An offshore booster station structure health monitoring method and system based on an internet of things

Abstract

The application discloses a kind of offshore booster station structural health monitoring method and system based on Internet of Things, and monitoring method includes the following steps: acceleration sensor is arranged in offshore booster station;Collect initial acceleration signal, obtain initial natural frequency and adopt genetic algorithm optimization;Collect acceleration signal under the action of seismic load, obtain natural frequency under the action of seismic load and adopt genetic algorithm optimization;Damage index is calculated, and compared with set threshold, alarm when higher than set threshold.The monitoring method and system disclosed in the application can realize real-time early warning of dangerous state of structure under extreme load such as earthquake, realize accurate prediction of long-term life of structure, and greatly reduce the possibility of collapse and other fatal damage of offshore booster station structure under the action of earthquake.

Description

Technical Field

[0001] This invention relates to the field of signal processing for system identification and damage identification, and particularly to a method and system for monitoring the structural health of offshore substations based on the Internet of Things. Background Technology

[0002] With the rapid development of the global economy, issues such as energy, environment, and climate change are becoming increasingly prominent, and traditional fossil fuels can no longer meet the requirements of sustainable social development. Wind energy resources, as a clean and renewable energy source, have always attracted much attention from countries around the world.

[0003] Offshore substations connect dozens of wind turbines, integrating collection, voltage boosting, and power transmission functions. They play a crucial role in efficiently transmitting the electricity generated by the wind turbines to land, making them an indispensable core structure in offshore wind farms. As offshore wind farms gradually expand into deeper waters, the size of wind turbine structures is becoming increasingly large, the service environment is becoming more severe, and the sources of loads are becoming more complex. Compared with onshore structures, offshore substations not only need to withstand the vibration of internal equipment, but also the combined effects of the constant external random loads such as sea winds and waves that change over time and space, and may sometimes be subjected to seismic loads.

[0004] If the status of offshore substation structures cannot be monitored in real time, it will not only affect the normal operation of offshore wind power structures, reduce the quality of output power, exacerbate the fatigue of structural components, and shorten the service life of structures, but also cause vibration problems due to the continuous wind and wave forces acting on the structures. In addition, the effects of disasters such as typhoons and earthquakes may lead to unpredictable safety accidents, resulting in significant economic losses and adverse social impacts.

[0005] Health monitoring methods for onshore wind power structures differ significantly from those for offshore structures in terms of site influences, hydrodynamic effects, and damping characteristics, making them unsuitable for monitoring the health of offshore substation structures. Using monitoring technologies from offshore oil platforms is also problematic, as their structural characteristics are incompatible with the top-heavy nature of offshore substations, and core electrical equipment is not within their monitoring scope, limiting the placement of measuring points and monitoring indicators. Furthermore, existing monitoring methods exhibit significant identification errors after modal parameter identification of offshore substation structures, which substantially impacts subsequent damage indicator calculations, making it impossible to accurately determine the structural damage status. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method and system for monitoring the structural health of offshore substations based on the Internet of Things, aiming to achieve real-time early warning of structural hazards under extreme loads such as earthquakes, and to accurately predict the long-term lifespan of the structure.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows:

[0008] A method for monitoring the structural health of offshore substations based on the Internet of Things includes the following steps:

[0009] Step 1: After the offshore substation is built, accelerometers are installed on its upper platform frame and pile foundation.

[0010] Step 2: In the initial stage of the construction and commissioning of the offshore substation, the accelerometer collects acceleration signals, and after preprocessing, the initial natural frequency is obtained using the random subspace method. Then, the initial natural frequency is optimized using a genetic algorithm.

[0011] Step 3: Under seismic load, the acceleration sensor collects the acceleration signal under seismic load, and after preprocessing, the natural frequency under seismic load is obtained by the random subspace method. Then, the natural frequency under seismic load is optimized by the genetic algorithm.

[0012] Step 4: Calculate the damage index based on the optimized initial natural frequency and the natural frequency under seismic load, and compare the calculated damage index with the set threshold. If the damage index is lower than the set threshold, no action is taken; if the damage index is higher than the set threshold, an alarm is triggered.

[0013] In the above scheme, the method for arranging the acceleration sensor in step one is as follows:

[0014] (1) Based on the design drawings of the offshore substation, a finite element model of the offshore substation structure was established using ABAQUS software.

[0015] (2) Perform dynamic analysis on the offshore substation structure in ABAQUS meta software to obtain the structural vibration modes;

[0016] (3) Determine the placement of the acceleration sensor based on the structural vibration mode, select the location with large amplitude, and avoid the location with small amplitude.

[0017] In the above scheme, there are no fewer than four acceleration sensor points on each layer of the upper platform frame of the offshore booster station, and the acceleration response in the X, Y and Z directions is collected at each point.

[0018] In the above scheme, the process of obtaining the natural frequency using the random subspace method in steps two and three is as follows:

[0019] (1) Set the model order to 12 and use the random subspace method to identify the natural frequency, damping ratio and mode shape based on the acceleration signal;

[0020] (2) Increase the model order by 2, and re-identify the natural frequency, damping ratio and mode shape using the random subspace method; use the discriminant formula to make a judgment and delete spurious modes;

[0021] (3) Repeat step (2) until the model order is 100 to obtain the final natural frequency, damping ratio and mode shape of the structure.

[0022] In a further technical solution, the discrimination formula is as follows:

[0023]

[0024]

[0025]

[0026] Where ω, ξ and φ are the natural frequency, damping ratio and mode shape of the structure, respectively. The subscript "order" indicates the parameter identified at the current model order, the subscript "order+2" indicates the parameter identified at the next adjacent next model order, and the superscript "*" indicates the complex conjugate transpose.

[0027] Judgment method: When the recognition parameters of two adjacent model orders meet the above three formulas, it is a true mode; when the recognition parameters of two adjacent model orders do not meet any of the three formulas, it is a false mode and is deleted.

[0028] In the above scheme, the method for optimizing the natural frequency using a genetic algorithm in steps two and three is as follows:

[0029] Within the genetic algorithm, multiple sets of natural frequencies are randomly generated within a given interval. Each set forms a state-space equation. The structural displacement and load are calculated using a Kalman filter. The objective function is calculated, and the algorithm is filtered according to the rules of the genetic algorithm to enter the next iteration. When the objective function converges, the genetic algorithm ends. The randomly generated natural frequencies in this iteration step are the optimized natural frequencies.

[0030] The objective function is calculated using the following formula:

[0031]

[0032] Among them, f RMS Let be the objective function. To measure structural acceleration, For structural loads, Calculate the structural load for the Kalman filter, where z(t) is the structural displacement. Calculate the structural displacement for the Kalman filter, where t is the sampling time and l is the sampling length.

[0033] In the above scheme, step four, the formula for calculating the damage index is as follows:

[0034]

[0035] Where Index is the damage index, the superscript “(1)” represents the first-order natural frequency in each direction, and ω initial Let ω be the initial natural frequency. extreme_load It is the natural frequency under seismic load.

[0036] In the above scheme, the method for selecting the threshold in step four is as follows: A finite element model of the offshore substation is established using ABAQUS software. Nonlinear pushover analysis is performed on the model, and the natural frequency ω of the model in the yield state is obtained when the structure enters this state. yield The threshold is set using the following formula:

[0037]

[0038] Where, value threshold To set the threshold, the superscript "(1)" represents the first-order natural frequency in each direction, ω initial The initial natural frequency.

[0039] An IoT-based offshore substation structural health monitoring system employing the method described above includes a signal acquisition module, a signal processing module, a signal transmission module, a cloud platform system module, and a remote monitoring module.

[0040] The signal acquisition module includes accelerometers and cables installed at various monitoring points on the upper platform frame and pile foundation of the offshore substation. The accelerometers transmit the acquired signals to the signal processing module via the cables.

[0041] The signal processing module is a server installed on the upper platform of the offshore substation, including a signal preprocessing module, a signal processing module, and a structural system identification module. The signal processing module uses the random subspace method to obtain the natural frequency of the offshore substation acceleration, and then uses a genetic algorithm to optimize the natural frequency. The data containing the offshore substation structural parameters and the input peak acceleration data are then transmitted to the cloud platform system module through the submarine optical fiber.

[0042] The cloud platform system module updates the structural parameters of the offshore substation, calculates damage indicators, saves historical data, and sends it to the remote monitoring module.

[0043] The remote monitoring module includes a display module and an early warning module. The display module can show the structural parameter information of the offshore substation, extreme load information such as earthquakes, and the corresponding structural damage index calculation information. The early warning module compares the damage index parameters with the set threshold. If the damage index exceeds the danger value, it will automatically send SMS and emails to the relevant management and technical personnel.

[0044] Through the above technical solution, the present invention provides a method and system for monitoring the structural health of offshore substations based on the Internet of Things, which has the following beneficial effects:

[0045] The health monitoring system for offshore substation structures built using this invention can monitor the structural performance in real time and promptly identify and warn of structural damage under extreme loads such as earthquakes, greatly reducing the possibility of fatal damage such as collapse of offshore substation structures under earthquakes and other loads.

[0046] This invention uses a genetic algorithm to optimize the identified natural frequencies, which can overcome the problem of inaccurate identification of modal parameters caused by the "top-heavy" structure and complex stress of the offshore substation. The optimized natural frequencies are closer to the true frequencies of the structure itself, and can more accurately reflect the current damage status of the structure when calculating structural damage indicators. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0048] Figure 1 This is a schematic diagram of an IoT-based offshore substation structural health monitoring system disclosed in an embodiment of the present invention;

[0049] Figure 2 This is a flowchart of an IoT-based method for monitoring the structural health of an offshore substation, as disclosed in an embodiment of the present invention.

[0050] In the diagram, 1. Offshore booster station; 2. Accelerometer; 3. Cable; 4. Signal processing module; 5. Submarine fiber optic cable; 6. Cloud platform system module; 7. 5G signal; 8. Remote monitoring module. Detailed Implementation

[0051] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0052] This invention provides an IoT-based structural health monitoring system for offshore substations, such as... Figure 1As shown, it consists of a three-layer structure, including a perception layer, a network layer, and an application layer; it includes a signal acquisition module, a signal processing module, a signal transmission module, a cloud platform system module, and a remote monitoring module.

[0053] 1. Signal Acquisition Module

[0054] The signal acquisition module includes accelerometers 2 and cables 3 installed at various monitoring points on the upper platform frame and pile foundation of the offshore substation 1. This module belongs to the sensing layer. Accelerometers 2 are triaxial accelerometers or uniaxial accelerometers installed in three directions respectively, used to measure the vibration response of the offshore substation structure in three directions. The accelerometer range should be no less than ±1g (±9.8m / s²). 2 The resolution should be no less than 0.001g (0.0098m / s). 2 The sampling frequency should be no less than 50Hz. The cable connects the accelerometer and the data acquisition and processing device, providing the necessary power to the accelerometer and transmitting data. The accelerometer transmits the acquired signals to the signal processing module via the cable.

[0055] 2. Signal processing module

[0056] Signal processing module 4 is a server installed on the upper platform of the offshore substation. This module belongs to the perception layer and includes a signal preprocessing module, a signal processing module, and a structural system identification module. The signal processing module uses the random subspace method to obtain the natural frequency of the offshore substation's acceleration, then uses a genetic algorithm to optimize the natural frequency before transmitting the data, containing the offshore substation's structural parameters and the input peak acceleration, to the cloud platform system module via submarine optical fiber. Submarine optical fiber 5 is laid together with cable 3, avoiding additional costs and solving the problem of no 5G signal in offshore wind farms.

[0057] 3. Cloud Platform System Module

[0058] The cloud platform system module 6 updates the structural parameters of the offshore substation, calculates damage indicators, saves historical data, and sends it to the remote monitoring module.

[0059] 4. Remote monitoring module

[0060] The remote monitoring module 8 includes a display module and an early warning module. The display module can show the structural parameter information of the offshore substation, extreme load information such as earthquakes, and the corresponding structural damage index calculation information. The early warning module compares the damage index parameters with the set thresholds. If the thresholds are exceeded, it will automatically send SMS and emails to the relevant management and technical personnel.

[0061] The display and early warning modules, accessible via devices such as mobile phones, tablets, and computers, belong to the application layer. The offshore substation structural health monitoring platform can be accessed through a webpage and can receive early warning SMS messages and emails from the cloud platform system. Data for the offshore substation structural health monitoring platform originates from the cloud platform system module and communicates via 5G signal. Users can add and change their mobile phone numbers and email addresses on the platform to receive early warning information about structural damage. Commands can be sent from the platform to collect vibration information of the current structural condition and analyze the acceleration data.

[0062] Based on the aforementioned health monitoring system, this invention provides an Internet of Things-based method for monitoring the structural health of offshore substations, such as... Figure 2 As shown, it includes the following steps:

[0063] Step 1: After the offshore substation is built, accelerometers are installed on its upper platform frame and pile foundation.

[0064] Step 2: In the initial stage of the construction and commissioning of the offshore substation, the accelerometer collects acceleration signals, and after preprocessing, the initial natural frequency is obtained using the random subspace method. Then, the initial natural frequency is optimized using a genetic algorithm.

[0065] Step 3: Under seismic load, the acceleration sensor collects the acceleration signal under seismic load, and after preprocessing, the natural frequency under seismic load is obtained by the random subspace method. Then, the natural frequency under seismic load is optimized by the genetic algorithm.

[0066] Step 4: Calculate the damage index based on the optimized initial natural frequency and the natural frequency under seismic load, and compare the calculated damage index with the set threshold. If the damage index is lower than the set threshold, no action is taken; if the damage index is higher than the set threshold, an alarm is triggered.

[0067] In step one, the method for setting up the accelerometer is as follows:

[0068] (1) Based on the design drawings of the offshore substation, the finite element model of the offshore substation structure was established using ABAQUS software. The pile foundation and the beam and column components of the upper frame structure of the offshore substation were selected as beam elements. The foundation was fixed. The interaction between pile and soil was not considered. The effect of water was considered. The value was taken according to the design height.

[0069] (2) Perform dynamic analysis on the offshore substation structure in ABAQUS meta software to obtain the structural vibration mode.

[0070] (3) Based on the first two modes of vibration of the structure in the two translational directions, select the position with large amplitude and avoid the position with small amplitude to place the acceleration sensor.

[0071] Preferably, each layer of the upper platform frame of the offshore booster station has no fewer than four accelerometer points, and the acceleration response in the X, Y, and Z directions is collected at each point.

[0072] In steps two and three, the process of obtaining the natural frequency using the random subspace method is as follows:

[0073] (1) Set the model order to 12 and use the random subspace method to identify the natural frequency, damping ratio and mode shape based on the acceleration signal;

[0074] (2) Increase the model order by 2, and re-identify the natural frequency, damping ratio and mode shape using the random subspace method; use the discriminant formula to make a judgment and delete spurious modes;

[0075] (3) Repeat step (2) until the model order is 100 to obtain the final natural frequency, damping ratio and mode shape of the structure.

[0076] Specifically, the discrimination formula is as follows:

[0077]

[0078]

[0079]

[0080] Where ω, ξ and φ are the natural frequency, damping ratio and mode shape of the structure, respectively. The subscript "order" indicates the parameter identified at the current model order, the subscript "order+2" indicates the parameter identified at the next adjacent next model order, and the superscript "*" indicates the complex conjugate transpose.

[0081] Judgment method: When the recognition parameters of two adjacent model orders meet the above three formulas, it is a true mode; when the recognition parameters of two adjacent model orders do not meet any of the three formulas, it is a false mode and is deleted.

[0082] In steps two and three, the method for optimizing the natural frequency using a genetic algorithm is as follows:

[0083] Within the genetic algorithm, multiple sets of natural frequencies are randomly generated within a given interval. Each set forms a state-space equation. The structural displacement and load are calculated using a Kalman filter. The objective function is calculated, and the algorithm is filtered according to the rules of the genetic algorithm to enter the next iteration. When the objective function converges, the genetic algorithm ends. The randomly generated natural frequencies in this iteration step are the optimized natural frequencies.

[0084] Within the genetic algorithm, the natural frequency is used as the independent variable, and the natural frequency is randomly generated within the following interval:

[0085]

[0086] in, The natural frequencies identified using the random subspace method;

[0087] Based on the randomly generated natural frequencies and the identified damping ratios and mode shapes, establish the continuous-time state-space equations under the modal equations for the offshore substation:

[0088]

[0089] y k =H c x k +ν k

[0090] z k =L c x k

[0091] in, Let y be the first derivative of the state vector with respect to time. k The observation vector is the measured structural acceleration. z k p represents the relative displacement of the structure. k Structural loads and ν k These represent process noise and observation noise, respectively; k represents a sampling time.

[0092] State vector x k The modal displacement v(k) and modal velocity constitute:

[0093]

[0094] The subscript represents the modal number, with a value ranging from 1 to n.

[0095] The coefficient matrix A of the continuous-time state-space equation c B c H c L c They are respectively:

[0096]

[0097] B c =[0 -1 … … 0 -1] T

[0098]

[0099]

[0100] Φ represents the structural vibration mode.

[0101]

[0102] Participation factor of the j-th mode shape Let M be the transpose of the j-th mode shape vector, M be the mass matrix of the structure (selected according to the design drawings), r be the unit column vector, and p be the transpose of the mode shape vector. k Structural loads y represents the observable quantity, specifically the measured structural acceleration. and ν k Let Q be the process noise and Q be the observation noise. The process noise covariance matrix and the observation noise covariance matrix are respectively Q... x R and k represent a certain sampling time.

[0103] Transform the continuous-time state-space equations into discrete-time state-space equations:

[0104]

[0105] y k =Hx k +ν k

[0106] in:

[0107]

[0108] Δt is the sampling time;

[0109] After setting the structural state vector, process noise covariance, and measurement noise covariance, based on the measured structural acceleration... Structural loads were obtained using a Kalman filter. and structural displacement z(t);

[0110] The objective function is calculated using the following formula:

[0111]

[0112] Among them, f RMS Let be the objective function. To measure structural acceleration, For structural loads, The structural load is calculated for the Kalman filter, z(t) is the structural displacement, t is the sampling time, and l is the sampling length.

[0113] In step four, the formula for calculating the damage index is as follows:

[0114]

[0115] Where Index is the damage index, the superscript “(1)” represents the first-order natural frequency in each direction, and ω initial Let ω be the initial natural frequency. extreme_load It is the natural frequency under seismic load.

[0116] In step four, the method for selecting the threshold is as follows: A finite element model of the offshore substation is established using ABAQUS software. Nonlinear pushover analysis is performed on the model, and the natural frequency ω of the model in the yield state is obtained when the structure enters this state. yield The threshold is calculated using the following formula:

[0117]

[0118] Where, value threshold To set the threshold, the superscript "(1)" represents the first-order natural frequency in each direction, ω initial The initial natural frequency.

[0119] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring the structural health of offshore substations based on the Internet of Things, characterized in that, Includes the following steps: Step 1: After the offshore substation is built, accelerometers are installed on its upper platform frame and pile foundation. Step 2: In the initial stage of the construction and commissioning of the offshore substation, the accelerometer collects acceleration signals, and after preprocessing, the initial natural frequency is obtained using the random subspace method. Then, the initial natural frequency is optimized using a genetic algorithm. Step 3: Under seismic load, the acceleration sensor collects the acceleration signal under seismic load, and after preprocessing, the natural frequency under seismic load is obtained by the random subspace method. Then, the natural frequency under seismic load is optimized by the genetic algorithm. Step 4: Calculate the damage index based on the optimized initial natural frequency and the natural frequency under seismic load, and compare the calculated damage index with the set threshold. If the damage index is lower than the set threshold, no action is taken; if the damage index is higher than the set threshold, an alarm is triggered.

2. The method for monitoring the structural health of an offshore substation based on the Internet of Things according to claim 1, characterized in that, In step one, the method for setting up the accelerometer is as follows: (1) Based on the design drawings of the offshore substation, a finite element model of the offshore substation structure was established using ABAQUS software. (2) Perform dynamic analysis on the offshore substation structure in ABAQUS meta software to obtain the structural vibration modes; (3) Determine the placement of the acceleration sensor based on the structural vibration mode, select the location with large amplitude, and avoid the location with small amplitude.

3. A method for monitoring the structural health of an offshore substation based on the Internet of Things, as described in claim 1 or 2, characterized in that, Each layer of the upper platform frame of the offshore booster station has no fewer than four accelerometer points, and the acceleration response in the X, Y, and Z directions is collected at each point.

4. The method for monitoring the structural health of an offshore substation based on the Internet of Things according to claim 1, characterized in that, In steps two and three, the process of obtaining the natural frequency using the random subspace method is as follows: (1) Set the model order to 12 and use the random subspace method to identify the natural frequency, damping ratio and mode shape based on the acceleration signal; (2) Increase the model order by 2, and re-identify the natural frequency, damping ratio and mode shape using the random subspace method; use the discriminant formula to make a judgment and delete spurious modes; (3) Repeat step (2) until the model order is 100 to obtain the final natural frequency, damping ratio and mode shape of the structure.

5. A method for monitoring the structural health of an offshore substation based on the Internet of Things, as described in claim 4, is characterized in that... The discriminant formula is as follows: Where ω, ξ and φ are the natural frequency, damping ratio and mode shape of the structure, respectively. The subscript "order" indicates the parameter identified at the current model order, the subscript "order+2" indicates the parameter identified at the next adjacent next model order, and the superscript "*" indicates the complex conjugate transpose. Judgment method: When the recognition parameters of two adjacent model orders meet the above three formulas, it is a true mode; when the recognition parameters of two adjacent model orders do not meet any of the three formulas, it is a false mode and is deleted.

6. The method for monitoring the structural health of an offshore substation based on the Internet of Things according to claim 1, characterized in that, In steps two and three, the method for optimizing the natural frequency using a genetic algorithm is as follows: Within the genetic algorithm, multiple sets of natural frequencies are randomly generated within a given interval. Each set forms a state-space equation. The structural displacement and load are calculated using a Kalman filter. The objective function is calculated, and the algorithm is filtered according to the rules of the genetic algorithm to enter the next iteration. When the objective function converges, the genetic algorithm ends. The randomly generated natural frequencies in this iteration step are the optimized natural frequencies. The objective function is calculated using the following formula: Among them, f RMS Let be the objective function. To measure structural acceleration, For structural loads, Calculate the structural load for the Kalman filter, where z(t) is the structural displacement. Calculate the structural displacement for the Kalman filter, where t is the sampling time and l is the sampling length.

7. A method for monitoring the structural health of an offshore substation based on the Internet of Things according to claim 1, characterized in that, In step four, the formula for calculating the damage index is as follows: Where Index is the damage index, the superscript "(1)" represents the first-order natural frequency in each direction, and ω initial Let ω be the initial natural frequency. extreme_load It is the natural frequency under seismic load.

8. A method for monitoring the structural health of an offshore substation based on the Internet of Things according to claim 1, characterized in that, In step four, the method for selecting the threshold is as follows: A finite element model of the offshore substation is established using ABAQUS software. Nonlinear pushover analysis is performed on the model, and the natural frequency ω of the model in the yield state is obtained when the structure enters this state. yield The threshold is set using the following formula: Where, value threshold To set the threshold, the superscript "(1)" represents the first-order natural frequency in each direction, ω initial The initial natural frequency.

9. A network-based offshore substation structural health monitoring system employing the method described in any one of claims 1-8, characterized in that, It includes a signal acquisition module, a signal processing module, a signal transmission module, a cloud platform system module, and a remote monitoring module; The signal acquisition module includes accelerometers and cables installed at various monitoring points on the upper platform frame and pile foundation of the offshore substation. The accelerometers transmit the acquired signals to the signal processing module via the cables. The signal processing module is a server installed on the upper platform of the offshore substation, including a signal preprocessing module, a signal processing module, and a structural system identification module. The signal processing module uses the random subspace method to obtain the natural frequency of the offshore substation acceleration, and then uses a genetic algorithm to optimize the natural frequency. The data containing the offshore substation structural parameters and the input peak acceleration data are then transmitted to the cloud platform system module through the submarine optical fiber. The cloud platform system module updates the structural parameters of the offshore substation, calculates damage indicators, saves historical data, and sends it to the remote monitoring module. The remote monitoring module includes a display module and an early warning module. The display module can show the structural parameter information of the offshore substation, extreme load information such as earthquakes, and the corresponding structural damage index calculation information. The early warning module compares the damage index parameters with the set threshold. If the damage index exceeds the danger value, it will automatically send SMS and emails to the relevant management and technical personnel.

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