High-voltage cable dielectric loss monitoring method and device, terminal equipment and storage medium

Abstract

The invention discloses a high-voltage cable dielectric loss monitoring method and device, terminal equipment and a storage medium. The method comprises the steps of obtaining to-be-processed multi-dimensional dielectric loss data of a target high-voltage cable in a preset time period; wherein the to-be-processed multi-dimensional dielectric loss data comprises to-be-processed dielectric loss data and associated data of the to-be-processed dielectric loss data; the associated data comprises temperature, electric field interference and electromagnetic interference; inputting the to-be-processed multi-dimensional dielectric loss data in the preset time period into a dielectric loss prediction model, so that the dielectric loss prediction model outputs a dielectric loss value of the to-be-processed multi-dimensional dielectric loss data in the preset time period at the next moment; and carrying out dielectric loss monitoring on the target high-voltage cable according to the dielectric loss value at the next moment. By implementing the method, the influence of high-voltage cable dielectric loss correlation factors can be comprehensively considered, the accuracy and comprehensiveness of high-voltage cable dielectric loss value prediction are improved, and then the accuracy of high-voltage cable dielectric loss monitoring is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cable insulation performance monitoring, and in particular to a method, device, terminal equipment and storage medium for monitoring dielectric loss of a high-voltage cable. Background Art

[0002] With the continuous improvement of the power network, the laying length of high-voltage cables is increasing, and the safe and stable operation of high-voltage cables is becoming more and more important. Monitoring of high-voltage cables can reduce the risk of power accidents caused by insulation defects and ensure the safety and stability of the power grid. Among the existing monitoring methods for high-voltage cables, the overall aging degree of high-voltage cables is mainly evaluated by the dielectric loss of high-voltage cables. It involves the internal heating phenomenon caused by the dielectric conductivity and polarization hysteresis effect under the influence of an external electric field. This dielectric loss not only dissipates electrical energy, but also causes heating and aging of the insulating material, and may even cause insulation damage.

[0003] The current high-voltage cable dielectric loss monitoring methods can be divided into two categories: absolute measurement method and relative measurement method. The absolute measurement method includes traditional methods such as bridge method and resonance method, and the relative measurement method includes zero-crossing comparison method, sine fitting method, harmonic analysis method, etc. These methods are all digital measurement methods, and the impact of related factors on dielectric loss is not considered during measurement. Summary of the invention

[0004] The embodiments of the present invention provide a high-voltage cable dielectric loss monitoring method, device, terminal equipment and storage medium, which can comprehensively consider the influence of factors related to the high-voltage cable dielectric loss, improve the accuracy and comprehensiveness of the prediction of the high-voltage cable dielectric loss value, and thus improve the accuracy of the high-voltage cable dielectric loss monitoring.

[0005] An embodiment of the present invention provides a method for monitoring dielectric loss of a high-voltage cable, comprising:

[0006] Acquire the multi-dimensional dielectric loss data to be processed for a preset period of time of the target high-voltage cable; wherein the multi-dimensional dielectric loss data to be processed includes: the dielectric loss data to be processed and the associated data of the dielectric loss data to be processed; the associated data includes temperature, electric field interference and electromagnetic interference;

[0007] Inputting the to-be-processed multidimensional dielectric loss data of the preset time period into the dielectric loss prediction model, so that the dielectric loss prediction model outputs the dielectric loss value of the to-be-processed multidimensional dielectric loss data of the preset time period at the next moment;

[0008] The dielectric loss of the target high-voltage cable is monitored according to the dielectric loss value at the next moment.

[0009] Furthermore, the construction of the dielectric loss prediction model includes:

[0010] Obtain a multidimensional dielectric loss time series training set; wherein the multidimensional dielectric loss time series training set is composed of multidimensional dielectric loss data samples at a number of consecutive moments, each multidimensional dielectric loss data sample includes a dielectric loss data sample, a temperature sample, an electric field interference sample, and an electromagnetic interference sample, and each multidimensional dielectric loss data sample is marked with a corresponding dielectric loss true value;

[0011] An initial dielectric loss prediction model is constructed, and the initial dielectric loss prediction model is iteratively trained with the multi-dimensional dielectric loss time series training set until the initial dielectric loss prediction model converges, thereby generating a dielectric loss prediction model.

[0012] Furthermore, the initial dielectric loss prediction model is iteratively trained using the multi-dimensional dielectric loss time series training set, including:

[0013] Taking a multidimensional dielectric loss data sample sequence of a preset period as the input of an initial dielectric loss prediction model, taking a true value of dielectric loss at the next moment of the preset period as the output of the initial dielectric loss prediction model, and iteratively training the initial dielectric loss prediction model;

[0014] In each iterative training process, the initial dielectric loss prediction model uses a convolutional attention mechanism to process dielectric loss data samples, temperature samples, electric field interference samples, and electromagnetic interference samples, and outputs a dielectric loss prediction value at the next moment in a preset time period;

[0015] Determine the dielectric loss value of the current iteration according to the dielectric loss prediction value at the next moment of the preset period and the dielectric loss actual value at the next moment of the preset period;

[0016] The weight of the initial dielectric loss prediction model is adjusted according to the dielectric loss value of the current iteration.

[0017] Furthermore, the multi-dimensional dielectric loss data sample also includes a real value of frequency;

[0018] The multi-dimensional dielectric loss time series training set iteratively trains the initial dielectric loss prediction model, including:

[0019] Taking a multidimensional dielectric loss data sample sequence of a preset period as the input of an initial dielectric loss prediction model, taking a true value of dielectric loss at the next moment of the preset period as the output of the initial dielectric loss prediction model, and iteratively training the initial dielectric loss prediction model;

[0020] In each iterative training process, the initial dielectric loss prediction model uses a convolutional attention mechanism to process dielectric loss data samples, temperature samples, electric field interference samples, and electromagnetic interference samples, and outputs a dielectric loss prediction value at the next moment of a preset period and a frequency prediction value at the next moment of a preset period;

[0021] Determine the dielectric loss value of the current iteration according to the dielectric loss prediction value at the next moment of the preset period and the dielectric loss actual value at the next moment of the preset period;

[0022] The weight of the initial dielectric loss prediction model is adjusted according to the dielectric loss value of the current iteration;

[0023] Determine the frequency loss value of the current iteration according to the frequency prediction value at the next moment of the preset period and the actual frequency value at the next moment of the preset period;

[0024] If the frequency loss value of the current iteration is less than the preset frequency loss threshold, the multidimensional dielectric loss data sample at the next moment of the preset period and the dielectric loss prediction value at the next moment of the preset period are added to the multidimensional dielectric loss time series training set. Further, the preset frequency loss threshold is determined according to the preset threshold adjustment curve and the current iteration number.

[0025] Furthermore, after obtaining the multi-dimensional dielectric loss time series training set, the method further includes:

[0026] The multidimensional dielectric loss time series training set is subjected to wavelet denoising to obtain the denoised multidimensional dielectric loss time series training set.

[0027] Based on the above method embodiment, the present invention provides a corresponding device embodiment;

[0028] An embodiment of the present invention provides a high-voltage cable dielectric loss monitoring device, including: a data acquisition module, a dielectric loss prediction module and a monitoring module;

[0029] The data acquisition module is used to acquire the multi-dimensional dielectric loss data to be processed for a preset period of time of the target high-voltage cable; wherein the multi-dimensional dielectric loss data to be processed includes: the dielectric loss data to be processed and the associated data of the dielectric loss data to be processed; the associated data includes temperature, electric field interference and electromagnetic interference;

[0030] The dielectric loss prediction module is used to input the multidimensional dielectric loss data to be processed in the preset time period into the dielectric loss prediction model, so that the dielectric loss prediction model outputs the dielectric loss value of the multidimensional dielectric loss data to be processed in the preset time period at the next moment;

[0031] The monitoring module is used to monitor the dielectric loss of the target high-voltage cable according to the dielectric loss value at the next moment.

[0032] Furthermore, it also includes a dielectric loss prediction model construction module;

[0033] The dielectric loss prediction model building module is used to obtain a multidimensional dielectric loss time series training set; wherein the multidimensional dielectric loss time series training set is composed of multidimensional dielectric loss data samples at a number of consecutive moments, each multidimensional dielectric loss data sample includes a dielectric loss data sample, a temperature sample, an electric field interference sample, and an electromagnetic interference sample, and each multidimensional dielectric loss data sample is marked with a corresponding dielectric loss true value;

[0034] An initial dielectric loss prediction model is constructed, and the initial dielectric loss prediction model is iteratively trained with the multi-dimensional dielectric loss time series training set until the initial dielectric loss prediction model converges, thereby generating a dielectric loss prediction model.

[0035] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, a high-voltage cable dielectric loss monitoring method described in the above-mentioned embodiment of the invention is implemented.

[0036] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a high-voltage cable dielectric loss monitoring method described in the above-mentioned embodiment of the invention.

[0037] The following beneficial effects are achieved by implementing the present invention:

[0038] The present invention provides a method, device, terminal equipment and storage medium for monitoring dielectric loss of high-voltage cables. The monitoring method obtains the dielectric loss data to be processed and the associated data of the dielectric loss data to be processed for a preset period of time of the target high-voltage cable, wherein the associated data includes temperature, electric field interference and electromagnetic interference; and combines them into multi-dimensional dielectric loss data to be processed, and then inputs them into a dielectric loss prediction model, so that the dielectric loss prediction model outputs the dielectric loss value of the multi-dimensional dielectric loss data to be processed for the preset period of time at the next moment, and the dielectric loss of the target high-voltage cable is monitored according to the dielectric loss value at the next moment. By obtaining the multi-dimensional dielectric loss data to be processed including the dielectric loss data to be processed and the associated data of the dielectric loss data to be processed, the associated factors affecting the dielectric loss can be fully considered when predicting the dielectric loss value, so that the prediction result is more comprehensive and accurate, thereby improving the accuracy of dielectric loss monitoring of the target high-voltage cable. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1It is a flow chart of a method for monitoring dielectric loss of a high-voltage cable provided in one embodiment of the present invention.

[0040] Figure 2 This is a training flow chart of an initial dielectric loss prediction model provided by an embodiment of the present invention.

[0041] Figure 3 It is a structural schematic diagram of a high-voltage cable dielectric loss monitoring device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0045] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0046] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0047] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0048] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0049] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0050] like Figure 1 As shown, a high-voltage cable dielectric loss monitoring method provided by an embodiment of the present invention includes:

[0051] Step S1: obtaining the multi-dimensional dielectric loss data to be processed for a preset period of time of the target high-voltage cable; wherein the multi-dimensional dielectric loss data to be processed includes: the dielectric loss data to be processed and the associated data of the dielectric loss data to be processed; the associated data includes temperature, electric field interference and electromagnetic interference;

[0052] Step S2: inputting the to-be-processed multidimensional dielectric loss data of the preset time period into the dielectric loss prediction model, so that the dielectric loss prediction model outputs the dielectric loss value of the to-be-processed multidimensional dielectric loss data of the preset time period at the next moment;

[0053] Step S3: monitoring the dielectric loss of the target high-voltage cable according to the dielectric loss value at the next moment.

[0054] For step S1, the multi-dimensional dielectric loss data to be processed of the target high-voltage cable to be monitored in the preset period is obtained, and the multi-dimensional dielectric loss data needs to include the target high-voltage cable to be processed dielectric loss data at each moment in the preset period and the temperature, electric field interference and electromagnetic interference and other related data of the target high-voltage cable. The preset period is a historical period of fixed time length, the end time of the preset period is the monitoring time of the current target high-voltage cable, and the start time of the preset period is determined according to the monitoring time of the current target high-voltage cable and the fixed time length.

[0055] For step S2, the multidimensional dielectric loss data to be processed for a preset time period is input into the dielectric loss prediction model, and the dielectric loss prediction model outputs the dielectric loss value of the multidimensional dielectric loss data to be processed for the preset time period at the next moment, that is, the dielectric loss value at the next moment of the current monitoring moment.

[0056] In a preferred embodiment, the construction of the dielectric loss prediction model includes: obtaining a multidimensional dielectric loss time series training set; wherein the multidimensional dielectric loss time series training set is composed of multidimensional dielectric loss data samples at several consecutive moments, each multidimensional dielectric loss data sample includes a dielectric loss data sample, a temperature sample, an electric field interference sample, and an electromagnetic interference sample, and each multidimensional dielectric loss data sample is marked with a corresponding dielectric loss true value; constructing an initial dielectric loss prediction model, and iteratively training the initial dielectric loss prediction model with the multidimensional dielectric loss time series training set until the initial dielectric loss prediction model converges, thereby generating a dielectric loss prediction model.

[0057] Specifically, the construction of the dielectric loss prediction model mainly includes the training data acquisition step and the model training step. When the training data is acquired, in order to minimize the interference of irrelevant external electromagnetic wave signals, the multi-dimensional dielectric loss data samples of each continuous moment of the present invention are obtained through simulation experiments. First, a laboratory cable dielectric loss data acquisition platform is constructed in an anechoic chamber, and a cable layout and model transmission path similar to the actual high-voltage cable field are set in the laboratory, and a signal transmitter and a spectrum analyzer are used to simulate industrial interference signals. Secondly, a voltage sensor and a current sensor are used to collect high-voltage cable analog data respectively, and the collected high-voltage cable analog signal is converted into a digital signal with a 50H power frequency as the sampling frequency. At the same time, temperature monitoring equipment, electric field probes, magnetic field probes and other equipment are used to detect real-time temperature, electric field interference, magnetic field interference and other parameters respectively, as temperature samples, electric field interference samples and electromagnetic interference samples corresponding to the multi-dimensional dielectric loss data samples at each moment. Thirdly, based on the high-voltage cable data, a variety of dielectric loss detection methods including harmonic analysis, bipolar zero-crossing comparison and high-order sine fitting are comprehensively adopted to obtain dielectric loss data at multiple continuous moments as dielectric loss data samples corresponding to multi-dimensional dielectric loss data samples at multiple continuous moments. It should be noted that the harmonic analysis method uses fast Fourier transform harmonic analysis of current and voltage signals, extracts the signal phase angle and obtains a dielectric loss data; the bipolar zero-crossing comparison method obtains the corresponding dielectric loss data by comparing the positive and negative phase zero-crossing time difference of voltage and current at the same time; the high-order sine fitting method approximates the original signal with the fundamental frequency, the amplitude and phase angle of each harmonic as variables, and obtains the corresponding dielectric loss data in combination with Hamming window interpolation. For each dielectric loss data sample, a multi-dimensional dielectric loss data sample is constructed by combining the temperature sample, the electric field interference sample, the electromagnetic interference sample and the true value of the dielectric loss, and the true value of the dielectric loss corresponding to the multi-dimensional dielectric loss data sample is obtained in combination with the harmonic analysis method. Finally, the sampling rate is adjusted in real time by using the true value of the adopted frequency and a dynamic algorithm to obtain multidimensional dielectric loss data samples at multiple consecutive moments to form a multidimensional dielectric loss time series training set.

[0058] In a preferred embodiment, after obtaining the multi-dimensional dielectric loss time series training set, the method further includes: performing wavelet denoising on the multi-dimensional dielectric loss time series training set to obtain a denoised multi-dimensional dielectric loss time series training set.

[0059] Specifically, after obtaining the multidimensional dielectric loss time series training set, the multidimensional dielectric loss data samples at each moment containing the noisy signal are placed in a two-dimensional space using wavelet transform. By utilizing the completely different characteristics of the signal and the noise, the denoising effect of each sample is achieved through wavelet decomposition, denoising and signal reconstruction of the noise signal, and the denoised multidimensional dielectric loss time series training set is obtained.

[0060] Preferably, the denoised multi-dimensional dielectric loss time series training set is divided into a validation set and a test set according to a certain ratio.

[0061] like Figure 2 As shown, an initial dielectric loss prediction model is constructed. The initial dielectric loss prediction model is a ConvTrans model. It should be noted that the ConvTrans model is a model that is improved and optimized for time series prediction tasks based on the Transformer model. The core structure of the ConvTrans model consists of two parts: Convolutional Self-Attention and LogSparse Transformer. The ConvTrans model uses the convolutional attention mechanism and the log sparse Transformer to achieve modeling of finer-grained long sequences with less memory. The convolutional attention mechanism generates query values ​​Q, keys K, and values ​​V through causal convolutions, as shown in the following formula:

[0062]

[0063] Among them, the query value Q and key K are generated through causal convolution as follows:

[0064] Q=Conv(X,W q ,padding)

[0065] K=Conv(X,W k ,padding)

[0066] Where X is the input sequence; W q and W k is the convolution kernel; padding ensures causality.

[0067] The initial dielectric loss prediction model is iteratively trained using the multi-dimensional dielectric loss time series training set.

[0068] In a preferred embodiment, the initial dielectric loss prediction model is iteratively trained with the multidimensional dielectric loss time series training set, including: taking the multidimensional dielectric loss data sample sequence of a preset time period as the input of the initial dielectric loss prediction model, taking the true value of the dielectric loss at the next moment of the preset time period as the output of the initial dielectric loss prediction model, and iteratively training the initial dielectric loss prediction model; wherein, in each iterative training process, the initial dielectric loss prediction model uses a convolutional attention mechanism to process dielectric loss data samples, temperature samples, electric field interference samples, and electromagnetic interference samples, and outputs a dielectric loss prediction value at the next moment of the preset time period; determines the dielectric loss loss value of the current iteration according to the dielectric loss prediction value at the next moment of the preset time period and the true value of the dielectric loss at the next moment of the preset time period; and adjusts the weight of the initial dielectric loss prediction model according to the dielectric loss loss value of the current iteration.

[0069] In a preferred embodiment, the multidimensional dielectric loss data sample also includes a true value of the frequency; the multidimensional dielectric loss time series training set iteratively trains the initial dielectric loss prediction model, including: taking the multidimensional dielectric loss data sample sequence of a preset time period as the input of the initial dielectric loss prediction model, taking the true value of the dielectric loss at the next moment of the preset time period as the output of the initial dielectric loss prediction model, and iteratively training the initial dielectric loss prediction model; wherein, in each iterative training process, the initial dielectric loss prediction model uses a convolutional attention mechanism to process dielectric loss data samples, temperature samples, electric field interference samples, and electromagnetic interference samples, and outputs a preset time series training set. The dielectric loss prediction value of the next moment of the preset period and the frequency prediction value of the next moment of the preset period are determined; the dielectric loss loss value of the current iteration is determined according to the dielectric loss prediction value of the next moment of the preset period and the actual value of the dielectric loss of the next moment of the preset period; the weight of the initial dielectric loss prediction model is adjusted according to the dielectric loss loss value of the current iteration; the frequency loss value of the current iteration is determined according to the frequency prediction value of the next moment of the preset period and the actual value of the frequency of the next moment of the preset period; if the frequency loss value of the current iteration is less than the preset frequency loss threshold, the multidimensional dielectric loss data sample of the next moment of the preset period and the dielectric loss prediction value of the next moment of the preset period are added to the multidimensional dielectric loss time series training set.

[0070] In a preferred embodiment, the preset frequency loss threshold is determined according to a preset threshold adjustment curve and the current number of iterations.

[0071] Specifically, when training the initial dielectric loss prediction model, a multidimensional dielectric loss data sample of a preset period is obtained from the multidimensional dielectric loss time series training set as the input of the initial dielectric loss prediction model, and the actual value of the dielectric loss at the next moment of the preset period is used as the output of the initial dielectric loss prediction model, and the initial dielectric loss prediction model is iteratively trained. In each iterative training process, after a multidimensional dielectric loss data sample of a preset period is obtained from the multidimensional dielectric loss time series training set and input into the initial dielectric loss prediction model, the initial dielectric loss prediction model uses a convolutional attention mechanism to process the dielectric loss data sample, temperature sample, electric field interference sample, and electromagnetic interference sample, and outputs the dielectric loss prediction value corresponding to the multidimensional dielectric loss data sample of the preset period. Exemplarily, if the preset period is [n, n+k], the fixed time length of the preset period is k, and the multidimensional dielectric loss data sample of [n, n+k] is obtained from the multidimensional dielectric loss time series training set as the input of the initial dielectric loss prediction model, so that the initial dielectric loss prediction model single-step predicts the dielectric loss prediction value at the moment n+k+1 and the frequency prediction value at the moment n+k+1. The actual value of dielectric loss and the actual value of frequency at the moment n+k+1 are obtained from the multi-dimensional dielectric loss time series training set, the dielectric loss loss value of the current iteration is determined according to the dielectric loss prediction value at the moment n+k+1 and the actual value of dielectric loss at the moment n+k+1, and the frequency loss value of the current iteration is determined according to the frequency prediction value at the moment n+k+1 and the actual value of frequency at the moment n+k+1. In the present invention, the dielectric loss loss value is used as the loss function of the initial dielectric loss prediction model, and the weight of the initial dielectric loss prediction model is adjusted by the dielectric loss loss value of the current iteration to assist in the optimization of the weight of the initial dielectric loss prediction model. When the dielectric loss loss value is the minimum, the initial dielectric loss prediction model converges to generate a dielectric loss prediction model. The frequency loss value is used to determine the reliability of the predicted data. If the frequency loss value at the next moment of the preset period is less than the preset frequency loss threshold value under the corresponding number of iterations, the dielectric loss prediction value at the next moment of the preset period is considered to be reliable data, and the multidimensional dielectric loss data sample and the dielectric loss prediction value at the next moment of the preset period are added to the multidimensional dielectric loss time series training set to assist the prediction of subsequent time steps; that is, in the next iterative training, the multidimensional dielectric loss data sample of [n+1,n+k+1] is used as the input of the initial dielectric loss prediction model, so that the initial dielectric loss prediction model predicts the dielectric loss prediction value at the moment n+k+2 and the frequency prediction value at the moment n+k+2 in a single step. This method can make the multidimensional dielectric loss time series training set continuously iteratively updated with the model training process. If the frequency loss value at the next moment of the preset period is greater than or equal to the preset frequency loss threshold value under the corresponding number of iterations, the dielectric loss prediction value at the next moment of the preset period is considered to be unreliable data, and the dielectric loss prediction value at the next moment of the preset period is discarded.Among them, the preset frequency loss threshold is determined according to the threshold adjustment curve, and decreases as the number of iterations increases. The threshold adjustment curve is a cosine decline curve, whose horizontal axis is the number of training rounds, that is, the number of iterations, and whose vertical axis is the preset frequency loss threshold.

[0072] Preferably, the performance of the generated dielectric loss prediction model is evaluated, and the prediction effect of the dielectric loss prediction model is evaluated by multiple evaluation indicators such as mean absolute error (MAE), relative absolute error (RAE), root mean square error (RMSE), and determination coefficient (R2), and the model weight of the dielectric loss prediction model is fine-tuned according to the prediction effect.

[0073] For step S3, the dielectric loss of the target high-voltage cable is monitored according to the dielectric loss value at the next moment. Preferably, the dielectric loss prediction model is set in the monitoring device of the target high-voltage cable to predict the dielectric loss value of the next moment of the multi-dimensional dielectric loss data to be processed in the preset period in real time according to the multi-dimensional dielectric loss data to be processed in the preset period, and the preset period is updated with the dielectric loss value at the next moment, and then the prediction is continuously iterated to realize the real-time and long-term monitoring of the target high-voltage cable. At the same time, in order to avoid error accumulation, the predicted dielectric loss value is regularly corrected.

[0074] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0075] like Figure 3 As shown, an embodiment of the present invention provides a high-voltage cable dielectric loss monitoring device, including: a data acquisition module, a dielectric loss prediction module and a monitoring module;

[0076] The data acquisition module is used to acquire the multi-dimensional dielectric loss data to be processed for a preset period of time of the target high-voltage cable; wherein the multi-dimensional dielectric loss data to be processed includes: the dielectric loss data to be processed and the associated data of the dielectric loss data to be processed; the associated data includes temperature, electric field interference and electromagnetic interference;

[0077] The dielectric loss prediction module is used to input the multidimensional dielectric loss data to be processed in the preset time period into the dielectric loss prediction model, so that the dielectric loss prediction model outputs the dielectric loss value of the multidimensional dielectric loss data to be processed in the preset time period at the next moment;

[0078] The monitoring module is used to monitor the dielectric loss of the target high-voltage cable according to the dielectric loss value at the next moment.

[0079] In a preferred embodiment, it also includes a dielectric loss prediction model building module;

[0080] The dielectric loss prediction model building module is used to obtain a multidimensional dielectric loss time series training set; wherein the multidimensional dielectric loss time series training set is composed of multidimensional dielectric loss data samples at a number of consecutive moments, each multidimensional dielectric loss data sample includes a dielectric loss data sample, a temperature sample, an electric field interference sample, and an electromagnetic interference sample, and each multidimensional dielectric loss data sample is marked with a corresponding dielectric loss true value;

[0081] An initial dielectric loss prediction model is constructed, and the initial dielectric loss prediction model is iteratively trained with the multi-dimensional dielectric loss time series training set until the initial dielectric loss prediction model converges, thereby generating a dielectric loss prediction model.

[0082] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0083] Those skilled in the art can clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0084] Based on the above method item embodiments, the present invention provides corresponding terminal device item embodiments.

[0085] An embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, a high-voltage cable dielectric loss monitoring method as described in any one of the present invention is implemented.

[0086] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0087] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0088] The memory can be used to store the computer program, and the processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0089] Based on the above method item embodiments, the present invention provides a corresponding storage medium item embodiment.

[0090] An embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a high-voltage cable dielectric loss monitoring method as described in any one of the present inventions.

[0091] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium.

[0092] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for monitoring dielectric loss of a high voltage cable, characterized in that: include: Acquire the multi-dimensional dielectric loss data to be processed for a preset period of time of the target high-voltage cable; wherein the multi-dimensional dielectric loss data to be processed includes: the dielectric loss data to be processed and the associated data of the dielectric loss data to be processed; the associated data includes temperature, electric field interference and electromagnetic interference; Inputting the to-be-processed multidimensional dielectric loss data of the preset time period into the dielectric loss prediction model, so that the dielectric loss prediction model outputs the dielectric loss value of the to-be-processed multidimensional dielectric loss data of the preset time period at the next moment; The dielectric loss of the target high-voltage cable is monitored according to the dielectric loss value at the next moment.

2. A high voltage cable dielectric loss monitoring method as claimed in claim 1, characterized in that: The construction of the dielectric loss prediction model includes: Obtain a multidimensional dielectric loss time series training set; wherein the multidimensional dielectric loss time series training set is composed of multidimensional dielectric loss data samples at a number of consecutive moments, each multidimensional dielectric loss data sample includes a dielectric loss data sample, a temperature sample, an electric field interference sample, and an electromagnetic interference sample, and each multidimensional dielectric loss data sample is marked with a corresponding dielectric loss true value; An initial dielectric loss prediction model is constructed, and the initial dielectric loss prediction model is iteratively trained with the multi-dimensional dielectric loss time series training set until the initial dielectric loss prediction model converges, thereby generating a dielectric loss prediction model.

3. A high voltage cable dielectric loss monitoring method as claimed in claim 2, characterized in that: The initial dielectric loss prediction model is iteratively trained using the multi-dimensional dielectric loss time series training set, including: Taking a multidimensional dielectric loss data sample sequence of a preset period as the input of an initial dielectric loss prediction model, taking a true value of dielectric loss at the next moment of the preset period as the output of the initial dielectric loss prediction model, and iteratively training the initial dielectric loss prediction model; In each iterative training process, the initial dielectric loss prediction model uses a convolutional attention mechanism to process dielectric loss data samples, temperature samples, electric field interference samples, and electromagnetic interference samples, and outputs a dielectric loss prediction value at the next moment in a preset time period; Determine the dielectric loss value of the current iteration according to the dielectric loss prediction value at the next moment of the preset period and the dielectric loss actual value at the next moment of the preset period; The weight of the initial dielectric loss prediction model is adjusted according to the dielectric loss value of the current iteration.

4. A high voltage cable dielectric loss monitoring method as claimed in claim 2, characterized in that: The multi-dimensional dielectric loss data sample also includes a true value of frequency; The multi-dimensional dielectric loss time series training set iteratively trains the initial dielectric loss prediction model, including: Taking a multidimensional dielectric loss data sample sequence of a preset period as the input of an initial dielectric loss prediction model, taking a true value of dielectric loss at the next moment of the preset period as the output of the initial dielectric loss prediction model, and iteratively training the initial dielectric loss prediction model; In each iterative training process, the initial dielectric loss prediction model uses a convolutional attention mechanism to process dielectric loss data samples, temperature samples, electric field interference samples, and electromagnetic interference samples, and outputs a dielectric loss prediction value at the next moment in a preset period and a frequency prediction value at the next moment in a preset period; Determine the dielectric loss value of the current iteration according to the dielectric loss prediction value at the next moment of the preset period and the dielectric loss actual value at the next moment of the preset period; The weight of the initial dielectric loss prediction model is adjusted according to the dielectric loss value of the current iteration; Determine the frequency loss value of the current iteration according to the frequency prediction value at the next moment of the preset period and the actual frequency value at the next moment of the preset period; If the frequency loss value of the current iteration is less than the preset frequency loss threshold, the multidimensional dielectric loss data sample at the next moment of the preset period and the dielectric loss prediction value at the next moment of the preset period are added to the multidimensional dielectric loss time series training set.

5. A high voltage cable dielectric loss monitoring method as claimed in claim 4, characterized in that: The preset frequency loss threshold is determined according to a preset threshold adjustment curve and the current number of iterations.

6. A method for monitoring dielectric loss of a high voltage cable according to claim 2, characterized in that: After obtaining the multi-dimensional dielectric loss time series training set, it also includes: The multidimensional dielectric loss time series training set is subjected to wavelet denoising to obtain the denoised multidimensional dielectric loss time series training set.

7. A high voltage cable dielectric loss monitoring device, characterized in that: include: Data acquisition module, dielectric loss prediction module and monitoring module; The data acquisition module is used to acquire the multi-dimensional dielectric loss data to be processed for a preset period of time of the target high-voltage cable; wherein the multi-dimensional dielectric loss data to be processed includes: the dielectric loss data to be processed and the associated data of the dielectric loss data to be processed; the associated data includes temperature, electric field interference and electromagnetic interference; The dielectric loss prediction module is used to input the multidimensional dielectric loss data to be processed in the preset time period into the dielectric loss prediction model, so that the dielectric loss prediction model outputs the dielectric loss value of the multidimensional dielectric loss data to be processed in the preset time period at the next moment; The monitoring module is used to monitor the dielectric loss of the target high-voltage cable according to the dielectric loss value at the next moment.

8. A high voltage cable dielectric loss monitoring device as claimed in claim 7, characterized in that: It also includes a dielectric loss prediction model building module; The dielectric loss prediction model building module is used to obtain a multidimensional dielectric loss time series training set; wherein the multidimensional dielectric loss time series training set is composed of multidimensional dielectric loss data samples at a number of consecutive moments, each multidimensional dielectric loss data sample includes a dielectric loss data sample, a temperature sample, an electric field interference sample, and an electromagnetic interference sample, and each multidimensional dielectric loss data sample is marked with a corresponding dielectric loss true value; An initial dielectric loss prediction model is constructed, and the initial dielectric loss prediction model is iteratively trained with the multi-dimensional dielectric loss time series training set until the initial dielectric loss prediction model converges, thereby generating a dielectric loss prediction model.

9. A terminal device, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a high-voltage cable dielectric loss monitoring method as claimed in any one of claims 1 to 6 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a high-voltage cable dielectric loss monitoring method as described in any one of claims 1 to 6.

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