Wire and cable partial discharge on-line detection and insulation defect evaluation system

Abstract

The invention discloses an electric wire and cable partial discharge online detection and insulation defect evaluation system. The system comprises a distributed sensor array, a signal preprocessing unit, a feature extraction module, a defect evaluation unit, a positioning module and an early warning unit. Multiple sensors cooperatively detect, a distributed sensor array realizes full-line coverage, weak discharge signals are effectively captured, and the detection sensitivity reaches a picobank (pC) level. According to the dual positioning algorithm, an improved TDOA algorithm and a PSO algorithm are combined, and the positioning error is smaller than or equal to 0.5 m (laboratory environment) and smaller than or equal to 1.5 m (actual engineering environment) and is improved by 30%-50% compared with a traditional method. Time-frequency feature depth extraction: wavelet packet transformation and S transformation are combined to realize 0.1 microsecond-level time resolution and 5kHz-level frequency resolution of discharge signals, and different types of discharge are accurately identified.

Description

Technical Field

[0001] This invention relates to partial discharge in wires and cables, specifically to an online detection system for partial discharge in wires and cables and an assessment system for insulation defects. Background Technology

[0002] The power industry urgently needs cable safety monitoring: As core infrastructure for power transmission and distribution, the insulation condition of wires and cables directly affects the reliability and safety of the power grid. According to statistics from the International Conference on Large Electric Systems (CIGRE), global economic losses due to power outages caused by cable insulation faults exceed US$300 billion annually. In my country's urban power distribution networks, approximately 40% of faults are caused by cable insulation aging. Traditional offline testing methods (such as periodic withstand voltage tests and insulation resistance tests) require power outages, which not only affects power supply continuity but may also accelerate insulation damage due to excessively high test voltages. Therefore, developing efficient online detection and evaluation technologies has become a key industry requirement.

[0003] Limitations of existing partial discharge detection technologies: Insufficient detection sensitivity: Traditional pulse current methods are severely affected by electromagnetic interference, with detection thresholds typically ranging from 10 to 100 nanocoulombs (nC), making it difficult to capture early, weak discharge signals (picocoulomb (pC) level), while the discharge quantity at the initiation stage of partial discharge is often below 10 pC. Single-sensor layouts cannot cover long-distance cable lines, resulting in a high rate of missed detections of concealed defects (such as internal discharges at cable joints). Low positioning and assessment accuracy: Traditional positioning algorithms based on Time Difference of Arrival (TDOA) rely on high-precision clock synchronization (error <10 ns). In practical engineering, the positioning error is typically greater than 5-10 meters due to signal propagation path influences, making it difficult to meet precise positioning requirements. Insulation condition assessments are mostly based on single parameters (such as discharge amplitude), failing to comprehensively consider multi-dimensional information such as discharge phase distribution and energy characteristics, resulting in an assessment accuracy of less than 65%, and an inability to accurately distinguish between different types of defects (such as electrical trees, water trees, and air gap discharges).

[0004] To address the shortcomings of the existing technologies, this invention provides an online monitoring system that integrates high-precision detection, intelligent evaluation, accurate positioning, and trend prediction. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the defects of the above-mentioned technologies and provide an online detection system for partial discharge and insulation defect assessment of wires and cables.

[0006] To solve the above-mentioned technical problems, the technical solution provided by this invention is an online detection system for partial discharge and insulation defect assessment of electric wires and cables:

[0007] include:

[0008] A distributed sensor array is used to collect partial discharge signals at different locations on the cable;

[0009] The signal preprocessing unit is used to denoise and enhance the acquired partial discharge signal;

[0010] The feature extraction module extracts the time-frequency features of the partial discharge signal based on wavelet transform and S-transform;

[0011] The defect assessment unit evaluates the degree of insulation defects based on a multi-parameter fusion model.

[0012] The positioning module uses an improved Time Difference of Arrival (TDOA) algorithm combined with a Particle Swarm Optimization (PSO) algorithm to determine the discharge location;

[0013] The early warning unit issues early warning signals based on preset thresholds and evaluation results.

[0014] As an improvement, the signal preprocessing unit employs a denoising method based on variational mode decomposition (VMD) to decompose the original signal s(t) into K mode functions u. k (t):

[0015]

[0016] The constraints are:

[0017]

[0018] Among them, {u k}={u1(t),u2(t),…,u K Let {ω(t)} be the set of modal functions obtained from the decomposition, and {ω} be the set of modal functions obtained from the decomposition. k}={ω1,ω2,…,ω K} represents the center frequency of each mode, δ(t) is the Dirac function, and * denotes convolution operation.

[0019] As an improvement, the feature extraction module uses wavelet packet transform (WPT) to perform time-frequency decomposition on the preprocessed signal to obtain wavelet coefficients W. j,k :

[0020]

[0021] Where x(t) is the preprocessed signal, ψ j,k (t) represents the wavelet function, j is the decomposition level, and k is the translation parameter. For ψ j,k The complex conjugate of (t).

[0022] As an improvement, the feature extraction module further employs the S-transform to perform time-frequency analysis on the signal, obtaining the S-transform matrix S(τ,f):

[0023]

[0024] Where x(t) is the preprocessed signal, τ is the time offset, and f is the frequency.

[0025] As an improvement, the defect assessment unit performs insulation defect assessment based on a multi-parameter fusion model. This model comprehensively considers discharge amplitude A, discharge frequency N, phase distribution φ, and energy characteristics E, and determines the weight ω of each parameter through the analytic hierarchy process (AHP). i :

[0026]

[0027] Where D is the assessment value for the degree of insulation defect, f i (x i ω is the normalization function for each parameter. i satisfy:

[0028]

[0029] And ω i Passed the consistency test:

[0030]

[0031] Where, λ max To determine the largest eigenvalue of the matrix, n is the order of the matrix.

[0032] As an improvement, the positioning module uses an improved Time Difference of Arrival (TDOA) algorithm to determine the discharge location. For m sensors, the distance from the discharge point P(x,y,z) to each sensor S... i (x i ,y i ,z i The time difference Δt i1 satisfy:

[0033]

[0034] Where c is the propagation speed of electromagnetic waves, and i = 2, 3, ..., m.

[0035] As an improvement, the positioning module uses the particle swarm optimization (PSO) algorithm to solve the TDOA equation, and iteratively updates the particle position X. i and speed V i :

[0036]

[0037]

[0038] Where w is the inertia weight, c1 and c2 are acceleration constants, r1 and r2 are random numbers, and pbest i is the historical best position of particle i, and gbest is the global best position.

[0039] As an improvement, the early warning unit classifies the insulation state based on the fuzzy C-means (FCM) clustering algorithm, by minimizing the objective function J. m Determine the cluster center v j and membership matrix u ij :

[0040]

[0041] The constraints are:

[0042]

[0043] Where m is the fuzzy index, d ij For sample x i To the cluster center v j The Euclidean distance is given by n, where n is the number of samples and c is the number of clusters.

[0044] As an improvement, the system also includes a historical data management module, which uses a grey prediction model GM(1,1) to predict the development trend of the insulation state. The prediction model is as follows:

[0045]

[0046] in, To accumulate the predicted values ​​of the generated sequence, x (0) (1) is the first value of the original sequence, and a and b are model parameters, which are solved by the least squares method:

[0047]

[0048] Where B is the data matrix and Y is the observation vector.

[0049] As an improvement, the system also includes a cloud server and user terminals. It achieves localized data processing through edge computing, collaborative processing of regional data through fog computing, and finally uploads the data to the cloud server for global analysis and decision-making, forming a distributed computing architecture.

[0050] T total =T edge +T fog +T cloud

[0051] Among them, T total T represents the total system processing time. edge For edge computing processing time,

[0052] T fog For fog processing time, T cloud This refers to the cloud computing processing time.

[0053] The advantages of this invention compared to existing technologies are: high-precision detection and positioning capabilities.

[0054] Multi-sensor collaborative detection: Distributed sensor arrays achieve full line coverage, effectively capturing weak discharge signals, with detection sensitivity reaching the picocun (pC) level.

[0055] Dual positioning algorithm: The improved TDOA+PSO algorithm is combined, with positioning errors ≤0.5m (laboratory environment) and ≤1.5m (actual engineering environment), which is 30%-50% higher than traditional methods.

[0056] Deep extraction of time and frequency features: Combining wavelet packet transform and S-transform, the discharge signal achieves a time resolution of 0.1μs and a frequency resolution of 5kHz, accurately identifying different types of discharge.

[0057] Intelligent evaluation system:

[0058] Multi-parameter fusion model: By integrating discharge amplitude, number of discharges, phase distribution and energy characteristics, the evaluation accuracy reaches over 92%, which is 25% higher than that of single-parameter methods.

[0059] Optimization of the Analytic Hierarchy Process (AHP): The reasonableness of the weight allocation is ensured by a consistency test (CI≤0.1), and the fluctuation range of the evaluation results is ≤3%.

[0060] Fuzzy clustering early warning: The FCM algorithm achieves a 5-level classification of insulation status (normal-attention-abnormal-severe-dangerous), with a false alarm rate of ≤5%. Attached Figure Description

[0061] Figure 1 This is a schematic diagram of the online detection system for partial discharge and insulation defect assessment of wires and cables according to the present invention. Detailed Implementation

[0062] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0064] It is understood that spatial relation terms such as "below," "under," "below," "below," "above," "over," etc., can be used here to describe the relationship between one element or feature shown in the figure and other elements or features. It should be understood that, in addition to the orientation shown in the figure, spatial relation terms also include different orientations of the device in use and operation. For example, if the device in the figure is flipped, the element or feature described as "below" or "under" or "below" of the other element or feature will be oriented "over" the other element or feature. Therefore, the exemplary terms "below" and "under" can include both upper and lower orientations. Furthermore, the device may also include other orientations, such as being rotated 90 degrees or other orientations, and the spatial descriptive terms used herein will be interpreted accordingly.

[0065] It should be noted that when one element is considered to be "connected" to another element, it can be directly connected to the other element or connected to the other element through an intermediary element. In the following embodiments, "connection" should be understood as "electrical connection," "communication connection," etc., if the connected circuits, modules, units, etc., have the transmission of electrical signals or data between them.

[0066] When used herein, the singular forms of “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising,” “including,” or “having,” etc., specify the presence of the stated feature, whole, step, operation, component, part, or combination thereof, but do not preclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof.

[0067] Referring to the attached diagram, the online partial discharge detection and insulation defect assessment system for electric wires and cables includes:

[0068] A distributed sensor array is used to collect partial discharge signals at different locations on the cable;

[0069] The signal preprocessing unit is used to denoise and enhance the acquired partial discharge signal;

[0070] The feature extraction module extracts the time-frequency features of the partial discharge signal based on wavelet transform and S-transform;

[0071] The defect assessment unit evaluates the degree of insulation defects based on a multi-parameter fusion model.

[0072] The positioning module uses an improved Time Difference of Arrival (TDOA) algorithm combined with a Particle Swarm Optimization (PSO) algorithm to determine the discharge location;

[0073] The early warning unit issues early warning signals based on preset thresholds and evaluation results.

[0074] As an improvement, the signal preprocessing unit employs a denoising method based on variational mode decomposition (VMD) to decompose the original signal s(t) into K mode functions u. k (t):

[0075]

[0076] The constraints are:

[0077]

[0078] Among them, {u k}={u1(t),u2(t),…,u K Let {ω(t)} be the set of modal functions obtained from the decomposition, and {ω} be the set of modal functions obtained from the decomposition. k}={ω1,ω2,…,ω K} represents the center frequency of each mode, δ(t) is the Dirac function, and * denotes convolution operation.

[0079] As an improvement, the feature extraction module uses wavelet packet transform (WPT) to perform time-frequency decomposition on the preprocessed signal to obtain wavelet coefficients W. j,k :

[0080]

[0081] Where x(t) is the preprocessed signal, ψ j,k (t) represents the wavelet function, j is the decomposition level, and k is the translation parameter. For ψ j,k The complex conjugate of (t).

[0082] As an improvement, the feature extraction module further employs the S-transform to perform time-frequency analysis on the signal, obtaining the S-transform matrix S(τ,f):

[0083]

[0084] Where x(t) is the preprocessed signal, τ is the time offset, and f is the frequency.

[0085] As an improvement, the defect assessment unit performs insulation defect assessment based on a multi-parameter fusion model. This model comprehensively considers discharge amplitude A, discharge frequency N, phase distribution φ, and energy characteristics E, and determines the weight ω of each parameter through the analytic hierarchy process (AHP).i :

[0086]

[0087] Where D is the assessment value for the degree of insulation defect, f i (x i ω is the normalization function for each parameter. i satisfy:

[0088]

[0089] And ω i Passed the consistency test:

[0090]

[0091] Where, λ max To determine the largest eigenvalue of the matrix, n is the order of the matrix.

[0092] As an improvement, the positioning module uses an improved Time Difference of Arrival (TDOA) algorithm to determine the discharge location. For m sensors, the distance from the discharge point P(x,y,z) to each sensor S... i (x i ,y i ,z i The time difference Δt i1 satisfy:

[0093]

[0094] Where c is the propagation speed of electromagnetic waves, and i = 2, 3, ..., m.

[0095] As an improvement, the positioning module uses the particle swarm optimization (PSO) algorithm to solve the TDOA equation, and iteratively updates the particle position X. i and speed V i :

[0096]

[0097]

[0098] Where w is the inertia weight, c1 and c2 are acceleration constants, r1 and r2 are random numbers, and pbest i is the historical best position of particle i, and gbest is the global best position.

[0099] As an improvement, the early warning unit classifies the insulation state based on the fuzzy C-means (FCM) clustering algorithm, by minimizing the objective function J. m Determine the cluster center v j and membership matrix uij :

[0100]

[0101] The constraints are:

[0102]

[0103] Where m is the fuzzy index, d ij For sample x i To the cluster center v j The Euclidean distance is given by n, where n is the number of samples and c is the number of clusters.

[0104] As an improvement, the system also includes a historical data management module, which uses a grey prediction model GM(1,1) to predict the development trend of the insulation state. The prediction model is as follows:

[0105]

[0106] in, To accumulate the predicted values ​​of the generated sequence, x (0) (1) is the first value of the original sequence, and a and b are model parameters, which are solved by the least squares method:

[0107]

[0108] Where B is the data matrix and Y is the observation vector.

[0109] As an improvement, the system also includes a cloud server and user terminals. It achieves localized data processing through edge computing, collaborative processing of regional data through fog computing, and finally uploads the data to the cloud server for global analysis and decision-making, forming a distributed computing architecture.

[0110] T total =T edge +T fog +T cloud

[0111] Among them, T total T represents the total system processing time. edge For edge computing processing time,

[0112] T fog For fog processing time, T cloud This refers to the cloud computing processing time.

[0113] System overall architecture:

[0114] The online partial discharge detection and insulation defect assessment system for electric wires and cables of this invention mainly consists of a distributed sensor array, a signal preprocessing unit, a feature extraction module, a defect assessment unit, a positioning module, an early warning unit, a historical data management module, a cloud server, and a user terminal. The system adopts a distributed computing architecture, achieving localized data processing through edge computing and collaborative processing of regional data through fog computing. Finally, the data is uploaded to the cloud server for global analysis and decision-making.

[0115] Distributed sensor array:

[0116] The distributed sensor array consists of multiple high-frequency current transformers (HFCTs) or ultrasonic sensors, arranged at regular intervals along the cable line to collect partial discharge signals at different locations on the cable. The sensors employ electromagnetic compatibility design to effectively suppress external electromagnetic interference, ensuring the accuracy of signal acquisition.

[0117] Signal preprocessing unit:

[0118] The signal preprocessing unit performs denoising and enhancement on the acquired partial discharge signal. A denoising method based on variational mode decomposition (VMD) is used to decompose the original signal s(t) into K mode functions u. k (t). VMD is an adaptive, fully non-recursive signal decomposition method that determines the center frequency and bandwidth of each mode by finding the optimal solution to the variational problem. The specific implementation steps are as follows:

[0119] Constructing a variational problem:

[0120]

[0121] The constraints are:

[0122]

[0123] Among them, {u k}={u1(t),u2(t),…,u K Let {ω(t)} be the set of modal functions obtained from the decomposition, and {ω} be the set of modal functions obtained from the decomposition. k}={ω1,ω2,…,ω K} represents the center frequency of each mode, δ(t) is the Dirac function, and * denotes convolution operation.

[0124] The variational problem is solved using the Lagrange multiplier method and the alternating direction multiplier method (ADMM) to obtain the modal functions u. k (t).

[0125] Based on the center frequency and spectral characteristics of each mode function, mode functions related to the partial discharge signal are selected for reconstruction to achieve noise reduction.

[0126] Feature extraction module:

[0127] The feature extraction module extracts the time-frequency features of the partial discharge signal based on wavelet transform and S-transform, specifically including:

[0128] Wavelet packet transform (WPT): Performs time-frequency decomposition on the preprocessed signal to obtain wavelet coefficients W. j,k :

[0129]

[0130] Where x(t) is the preprocessed signal, ψ j,k (t) represents the wavelet function, j is the decomposition level, and k is the translation parameter. For ψ j,k The complex conjugate of (t). By selecting appropriate wavelet basis functions and decomposition levels, the signal can be decomposed into different frequency bands, and the characteristic frequency components of the partial discharge signal can be extracted.

[0131] S-transform: Further time-frequency analysis of the signal yields the S-transform matrix S(τ,f):

[0132]

[0133] Where τ is the time offset and f is the frequency. The S-transform combines the advantages of the short-time Fourier transform and the wavelet transform, providing a high-resolution time-frequency representation and possessing phase-preserving properties, making it suitable for feature extraction of partial discharge signals.

[0134] Defect assessment unit:

[0135] The defect assessment unit evaluates the degree of insulation defects based on a multi-parameter fusion model. This model comprehensively considers multiple parameters, including discharge amplitude A, discharge frequency N, phase distribution φ, and energy characteristics E, and determines the weight ω of each parameter using the analytic hierarchy process (AHP). i :

[0136] Construct a judgment matrix to compare the relative importance of each parameter.

[0137] Calculate the largest eigenvalue λ of the judgment matrix max By combining the eigenvectors, we can obtain the weights ω of each parameter. i .

[0138] Perform a consistency check:

[0139]

[0140] Where n is the order of the judgment matrix. If the consistency ratio CR = CI / RI ≤ 0.1 (RI is the random consistency index), then the judgment matrix is ​​considered to have satisfactory consistency.

[0141] Calculate the assessment value of the degree of insulation defect:

[0142]

[0143] Among them, f i (x i ) is the normalization function for each parameter, which maps the original parameter values ​​to the interval [0,1].

[0144] Positioning module:

[0145] The positioning module uses an improved Time Difference of Arrival (TDOA) algorithm combined with a Particle Swarm Optimization (PSO) algorithm to determine the discharge location. The specific implementation steps are as follows:

[0146] The TDOA equation is established as follows: For m sensors, the distance from the discharge point P(x,y,z) to each sensor S... i (x i ,y i ,z i The time difference Δt i1 satisfy:

[0147]

[0148] Where c is the propagation speed of electromagnetic waves, and i = 2, 3, ..., m.

[0149] PSO algorithm solution: Iteratively update particle position X i and speed V i Find the optimal solution to the TDOA equation:

[0150]

[0151] Where w is the inertia weight, c1 and c2 are acceleration constants, r1 and r2 are random numbers, and pbest i is the historical best position of particle i, and gbest is the global best position.

[0152] Early warning unit

[0153] The early warning unit classifies the insulation state based on the fuzzy C-means (FCM) clustering algorithm. This is achieved by minimizing the objective function J. m Determine the cluster center v j and membership matrix u ij :

[0154] Initialize cluster center v j and membership matrix u ij .

[0155] Iterative updates to the membership matrix and cluster centers:

[0156]

[0157] Where, d ij For sample x i To the cluster center v j The Euclidean distance, where m is the fuzziness index.

[0158] Repeat step 2 until the objective function J is achieved. m convergence:

[0159]

[0160] The constraints are:

[0161]

[0162] The insulation state category of the sample is determined based on the membership matrix, and a corresponding warning signal is issued based on the preset threshold.

[0163] Historical data management module:

[0164] The historical data management module uses the grey prediction model GM(1,1) to predict the development trend of insulation status. The specific steps are as follows:

[0165] For the original data sequence x (0) ={x (0) (1),x (0) (2),…,x (0) The sequence x is generated by accumulating (n)}. (1) :

[0166]

[0167] Establish the GM(1,1) differential equation:

[0168]

[0169] Where a and b are model parameters.

[0170] Construct a data matrix B and an observation vector Y, and solve for parameters a and b using the least squares method:

[0171]

[0172] Solving the differential equation yields the prediction model:

[0173]

[0174] The predicted values ​​are then subtracted to restore the original sequence, resulting in the predicted values ​​for the original sequence.

[0175]

[0176] Distributed computing architecture:

[0177] The system employs a distributed computing architecture, utilizing edge computing for localized data processing and fog computing for collaborative regional data processing. Finally, the data is uploaded to a cloud server for global analysis and decision-making. The total system processing time is T. total for:

[0178] T total =T edge +T fog +T cloud

[0179] Among them, T edge For edge computing processing time, T fog Calculate the processing time for fog.

[0180] T cloud This reduces cloud computing processing time. By rationally allocating computing tasks, system latency is reduced and processing efficiency is improved.

[0181] Detailed implementation process:

[0182] A distributed sensor array collects partial discharge signals at different locations on the cable.

[0183] The signal preprocessing unit performs noise reduction and enhancement on the acquired signal, and uses the VMD method to decompose and reconstruct the signal.

[0184] The feature extraction module uses wavelet packet transform and S-transform to extract the time-frequency features of the signal.

[0185] The defect assessment unit evaluates the degree of insulation defects based on a multi-parameter fusion model and the AHP method.

[0186] The positioning module uses an improved TDOA algorithm and PSO algorithm to determine the discharge location.

[0187] The early warning unit classifies the insulation status and issues early warnings based on the FCM clustering algorithm.

[0188] The historical data management module uses the GM(1,1) model to predict the development trend of insulation status.

[0189] The system uses a distributed computing architecture to achieve efficient data processing and analysis, and finally displays the results to the user terminal.

[0190] Through the above specific embodiments, the present invention can detect the partial discharge of wires and cables in real time and accurately, assess the degree of insulation defects, locate the discharge location, and predict the development trend of insulation status, providing a scientific basis for the safe operation and maintenance of cables.

[0191] High-precision detection and positioning capabilities:

[0192] Multi-sensor collaborative detection: Distributed sensor arrays achieve full line coverage, effectively capturing weak discharge signals, with detection sensitivity reaching the picocun (pC) level.

[0193] Dual positioning algorithm: The improved TDOA+PSO algorithm is combined, with positioning errors ≤0.5m (laboratory environment) and ≤1.5m (actual engineering environment), which is 30%-50% higher than traditional methods.

[0194] Deep extraction of time and frequency features: Combining wavelet packet transform and S-transform, the discharge signal achieves a time resolution of 0.1μs and a frequency resolution of 5kHz, accurately identifying different types of discharge.

[0195] Intelligent evaluation system:

[0196] Multi-parameter fusion model: By integrating discharge amplitude, number of discharges, phase distribution and energy characteristics, the evaluation accuracy reaches over 92%, which is 25% higher than that of single-parameter methods.

[0197] Optimization of the Analytic Hierarchy Process (AHP): The reasonableness of the weight allocation is ensured by a consistency test (CI≤0.1), and the fluctuation range of the evaluation results is ≤3%.

[0198] Fuzzy clustering early warning: The FCM algorithm achieves a 5-level classification of insulation status (normal-attention-abnormal-severe-dangerous), with a false alarm rate of ≤5%.

[0199] Predictive maintenance capability:

[0200] Grey prediction model: The GM(1,1) model has an error of ≤10% in predicting insulation life (within the next 12 months) and provides early warning of potential faults 3-6 months in advance.

[0201] Trend Analysis: The historical data management module stores ≥10 years of monitoring data, supports annual / quarterly / monthly / daily trend analysis, and generates dynamic aging curves.

[0202] High-efficiency distributed architecture:

[0203] Edge-fog-cloud collaborative computing: system response time ≤2s, 70% improvement over centralized architecture, and edge node processing capacity up to 1000 points / s.

[0204] Load balancing: Intelligent allocation of computing tasks reduces cloud resource utilization by 40% and saves 30% on hardware costs.

[0205] Advantages in engineering applications:

[0206] Anti-interference capability: The VMD denoising algorithm effectively suppresses 50Hz power frequency interference (suppression ratio ≥40dB) and random noise (signal-to-noise ratio improved by 15dB).

[0207] Adaptability: The PSO algorithm converges within 100 iterations, adapting to changes in cable parameters (e.g., positioning error variation ≤ 0.8m when temperature ±50℃).

[0208] Scalability: Supports simultaneous operation of ≥1000 monitoring points, with a communication distance of ≥20km (fiber optic transmission).

[0209] Economic and social benefits:

[0210] Reduced maintenance costs: Preventive maintenance replaces scheduled inspections, reducing power outage time by 80% and saving more than 50% in maintenance costs.

[0211] Extended lifespan: Early detection of defects extends cable lifespan by 20%-30%.

[0212] Safety Assurance: Fault early warning accuracy rate ≥95%, avoiding major power outages and equipment damage, and ensuring the safe operation of the power grid.

[0213] Technological innovation:

[0214] Mathematical model integration: Innovatively applying various mathematical methods such as VMD, wavelet packet transform, S-transform, AHP, PSO, FCM, and GM(1,1) to the field of cable testing.

[0215] Interdisciplinary integration: Integrating technologies from multiple disciplines such as electromagnetics, signal processing, machine learning, and distributed computing to form a complete solution.

[0216] With the advantages mentioned above, this system provides a comprehensive, intelligent, and efficient technical means for the condition monitoring and maintenance of wires and cables, filling the gap in the field of high-precision detection and evaluation of partial discharge in cables both domestically and internationally.

[0217] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A system for online detection of partial discharge and assessment of insulation defects in electric wires and cables, characterized in that, include: A distributed sensor array is used to collect partial discharge signals at different locations on the cable; The signal preprocessing unit is used to denoise and enhance the acquired partial discharge signal; The feature extraction module extracts the time-frequency features of the partial discharge signal based on wavelet transform and S-transform; The defect assessment unit evaluates the degree of insulation defects based on a multi-parameter fusion model. The positioning module uses an improved Time Difference of Arrival (TDOA) algorithm combined with a Particle Swarm Optimization (PSO) algorithm to determine the discharge location; The early warning unit issues early warning signals based on preset thresholds and evaluation results.

2. The online detection system for partial discharge and insulation defect assessment of electric wires and cables according to claim 1, characterized in that, The signal preprocessing unit employs a denoising method based on variational mode decomposition (VMD) to decompose the original signal s(t) into K mode functions u. k (t): The constraints are: Among them, {u k }={u1(t),u2(t),…,u K Let {ω(t)} be the set of modal functions obtained from the decomposition, and {ω} be the set of modal functions obtained from the decomposition. k }={ω1,ω2,…,ω K } represents the center frequency of each mode, δ(t) is the Dirac function, and * denotes convolution operation.

3. The online detection system for partial discharge and insulation defect assessment of electric wires and cables according to claim 1, characterized in that, The feature extraction module uses wavelet packet transform (WPT) to perform time-frequency decomposition on the preprocessed signal to obtain wavelet coefficients W. j,k : Where x(t) is the preprocessed signal, ψ j,k (t) represents the wavelet function, j is the decomposition level, and k is the translation parameter. For ψ j,k The complex conjugate of (t).

4. The online detection system for partial discharge and insulation defect assessment of electric wires and cables according to claim 1, characterized in that, The feature extraction module further employs the S-transform to perform time-frequency analysis on the signal, obtaining the S-transform matrix S(τ,f): Where x(t) is the preprocessed signal, τ is the time offset, and f is the frequency.

5. The online detection system for partial discharge and insulation defect assessment of wires and cables according to claim 1, characterized in that, The defect assessment unit assesses insulation defects based on a multi-parameter fusion model. This model comprehensively considers discharge amplitude A, discharge frequency N, phase distribution φ, and energy characteristics E, and determines the weight ω of each parameter through the analytic hierarchy process (AHP). i : Where D is the assessment value for the degree of insulation defect, f i (x i ω is the normalization function for each parameter. i satisfy: And ω i Passed the consistency test: Where, λ max To determine the largest eigenvalue of the matrix, n is the order of the matrix.

6. The online detection system for partial discharge and insulation defect assessment of electric wires and cables according to claim 1, characterized in that, The positioning module uses an improved Time Difference of Arrival (TDOA) algorithm to determine the discharge location. For m sensors, the distance from the discharge point P(x,y,z) to each sensor S is... i (x i ,y i ,z i The time difference Δt i1 satisfy: Where c is the propagation speed of electromagnetic waves, and i = 2, 3, ..., m.

7. The online detection system for partial discharge and insulation defect assessment of wires and cables according to claim 6, characterized in that, The positioning module uses the Particle Swarm Optimization (PSO) algorithm to solve the TDOA equation and iteratively updates the particle position X. i and speed V i : Where w is the inertia weight, c1 and c2 are acceleration constants, r1 and r2 are random numbers, and pbest i is the historical best position of particle i, and gbest is the global best position.

8. The online detection system for partial discharge and insulation defect assessment of electric wires and cables according to claim 1, characterized in that, The early warning unit classifies the insulation state based on the fuzzy C-means (FCM) clustering algorithm, by minimizing the objective function J. m Determine the cluster center v j and membership matrix u ij : The constraints are: Where m is the fuzzy index, d ij For sample x i To the cluster center v j The Euclidean distance is given by n, where n is the number of samples and c is the number of clusters.

9. The online detection system for partial discharge and insulation defect assessment of electric wires and cables according to claim 1, characterized in that, The system also includes a historical data management module, which uses a grey prediction model GM(1,1) to predict the development trend of the insulation state. The prediction model is as follows: in, To accumulate the predicted values ​​of the generated sequence, x (0) (1) is the first value of the original sequence, and a and b are model parameters, which are solved by the least squares method: Where B is the data matrix and Y is the observation vector.

10. The online detection system for partial discharge and insulation defect assessment of electric wires and cables according to claim 1, characterized in that, The system also includes a cloud server and user terminals. It achieves localized data processing through edge computing, collaborative processing of regional data through fog computing, and finally uploads the data to the cloud server for global analysis and decision-making, forming a distributed computing architecture. T total =T edge +T fog +T cloud Among them, T total T represents the total system processing time. edge For edge computing processing time, T fog For fog processing time, T cloud This refers to the cloud computing processing time.

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