Method and device for monitoring and fault alarm of buried cable power collection line

Abstract

The application provides a buried cable power collection line state monitoring and fault alarm method and device, which is suitable for the technical field of power systems and comprises the following steps: acquiring line information set, target fault set and monitoring equipment information set of the buried cable power collection line; constructing a buried cable power collection line simulation model of a target area based on the line information set, simulating based on the target fault set and the simulation model, obtaining a fault information set, training a deep learning model to obtain an initial fault identification model; correcting the initial fault identification model based on the monitoring equipment information set to obtain a fault identification model of the buried cable power collection line; monitoring the state of the buried cable power collection line of the target area to obtain real-time operation data, obtaining fault information according to the fault identification model, and performing fault alarm based on the fault information. The application corrects the fault identification model through the parameters of the power collection line and the monitoring equipment, and improves the accuracy of fault identification.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of power systems, and particularly relates to a buried cable power collection line state monitoring and fault alarm method and device. BACKGROUND

[0002] As an important power transmission facility, buried cable plays a key role in power transmission. It can safely and stably transmit power from the power generation end to various power consumption areas, effectively avoiding the risk of failure of overhead lines due to adverse weather (such as strong winds, lightning, snow, etc.), reducing the damage to the surrounding environment and potential safety hazards caused by exposed lines, and improving the utilization efficiency of land resources. It plays an indispensable and important role in ensuring power supply in cities and various places.

[0003] At present, various methods are used for state monitoring of buried cable power collection lines, such as installing temperature sensors to monitor cable temperature changes in real time to determine whether there are overload and other abnormal conditions; using partial discharge monitoring devices to detect possible insulation defect discharge phenomena inside the cable; and monitoring current size and fluctuations through current transformers to assist in analyzing line operation status. As for fault alarm, once the monitoring system detects that the relevant parameters exceed the normal range, it will send alarm information to the monitoring terminal in time through network communication, and relevant personnel can quickly know and take measures. Some systems can also be associated with sound and light alarms to enhance the alarm prompt effect.

[0004] However, sensors are affected by complex underground environments (such as humidity, pH changes, etc.), and data accuracy is easily compromised. There may be measurement deviations due to moisture, and since buried cables are located underground, it is extremely difficult to locate faults once they occur, requiring a lot of time and effort to investigate the specific fault location. Moreover, the maintenance and updating of the entire system is relatively complex, which is not conducive to long-term and efficient protection of the normal operation of the cable line. SUMMARY

[0005] The present application provides a buried cable power collection line state monitoring and fault alarm method and device to solve the problem of sensor data accuracy being compromised due to the influence of complex underground environments.

[0006] The present application is achieved by the following technical solutions:

[0007] In a first aspect, the present application provides a buried cable power collection line state monitoring and fault alarm method, comprising:

[0008] obtaining a line information set, a target fault set, and a monitoring device information set of the buried cable power collection line in the target area;

[0009] construct a simulation model of the underground cable power collection line in the target area based on the line information set, simulate based on the target fault set and the simulation model, and obtain a fault information set;

[0010] train a pre-set deep learning model based on the fault information set, and obtain an initial fault identification model of the underground cable power collection line in the target area;

[0011] correct the initial fault identification model based on the monitoring device information set, and obtain a fault identification model of the underground cable power collection line in the target area;

[0012] monitor the state of the underground cable power collection line in the target area, obtain real-time operation data of the underground cable power collection line, obtain fault information based on the real-time operation data and the fault identification model, and perform fault alarm based on the fault information.

[0013] In a second aspect, an embodiment of the present application provides an underground cable power collection line state monitoring and fault alarm device, comprising:

[0014] an acquisition module configured to acquire a line information set, a target fault set, and a monitoring device information set of an underground cable power collection line in a target area;

[0015] a simulation module configured to construct a simulation model of the underground cable power collection line in the target area based on the line information set, simulate based on the target fault set and the simulation model, and obtain a fault information set;

[0016] a training module configured to train a pre-set deep learning model based on the fault information set, and obtain an initial fault identification model of the underground cable power collection line in the target area;

[0017] a correction module configured to correct the initial fault identification model based on the monitoring device information set, and obtain a fault identification model of the underground cable power collection line in the target area;

[0018] an alarm module configured to monitor the state of the underground cable power collection line in the target area, obtain real-time operation data of the underground cable power collection line, obtain fault information based on the real-time operation data and the fault identification model, and perform fault alarm based on the fault information.

[0019] The embodiment of the present application provides a buried cable power collection line state monitoring and fault alarm method and device, a simulation model is constructed through line information of the buried cable power collection line, a target fault is simulated through the simulation model, a fault information set is obtained, a pre-set deep learning model is trained through the fault information set, an initial fault identification model of the buried cable power collection line in a target area is obtained, the initial fault identification model is corrected through equipment information, a fault identification model is obtained, real-time operation data are obtained through state monitoring on the buried cable power collection line, fault information is obtained according to the real-time operation data and the fault identification model, and finally, alarm is performed according to the fault information. The fault identification model is corrected through the equipment parameters, data deviation caused by equipment influence is reduced, and the fault identification accuracy is improved. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or related technical descriptions. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative labor.

[0021] Figure 1 is a flowchart of a buried cable power collection line state monitoring and fault alarm method provided by an embodiment of the present application;

[0022] Figure 2 is a segmented processing schematic diagram of a buried cable power collection line of a buried cable power collection line state monitoring and fault alarm method provided by an embodiment of the present application;

[0023] Figure 3 is another segmented processing schematic diagram of a buried cable power collection line of a buried cable power collection line state monitoring and fault alarm method provided by an embodiment of the present application;

[0024] Figure 4 is a structural schematic diagram of a buried cable power collection line state monitoring and fault alarm device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0025] In the following description, specific details are set forth in order to provide a thorough understanding of embodiments of the present application. However, persons skilled in the art will understand that the present application can be practiced without these specific details, in other instances, well-known systems, structures, circuits, and methods have not been described in detail in order to not obscure the understanding of this description.

[0026] Figure 1is a flowchart of a method for monitoring and fault alarming of a buried cable power collection line according to an embodiment of the present application, referring to Figure 1 The method is described in detail as follows.

[0027] In S110, line information set, target fault set and device information set of the target buried cable power collection line are obtained.

[0028] In some embodiments, the line information set includes various types of information related to the physical structure and layout of the buried cable power collection line. It contains the laying information of the cable, such as the path direction, laying depth, cable length, etc., which determines the actual laying situation of the cable underground. At the same time, the line information set also includes node information, such as the location of the connection node of the cable, the type of the node (such as branch node, switching node, etc.). For example, in a city power grid, a buried cable may be laid along a certain street, and its path direction needs to be accurately recorded. The laying depth may be different due to different geological conditions and safety requirements of different road sections, and the cable length directly affects the distance and loss of power transmission.

[0029] It should be noted that before determining the target fault set, the buried cable power collection line needs to be segmented, referring to Figure 2 The buried cable power collection line can be divided into fixed lengths, such as 100 meters per segment; referring to Figure 3 The buried cable power collection line can be divided according to the line information in the line information set, for example, the cable between two connection nodes is divided into two or three segments according to the length, the cable between node a and node b is divided into two segments according to the length, and the cable between node c and node d is divided into three segments according to the length.

[0030] In some embodiments, the target fault set includes various fault types and related parameters that may occur in each segment of the buried cable power collection line. The fault types include short circuit faults (such as phase-to-phase short circuit, phase-to-ground short circuit), grounding faults (single-phase grounding, multi-point grounding, etc.), broken line faults (one-phase broken line, multi-phase broken line), insulation aging faults, and cable moisture faults, etc.

[0031] In some embodiments, the device information set mainly involves the related information of various types of devices used for monitoring and ensuring the operation of the buried cable power collection line. It includes the basic attributes of the device, such as device model, device manufacturer, device production date, etc. At the same time, the device information set covers the operating parameters of the device, such as the measurement range and accuracy of the temperature sensor, the detection sensitivity and frequency range of the partial discharge monitoring device, the transformation ratio and rated current of the current transformer, etc. In addition, it also contains the maintenance records of the device, such as the last maintenance time, maintenance content, whether there is a potential fault hidden danger in the device, etc.

[0032] S120, based on the line information set, a simulation model of the underground cable power collection line in the target region is constructed, and simulation is performed based on the target fault set and the simulation model to obtain a fault information set.

[0033] In some embodiments, the fault information set covers various fault-related information obtained by simulating the target fault set and the simulation model of the underground cable power collection line, including changes in electrical parameters (such as abnormal values of current, voltage, and partial discharge) exhibited by different fault types (such as short circuit, grounding, broken line, and insulation damage) at specific locations, time nodes of fault occurrence, degrees of influence of the fault on the overall operation state of the line (such as changes in high-frequency partial discharge peak value and high-frequency partial discharge pulse count), and chain reactions that may be triggered by the fault.

[0034] In one possible implementation, the specific processing procedure of step S120 is as follows: based on the target fault set, a plurality of target faults in the underground cable power collection line are obtained; based on the simulation model, each target fault is simulated respectively to obtain fault information of the corresponding target fault; and based on the fault information of all target faults, the fault information set is obtained.

[0035] In some embodiments, the number of target faults is related to the number of fault types in the target fault set and the segmentation result. For example, the underground cable power collection line in region a is divided into 55 segments, and the number of fault types includes 8 types such as inter-phase short circuit, phase-to-ground short circuit, single-phase grounding, multi-point grounding, single-phase broken line, multi-phase broken line, insulation aging fault, and cable moisture fault. Therefore, the number of target faults in region a is 440.

[0036] In some embodiments, the fault information of a certain target fault includes changes in relevant electrical parameters (partial discharge, current, voltage anomaly, etc.) of a segment of the underground cable power collection line corresponding to the target fault when different faults occur at different times, possible influences on the line and surrounding equipment, and specific parameters collected by each device related to the segment of the underground cable power collection line. Different faults include different fault types (short circuit, grounding, broken line, etc.), different fault locations (precise or approximate location), different fault degrees (mild, severe, etc.), and different fault occurrence times. Different times can be different time periods, different seasons, or different weather corresponding times. Simulating different times is mainly to obtain the fault information set of the underground cable power collection line when faults occur in different environments.

[0037] In some embodiments, the fault information set includes fault information of all target faults in the underground cable power collection line in the target region.

[0038] A plurality of target faults are obtained through the target fault set, and the buried cable power line is segmented before the target faults are obtained. After the target faults are obtained, each target fault is simulated respectively to obtain corresponding fault information, and a fault information set is obtained by comprehensive analysis. The fault scene can be accurately simulated, the actual layout and node conditions of the line are considered comprehensively, and various fault characteristics are restored truly. Various fault types and parameters can be covered, fault information can be obtained efficiently, and detailed conditions under a large number of different faults can be generated in a short time, which greatly improves the efficiency compared with actual line detection and analysis, and provides strong support for fault research, prevention and processing of the buried cable power line.

[0039] In S130, the pre-set deep learning model is trained based on the fault information set to obtain an initial fault identification model of the buried cable power line in the target area.

[0040] In some embodiments, the initial fault identification model is obtained by inputting the fault information set containing detailed electrical parameter changes, fault positions and other information under various fault conditions as training data into the pre-set deep learning model for learning and optimization. The model can analyze and process the operation data of the buried cable power line in the target area to determine whether there is a fault and the fault parameters when there is a fault.

[0041] In a possible implementation, the specific processing process of step S130 is as follows: a plurality of fault information at different times is determined based on the fault information set, and the pre-set deep learning model is trained based on all the fault information to obtain the initial fault identification model of the buried cable power line in the target area.

[0042] The pre-set deep learning model is a computing model with complex structure and strong learning ability based on a deep learning algorithm. It has multiple hidden layers and can automatically learn feature representation from a large amount of data. For state monitoring of the buried cable power line, the fault information set (covering electrical parameters, positions and other information under various fault scenes) input can be deeply analyzed, and the model can be continuously adjusted to find patterns and rules in the data, thereby providing a basis for accurately identifying the fault type and position of the buried cable power line. The basic architecture (such as the number of layers and nodes of the neural network) and initial parameter settings of the model are determined before training, so that the initial fault identification model suitable for the target area can be obtained by subsequent targeted training using the fault information set.

[0043] In some embodiments, the pre-configured deep learning model can include a convolutional neural network (CNN), a recurrent neural network (RNN) and its variants such as long short-term memory network LSTM, gated recurrent unit GRU, and deep belief network (DBN), etc. The CNN has the characteristics of local perception and weight sharing, can extract high-level features of input data, and is suitable for monitoring the insulation state of the cable; the RNN and its variants are good at processing sequence data, and the LSTM and GRU solve the long sequence problem through the gating mechanism and can predict the cable fault trend; the DBN is stacked by multiple restricted Boltzmann machines, can learn the probability distribution of data, and can accurately classify the cable fault type after unsupervised pre-training and supervised fine-tuning.

[0044] In some embodiments, the pre-configured deep learning model can be determined according to the needs of monitoring and fault alarm, for example, if accurate identification of the cable fault type is required, a deep belief network (DBN) model can be selected as the pre-configured deep learning model.

[0045] In some embodiments, the initial fault identification model is obtained by training the pre-configured deep learning model according to the fault information set of the buried cable power collection line.

[0046] Training the pre-configured deep learning model according to the fault information set can make full use of the rich fault information set, enable the deep learning model to deeply mine the complex fault patterns and features therein, and make the initial fault identification model obtained by training have strong self-learning and self-adaptive capabilities, accurately capture subtle signs of various faults of the buried cable power collection line in the target area, and effectively improve the accuracy, reliability and generalization ability of fault identification, thereby laying a solid foundation for subsequent stable operation of the cable line.

[0047] S140, correcting the initial fault identification model based on the equipment information set to obtain a fault identification model of the buried cable power collection line in the target area.

[0048] In some embodiments, the fault identification model is a precise model obtained by modifying and optimizing the initial model based on the initial fault identification model and considering the operation parameters of each device in the equipment information set. It can accurately analyze the real-time operation data of the buried cable power collection line, accurately identify various fault types (such as short circuit, grounding, broken line, etc.), fault positions and fault degrees, thereby providing reliable basis for timely and effective fault alarm and targeted maintenance measures, greatly improving the accuracy and reliability of fault diagnosis of the buried cable power collection line, and effectively ensuring the safe and stable operation of the power system.

[0049] In one possible implementation, step S140 is specifically processed as follows: the operating parameters of each monitoring device are determined based on the monitoring device information set, and the influence factors of each monitoring device are determined based on the operating parameters of each monitoring device; based on the influence factors of all monitoring devices in the monitoring device information set, the early warning deviation matrix of the underground cable collection line in the target area is obtained; the initial fault identification model is corrected based on the early warning deviation matrix to obtain the fault identification model of the underground cable collection line in the target area.

[0050] In some embodiments, the fault identification model can identify multiple fault parameters when a fault occurs. The number of fault parameters is related to the segmentation result of the underground cable collector line. If the underground cable collector line is divided into x segments, then a total of x fault parameters can be obtained.

[0051] In some embodiments, the influence factor of the monitoring equipment is a quantitative indicator reflecting the impact of the monitoring equipment on the fault identification results during the monitoring of the status of buried cable collector lines. It is closely related to the operating parameters of the monitoring equipment, including its stability, sensitivity, response time, and the relationship between the equipment's measurement deviation and the environment. The combined effect of these factors determines the relative importance and influence of the monitoring equipment on fault diagnosis within the entire monitoring system, thereby affecting the accuracy and reliability of the fault identification model.

[0052] In some embodiments, the impact factor is calculated as follows:

[0053]

[0054] Where, θ k Let ω1 be the influence factor of the k-th monitoring device, ω2 be the stability weight of the k-th monitoring device, ω3 be the response time weight of the k-th monitoring device, ω4 be the measurement deviation weight of the k-th monitoring device, Δk1 be the stability error of the k-th monitoring device, Δk2 be the sensitivity error of the k-th monitoring device, Δk3 be the response time error of the k-th monitoring device, and Δk4 be the measurement deviation of the k-th monitoring device caused by environmental influences. And ω1 + ω2 + ω3 + ω4 = 1, Δk p This represents the difference between the operating parameters of the k-th monitoring device and the standard operating parameters, and can be either positive or negative.

[0055] In some embodiments, the early warning deviation matrix includes data for each monitoring device and its influencing factors. The row factors of the early warning deviation matrix are the monitoring device number and its influencing factor, and the column factors are each monitoring device number and its corresponding influencing factor.

[0056] In the determination of the influence factor of each monitoring device, the influence of the environment on the monitoring device is considered, so as to compensate for the environmental interference deviation, optimize the feature extraction accuracy, improve the fault recognition accuracy, dynamically adapt the model to the environmental changes, enhance the anti-interference ability, improve the adaptability and robustness, stabilize the fault diagnosis result, reduce the false alarm and missing alarm risk, enhance the reliability and stability of the fault diagnosis, and provide strong support for the buried cable fault recognition and operation and maintenance.

[0057] In a possible implementation, the specific processing manner of step S140 is: updating the fault information set based on the early warning deviation matrix, and obtaining a fault information deviation set based on the fault information set and the updated fault information set; correcting the initial fault recognition model based on the fault information deviation set to obtain the fault recognition model of the buried cable power line in the target area.

[0058] In some embodiments, updating the fault information set according to the early warning deviation matrix means determining accurate deviation compensation values according to the deviation conditions of each device in the matrix, such as temperature sensors and other devices with measurement deviation, and performing corresponding numerical compensation on the data collected by the devices; then considering the mutual relationship between the devices, adjusting the affected fault feature data, such as correcting the fault current feature value due to the deviation of the current transformer; finally, comprehensively updating the fault information set by combining the device deviation compensation and the fault feature adjustment, so that the fault information set can more accurately reflect the actual operation state of the buried cable.

[0059] The fault information deviation set is obtained by comparing the fault information set with the updated fault information set, and the fault information deviation set includes the fault information deviation corresponding to each fault type of each fault link, which can reflect the fault information deviation caused by the influence of the device.

[0060] In a possible implementation, the specific processing manner of step S140 is: determining a repair parameter of each fault type-link based on the fault information deviation set, and correcting the initial fault recognition model based on the repair parameter to obtain the fault recognition model of the buried cable power line in the target area.

[0061] The repair parameter of the fault type-link is:

[0062]

[0063] wherein, X (i,j) is the repair parameter of the (i, j)th fault type-link, is the device accuracy of the kth monitoring device of the (i, j)th fault type-link, K is the total number of monitoring devices of the (i, j)th fault type-link, and Δm (i,j)is the fault deviation value of the (i, j)th fault type-link, Am j is the fault deviation value of the jth fault link of the ith fault type, a j is the weight parameter of the jth fault link of the ith fault type, J is the total number of fault links related to the ith fault type.

[0064] In some embodiments, a link refers to the segmentation of the underground cable power collection line in the target area, and the number of links is consistent with the number of segments of the underground cable power collection line in the target area.

[0065] In some embodiments, after obtaining the corrected model parameters, first, the adjustment direction and amplitude of the model weight are determined according to the repair parameters, the influence of the monitoring equipment in each fault type-link and the importance of the fault characteristics are comprehensively considered, the weights between different layers of neurons are redistributed, and the weight distribution is optimized to improve the accuracy and robustness; second, the neuron bias correction amount is calculated according to the fault deviation value in the repair parameter, and then it is applied to the bias parameter of the initial model to update the bias, so that the model can still accurately identify the fault type and location when the data is biased.

[0066] By determining the influence factor and the early warning deviation matrix through the equipment information set, and further obtaining the fault information deviation set, the repair coefficient of each fault type-link is calculated, and the initial fault identification model is corrected to obtain the fault identification model of the underground cable power collection line in the target area, which can effectively improve the accuracy of fault identification, compensate for equipment errors, adjust model parameters according to equipment operating parameters and influence factors, and accurately judge faults to avoid misjudgment; at the same time, the model adaptability can be optimized to dynamically respond to changes in equipment performance. And in complex fault conditions, it can accurately identify, and with the update of the equipment information set, the model can also be continuously optimized to provide stable and reliable diagnostic results for cable operation and maintenance, and effectively ensure the safe and stable operation of the power system.

[0067] S150, performing state monitoring on the underground cable power collection line in the target area to obtain real-time operation data of the underground cable power collection line, obtaining fault information based on the real-time operation data and the fault identification model, and performing fault alarm based on the fault information.

[0068] In some embodiments, the real-time operation data includes electrical parameter data such as partial discharge monitoring data (such as single discharge amount and statistical distribution of discharge amount, discharge phase related data such as initial phase, extinction phase and phase distribution characteristics, discharge frequency related data, discharge pulse waveform related data), current size, change rate, voltage and its fluctuation, active and reactive power, etc., as well as parameters monitored by temperature sensors, insulation performance monitoring sensors (such as partial discharge sensors, insulation resistance sensors), temperature and humidity sensors, gas sensors, etc.

[0069] In a possible implementation, the specific processing manner of step S150 is: inputting the real-time operation data into the fault identification model to obtain fault parameters, comparing the fault parameters with preset parameter thresholds, inputting the fault parameters into a pre-set fault parameter-fault information mapping table if the fault parameters exceed the preset parameter thresholds to obtain fault information corresponding to the fault parameters, and performing fault alarm based on the fault information, and not performing fault alarm if the fault parameters do not exceed the preset parameter thresholds.

[0070] In some embodiments, the real-time operation data are input into the fault identification model, and a plurality of fault parameters can be obtained through the real-time operation data, and further, fault information can be obtained through the fault parameters.

[0071] In some embodiments, the preset parameter thresholds can be determined by the electrical parameters of the target regional underground cable power collection line in the normal operation state and the fluctuation range of the monitoring results of each sensor. The specific determination manner is not limited here.

[0072] In some embodiments, the fault parameter-fault information mapping table includes fault information corresponding to each parameter range, and the fault information can be obtained through the fault parameter-fault information mapping table, and the fault information includes the fault type corresponding to the fault parameter of each position.

[0073] In a possible implementation, the specific processing manner of step S150 is: determining a fault position based on the fault information, obtaining real-time monitoring data of a plurality of monitoring devices corresponding to the fault position, determining a real-time fault state of the fault position based on the real-time monitoring data, and performing fault alarm based on the real-time fault state.

[0074] In some embodiments, the fault position refers to one or more sections of the target regional underground cable power collection line in which faults exist, and the fault type corresponding to each fault position can be determined through the fault information. The fault position can be one or more.

[0075] In some embodiments, the number of fault positions is related to the number of fault parameters exceeding the preset parameter thresholds, for example, three fault parameters exceed the preset parameter thresholds, and three fault parameters can be obtained.

[0076] The line information of the buried cable power collection line is used to construct a simulation model, and the simulation model is used to simulate a target fault to obtain a fault information set. A pre-set deep learning model is trained based on the fault information set to obtain an initial fault identification model of the buried cable power collection line in the target area. The initial fault identification model is corrected based on the monitoring device information to obtain a fault identification model. Real-time operation data is obtained by monitoring the state of the buried cable power collection line. The fault information is obtained based on the real-time operation data and the fault identification model. Finally, an alarm is given based on the fault information. When the correction parameters are obtained, the deviation caused by the monitoring device environment is considered, and the fault identification model is corrected based on the monitoring device parameters, so that the data deviation caused by the monitoring device in the fault identification process is reduced, and the accuracy of the fault identification of the buried cable power collection line is improved.

[0077] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0078] A buried cable power collection line state monitoring and fault alarm method corresponding to the above embodiment, Figure 4 A structure diagram of a buried cable power collection line state monitoring and fault alarm device provided by an embodiment of the present application is shown, and only parts related to the embodiment of the present application are shown for ease of description.

[0079] Referring to Figure 4 A buried cable power collection line state monitoring and fault alarm device 4 in an embodiment of the present application can include:

[0080] The acquisition module 41 is configured to acquire a line information set of a buried cable power collection line in a target area, a target fault set, and a monitoring device information set.

[0081] The simulation module 42 is configured to construct a buried cable power collection line simulation model of the target area based on the line information set, and simulate based on the target fault set and the simulation model to obtain a fault information set.

[0082] The training module 43 is configured to train a pre-set deep learning model based on the fault information set to obtain an initial fault identification model of the buried cable power collection line in the target area.

[0083] The correction module 44 is configured to correct the initial fault identification model based on the monitoring device information set to obtain a fault identification model of the buried cable power collection line in the target area.

[0084] The alarm module 45 is configured to monitor the state of the buried cable power collection line in the target area, obtain real-time operation data of the buried cable power collection line, obtain fault information based on the real-time operation data and the fault identification model, and perform fault alarm based on the fault information.

[0085] In a possible implementation, the simulation module 42 is specifically configured to: obtain a plurality of target faults in the buried cable power collection line based on the target fault set; simulate each target fault based on the simulation model to obtain fault information of the corresponding target fault; and obtain the fault information set based on the fault information of all target faults.

[0086] In a possible implementation, the training module 43 is specifically configured to: determine fault information of a plurality of different times based on the fault information set, and train the preconfigured deep learning model based on all fault information to obtain the initial fault identification model of the buried cable power collection line in the target area.

[0087] In a possible implementation, the correction module 44 is specifically configured to: determine the operation parameter of each monitoring device based on the monitoring device information set, and determine the influence factor of each monitoring device based on the operation parameter of each monitoring device; obtain the early warning deviation matrix of the buried cable power collection line in the target area based on the influence factors of all monitoring devices in the monitoring device information set; and correct the initial fault identification model based on the early warning deviation matrix to obtain the fault identification model of the buried cable power collection line in the target area.

[0088] In a possible implementation, the correction module 44 is further configured to: update the fault information set based on the early warning deviation matrix, and obtain a fault information deviation set based on the fault information set and the updated fault information set; and correct the initial fault identification model based on the fault information deviation set to obtain the fault identification model of the buried cable power collection line in the target area.

[0089] In a possible implementation, the correction module 44 is further configured to: determine the repair parameter of each fault type-link based on the fault information deviation set, and correct the initial fault identification model based on the repair parameter to obtain the fault identification model of the buried cable power collection line in the target area.

[0090] The repair parameter of the fault type-link is:

[0091]

[0092] X (i,j) = (X (i,j) - X (i,j) ) / X (i,j), where X (i,j) is the repair parameter of the (i,j)th fault type-link. (i,j) X (i,j) = (X (i,j) - X (i,j) ) / X (i,j), where X (i,j) is the repair parameter of the (i,j)th fault type-link. The device accuracy rate of the kth monitoring device of the (i, j)th fault type-link is K, the total number of monitoring devices of the (i, j)th fault type-link, and Δm (i,j) The fault deviation value of the (i, j)th fault type-link is Δm j The fault deviation value of the jth fault link of the ith fault type is α j The weight parameter of the jth fault link of the ith fault type is J, and the total number of fault links related to the ith fault type.

[0093] In a possible implementation, the alarm module 45 is specifically configured to input the real-time operation data into the fault identification model to obtain a fault parameter, compare the fault parameter with a preset parameter threshold, input the fault parameter into a pre-set fault parameter-fault information mapping table to obtain fault information corresponding to the fault parameter, and perform fault alarm based on the fault information if the fault parameter exceeds the preset parameter threshold, and not perform fault alarm if the fault parameter does not exceed the preset parameter threshold.

[0094] In a possible implementation, the alarm module 45 is further configured to determine a fault position based on the fault information, obtain real-time monitoring data of a plurality of monitoring devices corresponding to the fault position, determine a real-time fault state of the fault position based on the real-time monitoring data, and perform fault alarm based on the real-time fault state.

[0095] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the related description of other embodiments.

[0096] Those of ordinary skill in the art can realize that the templates, units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those of ordinary skill in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0097] The modules / units, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can implement the steps of each of the above-mentioned buried cable current collector line state monitoring and fault alarm method embodiments when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0098] The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for monitoring the state of a power collection circuit of a buried cable and for alarming a fault, characterized in that, The method comprises the following steps: acquiring a line information set of a buried cable power collection line in a target area, a target fault set, and a monitoring device information set; constructing a simulation model of the buried cable power collection line in the target area based on the line information set, simulating based on the target fault set and the simulation model to obtain a fault information set; training a pre-set deep learning model based on the fault information set to obtain an initial fault identification model of the buried cable power collection line in the target area; correcting the initial fault identification model based on the monitoring device information set to obtain a fault identification model of the buried cable power collection line in the target area; monitoring the state of the buried cable power collection line in the target area to obtain real-time operation data of the buried cable power collection line, acquiring fault information based on the real-time operation data and the fault identification model, and performing fault alarm based on the fault information; the step of correcting the initial fault identification model based on the monitoring device information set to obtain the fault identification model of the buried cable power collection line in the target area comprises the following steps: determining the operation parameters of each monitoring device based on the monitoring device information set, and determining the influence factor of each monitoring device based on the operation parameters of each monitoring device; obtaining a warning deviation matrix of the buried cable power collection line in the target area based on the influence factors of all monitoring devices in the monitoring device information set; correcting the initial fault identification model based on the warning deviation matrix to obtain the fault identification model of the buried cable power collection line in the target area.

2. The buried cable power line status monitoring and fault alarm method of claim 1, wherein, the step of correcting the initial fault identification model based on the warning deviation matrix to obtain the fault identification model of the buried cable power collection line in the target area comprises the following steps: updating the fault information set based on the warning deviation matrix, and obtaining a fault information deviation set based on the fault information set and the updated fault information set; correcting the initial fault identification model based on the fault information deviation set to obtain the fault identification model of the buried cable power collection line in the target area.

3. The buried cable power line status monitoring and fault alarm method of claim 2, wherein, the step of correcting the initial fault identification model based on the fault information deviation set to obtain the fault identification model of the buried cable power collection line in the target area comprises the following steps: determining the repair parameters of each fault type-link based on the fault information deviation set, correcting the initial fault identification model based on the repair parameters, and obtaining the fault identification model of the buried cable power collection line in the target area; the repair parameters of the fault type-link are: wherein, is a repair parameter for the th failure type-link, is a device accuracy rate for the th monitoring device of the th failure type-link, is a total number of monitoring devices for the th failure type-link, is a failure bias value for the th failure type-link, is a failure bias value for the th failure link of the th failure type, is a weight parameter for the th failure link of the th failure type, is a total number of failure links related to the th failure type.

4. The buried cable power line condition monitoring and fault alarm method of claim 1, wherein, the step of constructing the simulation model of the buried cable power collection line in the target area based on the line information set comprises the following steps: determining the layout information and node information of the buried cable based on the line information set; constructing an initial simulation model of the buried cable power collection line in the target area based on the layout information; adjusting the initial simulation model based on the node information to obtain the simulation model of the buried cable power collection line.

5. The buried cable conductor line status monitoring and fault alarm method of claim 1, wherein, the step of simulating based on the target fault set and the simulation model to obtain a fault information set comprises the following steps: Based on the target fault set, a plurality of target faults in the buried cable power collection line are obtained; Based on the simulation model, each target fault is simulated respectively to obtain fault information of the corresponding target fault; Based on the fault information of all target faults, a fault information set is obtained.

6. The buried cable conductor line status monitoring and fault alarm method as claimed in claim 1, wherein, The fault information is obtained based on the real-time operation data and the fault identification model, and fault alarm is performed based on the fault information, which includes: The real-time operation data is input into the fault identification model to obtain fault parameters, and the fault parameters are compared with preset parameter thresholds; If the fault parameters exceed the preset parameter thresholds, the fault parameters are input into a pre-set fault parameter-fault information mapping table to obtain the fault information corresponding to the fault parameters, and fault alarm is performed based on the fault information; If the fault parameters do not exceed the preset parameter thresholds, no fault alarm is performed.

7. The buried cable conductor line status monitoring and fault alarm method of claim 6, wherein, The fault alarm based on the fault information includes: Based on the fault information, the fault location is determined, and real-time monitoring data of a plurality of monitoring devices corresponding to the fault location are obtained; Based on the real-time monitoring data, the real-time fault state of the fault location is determined, and fault alarm is performed based on the real-time fault state.

8. The buried cable conductor line status monitoring and fault alarm method as claimed in claim 1, wherein, The pre-set deep learning model is trained based on the fault information set to obtain an initial fault identification model of the buried cable power collection line in the target area, which includes: Based on the fault information set, fault information at different times is determined, and the pre-set deep learning model is trained based on all fault information to obtain an initial fault identification model of the buried cable power collection line in the target area.

9. A buried cable power collection line state monitoring and fault alarm device, characterized in that, It includes: An acquisition module is configured to acquire a line information set, a target fault set, and a monitoring device information set of a buried cable power collection line in a target area; A simulation module is configured to construct a simulation model of the buried cable power collection line in the target area based on the line information set, and simulate based on the target fault set and the simulation model to obtain a fault information set; A training module is configured to train a pre-set deep learning model based on the fault information set to obtain an initial fault identification model of the buried cable power collection line in the target area; A correction module is configured to correct the initial fault identification model based on the monitoring device information set to obtain a fault identification model of the buried cable power collection line in the target area; An alarm module is configured to monitor the state of the buried cable power collection line in the target area to obtain real-time operation data of the buried cable power collection line, obtain fault information based on the real-time operation data and the fault identification model, and perform fault alarm based on the fault information; The correction module is further configured to determine the operating parameters of each monitoring device based on the monitoring device information set, and determine the influence factor of each monitoring device based on the operating parameters of each monitoring device; Based on the influence factors of all monitoring devices in the monitoring device information set, a warning deviation matrix of the buried cable power collection line in the target area is obtained; The initial fault identification model is corrected based on the early warning deviation matrix, so as to obtain a fault identification model of the power collection line of the buried cable in the target area.

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