Methods, devices and storage media for assessing thermal defects in cable joints

Abstract

This invention discloses a method, apparatus, and storage medium for evaluating the thermal defects of cable joints. The method includes: acquiring first initial temperature data of the conductor in the cable joint and second initial temperature data of the sheath; determining first target temperature data of the conductor based on the first initial temperature data, and determining second target temperature data of the sheath based on the second initial temperature data; inputting the first and second target temperature data into a target thermal defect evaluation model of the cable joint to evaluate the thermal defect data of the cable joint, and outputting the evaluation result of the cable joint. The target thermal defect evaluation model is established using first historical temperature data of the conductor, second historical temperature data of the sheath, and historical thermal defect data of the cable joint. The evaluation result is used to characterize the thermal defect state of the cable joint corresponding to the thermal defect data. This invention solves the technical problem of low efficiency in evaluating the thermal defects of cable joints.

Description

Technical Field

[0001] This invention relates to the field of cable joint technology, and more specifically, to a method, apparatus, and storage medium for evaluating the thermal defects of cable joints. Background Technology

[0002] During the long-term use of cable lines, cable joints are prone to thermal defects due to overheating, aging, and external damage, leading to malfunctions. Currently, infrared thermal imaging technology and regular inspections are commonly used to assess the thermal defects of cable joints. Infrared thermal imaging technology assesses the thermal condition of the joint through thermal radiation from its surface. However, this method is greatly affected by ambient temperature and humidity, and usually requires regular inspections. The high cost of regular inspections results in low efficiency in assessing thermal defects in cable joints.

[0003] There is currently no effective solution to the technical problem of low efficiency in thermal defect assessment of the aforementioned cable joints. Summary of the Invention

[0004] The present invention provides a method, apparatus and storage medium for evaluating the thermal defects of cable joints, thereby at least solving the technical problem of low efficiency in evaluating the thermal defects of cable joints.

[0005] According to one aspect of the invention, a method for evaluating the thermal defects of a cable joint is provided. The method may include: acquiring first initial temperature data of the conductor in the cable joint and second initial temperature data of the sheath in the cable joint; determining first target temperature data of the conductor based on the first initial temperature data, and determining second target temperature data of the sheath based on the second initial temperature data; inputting the first target temperature data and the second target temperature data into a target thermal defect evaluation model of the cable joint, evaluating the thermal defect data of the cable joint, and outputting the evaluation result of the cable joint, wherein the target thermal defect evaluation model is established using first historical temperature data of the conductor, second historical temperature data of the sheath, and historical thermal defect data of the cable joint, and the evaluation result is used to characterize the thermal defect state of the cable joint corresponding to the thermal defect data.

[0006] Optionally, the method further includes: using a simulation model of the cable joint to obtain first historical temperature data of the conductor and second historical temperature data of the sheath; using a finite element model of the cable joint to obtain historical thermal defect data of the cable joint; and training an initial thermal defect assessment model of the cable joint based on the first historical temperature data, the second historical temperature data, and the historical thermal defect data to obtain a target thermal defect assessment model, wherein the initial thermal defect assessment model is established using parameter information of the environment in which the cable joint is located.

[0007] Optionally, based on the first historical temperature data, the second historical temperature data, and historical thermal defect data, an initial thermal defect assessment model for the cable joint is trained to obtain a target thermal defect assessment model, including: determining a training dataset for the cable joint based on the first historical temperature data, the second historical temperature data, and historical thermal defect data; and using the training dataset to train the initial thermal defect assessment model to obtain the target thermal defect assessment model.

[0008] Optionally, a training dataset for the cable joint is determined based on the first historical temperature data, the second historical temperature data, and historical thermal defect data, including: extracting features from the first historical temperature data to obtain the first feature value of the conductor; extracting features from the second historical temperature data to obtain the second feature value of the sheath; and determining the training dataset based on the first feature value, the second feature value, and the historical thermal defect data.

[0009] Optionally, the initial thermal defect assessment model is trained using the training dataset to obtain the target thermal defect assessment model, including: training the initial thermal defect assessment model using the target loss function of the cable joint based on the training dataset to obtain the target thermal defect assessment model.

[0010] Optionally, determining a first target temperature data for the core based on the first initial temperature data, and determining a second target temperature data for the sheath based on the second initial temperature data, includes: performing noise reduction processing on the first initial temperature data to obtain the first target temperature data, and performing noise reduction processing on the second initial temperature data to obtain the second target temperature data.

[0011] Optionally, the evaluation results include thermal defect type and thermal defect fault information, wherein the thermal defect fault information is used to characterize the degree of thermal defect fault of the cable joint.

[0012] According to one aspect of the present invention, a thermal defect assessment device for a cable joint is provided. The device may include: a data acquisition unit for acquiring first initial temperature data of the conductor in the cable joint and second initial temperature data of the sheath in the cable joint; a determination unit for determining first target temperature data of the conductor based on the first initial temperature data and determining second target temperature data of the sheath based on the second initial temperature data; and an assessment unit for inputting the first and second target temperature data into a target thermal defect assessment model for the cable joint, assessing the thermal defect data of the cable joint, and outputting the assessment result of the cable joint. The target thermal defect assessment model is established using first historical temperature data of the conductor, second historical temperature data of the sheath, and historical thermal defect data of the cable joint. The assessment result is used to characterize the thermal defect state of the cable joint corresponding to the thermal defect data.

[0013] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein the program, when run by a processor, controls the device where the storage medium is located to execute the method of the present invention.

[0014] According to another aspect of the present invention, a processor is also provided for running a program, wherein the program executes the methods of the present invention during runtime.

[0015] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the method of the present invention.

[0016] According to another aspect of the present invention, an electronic device is also provided, comprising a processor and a memory for storing processor-executable instructions. The processor is configured to execute instructions to implement the methods of the embodiments of the present invention.

[0017] In this embodiment of the invention, first initial temperature data of the conductor in the cable joint and second initial temperature data of the sheath in the cable joint are collected. Based on the first initial temperature data, first target temperature data of the conductor is determined, and based on the second initial temperature data, second target temperature data of the sheath is determined. The first and second target temperature data are input into the target thermal defect assessment model of the cable joint to assess the thermal defect data of the cable joint and output the assessment result of the cable joint. The target thermal defect assessment model is established using the first historical temperature data of the conductor, the second historical temperature data of the sheath, and the historical thermal defect data of the cable joint. The assessment result is used to characterize the thermal defect state of the cable joint corresponding to the thermal defect data. In other words, this embodiment of the invention can first collect the first initial temperature data of the conductor in the cable joint and the second initial temperature data of the sheath in the cable joint. Then, based on the first initial temperature data obtained above, the first target temperature data of the conductor can be determined, and based on the second initial temperature data obtained above, the second target temperature data of the sheath can be determined. Finally, the first and second target temperature data can be input into the target thermal defect assessment model to assess the thermal defect data of the cable joint and output the assessment result of the cable joint. Since the first target temperature data is determined based on the first initial temperature data, and the second target temperature data is determined based on the second initial temperature data, and then the target thermal defect assessment model is called according to the first target temperature data and the second target temperature data, in order to obtain the assessment results of the cable joint, the technical problem of low thermal defect assessment efficiency of cable joints is solved, and the technical effect of improving the thermal defect assessment efficiency of cable joints is achieved. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0019] Figure 1 This is a flowchart of a method for evaluating the thermal defects of a cable joint according to an embodiment of the present invention;

[0020] Figure 2 This is a flowchart of a method for evaluating the thermal defects of cable joints based on real-time temperature sensing according to an embodiment of the present invention.

[0021] Figure 3 This is a schematic diagram of a deep neural network according to an embodiment of the present invention;

[0022] Figure 4 This is a schematic diagram of a thermal defect assessment device for a cable joint according to an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] According to an embodiment of the present invention, a method for evaluating thermal defects in cable joints is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0026] The following describes the method for evaluating the thermal defects of cable joints according to an embodiment of the present invention.

[0027] Figure 1 This is a flowchart of a method for evaluating the thermal defects of a cable joint according to an embodiment of the present invention, such as... Figure 1 As shown, the method may include the following steps:

[0028] Step S101: Collect the first initial temperature data of the wire core in the cable joint and the second initial temperature data of the sheath in the cable joint.

[0029] In the technical solution provided by step S101 of the present invention, first initial temperature data of the wire core in the cable joint and second initial temperature data of the cable sheath in the cable joint can be collected. The first initial temperature data of the wire core can be referred to as the initial temperature data of the wire core. The second initial temperature data of the sheath can be referred to as the initial temperature data of the sheath.

[0030] Optionally, a temperature sensor can be used to collect the first initial temperature data of the core and the second initial temperature data of the sheath in real time. For example, the initial temperature data of the core can be 20 degrees Celsius (°C) and the initial temperature data of the sheath can be 28 degrees Celsius.

[0031] It should be noted that this is only a preferred embodiment for obtaining the first initial temperature data of the wire core in the cable joint and the second initial temperature data of the sheath in the cable joint, and the process and method for obtaining the first initial temperature data of the wire core in the cable joint and the second initial temperature data of the sheath in the cable joint are not specifically limited.

[0032] Step S102: Based on the first initial temperature data, determine the first target temperature data of the core, and based on the second initial temperature data, determine the second target temperature data of the sheath.

[0033] In the technical solution provided by step S102 of the present invention, after obtaining the first initial temperature data and the second initial temperature data, the first target temperature data of the wire core can be determined based on the first initial temperature data, and the second target temperature data of the sheath can be determined based on the second initial temperature data.

[0034] Optionally, feature extraction can be performed on the first initial temperature data to obtain the first target temperature data of the wire core, and feature extraction can be performed on the second initial temperature data to obtain the second target temperature data of the sheath. The feature extraction methods can include statistical feature extraction, frequency domain feature extraction, and time series feature extraction, etc. This section only provides examples of data feature extraction methods and does not impose specific limitations on the methods used.

[0035] It is understood that this is only a preferred embodiment for determining the first target temperature data of the core and the second target temperature data of the sheath. The process and method for determining the first target temperature data of the core and the second target temperature data of the sheath are not specifically limited. As long as the process and method of determining the first target temperature data of the core based on the first initial temperature data and determining the second target temperature data of the sheath based on the second initial temperature data are within the protection scope of this invention, they will not be listed here.

[0036] Step S103: Input the first target temperature data and the second target temperature data into the target thermal defect evaluation model of the cable joint, evaluate the thermal defect data of the cable joint, and output the evaluation results of the cable joint.

[0037] In the technical solution provided by step S103 of the present invention, after obtaining the first target temperature data and the second target temperature data in step S102, the first target temperature data and the second target temperature data can be input into the target thermal defect evaluation model of the cable joint to evaluate the thermal defect data of the cable joint, thereby outputting the evaluation result of the cable joint. The target thermal defect evaluation model can be a trained artificial intelligence model.

[0038] Optionally, the target thermal defect assessment model is established using the first historical temperature data of the conductor, the second historical temperature data of the sheath, and the historical thermal defect data of the cable joint. The assessment results are used to characterize the thermal defect state of the cable joint corresponding to the thermal defect data. The thermal defect state is used to characterize whether there is an abnormally high temperature phenomenon at the cable joint.

[0039] For example, temperature sensors are used to collect temperature data of the cable core and sheath in real time, and the temperature data is denoised. The denoised temperature data is then input into a trained artificial intelligence model, which can output the evaluation results of the cable joint to achieve real-time assessment of thermal defects in the cable joint.

[0040] It should be noted that this is only a preferred embodiment for determining the evaluation results of cable joints. The process and method for determining the evaluation results of cable joints are not specifically limited. As long as the first target temperature data and the second target temperature data are input into the target thermal defect evaluation model of the cable joint to achieve the purpose of outputting the evaluation results of the cable joint, it is within the protection scope of this invention, and will not be listed here.

[0041] In steps S101 to S103 of this invention, the first initial temperature data of the conductor in the cable joint and the second initial temperature data of the sheath in the cable joint are first collected. Then, based on the obtained first initial temperature data, the first target temperature data of the conductor can be determined, and based on the obtained second initial temperature data, the second target temperature data of the sheath can be determined. Finally, the first and second target temperature data can be input into the target thermal defect assessment model to assess the thermal defect data of the cable joint and output the assessment results of the cable joint. Since the first target temperature data is determined based on the obtained first initial temperature data, and the second target temperature data is determined based on the second initial temperature data, and then the target thermal defect assessment model is called based on the first and second target temperature data to obtain the assessment results of the cable joint, the technical problem of low efficiency in the thermal defect assessment of cable joints is solved, and the technical effect of improving the efficiency of thermal defect assessment of cable joints is achieved.

[0042] The method described in this embodiment will be further described below.

[0043] As an optional embodiment, the method further includes: using a simulation model of the cable joint to obtain first historical temperature data of the conductor and second historical temperature data of the sheath; using a finite element model of the cable joint to obtain historical thermal defect data of the cable joint; and training an initial thermal defect assessment model of the cable joint based on the first historical temperature data, the second historical temperature data, and the historical thermal defect data to obtain a target thermal defect assessment model, wherein the initial thermal defect assessment model is established using parameter information of the environment in which the cable joint is located.

[0044] In this embodiment, a simulation model of the cable joint can be used to obtain the first historical temperature data of the core and the second historical temperature data of the sheath. Alternatively, a finite element model of the cable joint can be used to obtain the historical thermal defect data of the cable joint. Then, based on the first historical temperature data, the second historical temperature data, and the historical thermal defect data obtained above, the initial thermal defect evaluation model of the cable joint can be trained to obtain the target thermal defect evaluation model.

[0045] Optionally, a finite element model of the electromagnetic thermal coupling of the cable joint can be established using finite element analysis software. This model includes structures such as cable core, semiconductor layer, insulation layer, metal shielding layer, inner and outer sheaths, and sets the possible types and degrees of thermal defects, including core overheating, insulation layer damage, and sheath short circuit.

[0046] For example, by running a simulation model, setting up heat conduction analysis data for different defects in cable joints, obtaining temperature distribution data of cable joints under different defect conditions, and recording the temperature values ​​of the core and the sheath, so as to obtain the first historical temperature data of the core and the second historical temperature data of the sheath at this time.

[0047] For another example, after obtaining the first historical temperature data, the second historical temperature data, and historical thermal defect data, the parameters of the established initial thermal defect assessment model can be optimized to obtain the target thermal defect assessment model.

[0048] As an optional embodiment, an initial thermal defect assessment model for a cable joint is trained based on first historical temperature data, second historical temperature data, and historical thermal defect data to obtain a target thermal defect assessment model. This includes: determining a training dataset for the cable joint based on the first historical temperature data, second historical temperature data, and historical thermal defect data; and using the training dataset to train the initial thermal defect assessment model to obtain the target thermal defect assessment model.

[0049] In this embodiment, based on the first historical temperature data, the second historical temperature data, and historical thermal defect data, a training dataset for the cable joint can be determined. Then, using the training dataset, the initial thermal defect assessment model can be trained to obtain the target thermal defect assessment model.

[0050] For example, based on the acquired first historical temperature data, second historical temperature data, and historical thermal defect data, an initial thermal defect assessment model can be trained to obtain a target thermal defect assessment model. For instance, assuming this model optimization has m training samples, each containing 6 feature values, and using defect type and severity information as labels, the training dataset for training the initial thermal defect assessment model can be represented as:

[0051]

[0052] Among them, X i ∈R 6 Let y be the feature vector of the i-th sample. i For the corresponding tags.

[0053] As an optional embodiment, a training dataset for the cable joint is determined based on first historical temperature data, second historical temperature data, and historical thermal defect data, including: extracting features from the first historical temperature data to obtain a first feature value of the wire core; extracting features from the second historical temperature data to obtain a second feature value of the sheath; and determining the training dataset based on the first feature value, the second feature value, and the historical thermal defect data.

[0054] In this embodiment, feature extraction can be performed on the first historical temperature data to obtain the first feature value of the wire core, and feature extraction can be performed on the second historical temperature data to obtain the second feature value of the sheath. Then, based on the obtained first and second feature values, and historical thermal defect data, the training dataset for the cable joint can be determined. The first feature value of the wire core can be the temperature feature data of the wire core, and the second feature value of the sheath can be the temperature feature data of the sheath, and so on.

[0055] Optionally, the average temperature, temperature rise rate, or temperature rise difference before and after the defect can be extracted from the cable joint's core and sheath, respectively, and these data can be used as temperature characteristic data of the core and sheath. The average temperature of the core can be obtained through θ. c To represent this, the temperature rise rate of the wire core can be expressed as k. c The temperature rise difference before and after the defect in the wire core can be represented by Δθ. c The average temperature of the epidermis can be represented by θ. h To illustrate, the rate of temperature rise of the epidermis can be expressed as k. h The temperature rise difference before and after the defect in the epidermis can be represented by Δθ. h This is to be represented. It should be noted that this only provides examples of the temperature characteristic data of the wire core and the temperature characteristic data of the sheath, and does not specifically limit the data types of the temperature characteristic data of the wire core and the temperature characteristic data of the sheath.

[0056] Optionally, if the average temperature of the wire core and the average temperature of the sheath are calculated in the same way and can both be recorded as θ, then they can be expressed by the following formula:

[0057]

[0058] Where, θ i Let k be the temperature at the i-th sampling point, and n be the total number of sampling points. Over a period of time, the temperature rise rate of the wire core and the temperature rise rate of the sheath are calculated in the same way and can be uniformly recorded as k, expressed by the following formula:

[0059]

[0060] Where, θ i and θ i-1 The temperature can be the value at two consecutive time points, where Δt is the time interval. The temperature rise difference of the core before and after the defect is calculated in the same way as that of the sheath before and after the defect, and can be uniformly recorded as Δθ, and expressed by the following formula:

[0061] Δθ=θ max -θmin ,

[0062] Where, θ max and θ min These represent the highest and lowest temperatures measured within the total time period nΔt, respectively.

[0063] As an optional implementation method, the initial thermal defect assessment model is trained using a training dataset to obtain a target thermal defect assessment model, including: training the initial thermal defect assessment model using a target loss function of a cable joint based on the training dataset to obtain the target thermal defect assessment model.

[0064] In this embodiment, after obtaining the training dataset, the initial thermal defect assessment model can be trained using the target loss function of the cable joint to obtain the target thermal defect assessment model. The target loss function can be a preset loss function, such as the cross-entropy loss function. It should be noted that this is only an example of the target loss function and does not impose any specific limitations on it.

[0065] Optionally, the initial thermal defect assessment model can be trained using a deep neural network. This deep neural network consists of three layers: an input layer, a hidden layer, and an output layer. The input layer contains 6 neurons, corresponding to 6 feature values; the hidden layer contains k neurons with ReLU activation function; and the output layer outputs information about the defect type and severity.

[0066] For example, suppose there is a type c defect, the output layer contains c neurons, the activation function is softmax(), and the input of the i-th layer is a. (i) Then the calculation from the input layer to the hidden layer is as follows:

[0067] z (1) =W (1) X+b (1) ,

[0068] a (1) =ReLU(z) (1) ),

[0069] And the calculation from the hidden layer to the output layer is as follows:

[0070] z (2) =W (2) a (1) +b (2) ,

[0071] a (2) =softmax(z (2) ),

[0072] Among them, W (1) and W(2) Let b be the weight matrix. (1) and b (2) Let be the bias vector. The loss function chosen is the cross-entropy loss function, which can be expressed by the following formula:

[0073]

[0074] In the above formula, To predict the probability, y i These are the true label values. During the training of the initial thermal defect assessment model, the backpropagation algorithm is used to calculate and the model parameters W and b are updated using an optimization algorithm. The backpropagation calculation steps include:

[0075] First, calculate the gradient of the loss function with respect to the input of the output layer:

[0076] δ (2) =a (2) -y,

[0077] Calculate the gradient of the loss function with respect to the hidden layer input:

[0078] δ (1) =(W (2) ) T δ (2) ⊙ReLU ′ (z (1 )),

[0079] Calculate the gradients of the weights and biases:

[0080]

[0081]

[0082] Finally, update the parameters using the Adam optimizer:

[0083]

[0084] Where α can be the learning rate. and These represent the first-order and second-order momentum estimates, respectively, where ∈ is a small constant to prevent division by zero. After repeatedly performing forward propagation, loss calculation, backpropagation, and parameter updates on the initial thermal defect assessment model until the loss function converges, the target thermal defect assessment model can be obtained.

[0085] As an optional embodiment, determining a first target temperature data for the core based on first initial temperature data, and determining a second target temperature data for the sheath based on second initial temperature data, includes: performing noise reduction processing on the first initial temperature data to obtain the first target temperature data, and performing noise reduction processing on the second initial temperature data to obtain the second target temperature data.

[0086] In this embodiment, after obtaining the first initial temperature data, the first initial temperature data can be denoised to obtain the first target temperature data. Similarly, after obtaining the second initial temperature data, the second initial temperature data can be denoised to obtain the second target temperature data. For example, a temperature sensor can be used to collect the temperature data of the cable core and sheath in real time, and the temperature data can be denoised to obtain both the first and second target temperature data.

[0087] As an optional implementation method, the evaluation results include thermal defect type and thermal defect fault information, wherein the thermal defect fault information is used to characterize the degree of thermal defect fault of the cable joint.

[0088] In this embodiment, the evaluation results may include thermal defect type and thermal defect fault information, wherein the thermal defect type may be simply referred to as defect type, and the thermal defect fault information may be referred to as defect fault degree information.

[0089] Optionally, after obtaining the thermal defect data of the cable joint, the type of thermal defect can be determined based on the data. Based on the thermal defect state corresponding to the thermal defect data, thermal defect fault information can be determined. The thermal defect state can be cold, warm, overheated, hot, or extremely hot, etc. This is only an example of a thermal defect state and no specific limitations are imposed on it.

[0090] For example, after inputting the first target temperature data and the second target temperature data of the obtained wire core into the trained artificial intelligence model of the cable joint, the model can output information on the defect type and the degree of defect failure, thereby enabling real-time pre-assessment of the thermal defects of the cable joint.

[0091] In this embodiment, first initial temperature data of the conductor in the cable joint and second initial temperature data of the sheath in the cable joint can be collected. Then, based on the first initial temperature data, the first target temperature data of the conductor can be determined, and based on the second initial temperature data, the second target temperature data of the sheath can be determined. Finally, the first and second target temperature data can be input into the target thermal defect assessment model to assess the thermal defects of the cable joint and output the assessment results. Since the first target temperature data is determined based on the first initial temperature data, and the second target temperature data is determined based on the second initial temperature data, and then the target thermal defect assessment model is invoked based on the first and second target temperature data to obtain the assessment results of the cable joint, the technical problem of low efficiency in the thermal defect assessment of cable joints is solved, thus achieving the technical effect of improving the efficiency of thermal defect assessment of cable joints.

[0092] The technical solutions of the embodiments of the present invention will be illustrated below with reference to preferred embodiments.

[0093] With the rapid development of the national economy and the continuous growth of energy demand, the stable operation of the power system, as the foundation supporting socio-economic development, is of paramount importance. Cable lines, as a crucial component of the power system, bear the vital task of power transmission. Especially in urban distribution networks and high-voltage transmission lines, the use of cables is becoming increasingly widespread. Cable joints, the transitional parts connecting two cable sections, are characterized by their complex structure, small contact area, and poor heat dissipation, making them a weak link in cable lines. Furthermore, during long-term operation, cable joints are prone to thermal defects due to overheating, aging, and external damage, leading to malfunctions. Moreover, the existence of thermal defects not only affects the reliability and safety of the power system but may also trigger serious accidents such as fires.

[0094] Currently, thermal defect assessment of cable joints mainly relies on periodic inspections and offline testing methods, such as infrared thermal imaging, partial discharge detection, and resistance measurement. Infrared thermal imaging detects the thermal radiation on the surface of the cable joint to form a temperature distribution image, thereby assessing the thermal condition of the joint. This method can quickly detect temperature anomalies non-contactly. However, this method is greatly affected by ambient temperature and humidity, and usually requires periodic inspections. Furthermore, by detecting partial discharge signals around the cable joint, the insulation condition of the joint can be assessed; an abnormal increase in partial discharge is usually associated with thermal defects. However, this method may not be obvious enough for early and minor thermal defects, making detection or assessment difficult, and it is easily affected by electromagnetic interference in the field, affecting the accuracy of the prediction results.

[0095] Furthermore, the resistance measurement method assesses the presence of poor contact or overheating by periodically measuring the resistance of cable joints and comparing it with the resistance value during normal operation. However, this method only reflects the overall contact condition of the joint and cannot accurately locate the thermal defects. Moreover, it requires power outages for measurement, affecting the normal operation of the power system. In summary, while these methods can detect thermal defects to some extent, they still suffer from the technical problems of not being able to achieve real-time assessment, difficulty in timely detection of potential thermal defects, and high estimated costs, resulting in low efficiency in the assessment of thermal defects in cable joints.

[0096] Therefore, to address the aforementioned problems, this invention proposes a method for evaluating the thermal defects of cable joints based on real-time temperature sensing. This method utilizes a finite element model and a deep neural network model of the cable joint's thermal defects, based on real-time temperature sensing data, and combines data analysis and processing to assess the type and severity of thermal defects in the cable joint, ensuring the timely detection of potential safety hazards. This not only effectively improves the stability and safety of the power system but also provides strong support for the maintenance and management of power equipment, thereby enhancing the technical efficiency of thermal defect evaluation for cable joints.

[0097] In an embodiment of the present invention, Figure 2 This is a flowchart of a cable joint thermal defect assessment method based on real-time temperature sensing according to an embodiment of the present invention, as shown below. Figure 2 As shown, the flowchart includes the following steps:

[0098] Step S201: Using a simulation model, obtain thermal defect data of the cable joint, as well as the temperature values ​​of the cable core and sheath of the cable joint.

[0099] In this embodiment, a simulation model can be used to obtain thermal defect data of the cable joint.

[0100] Optionally, a finite element model of the electromagnetic thermal coupling of the cable joint can be established using finite element analysis software. This model includes structures such as cable core, semiconductor layer, insulation layer, metal shielding layer, inner and outer sheaths, and sets the possible types and degrees of thermal defects, including core overheating, insulation layer damage, and sheath short circuit.

[0101] Optionally, a simulation model can be run to set up heat conduction analysis data for different defects in the cable joint, obtain temperature distribution data of the cable joint under different defect conditions, and record the temperature values ​​of the core and the sheath.

[0102] Step S202: Extract the temperature characteristic values ​​of the core and sheath.

[0103] In this embodiment, the average temperature, temperature rise rate, or temperature rise difference before and after the defect of the cable joint's core and sheath are extracted respectively, and these data can be used as temperature characteristic data of the core and sheath. The average temperature of the core can be obtained through θ. c To represent this, the temperature rise rate of the wire core can be expressed as k. c The temperature rise difference before and after the defect in the wire core can be represented by Δθ. c The average temperature of the epidermis can be represented by θ. h To illustrate, the rate of temperature rise of the epidermis can be expressed as k. h The temperature rise difference before and after the defect in the epidermis can be represented by Δθ. h To express.

[0104] Optionally, if the average temperature of the wire core and the average temperature of the sheath are calculated in the same way and can both be recorded as θ, then they can be expressed by the following formula:

[0105]

[0106] Where, θ i Let k be the temperature at the i-th sampling point, and n be the total number of sampling points. Over a period of time, the temperature rise rate of the wire core and the temperature rise rate of the sheath are calculated in the same way and can be uniformly recorded as k, expressed by the following formula:

[0107]

[0108] Where, θ i and θ i-1 The temperature can be the value at two consecutive time points, where Δt is the time interval. The temperature rise difference of the core before and after the defect is calculated in the same way as that of the sheath before and after the defect, and can be uniformly recorded as Δθ, and expressed by the following formula:

[0109] Δθ=θ max -θ min ,

[0110] Where, θ max and θ min These represent the highest and lowest temperatures measured within the total time period nΔt, respectively.

[0111] Step S203: Obtain the training dataset.

[0112] In this embodiment, temperature feature values ​​and defect information can be organized into a training dataset. Assuming the model optimization has m training samples, each containing 6 feature values, and using defect type and severity information as labels to train the initial thermal defect assessment model, the training dataset can be represented as:

[0113]

[0114] Among them, X i ∈R 6 Let y be the feature vector of the i-th sample. i For the corresponding tags.

[0115] Step S204: Train the deep neural network.

[0116] In this embodiment, a deep neural network can be trained. Wherein, Figure 3 This is a schematic diagram of a deep neural network according to an embodiment of the present invention, such as... Figure 3 As shown, a deep neural network consists of three layers: an input layer, a hidden layer, and an output layer. The input layer contains 6 neurons, corresponding to 6 feature values; the hidden layer contains k neurons, and the activation function is ReLU(); the output layer can output information about the defect type and defect severity.

[0117] For example, suppose there is a type c defect, the output layer contains c neurons, the activation function is softmax(), and the input of the i-th layer is a. (i) Then the calculation from the input layer to the hidden layer is as follows:

[0118] z (1) =W (1) X+b (1) ,

[0119] a (1) =ReLU(z) (1) ),

[0120] And the calculation from the hidden layer to the output layer is as follows:

[0121] z (2) =W (2) a (1) +b (2) ,

[0122] a (2) =softmax(z (2) ),

[0123] Among them, W (1) and W (2) Let b be the weight matrix. (1) and b (2) Let be the bias vector. The loss function chosen is the cross-entropy loss function, which can be expressed by the following formula:

[0124]

[0125] In the above formula, To predict the probability, y iThese are the true label values. During the training of the initial thermal defect assessment model, the backpropagation algorithm is used to calculate and the model parameters W and b are updated using an optimization algorithm. The backpropagation calculation steps include:

[0126] First, calculate the gradient of the loss function with respect to the input of the output layer:

[0127] δ (2) =a (2 )-y,

[0128] Calculate the gradient of the loss function with respect to the hidden layer input:

[0129] δ (1) =(W (2) ) T δ (2) ⊙ReLU ′ (z (1) ),

[0130] Calculate the gradients of the weights and biases:

[0131]

[0132] Finally, update the parameters using the Adam optimizer:

[0133]

[0134]

[0135] Where α can be the learning rate. and These represent the first-order and second-order momentum estimates, respectively, where ∈ is a small constant to prevent division by zero. After repeatedly performing forward propagation, loss calculation, backpropagation, and parameter updates on the initial thermal defect assessment model until the loss function converges, the target thermal defect assessment model can be obtained.

[0136] Step S205: Real-time temperature acquisition and thermal defect assessment.

[0137] In this embodiment, a temperature sensor is used to collect temperature data of the cable core and sheath in real time, and the temperature data is denoised. The denoised temperature data is then input into a trained artificial intelligence model, which can output information on defect type and defect failure level, thereby enabling real-time pre-assessment of thermal defects in the cable joint.

[0138] In this embodiment, first initial temperature data of the conductor in the cable joint and second initial temperature data of the sheath in the cable joint can be collected. Then, based on the first initial temperature data, the first target temperature data of the conductor can be determined, and based on the second initial temperature data, the second target temperature data of the sheath can be determined. Finally, the first and second target temperature data can be input into the target thermal defect assessment model to assess the thermal defects of the cable joint and output the assessment results. Since the first target temperature data is determined based on the first initial temperature data, and the second target temperature data is determined based on the second initial temperature data, and then the target thermal defect assessment model is invoked based on the first and second target temperature data to obtain the assessment results of the cable joint, the technical problem of low efficiency in the thermal defect assessment of cable joints is solved, thus achieving the technical effect of improving the efficiency of thermal defect assessment of cable joints.

[0139] According to an embodiment of the present invention, a device for evaluating the thermal defects of a cable joint is provided. It should be noted that this device can be used to perform a method for evaluating the thermal defects of a cable joint as described in Embodiment 1.

[0140] Figure 4 This is a schematic diagram of a cable joint thermal defect assessment device according to an embodiment of the present invention. Figure 4 As shown, a thermal defect assessment device 400 for cable joints may include: a data acquisition unit 401, a determination unit 402, and an assessment unit 403.

[0141] The acquisition unit 401 is used to acquire the first initial temperature data of the wire core in the cable joint and the second initial temperature data of the sheath in the cable joint.

[0142] The determining unit 402 is used to determine the first target temperature data of the core based on the first initial temperature data, and to determine the second target temperature data of the sheath based on the second initial temperature data.

[0143] The evaluation unit 403 is used to input the first target temperature data and the second target temperature data into the target thermal defect evaluation model of the cable joint, evaluate the thermal defect data of the cable joint, and output the evaluation result of the cable joint. The target thermal defect evaluation model is established by using the first historical temperature data of the wire core, the second historical temperature data of the sheath, and the historical thermal defect data of the cable joint. The evaluation result is used to characterize the thermal defect state of the cable joint corresponding to the thermal defect data.

[0144] Optionally, the device further includes: a first acquisition unit for acquiring first historical temperature data of the conductor and second historical temperature data of the sheath using a simulation model of the cable joint; a second acquisition unit for acquiring historical thermal defect data of the cable joint using a finite element model of the cable joint; and a third acquisition unit for training an initial thermal defect assessment model of the cable joint based on the first historical temperature data, the second historical temperature data, and the historical thermal defect data to obtain a target thermal defect assessment model, wherein the initial thermal defect assessment model is established using parameter information of the environment in which the cable joint is located.

[0145] Optionally, the third acquisition unit further includes: a determination module, used to determine the training dataset of the cable joint based on the first historical temperature data, the second historical temperature data and historical thermal defect data; and a training module, used to train the initial thermal defect assessment model using the training dataset to obtain the target thermal defect assessment model.

[0146] Optionally, the determining module may include: a first acquisition submodule, used to extract features from the first historical temperature data to obtain a first feature value of the wire core; a second acquisition submodule, used to extract features from the second historical temperature data to obtain a second feature value of the sheath; and an acquisition submodule, used to determine a training dataset based on the first feature value, the second feature value, and historical thermal defect data.

[0147] Optionally, the training module may include: a training submodule, used to train the initial thermal defect assessment model based on the training dataset and using the target loss function of the cable joint to obtain the target thermal defect assessment model.

[0148] Optionally, the determining unit 402 includes: a processing module, used to perform noise reduction processing on the first initial temperature data to obtain the first target temperature data, and to perform noise reduction processing on the second initial temperature data to obtain the second target temperature data.

[0149] Optionally, the evaluation results include thermal defect type and thermal defect fault information, wherein the thermal defect fault information is used to characterize the degree of thermal defect fault of the cable joint.

[0150] In this embodiment, a data acquisition unit collects first initial temperature data of the conductor in the cable joint and second initial temperature data of the sheath in the cable joint. A determination unit determines first target temperature data of the conductor based on the first initial temperature data and second target temperature data of the sheath based on the second initial temperature data. An evaluation unit inputs the first and second target temperature data into the target thermal defect evaluation model of the cable joint to evaluate the thermal defect data of the cable joint and outputs the evaluation result of the cable joint. The target thermal defect evaluation model is established using the first historical temperature data of the conductor, the second historical temperature data of the sheath, and the historical thermal defect data of the cable joint. The evaluation result is used to characterize the thermal defect state of the cable joint corresponding to the thermal defect data, thereby solving the technical problem of low evaluation efficiency of thermal defects in cable joints and achieving the technical effect of improving the evaluation efficiency of thermal defects in cable joints.

[0151] According to an embodiment of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein, when the program is run by a processor, it controls the device where the readable storage medium is located to execute the thermal defect assessment method for cable joints in the embodiment.

[0152] According to an embodiment of the present invention, a processor is also provided for running a program, wherein the program executes the thermal defect assessment method for cable joints in the embodiment.

[0153] According to an embodiment of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the thermal defect assessment method for cable joints in the embodiments of the present invention.

[0154] According to embodiments of the present invention, an electronic device is also provided, comprising a processor and a memory for storing processor-executable instructions. The processor is configured to execute instructions to implement the thermal defect assessment method for cable joints according to embodiments of the present invention.

[0155] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0156] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0157] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed can be through some interfaces; the indirect coupling or communication connection of units or modules can be electrical or other forms.

[0158] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0159] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0160] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0161] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for evaluating thermal defects in cable joints, characterized in that, include: Collect the first initial temperature data of the wire core in the cable joint, and the second initial temperature data of the sheath in the cable joint; Based on the first initial temperature data, a first target temperature data for the core is determined, and based on the second initial temperature data, a second target temperature data for the sheath is determined. The first target temperature data and the second target temperature data are input into the target thermal defect evaluation model of the cable joint to evaluate the thermal defect data of the cable joint and output the evaluation result of the cable joint. The target thermal defect evaluation model is established by the first historical temperature data of the core, the second historical temperature data of the sheath and the historical thermal defect data of the cable joint. The evaluation result is used to characterize the thermal defect state of the cable joint corresponding to the thermal defect data. The method further includes: using the finite element model of the cable joint to obtain historical thermal defect data of the cable joint; using the simulation model of the cable joint to obtain temperature distribution data of the cable joint under different defect conditions, so as to determine the core temperature value and the sheath temperature value of the cable joint from the temperature distribution data, and based on the core temperature value and the sheath temperature value, obtaining the first historical temperature data of the core and the second historical temperature data of the sheath; and training the initial thermal defect evaluation model of the cable joint based on the first historical temperature data, the second historical temperature data and the historical thermal defect data to obtain the target thermal defect evaluation model, wherein the initial thermal defect evaluation model is established based on the parameter information of the environment in which the cable joint is located.

2. The method according to claim 1, characterized in that, Based on the first historical temperature data, the second historical temperature data, and the historical thermal defect data, the initial thermal defect assessment model of the cable joint is trained to obtain the target thermal defect assessment model, including: Based on the first historical temperature data, the second historical temperature data, and the historical thermal defect data, a training dataset for the cable joint is determined. The initial thermal defect assessment model is trained using the training dataset to obtain the target thermal defect assessment model.

3. The method according to claim 2, characterized in that, Based on the first historical temperature data, the second historical temperature data, and the historical thermal defect data, a training dataset for the cable joint is determined, including: Feature extraction is performed on the first historical temperature data to obtain the first feature value of the wire core; The feature extraction is performed on the second historical temperature data to obtain the second feature value of the epidermis; The training dataset is determined based on the first feature value, the second feature value, and the historical thermal defect data.

4. The method according to claim 2, characterized in that, Using the training dataset, the initial thermal defect assessment model is trained to obtain the target thermal defect assessment model, including: Based on the training dataset, the initial thermal defect assessment model is trained using the target loss function of the cable joint to obtain the target thermal defect assessment model.

5. The method according to claim 1, characterized in that, Based on the first initial temperature data, a first target temperature data for the core is determined, and based on the second initial temperature data, a second target temperature data for the sheath is determined, including: The first initial temperature data is denoised to obtain the first target temperature data, and the second initial temperature data is denoised to obtain the second target temperature data.

6. The method according to claim 1, characterized in that, The evaluation results include thermal defect type and thermal defect fault information, wherein the thermal defect fault information is used to characterize the degree of thermal defect fault of the cable joint.

7. A device for assessing thermal defects in cable joints, characterized in that, include: The acquisition unit is used to acquire the first initial temperature data of the wire core in the cable joint, and the second initial temperature data of the sheath in the cable joint. The determining unit is configured to determine a first target temperature data of the wire core based on the first initial temperature data, and to determine a second target temperature data of the sheath based on the second initial temperature data. The evaluation unit is used to input the first target temperature data and the second target temperature data into the target thermal defect evaluation model of the cable joint, evaluate the thermal defect data of the cable joint, and output the evaluation result of the cable joint. The target thermal defect evaluation model is established by the first historical temperature data of the wire core, the second historical temperature data of the sheath, and the historical thermal defect data of the cable joint. The evaluation result is used to characterize the thermal defect state of the cable joint corresponding to the thermal defect data. The device is further configured to: acquire historical thermal defect data of the cable joint using a finite element model of the cable joint; acquire temperature distribution data of the cable joint under different defect conditions using a simulation model of the cable joint, so as to determine the core temperature value and sheath temperature value of the cable joint from the temperature distribution data, and acquire first historical temperature data of the core and second historical temperature data of the sheath based on the core temperature value and the sheath temperature value; train an initial thermal defect assessment model of the cable joint based on the first historical temperature data, the second historical temperature data and the historical thermal defect data to obtain the target thermal defect assessment model, wherein the initial thermal defect assessment model is established using parameter information of the environment in which the cable joint is located.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein when the program is run by a processor, it controls the device in which the storage medium resides to perform the method according to any one of claims 1 to 6.

9. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the method described in any one of claims 1 to 6.

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