A stability monitoring method and system for offshore wind power piles

Abstract

The present application relates to the technical field of wind power generation, and specifically to a stability monitoring method and system for offshore wind power piles. The method comprises: obtaining the inclination time series data of the wind power pile; performing anomaly analysis on the inclination time series data to obtain the degree of abnormality of the data points in the inclination time series data; and determining the stability of the wind power pile according to the degree of abnormality of the data points. The embodiment of the present application performs an anomaly analysis on the acquired inclination time series data of the wind power pile, obtains the degree of abnormality of the data points in the inclination time series data, and determines the stability of the wind power pile according to the degree of abnormality of the data points. It is possible to achieve in-depth analysis and calculation of sea surface environmental interference, thereby improving the monitoring accuracy and monitoring performance of the stability of the wind power pile.

Description

Technical Field

[0001] The present application belongs to the technical field of wind power generation, and specifically relates to a stability monitoring method and system for offshore wind power piles. Background Art

[0002] Offshore wind power piles are structures used to support offshore wind turbines, usually including pile foundations and the tower body connected to them, providing stable support for wind turbines and ensuring stable operation of wind turbines in the marine environment. The stability of the pile body directly affects the operation of the wind turbine. If the pile body is unstable, it may cause increased vibration and noise of the wind turbine, affecting the normal operation of the wind turbine. Due to the complex and changeable marine environment, including waves, wind speed, ocean currents and other factors that change all the time, these changing factors will affect the stability of the pile body, so it is necessary to monitor the stability of the pile body in real time.

[0003] In order to monitor the stability of offshore wind turbine piles, the inclination angle of the wind turbine piles can be obtained in real time and its stability can be analyzed by installing inclination sensors. However, due to the complex sea environment, wind turbine piles will be affected by waves and sea breezes, and there will also be some bad weather, which will interfere with the inclination monitoring of wind turbine piles, and therefore will cause great interference to the stability analysis of wind turbine piles.

[0004] Therefore, there is an urgent need for a stability monitoring method and system for offshore wind power piles to solve the above problems. Summary of the invention

[0005] In order to solve the above problem, according to a first aspect of an embodiment of the present application, a stability monitoring method for an offshore wind power pile is provided, the method comprising:

[0006] Acquiring the inclination time series data of the wind power pile;

[0007] Performing an abnormality analysis on the inclination time series data to obtain the degree of abnormality of data points in the inclination time series data;

[0008] Determining the stability of the wind power pile according to the abnormality degree of the data point;

[0009] Among them, the abnormality of the i-th data point The calculation formula is:

[0010] Formula 2

[0011] in, represents an exponential function with the natural constant e as the base, is not zero, represents the probability of sea breeze impact, Indicates breakthrough degree, represents the probability of wave impact;

[0012] The calculation formula of breakthrough degree is:

[0013] Formula 1

[0014] in, is any data point in the dip time series data, is the first The breakthrough of data points, is the first The inclination value of the data point, is the first The inclination value of the data point, is the first -1 data point's inclination value, is a hyperbolic function;

[0015] The method for obtaining the probability of sea wave influence is as follows: obtaining the difference between any two adjacent data points in the evaluation data set of the data point; and determining the probability of sea wave influence of the data point according to the difference;

[0016] The method for obtaining the evaluation data set is as follows: obtaining the autocorrelation function of the tilt angle time series data; determining the period of the tilt angle time series data according to the maximum value of the autocorrelation function; determining the periodic data point of any data point in the tilt angle time series data according to the period; and using the maximum value point of the periodic data point as the evaluation data set of any data point in the tilt angle time series data;

[0017] The method for obtaining the probability of sea breeze influence is as follows: obtaining the descending anomaly coefficient of the data point; and determining the probability of sea breeze influence of the data point according to the descending anomaly coefficient;

[0018] The method for obtaining the descending anomaly coefficient is as follows: obtaining a fitting straight line of data in the judgment data set of the data point; obtaining a first number of data points of a first subset, the first subset being a data set consisting of data points in the judgment data set located on a first side of the fitting straight line; obtaining a second number of data points of a second subset, the second subset being a data set consisting of data points in the judgment data set located on a second side of the fitting straight line; determining the descending anomaly coefficient of the data point according to the first number and the second number;

[0019] Wherein, determining the probability of sea breeze influence on the data point according to the descending anomaly coefficient includes: obtaining a first slope, the first slope being the slope of the fitted straight line; obtaining a second slope, the second slope being the slope of the fitted straight line for the data points of the first subset; obtaining a third slope, the third slope being the slope of the fitted straight line for the data points of the second subset; determining the probability of sea breeze influence on the data point according to the descending anomaly coefficient, the first slope, the second slope and the third slope.

[0020] Optionally, obtaining the abnormality degree of data points in the inclination time series data includes:

[0021] Obtaining the breakthrough degree of the data point;

[0022] Acquire a judgment data set of the inclination time series data;

[0023] According to the evaluation data set, obtaining the wave impact probability of the data point;

[0024] According to the evaluation data set, obtaining the sea breeze influence probability of the data point;

[0025] The abnormality degree of the data point is determined according to the breakthrough degree, the wave influence probability and the sea breeze influence probability.

[0026] Optionally, the determining the stability of the wind power pile includes:

[0027] When the abnormality degree of the data point is greater than a first threshold, determining the data point as an abnormal data point;

[0028] Determining the data density of the abnormal data points according to the proportion of the abnormal data points in the inclination time series data;

[0029] The stability of the wind power pile is determined according to the data density of the abnormal data points.

[0030] Optionally, the determining the stability of the wind power pile includes:

[0031] When the data density of the abnormal data points is greater than or equal to a second threshold, determining that the wind power pile is unstable and issuing an alarm;

[0032] When the data density of the abnormal data points is less than the second threshold, it is determined that the wind power pile is stable.

[0033] According to a second aspect of an embodiment of the present application, a stability monitoring system for an offshore wind power pile is provided, the system comprising a monitoring platform, the monitoring platform comprising:

[0034] a memory having a computer program stored thereon;

[0035] A processor is used to execute the computer program in the memory to implement the steps of any one of the methods for monitoring the stability of offshore wind power piles described in the first aspect.

[0036] In summary, the embodiment of the present application provides a stability monitoring method for offshore wind power piles, the method comprising: obtaining the inclination time series data of the wind power pile; performing anomaly analysis on the inclination time series data to obtain the degree of abnormality of the data points in the inclination time series data; and determining the stability of the wind power pile according to the degree of abnormality of the data points. The embodiment of the present application performs anomaly analysis on the acquired inclination time series data of the wind power pile to obtain the degree of abnormality of the data points in the inclination time series data, and determines the stability of the wind power pile according to the degree of abnormality of the data points. It is possible to achieve in-depth analysis and calculation of sea surface environmental interference, thereby improving the monitoring accuracy and monitoring performance of wind power pile stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the implementation scheme of the present application, the drawings required for use in the implementation scheme will be briefly introduced below. It should be understood that the drawings only show certain implementation schemes of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on the drawings without paying creative work.

[0038] Figure 1 The present invention is a flow chart showing a method for monitoring the stability of offshore wind power piles according to an exemplary embodiment.

[0039] Figure 2 The present invention is a flowchart showing a method for obtaining the abnormality degree of a data point in inclination time series data according to an exemplary embodiment.

[0040] Figure 3 The present invention is a flowchart showing a method for obtaining a judgment data set of inclination time series data according to an exemplary embodiment.

[0041] Figure 4 The present invention is a flow chart showing a method for obtaining the probability of ocean wave impact of a data point according to an exemplary embodiment.

[0042] Figure 5 The present invention is a flow chart showing a method for obtaining the probability of sea breeze influence of a data point according to an exemplary embodiment.

[0043] Figure 6 The figure is a schematic diagram showing a unilateral decline of a data point according to an exemplary embodiment.

[0044] Figure 7is a schematic diagram showing an abnormal decrease of a data point according to an exemplary embodiment.

[0045] Figure 8 The invention is a flow chart showing a method for obtaining a decreasing anomaly coefficient of a data point according to an exemplary embodiment.

[0046] Fig. 9 The present invention is a flow chart showing a method for determining the probability of sea breeze influence of a data point according to an exemplary embodiment.

[0047] Fig.10 The figure is a flow chart showing a method for determining the stability of a wind power pile according to an exemplary embodiment.

[0048] Fig.11 The flowchart of another method for determining the stability of a wind power pile is shown according to an exemplary embodiment.

[0049] Fig.12 It is a block diagram of a stability monitoring system for offshore wind power piles according to an exemplary embodiment.

[0050] Fig.13 It is a block diagram of a monitoring platform according to an exemplary embodiment. DETAILED DESCRIPTION

[0051] In order to clearly illustrate the technical features of the present solution, the present application is described in detail below through specific implementation methods and in conjunction with the accompanying drawings.

[0052] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as being limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not intended to limit the scope of protection of the present application.

[0053] It should be understood that the various steps described in the method implementation of the present application can be performed in different orders and / or performed in parallel. In addition, the method implementation may include additional steps and / or omit the steps shown. The scope of the present application is not limited in this respect.

[0054] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0055] It should be noted that the concepts such as "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0056] It should be noted that the modifications of "one" and "multiple" mentioned in this application are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more". In the description of this application, unless otherwise specified, "multiple" means two or more than two, and other quantifiers are similar; "at least one item", "one or more items" or similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one item a can represent any number of a; for another example, one or more items of a, b and c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple; "and / or" is a kind of association relationship that describes the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural.

[0057] Although the operations or steps are described in a specific order in the drawings in the embodiments of the present application, it should not be understood that it is required to perform these operations or steps in the specific order shown or in a serial order, or to perform all the operations or steps shown to obtain the desired results. In the embodiments of the present application, these operations or steps can be performed in series; these operations or steps can also be performed in parallel; or some of these operations or steps can be performed.

[0058] At the same time, it is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) should comply with the requirements of the relevant laws and regulations and relevant provisions. The present application is described below in conjunction with specific embodiments.

[0059] Figure 1 FIG. 1 is a flow chart of a method for monitoring the stability of an offshore wind power pile according to an exemplary embodiment. Figure 1 As shown, an embodiment of the present application provides a stability monitoring method for offshore wind power piles, and the method may include the following steps:

[0060] In step S10, the inclination time series data of the wind power pile is obtained.

[0061] In this step, the inclination time series data of the wind power pile is obtained. For example, a tilt sensor can be installed inside the wind power pile, and the tilt sensor is used to measure the inclination angle and direction of an object or structure, and the inclination data collected by the tilt sensor is obtained and sent to the monitoring platform, so that the inclination time series data of the wind power pile can be obtained.

[0062] In step S20, an abnormality analysis is performed on the tilt angle time series data to obtain the abnormality degree of the data points in the tilt angle time series data.

[0063] In this step, the inclination time series data is analyzed for abnormality to obtain the degree of abnormality of the data point in the inclination time series data. Exemplarily, the breakthrough degree of the data point can be obtained first, and then the evaluation data set of the inclination time series data can be obtained, and then the probability of the wave influence of the data point can be obtained based on the evaluation data set, and then the probability of the sea breeze influence of the data point can be obtained based on the evaluation data set, and finally the degree of abnormality of the data point can be determined based on the breakthrough degree, the probability of the wave influence and the probability of the sea breeze influence.

[0064] In step S30, the stability of the wind power pile is determined according to the abnormality degree of the data point.

[0065] In this step, the stability of the wind farm pile is determined according to the abnormal degree of the data point. For example, when the abnormal degree of the data point is greater than the first threshold, the data point can be determined to be an abnormal data point, and then the data density of the abnormal data point can be determined according to the proportion of the abnormal data point in the inclination time series data, and then the stability of the wind farm pile can be determined according to the data density of the abnormal data point.

[0066] In summary, the embodiment of the present application provides a stability monitoring method for offshore wind power piles, the method comprising: obtaining the inclination time series data of the wind power pile; performing anomaly analysis on the inclination time series data to obtain the degree of abnormality of the data points in the inclination time series data; and determining the stability of the wind power pile according to the degree of abnormality of the data points. The embodiment of the present application performs anomaly analysis on the acquired inclination time series data of the wind power pile to obtain the degree of abnormality of the data points in the inclination time series data, and determines the stability of the wind power pile according to the degree of abnormality of the data points. It is possible to achieve in-depth analysis and calculation of sea surface environmental interference, thereby improving the monitoring accuracy and monitoring performance of wind power pile stability.

[0067] Figure 2 FIG. 1 is a flow chart showing a method for obtaining the abnormality degree of a data point in inclination time series data according to an exemplary embodiment. Figure 2 As shown, the obtaining of the abnormality degree of the data points in the inclination time series data may include the following steps:

[0068] In step S201, the breakthrough degree of the data point is obtained.

[0069] In this step, get the breakthrough degree of the i-th data point Since offshore wind power piles will be affected by waves and tidal phenomena and will have normal tilt and shaking, it is necessary to first analyze the inclination fluctuation of the data points in the inclination time series data. For example, the breakthrough degree of the i-th data point It can be obtained by the following formula:

[0070] Formula 1

[0071] in, is any data point in the dip time series data, is the first The breakthrough of data points, is the first The inclination value of the data point, is the first The inclination value of the data point, is the first -1 data point's inclination value, is a hyperbolic function.

[0072] The larger the value of indicates, the greater the fluctuation of the i-th data point relative to the two data points before and after it, and the value of the i-th data point The larger the value, the greater the inclination angle of the wind power pile, so the breakthrough degree The larger it is, the more likely a dangerous situation will occur.

[0073] In step S202, a judgment data set of the tilt angle time series data is obtained.

[0074] In this step, a judgment data set of the tilt angle time series data is obtained. Exemplarily, the autocorrelation function of the tilt angle time series data can be obtained first, and then the period of the tilt angle time series data can be determined based on the maximum value of the autocorrelation function, and then the periodic data point of any data point in the tilt angle time series data can be determined based on the period, and finally the maximum value points of all periodic data points are used as the judgment data set of any data point in the tilt angle time series data.

[0075] In step S203, the wave impact probability of the data point is obtained according to the evaluation data set.

[0076] In this step, the wave impact probability of the data point is obtained based on the evaluation data set. For example, the difference between any two adjacent data points in the evaluation data set of the data point can be obtained first, and then the wave impact probability of the data point can be determined based on the difference. .

[0077] In step S204, the sea breeze influence probability of the data point is obtained according to the evaluation data set.

[0078] In this step, the sea breeze influence probability of the data point is obtained based on the evaluation data set. For example, the descending anomaly coefficient of the i-th data point can be obtained first: , and then according to the decline anomaly coefficient , determine the probability of sea breeze influence at the i-th data point .

[0079] In step S205, the abnormality degree of the data point is determined according to the breakthrough degree, the wave influence probability and the sea breeze influence probability.

[0080] In this step, according to the breakthrough degree , Probability of Wave Impact Probability of sea breeze impact , determine the abnormality of the i-th data point For example, the abnormality of the i-th data point is It can be obtained by the following formula:

[0081] Formula 2

[0082] in, represents an exponential function with the natural constant e as the base, Not zero.

[0083] According to the probability of sea breeze influence at the i-th data point and the probability of waves affecting The product of and the breakthrough degree of the i-th data point The difference between the ratio and 1 can represent the abnormality of the i-th data point. The smaller the difference, the greater the abnormality. and The smaller the gap, the higher the abnormality of the i-th data point.

[0084] Figure 3 FIG. 1 is a flow chart showing a method for obtaining a judgment data set of inclination time series data according to an exemplary embodiment. Figure 3 As shown, the acquisition of the evaluation data set of the tilt angle time series data may include the following steps:

[0085] In step S2021, the autocorrelation function of the tilt angle time series data is obtained.

[0086] In this step, the autocorrelation function of the tilt time series data is obtained. Since periodic waves can cause wind turbine piles to swing, the data in the wind turbine pile inclination time series data is periodic under normal circumstances; for the maximum value points in the inclination time series data, the fluctuation period of the wind turbine pile can be obtained by analyzing the autocorrelation of the maximum value points. For example, the autocorrelation function of the inclination time series data is It can be obtained by the following formula:

[0087] Formula 3

[0088] in, is any data point in the dip time series data, is the number of data points in the inclination time series data, is the period of the inclination time series data, is the first The inclination value of the data point, is the first The inclination value of each data point.

[0089] In step S2022, the period of the tilt angle time series data is determined according to the maximum value of the autocorrelation function.

[0090] In this step, according to the autocorrelation function The maximum value of the dip angle time series data determines the period .

[0091] In step S2023, a periodic data point of any data point in the tilt angle time series data is determined according to the period.

[0092] In this step, according to the cycle , determine the periodic data point of any i-th data point in the inclination time series data .

[0093] In step S2024, the maximum value point of the periodic data point is used as a judgment data set for any data point in the tilt angle time series data.

[0094] In this step, the periodic data points All the maximum points of are used as the judgment data set for any i-th data point in the inclination time series data.

[0095] Figure 4 FIG. 1 is a flow chart showing a method for obtaining the probability of sea wave impact of a data point according to an exemplary embodiment. Figure 4 As shown, obtaining the wave impact probability of the data point may include the following steps:

[0096] In step S2031, the difference between any two adjacent data points in the evaluation data set of the data points is obtained.

[0097] In this step, get any two adjacent data points in the judgment data set of the i-th data point and The difference .

[0098] In step S2032, the wave influence probability of the data point is determined according to the difference.

[0099] In this step, the difference , determine the probability of wave impact at the i-th data point For example, the probability of sea wave impact at the i-th data point is It can be obtained by the following formula:

[0100] Formula 4

[0101] in, is any data point in the evaluation data set of the data point in the inclination time series data, is the number of data points in the judgment data set of the data points in the inclination time series data, It is the first The probability of wave impact for each data point is It is the first The first in the judgment data set of data points The value of the data point, It is the first The first in the judgment data set of data points The value of the data point, is a hyperbolic function.

[0102] The larger the value is, the more it indicates that the value of the data point is showing a downward trend, and therefore the data of the evaluation data set of the data point is showing a downward trend, which indicates that the location of the data point is consistent with the fluctuation caused by the influence of waves, and therefore the greater the probability value of its influence by waves.

[0103] Figure 5 FIG. 1 is a flow chart showing a method for obtaining the probability of sea breeze influence of a data point according to an exemplary embodiment. Figure 5 As shown, obtaining the sea breeze influence probability of the data point may include the following steps:

[0104] In step S2041, the descending anomaly coefficient of the data point is obtained.

[0105] In this step, the decreasing anomaly coefficient of the data point is obtained. When a wind turbine pile floats in the sea, it will not only be affected by the waves, but also by the sea breeze that blows the wind turbine pile, causing the wind turbine pile to change from the normal periodic swing caused by the waves to irregular swing; that is, in the short term, the inclination of the wind turbine pile will appear to be tilted to one side in the direction of the sea breeze, which will cause the inclination angle time series data of the wind turbine pile to change. That is, when the wind turbine pile is affected by the waves and swings, the swing in the direction of the sea breeze will be strengthened by the sea breeze, and the swing in the opposite direction of the sea breeze will be weakened by the sea breeze, resulting in a large and small change in its inclination angle, which will appear as a decreasing anomaly in the data. Figure 6 FIG. 1 is a schematic diagram showing a unilateral decline of a data point according to an exemplary embodiment. Figure 6 As shown, we can see that the maximum value of the data point shows a unilateral downward trend, which is often caused by waves. Figure 7 FIG. 1 is a schematic diagram showing an abnormal decrease in a data point according to an exemplary embodiment. Figure 7 As shown, we can see that the maximum value points of the data points show an abnormal downward trend, which is often caused by waves and sea breeze.

[0106] Exemplarily, a fitting straight line of data in a judgment data set of data points can be first obtained, and then a first number of data points in a first subset can be obtained, where the first subset is a data set consisting of data points in the judgment data set located on a first side of the fitting straight line, and then a second number of data points in a second subset can be obtained, where the second subset is a data set consisting of data points in the judgment data set located on a second side of the fitting straight line, and then the decreasing anomaly coefficient of the data point can be determined based on the first number and the second number.

[0107] In step S2042, the probability of sea breeze influence on the data point is determined according to the descent anomaly coefficient.

[0108] In this step, the probability of sea breeze influence of the data point is determined according to the descending anomaly coefficient. Exemplarily, a first slope may be obtained first, the first slope being the slope of the fitted straight line, a second slope being the slope of the fitted straight line of the data points of the first subset, a third slope being the slope of the fitted straight line of the data points of the second subset, and then the probability of sea breeze influence of the data point may be determined according to the descending anomaly coefficient, the first slope, the second slope and the third slope.

[0109] Figure 8 FIG. 1 is a flow chart showing a method for obtaining a decreasing anomaly coefficient of a data point according to an exemplary embodiment. Figure 8 As shown, the acquisition of the decline anomaly coefficient of the data point may include the following steps:

[0110] In step S20411, a fitting straight line of the data in the evaluation data set of the data point is obtained.

[0111] In this step, a fitting straight line of the data in the evaluation data set of the ith data point is obtained. Exemplarily, the method of obtaining the fitting straight line is prior art, and this application will not elaborate on it here.

[0112] In step S20412, a first number of data points in a first subset is obtained, where the first subset is a data set consisting of data points in the evaluation data set that are located on a first side of the fitting straight line.

[0113] In this step, get the first number of data points of the first subset The first subset is a data set consisting of data points located on the first side of the fitting line in the evaluation data set. Exemplarily, the first side of the fitting line can be the right side of the fitting line.

[0114] In step S20413, a second number of data points in a second subset is obtained, where the second subset is a data set consisting of data points in the evaluation data set that are located on a second side of the fitting straight line.

[0115] In this step, the second number of data points of the second subset is obtained. , the second subset is a data set consisting of data points located on the second side of the fitting line in the judgment data set. Exemplarily, the second side of the fitting line can be the left side of the fitting line.

[0116] In step S20414, the decreasing anomaly coefficient of the data point is determined based on the first quantity and the second quantity.

[0117] In this step, according to the first quantity and the second quantity , determine the descending anomaly coefficient of the ith data point For example, the decreasing anomaly coefficient of the ith data point is It can be obtained by the following formula:

[0118] Formula 5

[0119] in, represents an exponential function with the natural constant e as the base, or Not zero.

[0120] First quantity and the second quantity The closer it is, the closer the number of data points on both sides of the fitting line is, indicating that there is a downward anomaly in the periodic data of the i-th data point. The smaller it is, the lower the anomaly coefficient of the i-th data point is. The bigger.

[0121] Fig. 9 FIG. 1 is a flow chart showing a method for determining the probability of sea breeze influence of a data point according to an exemplary embodiment. Fig. 9 As shown, the determination of the sea breeze influence probability of the data point may include the following steps:

[0122] In step S20421, a first slope is obtained, where the first slope is the slope of the fitting straight line.

[0123] In this step, the first slope is obtained , the first slope is the slope of the fitted straight line.

[0124] In step S20422, a second slope is obtained, where the second slope is the slope of the fitted straight line of the data points of the first subset.

[0125] In this step, the second slope is obtained , the second slope is the slope of the fitted line for the first subset of data points.

[0126] In step S20423, a third slope is obtained, where the third slope is the slope of the fitted straight line of the data points of the second subset.

[0127] In this step, the third slope is obtained , the third slope is the slope of the fitted line for the second subset of data points.

[0128] In step S20424, the sea breeze influence probability of the data point is determined according to the descent anomaly coefficient, the first slope, the second slope and the third slope.

[0129] In this step, according to the descending anomaly coefficient , first slope , the second slope and the third slope , determine the probability of sea breeze influence at the i-th data point For example, the probability of sea breeze influence at the i-th data point is It can be obtained by the following formula:

[0130] Formula 6

[0131] in, Represents an exponential function with the natural constant e as the base.

[0132] First, the decreasing anomaly coefficient of the i-th data point The larger the value, the greater the influence of sea breeze on the i-th data point. Secondly, the slope of the fitted line of the judgment data set for the i-th data point , the smaller the value, the more the data in the data set tends to decrease gradually; the difference The smaller it is, the more similar the downward trend of the data points on both sides of the fitting line is, indicating that the i-th data point is more affected by the sea breeze, so the probability of its sea breeze influence is higher. The bigger.

[0133] Fig.10 FIG. 1 is a flow chart showing a method for determining the stability of a wind power pile according to an exemplary embodiment. Fig.10 As shown, the determination of the stability of the wind power pile may include the following steps:

[0134] In step S301, when the abnormality degree of the data point is greater than a first threshold, the data point is determined to be an abnormal data point.

[0135] In this step, the degree of abnormality of the data point Greater than the first threshold In the case of, the i-th data point is determined to be an abnormal data point. Exemplarily, the first threshold It can be 0.8.

[0136] In step S302, the data density of the abnormal data points is determined according to the proportion of the abnormal data points in the tilt angle time series data.

[0137] In this step, the data density of the abnormal data points is determined according to the proportion of the abnormal data points in the inclination time series data.

[0138] In step S303, the stability of the wind power pile is determined according to the data density of the abnormal data points.

[0139] In this step, the stability of the wind farm pile is determined according to the data density of the abnormal data points. For example, when the data density of the abnormal data points is greater than or equal to the second threshold, the wind farm pile is determined to be unstable and an alarm is issued; when the data density of the abnormal data points is less than the second threshold, the wind farm pile is determined to be stable.

[0140] Fig.11 FIG. 1 is a flow chart showing another method for determining the stability of a wind power pile according to an exemplary embodiment. Fig.11 As shown, the step of determining the stability of the wind power pile may further include the following steps:

[0141] In step S3031, when the data density of the abnormal data points is greater than or equal to the second threshold, the wind pile is determined to be unstable, and an alarm is issued to instruct the maintenance team to perform on-site inspection and maintenance of the wind pile to ensure the safety of the wind pile.

[0142] In this step, the data density of the abnormal data points is greater than or equal to the second threshold In the case of the wind power pile being unstable, an alarm is issued. It can be 0.8.

[0143] In step S3032, when the data density of the abnormal data points is less than the second threshold, it is determined that the wind power pile is stable.

[0144] In this step, the data density of the abnormal data point is less than the second threshold In the case of, it is determined that the wind power pile is stable. Exemplarily, the second threshold It can be 0.8.

[0145] In summary, the embodiment of the present application provides a stability monitoring method for offshore wind power piles, the method comprising: obtaining the inclination time series data of the wind power pile; performing anomaly analysis on the inclination time series data to obtain the degree of abnormality of the data points in the inclination time series data; and determining the stability of the wind power pile according to the degree of abnormality of the data points. The embodiment of the present application performs anomaly analysis on the acquired inclination time series data of the wind power pile to obtain the degree of abnormality of the data points in the inclination time series data, and determines the stability of the wind power pile according to the degree of abnormality of the data points. It is possible to achieve in-depth analysis and calculation of sea surface environmental interference, thereby improving the monitoring accuracy and monitoring performance of wind power pile stability.

[0146] The present application also provides a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the steps of the stability monitoring method for offshore wind power piles provided in the present application.

[0147] Fig.12 FIG. 1 is a block diagram of a stability monitoring system for offshore wind power piles according to an exemplary embodiment. Fig.12 As shown, an embodiment of the present application provides a stability monitoring system 1000 for an offshore wind power pile, including a monitoring platform 1100 .

[0148] Fig.13 1 is a block diagram of a monitoring platform according to an exemplary embodiment. For example, the monitoring platform 1100 may be provided as a server. Fig.13The monitoring platform 1100 includes a processor 1122, which further includes one or more processors, and a memory resource represented by a memory 1132 for storing instructions executable by the processor 1122, such as an application. The application stored in the memory 1132 may include one or more modules, each corresponding to a set of instructions. In addition, the processor 1122 is configured to execute instructions to perform the above-mentioned stability monitoring method for offshore wind power piles.

[0149] The monitoring platform 1100 may also include a power component 1126 configured to perform power management of the monitoring platform 1100, a communication component 1150 configured to connect the monitoring platform 1100 to a network, and an input / output interface 1158. The monitoring platform 1100 may operate based on an operating system stored in the memory 1132.

[0150] In another exemplary embodiment, a computer program product is also provided, which includes a computer program that can be executed by a programmable electronic device, and the computer program has a code portion for executing the above-mentioned stability monitoring method for offshore wind power piles when executed by the programmable electronic device.

[0151] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A stability monitoring method for offshore wind power piles, characterized in that: The method comprises: Acquiring the inclination time series data of the wind power pile; Performing an abnormality analysis on the inclination time series data to obtain the degree of abnormality of data points in the inclination time series data; Determining the stability of the wind power pile according to the abnormality degree of the data point; Among them, the abnormality of the i-th data point The calculation formula is: Formula 2 in, represents an exponential function with the natural constant e as the base, is not zero, represents the probability of sea breeze impact, Indicates breakthrough degree, represents the probability of wave impact; The calculation formula of breakthrough degree is: Formula 1 in, is any data point in the dip time series data, is the first The breakthrough of data points, is the first The inclination value of the data point, is the first The inclination value of the data point, is the first -1 data point's inclination value, is a hyperbolic function; The method for obtaining the probability of sea wave influence is as follows: obtaining the difference between any two adjacent data points in the evaluation data set of the data point; and determining the probability of sea wave influence of the data point according to the difference; The method for obtaining the evaluation data set is as follows: obtaining the autocorrelation function of the tilt angle time series data; determining the period of the tilt angle time series data according to the maximum value of the autocorrelation function; determining the periodic data point of any data point in the tilt angle time series data according to the period; and using the maximum value point of the periodic data point as the evaluation data set of any data point in the tilt angle time series data; The method for obtaining the probability of sea breeze influence is as follows: obtaining the descending anomaly coefficient of the data point; and determining the probability of sea breeze influence of the data point according to the descending anomaly coefficient; The method for obtaining the descending anomaly coefficient is as follows: obtaining a fitting straight line of data in the judgment data set of the data point; obtaining a first number of data points of a first subset, the first subset being a data set consisting of data points in the judgment data set located on a first side of the fitting straight line; obtaining a second number of data points of a second subset, the second subset being a data set consisting of data points in the judgment data set located on a second side of the fitting straight line; determining the descending anomaly coefficient of the data point according to the first number and the second number; Wherein, determining the probability of sea breeze influence on the data point according to the descending anomaly coefficient includes: obtaining a first slope, the first slope being the slope of the fitted straight line; obtaining a second slope, the second slope being the slope of the fitted straight line for the data points of the first subset; obtaining a third slope, the third slope being the slope of the fitted straight line for the data points of the second subset; determining the probability of sea breeze influence on the data point according to the descending anomaly coefficient, the first slope, the second slope and the third slope.

2. The stability monitoring method for offshore wind power piles according to claim 1, characterized in that: The obtaining of the abnormality degree of the data points in the inclination time series data includes: Obtaining the breakthrough degree of the data point; Acquire a judgment data set of the inclination time series data; According to the evaluation data set, obtaining the wave impact probability of the data point; According to the evaluation data set, obtaining the sea breeze influence probability of the data point; The abnormality degree of the data point is determined according to the breakthrough degree, the wave influence probability and the sea breeze influence probability.

3. The stability monitoring method for offshore wind power piles according to claim 1, characterized in that: The step of determining the stability of the wind power pile comprises: When the abnormality degree of the data point is greater than a first threshold, determining the data point as an abnormal data point; Determining the data density of the abnormal data points according to the proportion of the abnormal data points in the inclination time series data; The stability of the wind power pile is determined according to the data density of the abnormal data points.

4. The stability monitoring method for offshore wind power piles according to claim 3 is characterized in that: The step of determining the stability of the wind power pile comprises: When the data density of the abnormal data points is greater than or equal to a second threshold, determining that the wind power pile is unstable and issuing an alarm; When the data density of the abnormal data points is less than the second threshold, it is determined that the wind power pile is stable.

5. A stability monitoring system for offshore wind power piles, characterized in that: The system includes a monitoring platform, which includes: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the steps of the stability monitoring method for offshore wind power piles described in any one of claims 1-4.

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