CN114413971A - Corrosion monitoring system and method for shell of offshore wind turbine - Google Patents

Corrosion monitoring system and method for shell of offshore wind turbine Download PDF

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Publication number
CN114413971A
CN114413971A CN202210318189.3A CN202210318189A CN114413971A CN 114413971 A CN114413971 A CN 114413971A CN 202210318189 A CN202210318189 A CN 202210318189A CN 114413971 A CN114413971 A CN 114413971A
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seawater
monitoring
offshore wind
data
shell
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杨凯
李黎
郭浚安
黄小康
夏志才
黄晓宏
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Institute Of New Energy Wuhan Co ltd
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Institute Of New Energy Wuhan Co ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/043—Architecture, e.g. interconnection topology based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply

Abstract

The application relates to a system and a method for monitoring corrosion of an outer shell of an offshore wind turbine, which relate to the technical field of equipment monitoring, and the system comprises: the microclimate monitoring device is used for monitoring microclimate monitoring data around the offshore wind turbine; the seawater collecting device is used for collecting seawater in a sea area near the offshore wind driven generator according to a preset period, and detecting the seawater collected by the seawater collecting device by matching with a preset seawater monitoring probe to obtain corresponding seawater monitoring data; the resistance probe is used for monitoring and obtaining a shell resistance change value of the offshore wind turbine; and the comprehensive data processing device is used for acquiring the corrosion state information of the shell based on the microclimate monitoring data, the seawater monitoring data and the resistance change value of the shell. According to the method and the device, the corrosion condition of the shell of the offshore wind driven generator is monitored on line, and the corrosion condition is judged, so that the operation and maintenance period of the wind driven generator is planned at a later stage.

Description

Corrosion monitoring system and method for shell of offshore wind turbine
Technical Field
The application relates to the technical field of equipment monitoring, in particular to a system and a method for monitoring corrosion of an offshore wind turbine shell.
Background
Because offshore wind power resources are abundant, compared with land wind power, offshore wind power has the characteristic of being more stable, and the number of offshore wind power generators is continuously increased. However, the shell of the offshore wind driven generator is in a complex marine environment and is subjected to sunshine insolation, high-salt seawater corrosion, sea wave beating and the like for a long time, so that the shell of the wind driven generator is easily corroded, the running of the wind driven generator is damaged, and effective monitoring on the corrosion condition of the shell is a key for ensuring the stable running of the offshore wind driven generator.
Because the offshore wind driven generator is difficult to detect manually and high in cost, the corrosion condition of the shell of the wind driven generator needs to be monitored on line, so that the operation and maintenance period of the wind driven generator is effectively planned, and the operation and maintenance cost is reduced.
Therefore, there is a need for an on-line monitoring technique for corrosion of a housing of an offshore wind turbine, which addresses the above-mentioned technical problems.
Disclosure of Invention
The application provides a system and a method for monitoring corrosion of an outer shell of an offshore wind driven generator, which are used for monitoring the corrosion condition of the outer shell of the offshore wind driven generator on line and judging the corrosion condition so as to plan the operation and maintenance period of the wind driven generator at the later stage.
In a first aspect, the present application provides an offshore wind turbine housing corrosion monitoring system, the system comprising:
the microclimate monitoring device is used for monitoring microclimate monitoring data around the offshore wind turbine;
the sea water collecting device is used for collecting sea water in a sea area near the offshore wind driven generator according to a preset period, and detecting the sea water collected by the sea water collecting device in cooperation with a preset sea water monitoring probe to obtain corresponding sea water monitoring data;
the resistance probe is used for monitoring and obtaining a shell resistance change value of the offshore wind turbine;
the comprehensive data processing device is used for obtaining shell corrosion state information based on the microclimate monitoring data, the seawater monitoring data and the shell resistance change value; wherein the content of the first and second substances,
the microclimate monitoring data comprises ambient temperature and air humidity;
the seawater monitoring data comprises seawater salinity, seawater temperature and seawater conductivity.
Further, the system further comprises:
and the data transmitting and receiving device is used for uploading the microclimate monitoring data, the seawater monitoring data, the shell resistance change value, the shell corrosion state information, the offshore wind driven generator number and the position to a land central station.
Furthermore, the comprehensive data processing device is also used for training a new fuzzy RBF neural network and a full-connection layer classifier by using a self-adaptive genetic algorithm in combination with preset network training data to obtain a fuzzy RBF neural network model;
the comprehensive data processing device is also used for acquiring the corrosion state information of the shell by utilizing a fuzzy RBF neural network model based on the microclimate monitoring data, the seawater monitoring data and the shell resistance change value.
Furthermore, the comprehensive data processing device also comprises a meteorological data acquisition unit;
the meteorological data acquisition unit is used for carrying out normalization processing on the environmental temperature based on a preset environmental temperature normalization formula;
the meteorological data acquisition unit is used for carrying out normalization processing on the air humidity based on a preset air humidity normalization formula;
the environment temperature normalization formula is as follows:
Figure 584014DEST_PATH_IMAGE001
；
the air humidity normalization formula is as follows:
Figure 921585DEST_PATH_IMAGE002
(ii) a Wherein the content of the first and second substances,
Figure 3811DEST_PATH_IMAGE003
in order to normalize the ambient temperature after the treatment,
Figure 475243DEST_PATH_IMAGE004
in order to detect the obtained ambient temperature,
Figure 342836DEST_PATH_IMAGE005
is the average value of the ambient temperature,
Figure 975943DEST_PATH_IMAGE006
is the ambient temperature variance;
Figure 811044DEST_PATH_IMAGE007
in order to normalize the air humidity after the treatment,
Figure 820588DEST_PATH_IMAGE008
in order to detect the humidity of the air obtained,
Figure 274179DEST_PATH_IMAGE009
the average value of the air humidity is taken as the average value,
Figure 343766DEST_PATH_IMAGE010
is the air humidity variance.
Furthermore, the comprehensive data processing device also comprises a seawater data acquisition unit;
the seawater data acquisition unit is also used for carrying out normalization treatment on the seawater salinity based on a preset seawater salinity normalization formula;
the seawater data acquisition unit is also used for carrying out normalization treatment on the seawater temperature based on a preset seawater temperature normalization formula;
the seawater data acquisition unit is also used for carrying out normalization treatment on the seawater conductivity based on a preset seawater conductivity normalization formula;
the seawater salinity normalization formula is as follows:
Figure 400584DEST_PATH_IMAGE011
；
the seawater temperature normalization formula is as follows:
Figure 213819DEST_PATH_IMAGE012
；
the seawater conductivity normalization formula is as follows:
Figure 56004DEST_PATH_IMAGE013
(ii) a Wherein the content of the first and second substances,
Figure 30913DEST_PATH_IMAGE014
in order to normalize the salinity of the treated seawater,
Figure 840606DEST_PATH_IMAGE015
in order to detect the salinity of the obtained seawater,
Figure 191953DEST_PATH_IMAGE016
is the average value of the salinity of the seawater,
Figure 623066DEST_PATH_IMAGE017
the variance of the salinity of the seawater is obtained;
Figure 34455DEST_PATH_IMAGE018
in order to normalize the temperature of the treated seawater,
Figure 800286DEST_PATH_IMAGE019
in order to detect the temperature of the obtained seawater,
Figure 830690DEST_PATH_IMAGE020
is the average value of the temperature of the seawater,
Figure 506522DEST_PATH_IMAGE021
is the variance of the seawater temperature;
Figure 213447DEST_PATH_IMAGE022
in order to normalize the conductivity of the treated seawater,
Figure 631353DEST_PATH_IMAGE023
in order to measure the conductivity of the obtained seawater,
Figure 400202DEST_PATH_IMAGE024
the average value of the electric conductivity of the seawater is,
Figure 196119DEST_PATH_IMAGE025
is the variance of the conductivity of the seawater.
Furthermore, the comprehensive data processing device also comprises a resistance data acquisition unit;
the resistance data acquisition unit is also used for carrying out normalization processing on the shell resistance change value based on a preset shell resistance change value normalization formula;
the normalized formula of the resistance change value of the shell is as follows:
Figure 73946DEST_PATH_IMAGE026
；
Figure 299522DEST_PATH_IMAGE027
in order to normalize the processed resistance variation value of the case,
Figure 796362DEST_PATH_IMAGE028
in order to collect the obtained resistance variation values of the casing,
Figure 571420DEST_PATH_IMAGE029
the initial resistance value is the resistance value when not corroded.
Furthermore, the comprehensive data processing device also comprises a probe power supply which is used for supplying power to the resistance probe at constant current.
Further, the comprehensive data processing device is also provided with an offshore wind driven generator database;
the offshore wind turbine database is used for storing the number and the position of the offshore wind turbine and the parameters of the offshore wind turbine body.
Further, the integrated data processing device further comprises a data calculation unit;
the data calculation unit is used for merging the micrometeorological monitoring data, the seawater monitoring data, the shell resistance change value and the shell corrosion state information after normalization processing to obtain merged detection data;
the data calculation unit is also used for training a new fuzzy RBF neural network and a full-connection layer classifier by using a self-adaptive genetic algorithm in combination with preset network training data to obtain a fuzzy RBF neural network model;
the data calculation unit is further used for obtaining the shell corrosion state information by utilizing a fuzzy RBF neural network model based on the microclimate monitoring data, the seawater monitoring data and the shell resistance change value.
In a second aspect, the present application provides a method of monitoring corrosion of an offshore wind turbine housing, the method comprising the steps of:
monitoring microclimate monitoring data around the offshore wind turbine;
collecting seawater in a sea area near an offshore wind driven generator according to a preset period, and detecting the seawater collected by the seawater collection device by matching with a preset seawater monitoring probe to obtain corresponding seawater monitoring data;
monitoring to obtain a shell resistance change value of the offshore wind turbine;
acquiring shell corrosion state information based on the microclimate monitoring data, the seawater monitoring data and the shell resistance change value; wherein the content of the first and second substances,
the microclimate monitoring data comprises ambient temperature and air humidity;
the seawater monitoring data comprises seawater salinity, seawater temperature and seawater conductivity.
Further, the method comprises the following steps:
and uploading the microclimate monitoring data, the seawater monitoring data, the shell resistance change value, the shell corrosion state information, the offshore wind driven generator number and the position to a land central station.
Further, the method comprises the following steps:
training a new fuzzy RBF neural network and a full-connection layer classifier by using a self-adaptive genetic algorithm in combination with preset network training data to obtain a fuzzy RBF neural network model;
and acquiring the corrosion state information of the shell by utilizing a fuzzy RBF neural network model based on the microclimate monitoring data, the seawater monitoring data and the shell resistance change value.
Further, the method comprises the following steps:
normalizing the ambient temperature based on a preset ambient temperature normalization formula;
normalizing the air humidity based on a preset air humidity normalization formula;
the environment temperature normalization formula is as follows:
Figure 495514DEST_PATH_IMAGE030
；
the air humidity normalization formula is as follows:
Figure 739544DEST_PATH_IMAGE031
(ii) a Wherein the content of the first and second substances,
Figure 774497DEST_PATH_IMAGE003
in order to normalize the ambient temperature after the treatment,
Figure 404061DEST_PATH_IMAGE004
in order to detect the obtained ambient temperature,
Figure 233477DEST_PATH_IMAGE005
is the average value of the ambient temperature,
Figure 699224DEST_PATH_IMAGE006
is the ambient temperature variance;
Figure 537867DEST_PATH_IMAGE007
in order to normalize the air humidity after the treatment,
Figure 287517DEST_PATH_IMAGE008
in order to detect the humidity of the air obtained,
Figure 553414DEST_PATH_IMAGE009
the average value of the air humidity is taken as the average value,
Figure 509387DEST_PATH_IMAGE010
is the air humidity variance.
Further, the method comprises the following steps:
normalizing the seawater salinity based on a preset seawater salinity normalization formula;
normalizing the seawater temperature based on a preset seawater temperature normalization formula;
carrying out normalization treatment on the sea water conductivity based on a preset sea water conductivity normalization formula;
the seawater salinity normalization formula is as follows:
Figure 886142DEST_PATH_IMAGE032
；
the seawater temperature normalization formula is as follows:
Figure 490298DEST_PATH_IMAGE033
；
the seawater conductivity normalization formulaComprises the following steps:
Figure 927096DEST_PATH_IMAGE034
(ii) a Wherein the content of the first and second substances,
Figure 367436DEST_PATH_IMAGE014
in order to normalize the salinity of the treated seawater,
Figure 547881DEST_PATH_IMAGE015
in order to detect the salinity of the obtained seawater,
Figure 272124DEST_PATH_IMAGE016
is the average value of the salinity of the seawater,
Figure 879823DEST_PATH_IMAGE017
the variance of the salinity of the seawater is obtained;
Figure 541879DEST_PATH_IMAGE018
in order to normalize the temperature of the treated seawater,
Figure 526016DEST_PATH_IMAGE019
in order to detect the temperature of the obtained seawater,
Figure 104765DEST_PATH_IMAGE020
is the average value of the temperature of the seawater,
Figure 883365DEST_PATH_IMAGE021
is the variance of the seawater temperature;
Figure 32718DEST_PATH_IMAGE022
in order to normalize the conductivity of the treated seawater,
Figure 554966DEST_PATH_IMAGE023
in order to measure the conductivity of the obtained seawater,
Figure 988221DEST_PATH_IMAGE024
the average value of the electric conductivity of the seawater is,
Figure 937723DEST_PATH_IMAGE025
is the variance of the conductivity of the seawater.
Further, the method comprises the following steps:
normalizing the shell resistance change value based on a preset shell resistance change value normalization formula;
the normalized formula of the resistance change value of the shell is as follows:
Figure 837021DEST_PATH_IMAGE035
；
Figure 897381DEST_PATH_IMAGE036
in order to normalize the processed resistance variation value of the case,
Figure 450722DEST_PATH_IMAGE037
in order to collect the obtained resistance variation values of the casing,
Figure 180912DEST_PATH_IMAGE038
the initial resistance value is the resistance value when not corroded.
Further, the method comprises the following steps:
and storing the number and the position of the offshore wind turbine and the parameters of the offshore wind turbine body.
Further, the method comprises the following steps:
merging the micrometeorological monitoring data, the seawater monitoring data, the shell resistance change value and the shell corrosion state information after normalization processing to obtain merged detection data;
training a new fuzzy RBF neural network and a full-connection layer classifier by using a self-adaptive genetic algorithm in combination with preset network training data to obtain a fuzzy RBF neural network model;
and acquiring the corrosion state information of the shell by utilizing a fuzzy RBF neural network model based on the microclimate monitoring data, the seawater monitoring data and the shell resistance change value.
The beneficial effect that technical scheme that this application provided brought includes:
the method and the device are matched with specific monitoring hardware to monitor the corrosion condition of the shell of the offshore wind driven generator on line and judge the corrosion condition of the shell of the offshore wind driven generator, so that the operation and maintenance period of the wind driven generator is planned at a later stage, and the operation and maintenance cost is reduced.
Drawings
Interpretation of terms:
RBF: radial Basis Function, Radial Basis Function.
AGA: adaptive Genetic Algorithm, Adaptive Genetic Algorithm.
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a block diagram of a corrosion monitoring system for an offshore wind turbine housing provided in an embodiment of the present application;
FIG. 2 is a schematic block diagram of an offshore wind turbine housing corrosion monitoring system provided in an embodiment of the present application;
FIG. 3 is a block diagram illustrating a fuzzy RBF neural network model in an offshore wind turbine housing corrosion monitoring system according to an embodiment of the present application;
FIG. 4 is a schematic flow chart of a fuzzy RBF neural network model in an offshore wind turbine housing corrosion monitoring system provided in an embodiment of the present application;
FIG. 5 is a flow chart illustrating steps of a method for monitoring corrosion of an offshore wind turbine housing, provided in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
The embodiment of the application provides a system and a method for monitoring corrosion of an outer shell of an offshore wind turbine, which are matched with specific monitoring hardware to monitor the corrosion condition of the outer shell of the offshore wind turbine on line and judge the corrosion condition of the outer shell of the offshore wind turbine, so that the operation and maintenance period of the wind turbine is planned at the later stage, and the operation and maintenance cost is reduced.
In order to achieve the technical effects, the general idea of the application is as follows:
an offshore wind turbine housing corrosion monitoring system, the system comprising:
the microclimate monitoring device is used for monitoring microclimate monitoring data around the offshore wind turbine;
the sea water collecting device is used for collecting sea water in a sea area near the offshore wind driven generator according to a preset period, and detecting the sea water collected by the sea water collecting device in cooperation with a preset sea water monitoring probe to obtain corresponding sea water monitoring data;
the resistance probe is used for monitoring and obtaining a shell resistance change value of the offshore wind turbine;
the comprehensive data processing device is used for obtaining shell corrosion state information based on the microclimate monitoring data, the seawater monitoring data and the shell resistance change value; wherein the content of the first and second substances,
the microclimate monitoring data comprises ambient temperature and air humidity;
the seawater monitoring data comprises seawater salinity, seawater temperature and seawater conductivity.
Embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
In a first aspect, referring to fig. 1 to 4, an embodiment of the present application provides an offshore wind turbine housing corrosion monitoring system, including:
the microclimate monitoring device is used for monitoring microclimate monitoring data around the offshore wind turbine;
the sea water collecting device is used for collecting sea water in a sea area near the offshore wind driven generator according to a preset period, and detecting the sea water collected by the sea water collecting device in cooperation with a preset sea water monitoring probe to obtain corresponding sea water monitoring data;
the resistance probe is used for monitoring and obtaining a shell resistance change value of the offshore wind turbine;
the comprehensive data processing device is used for obtaining shell corrosion state information based on the microclimate monitoring data, the seawater monitoring data and the shell resistance change value; wherein the content of the first and second substances,
the microclimate monitoring data comprises ambient temperature and air humidity;
the seawater monitoring data comprises seawater salinity, seawater temperature and seawater conductivity.
According to the embodiment of the application, specific monitoring hardware is matched to perform online monitoring on the corrosion condition of the shell of the offshore wind turbine, and the corrosion condition of the shell of the offshore wind turbine is judged, so that the operation and maintenance period of the wind turbine is planned at a later stage, and the operation and maintenance cost is reduced.
Further, this offshore wind turbine shell corrosion monitoring system still includes:
and the data transmitting and receiving device is used for uploading the microclimate monitoring data, the seawater monitoring data, the shell resistance change value, the shell corrosion state information, the offshore wind driven generator number and the position to a land central station.
Furthermore, the comprehensive data processing device is also used for training a new fuzzy RBF neural network and a full-connection layer classifier by using a self-adaptive genetic algorithm in combination with preset network training data to obtain a fuzzy RBF neural network model;
the comprehensive data processing device is also used for acquiring the corrosion state information of the shell by utilizing a fuzzy RBF neural network model based on the microclimate monitoring data, the seawater monitoring data and the shell resistance change value.
Furthermore, the comprehensive data processing device also comprises a meteorological data acquisition unit;
the meteorological data acquisition unit is used for carrying out normalization processing on the environmental temperature based on a preset environmental temperature normalization formula;
the meteorological data acquisition unit is used for carrying out normalization processing on the air humidity based on a preset air humidity normalization formula;
the environment temperature normalization formula is as follows:
Figure 695070DEST_PATH_IMAGE039
；
the air humidity normalization formula is as follows:
Figure 418175DEST_PATH_IMAGE040
(ii) a Wherein the content of the first and second substances,
Figure 701389DEST_PATH_IMAGE003
in order to normalize the ambient temperature after the treatment,
Figure 868059DEST_PATH_IMAGE004
in order to detect the obtained ambient temperature,
Figure 603934DEST_PATH_IMAGE005
is the average value of the ambient temperature,
Figure 396309DEST_PATH_IMAGE006
is the ambient temperature variance;
Figure 268450DEST_PATH_IMAGE007
in order to normalize the air humidity after the treatment,
Figure 606022DEST_PATH_IMAGE008
for detecting the humidity of the air obtained，
Figure 829193DEST_PATH_IMAGE009
The average value of the air humidity is taken as the average value,
Figure 425259DEST_PATH_IMAGE010
is the air humidity variance.
Furthermore, the comprehensive data processing device also comprises a seawater data acquisition unit;
the seawater data acquisition unit is also used for carrying out normalization treatment on the seawater salinity based on a preset seawater salinity normalization formula;
the seawater data acquisition unit is also used for carrying out normalization treatment on the seawater temperature based on a preset seawater temperature normalization formula;
the seawater data acquisition unit is also used for carrying out normalization treatment on the seawater conductivity based on a preset seawater conductivity normalization formula;
the seawater salinity normalization formula is as follows:
Figure 41922DEST_PATH_IMAGE032
；
the seawater temperature normalization formula is as follows:
Figure 675028DEST_PATH_IMAGE033
；
the seawater conductivity normalization formula is as follows:
Figure 510129DEST_PATH_IMAGE034
(ii) a Wherein the content of the first and second substances,
Figure 519673DEST_PATH_IMAGE014
in order to normalize the salinity of the treated seawater,
Figure 241773DEST_PATH_IMAGE015
in order to detect the salinity of the obtained seawater,
Figure 311360DEST_PATH_IMAGE016
is the average value of the salinity of the seawater,
Figure 102598DEST_PATH_IMAGE017
the variance of the salinity of the seawater is obtained;
Figure 915834DEST_PATH_IMAGE018
in order to normalize the temperature of the treated seawater,
Figure 492440DEST_PATH_IMAGE019
in order to detect the temperature of the obtained seawater,
Figure 732928DEST_PATH_IMAGE020
is the average value of the temperature of the seawater,
Figure 277042DEST_PATH_IMAGE021
is the variance of the seawater temperature;
Figure 628389DEST_PATH_IMAGE022
in order to normalize the conductivity of the treated seawater,
Figure 325081DEST_PATH_IMAGE023
in order to measure the conductivity of the obtained seawater,
Figure 595525DEST_PATH_IMAGE024
the average value of the electric conductivity of the seawater is,
Figure 767880DEST_PATH_IMAGE025
is the variance of the conductivity of the seawater.
Furthermore, the comprehensive data processing device also comprises a resistance data acquisition unit;
the resistance data acquisition unit is also used for carrying out normalization processing on the shell resistance change value based on a preset shell resistance change value normalization formula;
the normalized formula of the resistance change value of the shell is as follows:
Figure 529775DEST_PATH_IMAGE035
；
Figure 205607DEST_PATH_IMAGE036
in order to normalize the processed resistance variation value of the case,
Figure 912532DEST_PATH_IMAGE037
in order to collect the obtained resistance variation values of the casing,
Figure 572184DEST_PATH_IMAGE038
the initial resistance value is the resistance value when not corroded.
Furthermore, the comprehensive data processing device also comprises a probe power supply which is used for supplying power to the resistance probe at constant current.
Further, the comprehensive data processing device is also provided with an offshore wind driven generator database;
the offshore wind turbine database is used for storing the number and the position of the offshore wind turbine and the parameters of the offshore wind turbine body.
Further, the integrated data processing device further comprises a data calculation unit;
the data calculation unit is used for merging the micrometeorological monitoring data, the seawater monitoring data, the shell resistance change value and the shell corrosion state information after normalization processing to obtain merged detection data;
the data calculation unit is also used for training a new fuzzy RBF neural network and a full-connection layer classifier by using a self-adaptive genetic algorithm in combination with preset network training data to obtain a fuzzy RBF neural network model;
the data calculation unit is further used for obtaining the shell corrosion state information by utilizing a fuzzy RBF neural network model based on the microclimate monitoring data, the seawater monitoring data and the shell resistance change value.
During specific operation, the comprehensive data processing device also comprises a data merging unit and a data exchange unit, and the data merging unit collects the sending data of each acquisition unit and performs format verification and data splicing;
transmitting the spliced data to a fuzzy RBF neural network and a full-connection layer classifier for corrosion state evaluation;
transmitting the corrosion state evaluation result, the position parameters of the wind driven generator and the actual data acquired by the acquisition unit to a data communication exchanger through a data exchange unit, and transmitting the corrosion state evaluation result, the position parameters of the wind driven generator and the actual data to a land central station through a data transmitting and receiving device;
the data exchange unit receives the land central station network training data transmitted by the data communication exchanger, transmits the network training data to the fuzzy RBF neural network, trains the network by using the self-adaptive genetic algorithm, and the trained network is used for corrosion state evaluation.
When the technical scheme of the embodiment of the application is used for corrosion evaluation of the shell of the offshore wind turbine, the evaluation can be assisted according to a corrosion condition grade evaluation table, and the following table 1 shows:
Figure 875120DEST_PATH_IMAGE042
the actual structure diagram of the offshore wind turbine housing corrosion monitoring system is explained based on fig. 1 of the attached drawings, wherein the diagram comprises:
the system comprises an offshore wind turbine generator shell 1, a microclimate monitoring device 2, a data transmitting and receiving device 3, seawater 4, a comprehensive data processing device 5, a seawater collecting device 6, a resistance probe 7 and a land central station 8;
the comprehensive data processing device 5 comprises a resistance data acquisition unit, a meteorological data acquisition unit, a seawater data acquisition unit, a data communication exchanger, an offshore wind turbine serial number and position database, a probe power supply and a data calculation module.
It should be noted that, as shown in figure 3 of the drawings accompanying the specification,
Figure 671038DEST_PATH_IMAGE044
…
Figure 283285DEST_PATH_IMAGE046
、
Figure 164653DEST_PATH_IMAGE048
to blur the input parameters of the input layer of the RBF neural network model,
Figure 536860DEST_PATH_IMAGE050
…
Figure 187284DEST_PATH_IMAGE052
、
Figure 236011DEST_PATH_IMAGE054
to blur the output parameters of the blur layer of the RBF neural network model,
Figure 339097DEST_PATH_IMAGE056
a receiving unit of a fuzzy inference layer for a fuzzy RBF neural network model,
Figure 983836DEST_PATH_IMAGE058
…
Figure 19925DEST_PATH_IMAGE060
、
Figure 973974DEST_PATH_IMAGE062
in order to output parameters of a fuzzy inference layer of the fuzzy RBF neural network model,
Figure 829935DEST_PATH_IMAGE064
…
Figure 281295DEST_PATH_IMAGE066
、
Figure 906311DEST_PATH_IMAGE068
is an output parameter after the normalization calculation processing of the fuzzy RBF neural network model,
Figure 296841DEST_PATH_IMAGE070
…
Figure 374518DEST_PATH_IMAGE072
、
Figure 361060DEST_PATH_IMAGE074
is the output parameter of the output layer of the fuzzy RBF neural network model.
In a second aspect, referring to fig. 5, an embodiment of the present application provides a method for monitoring corrosion of an offshore wind turbine housing based on the technology of the offshore wind turbine housing corrosion monitoring system mentioned in the first aspect, the method including the following steps:
s1, monitoring microclimate monitoring data around the offshore wind turbine;
s2, collecting seawater in a sea area near the offshore wind driven generator according to a preset period, and detecting the seawater collected by the seawater collection device by matching with a preset seawater monitoring probe to obtain corresponding seawater monitoring data;
s3, monitoring and obtaining a shell resistance change value of the offshore wind driven generator;
s4, obtaining corrosion state information of the shell based on the microclimate monitoring data, the seawater monitoring data and the resistance change value of the shell; wherein the content of the first and second substances,
the microclimate monitoring data comprises ambient temperature and air humidity;
the seawater monitoring data comprises seawater salinity, seawater temperature and seawater conductivity.
According to the embodiment of the application, specific monitoring hardware is matched to perform online monitoring on the corrosion condition of the shell of the offshore wind turbine, and the corrosion condition of the shell of the offshore wind turbine is judged, so that the operation and maintenance period of the wind turbine is planned at a later stage, and the operation and maintenance cost is reduced.
Further, the method for monitoring the corrosion of the shell of the offshore wind turbine further comprises the following steps:
and uploading the microclimate monitoring data, the seawater monitoring data, the shell resistance change value, the shell corrosion state information, the offshore wind driven generator number and the position to a land central station.
Further, the method for monitoring the corrosion of the shell of the offshore wind turbine further comprises the following steps:
training a new fuzzy RBF neural network and a full-connection layer classifier by using a self-adaptive genetic algorithm in combination with preset network training data to obtain a fuzzy RBF neural network model;
and acquiring the corrosion state information of the shell by utilizing a fuzzy RBF neural network model based on the microclimate monitoring data, the seawater monitoring data and the shell resistance change value.
Further, the method for monitoring the corrosion of the shell of the offshore wind turbine further comprises the following steps:
normalizing the ambient temperature based on a preset ambient temperature normalization formula;
normalizing the air humidity based on a preset air humidity normalization formula;
the environment temperature normalization formula is as follows:
Figure 965217DEST_PATH_IMAGE030
；
the air humidity normalization formula is as follows:
Figure 402014DEST_PATH_IMAGE031
(ii) a Wherein the content of the first and second substances,
Figure 232567DEST_PATH_IMAGE003
in order to normalize the ambient temperature after the treatment,
Figure 22800DEST_PATH_IMAGE004
in order to detect the obtained ambient temperature,
Figure 481463DEST_PATH_IMAGE005
is the average value of the ambient temperature,
Figure 89162DEST_PATH_IMAGE006
is the ambient temperature variance;
Figure 16798DEST_PATH_IMAGE007
in order to normalize the air humidity after the treatment,
Figure 934DEST_PATH_IMAGE008
in order to detect the humidity of the air obtained,
Figure 314104DEST_PATH_IMAGE009
the average value of the air humidity is taken as the average value,
Figure 92704DEST_PATH_IMAGE010
is the air humidity variance.
Further, the method for monitoring the corrosion of the shell of the offshore wind turbine further comprises the following steps:
normalizing the seawater salinity based on a preset seawater salinity normalization formula;
normalizing the seawater temperature based on a preset seawater temperature normalization formula;
carrying out normalization treatment on the sea water conductivity based on a preset sea water conductivity normalization formula;
the seawater salinity normalization formula is as follows:
Figure 504706DEST_PATH_IMAGE011
；
the seawater temperature normalization formula is as follows:
Figure 26954DEST_PATH_IMAGE012
；
the seawater conductivity normalization formula is as follows:
Figure 460210DEST_PATH_IMAGE013
(ii) a Wherein the content of the first and second substances,
Figure 409711DEST_PATH_IMAGE014
in order to normalize the salinity of the treated seawater,
Figure 311939DEST_PATH_IMAGE015
in order to detect the salinity of the obtained seawater,
Figure 372299DEST_PATH_IMAGE016
is the average value of the salinity of the seawater,
Figure 394482DEST_PATH_IMAGE017
the variance of the salinity of the seawater is obtained;
Figure 780464DEST_PATH_IMAGE018
in order to normalize the temperature of the treated seawater,
Figure 904409DEST_PATH_IMAGE019
in order to detect the temperature of the obtained seawater,
Figure 768460DEST_PATH_IMAGE020
is the average value of the temperature of the seawater,
Figure 176307DEST_PATH_IMAGE021
is the variance of the seawater temperature;
Figure 467611DEST_PATH_IMAGE022
in order to normalize the conductivity of the treated seawater,
Figure 813273DEST_PATH_IMAGE023
in order to measure the conductivity of the obtained seawater,
Figure 746594DEST_PATH_IMAGE024
the average value of the electric conductivity of the seawater is,
Figure 743369DEST_PATH_IMAGE025
is the variance of the conductivity of the seawater.
Further, the method for monitoring the corrosion of the shell of the offshore wind turbine further comprises the following steps:
normalizing the shell resistance change value based on a preset shell resistance change value normalization formula;
the normalized formula of the resistance change value of the shell is as follows:
Figure 471153DEST_PATH_IMAGE035
；
Figure 295322DEST_PATH_IMAGE036
in order to normalize the processed resistance variation value of the case,
Figure 625809DEST_PATH_IMAGE037
in order to collect the obtained resistance variation values of the casing,
Figure 618036DEST_PATH_IMAGE038
the initial resistance value is the resistance value when not corroded.
Further, the method for monitoring the corrosion of the shell of the offshore wind turbine further comprises the following steps:
and storing the number and the position of the offshore wind turbine and the parameters of the offshore wind turbine body.
Further, the method for monitoring the corrosion of the shell of the offshore wind turbine further comprises the following steps:
merging the micrometeorological monitoring data, the seawater monitoring data, the shell resistance change value and the shell corrosion state information after normalization processing to obtain merged detection data;
training a new fuzzy RBF neural network and a full-connection layer classifier by using a self-adaptive genetic algorithm in combination with preset network training data to obtain a fuzzy RBF neural network model;
and acquiring the corrosion state information of the shell by utilizing a fuzzy RBF neural network model based on the microclimate monitoring data, the seawater monitoring data and the shell resistance change value.
It is noted that, in the present application, relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present application and are presented to enable those skilled in the art to understand and practice the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended 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 (10)

1. An offshore wind turbine housing corrosion monitoring system, the system comprising:
the microclimate monitoring device is used for monitoring microclimate monitoring data around the offshore wind turbine;
the sea water collecting device is used for collecting sea water in a sea area near the offshore wind driven generator according to a preset period, and detecting the sea water collected by the sea water collecting device in cooperation with a preset sea water monitoring probe to obtain corresponding sea water monitoring data;
the resistance probe is used for monitoring and obtaining a shell resistance change value of the offshore wind turbine;
the comprehensive data processing device is used for obtaining shell corrosion state information based on the microclimate monitoring data, the seawater monitoring data and the shell resistance change value; wherein the content of the first and second substances,
the microclimate monitoring data comprises ambient temperature and air humidity;
the seawater monitoring data comprises seawater salinity, seawater temperature and seawater conductivity.
2. The offshore wind turbine shroud corrosion monitoring system of claim 1, further comprising:
and the data transmitting and receiving device is used for uploading the microclimate monitoring data, the seawater monitoring data, the shell resistance change value, the shell corrosion state information, the offshore wind driven generator number and the position to a land central station.
3. The offshore wind turbine shroud corrosion monitoring system of claim 1, wherein:
the comprehensive data processing device is also used for training a new fuzzy RBF neural network and a full-connection layer classifier by utilizing a self-adaptive genetic algorithm in combination with preset network training data to obtain a fuzzy RBF neural network model;
the comprehensive data processing device is also used for acquiring the corrosion state information of the shell by utilizing a fuzzy RBF neural network model based on the microclimate monitoring data, the seawater monitoring data and the shell resistance change value.
4. The offshore wind turbine shroud corrosion monitoring system of claim 1, wherein:
the comprehensive data processing device also comprises a meteorological data acquisition unit;
the meteorological data acquisition unit is used for carrying out normalization processing on the environmental temperature based on a preset environmental temperature normalization formula;
the meteorological data acquisition unit is used for carrying out normalization processing on the air humidity based on a preset air humidity normalization formula;
the environment temperature normalization formula is as follows:
Figure 671780DEST_PATH_IMAGE001
；
the air humidity normalization formula is as follows:
Figure 57762DEST_PATH_IMAGE002
(ii) a Wherein the content of the first and second substances,
Figure 165395DEST_PATH_IMAGE003
in order to normalize the ambient temperature after the treatment,
Figure 295025DEST_PATH_IMAGE004
in order to detect the obtained ambient temperature,
Figure 702873DEST_PATH_IMAGE005
is the average value of the ambient temperature,
Figure 994177DEST_PATH_IMAGE006
is the ambient temperature variance;
Figure 90571DEST_PATH_IMAGE007
in order to normalize the air humidity after the treatment,
Figure 758313DEST_PATH_IMAGE008
in order to detect the humidity of the air obtained,
Figure 20667DEST_PATH_IMAGE009
the average value of the air humidity is taken as the average value,
Figure 482872DEST_PATH_IMAGE010
is the air humidity variance.
5. The offshore wind turbine shroud corrosion monitoring system of claim 4, wherein:
the comprehensive data processing device also comprises a seawater data acquisition unit;
the seawater data acquisition unit is also used for carrying out normalization treatment on the seawater salinity based on a preset seawater salinity normalization formula;
the seawater data acquisition unit is also used for carrying out normalization treatment on the seawater temperature based on a preset seawater temperature normalization formula;
the seawater data acquisition unit is also used for carrying out normalization treatment on the seawater conductivity based on a preset seawater conductivity normalization formula;
the seawater salinity normalization formula is as follows:
Figure 830677DEST_PATH_IMAGE011
；
the seawater temperature normalization formula is as follows:
Figure 36530DEST_PATH_IMAGE012
；
the seawater conductivity normalization formula is as follows:
Figure 153391DEST_PATH_IMAGE013
(ii) a Wherein the content of the first and second substances,
Figure 848814DEST_PATH_IMAGE014
in order to normalize the salinity of the treated seawater,
Figure 559281DEST_PATH_IMAGE015
in order to detect the salinity of the obtained seawater,
Figure 926415DEST_PATH_IMAGE016
is the average value of the salinity of the seawater,
Figure 38728DEST_PATH_IMAGE017
the variance of the salinity of the seawater is obtained;
Figure 967370DEST_PATH_IMAGE018
in order to normalize the temperature of the treated seawater,
Figure 165133DEST_PATH_IMAGE019
in order to detect the temperature of the obtained seawater,
Figure 837423DEST_PATH_IMAGE020
is the average value of the temperature of the seawater,
Figure 804242DEST_PATH_IMAGE021
is the variance of the seawater temperature;
Figure 169364DEST_PATH_IMAGE022
in order to normalize the conductivity of the treated seawater,
Figure 588844DEST_PATH_IMAGE023
in order to measure the conductivity of the obtained seawater,
Figure 566289DEST_PATH_IMAGE024
the average value of the electric conductivity of the seawater is,
Figure 387615DEST_PATH_IMAGE025
is the variance of the conductivity of the seawater.
6. The offshore wind turbine shroud corrosion monitoring system of claim 5, wherein:
the comprehensive data processing device also comprises a resistance data acquisition unit;
the resistance data acquisition unit is also used for carrying out normalization processing on the shell resistance change value based on a preset shell resistance change value normalization formula;
the normalized formula of the resistance change value of the shell is as follows:
Figure 923638DEST_PATH_IMAGE026
；
Figure 830415DEST_PATH_IMAGE027
in order to normalize the processed resistance variation value of the case,
Figure 844507DEST_PATH_IMAGE028
in order to collect the obtained resistance variation values of the casing,
Figure 785918DEST_PATH_IMAGE029
the initial resistance value is the resistance value when not corroded.
7. The offshore wind turbine shroud corrosion monitoring system of claim 1, wherein:
the comprehensive data processing device also comprises a probe power supply which is used for the resistance probe to supply power at constant current.
8. The offshore wind turbine shroud corrosion monitoring system of claim 1, wherein:
the comprehensive data processing device is also provided with an offshore wind driven generator database;
the offshore wind turbine database is used for storing the number and the position of the offshore wind turbine and the parameters of the offshore wind turbine body.
9. The offshore wind turbine shroud corrosion monitoring system of claim 6, wherein:
the integrated data processing device also comprises a data calculation unit;
the data calculation unit is used for merging the micrometeorological monitoring data, the seawater monitoring data, the shell resistance change value and the shell corrosion state information after normalization processing to obtain merged detection data;
the data calculation unit is also used for training a new fuzzy RBF neural network and a full-connection layer classifier by using a self-adaptive genetic algorithm in combination with preset network training data to obtain a fuzzy RBF neural network model;
the data calculation unit is further used for obtaining the shell corrosion state information by utilizing a fuzzy RBF neural network model based on the microclimate monitoring data, the seawater monitoring data and the shell resistance change value.
10. An offshore wind turbine housing corrosion monitoring method, comprising the steps of:
monitoring microclimate monitoring data around the offshore wind turbine;
collecting seawater in a sea area near an offshore wind driven generator according to a preset period, and detecting the seawater collected by the seawater collection device by matching with a preset seawater monitoring probe to obtain corresponding seawater monitoring data;
monitoring to obtain a shell resistance change value of the offshore wind turbine;
acquiring shell corrosion state information based on the microclimate monitoring data, the seawater monitoring data and the shell resistance change value; wherein the content of the first and second substances,
the microclimate monitoring data comprises ambient temperature and air humidity;
the seawater monitoring data comprises seawater salinity, seawater temperature and seawater conductivity.
CN202210318189.3A 2022-03-29 2022-03-29 Corrosion monitoring system and method for shell of offshore wind turbine Pending CN114413971A (en)

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