Method for monitoring dynamic of marine cable biofouling based on fiber-optic sensing

Abstract

The application relates to the technical field of dynamic submarine cable monitoring, and discloses a floating type wind turbine submarine cable bio-attachment dynamic monitoring method based on optical fiber sensing, which comprises the following steps: acquiring dynamic submarine cable optical fiber initial temperature data, and establishing a dynamic submarine cable optical fiber temperature data set; acquiring a first measured temperature data set of the dynamic submarine cable under a first current working condition and a second measured temperature data set of the dynamic submarine cable under a second current working condition; establishing a dynamic submarine cable transient thermal circuit model; calculating a theoretical temperature rise under a marine bio-attachment working condition according to the dynamic submarine cable transient thermal circuit model; collecting a measured temperature rise of the dynamic submarine cable, comparing the measured temperature rise with theoretical temperature rises under different bio-attachment thickness conditions, judging whether an abnormality caused by marine bio-attachment occurs, calculating the thickness of a bio-attachment layer, and outputting the thickness; and the bio-attachment data of the dynamic submarine cable can be calculated on line in real time.

Description

Technical Field

[0001] This application relates to the technical field of dynamic submarine cable monitoring, and in particular to a dynamic monitoring method for organisms attached to floating wind turbine submarine cables based on fiber optic sensing. Background Technology

[0002] The dynamic submarine cable of a floating wind turbine is a key component connecting the deep-sea floating wind power platform to the submarine static cable. Its core function is to stably transmit electrical energy, communication, and control signals when the platform experiences dynamic movements such as swaying and rising / falling due to waves, wind, and ocean currents. Unlike the static submarine cable of a shallow-sea fixed wind turbine, it must withstand repeated bending, stretching, and torsion, possess strong fatigue resistance, and balance flexibility and rigidity to resist seawater corrosion, biofouling, and other erosion.

[0003] Dynamic submarine cables are crucial media for power and communication transmission. One end connects to the bottom of a floating wind turbine, suspended in the seawater in an "S" shape, while the other end connects to a fixed platform. The middle section is buried under the seabed. This S-shaped suspended section connects the platform to the buried cable on the seabed, serving as a transition channel for the submarine cable. In shallow waters, this suspended section is subject to significant tidal level differences and wave action, making it prone to marine organism attachment on the cable surface between the wave crest and low tide line. Due to tidal forces, this portion of the cable is periodically exposed to air, and the attachment of organisms alters its heat dissipation conditions, leading to overheating. Furthermore, due to the high cost of submarine cables, the difficulty of maintenance in the marine environment, and the long maintenance cycle, a failure can often paralyze the entire offshore wind power transmission link, causing significant economic losses. Therefore, timely and accurate detection of abnormal cable conditions caused by marine organism attachment is of paramount importance for ensuring the safe and stable operation of offshore wind power transmission systems.

[0004] Currently, monitoring of biofouling on dynamic submarine cables mainly relies on finite element analysis software to model the cable and its biofouling state, thereby analyzing the changing characteristics of cable current carrying capacity under different biofouling conditions and proposing monitoring methods accordingly. However, finite element software is not suitable for online real-time calculations, and the established models often lack verification with measured data, making it difficult to effectively reflect the mapping relationship between the actual current carrying capacity of submarine cables and marine biofouling, thus limiting its application in practical engineering. Summary of the Invention

[0005] To calculate biofouling data of dynamic submarine cables in real time, this application provides a dynamic monitoring method for biofouling on floating wind turbine submarine cables based on fiber optic sensing, employing the following technical solution:

[0006] A dynamic monitoring method for organisms attached to floating wind turbine submarine cables based on fiber optic sensing includes the following steps:

[0007] A dynamic submarine cable fiber temperature dataset is established by acquiring initial temperature data of dynamic submarine cable fibers based on a distributed fiber temperature system.

[0008] The first measured temperature dataset of the dynamic submarine cable under the first current condition and the second measured temperature dataset under the second current condition are obtained based on the dynamic submarine cable fiber temperature dataset; wherein, the current under the first current condition is greater than the current under the second current condition.

[0009] Establish a dynamic transient thermal path model of submarine cables under marine organism attachment conditions;

[0010] The influence function of marine organisms on temperature characteristics under different attachment thicknesses of the outer layer of the dynamic submarine cable is simulated by changing the thickness of the outer layer attachment material. Under the condition of marine organism attachment, the theoretical temperature rise under the condition of marine organism attachment is calculated according to the transient thermal circuit model of the dynamic submarine cable.

[0011] The measured temperature rise of the dynamic submarine cable is collected and compared with the theoretical temperature rise under different attachment thickness conditions to determine whether there is any abnormality caused by marine organism attachment, and the thickness of the biological attachment layer is calculated and output.

[0012] By adopting the above technical solution, combining distributed optical fiber temperature measurement technology with dynamic submarine cable transient thermal path modeling and analysis, and making full use of the abnormal temperature rise caused by the influence of marine organism attachment on the heat dissipation characteristics of submarine cables, online detection and equivalent thickness estimation of the presence of biological attachment on dynamic submarine cables are realized. This effectively solves the shortcomings of traditional finite element offline simulation and lack of experimental verification, and significantly improves the real-time performance, accuracy and engineering feasibility of monitoring the biological attachment status of dynamic submarine cables. It has significant application value for ensuring the safe and stable operation of offshore wind power transmission systems.

[0013] Optionally, the steps for establishing a dynamic submarine cable fiber optic temperature dataset include:

[0014] Based on the preset target location, target time period, and dynamic submarine cable fiber temperature dataset collected by the distributed fiber optic sensing system, the initial dynamic submarine cable fiber temperature dataset is a collection of initial dynamic submarine cable fiber temperature data at the target location and target time period. The dynamic submarine cable fiber temperature dataset is obtained by smoothing all the initial dynamic submarine cable fiber temperature data through Savitzky-Golay filtering.

[0015] By adopting the above technical solution and combining it with Savitzky-Golay filtering, a more accurate and smooth dynamic submarine cable fiber temperature dataset can be obtained, providing a high-quality data foundation for subsequent temperature analysis and biofouling monitoring, and improving the accuracy and reliability of monitoring.

[0016] Optionally, the steps for obtaining the first measured temperature dataset include:

[0017] The load current data of the dynamic submarine cable is obtained, and the load current data records the current carrying capacity and the acquisition time.

[0018] Based on the load current data, a first time period is identified where the dynamic submarine cable is in the first current condition and the duration exceeds the preset first duration. When the temperature fluctuation of the dynamic submarine cable within the first time period does not exceed the preset set range, the temperature data within the first time period is taken as the measured temperature data under the first current condition, and the first measured temperature dataset is output. The first current condition is a current condition with a current carrying capacity greater than 70%.

[0019] By adopting the above technical solution, and by accurately screening temperature data that meets the conditions of the first current condition, the first duration, and temperature stability, it is ensured that the first measured temperature dataset can truly reflect the temperature characteristics of the submarine cable under the corresponding first current condition, providing a reliable benchmark for subsequent comparison with theoretical temperature rise and improving the accuracy of biological attachment judgment.

[0020] Optionally, the steps for obtaining the second measured temperature dataset include:

[0021] The load current data of the dynamic submarine cable is obtained, and the load current data records the current carrying capacity and the acquisition time.

[0022] Based on the load current data, a second time period is identified where the dynamic submarine cable is in the second current condition and the duration exceeds the preset second duration. When the temperature fluctuation of the dynamic submarine cable within the second time period does not exceed the preset set range, the temperature data within the second time period is taken as the measured temperature data under the second current condition, and the second measured temperature dataset is output. The second current condition is a current condition with a current carrying capacity of less than 30%.

[0023] By adopting the above technical solution, and by accurately selecting temperature data that meets the conditions of the second current condition, the first duration of operation, and temperature stability, it is ensured that the second measured temperature dataset can truly reflect the temperature characteristics of the submarine cable under the corresponding second current condition, providing a reliable benchmark for subsequent comparison with the theoretical temperature rise.

[0024] Optionally, the structure of the dynamic submarine cable includes a conductor, an insulation layer, a metal sheath layer, an inner padding layer, a filling layer, an optical fiber, an armor layer, and an outer sheath; the thermal parameters of the dynamic submarine cable include the thermal conductivity and specific heat capacity of each layer.

[0025] Based on the first measured temperature dataset, the second measured temperature dataset, and the thermal parameters of the dynamic submarine cable, a transient thermal path model of the dynamic submarine cable is established:

[0026] The transient thermal circuit model of a dynamic submarine cable models the multiple structural layers of the cable as an equivalent network composed of thermal resistance and thermal capacity units. Heat flow is equivalent to current, thermal capacity is equivalent to capacitance, and thermal resistance is equivalent to resistance. The corresponding node voltage equations are constructed, forming the following set of differential equations:

[0027] ;

[0028] Where C is the thermal capacity coefficient matrix, R is the thermal resistance coefficient matrix, T is the nodal temperature vector, Q is the heat source vector, and dT / dt is the rate of change of T with time t; the differential equation system is solved using the Runge-Kutta method.

[0029] By adopting the above technical solution, combining measured temperature data with the thermal parameters of each layer of the submarine cable, the multi-layer structure is equivalent to a thermal resistance and thermal capacity network, and a set of differential equations is constructed. The Runge-Kutta method is used to solve the equations, so that the transient thermal circuit model can accurately reflect the heat transfer characteristics of the submarine cable.

[0030] Optionally, the theoretical temperature rise curve under no-biological-attachment conditions calculated based on the dynamic submarine cable transient thermal circuit model is compared with the measured temperature rise curve of the submarine cable during its initial operation to verify the correctness of the submarine cable transient thermal circuit model. The parameters of the submarine cable are then adjusted until the calculation results of the dynamic submarine cable transient thermal circuit model are within the preset error range, including:

[0031] Based on a dynamic transient thermal circuit model of submarine cables, theoretical temperature sequences at different time points are calculated under the same load conditions as measured in actual experiments. A theoretical temperature dataset under no-attachment conditions was generated; the theoretical temperature curves calculated by the dynamic submarine cable transient thermal path model were compared with the measured temperature curves, and the mean square error between the two was used as the objective function.

[0032] ;

[0033] Where n is the number of temperature sampling points, and α is the parameter vector to be corrected. This is a dataset of measured temperatures under conditions where no marine organisms were attached during the initial operation of the submarine cable.

[0034] The thermal parameters in the dynamic transient thermal circuit model of the submarine cable were optimized so that the root mean square error between the calculated results of the dynamic transient thermal circuit model and the measured temperature curve was less than 5% of the measured maximum temperature rise under conditions without biofouling. , It is the difference between the measured temperature and the initial temperature of the submarine cable at the i-th time point ti, that is, the actual measured temperature rise.

[0035] By adopting the above technical solution, the theoretical temperature rise curve without biological attachment calculated by the transient thermal circuit model of the submarine cable is compared with the initial measured temperature rise curve. The thermal parameters are optimized with the mean square error as the objective function, ensuring that the model error is controlled within a strict range. It can also effectively calibrate the model and improve its simulation accuracy of the real thermal characteristics of dynamic submarine cables.

[0036] Optionally, the method for optimizing the thermal parameters of the submarine cable transient model includes the following steps:

[0037] Optimization was performed using the particle swarm optimization algorithm.

[0038] The particle swarm optimization algorithm pre-sets an initial number of particles, each representing a set of thermal parameters to be optimized. Guided by the objective function, the particles iteratively update their search paths in the parameter space based on their individual optimal positions and the population optimal positions. The objective function is the function for calculating the root mean square error.

[0039] By adopting the above technical solution, global search and convergence of the optimal thermal parameter combination can be achieved.

[0040] Optionally, an equivalent bio-attachment structure layer is set in the dynamic submarine cable transient thermal circuit model. The equivalent bio-attachment structure layer is used in the model to correspond to the additional thermal resistance unit that is wrapped around the outer sheath as a ring.

[0041] The process of comparing the measured temperature rise with the theoretical temperature rise under different adhesion thickness conditions and estimating the equivalent thickness includes the following steps:

[0042] When the dynamic submarine cable switches from the second current condition to the first current condition, the measured temperature rise value is obtained. ;

[0043] Based on a dynamic transient thermal circuit model of submarine cables, the theoretical temperature rise sequence of the equivalent bio-attached structural layer under different thicknesses is calculated under the same load current conditions. , where d is the thickness parameter of the equivalent bio-attachment structure layer;

[0044] When the measured temperature rise Theoretical temperature rise under conditions of relative absence of biofouling Exceeding the predetermined threshold ,Right now If so, it is determined that the dynamic submarine cable has an anomaly caused by biological attachment;

[0045] The fitted bioattachment thickness is determined by minimizing the residuals. ;

[0046] threshold The following method can be used to determine the residual ratio: obtain the relative deviations between the temperature rise values ​​under conditions with and without biofouling, forming a set of residual ratios. Assume that the set of residual ratios approximately follows a normal distribution, and that each sample is statistically independent and identically distributed.

[0047] ;

[0048] Where μ is the sample mean. To ensure that the confidence level for anomaly detection is not less than 95%, the above... The threshold value is ,in, This represents the upper quantile of the confidence interval.

[0049] By adopting the above technical solution, an additional thermal resistance unit is used to simulate the impact of marine organism attachment of different thicknesses on the heat dissipation performance of submarine cables. Its thermophysical parameters are preferably simulated using calcium carbonate materials to reflect the inhibitory effect of typical shell organism attachment layers such as barnacles and oysters on the heat dissipation performance of submarine cables. An equivalent biological attachment structure layer is set to simulate the attachment effect. Combined with the temperature rise value comparison during the switching of operating conditions, the modeling accuracy of the correlation between biological attachment and submarine cable temperature rise is improved. The threshold is determined based on the normal distribution and combined with the confidence level to enhance the reliability of anomaly judgment. By minimizing the residual to estimate the thickness, the accuracy of attachment amount assessment is improved.

[0050] Optionally, the measured temperature rise The difference between the average steady-state temperature under the first current condition and the average steady-state temperature under the second current condition when the dynamic submarine cable switches from the second current condition to the first current condition.

[0051] Theoretical temperature rise The preferred value is the difference between the average steady-state temperature under the first current condition and the average steady-state temperature under the second current condition, obtained by simulating the transient thermal circuit model of the dynamic submarine cable under the same load current condition.

[0052] By adopting the above technical solution, the average difference in steady-state temperature during current switching is taken as the temperature rise value, eliminating the influence of transient fluctuations, making the comparison benchmark between the measured temperature rise and the theoretical temperature rise more stable, and improving the accuracy of biological adhesion anomaly judgment and thickness estimation. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating a method for dynamic monitoring of organisms attached to floating wind turbine submarine cables based on fiber optic sensing.

[0054] Figure 2 It is a transient thermal path model of submarine cables without biological attachment.

[0055] Figure 3It is a transient thermal circuit model of a submarine cable with biological attachment. Detailed Implementation

[0056] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0057] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0058] This application discloses a method for dynamic monitoring of organisms attached to floating wind turbine submarine cables based on fiber optic sensing, referring to... Figure 1 It includes the following steps:

[0059] Step S1: Obtain initial temperature data of the dynamic submarine cable fiber optic cable based on a distributed optical fiber temperature system, and establish a dynamic submarine cable fiber optic temperature dataset. The dynamic submarine cable is a three-core dynamic submarine cable. The structural parameters of the dynamic submarine cable include conductor, insulation layer (including conductor shielding, XLPE insulation and insulation shielding), metal sheath layer (including water-blocking tape, alloy lead sheath and semiconductor PE sheath), fiber inner filling layer, optical fiber, fiber outer filling layer, and armor layer (including PP inner padding layer, galvanized steel wire, asphalt and outer sheath). In this embodiment, a target location and a target time period are selected, and initial temperature data of the dynamic submarine cable fiber optic cable are obtained based on a distributed optical fiber temperature system. The dynamic submarine cable fiber optic temperature dataset is a collection of initial temperature data of the dynamic submarine cable fiber optic cable at the target location and during the target time period. The dynamic cable fiber optic temperature dataset is obtained by smoothing the initial temperature data of the dynamic submarine cable fiber optic cable using Savitzky-Golay filtering. The Savitzky-Golay filtering sliding window length is 11, and the fitting order is 2. By combining Savitzky-Golay filtering, a more accurate and smooth dynamic submarine cable fiber temperature dataset can be obtained, providing a high-quality data foundation for subsequent temperature analysis and biofouling monitoring, and improving the accuracy and reliability of monitoring.

[0060] Step S2: Obtain the first measured temperature dataset of the dynamic submarine cable under the first current condition and the second measured temperature dataset under the second current condition based on the dynamic submarine cable fiber temperature dataset; wherein, the current under the first current condition is greater than the current under the second current condition.

[0061] The steps for obtaining the first measured temperature dataset include: acquiring the load current data of the dynamic submarine cable, which records the current carrying capacity and acquisition time; matching the load current data to determine a first time period in which the dynamic submarine cable is under a first current condition and the duration exceeds a preset first duration; and taking the temperature data within the first time period as the measured temperature data under the first current condition as the measured temperature data, and outputting the first measured temperature dataset; wherein, the first current condition is a current condition greater than 70% of the current carrying capacity. In this embodiment, taking a certain type of submarine cable with a rated current carrying capacity of approximately 500A as an example, the first duration and the second duration are both 3 hours, and the set range is 0.5℃. The temperature dataset of dynamic submarine cables under high-current conditions is obtained based on the dynamic submarine cable fiber optic temperature dataset. This can be achieved through the following steps: Select a time period from the load current data where the submarine cable is under high current for more than 3 hours. If the temperature fluctuation of the dynamic submarine cable during this time period does not exceed 0.5℃, then the temperature data within this time period is taken as the measured temperature data under high-current conditions. High-current conditions are preferably defined as the submarine cable's current carrying capacity exceeding 70% of the design rated current carrying capacity, i.e., a current value exceeding 350A. By accurately selecting temperature data that meets the conditions of the first current condition, the first duration, and temperature stability, it is ensured that the first measured temperature dataset accurately reflects the temperature characteristics of the submarine cable under the corresponding first current condition. This provides a reliable benchmark for subsequent comparison with theoretical temperature rise and improves the accuracy of biofouling assessment.

[0062] The steps for obtaining the second measured temperature dataset include: acquiring the load current data of the dynamic submarine cable, which records the current carrying capacity and acquisition time; matching the load current data to determine a second time period in which the dynamic submarine cable is under a second current condition and the duration exceeds a preset second duration; and taking the temperature data within the second time period as the measured temperature data under the second current condition when the temperature fluctuation of the dynamic submarine cable within the second time period does not exceed a preset set range, and outputting the second measured temperature dataset; wherein, the second current condition is a current condition less than 30% of the current carrying capacity. In this embodiment, taking a certain type of submarine cable with a designed rated current carrying capacity of approximately 500A as an example, a time period in which the submarine cable is under low current and the duration exceeds 3 hours is selected from the load current data. When the temperature fluctuation of the dynamic submarine cable within this time period does not exceed 0.5℃, the temperature data within this time period is taken as the measured temperature data under the low current condition. The low current condition preferably refers to the submarine cable current carrying capacity being less than 30% of the designed rated current carrying capacity, i.e., the current value is less than 150A. By accurately selecting temperature data that meets the conditions of the second current condition, the first duration of operation, and temperature stability, we ensure that the second measured temperature dataset can truly reflect the temperature characteristics of the submarine cable under the corresponding second current condition, providing a reliable benchmark for subsequent comparison with theoretical temperature rise.

[0063] Step S3: Establish a transient thermal path model of the dynamic submarine cable under conditions of marine organism attachment. The thermal parameters of the dynamic submarine cable include the thermal conductivity and specific heat capacity of each of the above-mentioned layers. Referring to heat transfer theory, materials with the same thermal resistivity are grouped into one layer. The insulation layer includes conductor shielding, XLPE insulation, and insulating shielding; the metal sheath layer includes water-blocking tape, alloy lead sheath, and semiconductor PE sheath.

[0064] All structural data and thermal parameters of each layer of the three-core dynamic submarine cable were obtained, as shown in Table 1:

[0065] Table 1. Structural and thermal parameters of the three-core dynamic submarine cable

[0066]

[0067] In this embodiment, a transient thermal path model of the dynamic submarine cable is established based on the first measured temperature dataset, the second measured temperature dataset, and the thermal parameters of the dynamic submarine cable:

[0068] like Figure 2 and Figure 3 As shown, the transient thermal circuit model of a dynamic submarine cable models the multiple structural layers of the cable as an equivalent network composed of thermal resistance and thermal capacity units. The thermal circuit is equivalent to a resistor, the thermal capacity is equivalent to a capacitor, and the thermal resistance is equivalent to a resistor. The corresponding node voltage equations are constructed, forming the following set of differential equations:

[0069] ;

[0070] Where C is the thermal capacity coefficient matrix, R is the thermal resistance coefficient matrix, T is the nodal temperature vector, Q is the heat source vector, and dT / dt is the rate of change of T with time t; the differential equations are solved using the Runge-Kutta method. The Runge-Kutta method is an important method for solving numerical solutions to ordinary differential equations. It approximates the derivative by selecting multiple points within each solution interval and using a weighted average of the slopes of the function at these points, thus obtaining a more accurate numerical solution than methods such as the Euler method. A common example is the fourth-order Runge-Kutta method, which calculates the slope values ​​at four different points and combines them with specific weights, providing high solution accuracy while ensuring computational efficiency. It is widely used in dynamic process simulations in physics, engineering, and other fields. Combining measured temperature data with the thermal parameters of each layer of the submarine cable, the multi-layer structure is equivalent to a thermal resistance and thermal capacity network, and a system of differential equations is constructed and solved using the Runge-Kutta method, enabling the transient thermal path model to accurately reflect the heat transfer characteristics of the submarine cable.

[0071] Step S4: Based on the correctness of the submarine cable transient thermal circuit model, the thickness of the outer layer attachment material is changed to simulate the influence of marine organisms on temperature characteristics under different attachment thickness conditions. Under the condition of biological attachment, the temperature rise change under the attachment condition is calculated according to the submarine cable transient thermal circuit model.

[0072] Step S5: Compare the measured temperature rise with the theoretical temperature rise under different adhesion thickness conditions to determine whether there is any abnormality caused by marine organism attachment, and estimate the thickness of the biological attachment layer.

[0073] The theoretical temperature rise curve under no-biological-attachment conditions was calculated based on the dynamic submarine cable transient thermal circuit model. This curve was then compared with the measured temperature rise curve of the submarine cable during its initial operation to verify the correctness of the transient thermal circuit model. The parameters of the submarine cable were adjusted until the calculation results of the dynamic submarine cable transient thermal circuit model were within the preset error range, including:

[0074] Based on a dynamic transient thermal circuit model of submarine cables, theoretical temperature sequences at different time points are calculated under the same load conditions as measured in actual experiments. A theoretical temperature dataset under no-attachment conditions was generated; the theoretical temperature curves calculated by the dynamic submarine cable transient thermal path model were compared with the measured temperature curves, and the mean square error between the two was used as the objective function.

[0075] ;

[0076] Where n is the number of temperature sampling points, and α is the parameter vector to be corrected. This is a dataset of measured temperatures under conditions where no marine organisms were attached during the initial operation of the submarine cable.

[0077] The thermal parameters in the dynamic transient thermal circuit model of the submarine cable were optimized so that the root mean square error between the calculated results of the dynamic transient thermal circuit model and the measured temperature curve was less than 5% of the measured maximum temperature rise under conditions without biofouling. , It is the difference between the measured temperature and the initial temperature of the submarine cable at the i-th time point ti, that is, the actual measured temperature rise.

[0078] By comparing the theoretical temperature rise curve without biological attachment calculated by the transient thermal circuit model of the submarine cable with the initial measured temperature rise curve, and optimizing the thermal parameters with the mean square error as the objective function, the model error is ensured to be controlled within a strict range. This also effectively calibrates the model and improves its simulation accuracy of the real thermal characteristics of dynamic submarine cables.

[0079] In this embodiment, Figure 2 and Figure 3 middle , , , These are conductor loss, dielectric loss, metal sheath loss, and armor loss, respectively. For the temperature of each node, The ambient temperature; For the heat capacity of a conductor, and These are the total thermal resistance and thermal capacity of the conductor shield, respectively; The total heat capacity of the water-blocking strip, alloy lead sleeve, and semiconductor PE sheath; These represent the thermal resistance of the inner filling layer of the optical fiber, the optical fiber itself, and the outer filling layer of the optical fiber. These represent the heat capacities of the inner filling layer of the optical fiber, the optical fiber itself, and the outer filling layer of the optical fiber. and These are the total thermal resistance and total heat capacity of the inner sheath, armor, asphalt, and outer sheath, respectively. and These are the thermal resistance and heat capacity of the environment, respectively; 7 represents the temperature of the inner surface of the biofilm layer, T7 represents the thermal resistance of the biofilm layer, and C9 represents the heat capacity of the biofilm layer.

[0080] The thermal parameters of a submarine cable transient model are optimized using a particle swarm optimization algorithm. The PSO algorithm pre-sets an initial number of particles, each representing a set of thermal parameters to be optimized. Guided by an objective function, the particles iteratively update, adjusting their search paths in the parameter space based on their individual optimal positions and the population's optimal positions. The objective function is the root mean square error (RMSE) calculation function. This achieves global search and convergence for the optimal combination of thermal parameters. In this embodiment, the method for optimizing the thermal parameters in the submarine cable transient model employs the particle swarm optimization algorithm, PSO. The PSO algorithm uses an initial number of particles, each representing a set of thermal parameter values ​​to be optimized (heat capacity and thermal resistance of each structural layer). Guided by an objective function (the mean square error between the measured temperature and the model's predicted temperature), the particles iteratively update, continuously adjusting their search paths in the parameter space based on their individual optimal positions and the population's optimal positions, thereby achieving global search and convergence for the optimal combination of thermal parameters.

[0081] In this embodiment, assuming the transient thermal path model is verified to be correct, an equivalent bio-attachment structure layer is introduced into the thermal path model. This attachment layer serves as an additional thermal resistance unit, annularly wrapped around the outer sheath of the submarine cable. The material of this attachment layer is equivalent to calcium carbonate, and its thermal conductivity is [insert value here]. Specific heat capacity is .

[0082] In this embodiment, the process of comparing the measured temperature rise with the theoretical temperature rise under different adhesion thickness conditions and estimating the equivalent thickness can be implemented through the following steps:

[0083] When the dynamic submarine cable switches from low-current to high-current operation, the measured temperature rise value during this process is obtained. ;

[0084] Based on a dynamic transient thermal circuit model of submarine cables, the theoretical temperature rise sequence is calculated under the same load conditions and with biofouling thicknesses of 10mm, 20mm, 30mm, 40mm, and 50mm. ,in For the thickness parameter of the bio-attachment layer;

[0085] When the measured temperature rise Theoretical temperature rise under conditions of relative absence of biofouling Exceeding the predetermined threshold ,Right now If the dynamic submarine cable exhibits anomalies caused by biofouling, then the optimal biofouling thickness is determined by minimizing the residuals. : And based on this, the thickness of the bio-attachment layer is estimated.

[0086] In this embodiment, the threshold The following steps can be used to determine this: Obtain the relative deviations between the temperature rise values ​​under conditions with and without biofouling, forming a set of residual ratios. Assume that this set approximately follows a normal distribution, and that each sample is statistically independent and identically distributed.

[0087]

[0088] in, The sample mean. To ensure that the confidence level for anomaly detection is not less than 95%, the above... The threshold value is ,in This represents the upper quantile of the confidence interval.

[0089] An additional thermal resistance unit is used to simulate the impact of marine organism attachment of different thicknesses on the heat dissipation performance of submarine cables. Its thermophysical parameters are preferably simulated using calcium carbonate materials to reflect the inhibitory effect of typical shell organism attachment layers such as barnacles and oysters on the heat dissipation performance of submarine cables. An equivalent bio-attachment structure layer is set to simulate the attachment effect. Combined with the temperature rise value comparison during the switching of operating conditions, the modeling accuracy of the correlation between bio-attachment and submarine cable temperature rise is improved. The threshold is determined based on the normal distribution and combined with the confidence level to enhance the reliability of anomaly judgment. By minimizing the residual to estimate the thickness, the accuracy of attachment amount assessment is improved.

[0090] In this embodiment, the measured temperature rise ΔT meas This represents the difference between the average steady-state temperature at high current and the average steady-state temperature at low current when a dynamic submarine cable switches from low current to high current operation. Theoretical temperature rise. This is the difference between the average steady-state temperature under high current and low current conditions obtained from the transient thermal circuit model simulation under the same load conditions. Taking the difference in the average steady-state temperature during current switching as the temperature rise value eliminates the influence of transient fluctuations, making the comparison benchmark between the measured and theoretical temperature rise more stable, and improving the accuracy of bio-attachment anomaly judgment and thickness estimation.

[0091] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for monitoring the dynamic of the biofouling of a floating wind farm submarine cable based on optical fiber sensing, characterized in that, The method comprises the following steps: Based on the distributed optical fiber temperature system, the initial temperature data of the dynamic submarine cable optical fiber is obtained, and a dynamic submarine cable optical fiber temperature data set is established; According to the dynamic submarine cable optical fiber temperature data set, a first measured temperature data set of the dynamic submarine cable under a first current working condition and a second measured temperature data set of the dynamic submarine cable under a second current working condition are obtained; wherein the current of the first current working condition is greater than the current of the second current working condition; A dynamic submarine cable transient thermal circuit model under the condition of existence of marine organism attachment is established; The thickness of the outer layer of the dynamic submarine cable is changed to simulate the influence function of the marine organism on the temperature characteristics under different attachment thickness conditions, and the theoretical temperature rise under the condition of existence of marine organism attachment is calculated according to the dynamic submarine cable transient thermal circuit model; The measured temperature rise of the dynamic submarine cable is collected, the measured temperature rise is compared with the theoretical temperature rise under different attachment thickness conditions, it is judged whether the abnormality caused by marine organism attachment occurs, the thickness of the biological attachment layer is calculated, and the thickness is output.

2. The fiber-optic sensing based dynamic monitoring method of marine growth on a floating wind farm cable as claimed in claim 1, wherein, The step of establishing the dynamic submarine cable optical fiber temperature data set comprises: Based on the preset target position, target time period and distributed optical fiber sensing system, the dynamic submarine cable optical fiber temperature data set is collected, the dynamic submarine cable optical fiber initial data set is the collection of the dynamic submarine cable optical fiber initial temperature data of the target position and the target time period, and all the dynamic submarine cable optical fiber initial optical fiber temperature data are smoothed by Savitzky-Golay filtering to obtain the dynamic submarine cable optical fiber temperature data set.

3. The fiber optic sensing based dynamic monitoring method of marine growth on a floating wind farm cable as claimed in claim 1, wherein, The step of obtaining the first measured temperature data set comprises: The load current data of the dynamic submarine cable is obtained, and the load current data records the load current and the collection time; According to the load current data, a first time period in which the dynamic submarine cable is in the first current working condition and the duration exceeds the preset first duration is matched out, when the fluctuation amplitude of the temperature of the dynamic submarine cable in the first time period does not exceed the preset set amplitude, the temperature data in the first time period is taken as the measured temperature data under the first current working condition, and the first measured temperature data set is output; wherein the first current working condition is a current working condition greater than 70% of the load current.

4. The fiber-optic sensing based dynamic monitoring method of marine growth on a floating wind farm cable as claimed in claim 1, wherein, The step of obtaining the second measured temperature data set comprises: The load current data of the dynamic submarine cable is obtained, and the load current data records the load current and the collection time; According to the load current data, a second time period in which the dynamic submarine cable is in the second current working condition and the duration exceeds the preset second duration is matched out, when the fluctuation amplitude of the temperature of the dynamic submarine cable in the second time period does not exceed the preset set amplitude, the temperature data in the second time period is taken as the measured temperature data under the second current working condition, and the second measured temperature data set is output; wherein the second current working condition is a current working condition less than 30% of the load current.

5. The fiber-optic sensing based dynamic monitoring method of marine growth on a floating wind farm cable as claimed in claim 1, wherein, The structure of the dynamic submarine cable comprises a conductor, an insulating layer, a metal sheath layer, an inner pad layer, a filling layer, an optical fiber, an armor layer and an outer protective layer; the thermal parameters of the dynamic submarine cable include the thermal conductivity and the specific heat capacity of each layer structure; According to the first measured temperature data set, the second measured temperature data set and the thermal parameters of the dynamic submarine cable, a dynamic submarine cable transient thermal circuit model is established: The dynamic submarine cable transient thermal circuit model models multiple structural layers of the dynamic submarine cable as an equivalent network composed of thermal resistance and thermal capacity units, equivalently regards the thermal circuit as a resistance, equivalently regards the thermal capacity as a capacitor, equivalently regards the thermal resistance as a resistance, constructs corresponding node voltage equations and forms the following differential equation set: ； Wherein, C is a thermal capacity coefficient matrix, R is a thermal resistance coefficient matrix, T is a node temperature vector, Q is a heat source vector, and dT / dt is a change rate of T with time t; the differential equation set is solved by using the Runge-Kutta method.

6. The fiber-optic sensing based dynamic monitoring method of marine growth on a floating wind farm cable as claimed in claim 5, wherein, According to the dynamic submarine cable transient thermal circuit model, a theoretical temperature rise curve under the condition of no biological attachment is calculated, and the calculated temperature rise curve is compared with a measured temperature rise curve of the submarine cable when the submarine cable is initially put into operation, so as to verify the correctness of the submarine cable transient thermal circuit model, and the parameter conditions of the submarine cable are adjusted until the calculation result of the dynamic submarine cable transient thermal circuit model is within a preset error range; Based on the dynamic transient thermal model of submarine cable, the theoretical temperature sequence at different time nodes is calculated under the same load condition as the measured one , forming the theoretical temperature dataset under the condition of no adhesion; the theoretical temperature curve calculated by the dynamic transient thermal model of submarine cable is compared with the measured temperature curve, and the mean square error of the two is taken as the objective function: ； Wherein, n is the number of temperature sampling points, and a is the parameter vector to be corrected, is the measured temperature data set under the condition of no marine organism attachment when the submarine cable is initially put into operation. The thermal parameters in the dynamic submarine cable transient thermal circuit model are optimized so that the root mean square error between the calculation results of the dynamic submarine cable transient thermal circuit model and the measured temperature curve is less than 5% of the maximum temperature rise measured under the condition of no biological attachment, that is , is the difference between the measured temperature of the submarine cable and the initial temperature at the i th time point t i, that is, the actual measured temperature rise value.

7. The fiber-optic sensing based dynamic monitoring method of marine growth on a floating wind farm cable as claimed in claim 6, wherein, The method for optimizing the thermal parameters of the submarine cable transient model comprises the following steps: The particle swarm optimization algorithm is used for optimization. The particle swarm optimization algorithm is preset with an initial number of particle swarms, each particle represents a set of thermal parameters to be optimized, and is iteratively updated under the guidance of a target function, and the particles adjust the search path in the parameter space according to the individual optimal position and the population optimal position, wherein the target function is a calculation function of the root mean square error.

8. The fiber-optic sensing based dynamic monitoring method of marine growth on a floating wind farm cable as claimed in claim 7, wherein, An equivalent biological attachment structural layer is arranged in the dynamic submarine cable transient thermal circuit model, and the equivalent biological attachment structural layer is used to correspond to an additional thermal resistance unit wrapped outside the outer sheath in the model; The process of comparing the measured temperature rise with the theoretical temperature rise under different attachment thickness conditions and estimating the equivalent thickness comprises the following steps: The process of comparing the measured temperature rise with the theoretical temperature rise under different attachment thickness conditions and estimating the equivalent thickness comprises the following steps: When the dynamic submarine cable is switched from the second current working condition to the first current working condition, the measured temperature rise value is acquired ; Based on the dynamic transient thermal circuit model of submarine cable, the theoretical temperature rise value sequence of equivalent biofouling structure layer with different thickness under the same load current condition is calculated wherein d is the thickness parameter of the equivalent biofouling structure layer when the measured temperature rise value the theoretical temperature rise value under the condition of no bio-attachment exceeds a predetermined threshold , i.e. abnormality caused by bio-attachment is determined to exist in the dynamic submarine cable; determining the fitted bioattachment thickness by minimizing the residual ; Threshold value The threshold value can be determined by the following method: obtaining the relative deviation of the temperature rise value under the biological attachment condition from the temperature rise value under the non-biological attachment condition, constructing a residual ratio set, setting the residual ratio set to approximately obey a normal distribution, and each sample is statistically independent and identically distributed: ； where μ is the sample mean, is the sample standard deviation, and the threshold value is where is the upper quantile of the confidence interval.

9. The fiber-optic sensing based dynamic monitoring method of marine growth on a floating wind farm cable according to claim 8, wherein, measured temperature rise value for a dynamic submarine cable, the difference between the average steady-state temperature of the current in the first current operating condition and the average steady-state temperature of the current in the second current operating condition when switching from the second current operating condition to the first current operating condition; Theoretical temperature rise value The difference between the first current steady-state temperature average value and the second current steady-state temperature average value is preferably simulated by a dynamic submarine cable transient thermal circuit model under the same load current working condition.

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