Specifically, by calculating the manifold curvature and sensitivity distribution, determine the parameter region with the highest signal-to-noise discrimination, and select the optimal parameter combination in this region as the parameters of the final oscillating ultra-low-frequency voltage signal. For example, for a certain 10kV XLPE cable, the finally determined optimal parameters can be: frequency 0.035Hz, amplitude 1.6U0, waveform type is a modified sine wave, and the correction coefficient is 0.8.

[0038] It should be noted that when determining the optimal parameter region, the present invention uses the Fisher discriminant ratio to evaluate the signal-to-noise discrimination:

[0039] ;

[0040] Among them, represents the Fisher discriminant ratio under parameter p, and are the means of the signal and noise respectively, and They are the variances of the signal and the noise respectively. It should be noted that the parameter p refers to the parameter combination of the oscillating ultra-low frequency voltage signal, specifically including three key parameters: frequency, amplitude, and waveform type (including waveform correction coefficient). The system needs to search for the optimal parameter combination in the parameter space so that, under this parameter combination, the discrimination between the cable dielectric loss characteristics and the partial discharge signal and the background noise is the highest, that is, the Fisher discrimination ratio is the largest. This discrimination ratio is not only used for conventional signal discrimination in the present invention, but also applied to the parameter optimization process of the oscillating ultra-low frequency signal. Through the maximization of the discrimination ratio, a technical breakthrough has been achieved in sharing one excitation source for the withstand voltage test, dielectric loss detection, and partial discharge detection, which cannot be achieved in the prior art. Traditional methods require different test signals to complete these three tests separately, while the present invention has found the optimal signal parameters that can simultaneously meet the requirements of the three tests through parameter optimization guided by the Fisher discrimination ratio.

[0041] In an alternative embodiment, the optimal parameters can be selected by combining the deep reinforcement learning method. By establishing a comprehensive reward function for dielectric loss detection accuracy, partial discharge sensitivity, and test safety, an intelligent agent is trained to explore the optimal strategy in the parameter space. This method has better adaptability for complex insulation structures (such as multi-branch cable systems). Another alternative is to use the genetic algorithm to optimize the parameter selection process. By simulating the biological evolution process, the optimal parameter combination is iteratively searched, and the search efficiency for a large parameter space is relatively high.

[0042] After completing the parameter optimization, the digital signal processor DSP is used to control the power inverter unit to generate an oscillating ultra-low frequency voltage signal that meets the requirements of the optimized parameters, and it is applied to the cable under test through a high-voltage transformer, realizing the shared excitation source for the withstand voltage test, dielectric loss detection, and partial discharge detection, and providing a high-quality basic signal excitation for the subsequent comprehensive cable insulation condition assessment.

[0043] Based on the above optimization process, the present invention proposes a modified sine wave oscillating ultra-low frequency voltage signal with a special structure:

[0044] ;

[0045] where is the voltage value at time t, A is the optimized amplitude, f is the optimized frequency, is the waveform correction coefficient (usually taking values between 0.1 - 0.3). This formula synthesizes the basic sine wave and its third harmonic to generate a special waveform. While meeting the requirements of the cable withstand voltage test, by adjusting the parameter, the sensitivity to dielectric loss characteristics and partial discharge pulses is significantly enhanced. Experiments show that when When taking 0.2, compared with the pure sine wave signal, the corrected waveform can improve the dielectric loss measurement accuracy by 30%, and at the same time increase the partial discharge detection sensitivity by about 25 pC.

[0046] In an optional embodiment, the present invention can have the ability of online parameter fine-tuning. During the test process, according to the real-time response characteristics of the cable, the frequency and amplitude of the oscillating ultra-low frequency signal are dynamically adjusted to make the test process safer and more efficient. For example, when a sudden change in dielectric loss characteristics is detected, the voltage amplitude can be appropriately reduced or the frequency can be adjusted to prevent potential breakdown risks.

[0047] The present invention improves the test efficiency by integrating three traditional tests (dielectric withstand, dielectric loss, and partial discharge) that need to be carried out separately into a combined test completed at one time. The test time is shortened from the original 4 - 6 hours to 1 - 2 hours. Moreover, due to the use of an optimized ultra-low frequency signal, the volume and weight of the test equipment are greatly reduced, which is convenient for on-site carrying and operation.

[0048] S2: Obtain the voltage signal and current signal of the cable under test under the excitation of the oscillating ultra-low frequency voltage signal, analyze the deviation degree of the voltage signal and the current signal from the pre-established feature manifold, and extract the dielectric loss characteristic signal and the partial discharge pulse signal.

[0049] Specifically, the obtaining of the voltage signal and current signal of the cable under test under the excitation of the oscillating ultra-low frequency voltage signal includes: collecting voltage sampling data and current sampling data; performing signal preprocessing on the voltage sampling data and the current sampling data; converting the preprocessed sampling data into the voltage signal and the current signal, and mapping them to a preset feature space.

[0050] Specifically, the extraction of the dielectric loss characteristic signal and the partial discharge pulse signal includes: performing orthogonal decomposition on the voltage signal and the current signal to obtain orthogonal components; performing time-frequency analysis on the orthogonal components to obtain energy distribution characteristics; classifying the orthogonal components into dielectric loss-related components and partial discharge-related components based on the energy distribution characteristics; extracting partial discharge characteristics from the partial discharge-related components to form the partial discharge pulse signal; extracting phase difference and amplitude ratio parameters from the dielectric loss-related components to form the dielectric loss characteristic signal.

[0051] Specifically, the analysis of the deviation degree of the voltage signal and the current signal from the pre-established feature manifold includes: calculating the distance parameter between the characteristic parameters of the voltage signal and the current signal and the standard feature manifold; determining the deviation state according to the distance parameter; if the deviation state exceeds the reference range, adjust the frequency and amplitude of the oscillating ultra-low frequency voltage signal to make the deviation state return to the reference range; otherwise, maintain the current oscillating ultra-low frequency voltage signal parameters and continue the test process.

[0052] In the embodiment of the present application, the specific implementation process of step S2 can be as follows:

[0053] First, obtain the voltage signal and current signal of the cable under test under the excitation of a modified sine wave oscillating ultra-low frequency voltage signal. The acquisition is carried out using a high-precision sensor array. Among them, the voltage acquisition adopts a dual-channel scheme combining a resistor voltage divider and a high-voltage probe, and the current acquisition adopts a dual-channel scheme combining a current transformer and a Rogowski coil. The voltage sampling frequency is set to 10 MHz, and the current sampling frequency is set to 20 MHz to ensure that low-frequency dielectric loss characteristics and high-frequency partial discharge pulses can be captured simultaneously.

[0054] In an optional embodiment, an optoelectronic hybrid sensor array can also be used for acquisition, converting the voltage and current signals into optical signals for transmission, effectively avoiding the interference of strong electromagnetic fields on weak signals, especially suitable for ultra-high voltage cable test environments, and can improve the signal-to-noise ratio by 5 - 10 dB.

[0055] Among them, the obtained voltage signal and current signal need to go through key steps such as signal preprocessing, feature space mapping, and orthogonal decomposition. The specific implementation is as follows:

[0056] First, perform signal preprocessing on the voltage sampling data and the current sampling data:

[0057] Perform preprocessing operations through methods such as adaptive wavelet denoising, baseline drift correction, outlier processing, and phase synchronization correction. The same wavelet basis function selection strategy as in step S1 is adopted in the denoising process. The symlet wavelet is used for low-frequency signals, and the Daubechies wavelet is used for high-frequency pulse signals. The baseline correction window length is set to 3 - 5 oscillation periods, and the phase correction accuracy is better than 0.01° to ensure the accuracy of dielectric loss measurement.

[0058] After preprocessing, reconstruct the continuous voltage signal and current signal through the B-spline interpolation algorithm and map them to a preset feature space. When the preprocessed signal is mapped to the preset feature space, a manifold learning technique based on the kernel method is adopted. Specifically, the Gaussian radial basis function is used as the kernel function, and the bandwidth parameter is adaptively set to 0.5 times the average distance between samples. The mapping process includes two steps: feature embedding and manifold reconstruction. The embedding dimension is determined according to the cumulative variance contribution rate, and generally, the number of dimensions with a cumulative contribution rate reaching 95% is selected. This feature space includes a phase-amplitude plane, a harmonic component space, a time-frequency distribution plane, and a statistical feature space, which is consistent with the feature manifold framework established in step S1. Through dimensionality reduction techniques such as principal component analysis, the most discriminative feature subspace is extracted to lay a foundation for subsequent analysis.

[0059] Secondly, orthogonal decomposition is performed on the voltage signal and the current signal to obtain mutually orthogonal sub-components. Specifically, the ensemble empirical mode decomposition algorithm is adopted, and by introducing noise assistance and extreme point optimization strategies, the mode mixing problem of the traditional EMD method is overcome. Time-frequency analysis is performed on each decomposed sub-component, and a hybrid algorithm of S-transform and synchrosqueezing transform is used to achieve high-resolution time-frequency representation and reveal the distribution characteristics of signal energy in the time-frequency plane.

[0060] When performing time-frequency analysis on the orthogonal components, the instantaneous energy density at each time-frequency point is calculated, and a three-dimensional energy distribution map is constructed. The map uses a logarithmic scale on the time axis to better display the energy distribution at different time scales. The energy distribution characteristics are obtained by calculating the statistical moments in the time-frequency plane, including the first moment (energy center), the second moment (energy diffusivity), and the higher-order moments (energy skewness and kurtosis), forming a 12-dimensional feature vector in total.

[0061] Based on the time-frequency characteristics, the fuzzy C-means clustering algorithm is applied to divide all orthogonal components into dielectric loss-related components and partial discharge-related components. The same weight adjustment strategy as in the Fisher discriminant analysis in step S1 is adopted in the clustering process to ensure classification accuracy. For the components in the fuzzy region, the system further applies wavelet packet energy entropy analysis for fine classification.

[0062] From the partial discharge-related components, the dynamic threshold detection algorithm is used to identify partial discharge pulse events, and the sensitivity coefficient is set to 3.5 - 4.5. For each detected pulse event, the system extracts its waveform characteristics, time characteristics, and statistical characteristics, and classifies the discharge type into internal discharge, surface discharge, corona discharge, or floating discharge, etc. through the pattern recognition algorithm.

[0063] When extracting partial discharge characteristics from the partial discharge-related components to form partial discharge pulse signals, first, 6 key parameters are extracted for each detected partial discharge event: peak value, rise time, duration, phase angle, energy, and waveform factor. Then these parameters are integrated into a standardized partial discharge pulse signal through the weighted synthesis method, and the weight coefficients are determined by the Fisher discriminant ratio. The finally formed partial discharge pulse signal retains the timing information and amplitude characteristics of the original partial discharge event, while removing random noise and interference components, and the signal-to-noise ratio is increased by 8 - 12 dB.

[0064] From the dielectric loss-related components, first, the fundamental frequency component is extracted, the center frequency is set to the fundamental frequency (0.1 Hz) of the oscillating ultra-low-frequency voltage signal, and the bandwidth is 2% of the fundamental frequency. By combining zero-crossing detection and Hilbert transform, the phase difference between the voltage and current fundamental components is accurately calculated, and the dielectric loss tangent value is calculated based on this. At the same time, the system analyzes the variation characteristics of the dielectric loss value with frequency to obtain the frequency response characteristics reflecting the dielectric polarization loss mechanism.

[0065] Finally, analyze the deviation degree of the acquired voltage signal and current signal from the pre - constructed feature manifold. First, extract the key feature parameters to form a feature vector with the same dimension as in step S1, and calculate the geodesic distance between it and the standard feature manifold. Evaluate the deviation degree at multiple scales and comprehensively calculate the overall deviation index.

[0066] According to the deviation index value, classify the current test state into three categories: normal state (deviation index < 1.0), slight deviation (1.0 ≤ deviation index < 1.5), and severe deviation (deviation index ≥ 1.5). For the severe deviation state, start the parameter adjustment process; otherwise, keep the current parameter settings and continue the test process.

[0067] When parameter adjustment is required, based on manifold learning and optimal control theory, calculate the optimal adjustment amounts of frequency and amplitude. The goal is to make the test features return to the standard manifold while minimizing the parameter change amplitude. The parameter adjustment is carried out smoothly to avoid sudden interference. After adjustment, the system automatically re - evaluates the deviation state to confirm the adjustment effect.

[0068] In an optional embodiment, a model predictive control method can be used to optimize the parameter adjustment process, considering the cumulative effect of multi - step adjustments to avoid oscillations that may be caused by simple adjustments. In addition, the system can also integrate a reinforcement learning module. By recording the parameter adjustment history and effects, continuously optimize the adjustment strategy to achieve adaptive control.

[0069] Through the implementation of step S2 above, the present invention can stably acquire the response signal of the cable under test under the optimized oscillating ultra - low - frequency voltage signal excitation, accurately extract the dielectric loss characteristics and partial discharge pulse signals, and dynamically monitor and adjust the test state during the test to ensure the data quality. This provides high - quality basic data support for subsequent insulation state assessment.

[0070] S3: Calculate the dielectric loss parameter based on the dielectric loss characteristic signal, determine the partial discharge position based on the partial discharge pulse signal, and generate a cable insulation state diagnosis report according to the dielectric loss parameter and the partial discharge position.

[0071] Specifically, the determining the partial discharge position based on the partial discharge pulse signal includes: extracting the time characteristics of the partial discharge pulse signal; calculating the pulse propagation time difference; calculating the distance of the partial discharge source according to the propagation time difference and the cable propagation speed; and calibrating the physical position of the cable corresponding to the distance of the partial discharge source.

[0072] Specifically, the generation of the cable insulation status diagnosis report includes: determining the status of the insulating medium based on the dielectric loss parameter; determining the defect characteristics based on the partial discharge position; if the dielectric loss parameter exceeds the standard range and the partial discharge position shows a regular distribution, it is determined as a composite fault; if only the dielectric loss parameter exceeds the standard range, it is determined as insulation deterioration; if only the partial discharge position shows a regular distribution, it is determined as a local defect; combining the status of the insulating medium and the defect characteristics to form a diagnosis result including fault location information and reliability assessment.

[0073] In a preferred embodiment of the present invention, the specific implementation process of step S3 is as follows:

[0074] Based on the dielectric loss characteristic signal and partial discharge pulse signal obtained in step S2, insulation status diagnosis is performed. First, calculate the dielectric loss parameter, extract multi-dimensional features, and reflect the overall deterioration status of the insulating medium; secondly, determine the partial discharge position and accurately locate local defects; finally, comprehensively analyze these two types of information to generate a comprehensive insulation status diagnosis report.

[0075] The calculation of the dielectric loss parameter adopts the frequency-band vector analysis method. The system calculates the fundamental frequency dielectric loss tangent value according to the phase difference and amplitude ratio extracted in step S2 , through the following vector formula:

[0076] ;

[0077] where represents the phase difference angle between voltage and current. The measurement accuracy is better than 0.0001, meeting the measurement requirements of ultra-low loss XLPE cables.

[0078] Analyze the variation relationship of dielectric loss with frequency and construct the dielectric loss spectrum characteristics. Utilize multiple harmonic components in the oscillating ultra-low frequency signal to obtain the to range to obtain the -frequency curve. Fit through the Cole-Cole model:

[0079] ;

[0080] where represents the zero-frequency dielectric loss value, represents the high-frequency limit dielectric loss value, represents the characteristic frequency, represents the frequency dispersion index, is the test frequency. The system extracts these model parameters as dielectric loss spectrum characteristics to reflect the loss characteristics under different polarization mechanisms.

[0081] In an optional embodiment, a three-dimensional characterization technique of dielectric loss can be adopted to construct a three-dimensional characteristic surface based on the variation of dielectric loss values with frequency and electric field strength, which can more comprehensively reflect the insulation deterioration characteristics. This method is particularly suitable for the deterioration diagnosis of complex insulation systems and can improve the accuracy of water tree aging identification by 15 - 20%.

[0082] Perform temperature correction on the measured dielectric loss values to ensure the comparability of test results at different temperatures. The correction uses an exponential model:

[0083] ;

[0084] Wherein, represents the dielectric loss value at the standard temperature, represents the dielectric loss value at the measured temperature, represents the temperature correction coefficient, is the measured temperature, is the standard temperature (20 °C). For XLPE insulation, takes values from 0.025 to 0.035; for oil-paper insulation, takes values from 0.045 to 0.055.

[0085] Analyze the voltage non-linearity characteristics of dielectric loss, which is a sensitive indicator of water tree aging. By fitting the voltage-dielectric loss curve:

[0086] ;

[0087] Wherein, represents the dielectric loss value when the electric field strength is , represents the dielectric loss value under the reference electric field strength , is the non-linear exponent. For healthy insulation, is close to 0, and for water tree aging insulation, is significantly greater than 0.

[0088] Calculate the historical change rate of dielectric loss , and adopt a normalization processing method to eliminate the influence of test condition differences:

[0089] ;

[0090] Wherein, represents the time interval (years) between two tests. Through the above calculations, the system forms a multi-dimensional dielectric loss feature vector including fundamental frequency dielectric loss values, frequency characteristic parameters, temperature correction coefficients, voltage non-linear exponents, and historical change rates.

[0091] For the determination of the partial discharge location, the time characteristics of the partial discharge pulse signal are first extracted. The multi-resolution singularity detection algorithm based on wavelet transform is adopted to accurately locate the pulse wavefront. This algorithm uses the adaptive threshold technology to process the maximum value of the wavelet coefficients, and the positioning accuracy is better than 1 / 10 of the sampling time interval, realizing sub-sampling accuracy. The system simultaneously identifies the pulse peak time and the termination time to establish a complete timing feature model.

[0092] The double-ended synchronous measurement or the single-ended reflection method is used to calculate the time difference of the partial discharge pulse propagation. The double-ended measurement adopts the high-precision time synchronization technology based on the PTP protocol, and the synchronization error is less than 50 ns. The system calculates the time difference of the same discharge event reaching both ends. :

[0093] ;

[0094] Among them, and are the times when the pulse reaches both ends of the cable respectively. In the reflection method, the system measures the time interval between the original pulse and its end-reflected echo :

[0095] ;

[0096] Based on the time difference of propagation and the pulse propagation speed, the system calculates the location of the partial discharge source. For the double-ended measurement method, the distance from the partial discharge source to the reference end is calculated as:

[0097] ;

[0098] Among them, is the total length of the cable, is the pulse propagation speed in the cable. For the reflection method, the distance from the partial discharge source to the measurement end is calculated as:

[0099] ;

[0100] The present invention adopts the adaptive calibration technology to determine the pulse propagation speed. By injecting calibration pulses at known positions of the cable, measuring the actual propagation time, and dynamically calculating the propagation speed. The present invention takes into account the dispersion characteristics of different frequency components and improves the positioning accuracy through the dispersion compensation algorithm. For XLPE insulated cables, the typical propagation speed is 0.5 - 0.6 times the speed of light; for oil-paper insulated cables, the propagation speed is 0.3 - 0.4 times the speed of light.

[0101] Furthermore, map the calculated distance of the partial discharge source to the actual physical location of the cable. The present invention integrates a cable path geographic information system, including key node information such as laying paths, joint positions, and branch points. Through piecewise linear mapping and node matching algorithms, convert the distance value into geographical coordinates or physical description locations. The system considers non-linear factors in cable laying, such as the coiling coefficient and length changes caused by temperature, further improving the calibration accuracy. The final positioning accuracy is within 1% of the cable length.

[0102] Based on the calculated dielectric loss parameters, the system evaluates the state of the insulating medium. The system adopts a multi-threshold grading evaluation method, and based on standards specific to cable types, divides the insulation state into five levels:

[0103] 1. The excellent state is:

[0104] , , ;

[0105] 2. The good state is:

[0106] , , ;

[0107] 2. The attention state is:

[0108] , , ;

[0109] 3. The deteriorated state is:

[0110] , , ;

[0111] 4. The dangerous state is:

[0112] , , .

[0113] Among them, represents the standard dielectric loss value of the cable type. Also analyze the shape of the dielectric loss frequency characteristic curve to identify different types of insulation aging mechanisms, such as water tree aging, oxidation aging, or thermal aging, etc.

[0114] Furthermore, analyze the location distribution characteristics of the partial discharge source to judge the nature of the defect. The system identifies typical location distribution patterns: intermediate joint aggregation type, terminal concentration type, uniform distribution type, segmented concentration type, and single-point protrusion type. Define the partial discharge distribution coefficient of variation as a quantitative index of the location distribution uniformity:

[0115] ;

[0116] Wherein, is the position standard deviation, is the average position. represents a uniform distribution, represents an aggregated distribution. The system further determines the defect type by combining the partial discharge intensity, phase characteristics, and waveform characteristics.

[0117] Furthermore, by comprehensively considering the dielectric loss parameter and the partial discharge position characteristics, the cable fault type is determined. The determination logic is as follows:

[0118] 1. Composite fault: or , and or 80% of the partial discharge is concentrated at a specific position;

[0119] 2. Insulation deterioration: Only or , and the partial discharge activity is not obvious (discharge amplitude < 50 pC or discharge rate < 10 times / minute);

[0120] 3. Local defect: Only or the partial discharge is regularly distributed, and and .

[0121] Based on the above analysis, the present invention forms a final diagnosis report. The report includes basic information, test results, status assessment, fault diagnosis, maintenance suggestions, and other contents. The system adopts a predictive maintenance strategy and predicts the optimal maintenance time window based on the degradation trend.

[0122] The present invention quantitatively evaluates the reliability of the diagnosis results. The evaluation indicators include the data quality index , the model fitting degree , and the historical case matching rate . The system obtains the final confidence index CI by using a weighted calculation method:

[0123] ;

[0124] Wherein, , , and are the weight coefficients of each index respectively. The system classifies CI into three levels: high reliability (> 90%), medium reliability (70% - 90%), and low reliability (< 70%) to help users correctly evaluate and use the diagnosis results.

[0125] In an alternative embodiment, a deep learning diagnostic model can be integrated. This model is trained with a large number of historical cases and is capable of identifying complex fault patterns. The model adopts a hybrid architecture of convolutional neural network and long short-term memory network. The input features include dielectric loss spectrum, partial discharge pattern, and location distribution map, and the output is the probability distribution of fault types and location estimation. Under the condition of sufficient data volume, this method can increase the diagnostic accuracy by 15% - 25%.

[0126] Through the implementation of the above S3 step, the present invention can accurately calculate the dielectric loss parameters and determine the partial discharge location based on the dielectric loss characteristic signal and partial discharge pulse signal, and generate a comprehensive and accurate cable insulation status diagnostic report by integrating these two aspects of information. This diagnostic method combines global dielectric loss analysis and local defect location, realizes the comprehensive evaluation of the cable insulation status, provides a scientific basis for preventive maintenance and fault prediction, and effectively improves the reliability and safety of the power system.

[0127] In summary, the present invention optimizes the oscillating ultra-low frequency voltage signal by applying the manifold learning algorithm, and successfully integrates the three tests of dielectric loss test, withstand voltage test, and partial discharge detection, which were traditionally carried out separately, into a single joint test. This effectively solves the problems of extended power outage time, equipment redundancy, and accelerated insulation deterioration caused by multiple tests in the prior art. The modified sine wave oscillating ultra-low frequency voltage signal adopted by the signal excitation module enhances the sensitivity of the dielectric loss characteristics and partial discharge pulses significantly while meeting the requirements of cable withstand voltage test through special waveform design, improving the measurement accuracy and detection sensitivity. The signal processing module combines the multi-resolution analysis method with the ensemble empirical mode decomposition algorithm, overcomes the mode mixing problem of the traditional EMD method, realizes the efficient separation of the dielectric loss characteristic signal and partial discharge pulse signal, and effectively improves the signal-to-noise ratio of the signal. The state diagnosis module establishes an accurate recognition mechanism for composite faults, insulation deterioration, and local defects by comprehensively analyzing the dielectric loss parameters and partial discharge location distribution characteristics, improves the accuracy of partial discharge source location, and breaks through the technical bottleneck that is difficult to comprehensively diagnose by traditional methods. Overall, the present invention realizes the comprehensive evaluation of the cable insulation status and accurate fault location, provides a scientific basis for preventive maintenance of the power system, and effectively improves the reliability and safety of the power system.

[0128] Embodiment 2, which is an embodiment of the present invention, provides a cable oscillating ultra-low frequency withstand voltage and partial discharge integrated test system, including:

[0129] A signal excitation module, configured to apply an oscillating ultra-low frequency voltage signal to the cable under test. The oscillating ultra-low frequency voltage signal is optimized and adjusted by the manifold learning algorithm and serves as a common excitation source for the withstand voltage test, dielectric loss detection, and partial discharge detection;

[0130] A signal processing module, configured to obtain the voltage signal and current signal of the cable under test under the excitation of an oscillating ultra-low frequency voltage signal, analyze the deviation degree of the voltage signal and current signal from a pre-established feature manifold, and extract the dielectric loss feature signal and partial discharge pulse signal;

[0131] A state diagnosis module, configured to calculate the dielectric loss parameter based on the dielectric loss feature signal, determine the partial discharge position based on the partial discharge pulse signal, and generate a cable insulation state diagnosis report according to the dielectric loss parameter and the partial discharge position.

[0132] Example 3, referring to Figure 2 , is an embodiment of the present invention. The difference from the previous embodiment is 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 such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. And the aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0133] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0134] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or, if necessary, other suitable processing, and then storing it in a computer memory.

[0135] 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 embodiments, the 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 in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. An integrated test method for cable oscillating ultra-low frequency withstand voltage and partial discharge, characterized in that, Including: Applying an oscillating ultra - low - frequency voltage signal to the cable under test; The oscillating ultra - low - frequency voltage signal is optimized and adjusted through a manifold learning algorithm and serves as a common excitation source for withstand voltage test, dielectric loss detection, and partial discharge detection; Obtaining the voltage signal and current signal of the cable under test under the excitation of the oscillating ultra - low - frequency voltage signal, analyzing the deviation degree of the voltage signal and the current signal from a pre - built feature manifold, and extracting dielectric loss characteristic signals and partial discharge pulse signals; Calculating dielectric loss parameters based on the dielectric loss characteristic signals, determining the partial discharge position based on the partial discharge pulse signals, and generating a cable insulation state diagnosis report according to the dielectric loss parameters and the partial discharge position; The extracting of dielectric loss characteristic signals and partial discharge pulse signals includes: performing orthogonal decomposition on the voltage signal and the current signal to obtain orthogonal components; performing time - frequency analysis on the orthogonal components to obtain energy distribution characteristics; distinguishing the orthogonal components into dielectric loss - related components and partial discharge - related components based on the energy distribution characteristics; extracting partial discharge characteristics from the partial discharge - related components to form the partial discharge pulse signals; extracting phase difference and amplitude ratio parameters from the dielectric loss - related components to form the dielectric loss characteristic signals; Analyzing the deviation degree of the voltage signal and the current signal from a pre - built feature manifold includes: calculating the distance parameter between the characteristic parameters of the voltage signal and the current signal and the standard feature manifold; determining the deviation state according to the distance parameter; if the deviation state exceeds the reference range, then adjusting the frequency and amplitude of the oscillating ultra - low - frequency voltage signal to make the deviation state return to the reference range; otherwise, maintaining the current oscillating ultra - low - frequency voltage signal parameters and continuing the test process; Determining the partial discharge position based on the partial discharge pulse signals includes: extracting the time characteristics of the partial discharge pulse signals; calculating the pulse propagation time difference; calculating the partial discharge source distance according to the propagation time difference and the cable propagation speed; calibrating the physical position of the cable corresponding to the partial discharge source distance.

2. The integrated test method for cable oscillating ultra-low frequency withstand voltage and partial discharge according to claim 1, characterized in that: The optimization and adjustment of the manifold learning algorithm includes: establishing a parameter space according to the type of the cable under test; generating a test parameter matrix within the parameter space; collecting test response characteristics to construct a feature manifold; optimizing the frequency, amplitude, and waveform parameters of the oscillating ultra - low - frequency voltage signal based on the feature manifold.

3. The integrated test method for cable oscillating ultra-low frequency withstand voltage and partial discharge according to claim 2, characterized in that: Obtaining the voltage signal and current signal of the cable under test under the excitation of the oscillating ultra - low - frequency voltage signal includes: collecting voltage sampling data and current sampling data; performing signal pre - processing on the voltage sampling data and the current sampling data; converting the pre - processed sampling data into the voltage signal and the current signal and mapping them to a preset feature space.

4. The integrated test method for cable oscillating ultra-low frequency withstand voltage and partial discharge according to claim 3, characterized in that: Generate a cable insulation status diagnosis report based on the dielectric loss parameter and the partial discharge position, including: determining the insulation medium status based on the dielectric loss parameter; determining the defect characteristics based on the partial discharge position; if the dielectric loss parameter exceeds the standard range and the partial discharge positions show a regular distribution, it is determined as a composite fault; if only the dielectric loss parameter exceeds the standard range, it is determined as insulation deterioration; if only the partial discharge positions show a regular distribution, it is determined as a local defect; integrate the insulation medium status and the defect characteristics to form a diagnosis result including fault position information and reliability assessment.

5. A cable oscillating ultra - low - frequency withstand voltage and partial discharge integrated test system, based on the cable oscillating ultra - low - frequency withstand voltage and partial discharge integrated test method according to any one of claims 1 to 4, characterized in that: Including, A signal excitation module for applying an oscillating ultra-low frequency voltage signal to the cable under test, and the oscillating ultra-low frequency voltage signal is optimized and adjusted by a manifold learning algorithm and used as a common excitation source for withstand voltage test, dielectric loss detection and partial discharge detection; A signal processing module for obtaining the voltage signal and current signal of the cable under test under the excitation of the oscillating ultra-low frequency voltage signal, analyzing the deviation degree of the voltage signal and the current signal from a pre-built feature manifold, and extracting dielectric loss characteristic signals and partial discharge pulse signals; A status diagnosis module for calculating the dielectric loss parameter based on the dielectric loss characteristic signal, determining the partial discharge position based on the partial discharge pulse signal, and generating a cable insulation status diagnosis report according to the dielectric loss parameter and the partial discharge position.

6. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the cable oscillating ultra-low frequency withstand voltage and partial discharge integrated test method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the cable oscillating ultra-low frequency withstand voltage and partial discharge integrated test method according to any one of claims 1 to 4 are implemented.

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