CN115839320B - Deicing control method and system for wind power blade - Google Patents
Deicing control method and system for wind power blade Download PDFInfo
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Abstract
The application provides a wind turbine blade deicing control method and system, belongs to the technical field of wind turbine blades, and comprises the steps of embedding a gas-heat cycle detection device in a wind turbine blade deicing control system, conducting data sensing on a power generation closed cavity, conducting data sensing on an air inlet, acquiring real-time working condition data of a generator, conducting gas-heat prediction, comparing inner cavity gas-heat sensing data with predicted gas-heat data, acquiring optimization control parameters, and conducting deicing optimization control on a target wind turbine blade. The method solves the technical problems that in the prior art, the gas heat loss cannot be controlled, so that the energy loss is high and the deicing efficiency is low in the deicing process of the wind power blade, and reduces the gas heat loss by optimizing control parameters, so that the energy consumption is reduced, the deicing effect is enhanced, and the deicing efficiency is improved.
Description
Technical Field
The application belongs to the technical field of wind power blades, and particularly relates to a wind power blade deicing control method and system.
Background
Wind energy is one of important green energy, and the wind energy is a safe, clean and uninteresting power generation form breeds huge energy and business opportunity, and wind energy is particularly rich in resources of the plateau, cold, ridge and mountain top, has huge development value, but the places are high in altitude, high in humidity and low in temperature, and can easily cause icing of blades, the pneumatic performance of the blades is affected by icing, on one hand, overload of the blades and uneven load distribution of the blades can be caused, and further, continuously produced wind energy is greatly affected, and on the other hand, when the blades are in the rotating process, operation accidents caused by ice dropping are extremely easy to occur. The load is increased after the blades are frozen, the service life of the blades is directly influenced, and the ice load on each blade is different, so that the unbalanced load of the unit is increased, serious harm is generated to the unit if countermeasures are not timely taken, the possibility of off-grid shutdown of the fan blade without preventing and removing ice protection is faced, and the whole annual generating capacity is greatly reduced in a low-temperature area.
The existing wind power blade deicing technology cannot control the air heat loss, so that the energy loss is high and the deicing efficiency is low in the wind power blade deicing process.
Disclosure of Invention
The application provides a wind power blade deicing control method and system, which aim to solve the technical problems that in the prior art, the gas heat loss cannot be controlled, so that the energy loss is high and the deicing efficiency is low in the deicing process of the wind power blade.
In a first aspect, an embodiment of the present application provides a wind power blade deicing control method, including: embedding the gas-heat cycle detection device in the wind power blade deicing control system, wherein the gas-heat cycle detection device comprises an inner cavity sensor and an outer cavity sensor; performing data sensing on a power generation closed cavity in the target wind power blade according to the inner cavity sensor to obtain inner cavity gas-heat sensing data; performing data sensing on an air inlet of the target wind power blade according to the external cavity sensor to obtain external cavity air heat sensing data; acquiring real-time working condition data of a generator in the target wind power blade; carrying out gas-heat prediction by using the real-time working condition data and the external cavity gas-heat sensing data to obtain predicted gas-heat data; comparing the inner cavity gas heat sensing data with the predicted gas heat data to obtain an optimized control parameter; and deicing optimization control is carried out on the target wind power blade according to the optimization control parameters.
In a second aspect, embodiments of the present application provide a wind turbine blade deicing control system, including: the sensor installation module is used for embedding the gas-heat cycle detection device into the wind power blade deicing control system, wherein the gas-heat cycle detection device comprises an inner cavity sensor and an outer cavity sensor; the inner cavity gas-heat sensing data acquisition module is used for carrying out data sensing on a power generation closed cavity in the target wind power blade according to the inner cavity sensor to acquire inner cavity gas-heat sensing data; the external cavity gas-heat sensing data acquisition module is used for carrying out data sensing on the air inlet of the target wind power blade according to the external cavity sensor to acquire external cavity gas-heat sensing data; the real-time working condition data acquisition module is used for acquiring real-time working condition data of the generator in the target wind power blade; the prediction gas heat data acquisition module is used for performing gas heat prediction by using the real-time working condition data and the external cavity gas heat sensing data to acquire prediction gas heat data; the optimal control parameter acquisition module is used for comparing the inner cavity gas heat sensing data with the predicted gas heat data to acquire optimal control parameters; and the deicing optimization control module is used for carrying out deicing optimization control on the target wind power blade according to the optimization control parameters.
By adopting the technical scheme, the technical problems that in the prior art, the air heat loss cannot be controlled, so that the energy loss is high and the deicing efficiency is low in the deicing process of the wind power blade are solved. By optimizing the control parameters, the method reduces the gas heat loss, further reduces the energy consumption, and achieves the technical effects of enhancing the deicing effect and improving the deicing efficiency.
Further, comparing the inner cavity gas-heat sensing data with the predicted gas-heat data, and building a gas-heat comparison model, wherein the gas-heat comparison model comprises a variable adjustment sub-model and a comparison output sub-model; inputting the predicted gas-heat data into the variable adjustment sub-model, and performing multivariate index analysis according to the variable adjustment sub-model to obtain adjustment gas-heat data; and inputting the adjustment gas-heat data and the inner cavity gas-heat sensing data into the comparison output submodel to obtain the optimization control parameters.
By adopting the technical scheme, the linear relation between the wind power blade equipment data and the air heat data is mastered, and the air heat data is obtained by adjusting the undetermined parameters, so that the data support is provided for improving the deicing effect.
Further, before the predicted aero-thermal data is input into the variable adjustment sub-model, acquiring equipment geometric data, equipment component attributes and equipment connection structures of the target wind power blade; obtaining air inlet distribution information, air return channel information and heat absorption cavity structure information according to the equipment geometric data, the equipment component attribute and the equipment connection structure; and constructing the variable adjustment sub-model by taking the air inlet distribution information, the air return channel information and the heat absorption cavity structure information as multivariate indexes.
By adopting the technical scheme, the statistical analysis of the data of different wind power blade equipment is realized, and the effect of intuitively reflecting the influence of the data of each equipment on the gas-heat loss is achieved by constructing a variable regulator sub-model.
Further, when the optimized control parameters are obtained, obtaining the gas-heat loss data according to the adjustment gas-heat data and the inner cavity gas-heat sensing data; and introducing a first loss function to analyze the gas-heat loss data to obtain a feedback regulation parameter, and outputting the feedback regulation parameter as the optimal control parameter.
By adopting the technical scheme, the model reaches a convergence state by minimizing the loss function, and the error of the model predicted value is reduced, so that the aim of accurate prediction is fulfilled.
Further, the calculation formula of the first loss function is as follows:
wherein ,
for prediction of qi-heat data +.>
For inner cavity gas heat sensing data, +.>
For the predicted aero-thermal data of column i, < >>
For the inner cavity gas-heat sensing data of column i, < >>
For the variable adjustment factor, +.>
，/>
For a variable adjustment factor based on the air intake distribution information, < >>
For the variable adjustment factor based on the gas return channel information, < >>
For the variable adjustment coefficient based on the endothermic chamber structure information, n is the total group number of the sensing data, and is n columns in one row of data.
By adopting the technical scheme, the model is secondarily optimized based on the equipment data, so that the effect of continuously optimizing and improving the accuracy of the prediction model is achieved.
Further, the deicing optimization control of the target wind power blade further comprises the steps of obtaining a wind valve distribution node of the target wind power blade, and generating an air volume control node according to the wind valve distribution node; and obtaining the air valve control parameters correspondingly output at the air volume control node according to the optimized control parameters, and controlling the circulating air volume according to the air valve control parameters.
By adopting the technical scheme, the wind power blade deicing control system realizes the adjustment of newly added control parameters from multiple aspects, and further improves the deicing effect.
Further, after air inlet distribution information is obtained, assembling N outer cavity sensors according to the air inlet distribution information, and acquiring N outer cavity gas-heat sensing data of the N outer cavity sensors, wherein the N outer cavity sensors are provided with the same data transmission terminal; performing average value calculation on the N pieces of external cavity air heat sensing data to obtain external cavity average value air heat sensing data; and outputting the outer cavity mean gas-heat sensing data as the outer cavity gas-heat sensing data.
By adopting the technical scheme, the detection of a plurality of air inlets is realized, and the effect of improving the data accuracy is achieved.
The beneficial effects of this application are:
1. according to the method, the optimization control parameters are obtained by comparing the inner cavity gas heat sensing data with the prediction gas heat data, so that the accurate control of the gas heat loss is realized.
2. According to the deicing optimization control method and device for the target wind power blade, deicing optimization control is carried out on the target wind power blade according to the optimization control parameters, so that the reduction of air heat loss is achieved, the energy consumption is further reduced, and the technical effects of enhancing deicing effect and improving deicing efficiency are achieved.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and drawings.
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The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate the application and together with the embodiments of the application, and not constitute a limitation to the application. In the drawings:
FIG. 1 is a schematic flow chart of a deicing control method for wind power blades according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of obtaining optimized control parameters in a wind turbine blade deicing control method according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of building a variable adjustment sub-model in a wind turbine blade deicing control method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a deicing control system for wind power blades according to an embodiment of the present application.
Reference numerals: the system comprises a sensor installation module 10, an inner cavity gas-heat sensing data acquisition module 20, an outer cavity gas-heat sensing data acquisition module 30, a real-time working condition data acquisition module 40, a prediction gas-heat data acquisition module 50, an optimization control parameter acquisition module 60 and a deicing optimization control module 70.
Detailed Description
In order to make the objects, technical solutions and advantages of the technical solutions of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings of the specific embodiments of the present application. Like reference numerals in the drawings denote like parts. It should be noted that the described embodiments are some, but not all embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without the benefit of the present disclosure, are intended to be within the scope of the present application based on the described embodiments.
Referring to fig. 1, an embodiment of the present application provides a wind power blade deicing control method, including:
step S100: embedding the gas-heat cycle detection device in the wind power blade deicing control system, wherein the gas-heat cycle detection device comprises an inner cavity sensor and an outer cavity sensor;
the implementation mode specifically comprises the following steps: the wind power blade is a core component for converting natural wind energy into wind power generation set electric energy in the wind power generation set, and is also a main basis for measuring the design and technical level of the wind power generation set. The embodiment of the application provides a wind power blade deicing control method, which is applied to a wind power blade deicing control system. For achieving better deicing effect, detect wind-powered electricity generation blade's air through embedding gas thermal cycle detection device, gas thermal cycle detection device is for detecting the device such as temperature, humidity of the air that gets into wind-powered electricity generation blade, including inner chamber sensor and outer chamber sensor, the inner chamber sensor is used for detecting wind-powered electricity generation blade's the airtight cavity of electricity generation air, and outer chamber sensor is used for detecting wind-powered electricity generation blade's air intake.
Step S200: performing data sensing on a power generation closed cavity in the target wind power blade according to the inner cavity sensor to obtain inner cavity gas-heat sensing data;
the implementation mode specifically comprises the following steps: the power generation closed cavity is a power generation part of the wind power blade, and generates a large amount of heat in the running process of the wind power blade, and the extracted air absorbs the heat in the power generation closed cavity to enable the temperature of the air to rise so as to be used for deicing the wind power blade. The inner cavity sensor is a device for detecting the temperature and humidity of air in the power generation closed cavity in real time, and is generally a temperature and humidity sensor. The inner cavity sensor takes a temperature and humidity integrated probe as a temperature measuring element, collects temperature and humidity signals, converts the temperature and humidity signals into current signals or voltage signals which are in linear relation with the temperature and the humidity after circuit processing, outputs the current signals or the voltage signals, and uploads the collected signals to the deicing control system of the wind power blade. The remote data acquisition and transmission are realized, and the technical effects of reducing the workload and improving the working efficiency are achieved.
Step S300: performing data sensing on an air inlet of the target wind power blade according to the external cavity sensor to obtain external cavity air heat sensing data;
the implementation mode specifically comprises the following steps: the air inlet is the end equipment that wind-powered electricity generation blade was used for supplying air and return air, also refers to the distribution equipment of air, can carry out the extraction with the air and send into the airtight chamber of electricity generation through the air inlet in to there is a return air inlet in air inlet department for with the impurity air in the wind-powered electricity generation blade send out through the return air inlet. And an outer cavity sensor is arranged at the air inlet of the wind power blade and used for collecting the temperature and the humidity of air at the air inlet, and the collected information is uploaded to a wind power blade deicing control system according to the same method. The remote data acquisition and transmission are realized, and the technical effects of reducing the workload and improving the working efficiency are achieved.
Step S400: acquiring real-time working condition data of a generator in the target wind power blade;
the implementation mode specifically comprises the following steps: in the running process of the generator, mechanical energy generated by rotation of the rotating shaft is converted into magnetic field energy storage increment, heat loss and electric energy, and the loss of the heat energy converted into the motor causes the generator to generate heat, and the loss of the part is divided into the following three types: firstly, the resistance loss in the circuit, namely the basic copper loss; the second is the core loss in the magnetic circuit, including the magnetic domain continuously rotates due to the magnetic field, the magnetic domain rubs with each other, the hysteresis loss is generated, and the induced electromotive force and the induced current of the ferromagnetic material form eddy current under the magnetic field, so as to generate eddy current loss; thirdly, various mechanical friction losses. The identification data such as rated voltage, resistance and the like of the wind power blade generator are obtained through the wind power blade management system, and real-time rotating speed, power and the like during operation are obtained through calculation, so that how much heat can be absorbed during operation of the wind power blade generator. The real-time working condition data of the generator is obtained, so that the operation data of the generator is mastered, and a foundation is laid for subsequent gas-heat prediction.
Step S500: carrying out gas-heat prediction by using the real-time working condition data and the external cavity gas-heat sensing data to obtain predicted gas-heat data;
the implementation mode specifically comprises the following steps: the heat generated during the operation of the generator is a main source of heat absorption of air, the thermal power of the generator refers to the power lost by heat generation on a section of circuit, and the magnitude of the thermal power is determined by the product of the square of the current intensity in the section of conductor and the conductor resistance R, namely the motor heating power p=i, R, so that the heat q=pt generated by the generator in the time t can be obtained. In general, the specific heat capacity c.apprxeq.1000J/Kg℃and the air density of air
Is fixed, the space volume in the power generation closed cavity, namely the volume V of air is obtained, and the temperature variation is calculated>
Acquiring initial temperature of air according to external cavity gas heat sensing data>
Theoretically, the temperature of the air after heat absorption in the power generation sealed chamber is +.>
Will->
As predicted aero-thermal data. The calculation of the predicted gas heat data is realized, the grasp of the ideal state is achieved, and a foundation is laid for the follow-up acquisition of the optimized control parameters.
Step S600: comparing the inner cavity gas heat sensing data with the predicted gas heat data to obtain an optimized control parameter;
the implementation mode specifically comprises the following steps: in the actual operation process, the actually measured air heat transmission data of the inner cavity is lower than the predicted air heat data due to the existence of air heat loss, namely, the heat lost by a closed system or space, such as that a part of heat can be taken away by air flow, the wind power blade equipment is exposed out of metal structures in the atmosphere and the like to dissipate heat to the outside air, and the dissipated heat is related to the size of the cavity of the wind power blade, the heat insulation property of materials and the heat insulation condition, and is also related to the quantity and the position of the distribution of air inlets according to the air inlet condition and the like. In order to enable the actually measured inner cavity gas-heat transmission data to be infinitely close to the predicted gas-heat data, the gas-heat loss needs to be minimized, the gas-heat loss data of a plurality of wind power blades with different conditions are obtained, and the optimal equipment data are obtained by comparing the equipment data of each wind power blade, so that the optimal equipment data are used as optimal control parameters. The method has the advantages that the control of the gas-heat loss condition of the wind power blade equipment is realized, and the influence of the data of each equipment on the gas-heat loss is intuitively reflected by optimizing the acquisition of control parameters.
Step S700: and deicing optimization control is carried out on the target wind power blade according to the optimization control parameters.
The implementation mode specifically comprises the following steps: the optimal control parameters are optimal equipment data obtained by comparing the equipment data of each wind power blade, so that the target wind power blade is adjusted, the air heat loss of the air heat absorption in the power generation closed cavity is reduced, the air heat data of the air injected into the air backflow cavity is close to a predicted value as much as possible, the energy consumption is saved, and the deicing efficiency is improved.
Further, referring to fig. 2, in the embodiment of the present application, the step of comparing the inner cavity gas-heat sensing data and the predicted gas-heat data to obtain the optimized control parameter includes:
step S610: building an air-heat comparison model, wherein the air-heat comparison model comprises a variable adjustment sub-model and a comparison output sub-model;
step S620: inputting the predicted gas-heat data into the variable adjustment sub-model, and performing multivariate index analysis according to the variable adjustment sub-model to obtain adjustment gas-heat data;
step S630: and inputting the adjustment gas-heat data and the inner cavity gas-heat sensing data into the comparison output submodel to obtain the optimization control parameters.
The implementation mode specifically comprises the following steps: acquiring equipment data of the wind power blade, acquiring air inlet distribution information, air return channel information and heat absorption cavity structure information according to the equipment data of the wind power blade, constructing a variable adjustment sub-model according to the linear relation between the variable index and the air heat data, predicting the air temperature of the air heat in the power generation closed cavity after heat absorption in an ideal state, taking the air temperature as a variable adjustment target, performing multi-variable index analysis on the variable adjustment sub-model, and comparing an output sub-model with the variable adjustment sub-model for receiving the adjustment air heat data output by the variable adjustment sub-model.
Further, the multi-variable index is analyzed by a least square method to find each index parameter
Is used to determine the optimal estimate of (1). Known tuyere distribution information->
Information about gas return channel>
And endothermic chamber structural information->
Data of qi and heat->
There is a linear relationship, let (x, y) be a set of observables, and +.>
∈R，/>
The following theoretical functions are satisfied:
, wherein />
For undetermined parameters, for finding the function +.>
Parameter of->
For a given m groups of observations +.>
Solving an objective function
Thus, the parameter +.>
(i=1, 2, 3), parameter of minimum value +.>
Corresponding optimal aero-thermal data->
And as the adjustment gas-heat data, the optimization control parameters are obtained by comparing the adjustment gas-heat data with the inner cavity gas-heat sensing data.
Through multivariate index analysis, the linear relation between wind power blade equipment data and air heat data is mastered, and through adjusting undetermined parameters, air heat data is obtained, and data support is provided for improving deicing effect.
Further, referring to fig. 3, the building variable adjustment sub-model according to the embodiment of the present application specifically includes:
step S611: acquiring equipment geometric data, equipment component attributes and equipment connection structures of the target wind power blade;
step S612: obtaining air inlet distribution information, air return channel information and heat absorption cavity structure information according to the equipment geometric data, the equipment component attribute and the equipment connection structure;
step S613: and constructing the variable adjustment sub-model by taking the air inlet distribution information, the air return channel information and the heat absorption cavity structure information as multivariate indexes.
The implementation mode specifically comprises the following steps: and acquiring equipment geometric data, equipment component properties, equipment connection structures and the like of the target wind power blade according to the wind power blade equipment identifier, wherein the geometric data are the size, the surface area, the diameter and the like of equipment, the component properties are the materials, the performances and the like of the equipment, and the equipment connection structures are the connection relations among a plurality of pieces of equipment in the target wind power blade, the length, the capacity and the like of a connection channel. The method comprises the steps of obtaining air port distribution information, air backflow channel information and heat absorption cavity structure information, wherein the air port distribution information comprises the number and the position of air inlet distribution, the air backflow channel information comprises channel wall materials, heat preservation property, thickness and the like, and the heat absorption cavity structure information comprises the size and heat loss of a cavity.
Distributing information of air openings
Information about gas return channel>
And endothermic chamber structural information->
As regression variable, data of aero-thermal +.>
As dependent variables, regression analysis is carried out on the dependent variables, firstly, statistical rules of influence of the regression variables on the dependent variables y are calculated, namely, structures of a plurality of wind power blades are compared, stability of frequency of occurrence of random events in a large number of observation or experiments is compared, secondly, factor analysis is carried out, namely, which of a plurality of regression variables can influence the dependent variables y is found, finally, after influence of other variables is fixed, correlation degree of each regression variable on the dependent variables y is examined, and finally wind gap distribution information is obtained>
Information about gas return channel>
And endothermic chamber structural information->
Data of qi and heat->
And (3) constructing a variable adjustment sub-model according to the linear relation between the two.
The statistical analysis of the data of different wind power blade equipment is realized, and the effect of intuitively reflecting the influence of the data of each equipment on the gas-heat loss is achieved by constructing a variable adjustment sub-model.
Further, in the embodiment of the present application, the step of inputting the adjustment gas-heat data and the inner cavity gas-heat sensing data into the comparison output sub-model to obtain the optimized control parameter includes:
step S631: acquiring gas heat loss data according to the adjustment gas heat data and the inner cavity gas heat sensing data;
step S632: and introducing a first loss function to analyze the gas-heat loss data to obtain a feedback regulation parameter, and outputting the feedback regulation parameter as the optimal control parameter.
The implementation mode specifically comprises the following steps: the air heat data are optimal air heat data obtained when the undetermined parameters are adjusted to be optimal, namely the highest air heat data which can be achieved in the current capacity state of the wind power blade, the air heat sensing data of the inner cavity are real-time air heat data of the target wind power blade, air heat loss data can be obtained according to the difference value of the air heat sensing data and the real-time air heat data, and the air heat loss data represent the air heat absorption difference between the target wind power blade and the optimal wind power blade.
The loss function is a function which maps a random event or a value of a related random variable thereof into a non-negative real number to represent risk or loss of the random event, namely, the loss function is minimized to solve and evaluate the gas-heat loss data, the common loss function comprises a 0-1 loss function, an absolute value loss function, a log logarithm loss function and the like, different loss functions are adopted to have different influences on a model, and parameters are continuously adjusted through the minimization of the loss function, so that predicted gas-heat data and inner cavity gas-heat sensing data are infinitely close to each other to obtain optimal parameters, and the optimal parameters are used as feedback adjustment parameters. The model reaches a convergence state by minimizing the loss function, so that the error of the model predicted value is reduced, and the aim of accurate prediction is fulfilled.
Further, step S632 of the embodiment of the present application further includes:
step S6321: the calculation formula of the first loss function is as follows:
wherein ,
for prediction of qi-heat data +.>
For inner cavity gas heat sensing data, +.>
For the predicted aero-thermal data of column i, < >>
For the inner cavity gas-heat sensing data of column i, < >>
For the variable adjustment factor, +.>
，/>
For a variable adjustment factor based on the air intake distribution information, < >>
For the variable adjustment factor based on the gas return channel information, < >>
For the variable adjustment coefficient based on the endothermic chamber structure information, n is n columns in the data.
The implementation mode specifically comprises the following steps: acquiring multiple groups of loss data according to a mean square error loss function
Wherein the predicted gas-heat data and the inner cavity gas-heat sensing data respectively corresponding to the plurality of time point sequences form n groups, the calculation of the loss function is used for calculating comparison loss data of the n groups of data, and the comparison loss data is represented by->
、/>
、/>
Three-dimensional variable control factor, determining the final variable control factor +.>
Adjusting the corresponding predicted aero-thermal data, constructing a first loss function +.>
For measuring the distance from the sample point to the regression curve, the sample point can be better fitted to the regression curve by minimizing the square loss. And inputting the obtained loss function and loss data into a comparison output sub-model, and performing secondary optimization on the model based on equipment data to achieve the effects of continuous optimization and improvement of the accuracy of the prediction model.
Further, the step of performing deicing optimization control on the target wind power blade according to the optimization control parameter in the embodiment of the application includes:
step S810: acquiring a wind valve distribution node of the target wind power blade, and generating an air quantity control node according to the wind valve distribution node;
step S820: and obtaining the air valve control parameters correspondingly output at the air volume control node according to the optimized control parameters, and controlling the circulating air volume according to the air valve control parameters.
The implementation mode specifically comprises the following steps: the air valve is also called an air quantity regulating valve, is an indispensable end fitting in ventilation and air extraction of a wind power blade, is generally used in a ventilation pipeline and is used for regulating the air quantity of a branch pipe and also can be used for mixing and regulating fresh air and return air. The method comprises the steps of obtaining the air quantity x of a distribution position of an air valve, the maximum air quantity y of the air valve and the quantity z of the air valve, constructing a space rectangular coordinate system according to the air quantity x of the air valve, f (x, y, z) is the air quantity of a target wind power blade, control parameters of x, y and z are air quantity control nodes, performing standardization processing on f (x, y, z) and optimization control parameters, namely, enabling all parameters to be in the same order of magnitude so as to perform comprehensive regulation, generally speaking, arranging the air valve in a wind turbine generator, regulating the air quantity, and arranging a real-time detector on the air valve, sensing the real-time air quantity, feeding corresponding sensing data back to a control system of the wind turbine generator, and generating feedback regulation parameters by using a set position point of the air valve, the real-time sensed air quantity, the flow quantity and the like to reach the opening degree of the air valve, regulating the air valve control parameters (the opening degree of each air valve) to enable the optimization control parameters to reach the optimal control parameters, and outputting the air valve control parameters, namely, the optimal air valve control parameters are circulated, and the air valve control parameters are controlled. The wind power blade deicing control system has the advantages that the newly added control parameters are realized, the wind power blade deicing control system is adjusted from multiple aspects, and the deicing effect is further improved.
Further, in the embodiment of the application, the step of acquiring the external cavity air heat sensing data according to the external cavity sensor to perform data sensing on the air inlet of the target wind power blade includes:
step S310: assembling N outer cavity sensors according to the air inlet distribution information to obtain N outer cavity air-heat sensing data of the N outer cavity sensors, wherein the N outer cavity sensors are provided with the same data transmission terminal;
step S320: performing average value calculation on the N pieces of external cavity air heat sensing data to obtain external cavity average value air heat sensing data;
step S330: and outputting the outer cavity mean gas-heat sensing data as the outer cavity gas-heat sensing data.
The implementation mode specifically comprises the following steps: different positions of the air inlets can cause different air inlet amounts, if the air inlet A is in a position of upwind, the air inlet A is in a position of downwind, if the air inlet B is in a position of downwind, the distribution positions of a plurality of air inlets are obtained according to the distribution information of the air inlets, and the plurality of air inlets with larger distribution positions are assembled by the external cavity sensor, such as the air inletThe opening A and the air inlet B are required to be arranged one by one, and the air inlet C which is positioned at the lee position with the air inlet B is not required. The plurality of external cavity sensors detect the air inlet at the same time, and upload data to the wind power blade deicing control system in real time to obtain air heat sensing data
N pieces of gas-heat sensing data are data of air inlets at different positions, and mean value calculation is carried out on the N pieces of gas-heat sensing data to obtain outer cavity mean value gas-heat sensing data +.>
The central position of the air inlet observation values, which are relatively concentrated, is used as the outer cavity air heat sensing data. The detection of a plurality of air inlets is realized, and the effect of improving the data accuracy is achieved.
Referring to fig. 4, based on the same application concept as a wind turbine blade deicing control method in the foregoing embodiment, an embodiment of the present application provides a wind turbine blade deicing control system, including:
the sensor installation module 10 is used for embedding the gas-heat cycle detection device into the wind power blade deicing control system, wherein the gas-heat cycle detection device comprises an inner cavity sensor and an outer cavity sensor;
the inner cavity gas-heat sensing data acquisition module 20 is used for carrying out data sensing on a power generation closed cavity in the target wind power blade according to the inner cavity sensor to acquire inner cavity gas-heat sensing data;
the external cavity gas-heat sensing data acquisition module 30 is used for carrying out data sensing on the air inlet of the target wind power blade according to the external cavity sensor by the external cavity gas-heat sensing data acquisition module 30 so as to acquire external cavity gas-heat sensing data;
the real-time working condition data acquisition module 40 is used for acquiring real-time working condition data of the generator in the target wind power blade by the real-time working condition data acquisition module 40;
the predicted gas-heat data acquisition module 50 is used for performing gas-heat prediction by using the real-time working condition data and the external cavity gas-heat sensing data to acquire predicted gas-heat data;
the optimal control parameter acquisition module 60 is used for comparing the inner cavity gas heat sensing data with the predicted gas heat data to acquire optimal control parameters;
the deicing optimization control module 70 is used for performing deicing optimization control on the target wind power blade according to the optimization control parameters.
Further, the optimization control parameter obtaining module 60 includes:
the gas-heat comparison model building module is used for building a gas-heat comparison model, wherein the gas-heat comparison model comprises a variable adjustment sub-model and a comparison output sub-model;
the multivariate index analysis module is used for inputting the predicted gas-heat data into the variable adjustment sub-model, and performing multivariate index analysis according to the variable adjustment sub-model to obtain adjustment gas-heat data;
and the data input module is used for inputting the adjustment gas-heat data and the inner cavity gas-heat sensing data into the comparison output submodel to obtain the optimization control parameters.
Further, the air-heat comparison model building module comprises:
the device comprises a target wind power blade information acquisition module, a device connection module and a device control module, wherein the target wind power blade information acquisition module is used for acquiring device geometric data, device component attributes and device connection structures of the target wind power blade;
the air inlet information acquisition module is used for acquiring air inlet distribution information, gas backflow channel information and heat absorption cavity structure information according to the equipment geometric data, the equipment component attribute and the equipment connection structure;
and the variable adjustment sub-model building module is used for building the variable adjustment sub-model by taking the air inlet distribution information, the air return channel information and the heat absorption cavity structure information as multivariate indexes.
Further, the data input module includes:
the gas-heat loss data acquisition module is used for acquiring gas-heat loss data according to the adjustment gas-heat data and the inner cavity gas-heat sensing data;
and the feedback adjustment parameter acquisition module is used for introducing a first loss function to analyze the gas-heat loss data to obtain a feedback adjustment parameter, and outputting the feedback adjustment parameter as the optimal control parameter.
Further, the feedback adjustment parameter obtaining module includes:
the first loss function calculation module is used for calculating the following formula:
wherein ,
for prediction of qi-heat data +.>
For inner cavity gas heat sensing data, +.>
For the predicted aero-thermal data of column i, < >>
For the inner cavity gas-heat sensing data of column i, < >>
For the variable adjustment factor, +.>
，/>
For a variable adjustment factor based on the air intake distribution information, < >>
Is based onVariable adjustment coefficient of gas return channel information, +.>
For the variable adjustment coefficient based on the endothermic chamber structure information, n is the total group number of the sensing data, and is n columns in one row of data.
Further, the deicing optimization control module 70 includes:
the air quantity control node generation module is used for acquiring air valve distribution nodes of the target wind power blade and generating air quantity control nodes according to the air valve distribution nodes;
and the air valve control parameter acquisition module is used for acquiring air valve control parameters corresponding to the output of the air volume control node according to the optimized control parameters, and performing circulating air volume control by using the air valve control parameters.
Further, the predicted gas-heat data acquisition module 50 includes:
the outer cavity sensor assembly module is used for assembling N outer cavity sensors according to the air inlet distribution information to obtain N outer cavity gas-heat sensing data of the N outer cavity sensors, wherein the N outer cavity sensors are provided with the same data transmission terminal;
the mean value calculation module is used for carrying out mean value calculation on the N pieces of external cavity gas-heat sensing data to obtain external cavity mean value gas-heat sensing data;
and the outer cavity gas-heat sensing data output module is used for outputting the outer cavity mean gas-heat sensing data as the outer cavity gas-heat sensing data.
The foregoing has outlined and described the basic principles and main features of the present application and the advantages of the present application. It will be appreciated by persons skilled in the art that the present application is not limited to the embodiments described above, and that the embodiments and descriptions described herein are merely illustrative of the principles of the present application, and that various changes and modifications may be made therein without departing from the spirit and scope of the application, which is defined by the appended claims. The scope of the application is defined by the appended claims and equivalents thereof.
Claims (4)
1. A wind turbine blade de-icing control method, wherein the method is applied to a wind turbine blade de-icing control system, the system is in communication connection with a gas-thermal cycle detection device, and the method comprises:
embedding the gas-heat cycle detection device in the wind power blade deicing control system, wherein the gas-heat cycle detection device comprises an inner cavity sensor and an outer cavity sensor;
performing data sensing on a power generation closed cavity in the target wind power blade according to the inner cavity sensor to obtain inner cavity gas-heat sensing data;
performing data sensing on an air inlet of the target wind power blade according to the external cavity sensor to obtain external cavity air heat sensing data;
acquiring real-time working condition data of a generator in the target wind power blade;
carrying out gas-heat prediction by using the real-time working condition data and the external cavity gas-heat sensing data to obtain predicted gas-heat data;
comparing the inner cavity gas heat sensing data with the predicted gas heat data to obtain an optimized control parameter;
deicing optimization control is carried out on the target wind power blade according to the optimization control parameters;
comparing the inner cavity gas-heat sensing data with the predicted gas-heat data to obtain optimized control parameters, wherein the method comprises the following steps:
building an air-heat comparison model, wherein the air-heat comparison model comprises a variable adjustment sub-model and a comparison output sub-model;
inputting the predicted gas-heat data into the variable adjustment sub-model, and performing multivariate index analysis according to the variable adjustment sub-model to obtain adjustment gas-heat data;
inputting the adjustment gas-heat data and the inner cavity gas-heat sensing data into the comparison output sub-model to obtain the optimization control parameters;
before inputting the predicted aero-thermal data into the variable adjustment sub-model, further comprising:
acquiring equipment geometric data, equipment component attributes and equipment connection structures of the target wind power blade;
obtaining air inlet distribution information, air return channel information and heat absorption cavity structure information according to the equipment geometric data, the equipment component attribute and the equipment connection structure;
taking the air inlet distribution information, the air return channel information and the heat absorption cavity structure information as multivariate indexes to build the variable adjustment sub-model;
acquiring the optimized control parameters, further comprising:
acquiring gas heat loss data according to the adjustment gas heat data and the inner cavity gas heat sensing data;
introducing a first loss function to analyze the gas-heat loss data to obtain a feedback regulation parameter, and outputting the feedback regulation parameter as the optimal control parameter;
the calculation formula of the first loss function is as follows:
wherein ,
for prediction of qi-heat data +.>
For inner cavity gas heat sensing data, +.>
Is->
Predicted aero-thermal data of column,/-)>
Is->
Inner cavity gas heat sensing data of column, +.>
For the variable adjustment factor, +.>
，/>
For a variable adjustment factor based on the air intake distribution information, < >>
For the variable adjustment factor based on the gas return channel information, < >>
And based on the variable adjustment coefficient of the heat absorption cavity structure information, n is the total group number of the sensing data, and n columns in one row of data.
2. A wind power blade deicing control method according to claim 1, characterized in that deicing optimization control is performed on the target wind power blade according to the optimization control parameter, further comprising:
acquiring a wind valve distribution node of the target wind power blade, and generating an air quantity control node according to the wind valve distribution node;
and obtaining the air valve control parameters correspondingly output at the air volume control node according to the optimized control parameters, and controlling the circulating air volume according to the air valve control parameters.
3. The method for controlling deicing of a wind power blade according to claim 1, further comprising, after obtaining the wind inlet distribution information:
assembling N outer cavity sensors according to the air inlet distribution information to obtain N outer cavity air-heat sensing data of the N outer cavity sensors, wherein the N outer cavity sensors are provided with the same data transmission terminal;
performing average value calculation on the N pieces of external cavity air heat sensing data to obtain external cavity average value air heat sensing data;
and outputting the outer cavity mean gas-heat sensing data as the outer cavity gas-heat sensing data.
4. A wind blade de-icing control system, wherein the system is communicatively coupled to a thermal air cycle detection device, the system comprising:
the sensor installation module is used for embedding the gas-heat cycle detection device into the wind power blade deicing control system, wherein the gas-heat cycle detection device comprises an inner cavity sensor and an outer cavity sensor;
the inner cavity gas-heat sensing data acquisition module is used for carrying out data sensing on a power generation closed cavity in the target wind power blade according to the inner cavity sensor to acquire inner cavity gas-heat sensing data;
the external cavity gas-heat sensing data acquisition module is used for carrying out data sensing on the air inlet of the target wind power blade according to the external cavity sensor to acquire external cavity gas-heat sensing data;
the real-time working condition data acquisition module is used for acquiring real-time working condition data of the generator in the target wind power blade;
the prediction gas heat data acquisition module is used for performing gas heat prediction by using the real-time working condition data and the external cavity gas heat sensing data to acquire prediction gas heat data;
the optimal control parameter acquisition module is used for comparing the inner cavity gas heat sensing data with the predicted gas heat data to acquire optimal control parameters;
the deicing optimization control module is used for carrying out deicing optimization control on the target wind power blade according to the optimization control parameters;
the gas-heat comparison model building module is used for building a gas-heat comparison model, wherein the gas-heat comparison model comprises a variable adjustment sub-model and a comparison output sub-model;
the multivariate index analysis module is used for inputting the predicted gas-heat data into the variable adjustment sub-model, and performing multivariate index analysis according to the variable adjustment sub-model to obtain adjustment gas-heat data;
the data input module is used for inputting the adjustment gas heat data and the inner cavity gas heat sensing data into the comparison output sub-model to acquire the optimization control parameters;
the device comprises a target wind power blade information acquisition module, a device connection module and a device control module, wherein the target wind power blade information acquisition module is used for acquiring device geometric data, device component attributes and device connection structures of the target wind power blade;
the air inlet information acquisition module is used for acquiring air inlet distribution information, gas backflow channel information and heat absorption cavity structure information according to the equipment geometric data, the equipment component attribute and the equipment connection structure;
the variable adjustment sub-model building module is used for building the variable adjustment sub-model by taking the air inlet distribution information, the air return channel information and the heat absorption cavity structure information as multivariate indexes;
the gas-heat loss data acquisition module is used for acquiring gas-heat loss data according to the adjustment gas-heat data and the inner cavity gas-heat sensing data;
the feedback adjustment parameter acquisition module is used for introducing a first loss function to analyze the gas-heat loss data to obtain a feedback adjustment parameter, and outputting the feedback adjustment parameter as the optimal control parameter;
the first loss function calculation module is used for calculating the following formula:
wherein ,
for prediction of qi-heat data +.>
For inner cavity gas heat sensing data, +.>
Is->
Predicted aero-thermal data of column,/-)>
Is->
Inner cavity gas heat sensing data of column, +.>
For the variable adjustment factor, +.>
，/>
For a variable adjustment factor based on the air intake distribution information, < >>
For the variable adjustment factor based on the gas return channel information, < >>
And based on the variable adjustment coefficient of the heat absorption cavity structure information, n is the total group number of the sensing data, and n columns in one row of data.
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| ITTO20060401A1 (en) * | 2006-05-31 | 2007-12-01 | Lorenzo Battisti | METHOD FOR THE CONSTRUCTION OF WIND FACILITIES |
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