CN116771601A - Temperature sensing-based wind turbine generator set optimal control method and system - Google Patents
Temperature sensing-based wind turbine generator set optimal control method and system Download PDFInfo
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- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
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Abstract
The application discloses a wind turbine generator set optimal control method and system based on temperature sensing, and relates to the technical field of optimal control, wherein the method comprises the following steps: acquiring N temperature sensor layout information of a target wind turbine generator; determining N position influence coefficients; acquiring service life information of N sensors and preset service life information of N sensors; obtaining N pieces of fault information; obtaining N reliability coefficients; determining N first extraction frequencies according to N position influence coefficients, and determining N second extraction frequencies according to N reliability coefficients; obtaining N temperature sensor data sets; obtaining a temperature sensing result; and obtaining an optimal control scheme of the wind turbine, and optimally controlling the target wind turbine according to the optimal control scheme of the wind turbine. The method solves the technical problems of lack of deep analysis of the temperature sensor, low reliability of temperature sensing results and poor control effect of the wind turbine generator in the prior art, and achieves the technical effect of improving the temperature sensing accuracy.
Description
Technical Field
The application relates to the technical field of optimal control, in particular to a temperature sensing-based optimal control method and system for a wind turbine.
Background
In the wind power generation process, the power output by the unit needs to be adjusted according to the temperature of the unit, so that the aims of high efficiency and energy conservation are fulfilled. At present, temperature acquisition is realized mainly by installing a temperature sensor on a wind measuring bracket. However, different manufacturers and different matched temperature sensors have different working performances, and along with the use of the sensors, the working reliability of the temperature sensors cannot be guaranteed, so that the control of the wind turbine generator cannot reach the expected effect. The prior art has the technical problems of lack of deep analysis of a temperature sensor, low reliability of a temperature sensing result and poor control effect of the wind turbine generator.
Disclosure of Invention
The application provides a temperature sensing-based wind turbine generator set optimal control method and system, which are used for solving the technical problems of lack of deep analysis of a temperature sensor, low reliability of a temperature sensing result and poor control effect of a wind turbine generator set in the prior art.
In view of the above problems, the application provides a wind turbine generator set optimal control method and system based on temperature sensing.
According to a first aspect of the application, a temperature sensing-based wind turbine generator set optimal control method is provided, and the method comprises the following steps:
acquiring N pieces of temperature sensor layout information of a target wind turbine generator, wherein the N pieces of temperature sensor layout information comprise N pieces of temperature sensor position information and N pieces of temperature sensor configuration information;
determining N position influence coefficients based on the N temperature sensor position information;
respectively extracting service life and preset service life information of the sensors from the N temperature sensor configuration information to obtain N service life information of the sensors and N preset service life information of the sensors;
collecting fault information of the N temperature sensors in a preset historical time period to obtain N pieces of fault information;
performing reliability analysis based on the N fault information, the N sensor service life information and the N sensor preset service life information to obtain N reliability coefficients;
determining N first extraction frequencies according to the N position influence coefficients, and determining N second extraction frequencies according to the N reliability coefficients;
acquiring data information of N temperature sensors in a preset time window to obtain N temperature sensor data sets;
inputting the N first extraction frequencies, the N second extraction frequencies and the N temperature sensor data sets into a temperature sensing model to obtain a temperature sensing result;
and searching based on a mapping relation of the temperature-unit control scheme by taking the temperature sensing result as an index to obtain a wind turbine unit optimal control scheme, and optimally controlling a target wind turbine unit according to the wind turbine unit optimal control scheme.
In a second aspect of the present application, a temperature sensing-based wind turbine optimizing control system is provided, the system comprising:
the system comprises a layout information acquisition module, a control module and a control module, wherein the layout information acquisition module is used for acquiring N temperature sensor layout information of a target wind turbine generator, and the N temperature sensor layout information comprises N temperature sensor position information and N temperature sensor configuration information;
the position influence coefficient determining module is used for determining N position influence coefficients based on the N temperature sensor position information;
the preset service life obtaining module is used for respectively extracting service life and preset service life information of the sensors from the N temperature sensor configuration information to obtain N service life information of the sensors and N preset service life information of the sensors;
the fault information acquisition module is used for acquiring fault information of the N temperature sensors in a preset historical time period and acquiring N fault information;
the reliability coefficient obtaining module is used for carrying out reliability analysis based on the N pieces of fault information, the N pieces of service life information of the sensors and the N pieces of preset service life information of the sensors to obtain N reliability coefficients;
the extraction frequency determining module is used for determining N first extraction frequencies according to the N position influence coefficients and N second extraction frequencies according to the N reliability coefficients;
the sensor data acquisition module is used for acquiring data information of N temperature sensors in a preset time window and acquiring N temperature sensor data sets;
the temperature sensing result obtaining module is used for inputting the N first extraction frequencies, the N second extraction frequencies and the N temperature sensor data sets into a temperature sensing model to obtain a temperature sensing result;
and the optimizing control module is used for searching based on a mapping relation of the temperature sensing result serving as an index and the control scheme of the temperature-unit to obtain an optimizing control scheme of the wind turbine, and optimizing control of the target wind turbine is carried out according to the optimizing control scheme of the wind turbine.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
according to the application, N pieces of temperature sensor layout information of a target wind turbine are obtained, wherein the N pieces of temperature sensor layout information comprise N pieces of temperature sensor position information and N pieces of temperature sensor configuration information, N pieces of position influence coefficients are determined based on the N pieces of temperature sensor position information, further N pieces of temperature sensor service life information and N pieces of temperature sensor preset life information are obtained in advance respectively from the N pieces of temperature sensor configuration information, N pieces of temperature sensor service life information and N pieces of temperature sensor preset life information are obtained, N pieces of fault information are obtained by collecting fault information of the N pieces of temperature sensor in a preset historical time period, then reliability analysis is carried out based on the N pieces of fault information, the N pieces of temperature sensor service life information and the N pieces of temperature sensor preset life information, N pieces of reliability coefficients are obtained, N pieces of first extraction frequencies are determined according to the N pieces of position influence coefficients, N pieces of second extraction frequencies are determined according to the N pieces of reliability coefficients, N pieces of temperature sensor data information in a preset time window are collected, N pieces of temperature sensor data set are obtained, then N pieces of temperature sensor data are mapped and the N pieces of first extraction frequencies and N pieces of temperature sensor data are obtained, the wind turbine are input into a temperature control set to obtain a sensing and optimal sensing result, a temperature control scheme is obtained, a sensing and a sensing result is optimized, a sensing result is obtained, a sensing result is based on a temperature sensing control scheme is optimized, and a temperature sensing control result is obtained, and a sensing result is based on a temperature sensing and a temperature sensing control scheme is optimized. The technical effects of improving the control quality of the wind turbine generator and improving the control accuracy are achieved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a temperature-sensing-based wind turbine optimization control method provided by an embodiment of the application;
FIG. 2 is a schematic flow chart of obtaining N position influence coefficients in a temperature-sensing-based wind turbine generator optimizing control method according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of obtaining N second extraction frequencies in the temperature-sensing-based wind turbine optimizing control method according to the embodiment of the application;
fig. 4 is a schematic structural diagram of a wind turbine generator optimizing control system based on temperature sensing according to an embodiment of the present application.
Reference numerals illustrate: the device comprises a layout information acquisition module 11, a position influence coefficient determination module 12, a preset service life acquisition module 13, a fault information acquisition module 14, a reliability coefficient acquisition module 15, an extraction frequency determination module 16, a sensor data acquisition module 17, a temperature sensing result acquisition module 18 and an optimization control module 19.
Detailed Description
The application provides a temperature sensing-based wind turbine generator set optimal control method and a temperature sensing-based wind turbine generator set optimal control system, which are used for solving the technical problems of lack of deep analysis of a temperature sensor, low reliability of a temperature sensing result and poor control effect of a wind turbine generator set in the prior art.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that the terms "comprises" and "comprising," along with any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus, but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
As shown in fig. 1, the application provides a temperature sensing-based wind turbine generator optimizing control method, wherein the method comprises the following steps:
step S100: acquiring N pieces of temperature sensor layout information of a target wind turbine generator, wherein the N pieces of temperature sensor layout information comprise N pieces of temperature sensor position information and N pieces of temperature sensor configuration information;
in the embodiment of the application, the target wind turbine is any wind turbine which needs to be subjected to unit optimization control based on a temperature sensing result. The N temperature sensor layout information is used for describing the layout condition of the temperature sensors in the target wind turbine generator and comprises N temperature sensor position information and N temperature sensor configuration information. The N temperature sensor position information describes the corresponding positions of the N temperature sensors in the target wind turbine. The configuration information of the N temperature sensors describes the configuration characteristics of the N temperature sensors different from other temperature sensors, including service life, design life, manufacturers and the like.
Step S200: determining N position influence coefficients based on the N temperature sensor position information;
further, as shown in fig. 2, step S200 of the embodiment of the present application further includes:
step S210: acquiring a unit area boundary of the target wind turbine;
step S220: traversing the position information of the N temperature sensors, determining distance values of the N temperature sensors and the boundary of the unit area, and obtaining N longitudinal distance sets and N transverse distance sets;
step S230: determining N first location influence coefficients based on the N sets of longitudinal distances;
step S240: determining N second location influence coefficients based on the N sets of lateral distances;
step S250: and respectively carrying out weighted calculation on the N first position influence coefficients and the N second position influence coefficients according to the preset weight ratio to obtain N position influence coefficients.
In one possible embodiment, the influence degree of different positions distributed in the target wind turbine set due to N temperature sensors on the temperature sensor acquired data, namely the N position influence coefficients, is determined according to the N temperature sensor position information. Thereby, the object of analyzing the accuracy of the data acquired by the sensor from the angle of the sensor position is realized.
Specifically, the unit region boundary of the target wind turbine is a boundary obtained after the serial connection of the positions of the most edge units of the target wind turbine. And determining the position points of the N temperature sensors in the target wind turbine generator by the position information of the N temperature sensors, and respectively calculating the transverse and longitudinal distance values of the position points of the N temperature sensors in the target wind turbine generator from the boundary of the generator area of the target wind turbine generator to obtain N longitudinal distance sets and N transverse distance sets. Each longitudinal distance set comprises two distance values of each temperature sensor from the unit region boundaries at the two longitudinal sides of the target wind turbine. Each transverse distance set comprises two distance values of each temperature sensor from the unit region boundaries at the two transverse sides of the target wind turbine. And respectively selecting smaller distance values in the N longitudinal distance sets, comparing the smaller distance values with the longitudinal distance values of the unit region boundary of the upper target wind turbine, and taking the ratio as N first position influence coefficients. The greater the ratio, the greater the corresponding N first position influence coefficients, and the greater the degree to which the temperature sensor is affected by position. And respectively selecting smaller distance values in the N transverse distance sets, comparing the smaller distance values with the transverse distance values of the unit region boundary of the upper target wind turbine, and taking the ratio as N second position influence coefficients. The greater the ratio, the greater the corresponding N second position influence coefficients, and the greater the degree to which the temperature sensor is affected by position. The preset weight ratio is a weight ratio preset by a person skilled in the art, and the N first position influence coefficients and the N second position influence coefficients are respectively weighted according to the preset weight ratio to obtain N position influence coefficients. The technical effect of improving the accuracy of calculating the position influence coefficient and further improving the accuracy of temperature sensing is achieved.
Step S300: respectively extracting service life and preset service life information of the sensors from the N temperature sensor configuration information to obtain N service life information of the sensors and N preset service life information of the sensors;
step S400: collecting fault information of the N temperature sensors in a preset historical time period to obtain N pieces of fault information;
in one possible embodiment, the sensor service life and the preset service life information are extracted from the N pieces of temperature sensor configuration information, so as to obtain N pieces of sensor service life information and N pieces of sensor preset service life information. Wherein the N sensor lifetime information is a length of time that N sensors have been used. The N sensor preset life information is preset time length which can be used under the condition of no failure.
In one embodiment, N pieces of fault information are obtained by collecting the fault conditions of N temperature sensors in a preset historical time period. The N pieces of fault information are used for carrying out fault recording on the N pieces of temperature sensors, and the N pieces of fault information comprise fault time, fault type, fault degree and the like.
Step S500: performing reliability analysis based on the N fault information, the N sensor service life information and the N sensor preset service life information to obtain N reliability coefficients;
further, step S500 of the embodiment of the present application further includes:
step S510: the reliability formula is:
wherein G is i Is the reliability coefficient of the ith temperature sensor, x 1 Duty ratio, x, of the temperature sensor life affecting reliability 2 The duty ratio, x of the temperature sensor which influences the reliability due to the occurrence of faults 1 +x 2 ＝1，t 0i For the preset life of the ith temperature sensor, t 1i Is the ithThe service life of the temperature sensor is i is the number of the temperature sensors, n is the total failure times of the ith temperature sensor, j is the failure sequence value of the ith temperature sensor in a preset historical time period, i is an integer greater than or equal to 1, n is an integer greater than or equal to 1, j is an integer greater than or equal to 1, y ij Is the damage to the ith temperature sensor when the jth fault occurs, Y ij The service performance value of the sensor when the ith temperature sensor generates the jth fault is used, and alpha is an empirical coefficient for performing fault influence analysis;
step S520: and respectively inputting the N pieces of fault information, the N pieces of service life information of the sensors and the N pieces of preset service life information of the sensors into a reliability analysis formula to perform reliability analysis to obtain the N pieces of reliability coefficients.
Further, step S500 of the embodiment of the present application further includes:
step S530: acquiring performance data of an ith temperature sensor before occurrence of a jth fault to obtain a jth performance data set, wherein the jth performance data set comprises a data transmission speed and a temperature data acquisition lag time;
step S540: and evaluating the jth performance data set by using an expert investigation method to generate a use performance value of the sensor when the jth fault occurs in the ith temperature sensor.
Specifically, N reliability coefficients are obtained by quantitatively analyzing the degrees of influence of the N temperature sensors due to life and failure on the temperature sensors by using a reliability formula. The N reliability coefficients reflect the reliability of the N temperature sensors after use and after fault maintenance.
Specifically, the N fault information, the N sensor service life information and the N sensor preset service life information are respectively input into a reliability analysis formula to perform reliability analysis, so as to obtain the N reliability coefficients.
Specifically, performance data of the ith temperature sensor before the jth fault occurs are collected to obtain a jth performance data set, wherein the jth performance data set comprises a data transmission speed and a temperature data collection lag time. And evaluating the jth performance data set by using an expert investigation method to generate a use performance value of the sensor when the jth fault occurs in the ith temperature sensor. For example, 10 experts are adopted to evaluate the jth performance data set respectively to obtain 10 evaluation values, the 10 evaluation values are subjected to mean value processing, and the processed result is used as the service performance value of the sensor when the jth fault occurs in the ith temperature sensor.
Step S600: determining N first extraction frequencies according to the N position influence coefficients, and determining N second extraction frequencies according to the N reliability coefficients;
further, as shown in fig. 3, step S600 of the embodiment of the present application further includes:
step S610: acquiring a preset extraction frequency;
step S620: respectively comparing N position influence coefficients with the total value of the N position influence coefficients, and multiplying the ratio with a preset extraction frequency to obtain N first extraction frequencies;
step S630: and respectively comparing the N reliability coefficients with the total value of the N reliability coefficients, and multiplying the ratio with a preset extraction frequency to obtain N second extraction frequencies.
In one possible embodiment, the N first extraction frequencies are determined by determining the N position influence coefficients, wherein the first extraction frequencies are intervals of acquisition of data of the temperature sensor. And determining N second extraction frequencies according to the N reliability coefficients, wherein the second extraction frequencies are intervals for acquiring data of the temperature sensor according to the reliability coefficients.
Specifically, the preset extraction frequency is a preset interval time set by a person skilled in the art for collecting temperature sensors in the target wind turbine. And respectively comparing the N position influence coefficients with the N position influence coefficient total values, multiplying the ratio with a preset extraction frequency to obtain N first extraction frequencies, and similarly respectively comparing the N reliability coefficients with the N reliability coefficient total values, and multiplying the ratio with the preset extraction frequency to obtain N second extraction frequencies.
Step S700: acquiring data information of N temperature sensors in a preset time window to obtain N temperature sensor data sets;
step S800: inputting the N first extraction frequencies, the N second extraction frequencies and the N temperature sensor data sets into a temperature sensing model to obtain a temperature sensing result;
further, step S800 of the embodiment of the present application further includes:
step S810: constructing the temperature sensing model, wherein the temperature sensing model comprises a first sensing branch, a second sensing branch and a temperature sensing analysis layer;
step S820: inputting the N first extraction frequencies and the N temperature sensor data sets into a first perception branch to obtain a first perception data extraction result set;
step S830: inputting the N second extraction frequencies and the N temperature sensor data sets into a second perception branch to obtain a second perception data extraction result set;
step S840: and inputting the first sensing data extraction result and the second sensing data extraction result into a temperature sensing analysis layer to obtain a temperature sensing result.
Further, step S800 of the embodiment of the present application further includes:
step S850: acquiring a plurality of sample first extraction frequencies, a plurality of sample temperature sensor data sets and a plurality of sample first perception data extraction result sets as a first training set;
step S860: and performing supervision training on the framework constructed based on the feedforward neural network by using the first training set until the output reaches convergence, so as to obtain a first perception branch.
In one possible embodiment, the preset time window is a preset time period for temperature sensor data acquisition. And acquiring data information acquired by the N temperature sensors within a preset time window to acquire N temperature sensor data sets. The N temperature sensor data sets reflect the temperature change condition of the target wind turbine generator set in a preset time window.
In one embodiment, the temperature sensing model is an intelligent model for analyzing the temperature of the target wind turbine generator, the input data is a data set of N first extraction frequencies, N second extraction frequencies and N temperature sensors, and the output data is a temperature sensing result. The temperature sensing result reflects the temperature change condition of the target wind turbine generator.
Specifically, the temperature sensing model includes a first sensing branch, a second sensing branch, and a temperature sensing analysis layer. Inputting the N first extraction frequencies and the N temperature sensor data sets into a first sensing branch to obtain a first sensing data extraction result set, inputting the N second extraction frequencies and the N temperature sensor data sets into a second sensing branch to obtain a second sensing data extraction result set, and inputting the first sensing data extraction result and the second sensing data extraction result into a temperature sensing analysis layer to obtain a temperature sensing result.
Specifically, a first sampling frequency, a plurality of sampling temperature sensor data sets and a plurality of sampling first perception data extraction result sets are obtained to serve as a first training set, and a framework constructed based on a feedforward neural network is supervised and trained by using the first training set until output reaches convergence, so that a first perception branch is obtained. The technical effect of improving the intelligent degree of temperature sensing analysis is achieved.
Step S900: and searching based on a mapping relation of the temperature-unit control scheme by taking the temperature sensing result as an index to obtain a wind turbine unit optimal control scheme, and optimally controlling a target wind turbine unit according to the wind turbine unit optimal control scheme.
Specifically, the temperature sensing result is used as an index, searching is carried out based on a mapping relation of a temperature-unit control scheme, a wind turbine unit optimal control scheme is obtained, and optimal control is carried out on a target wind turbine unit according to the wind turbine unit optimal control scheme. The wind turbine generator optimizing control scheme is used for optimizing control of the wind turbine generator and comprises a turbine generator control parameter, a turbine generator output power and the like.
Specifically, the mapping relation of the temperature-unit control scheme is used for describing the corresponding relation between the temperature sensing result and the optimized control scheme of the wind turbine. Preferably, the mapping relation of the temperature-unit control scheme is generated according to the corresponding relation between every two of the historical temperature sensing results and the optimal control schemes of the wind turbine units.
In summary, the embodiment of the application has at least the following technical effects:
according to the method, the position, the service life and the faults of the temperature sensors in the target wind turbine are analyzed, N position influence coefficients and N reliability coefficients are determined, then N temperature sensor data sets of a preset time window are acquired, N first extraction frequencies, N second extraction frequencies and N temperature sensor data sets are input into a temperature sensing model to obtain a temperature sensing result, then the temperature sensing result is used as an index, retrieval is carried out based on a mapping relation of a temperature-turbine control scheme, a wind turbine optimal control scheme is obtained, and the target wind turbine is optimally controlled according to the wind turbine optimal control scheme. The technical effects of improving the optimal control accuracy of the wind turbine generator and improving the control efficiency are achieved.
Example two
Based on the same inventive concept as the temperature sensing-based wind turbine optimization control method in the foregoing embodiment, as shown in fig. 4, the present application provides a temperature sensing-based wind turbine optimization control system, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:
the layout information acquisition module 11 is used for acquiring N pieces of temperature sensor layout information of the target wind turbine generator, wherein the N pieces of temperature sensor layout information comprise N pieces of temperature sensor position information and N pieces of temperature sensor configuration information;
a position-affecting coefficient determination module 12, the position-affecting coefficient determination module 12 being configured to determine N position-affecting coefficients based on the N temperature sensor position information;
the preset life obtaining module 13 is configured to extract service life and preset life information of the sensors from the N temperature sensor configuration information, and obtain service life information of the N sensors and preset life information of the N sensors;
the fault information obtaining module 14, the fault information obtaining module 14 is configured to collect fault information of the N temperature sensors occurring in a preset historical time period, and obtain N fault information;
the reliability coefficient obtaining module 15 is configured to perform reliability analysis based on the N pieces of fault information, the N pieces of sensor service life information, and the N pieces of sensor preset service life information, to obtain N reliability coefficients;
an extraction frequency determination module 16, where the extraction frequency determination module 16 is configured to determine N first extraction frequencies according to the N position influence coefficients and determine N second extraction frequencies according to the N reliability coefficients;
the sensor data acquisition module 17 is used for acquiring data information of N temperature sensors in a preset time window to acquire N temperature sensor data sets;
the temperature sensing result obtaining module 18, where the temperature sensing result obtaining module 18 is configured to input the N first extraction frequencies, the N second extraction frequencies, and the N temperature sensor data sets into a temperature sensing model to obtain a temperature sensing result;
and the optimizing control module 19 is used for searching based on a mapping relation of the temperature-unit control scheme by taking the temperature sensing result as an index to obtain a wind turbine unit optimizing control scheme, and optimizing and controlling a target wind turbine unit according to the wind turbine unit optimizing control scheme.
Further, the location influence coefficient determining module 12 is configured to perform the following method:
acquiring a unit area boundary of the target wind turbine;
traversing the position information of the N temperature sensors, determining distance values of the N temperature sensors and the boundary of the unit area, and obtaining N longitudinal distance sets and N transverse distance sets;
determining N first location influence coefficients based on the N sets of longitudinal distances;
determining N second location influence coefficients based on the N sets of lateral distances;
and respectively carrying out weighted calculation on the N first position influence coefficients and the N second position influence coefficients according to the preset weight ratio to obtain N position influence coefficients.
Further, the reliability coefficient obtaining module 15 is configured to perform the following method:
the reliability formula is:
wherein G is i Is the reliability coefficient of the ith temperature sensor, x 1 Duty ratio, x, of the temperature sensor life affecting reliability 2 The duty ratio, x of the temperature sensor which influences the reliability due to the occurrence of faults 1 +x 2 ＝1，t 0i For the preset life of the ith temperature sensor, t 1i For the service life of the ith temperature sensor, i is the number of the temperature sensors, n is the total failure times of the ith temperature sensor, j is the failure sequence value of the ith temperature sensor in a preset historical time period, i is an integer greater than or equal to 1, n is an integer greater than or equal to 1, j is an integer greater than or equal to 1, y ij Is the damage to the ith temperature sensor when the jth fault occurs, Y ij The service performance value of the sensor when the ith temperature sensor generates the jth fault is used, and alpha is an empirical coefficient for performing fault influence analysis;
and respectively inputting the N pieces of fault information, the N pieces of service life information of the sensors and the N pieces of preset service life information of the sensors into a reliability analysis formula to perform reliability analysis to obtain the N pieces of reliability coefficients.
Further, the reliability coefficient obtaining module 15 is configured to perform the following method:
acquiring performance data of an ith temperature sensor before occurrence of a jth fault to obtain a jth performance data set, wherein the jth performance data set comprises a data transmission speed and a temperature data acquisition lag time;
and evaluating the jth performance data set by using an expert investigation method to generate a use performance value of the sensor when the jth fault occurs in the ith temperature sensor.
Further, the extraction frequency determining module 16 is configured to perform the following method:
acquiring a preset extraction frequency;
respectively comparing N position influence coefficients with the total value of the N position influence coefficients, and multiplying the ratio with a preset extraction frequency to obtain N first extraction frequencies;
and respectively comparing the N reliability coefficients with the total value of the N reliability coefficients, and multiplying the ratio with a preset extraction frequency to obtain N second extraction frequencies.
Further, the temperature sensing result obtaining module 18 is configured to perform the following method:
constructing the temperature sensing model, wherein the temperature sensing model comprises a first sensing branch, a second sensing branch and a temperature sensing analysis layer;
inputting the N first extraction frequencies and the N temperature sensor data sets into a first perception branch to obtain a first perception data extraction result set;
inputting the N second extraction frequencies and the N temperature sensor data sets into a second perception branch to obtain a second perception data extraction result set;
and inputting the first sensing data extraction result and the second sensing data extraction result into a temperature sensing analysis layer to obtain a temperature sensing result.
Further, the temperature sensing result obtaining module 18 is configured to perform the following method:
acquiring a plurality of sample first extraction frequencies, a plurality of sample temperature sensor data sets and a plurality of sample first perception data extraction result sets as a first training set;
and performing supervision training on the framework constructed based on the feedforward neural network by using the first training set until the output reaches convergence, so as to obtain a first perception branch.
It should be noted that the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.
The specification and figures are merely exemplary illustrations of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, the present application is intended to include such modifications and alterations insofar as they come within the scope of the application or the equivalents thereof.
Claims (8)
1. The optimal control method of the wind turbine generator set based on temperature sensing is characterized by comprising the following steps of:
acquiring N pieces of temperature sensor layout information of a target wind turbine generator, wherein the N pieces of temperature sensor layout information comprise N pieces of temperature sensor position information and N pieces of temperature sensor configuration information;
determining N position influence coefficients based on the N temperature sensor position information;
respectively extracting service life and preset service life information of the sensors from the N temperature sensor configuration information to obtain N service life information of the sensors and N preset service life information of the sensors;
collecting fault information of the N temperature sensors in a preset historical time period to obtain N pieces of fault information;
performing reliability analysis based on the N fault information, the N sensor service life information and the N sensor preset service life information to obtain N reliability coefficients;
determining N first extraction frequencies according to the N position influence coefficients, and determining N second extraction frequencies according to the N reliability coefficients;
acquiring data information of N temperature sensors in a preset time window to obtain N temperature sensor data sets;
inputting the N first extraction frequencies, the N second extraction frequencies and the N temperature sensor data sets into a temperature sensing model to obtain a temperature sensing result;
and searching based on a mapping relation of the temperature-unit control scheme by taking the temperature sensing result as an index to obtain a wind turbine unit optimal control scheme, and optimally controlling a target wind turbine unit according to the wind turbine unit optimal control scheme.
2. The method of claim 1, wherein the method comprises:
acquiring a unit area boundary of the target wind turbine;
traversing the position information of the N temperature sensors, determining distance values of the N temperature sensors and the boundary of the unit area, and obtaining N longitudinal distance sets and N transverse distance sets;
determining N first location influence coefficients based on the N sets of longitudinal distances;
determining N second location influence coefficients based on the N sets of lateral distances;
and respectively carrying out weighted calculation on the N first position influence coefficients and the N second position influence coefficients according to the preset weight ratio to obtain N position influence coefficients.
3. The method of claim 1, wherein the method comprises:
the reliability formula is:
wherein G is i Is the reliability coefficient of the ith temperature sensor, x 1 Duty ratio, x, of the temperature sensor life affecting reliability 2 The duty ratio, x of the temperature sensor which influences the reliability due to the occurrence of faults 1 +x 2 ＝1，t 0i For the preset life of the ith temperature sensor, t 1i For the service life of the ith temperature sensor, i is the number of the temperature sensors, n is the total failure times of the ith temperature sensor, j is the failure sequence value of the ith temperature sensor in a preset historical time period, i is an integer greater than or equal to 1, n is an integer greater than or equal to 1, j is an integer greater than or equal to 1, y ij Is the damage to the ith temperature sensor when the jth fault occurs, Y ij The service performance value of the sensor when the ith temperature sensor generates the jth fault is used, and alpha is an empirical coefficient for performing fault influence analysis;
and respectively inputting the N pieces of fault information, the N pieces of service life information of the sensors and the N pieces of preset service life information of the sensors into a reliability analysis formula to perform reliability analysis to obtain the N pieces of reliability coefficients.
4. A method according to claim 3, wherein the method comprises:
acquiring performance data of an ith temperature sensor before occurrence of a jth fault to obtain a jth performance data set, wherein the jth performance data set comprises a data transmission speed and a temperature data acquisition lag time;
and evaluating the jth performance data set by using an expert investigation method to generate a use performance value of the sensor when the jth fault occurs in the ith temperature sensor.
5. The method of claim 1, wherein the method comprises:
acquiring a preset extraction frequency;
respectively comparing N position influence coefficients with the total value of the N position influence coefficients, and multiplying the ratio with a preset extraction frequency to obtain N first extraction frequencies;
and respectively comparing the N reliability coefficients with the total value of the N reliability coefficients, and multiplying the ratio with a preset extraction frequency to obtain N second extraction frequencies.
6. The method of claim 1, wherein the method comprises:
constructing the temperature sensing model, wherein the temperature sensing model comprises a first sensing branch, a second sensing branch and a temperature sensing analysis layer;
inputting the N first extraction frequencies and the N temperature sensor data sets into a first perception branch to obtain a first perception data extraction result set;
inputting the N second extraction frequencies and the N temperature sensor data sets into a second perception branch to obtain a second perception data extraction result set;
and inputting the first sensing data extraction result and the second sensing data extraction result into a temperature sensing analysis layer to obtain a temperature sensing result.
7. The method of claim 6, wherein the method comprises:
acquiring a plurality of sample first extraction frequencies, a plurality of sample temperature sensor data sets and a plurality of sample first perception data extraction result sets as a first training set;
and performing supervision training on the framework constructed based on the feedforward neural network by using the first training set until the output reaches convergence, so as to obtain a first perception branch.
8. Wind turbine generator system optimizing control system based on temperature perception, which is characterized in that the system comprises:
the system comprises a layout information acquisition module, a control module and a control module, wherein the layout information acquisition module is used for acquiring N temperature sensor layout information of a target wind turbine generator, and the N temperature sensor layout information comprises N temperature sensor position information and N temperature sensor configuration information;
the position influence coefficient determining module is used for determining N position influence coefficients based on the N temperature sensor position information;
the preset service life obtaining module is used for respectively extracting service life and preset service life information of the sensors from the N temperature sensor configuration information to obtain N service life information of the sensors and N preset service life information of the sensors;
the fault information acquisition module is used for acquiring fault information of the N temperature sensors in a preset historical time period and acquiring N fault information;
the reliability coefficient obtaining module is used for carrying out reliability analysis based on the N pieces of fault information, the N pieces of service life information of the sensors and the N pieces of preset service life information of the sensors to obtain N reliability coefficients;
the extraction frequency determining module is used for determining N first extraction frequencies according to the N position influence coefficients and N second extraction frequencies according to the N reliability coefficients;
the sensor data acquisition module is used for acquiring data information of N temperature sensors in a preset time window and acquiring N temperature sensor data sets;
the temperature sensing result obtaining module is used for inputting the N first extraction frequencies, the N second extraction frequencies and the N temperature sensor data sets into a temperature sensing model to obtain a temperature sensing result;
and the optimizing control module is used for searching based on a mapping relation of the temperature sensing result serving as an index and the control scheme of the temperature-unit to obtain an optimizing control scheme of the wind turbine, and optimizing control of the target wind turbine is carried out according to the optimizing control scheme of the wind turbine.
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| CN117434979B (en) * | 2023-12-06 | 2024-03-12 | 徐州优博电子科技有限公司 | Temperature control box control and temperature measurement method |
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