Submarine cable current-carrying capacity dynamic detection method and system based on optical fiber sensing and medium

Abstract

The invention discloses a submarine cable current-carrying capacity dynamic detection method and system based on optical fiber sensing, and a medium, and the method comprises the steps: obtaining the current data, optical fiber temperature data and time data of a submarine cable based on distributed optical fiber sensing equipment, building a steady-state data set, dividing an environment current sequence and an operation current sequence, and carrying out the steady-state data set; calculating the thermal resistance of the submarine cable structure in combination with an equivalent thermal resistance algorithm and a finite element simulation algorithm, establishing a multi-layer thermal resistance network model, generating a thermal resistance estimation strategy, calculating the current conductor temperature, and calculating the maximum allowable current value when the conductor temperature reaches a temperature safety threshold in combination with environment temperature data and matched cable loss; determining an optical fiber temperature threshold value corresponding to the temperature safety threshold value, wherein the optical fiber temperature threshold value serves as a reference control value of the submarine cable current-carrying capacity; based on optical fiber monitoring and real-time analysis, the method does not depend on the temperature of the conductor, considers the influence of environmental change, achieves the dynamic estimation of the temperature of the conductor and the optimization of the current-carrying capacity, and improves the operation safety and thermal stability of the three-core submarine cable.

Description

Technical Field

[0001] This application relates to the technical field of submarine cable temperature detection, and in particular to a dynamic detection method, system and medium for submarine cable current carrying capacity based on optical fiber sensing. Background Technology

[0002] Submarine cables are critical infrastructure connecting offshore wind farms to onshore power grids, undertaking the transmission of electricity and communications, and playing an irreplaceable role in wind power projects. With the development of offshore wind power projects, three-core AC submarine cables have become the most commonly used cable type in offshore wind power projects due to their compact structure, simple construction, and strong anti-interference capabilities.

[0003] Conductor temperature is a core indicator reflecting the thermal state of submarine cables and assessing their current-carrying capacity. However, since it is located in the central area of ​​the cable structure, it is difficult to directly deploy sensors for real-time measurement in engineering practice. Distributed optical fiber temperature measurement technology is often used to indirectly obtain the internal temperature distribution of submarine cables by laying optical fibers.

[0004] However, since optical fibers are typically laid within a metal sheath or armor layer, and there is a spatial distance between them and the conductor, there is a significant difference between the measured fiber temperature and the conductor temperature. This necessitates the use of a thermal circuit model to correct for this intermediate temperature difference. Furthermore, the environmental parameters of submarine cables exhibit significant time-varying and spatial uncertainties, causing continuous disturbances to thermal resistance characteristics. This makes traditional static thermal circuit models unable to accurately predict conductor temperature, especially during current step changes or fluctuations in the seabed environment, where the errors become more pronounced, thus affecting the dynamic control capability of the cable's current-carrying capacity. Summary of the Invention

[0005] To improve the accuracy of dynamic detection of conductor temperature, this application provides a method, system, and medium for dynamic detection of submarine cable current carrying capacity based on optical fiber sensing.

[0006] Firstly, this application provides a dynamic detection method for submarine cable current carrying capacity based on optical fiber sensing, employing the following technical solution:

[0007] A dynamic detection method for submarine cable current carrying capacity based on fiber optic sensing includes the following steps:

[0008] Current data, fiber temperature data, and time data of submarine cables are acquired using distributed fiber optic sensing devices. The current data, fiber temperature data, and time data are correlated and correspond to each other. Based on the time data, the real-time current data and fiber temperature data are judged for steady-state conditions using a sliding filter algorithm to obtain steady-state current data, steady-state fiber temperature data, and steady-state time data. A steady-state dataset is established based on the steady-state current data, steady-state fiber temperature data, and steady-state time data.

[0009] The steady-state dataset is divided into an ambient current sequence and an operating current sequence according to a first partitioning algorithm. The steady-state current data corresponding to the operating current sequence is greater than the steady-state current data corresponding to the ambient current sequence. The steady-state fiber temperature data is extracted from the ambient current sequence to form an ambient temperature sequence. The steady-state current data with the closest time to the operating current sequence is matched in the ambient temperature sequence through time backward search and used as the ambient temperature data corresponding to the operating current sequence.

[0010] Obtain structural material data and shape characteristic data of submarine cable. Calculate the thermal resistance of submarine cable structure based on the structural material data and shape characteristic data, combined with the equivalent thermal resistance algorithm and finite element simulation algorithm. Establish a multi-layer thermal resistance network model, including conductor-to-fiber segment model and fiber-to-environment segment model, with the optical fiber as the dividing point.

[0011] A thermal resistance estimation strategy is generated based on the Kalman filter algorithm and the recursive least squares method. The fiber-to-environment segment model is dynamically updated in real time using the thermal resistance estimation strategy based on the ambient current sequence and the operating current sequence.

[0012] Based on the real-time acquired fiber optic temperature data, the current conductor temperature is calculated using the conductor-to-fiber segment model, and it is determined whether the current conductor temperature is greater than the set temperature safety threshold. If the current conductor temperature is greater than the temperature safety threshold, then based on the fiber-to-environment segment model, combined with the ambient temperature data and the matched cable loss, the maximum allowable current value when the conductor temperature reaches the temperature safety threshold is calculated, and the fiber optic temperature threshold corresponding to the temperature safety threshold is determined. The fiber optic temperature threshold is used as a reference control value for the current carrying capacity of the submarine cable.

[0013] By adopting the above technical solutions and comprehensively applying fiber optic temperature monitoring and real-time data analysis methods, this paper proposes a method for conductor temperature estimation and dynamic optimization of current carrying capacity that integrates fiber optic temperature measurement data and thermal circuit model to address practical engineering problems such as the difficulty in directly measuring the conductor temperature of three-core submarine cables and the variation of thermal environment parameters with time and operating conditions. Without relying on conductor temperature, this method considers the impact of environmental changes and achieves dynamic estimation of conductor temperature and intelligent optimization of current carrying capacity, thereby improving the operational safety and thermal stability of three-core submarine cables and meeting the intelligent requirements of future offshore wind power scenarios.

[0014] Optionally, the method further includes the following steps:

[0015] The current data is The fiber temperature data corresponding to the current data is The time data is Among the existing data, the time data corresponding to the aforementioned data is: The current data is The fiber optic temperature data is ;

[0016] The sliding filtering algorithm is a sliding time window method. It performs steady-state condition judgment on the current data and the fiber optic temperature data to obtain the steady-state dataset. The steady-state current data in the steady-state dataset is... The corresponding steady-state fiber temperature data is The steady-state time data is ;

[0017] The sliding filter algorithm includes: defining a time window of duration A, with a sliding step size of a minutes, to implement the current data. The fiber optic temperature data and the time data Real-time updates; for the data in the current time window, the time range for obtaining the window length is [ , ] A temporary interval is defined, and the maximum value of the current data within the temporary interval is calculated. and minimum value Set the current change threshold to If the following conditions are met: Then mark the temporary interval as the steady-state interval, and update the starting point of the next time window to [value]. ; Calculate the average current within the b-th steady-state interval. As the steady-state current data Take the end point of the steady-state interval as... The fiber optic temperature data corresponding to the time. As the submarine cable in the steady-state current data The steady-state fiber temperature data under the action.

[0018] By adopting the above technical solution, a three-core submarine cable steady-state dataset is established. The steady-state data includes current data, optical fiber temperature data, and time data that satisfy the steady-state judgment rule.

[0019] Optionally, the method further includes the following steps:

[0020] The first partitioning algorithm is based on a current threshold.

[0021] The steady-state current data corresponding to the steady-state fiber temperature data is divided into an ambient current sequence and an operating current sequence according to a set current threshold.

[0022] Define the ambient current threshold as If the steady-state current data The value of the c-th steady-state current data satisfies The steady-state current data is then assigned to the ambient current sequence, the corresponding steady-state fiber optic temperature data is denoted as the ambient temperature sequence, and the corresponding steady-state time data is denoted as the ambient temperature time sequence. The ambient current sequence is... The ambient temperature sequence is The ambient temperature time sequence ;

[0023] If the steady-state current data The c-th current value satisfies Then, this steady-state current data is divided into the operating current sequence. The corresponding steady-state fiber temperature data Recorded as the operating temperature value and included in the temperature series. In this context, the corresponding steady-state time data is recorded as the operating temperature time. ;

[0024] The operating current sequence The steady-state current data, i.e., the operating current, of the j-th data is: The corresponding steady-state time data is ;

[0025] The ambient temperature time sequence In the process, a reverse search is performed along the time axis to find the steady-state time data. The closest ambient temperature moment Then the ambient temperature at any time The corresponding steady-state fiber temperature data denoted as operating current The corresponding ambient temperature value;

[0026] The resulting operating current sequence is The operating fiber temperature sequence is Operating temperature time sequence .

[0027] By adopting the above technical solution, the data in the steady-state dataset are sequentially filtered and divided into environmental current sequence and operating current sequence, which is beneficial for subsequent calculations.

[0028] Optionally, the method further includes the following steps:

[0029] The thermal resistance of the submarine cable structure includes the regular annular thermal resistance of the insulation layer and the outer sheath, and the thermal resistance of the irregular filling layer divided into two parts by the optical fiber.

[0030] The regular annular thermal resistance is calculated based on the shape factor method;

[0031] The thermal resistance of the irregular filling layer is obtained by partitioning and modeling based on the finite element simulation method to obtain the thermal resistance of the two parts inside and outside the optical fiber: first, the heat flow and temperature difference are calculated, the thermal resistance distribution value of the filling layer inside and outside the optical fiber is calculated, and finally the thermal resistance of the conductor to optical fiber segment model and the thermal resistance of the optical fiber to environment segment model are output.

[0032] The quantitative relationships between conductor temperature and fiber temperature, and between fiber temperature and ambient temperature, are obtained by simultaneously calculating the submarine cable loss, the thermal resistance of the conductor-to-fiber segment model, and the thermal resistance of the fiber-to-ambient segment model. These relationships are as follows:

[0033] ;

[0034] ;

[0035] in For conductor temperature, For fiber optic temperature, For ambient temperature, The thermal resistance of the conductor-to-fiber segment model. The thermal resistance of the fiber-to-environment segment model. The AC resistance per unit length of the conductor. For submarine cable loss, The steady-state current data.

[0036] By adopting the above technical solution, a three-layer steady-state thermal circuit model of the conductor-optical fiber-environment of the three-core submarine cable is constructed, and the relationship between the optical fiber temperature, conductor temperature and ambient temperature is established.

[0037] Optionally, the thermal resistance estimation strategy includes the following steps:

[0038] The single operating current sequence is further partitioned to obtain multiple operating intervals;

[0039] The thermal resistance prediction in the multilayer thermal resistance network model is performed using a state transition model, that is, the thermal resistance of the k-th operating interval. Dynamic change modeling is as follows:

[0040] ;

[0041] Based on the multilayer thermal resistance network model, the observation relationship between thermal resistance and the steady-state fiber temperature data is established as follows:

[0042] ;in, For process noise, To observe noise;

[0043] Predicting thermal resistance based on Kalman filtering and error covariance Where Q is the process noise covariance;

[0044] Calculate the gain using the recursive least squares method And correct the thermal resistance estimate. ,in:

[0045] Where U is the observation noise covariance;

[0046] ;

[0047] Introducing the forgetting factor Update covariance as ,in, The process noise covariance and observation noise covariance are dynamically adjusted based on the residuals.

[0048] By adopting the above technical solution, the accuracy and stability of the estimation are improved. Q is set to 0.01, and for a sensing device with a temperature measurement accuracy of ±5℃, U is set to 0.25.

[0049] Optionally, the method further includes the following steps:

[0050] Based on the multilayer thermal resistance network model, the steady-state fiber temperature data, steady-state current data, and ambient temperature data within the operating range are input to calculate the estimated conductor temperature.

[0051] When the estimated conductor temperature exceeds a preset temperature safety threshold, a mapping relationship between fiber temperature rise and current under the current operating condition is constructed. Based on the multilayer thermal resistance network model, the estimated conductor temperature, and the ambient temperature data, the maximum withstand current and the corresponding fiber temperature are calculated in reverse. This refers to the current capacity limit;

[0052] The maximum withstand current is used as the upper limit of the dynamic current carrying capacity under the current operating conditions, and the fiber temperature... This represents the upper limit of the corresponding optical fiber temperature.

[0053] By adopting the above technical solution, the fiber optic temperature rise prediction function is available, which helps the submarine cable to always operate within the safe and permissible range.

[0054] Optionally, the method further includes the following steps:

[0055] The formula for calculating the fiber optic temperature threshold is as follows: ℃;

[0056] Calculate the fiber temperature threshold When calculating the difference between the steady-state fiber temperature data and the steady-state fiber temperature data, if the difference is less than a preset reference difference, the average value of the steady-state fiber temperature data corresponding to the steady-state range is calculated. If the average value is greater than the preset reference average value, an over-temperature warning is issued, and the load current of the submarine cable is adjusted according to the positive correlation of the average value.

[0057] By adopting the above technical solution and introducing a temperature fault-tolerant mechanism, the system's operational safety is enhanced. This mechanism combines fiber optic temperature rise prediction and fault-tolerant control to ensure that the three-core submarine cable always operates within the safe and permissible range.

[0058] Secondly, this application provides a dynamic detection system for submarine cable current carrying capacity based on optical fiber sensing, employing the following technical solution:

[0059] A dynamic detection system for submarine cable current carrying capacity based on fiber optic sensing includes a processor, wherein the processor executes the steps of the dynamic detection method for submarine cable current carrying capacity based on fiber optic sensing as described in any one of the preceding claims.

[0060] Thirdly, this application provides a medium, which adopts the following technical solution:

[0061] A medium storing a program, which, when executed by a processor, implements the steps of the dynamic detection method for submarine cable current carrying capacity based on fiber optic sensing as described above.

[0062] In summary, this application offers at least one of the following beneficial technical effects: By comprehensively applying fiber optic temperature monitoring and real-time data analysis methods, and addressing practical engineering problems such as the difficulty in directly measuring the conductor temperature of three-core submarine cables and the variation of thermal environment parameters over time and under operating conditions, a method for conductor temperature estimation and dynamic optimization of current carrying capacity by integrating fiber optic temperature measurement data and a thermal circuit model is proposed. Compared to existing thermal analysis methods with fixed parameters and static limits, this application employs a thermal resistance update mechanism that integrates Kalman filtering and least squares, enabling real-time correction of thermal resistance parameters to adapt to changes in environment and operating conditions. In terms of control strategy, it combines mapping inference and fault-tolerant mechanisms, inferring the conductor temperature from the fiber optic temperature and adjusting the cable current carrying capacity limit accordingly, thereby achieving dynamic control of overheating risks and solving the problem of monitoring the current carrying capacity operation of three-core submarine cables. Attached Figure Description

[0063] Figure 1 This is a flowchart illustrating a dynamic detection method for submarine cable current carrying capacity based on fiber optic sensing.

[0064] Figure 2 This is a schematic diagram of the cross-sectional structure of a three-core submarine cable.

[0065] Figure 3 This is a schematic diagram of the equivalent thermal resistance model. Detailed Implementation

[0066] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0067] 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.

[0068] This application discloses a dynamic detection method for submarine cable current carrying capacity based on fiber optic sensing, referring to... Figure 1 It includes the following steps:

[0069] Current, fiber temperature, and time data of the submarine cable are acquired using distributed fiber optic sensing devices. The submarine cable is a three-core cable, model HYJQF41-F3×300mm. 2 Its structure is as follows Figure 2 As shown, the structure includes a copper conductor, conductor shield, insulation layer, insulation shield, semi-conductive resistive water tape, lead sheath, PE sheath, filling layer, wrapping tape, inner padding layer, galvanized steel wire, asphalt, outer sheath, and optical fiber unit structure. The circle containing the two optical cables divides the filling layer into inner and outer optical fiber parts. Distributed optical fiber sensing equipment can utilize BOTDA equipment from OZ Corporation of Canada, acquiring data based on the stimulated Brillouin scattering effect and optical time-domain reflectometry. Current data, optical fiber temperature data, and time data are interconnected. Based on the time data, a sliding filter algorithm is used to determine steady-state conditions for real-time current data and optical fiber temperature data, obtaining steady-state current data, steady-state optical fiber temperature data, and steady-state time data. A steady-state dataset is then established based on these data.

[0070] Current data is The fiber optic temperature data corresponding to the current data is Time data is The corresponding time data in the existing data is The current data is The fiber optic temperature data is .

[0071] The sliding filter algorithm is a sliding time window method. It obtains a steady-state dataset by judging the steady-state conditions of current data and fiber optic temperature data. The steady-state dataset contains steady-state current data. The corresponding steady-state fiber temperature data is Steady-state time data are .

[0072] The sliding filter algorithm includes: defining a time window of duration A, setting the sliding step size of the time window to a minutes, and implementing current data... Fiber optic temperature data and time data Real-time updates. For example, if A is 4 hours and a is 20 minutes, then a time window of length 4 hours is defined, and the sliding step of the time window is set to 20 minutes.

[0073] For the data in the current time window, the time range for obtaining the window length is [ , ] Calculate the maximum value of the current data within the temporary interval. and minimum value Set the current change threshold to If the following conditions are met: Then mark the temporary interval as the steady-state interval and update the starting point of the next time window to ; .

[0074] Calculate the average current in the b-th steady-state interval. As steady-state current data Take the end point of the steady-state interval, i.e. Fiber optic temperature data at any given time As for submarine cables in steady-state current data Steady-state fiber temperature data under action. A steady-state dataset for a three-core submarine cable is established, including current data, fiber temperature data, and time data that satisfy the steady-state judgment criteria.

[0075] The steady-state dataset is divided into an ambient current sequence and an operating current sequence according to a first partitioning algorithm. The first partitioning algorithm divides the data based on a current threshold, where the steady-state current data corresponding to the operating current sequence is greater than that corresponding to the ambient current sequence. Steady-state fiber optic temperature data is extracted from the ambient current sequence to form an ambient temperature sequence. Within the ambient temperature sequence, a time-reverse search is performed to match the most recent steady-state current data of the operating current sequence, which is then used as the ambient temperature data corresponding to the operating current sequence.

[0076] Specifically, the steady-state current data corresponding to the steady-state fiber temperature data are divided into ambient current sequences according to a set current threshold. With operating current sequence Define the ambient current threshold as... =100A, if steady-state current data The value of the c-th steady-state current data satisfies This steady-state current data is then assigned to the ambient current sequence, the corresponding steady-state fiber optic temperature data is denoted as the ambient temperature sequence, and the corresponding steady-state time data is denoted as the ambient temperature time sequence. The ambient current sequence is... The ambient temperature sequence is Ambient temperature time series .

[0077] If steady-state current data The c-th current value satisfies Then this steady-state current data is divided into the operating current sequence. The corresponding steady-state fiber temperature data Recorded as the operating temperature value and included in the temperature series. In this context, the corresponding steady-state time data is recorded as the operating temperature time. Operating current sequence The steady-state current data, i.e., the operating current, of the j-th data is: The corresponding steady-state time data is .

[0078] Time sequence of ambient temperature In the middle, reverse search along the time axis and steady-state time data closest ambient temperature moment So, the ambient temperature at all times Corresponding steady-state fiber temperature data denoted as operating current The corresponding ambient temperature value.

[0079] The resulting operating current sequence is The operating fiber temperature sequence is Operating temperature time sequence The data in the steady-state dataset are sequentially filtered and divided into ambient current sequences and operating current sequences to facilitate subsequent calculations.

[0080] like Figure 3 As shown, the structural material data and shape characteristic data of the submarine cable are obtained. Based on the structural material data and shape characteristic data, the equivalent thermal resistance algorithm and the finite element simulation algorithm are combined to calculate the thermal resistance of the submarine cable structure. Taking the optical fiber as the dividing point, a multi-layer thermal resistance network model including the conductor-to-optical fiber segment model and the optical fiber-to-environment segment model is established.

[0081] The thermal resistance of a submarine cable structure includes the regular annular thermal resistance of the insulation layer and the outer sheath, and the thermal resistance of the irregular filling layer divided into two parts by the optical fiber. The regular annular thermal resistance is calculated based on the form factor method, which is based on the principle of equivalent thermal resistance.

[0082] The thermal resistance of the irregular filling layer is obtained by partitioning the model based on the finite element simulation method to obtain the thermal resistance of the two parts inside and outside the optical fiber: first, the heat flow and temperature difference are calculated, the thermal resistance distribution values ​​of the filling layer inside and outside the optical fiber are calculated, and finally the thermal resistance of the conductor to the optical fiber segment model and the thermal resistance of the optical fiber to the environment segment model are output.

[0083] The quantitative relationships between conductor temperature and fiber temperature, and between fiber temperature and ambient temperature, are obtained by simultaneously calculating the thermal resistance of the submarine cable loss and the conductor-to-fiber segment model, and the fiber temperature and ambient temperature, respectively:

[0084] ; ;

[0085] in For conductor temperature, For fiber optic temperature, For ambient temperature, The thermal resistance of the conductor-to-fiber segment model. The thermal resistance of the fiber-to-environment segment model. The AC resistance per unit length of the conductor. For submarine cable loss, Steady-state current data. For HYJQF41-F3×300mm specification. 2 Three-core submarine cable The thermal resistance of the filling layers inside and outside the optical fiber is 0.12. A three-layer steady-state thermal path model of the conductor-optical fiber-environment of a three-core submarine cable is constructed to establish the relationship between the fiber temperature, conductor temperature, and ambient temperature.

[0086] Considering that the environmental thermal resistance will fluctuate dynamically with changes in external environmental conditions, a thermal resistance estimation strategy is generated based on the Kalman filter algorithm and the recursive least squares method. The fiber-to-environment segment model is dynamically updated in real time based on the environmental current sequence and the operating current sequence.

[0087] The thermal resistance estimation strategy includes the following steps:

[0088] A single operating current sequence is further divided into multiple operating intervals.

[0089] The thermal resistance prediction in the multilayer thermal resistance network model is performed using a state transition model, that is, the thermal resistance of the k-th operating interval. Dynamic change modeling is as follows: .

[0090] Based on a multilayer thermal resistance network model, the observational relationship between thermal resistance and steady-state fiber temperature data is established as follows: ;in, For process noise, To observe noise.

[0091] Predicting thermal resistance based on Kalman filtering and error covariance , where Q is the process noise covariance.

[0092] Calculate the gain using the recursive least squares method And correct the thermal resistance estimate. ,in:

[0093] , where U is the observation noise covariance.

[0094] Introducing a forgetting factor Update covariance as ,in, Calculate the observation residuals at the current time: The process noise covariance and observation noise covariance are dynamically adjusted based on the residuals. If the residuals continuously deviate from the zero mean, the state noise covariance is increased to enhance tracking capability; if the residuals are stable, the observation noise covariance is decreased to improve accuracy. This improves the accuracy and stability of the estimation. Additionally, Q is set to 0.01, and for a sensing device with a temperature measurement accuracy of ±5℃, U is set to 0.25. Regarding process noise... and observation noise Instantaneous values ​​are uncontrollable environmental factors, while Q and U are controllable algorithm parameters.

[0095] In the recursive process of Kalman filtering, the symbol "|" represents "an estimate based on information at a certain moment". To predict the prior error covariance at time k based on information from time k-1. To fuse the posterior error covariance after observations at time k. Similarly, This is a priori estimation, predicting the current thermal resistance based solely on historical conditions; For posterior estimation, the thermal resistance estimate is optimized by integrating current observation data.

[0096] Based on real-time acquired fiber optic temperature data, the current conductor temperature is calculated using a conductor-to-fiber segment model, and it is determined whether the current conductor temperature exceeds a set temperature safety threshold. If the current conductor temperature exceeds the temperature safety threshold, the maximum allowable current value when the conductor temperature reaches the temperature safety threshold is calculated based on the fiber-to-environment segment model, combined with ambient temperature data and the matched cable loss. The fiber optic temperature threshold corresponding to the temperature safety threshold is then determined and used as a reference control value for the current carrying capacity of the submarine cable.

[0097] Based on a multilayer thermal resistance network model, steady-state fiber temperature data, steady-state current data, and ambient temperature data within the operating range are input to calculate the estimated conductor temperature. When the estimated conductor temperature exceeds a preset temperature safety threshold, a mapping relationship between fiber temperature rise and current under the current operating condition is constructed. Based on the multilayer thermal resistance network model, the estimated conductor temperature, and ambient temperature data, the maximum withstand current and the corresponding fiber temperature are calculated in reverse. This refers to the current current carrying capacity boundary. The maximum tolerable current is used as the upper limit of the dynamic current carrying capacity under the current operating conditions, along with the fiber temperature. This represents the upper limit of the corresponding optical fiber temperature.

[0098] The formula for calculating the fiber optic temperature threshold is as follows: ℃, calculate the fiber optic temperature threshold When calculating the difference between the steady-state fiber temperature data and the average steady-state fiber temperature data, if the difference is less than a preset reference difference, the average steady-state fiber temperature data corresponding to the steady-state range is calculated. If the average value is greater than the preset reference average value, an over-temperature warning is issued, and the load current of the submarine cable is adjusted according to the positive correlation of the average value. The method introduces a temperature fault-tolerance mechanism to enhance system operational safety. This mechanism combines fiber temperature rise prediction and fault-tolerance control to ensure that the three-core submarine cable always operates within a safe and permissible range. By comprehensively applying fiber temperature monitoring and real-time data analysis methods, and addressing practical engineering problems such as the difficulty in directly measuring the conductor temperature of three-core submarine cables and the variation of thermal environment parameters with time and operating conditions, a method for conductor temperature estimation and dynamic optimization of current carrying capacity that integrates fiber temperature measurement data and a thermal circuit model is proposed. Without relying on conductor temperature, the method considers the impact of environmental changes to achieve dynamic estimation of conductor temperature and intelligent optimization of current carrying capacity, thereby improving the operational safety and thermal stability of the three-core submarine cable and meeting the intelligent requirements of future offshore wind power scenarios.

[0099] In summary, this method first acquires current and temperature data of submarine cables during actual operation based on distributed optical fiber sensing technology, and constructs a steady-state current and temperature dataset using a sliding time window and steady-state criteria. Then, it identifies ambient temperature through current segmentation and constructs a three-layer steady-state thermal path model from conductor to fiber to environment. The thermal resistance of each layer in the submarine cable structure is calculated using finite element simulation and the shape factor method, and mathematical relationships are established between conductor temperature and fiber temperature, and between fiber temperature and ambient temperature. Furthermore, a joint algorithm combining Kalman filtering and recursive least squares is introduced to dynamically estimate the thermal resistance from fiber to environment, thereby improving the model's adaptability to environmental changes. Finally, based on the dynamically updated thermal resistance value, the conductor temperature estimation results are corrected in real time, and the maximum allowable current and fiber temperature warning threshold are calculated to adjust the operating current-carrying capacity of the submarine cable to ensure its operation within a safe temperature range. This application can be widely used in power systems with high reliability requirements, such as offshore wind power and submarine cable monitoring.

[0100] This application also discloses a dynamic detection system for submarine cable current carrying capacity based on optical fiber sensing, including a processor, which executes the steps of the dynamic detection method for submarine cable current carrying capacity based on optical fiber sensing as described above.

[0101] This application also discloses a medium storing a program, which, when executed by a processor, implements the steps of the dynamic detection method for submarine cable current carrying capacity based on fiber optic sensing, as described above.

[0102] 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 dynamic detection method for submarine cable current carrying capacity based on fiber optic sensing, characterized in that, Includes the following steps: Current data, fiber temperature data, and time data of submarine cables are acquired using distributed fiber optic sensing devices. The current data, fiber temperature data, and time data are correlated and correspond to each other. Based on the time data, the real-time current data and fiber temperature data are judged for steady-state conditions using a sliding filter algorithm to obtain steady-state current data, steady-state fiber temperature data, and steady-state time data. A steady-state dataset is established based on the steady-state current data, steady-state fiber temperature data, and steady-state time data. The steady-state dataset is divided into an ambient current sequence and an operating current sequence according to a first partitioning algorithm. The steady-state current data corresponding to the operating current sequence is greater than the steady-state current data corresponding to the ambient current sequence. The steady-state fiber temperature data is extracted from the ambient current sequence to form an ambient temperature sequence. The steady-state current data with the closest time to the operating current sequence is matched in the ambient temperature sequence through time backward search and used as the ambient temperature data corresponding to the operating current sequence. Obtain structural material data and shape characteristic data of submarine cable. Calculate the thermal resistance of submarine cable structure based on the structural material data and shape characteristic data, combined with the equivalent thermal resistance algorithm and finite element simulation algorithm. Establish a multi-layer thermal resistance network model, including conductor-to-fiber segment model and fiber-to-environment segment model, with the optical fiber as the dividing point. A thermal resistance estimation strategy is generated based on the Kalman filter algorithm and the recursive least squares method. The fiber-to-environment segment model is dynamically updated in real time using the thermal resistance estimation strategy based on the ambient current sequence and the operating current sequence. Based on the real-time acquired fiber optic temperature data, the current conductor temperature is calculated using the conductor-to-fiber segment model, and it is determined whether the current conductor temperature is greater than the set temperature safety threshold. If the current conductor temperature is greater than the temperature safety threshold, then based on the fiber-to-environment segment model, combined with the ambient temperature data and the matched cable loss, the maximum allowable current value when the conductor temperature reaches the temperature safety threshold is calculated, and the fiber temperature threshold corresponding to the temperature safety threshold is determined. The fiber temperature threshold is used as a reference control value for the current carrying capacity of the submarine cable.

2. The dynamic detection method for submarine cable current carrying capacity based on fiber optic sensing according to claim 1, characterized in that, The method also includes the following steps: The current data is The fiber temperature data corresponding to the current data is The time data is Among the existing data, the time data corresponding to the aforementioned data is: The current data is The fiber optic temperature data is ; The sliding filtering algorithm is a sliding time window method. It performs steady-state condition judgment on the current data and the fiber optic temperature data to obtain the steady-state dataset. The steady-state current data in the steady-state dataset is... The corresponding steady-state fiber temperature data is The steady-state time data is ; The sliding filter algorithm includes: defining a time window of duration A, with a sliding step size of a minutes, to implement the current data. The fiber optic temperature data and the time data Real-time updates; for the data in the current time window, the time range for obtaining the window length is [ , ] A temporary interval is defined, and the maximum value of the current data within the temporary interval is calculated. and minimum value Set the current change threshold to If the following conditions are met: Then mark the temporary interval as the steady-state interval, and update the starting point of the next time window to [value]. ; Calculate the average current within the b-th steady-state interval. As the steady-state current data Take the end point of the steady-state interval as... The fiber optic temperature data corresponding to the time. As the submarine cable in the steady-state current data The steady-state fiber temperature data under the action.

3. The dynamic detection method for submarine cable current carrying capacity based on fiber optic sensing according to claim 1, characterized in that, The method also includes the following steps: The first partitioning algorithm is based on a current threshold. The steady-state current data corresponding to the steady-state fiber temperature data are divided into an ambient current sequence and an operating current sequence according to a set current threshold. Define the ambient current threshold as If the steady-state current data The value of the c-th steady-state current data satisfies The steady-state current data is then assigned to the ambient current sequence, the corresponding steady-state fiber optic temperature data is denoted as the ambient temperature sequence, and the corresponding steady-state time data is denoted as the ambient temperature time sequence. The ambient current sequence is... The ambient temperature sequence is The ambient temperature time sequence ; If the steady-state current data The c-th current value satisfies Then, this steady-state current data is divided into the operating current sequence. The corresponding steady-state fiber temperature data Recorded as the operating temperature value and included in the temperature series. In this context, the corresponding steady-state time data is recorded as the operating temperature time. ; The operating current sequence The steady-state current data, i.e., the operating current, of the j-th data is: The corresponding steady-state time data is ; The ambient temperature time sequence In the process, a reverse search is performed along the time axis to find the steady-state time data. The closest ambient temperature moment Then the ambient temperature at any time The corresponding steady-state fiber temperature data denoted as operating current The corresponding ambient temperature value; The resulting operating current sequence is The operating fiber temperature sequence is Operating temperature time sequence .

4. The dynamic detection method for submarine cable current carrying capacity based on fiber optic sensing according to claim 1, characterized in that, The method also includes the following steps: The thermal resistance of the submarine cable structure includes the regular annular thermal resistance of the insulation layer and the outer sheath, and the thermal resistance of the irregular filling layer divided into two parts by the optical fiber. The regular annular thermal resistance is calculated based on the shape factor method; The thermal resistance of the irregular filling layer is obtained by partitioning and modeling based on the finite element simulation method to obtain the thermal resistance of the two parts inside and outside the optical fiber: first, the heat flow and temperature difference are calculated, the thermal resistance distribution value of the filling layer inside and outside the optical fiber is calculated, and finally the thermal resistance of the conductor to optical fiber segment model and the thermal resistance of the optical fiber to environment segment model are output. The quantitative relationships between conductor temperature and fiber temperature, and between fiber temperature and ambient temperature, are obtained by simultaneously calculating the submarine cable loss, the thermal resistance of the conductor-to-fiber segment model, and the thermal resistance of the fiber-to-ambient segment model. These relationships are as follows: ； ； in For conductor temperature, For fiber optic temperature, For ambient temperature, The thermal resistance of the conductor-to-fiber segment model. The thermal resistance of the fiber-to-environment segment model. The AC resistance per unit length of the conductor. For submarine cable loss, The steady-state current data.

5. The dynamic detection method for submarine cable current carrying capacity based on fiber optic sensing according to claim 4, characterized in that, The thermal resistance estimation strategy includes the following steps: The single operating current sequence is further partitioned to obtain multiple operating intervals; The thermal resistance prediction in the multilayer thermal resistance network model is performed using a state transition model, that is, the thermal resistance of the k-th operating interval. Dynamic change modeling is as follows: ； Based on the multilayer thermal resistance network model, the observation relationship between thermal resistance and the steady-state fiber temperature data is established as follows: ;in, For process noise, To observe noise; Predicting thermal resistance based on Kalman filtering and error covariance Where Q is the process noise covariance; Calculate the gain using the recursive least squares method And correct the thermal resistance estimate. ,in: Where U is the observation noise covariance; ； Introducing the forgetting factor Update covariance as ,in, The process noise covariance and observation noise covariance are dynamically adjusted based on the residuals.

6. The dynamic detection method for submarine cable current carrying capacity based on fiber optic sensing according to claim 5, characterized in that, The method also includes the following steps: Based on the multilayer thermal resistance network model, the steady-state fiber temperature data, steady-state current data, and ambient temperature data within the operating range are input to calculate the estimated conductor temperature. When the estimated conductor temperature exceeds the preset temperature safety threshold, a mapping relationship between fiber temperature rise and current under the current operating condition is constructed. Based on the multilayer thermal resistance network model, the estimated conductor temperature, and the ambient temperature data, the maximum withstand current and the corresponding fiber temperature are calculated in reverse. This refers to the current capacity limit; The maximum withstand current is used as the upper limit of the dynamic current carrying capacity under the current operating conditions, and the fiber temperature... This represents the upper limit of the corresponding optical fiber temperature.

7. The dynamic detection method for submarine cable current carrying capacity based on fiber optic sensing according to claim 6, characterized in that, The method also includes the following steps: The formula for calculating the fiber optic temperature threshold is as follows: ℃; Calculate the fiber temperature threshold When calculating the difference between the steady-state fiber temperature data and the steady-state fiber temperature data, if the difference is less than a preset reference difference, the average value of the steady-state fiber temperature data corresponding to the steady-state range is calculated. If the average value is greater than the preset reference average value, an over-temperature warning is issued, and the load current of the submarine cable is adjusted according to the positive correlation of the average value.

8. A dynamic detection system for submarine cable current carrying capacity based on fiber optic sensing, characterized in that, The device includes a processor that performs the steps of the dynamic detection method for submarine cable current carrying capacity based on fiber optic sensing as described in any one of claims 1-7.

9. A medium, characterized in that, The medium stores a program that, when executed by a processor, implements the steps of the dynamic detection method for submarine cable current carrying capacity based on fiber optic sensing as described in any one of claims 1-7.

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