Submarine cable fault identification method and device
By analyzing the environmental data and operating temperature of submarine cables using a pre-trained BP neural network model, identifying cable faults, solving the problem of accuracy and low speed of submarine cable fault identification, and realizing timely early warning and efficient maintenance.
Patent Information
- Application Number
- CN202411882200.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-19
- Publication Date
- 2025-04-18
AI Technical Summary
Under the influence of ship anchor damage and other factors, submarine cables in offshore wind farms are prone to failure, resulting in difficulty in repairing and high shutdown losses. It is difficult for existing technology to accurately and promptly identify submarine cable failures.
Using a pre-trained BP neural network model, the theoretical value of the cable operating temperature is calculated by obtaining the environmental data of the submarine cable and the actual value of the operating temperature, and the residual analysis is used to determine whether there is a fault.
It realizes timely identification and early warning of the operating status of submarine cables, improves the accuracy and speed of fault identification, and reduces maintenance costs and downtime losses.
Smart Images
Figure CN119846347A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the technical field of submarine cable monitoring, and in particular to a method and device for identifying faults of submarine cables. Background Art
[0002] The submarine cables of offshore wind farms are optoelectronic composite cables, which play the role of transmitting wind farm electricity to users and providing sea-land Internet communications. With the active marine activities, the risk of ship anchor damage to offshore wind farm submarine cables has increased. It is difficult to repair severely damaged submarine cables, which require troubleshooting, salvaging, repairing, laying, etc., which takes dozens of days to complete. In addition to the direct costs of several million yuan, the downtime losses caused by the inability to transmit electricity are even higher. Therefore, how to timely and accurately identify the operating status of submarine cables is an important task in offshore wind power operation and maintenance. Summary of the invention
[0003] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.
[0004] To this end, a first aspect of the present disclosure provides a method for identifying a fault of a submarine cable, comprising the following steps:
[0005] Obtain the environmental data of the submarine cable to be tested and the actual value of the cable operating temperature at the same time;
[0006] Inputting the environmental data into a pre-trained BP neural network model to obtain a theoretical value of the cable operating temperature output by the BP neural network model; wherein the BP neural network model has learned a mapping relationship between the environmental data and the theoretical value of the cable operating temperature;
[0007] Determining a residual between an actual value of the cable operating temperature and a theoretical value of the cable operating temperature;
[0008] In response to the residual satisfying a preset fault condition, it is determined that a fault occurs in the submarine cable to be tested.
[0009] In some embodiments of the present disclosure, the BP neural network model is pre-trained through the following steps: obtaining the historical normal environmental data of the submarine cable during the cable fault-free period and the label value of the normal cable operating temperature corresponding to the historical normal environmental data; inputting the historical normal environmental data into the initial BP neural network model to obtain the theoretical value of the historical cable operating temperature output by the initial BP neural network model; training the initial BP neural network model based on the theoretical value of the historical cable operating temperature and the label value of the normal cable operating temperature to obtain the BP neural network model.
[0010] In some embodiments of the present disclosure, the residual meeting the fault condition includes: the residual being within a residual range of the cable fault type.
[0011] In some embodiments of the present disclosure, the residual range of at least one cable fault type is obtained by the following steps: obtaining historical environmental data of the submarine cable within a preset time period and the label value of the corresponding cable operating temperature, the preset time period including a cable fault-free time period and a cable fault time period; inputting the historical environmental data into the BP neural network model to obtain a theoretical value of the historical cable operating temperature corresponding to the historical environmental data; determining the residual between the label value of the cable operating temperature and the theoretical value of the historical cable operating temperature; performing cluster analysis on the residuals, and combining the fault records of the submarine cable within the preset time period to determine at least one of the cable fault types and the residual range of each of the cable fault types.
[0012] In some embodiments of the present disclosure, the method further includes: determining a fault type of the submarine cable to be tested according to the residual range in which the residual is located.
[0013] In some embodiments of the present disclosure, the residual meeting the fault condition includes: the residual being greater than or equal to a preset residual threshold.
[0014] The second aspect of the present disclosure provides a submarine cable fault identification device, comprising:
[0015] An acquisition module is used to obtain the environmental data of the submarine cable to be tested and the actual value of the cable operating temperature at the same time;
[0016] A first determination module is used to input the environmental data into a pre-trained BP neural network model to obtain a theoretical value of the cable operating temperature output by the BP neural network model; wherein the BP neural network model has learned a mapping relationship between the environmental data and the theoretical value of the cable operating temperature;
[0017] A second determination module is used to determine a residual between an actual value of the cable operating temperature and a theoretical value of the cable operating temperature;
[0018] The third determination module is configured to determine that a fault occurs in the submarine cable to be tested in response to the residual satisfying a preset fault condition.
[0019] In some embodiments of the present disclosure, the device also includes a training module; wherein the training module is used to: obtain historical normal environmental data of the submarine cable during the cable fault-free period and the label value of the normal cable operating temperature corresponding to the historical normal environmental data; input the historical normal environmental data into the initial BP neural network model to obtain the theoretical value of the historical cable operating temperature output by the initial BP neural network model; train the initial BP neural network model based on the theoretical value of the historical cable operating temperature and the label value of the normal cable operating temperature to obtain the BP neural network model.
[0020] A third aspect of the present disclosure provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;
[0021] The memory stores computer-executable instructions;
[0022] The processor executes the computer-executable instructions stored in the memory to implement the method described in the first aspect.
[0023] The fourth aspect of the present disclosure provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the method described in the first aspect.
[0024] The submarine cable fault identification method provided by the present disclosure fully considers the influence of the submarine cable environment on the cable operating temperature, and adopts the BP neural network model to obtain the theoretical value of the cable operating temperature corresponding to the cable environment data. The operating status of the submarine cable can be timely identified through residual analysis, and the cable fault can be warned in time, and the relevant personnel can be reminded to repair in time to avoid further deterioration of the submarine cable fault. The present disclosure can also improve the accuracy and speed of submarine cable fault identification.
[0025] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS
[0026] The above and / or additional aspects and advantages of the present disclosure will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:
[0027] Figure 1 A schematic flow chart of a method for identifying a fault of a submarine cable provided in an embodiment of the present disclosure;
[0028] Figure 2 A schematic diagram of a submarine cable fault identification device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION
[0029] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.
[0030] In traditional submarine cable monitoring technology, a single submarine cable operating temperature threshold can be used to determine whether a submarine cable is faulty. When the submarine cable operating temperature is monitored to exceed the threshold, it is determined that the submarine cable is faulty. However, since the operating environment of the submarine cable is relatively complex and changeable, the operating temperature of the submarine cable is affected, resulting in low accuracy of submarine cable fault monitoring, and problems of false fault reporting or delayed fault reporting. To this end, the present disclosure provides a method and device for identifying a submarine cable fault. Specifically, the method and device for identifying a submarine cable fault according to an embodiment of the present disclosure are described below with reference to the accompanying drawings.
[0031] Figure 1 The following is a flow chart of a method for identifying a submarine cable fault provided by an embodiment of the present disclosure. Figure 1 As shown, the submarine cable fault identification method may include the following steps:
[0032] Step 101, obtaining environmental data of the submarine cable to be tested and the actual value of the cable operating temperature at the same time.
[0033] In some embodiments of the present disclosure, the environmental data of the submarine cable to be tested mainly relates to data that affects the operating temperature of the cable. As an example, the environmental data may include one or more of the following: seawater temperature, submarine cable depth, water flow velocity, or other environmental data.
[0034] In some embodiments of the present disclosure, Brillouin scattering may be used to obtain the actual value of the cable operating temperature of the submarine cable to be tested.
[0035] Step 102: Input the environmental data into the pre-trained BP neural network model to obtain the theoretical value of the cable operating temperature output by the BP neural network model. The BP neural network model has learned the mapping relationship between the environmental data and the theoretical value of the cable operating temperature.
[0036] It should be noted that the BP neural network model is trained using the environmental data and cable operating temperature during the cable fault-free period, and can predict the theoretical value of the cable operating temperature corresponding to the environmental data when the submarine cable does not fail. In one implementation, the historical normal environmental data T' of the submarine cable during the cable fault-free period can be obtained. inThe label value T' of the normal cable operating temperature corresponding to the historical normal environmental data out . The historical normal environment data T' in Input into the initial BP neural network model to obtain the theoretical value of the historical cable operating temperature output by the initial BP neural network model Theoretical values based on historical cable operating temperatures and the label value of normal cable operating temperature T' out The initial BP neural network model is trained to obtain the BP neural network model. Optionally, the theoretical value of the historical cable operating temperature can be and the label value of normal cable operating temperature T' out Perform loss calculation and train the initial BP neural network model based on the loss value.
[0037] Step 103: determining a residual between an actual value of the cable operating temperature and a theoretical value of the cable operating temperature.
[0038]
[0039] Among them, C is the residual, is the theoretical value of the cable operating temperature output by the BP neural network model, and T is the actual value of the cable operating temperature.
[0040] Step 104: In response to the residual satisfying a preset fault condition, it is determined that a fault occurs in the submarine cable to be tested.
[0041] It should be noted that the residual is the residual between the actual value of the cable operating temperature and the theoretical value under fault-free conditions. Therefore, the residual can be used to characterize the gap between the current actual state of the submarine cable and the fault-free state. In some embodiments of the present disclosure, the fault condition may include a residual threshold for dividing faults and fault-free conditions, and the residual and the residual threshold are compared to determine whether the residual meets the fault condition. The fault condition may also include a residual range for distinguishing different states, and determine whether the residual falls within the residual range of the fault state. After determining that a fault has occurred in the submarine cable to be tested, a submarine cable early warning may be performed to facilitate timely maintenance to avoid further deterioration of the fault condition. Among them, the residual threshold or residual range used for the fault condition may be set based on an empirical value, or may be set using a trained BP neural network model.
[0042] As an example, the historical environmental data of the submarine cable within a preset time period and the label value of the corresponding cable operating temperature can be obtained. The preset time period includes a cable fault-free time period and a cable fault time period. The historical environmental data is input into the BP neural network model to obtain the theoretical value of the historical cable operating temperature corresponding to the historical environmental data. The residual between the label value of the cable operating temperature and the theoretical value of the historical cable operating temperature is determined. Combined with the fault records of the submarine cable within the preset time period, the residuals are divided into two categories: faults and no faults. The residual threshold is determined according to the minimum residual value in the fault class and / or the maximum residual value in the no fault class. Optionally, the minimum residual value in the fault class or the maximum residual value in the no fault class can be used as the residual threshold. Alternatively, the average value or the middle value of the minimum residual value in the fault class and the maximum residual value in the no fault class can be used as the residual threshold.
[0043] As another example, the historical environmental data of the submarine cable within a preset time period and the label value of the corresponding cable operating temperature can be obtained. The preset time period includes a cable fault-free time period and a cable fault time period. The historical environmental data is input into the BP neural network model to obtain the theoretical value of the historical cable operating temperature corresponding to the historical environmental data. The residual between the label value of the cable operating temperature and the theoretical value of the historical cable operating temperature is determined. The residual is clustered and analyzed, and combined with the fault records of the submarine cable within the preset time period, at least one cable fault type and the residual range of each cable fault type are determined.
[0044] In one implementation, the residuals can be divided into multiple types, such as two types, which are divided into cable fault types and cable non-fault types in combination with fault records, and the two types correspond to different residual ranges. If the residual is within the residual range of the cable fault type, it is determined that the residual meets the fault condition, and it is determined that the submarine cable to be tested has a fault.
[0045] In another implementation, the residuals can be divided into a cable fault-free type and a plurality of cable fault types of different degrees, such as a mild fault type, a moderate fault type, and a severe fault type, in combination with the fault records. If the residual is within the residual range of any cable fault type, it is determined that the residual meets the fault condition, and it is determined that the submarine cable to be tested has a fault. Furthermore, the fault type of the submarine cable to be tested can also be determined based on the residual range in which the residual is located. For example, if it is determined that the residual falls within the residual range of the mild fault type, it is determined that the submarine cable to be tested has a fault, and the fault type is a mild fault type.
[0046] By implementing the embodiments of the present disclosure, the influence of the environment of the submarine cable on the operating temperature of the cable is fully considered, and the BP neural network model is used to obtain the theoretical value of the cable operating temperature corresponding to the cable environment data. The residual analysis can timely identify the operating status of the submarine cable, timely warn the cable fault, and timely remind relevant personnel to repair it, so as to avoid further deterioration of the submarine cable fault. The present disclosure can also improve the accuracy and speed of submarine cable fault identification.
[0047] Figure 2 A schematic diagram of a submarine cable fault identification device provided by an embodiment of the present disclosure. Figure 2 As shown, the submarine cable fault identification device may include: an acquisition module 201 , a first determination module 202 , a second determination module 203 and a third determination module 204 .
[0048] The acquisition module 201 is used to acquire the environmental data of the submarine cable to be tested and the actual value of the cable operating temperature at the same time.
[0049] The first determination module 202 is used to input the environmental data into a pre-trained BP neural network model to obtain the theoretical value of the cable operating temperature output by the BP neural network model, wherein the BP neural network model has learned the mapping relationship between the environmental data and the theoretical value of the cable operating temperature.
[0050] The second determination module 203 is used to determine the residual between the actual value of the cable operating temperature and the theoretical value of the cable operating temperature.
[0051] The third determination module 204 is configured to determine that a fault occurs in the submarine cable to be tested in response to the residual satisfying a preset fault condition.
[0052] In some embodiments of the present disclosure, Figure 2 Based on the embodiment shown, a training module is also included. The training module is used to: obtain the historical normal environment data of the submarine cable within the cable fault-free period and the label value of the normal cable operating temperature corresponding to the historical normal environment data; input the historical normal environment data into the initial BP neural network model to obtain the theoretical value of the historical cable operating temperature output by the initial BP neural network model; train the initial BP neural network model based on the theoretical value of the historical cable operating temperature and the label value of the normal cable operating temperature to obtain the BP neural network model.
[0053] In some embodiments of the present disclosure, the residual meeting the fault condition includes: the residual being within a residual range of the cable fault type.
[0054] In some embodiments of the present disclosure, the residual range of at least one cable fault type is obtained by the following steps: obtaining historical environmental data of the submarine cable within a preset time period and the label value of the corresponding cable operating temperature, the preset time period including a cable fault-free time period and a cable fault time period; inputting the historical environmental data into a BP neural network model to obtain a theoretical value of the historical cable operating temperature corresponding to the historical environmental data; determining the residual between the label value of the cable operating temperature and the theoretical value of the historical cable operating temperature; performing cluster analysis on the residuals, and combining the fault records of the submarine cable within the preset time period to determine at least one cable fault type and the residual range of each cable fault type.
[0055] In some embodiments of the present disclosure, the third determination module 204 is further configured to: determine the fault type of the submarine cable to be tested according to the residual range in which the residual is located.
[0056] In some embodiments of the present disclosure, the residual meeting the fault condition includes: the residual being greater than or equal to a preset residual threshold.
[0057] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.
[0058] In order to implement the above embodiments, the present disclosure also proposes an electronic device, including: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.
[0059] In order to implement the above embodiments, the present disclosure further proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.
[0060] In order to implement the above embodiments, the present disclosure also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.
[0061] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.
[0062] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, "plurality" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.
[0063] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present disclosure belong.
[0064] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.
[0065] It should be understood that the various parts of the present disclosure can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.
[0066] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.
[0067] In addition, each functional unit in each embodiment of the present disclosure may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.
[0068] The storage medium mentioned above may be a read-only memory, a disk or an optical disk, etc. Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present disclosure. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present disclosure.
Claims
1. A method for identifying faults of submarine cables, characterized in that: The following steps are involved: Obtain the environmental data of the submarine cable to be tested and the actual value of the cable operating temperature at the same time; Inputting the environmental data into a pre-trained BP neural network model to obtain a theoretical value of the cable operating temperature output by the BP neural network model; wherein the BP neural network model has learned a mapping relationship between the environmental data and the theoretical value of the cable operating temperature; Determining a residual between an actual value of the cable operating temperature and a theoretical value of the cable operating temperature; In response to the residual satisfying a preset fault condition, it is determined that a fault occurs in the submarine cable to be tested.
2. The method according to claim 1, characterized in that: The BP neural network model is pre-trained by the following steps: Acquire historical normal environmental data of the submarine cable within a cable fault-free period and a label value of a normal cable operating temperature corresponding to the historical normal environmental data; Inputting the historical normal environment data into an initial BP neural network model to obtain a theoretical value of the historical cable operating temperature output by the initial BP neural network model; The initial BP neural network model is trained based on the theoretical value of the historical cable operating temperature and the label value of the normal cable operating temperature to obtain the BP neural network model.
3. The method according to claim 1 or 2, characterized in that: The residual meeting the fault condition includes: the residual being within a residual range of the cable fault type.
4. The method according to claim 3, characterized in that The residual range of at least one cable fault type is obtained by the following steps: Acquire historical environmental data of the submarine cable within a preset time period and a label value of the corresponding cable operating temperature, wherein the preset time period includes a cable fault-free time period and a cable fault time period; Inputting the historical environmental data into the BP neural network model to obtain a theoretical value of the historical cable operating temperature corresponding to the historical environmental data; Determining a residual between the label value of the cable operating temperature and a theoretical value of the historical cable operating temperature; A cluster analysis is performed on the residuals, and combined with the fault records of the submarine cables within the preset time period, at least one cable fault type and a residual range of each cable fault type are determined.
5. The method according to claim 3, characterized in that: Also includes: The fault type of the submarine cable to be tested is determined according to the residual range in which the residual is located.
6. The method according to claim 1, characterized in that: The residual meeting the fault condition includes: the residual being greater than or equal to a preset residual threshold.
7. A submarine cable fault identification device, characterized in that: include: An acquisition module is used to obtain the environmental data of the submarine cable to be tested and the actual value of the cable operating temperature at the same time; A first determination module is used to input the environmental data into a pre-trained BP neural network model to obtain a theoretical value of the cable operating temperature output by the BP neural network model; wherein the BP neural network model has learned a mapping relationship between the environmental data and the theoretical value of the cable operating temperature; A second determination module is used to determine a residual between an actual value of the cable operating temperature and a theoretical value of the cable operating temperature; The third determination module is configured to determine that a fault occurs in the submarine cable to be tested in response to the residual satisfying a preset fault condition.
8. The device according to claim 7, characterized in that: Also includes a training module; wherein the training module is used to: Acquire historical normal environmental data of the submarine cable within a cable fault-free period and a label value of a normal cable operating temperature corresponding to the historical normal environmental data; Inputting the historical normal environment data into an initial BP neural network model to obtain a theoretical value of the historical cable operating temperature output by the initial BP neural network model; The initial BP neural network model is trained based on the theoretical value of the historical cable operating temperature and the label value of the normal cable operating temperature to obtain the BP neural network model.
9. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.