A method and system for online monitoring of fault status in high-voltage parallel cable lines

Abstract

This invention discloses an online monitoring method and system for fault status of high-voltage parallel cable lines, relating to the field of cable line monitoring technology. The method involves acquiring electrical quantity parameters at various monitoring points of a high-voltage parallel cable line within a preset sampling interval, preprocessing these parameters based on the sampling interval to obtain corresponding feature data, extracting features from the feature data using a first detection network to obtain a first feature map, and then extracting features from the first feature map using a second detection network to obtain a second feature map. Finally, a Leaky ReLU activation layer is used to evaluate the state of the second feature map, yielding the fault status assessment result for the high-voltage parallel cable line. This invention solves the technical problem that existing cable line fault status monitoring methods mainly rely on manual experience and simple statistical analysis, making it difficult to reflect the actual fault status of the cable in real time and reducing the reliability of cable line operation.

Description

Technical Field

[0001] This invention relates to the field of cable line monitoring technology, and in particular to an online monitoring method and system for fault status of high-voltage parallel cable lines. Background Technology

[0002] High-voltage parallel cables are an important component of urban power grids. Cable failures not only cause economic losses and adverse social impacts, but also affect the safe and stable operation of urban power grids. Therefore, real-time monitoring of the fault status of high-voltage parallel cable lines is crucial.

[0003] Currently, existing methods for monitoring cable line fault conditions mainly rely on manual experience and simple statistical analysis, which makes it difficult to reflect the actual fault condition of the cable in real time and reduces the reliability of cable line operation. Summary of the Invention

[0004] This invention provides an online monitoring method and system for fault status of high-voltage parallel cable lines, which solves the technical problem that existing cable line fault status monitoring methods mainly rely on manual experience and simple statistical analysis, making it difficult to reflect the actual fault status of the cable in real time and reducing the reliability of cable line operation.

[0005] The first aspect of this invention provides an online monitoring method for fault status of high-voltage parallel cable lines, comprising:

[0006] The electrical quantity parameters of each monitoring point of the high-voltage parallel cable line within a preset sampling interval are obtained, and the data of each electrical quantity parameter is preprocessed based on the sampling interval to obtain the corresponding feature data.

[0007] The feature data is input into a pre-trained line fault state assessment model, wherein the line fault state assessment model includes a first detection network, a second detection network, and a Leaky ReLU activation layer connected in sequence.

[0008] The first feature map is obtained by extracting features from the feature data using the first detection network.

[0009] The second feature map is obtained by extracting features from the first feature map using the second detection network.

[0010] The second feature map is evaluated using a Leaky Relu activation layer to obtain the fault status evaluation result corresponding to the high-voltage parallel cable line.

[0011] Optionally, the electrical quantity parameters include a first grounding current, a second grounding current, a load current, a sheath induced voltage, a fault time, a start time, and an end time. The step of preprocessing each of the electrical quantity parameters based on the sampling interval to obtain corresponding feature data includes:

[0012] Each second grounding current is compared with the associated first grounding current to obtain a first difference value.

[0013] The first difference is compared with the sampling interval to obtain the ground current change rate corresponding to each monitoring point.

[0014] By inputting two load currents connected in parallel at the same node in each of the load currents into a preset load current imbalance function, multiple load current imbalances are obtained.

[0015] Each load current is input into a preset phase-to-phase current imbalance function to obtain the phase-to-phase current imbalance corresponding to each monitoring point.

[0016] The ratios of each second grounding current to the associated load current are calculated to obtain the grounding current-load ratio for each monitoring point.

[0017] The following are used as characteristic data: the change rate of each ground current, the unbalance of each load current, the unbalance of each phase current, the ground current-load ratio, the sheath induced voltage, the second ground current, the load current, the fault time, the start time, and the end time.

[0018] Optionally, the first detection network includes a 10×10 standard convolutional layer and a data normalization layer connected in sequence. The step of extracting features from the feature data through the first detection network to obtain a first feature map includes:

[0019] A 10×10 standard convolutional layer is used to extract features from the feature data to obtain a first data feature map;

[0020] The first feature map is obtained by standardizing the first data feature map through a data normalization layer.

[0021] Optionally, the second detection network includes a first detection module, a 1×1 convolutional layer, a second detection module, a 1×1 convolutional layer, and a feature fusion layer. The step of extracting features from the first feature map using the second detection network to obtain the second feature map includes:

[0022] The first detection module is used to extract features from the first feature map to obtain a first detection feature map. The first detection module includes a first extraction branch, a feature fusion layer, a batch normalization layer, and a Tanh activation layer.

[0023] The first detected feature map is downsampled by a 1×1 convolutional layer to obtain the first sampled feature map;

[0024] The second detection module extracts features from the first detection feature map to obtain a second detection feature map. The second detection module includes a second extraction branch, a 1×1 convolutional layer, a feature fusion layer, a batch normalization layer, and a ReLU activation layer.

[0025] The second detection feature map is extracted by a 1×1 convolutional layer to obtain the second sampling feature map;

[0026] A feature fusion layer is used to fuse the first sampled feature map and the second sampled feature map to obtain a second feature map.

[0027] Optionally, the step of using the first detection module to extract features from the first feature map to obtain a first detection feature map includes:

[0028] The first feature map is extracted by the first extraction branch to obtain the first residual feature map. The first extraction branch includes a 1×1 convolutional layer, a 3×3 convolutional layer, a batch normalization layer, a Tanh activation layer and a 1×1 standard convolutional layer connected in sequence.

[0029] A feature fusion layer is used to fuse the first residual feature map and the first feature map to obtain a first fused feature map;

[0030] The first fused feature map is obtained by sequentially passing it through a batch normalization layer and a ReLU activation layer to perform a nonlinear transformation.

[0031] Optionally, the step of extracting features from the first detection feature map using the second detection module to obtain the second detection feature map includes:

[0032] The second extraction branch extracts features from the first detection feature map to obtain a second residual feature map. The second extraction branch includes a 1×1 standard convolutional layer, a batch normalization layer, a ReLU activation layer, a 3×3 convolutional layer, a ReLU activation layer, and a 1×1 standard convolutional layer connected in sequence.

[0033] The first detection feature map is processed by a 1×1 convolutional layer to extract features, resulting in a third residual feature map.

[0034] A feature fusion layer is used to fuse the second residual feature map and the third residual feature map to obtain a second fused feature map;

[0035] The second fused feature map is nonlinearly mapped by passing it through a batch normalization layer and a ReLU activation layer in sequence to obtain the second detection feature map.

[0036] The second aspect of this invention provides an online monitoring system for fault status of high-voltage parallel cable lines, comprising:

[0037] The acquisition module is used to acquire electrical quantity parameters of each monitoring point of the high-voltage parallel cable line within a preset sampling interval, and to perform data preprocessing on each electrical quantity parameter based on the sampling interval to obtain corresponding feature data.

[0038] The input module is used to input the feature data into a pre-trained line fault state assessment model, wherein the line fault state assessment model includes a first detection network, a second detection network, and a Leaky ReLU activation layer connected in sequence.

[0039] The first extraction module is used to extract features from the feature data through the first detection network to obtain a first feature map;

[0040] The second extraction module is used to extract features from the first feature map through the second detection network to obtain a second feature map;

[0041] The evaluation module is used to perform state evaluation on the second feature map using the Leaky Relu activation layer to obtain the fault state evaluation result corresponding to the high-voltage parallel cable line.

[0042] A third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the online monitoring method for fault status of high-voltage parallel cable lines as described in any of the preceding claims.

[0043] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the online monitoring method for fault status of high-voltage parallel cable lines as described in any of the preceding claims.

[0044] The fifth aspect of the present invention provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein, when the program instructions are executed by a computer, the computer performs the online monitoring method for fault status of high-voltage parallel cable lines as described in any of the preceding claims.

[0045] As can be seen from the above technical solutions, the present invention has the following advantages:

[0046] By acquiring electrical parameters at various monitoring points of a high-voltage parallel cable line within a preset sampling interval and preprocessing these parameters to obtain feature data—which contains state information of each monitoring point within the high-voltage parallel cable line—and inputting this feature data into a pre-trained line fault state assessment model, the fault state assessment results of the high-voltage parallel cable line can be obtained quickly and accurately. This overcomes the technical problem that existing cable line fault state monitoring methods mainly rely on manual experience and simple statistical analysis, making it difficult to reflect the actual fault state of the cable in real time and reducing the reliability of cable line operation. Compared with traditional cable line fault state monitoring methods, this invention acquires feature data from the high-voltage parallel cable line and inputs it into a preset line fault state assessment model. By optimizing the internal structure of the line fault state assessment model, it achieves accurate assessment of the fault state of the high-voltage parallel cable line, thereby improving the reliability of cable line operation. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart illustrating the steps of an online monitoring method for fault status of a high-voltage parallel cable line provided in Embodiment 1 of the present invention.

[0049] Figure 2 This is a schematic diagram of a high-voltage parallel cable line structure provided in Embodiment 1 of the present invention;

[0050] Figure 3 This is a flowchart illustrating the steps of an online monitoring method for fault status of a high-voltage parallel cable line provided in Embodiment 2 of the present invention.

[0051] Figure 4 This is a schematic diagram of the structure of the line fault status assessment model provided in Embodiment 2 of the present invention;

[0052] Figure 5 This is a schematic diagram of the structure of the first detection module provided in Embodiment 2 of the present invention;

[0053] Figure 6 This is a schematic diagram of the structure of the second detection module provided in Embodiment 2 of the present invention;

[0054] Figure 7 This is a structural block diagram of an online monitoring system for fault status of high-voltage parallel cable lines provided in Embodiment 3 of the present invention;

[0055] Figure 8 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0056] This invention provides a method and system for online monitoring of fault status in high-voltage parallel cable lines, which addresses the technical problem that existing cable line fault status monitoring methods mainly rely on manual experience and simple statistical analysis, making it difficult to reflect the actual fault status of the cable in real time and reducing the reliability of cable line operation.

[0057] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0058] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of an online monitoring method for fault status of a high-voltage parallel cable line provided in Embodiment 1 of the present invention.

[0059] This invention provides an online monitoring method for fault status of high-voltage parallel cable lines, comprising:

[0060] Step 101: Obtain the electrical quantity parameters of each monitoring point of the high-voltage parallel cable line within the preset sampling interval, and perform data preprocessing on each electrical quantity parameter based on the sampling interval to obtain the corresponding feature data;

[0061] In this embodiment of the invention, electrical quantity parameters of each monitoring point are collected by the monitoring terminal of each monitoring point according to a preset sampling interval, and the electrical quantity parameters are preprocessed based on the sampling interval to generate corresponding feature data.

[0062] It should be noted that electrical parameters include, but are not limited to, monitoring nodes, sampling intervals, start time, fault time, end time, load current, sheath grounding current, and sheath induced voltage.

[0063] It should be noted that, for reference Figure 2As shown, the monitoring terminal includes a high-speed data acquisition module, a data analysis module, a clock module, a communication module, a main controller, and a power supply module. The clock module is used to adjust time synchronization between different devices; the power supply module provides power to the monitoring terminal; the high-speed data acquisition module converts the obtained analog voltage signals into digital signals; the data analysis module performs post-processing on basic parameters; and the clock module provides time for the high-speed data acquisition module and is used for time synchronization between different devices. The main controller is used to preprocess the electrical parameters of the acquisition modules and transmit the feature data to the monitoring center. The high-speed data acquisition module includes, but is not limited to, a sheath induced voltage sensor, a sheath current sensor, and a load current sensor.

[0064] Step 102: Input the feature data into the pre-trained line fault state assessment model, wherein the line fault state assessment model includes a first detection network, a second detection network and a Leaky ReLU activation layer connected in sequence;

[0065] In this embodiment of the invention, feature data is used as input to a pre-trained line fault state assessment model, resulting in a line fault state assessment model comprising a first detection network, a second detection network, and a LeakyRelu activation layer connected in sequence.

[0066] It is worth mentioning that before inputting the feature data into the pre-trained line fault state assessment model, multiple historical electrical quantity parameters of the high-voltage parallel cable line are obtained. These historical electrical quantity parameters are preprocessed to obtain a training feature dataset. This training feature dataset is then used to train the line fault state assessment model, resulting in a trained model. The training process is as follows: 1. Input the training feature dataset into the line fault state assessment model to obtain the line assessment result. 2. Select the standard assessment result corresponding to the line assessment result from the training feature dataset. 3. Calculate the deviation between the line assessment result and the standard assessment result. 4. When the deviation is less than a preset deviation threshold, the trained line fault state assessment model is generated. 5. When the deviation is greater than or equal to the preset deviation threshold, the parameters of the line fault state assessment model are adjusted using the gradient descent method, and steps 1-3 are repeated until the deviation is less than the preset deviation threshold.

[0067] Step 103: Extract features from the feature data using the first detection network to obtain the first feature map;

[0068] In this embodiment of the invention, a first detection network is used to extract features from the feature data to obtain a first feature map.

[0069] Step 104: Extract features from the first feature map using the second detection network to obtain the second feature map;

[0070] In this embodiment of the invention, a second detection network is used to extract features from the first feature map to obtain a second feature map.

[0071] Step 105: Use the Leaky Relu activation layer to perform state evaluation on the second feature map to obtain the fault state evaluation results corresponding to the high-voltage parallel cable line.

[0072] In this embodiment of the invention, the second feature map is evaluated by the Leaky Relu activation layer to obtain the fault status evaluation result corresponding to the high-voltage parallel cable line. The fault status evaluation result includes the overall status of the line (the overall status includes normal 1, attention 2, abnormal 3, danger 4) and the fault type corresponding to the fault monitoring point (the fault type includes none, single-phase short-circuit ground fault, two-phase short-circuit fault, two-phase short-circuit ground fault, three-phase short circuit, etc.).

[0073] In this embodiment of the invention, electrical parameters of each monitoring point in a high-voltage parallel cable line within a preset sampling interval are acquired, and data preprocessing is performed on each electrical parameter to obtain feature data. This feature data contains the state information of each monitoring point within the high-voltage parallel cable line. By inputting the feature data into a pre-trained line fault state assessment model, the fault state assessment result of the high-voltage parallel cable line can be obtained quickly and accurately. This overcomes the technical problem that existing cable line fault state monitoring methods mainly rely on manual experience and simple statistical analysis, making it difficult to reflect the actual fault state of the cable in real time and reducing the reliability of cable line operation. Compared with traditional cable line fault state monitoring methods, this invention acquires feature data of the high-voltage parallel cable line and inputs it into a preset line fault state assessment model. By optimizing the internal structure of the line fault state assessment model, it achieves accurate assessment of the fault state of the high-voltage parallel cable line, thereby improving the reliability of cable line operation.

[0074] Please see Figure 3 , Figure 3 The flowchart illustrates the steps of an online monitoring method for fault status of a high-voltage parallel cable line provided in Embodiment 2 of the present invention.

[0075] This invention provides an online monitoring method for fault status of high-voltage parallel cable lines, comprising:

[0076] Step 201: Obtain the electrical quantity parameters of each monitoring point of the high-voltage parallel cable line within the preset sampling interval, and perform data preprocessing on each electrical quantity parameter based on the sampling interval to obtain the corresponding feature data;

[0077] Furthermore, the electrical parameters include the first grounding current, the second grounding current, the load current, the sheath induced voltage, the fault time, the start time, and the end time. Step 201 includes the following sub-steps:

[0078] S11. Perform difference processing on each second grounding current and the associated first grounding current to obtain the first difference;

[0079] S12. Ratio the first difference with the sampling interval to obtain the grounding current change rate corresponding to each monitoring point;

[0080] The first grounding current refers to the grounding current value at the monitoring point at the previous moment.

[0081] The second grounding current refers to the grounding current value at the monitoring point at the current moment.

[0082] In this embodiment of the invention, the sampling interval, each second grounding current and the associated first grounding current are respectively input into a preset grounding current change rate function to obtain the grounding current change rate corresponding to each monitoring point.

[0083] It should be noted that the grounding current rate of change function is specifically as follows:

[0084]

[0085] in, The grounding current change rate, The first grounding current, This is the second grounding current. The sampling interval is denoted as .

[0086] S13. Input the two load currents connected in parallel at the same node in each load current into the preset load current imbalance function to obtain multiple load current imbalances.

[0087] In this embodiment of the invention, the load current imbalance of two load currents connected in parallel at the same node in each load current is calculated by a preset load current imbalance function.

[0088] It should be noted that the load current unbalance function is as follows:

[0089]

[0090] in, For load current imbalance, This represents the load current value of parallel circuit 1. This represents the load current value of parallel circuit 2.

[0091] It should be noted that the two load currents connected in parallel at the same node are the load currents collected by two consecutively numbered sensors.

[0092] It should be noted that the load current refers to the load current of the three phases at monitoring points a, b, and c, and the load current imbalance refers to the load current imbalance of the three phases at monitoring points a, b, and c.

[0093] S14. Input each load current into the preset phase-to-phase current imbalance function to obtain the phase-to-phase current imbalance corresponding to each monitoring point.

[0094] In this embodiment of the invention, the phase-to-phase current imbalance of each load current is calculated using a preset phase-to-phase current imbalance function.

[0095] It should be noted that the phase-to-phase current imbalance function is as follows:

[0096]

[0097]

[0098]

[0099] in, To monitor the phase-to-phase current imbalance at monitoring point a, To monitor the phase-to-phase current imbalance at monitoring point b, To monitor the phase-to-phase current imbalance at monitoring point c, To monitor the load current of phase a, To monitor the load current of phase b, The load current of phase c at monitoring point.

[0100] S15. Ratio the second grounding current with the associated load current for each monitoring point to obtain the grounding current-load ratio for each monitoring point.

[0101] In this embodiment of the invention, the ground current load ratio between each second ground current and the associated load current is calculated.

[0102] It should be noted that the expression for the grounding current-to-load ratio is:

[0103]

[0104] in, The grounding current load ratio, This is the load current.

[0105] It should be noted that the grounding current load ratio includes the grounding current load ratio of the three phases at monitoring points a, b, and c.

[0106] S16. The characteristic data include the change rate of each ground current, the unbalance of each load current, the unbalance of each phase current, the load ratio of each ground current, the induced voltage of each sheath, the second ground current, the load current, the fault time, the start time, and the end time.

[0107] In this embodiment of the invention, the following are used as feature data: the rate of change of each ground current, the degree of imbalance of each load current, the degree of imbalance of each phase current, the ratio of each ground current to the load, the induced voltage of each sheath, the second ground current, the load current, the fault time, the start time, and the end time.

[0108] Step 202: Input the feature data into the pre-trained line fault state assessment model, wherein the line fault state assessment model includes a first detection network, a second detection network and a Leaky ReLU activation layer connected in sequence;

[0109] In this embodiment of the invention, feature data is input into a pre-trained line fault state assessment model for state assessment. The line fault state assessment model includes a first detection network, a second detection network, and a Leaky ReLU activation layer connected in sequence.

[0110] In another embodiment, feature data is input into a pre-trained line fault state assessment model, wherein the line fault state assessment model can be a support vector machine or a random forest model.

[0111] It is worth mentioning that before inputting the feature data into the pre-trained line fault state assessment model for state assessment, the feature data can be preprocessed to obtain preprocessed feature data. Preprocessing specifically includes, but is not limited to, data standardization, calculation of the covariance matrix, or dimensionality reduction.

[0112] Step 203: Extract features from the feature data using the first detection network, wherein the first detection network includes a 10×10 standard convolutional layer and a data normalization layer connected in sequence;

[0113] In the embodiments of the present invention, see Figure 4 As shown, a first detection network is used to extract features from the feature data. The first detection network includes a 10×10 standard convolutional layer and a data normalization layer connected in sequence.

[0114] Step 204: Use a 10×10 standard convolutional layer to extract features from the feature data to obtain the first data feature map;

[0115] In this embodiment of the invention, a first data feature map is obtained by extracting features from the feature data through a 10×10 standard convolutional layer.

[0116] Step 205: Standardize the first data feature map through a data normalization layer to obtain the first feature map.

[0117] In this embodiment of the invention, the first data feature map is normalized by a data normalization layer to obtain the first feature map.

[0118] It is worth mentioning that by normalizing the first feature map of the input, the data has specific distribution characteristics, thereby improving the training efficiency and performance of the line fault status assessment model.

[0119] Step 206: Extract features from the first feature map using the second detection network to obtain the second feature map;

[0120] Further, see Figure 4 As shown, the second detection network includes a first detection module, a 1×1 convolutional layer, a second detection module, a 1×1 convolutional layer, and a feature fusion layer. Step 206 includes the following sub-steps:

[0121] S21. The first detection module is used to extract features from the first feature map to obtain the first detection feature map. The first detection module includes a first extraction branch, a feature fusion layer, a batch normalization layer and a Tanh activation layer.

[0122] Further, see Figure 5 As shown, S21 includes the following sub-steps:

[0123] S211. The first feature map is extracted through the first extraction branch to obtain the first residual feature map. The first extraction branch includes a 1×1 convolutional layer, a 3×3 convolutional layer, a batch normalization layer, a Tanh activation layer and a 1×1 standard convolutional layer connected in sequence.

[0124] In this embodiment of the invention, the first feature map is extracted sequentially through a 1×1 convolutional layer, a 3×3 convolutional layer, a batch normalization layer, a Tanh activation layer, and a 1×1 standard convolutional layer to obtain the first residual feature map.

[0125] S212. A feature fusion layer is used to fuse the first residual feature map and the first feature map to obtain the first fused feature map.

[0126] In this embodiment of the invention, a first fused feature map is obtained by fusing the first residual feature map and the first feature map through a feature fusion layer.

[0127] S213. The first fused feature map is subjected to nonlinear transformation through batch normalization layer and ReLU activation layer in sequence to obtain the first detection feature map.

[0128] In this embodiment of the invention, the first fused feature map is nonlinearly transformed by passing it through a batch normalization layer and a ReLU activation layer in sequence to obtain the first detection feature map.

[0129] S22. The first detection feature map is downsampled through a 1×1 convolutional layer to obtain the first sampled feature map;

[0130] In this embodiment of the invention, a 1×1 convolutional layer is used to downsample the first detection feature map to obtain the first sampled feature map.

[0131] S23. The second detection module extracts features from the first detection feature map to obtain the second detection feature map. The second detection module includes a second extraction branch, a 1×1 convolutional layer, a feature fusion layer, a batch normalization layer, and a ReLU activation layer.

[0132] Further, see Figure 6 As shown, S23 includes the following sub-steps:

[0133] S231. The first detection feature map is extracted through the second extraction branch to obtain the second residual feature map. The second extraction branch includes a 1×1 standard convolutional layer, a batch normalization layer, a ReLU activation layer, a 3×3 convolutional layer, a ReLU activation layer and a 1×1 standard convolutional layer connected in sequence.

[0134] In this embodiment of the invention, the first detection feature map is processed sequentially through a 1×1 standard convolutional layer, a batch normalization layer, a ReLU activation layer, a 3×3 convolutional layer, a ReLU activation layer, and a 1×1 standard convolutional layer to extract features, thereby obtaining the second residual feature map.

[0135] S232. The first detection feature map is extracted by a 1×1 convolutional layer to obtain the third residual feature map;

[0136] In this embodiment of the invention, a 1×1 convolutional layer is used to extract features from the first detection feature map to obtain the third residual feature map.

[0137] S233. A feature fusion layer is used to fuse the second residual feature map and the third residual feature map to obtain the second fused feature map.

[0138] In this embodiment of the invention, the second residual feature map and the third residual feature map are fused by a feature fusion layer to obtain the second fused feature map.

[0139] S234. The second fused feature map is nonlinearly mapped by passing it through a batch normalization layer and a ReLU activation layer in sequence to obtain the second detection feature map.

[0140] In this embodiment of the invention, the second fused feature map is nonlinearly mapped by a batch normalization layer and a ReLU activation layer in sequence to obtain the second detection feature map.

[0141] It is worth mentioning that, during feature data analysis, the ReLU activation layer has a fast computation speed and can alleviate the gradient vanishing problem.

[0142] S245. Extract features from the second detection feature map using a 1×1 convolutional layer to obtain the second sampling feature map;

[0143] In this embodiment of the invention, a 1×1 convolutional layer is used to extract features from the second detection feature map to obtain the second sampling feature map.

[0144] S25. A feature fusion layer is used to fuse the first sampled feature map and the second sampled feature map to obtain the second feature map.

[0145] In this embodiment of the invention, a feature fusion layer is used to fuse the first sampled feature map and the second sampled feature map to obtain a second feature map.

[0146] Step 207: Use the Leaky Relu activation layer to perform state evaluation on the second feature map to obtain the fault state evaluation results corresponding to the high-voltage parallel cable line.

[0147] In this embodiment of the invention, the second feature map is evaluated by the Leaky Relu activation layer to obtain the fault status evaluation result corresponding to the high-voltage parallel cable line, and the fault status evaluation result is loaded onto the monitoring page for visualization.

[0148] It should be noted that the visual display of fault status assessment results can be any of the following: dynamic charts, heat maps, or visual reports.

[0149] It is worth mentioning that the Leaky ReLU activation layer can accurately analyze feature data even when there are some negative fluctuations or special cases.

[0150] In this embodiment of the invention, electrical parameters of each monitoring point in a high-voltage parallel cable line within a preset sampling interval are acquired, and data preprocessing is performed on each electrical parameter to obtain feature data. This feature data contains the state information of each monitoring point within the high-voltage parallel cable line. By inputting the feature data into a pre-trained line fault state assessment model, the fault state assessment result of the high-voltage parallel cable line can be obtained quickly and accurately. This overcomes the technical problem that existing cable line fault state monitoring methods mainly rely on manual experience and simple statistical analysis, making it difficult to reflect the actual fault state of the cable in real time and reducing the reliability of cable line operation. Compared with traditional cable line fault state monitoring methods, this invention acquires feature data of the high-voltage parallel cable line and inputs it into a preset line fault state assessment model. By optimizing the internal structure of the line fault state assessment model, it achieves accurate assessment of the fault state of the high-voltage parallel cable line, thereby improving the reliability of cable line operation.

[0151] Please see Figure 7 , Figure 7 This is a structural block diagram of an online fault status monitoring system for high-voltage parallel cable lines provided in Embodiment 3 of the present invention.

[0152] This invention provides an online monitoring system for fault status of high-voltage parallel cable lines, comprising:

[0153] The acquisition module 301 is used to acquire electrical quantity parameters of each monitoring point of the high-voltage parallel cable line within a preset sampling interval, and to perform data preprocessing on each electrical quantity parameter based on the sampling interval to obtain the corresponding feature data.

[0154] The input module 302 is used to input feature data into a pre-trained line fault state assessment model, wherein the line fault state assessment model includes a first detection network, a second detection network, and a Leaky ReLU activation layer connected in sequence.

[0155] The first extraction module 303 is used to extract features from the feature data through the first detection network to obtain a first feature map;

[0156] The second extraction module 304 is used to extract features from the first feature map through the second detection network to obtain the second feature map;

[0157] The evaluation module 305 is used to perform state evaluation on the second feature map using the Leaky Relu activation layer to obtain the fault state evaluation result corresponding to the high-voltage parallel cable line.

[0158] Furthermore, the electrical parameters include the first grounding current, the second grounding current, the load current, the sheath induced voltage, the fault time, the start time, and the end time. The acquisition module 301 includes:

[0159] The first data analysis submodule is used to perform difference processing on each second grounding current and the associated first grounding current to obtain the first difference;

[0160] The first difference is compared with the sampling interval to obtain the grounding current change rate corresponding to each monitoring point.

[0161] The second data analysis submodule is used to input two load currents connected in parallel at the same node in each load current into a preset load current imbalance function to obtain multiple load current imbalances.

[0162] The third data analysis submodule is used to input each load current into a preset phase-to-phase current imbalance function to obtain the phase-to-phase current imbalance corresponding to each monitoring point.

[0163] The fourth data analysis submodule is used to process the ratio of each second grounding current to the associated load current to obtain the grounding current-load ratio corresponding to each monitoring point.

[0164] The fifth data analysis submodule is used to use the following as feature data: the change rate of each ground current, the unbalance of each load current, the unbalance of each phase current, the ground current load ratio, the induced voltage of each sheath, the second ground current, the load current, the fault time, the start time, and the end time.

[0165] Furthermore, the first detection network includes a 10×10 standard convolutional layer and a data normalization layer connected in sequence, and the first extraction module 303 includes:

[0166] The first extraction submodule is used to extract features from the feature data using a 10×10 standard convolutional layer to obtain the first data feature map;

[0167] The second extraction submodule is used to standardize the first data feature map through a data normalization layer to obtain the first feature map.

[0168] Furthermore, the second detection network includes a first detection module, a 1×1 convolutional layer, a second detection module, a 1×1 convolutional layer, and a feature fusion layer; the second extraction module 304 includes:

[0169] The third extraction submodule is used to extract features from the first feature map using the first detection module to obtain the first detection feature map. The first detection module includes a first extraction branch, a feature fusion layer, a batch normalization layer, and a Tanh activation layer.

[0170] The sampling submodule is used to perform a downsampling operation on the first detection feature map through a 1×1 convolutional layer to obtain the first sampled feature map;

[0171] The fourth extraction submodule is used to extract features from the first detection feature map through the second detection module to obtain the second detection feature map. The second detection module includes a second extraction branch, a 1×1 convolutional layer, a feature fusion layer, a batch normalization layer, and a ReLU activation layer.

[0172] The fifth extraction submodule is used to extract features from the second detection feature map through a 1×1 convolutional layer to obtain the second sampled feature map;

[0173] The first fusion submodule is used to perform feature fusion on the first sampled feature map and the second sampled feature map using a feature fusion layer to obtain the second feature map.

[0174] Furthermore, the third extraction submodule includes:

[0175] The first extraction unit is used to extract features from the first feature map through the first extraction branch to obtain the first residual feature map. The first extraction branch includes a 1×1 convolutional layer, a 3×3 convolutional layer, a batch normalization layer, a Tanh activation layer and a 1×1 standard convolutional layer connected in sequence.

[0176] The first fusion unit is used to perform feature fusion on the first residual feature map and the first feature map using a feature fusion layer to obtain the first fused feature map;

[0177] The first transformation unit is used to perform a nonlinear transformation on the first fused feature map sequentially through a batch normalization layer and a ReLU activation layer to obtain the first detection feature map.

[0178] Furthermore, the fourth extraction submodule includes:

[0179] The second extraction unit is used to extract features from the first detection feature map through the second extraction branch to obtain the second residual feature map. The second extraction branch includes a 1×1 standard convolutional layer, a batch normalization layer, a ReLU activation layer, a 3×3 convolutional layer, a ReLU activation layer and a 1×1 standard convolutional layer connected in sequence.

[0180] The third extraction unit is used to extract features from the first detection feature map through a 1×1 convolutional layer to obtain the third residual feature map;

[0181] The second fusion unit is used to perform feature fusion on the second residual feature map and the third residual feature map using the feature fusion layer to obtain the second fused feature map;

[0182] The second transformation unit is used to perform nonlinear mapping on the second fused feature map by sequentially passing it through a batch normalization layer and a ReLU activation layer to obtain the second detection feature map.

[0183] Please see Figure 8, Figure 8 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention.

[0184] An electronic device according to an embodiment of the present invention includes: a memory 401 and a processor 402. The memory 402 stores a computer program. When the computer program is executed by the processor 402, the processor 402 performs the online monitoring method for fault status of high-voltage parallel cable lines as described in any of the above embodiments.

[0185] Memory 401 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 401 has storage space 403 for program code 413 for performing any of the method steps described above. For example, storage space 403 for program code may include individual program codes 413 for implementing the various steps in the methods described above. This program code may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact discs (CDs), memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When run by a computing processing device, this code causes the computing processing device to perform the various steps in the methods described above.

[0186] Embodiment 5 of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the online monitoring method for fault status of high-voltage parallel cable lines as described in any of the above embodiments.

[0187] Embodiment 6 of the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer performs the online monitoring method for fault status of high-voltage parallel cable lines as described in any of the above embodiments.

[0188] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0189] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0190] 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 network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0191] 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.

[0192] 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, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0193] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for on-line monitoring of the state of a high-voltage parallel cable line fault, characterized by The method comprises the following steps: obtaining electrical quantity parameters of each monitoring point of a high-voltage parallel cable line within a preset sampling interval, and performing data preprocessing on each electrical quantity parameter based on the sampling interval to obtain corresponding feature data; inputting the feature data into a pre-trained line fault state evaluation model, wherein the line fault state evaluation model comprises a first detection network, a second detection network and a Leaky Relu activation layer connected in sequence; performing feature extraction on the feature data through the first detection network to obtain a first feature map; performing feature extraction on the first feature map through the second detection network to obtain a second feature map; performing state evaluation on the second feature map using the Leaky Relu activation layer to obtain a corresponding fault state evaluation result of the high-voltage parallel cable line; the electrical quantity parameters include first ground currents, second ground currents, load currents, shield layer induced voltages, fault times, start times and end times, the step of performing data preprocessing on each electrical quantity parameter based on the sampling interval to obtain corresponding feature data comprises: respectively, the first difference is obtained by performing difference processing on each second ground current and the associated first ground current; respectively, the ground current change rate corresponding to each monitoring point is obtained by performing ratio processing on the first difference and the sampling interval; respectively, a plurality of load current unbalance degrees are obtained by inputting two load currents in parallel at the same node in each load current into a preset load current unbalance degree function; respectively, the phase-to-phase current unbalance degree corresponding to each monitoring point is obtained by inputting each load current into a preset phase-to-phase current unbalance degree function; respectively, the ground current load ratio corresponding to each monitoring point is obtained by performing ratio processing on each second ground current and the associated load current; each ground current change rate, each load current unbalance degree, each phase-to-phase current unbalance degree, each ground current load ratio, each shield layer induced voltage, each second ground current, each load current, each fault time, each start time and each end time are used as feature data; the ground current change rate function is specifically: ； wherein, is a ground current rate of change, is a first ground current, is a second ground current, is a sampling interval; the load current unbalance degree function is specifically: ； wherein, is the load current unbalance degree, is the load current value of parallel circuit 1, is the load current value of parallel circuit 2.

2. The high voltage parallel cable line fault condition on-line monitoring method according to claim 1, characterized in that, the first detection network comprises a 10x10 standard convolution layer and a data normalization layer connected in sequence, and the step of performing feature extraction on the feature data through the first detection network to obtain a first feature map comprises: performing feature extraction on the feature data using a 10x10 standard convolution layer to obtain a first data feature map; performing standardization processing on the first data feature map through a data normalization layer to obtain a first feature map.

3. The high voltage parallel cable line fault condition on-line monitoring method according to claim 1, characterized in that, the second detection network comprises a first detection module, a 1x1 convolution layer, a second detection module, a 1x1 convolution layer and a feature fusion layer, and the step of performing feature extraction on the first feature map through the second detection network to obtain a second feature map comprises: The first detection module is used for feature extraction on the first feature map, and a first detection feature map is obtained, wherein the first detection module comprises a first extraction branch, a feature fusion layer, a batch normalization layer and a Tanh activation layer; The first detection feature map is down-sampled by a 1*1 convolution layer to obtain a first sampling feature map; The second detection module is used for feature extraction on the first detection feature map, and a second detection feature map is obtained, wherein the second detection module comprises a second extraction branch, a 1*1 convolution layer, a feature fusion layer, a batch normalization layer and a Relu activation layer; The second detection feature map is feature-extracted by a 1*1 convolution layer to obtain a second sampling feature map; The first sampling feature map and the second sampling feature map are fused by a feature fusion layer to obtain a second feature map.

4. The high voltage parallel cable line fault condition on-line monitoring method according to claim 3, characterized in that, The step of using the first detection module to perform feature extraction on the first feature map to obtain a first detection feature map comprises: The first feature map is feature-extracted by the first extraction branch to obtain a first residual feature map, wherein the first extraction branch comprises a 1*1 convolution layer, a 3*3 convolution layer, a batch normalization layer, a Tanh activation layer and a 1*1 standard convolution layer connected in sequence; The first residual feature map and the first feature map are fused by a feature fusion layer to obtain a first fusion feature map; The first fusion feature map is nonlinearly transformed by a batch normalization layer and a Relu activation layer in sequence to obtain a first detection feature map.

5. The high voltage parallel cable line fault condition on-line monitoring method according to claim 3, characterized in that, The step of using the second detection module to perform feature extraction on the first detection feature map to obtain a second detection feature map comprises: The first detection feature map is feature-extracted by the second extraction branch to obtain a second residual feature map, wherein the second extraction branch comprises a 1*1 standard convolution layer, a batch normalization layer, a ReLU activation layer, a 3*3 convolution layer, a ReLU activation layer and a 1*1 standard convolution layer connected in sequence; The first detection feature map is feature-extracted by a 1*1 convolution layer to obtain a third residual feature map; The second residual feature map and the third residual feature map are fused by a feature fusion layer to obtain a second fusion feature map; The second fusion feature map is nonlinearly mapped by a batch normalization layer and a Relu activation layer in sequence to obtain a second detection feature map.

6. A high voltage parallel cable line fault condition on-line monitoring system, characterized by The step of using the first detection module to perform feature extraction on the first feature map to obtain a first detection feature map comprises: The acquisition module is configured to acquire electrical quantity parameters of each monitoring point of a high-voltage parallel cable line within a preset sampling interval, and perform data preprocessing on each electrical quantity parameter based on the sampling interval to obtain corresponding feature data; The input module is configured to input the feature data into a pre-trained line fault state evaluation model, wherein the line fault state evaluation model comprises a first detection network, a second detection network and a Leaky Relu activation layer connected in sequence; The first extraction module is configured to perform feature extraction on the feature data by the first detection network to obtain a first feature map; a second extraction module configured to perform feature extraction on the first feature map through the second detection network to obtain a second feature map; an evaluation module configured to perform state evaluation on the second feature map using a Leaky Relu activation layer to obtain a fault state evaluation result corresponding to the high-voltage parallel cable line; The electrical quantity parameters include first ground currents, second ground currents, load currents, shield induction voltages, fault times, start times, and end times. The step of pre-processing the data of each electrical quantity parameter based on the sampling interval to obtain corresponding feature data includes: respectively, the first difference value is processed by the sampling interval to obtain the ground current change rate corresponding to each monitoring point; respectively, the first difference value is processed by the sampling interval to obtain the ground current change rate corresponding to each monitoring point; respectively, two load currents of the same node in parallel in each load current are input into a preset load current unbalance degree function to obtain a plurality of load current unbalance degrees; respectively, each load current is input into a preset phase-to-phase current unbalance degree function to obtain a phase-to-phase current unbalance degree corresponding to each monitoring point; respectively, each second ground current is processed by the associated load current to obtain a ground current load ratio corresponding to each monitoring point; Each of the ground current change rate, the load current unbalance degree, the phase-to-phase current unbalance degree, the ground current load ratio, the shield induction voltage, the second ground current, the load current, the fault time, the start time, and the end time is used as feature data. The ground current change rate function is specifically: ； wherein, is a ground current rate of change, is a first ground current, is a second ground current, is a sampling interval; The load current unbalance degree function is specifically: ； wherein, is the load current unbalance degree, is the load current value of parallel circuit 1, is the load current value of parallel circuit 2.

7. An electronic device, comprising: The computer program is executed by the processor to make the processor execute the steps of the high-voltage parallel cable line fault state online monitoring method according to any one of claims 1-5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed to implement the high-voltage parallel cable line fault state online monitoring method according to any one of claims 1-5.

9. A computer program product, characterised in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the high-voltage parallel cable line fault state online monitoring method according to any one of claims 1-5.

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