CN115467793A - Wind direction prediction and yaw control method and device for wind turbine generator and wind turbine generator - Google Patents
Wind direction prediction and yaw control method and device for wind turbine generator and wind turbine generator Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- 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
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- 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
- F03D7/0204—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor for orientation in relation to wind direction
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- 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
- F03D7/04—Automatic control; Regulation
- F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
- F03D7/043—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic
- F03D7/045—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic with model-based controls
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- 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
- F03D7/04—Automatic control; Regulation
- F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
- F03D7/048—Automatic control; Regulation by means of an electrical or electronic controller controlling wind farms
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2260/00—Function
- F05B2260/82—Forecasts
- F05B2260/821—Parameter estimation or prediction
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2260/00—Function
- F05B2260/84—Modelling or simulation
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/30—Control parameters, e.g. input parameters
- F05B2270/321—Wind directions
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
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Abstract
The invention provides a wind direction prediction and yaw control method and device for a wind turbine generator and the wind turbine generator, wherein the wind direction prediction method for the wind turbine generator comprises the following steps: acquiring position data of each fan in a target area, a target fan to be monitored and first wind direction data of the target fan; determining a first fan closest to the target fan based on the fan position data, and acquiring second wind direction data of the first fan; inputting the second wind direction data, the target fan and the position data of the first fan into a wind direction prediction model for wind direction prediction to obtain a wind direction prediction result of the target fan; and determining the current wind direction of the target fan based on the relation between the wind direction prediction result and the first wind direction data. By introducing the second wind direction data corresponding to the first fan, even if the anemoscope corresponding to the target fan fails under a special working condition, the wind direction data of the target fan can be effectively predicted through the second wind direction data, and the accuracy of wind direction prediction is greatly improved.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to a wind direction prediction and yaw control method and device for a wind turbine generator and the wind turbine generator.
Background
In the prior art, the wind direction is mostly monitored through a wind direction indicator, so that a wind turbine generator (namely a fan) is controlled to yaw to wind, but due to uncertainty of the wind direction, certain hysteresis exists when the fan yaws to wind according to wind direction data monitored by the wind direction indicator, the wind wheel of the fan is stressed unevenly, and the load of the wind turbine generator is greatly increased. Under special conditions, such as rain and snow weather, the wind direction cannot be effectively predicted due to failure of the anemoscope, and the fan cannot be controlled to accurately yaw to wind.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to overcome the defects that the wind direction cannot be effectively predicted and the fan cannot be controlled to accurately yaw to wind due to the failure of a anemoscope caused by special working conditions in the prior art, so that wind direction prediction and yaw control methods and devices of the wind turbine generator and the wind turbine generator are provided.
According to a first aspect, an embodiment of the present invention provides a wind turbine wind direction prediction method, where the method includes:
acquiring position data of each fan in a target area, a target fan to be monitored and first wind direction data acquired by a wind direction indicator corresponding to the target fan, wherein the target fan is positioned in the target area;
determining a first fan closest to the target fan based on the fan position data, and acquiring second wind direction data acquired by a corresponding wind direction indicator of the first fan;
inputting the second wind direction data and the position data of the target fan and the first fan into a wind direction prediction model for wind direction prediction to obtain a wind direction prediction result of the target fan;
and determining the current wind direction of the target fan based on the relation between the wind direction prediction result and the first wind direction data.
Optionally, the wind direction prediction model is constructed by:
acquiring climate data of the target area and position data of each fan in the target area;
analyzing and calculating the influence of wind directions among the fans in different wind directions based on the climate data and the position data of each fan to obtain a fan correlation calculation result;
constructing a wind turbine influence topological graph under different wind directions based on the wind turbine correlation calculation result;
and constructing a wind direction prediction model based on the wind turbine influence topological graph and the climate data under different wind directions.
Optionally, the constructing a wind direction prediction model based on the wind turbine influence topological graph under different wind directions and the climate data includes:
calculating wind direction lag time between the current fan and the adjacent fan under different wind direction data based on the wind direction influence topological graph under different wind directions;
and constructing a wind direction prediction model based on the wind direction lag time, the wind turbine influence topological graph under different wind directions and the climate data.
Optionally, the determining the current wind direction of the target wind turbine based on the relationship between the wind direction prediction result and the first wind direction data includes:
calculating a difference value between the wind direction prediction result and the first wind direction data;
judging whether the difference value is larger than a preset threshold value or not;
when the difference value is larger than the preset threshold value, correcting the wind direction prediction result according to the second wind direction data, and determining the current wind direction of the target fan based on the corrected wind direction prediction result;
and when the difference value is not larger than the preset threshold value, determining the current wind direction of the target fan based on the wind direction prediction result.
According to a second aspect, an embodiment of the present invention provides a wind turbine yaw control method, where the method includes:
determining a current wind direction of a target fan in a target area by using the wind turbine generator direction prediction method according to the first aspect or any one of optional embodiments of the first aspect, where the target area includes a plurality of fans;
and controlling each fan in the target area to yaw and align the wind based on the current wind direction.
Optionally, the method further comprises:
acquiring the running state of a current fan, wherein the current fan is a fan in a target area;
and when the current fan is in a shutdown state and the difference value between the wind direction prediction result and the first wind direction data does not exceed a preset threshold value, performing advanced yaw wind alignment on the current fan by using the wind direction prediction result.
Optionally, before yawing the current wind turbine in advance by using the wind direction prediction result, the method further includes:
obtaining the duration of the current wind direction;
judging whether the current wind is gust or not based on the duration of the current wind direction;
and when the current wind is gust, utilizing the current wind direction to yaw the current fan in advance to wind.
According to a third aspect, an embodiment of the present invention provides a wind turbine wind direction prediction apparatus, where the apparatus includes:
the system comprises an acquisition module, a monitoring module and a monitoring module, wherein the acquisition module is used for acquiring position data of each fan in a target area, a target fan to be monitored and first wind direction data acquired by a wind direction instrument corresponding to the target fan, and the target fan is positioned in the target area;
the first processing module is used for determining a first fan closest to the target fan based on the fan position data and acquiring second wind direction data acquired by a corresponding anemoscope of the first fan;
the second processing module is used for inputting the second wind direction data, the target fan and the position data of the first fan into a wind direction prediction model for wind direction prediction to obtain a wind direction prediction result of the target fan;
and the third processing module is used for determining the current wind direction of the target fan based on the relation between the wind direction prediction result and the first wind direction data.
According to a fourth aspect, an embodiment of the present invention provides a wind turbine yaw control apparatus, including:
the wind power generation set wind direction prediction device comprises an acquisition module, a prediction module and a prediction module, wherein the acquisition module is used for determining the current wind direction of a target fan in a target area by adopting the wind power generation set wind direction prediction device in the third aspect, and the target area comprises a plurality of fans;
and the fourth processing module is used for controlling each fan in the target area to yaw and align wind based on the current wind direction.
According to a fifth aspect, an embodiment of the present invention provides a wind turbine generator, including: the fan and with the controller that the fan is connected, wherein, the controller includes: a memory and a processor, the memory and the processor being communicatively coupled to each other, the memory having stored therein computer instructions, the processor being configured to execute the computer instructions to perform the method according to the first aspect, or any one of the alternative embodiments of the first aspect, or to perform the method according to the second aspect, or any one of the alternative embodiments of the second aspect.
The technical scheme of the invention has the following advantages:
1. the wind direction prediction method of the wind turbine generator system comprises the steps of obtaining position data of each fan in a target area, a target fan to be monitored and first wind direction data collected by a anemoscope corresponding to the target fan; determining a first fan closest to the target fan based on the fan position data, and acquiring second wind direction data acquired by a corresponding wind direction indicator of the first fan; inputting the second wind direction data and the position data of the target fan and the first fan into a wind direction prediction model for wind direction prediction to obtain a wind direction prediction result of the target fan; and determining the current wind direction of the target fan based on the relation between the wind direction prediction result and the first wind direction data. The method comprises the steps of obtaining first wind direction data of a target fan and second wind direction data of a first fan closest to the target fan, inputting the second wind direction data, the target fan and position data of the first fan into a wind direction prediction model to obtain a wind direction prediction result of the target fan, and finally determining the current wind direction of the target fan by comparing the relationship between the wind direction prediction result and the first wind direction data. By introducing the second wind direction data corresponding to the first fan closest to the target fan, even if the anemoscope corresponding to the target fan fails under a special working condition, the wind direction data of the target fan can be effectively predicted through the second wind direction data, so that the accuracy of wind direction prediction is greatly improved, and meanwhile, the accurate yaw wind control of the target fan is realized by utilizing a wind direction prediction result.
2. According to the wind turbine yaw control method provided by the invention, the current wind direction of a target fan in a target area is determined by adopting the wind turbine wind direction prediction method provided by another embodiment of the invention, and the target area comprises a plurality of fans; and controlling each fan in the target area to yaw and align wind based on the current wind direction. The wind direction prediction method of the wind generation set is used for effectively predicting the current wind direction, and the wind generation set is controlled in a yaw mode in time according to the current wind direction, so that the wind direction data are accurate, and meanwhile the yaw control efficiency and accuracy are further improved.
3. The wind turbine generator comprises a fan and a controller connected with the fan, first wind direction data of a target fan and second wind direction data of a first fan closest to the target fan are obtained through the controller, the second wind direction data, the target fan and position data of the first fan are input into a wind direction prediction model, a wind direction prediction result of the target fan can be obtained, and the current wind direction of the target fan is finally determined through comparing the relation between the wind direction prediction result and the first wind direction data. By introducing second wind direction data corresponding to a first fan nearest to a target fan, even if a wind direction indicator corresponding to the target fan fails under a special working condition, the wind direction data of the target fan can be effectively predicted through the second wind direction data, the accuracy of wind direction prediction is greatly improved, and meanwhile, the accurate yaw wind control of the target fan is realized by using a wind direction prediction result; on the basis, the current wind direction is effectively predicted by using a wind turbine generator wind direction prediction method, and the wind turbine generator is timely controlled in a yawing mode according to the current wind direction, so that the wind direction data are accurate, and meanwhile, the yawing control efficiency and accuracy are further improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a wind turbine generator wind direction prediction method according to an embodiment of the present invention;
FIG. 2 is a topological diagram of fan impact in different wind directions according to an embodiment of the present invention;
FIG. 3 is a fan influence topological diagram under the wind direction working condition of 20-50 degrees;
FIG. 4 is a fan influence topological diagram under the condition of 300-330 degrees wind direction;
fig. 5 is a schematic structural diagram of a wind turbine generator wind direction prediction device according to an embodiment of the present invention;
FIG. 6 is a flow chart of a yaw control method of a wind turbine generator according to an embodiment of the present invention;
FIG. 7 is a schematic structural diagram of a yaw control device of a wind turbine generator according to an embodiment of the present invention;
FIG. 8 is a schematic structural diagram of a wind turbine generator according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of a controller of a wind turbine generator according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplification of description, but do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," "third," and "fourth" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be connected through the inside of the two elements, or may be connected wirelessly or through a wire. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Furthermore, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The embodiment of the invention provides a wind turbine generator wind direction prediction method, which specifically comprises the following steps of:
step S101: the method comprises the steps of obtaining position data of each fan in a target area, a target fan to be monitored and first wind direction data collected by a target fan corresponding to a wind direction indicator, wherein the target fan is located in the target area.
Step S102: and determining a first fan closest to the target fan based on the fan position data, and acquiring second wind direction data acquired by a corresponding anemoscope of the first fan.
Specifically, in practical application, because the fans in the target area are influenced by each other, the embodiment of the present invention fully utilizes the phenomenon that the fans are influenced by each other, and obtains the first wind direction data collected by the anemoscope corresponding to the target fan, and at the same time obtains the second wind direction data collected by the anemoscope corresponding to the first fan closest to the target fan, so that when the anemoscope corresponding to the target fan fails, the wind direction of the position of the target fan can be determined by the second wind direction data of the first fan. In the embodiment of the invention, the fan closest to the target fan is used as the first fan and the second wind direction data of the wind direction indicator corresponding to the fan is adopted, but the actual situation is not limited to the first fan, and the position and the number of the first fans can be changed according to the mutual influence network diagram among the fans, so that the accuracy of the subsequent wind direction prediction result is ensured.
Step S103: and inputting the second wind direction data, the target fan and the position data of the first fan into a wind direction prediction model for wind direction prediction to obtain a wind direction prediction result of the target fan.
Specifically, in practical application, when the anemoscope corresponding to the target fan fails, the embodiment of the invention may input the second risk data and the position data of the target fan and the first fan into the wind direction prediction model to perform wind direction prediction, so as to obtain a wind direction prediction result of the target fan. On the premise of ensuring the accuracy of the second wind direction data, the wind direction prediction result of the target fan can be obtained by analyzing the positions of the target fan and the first fan.
By the method, the embodiment of the invention effectively avoids the condition that the anemoscope corresponding to the target fan cannot acquire accurate wind direction data due to failure under special working conditions. In practical application, the wind direction data collected by the anemoscope corresponding to the first wind direction data and other fans can be compared, so that the invalid anemoscope is checked before wind direction prediction, the accuracy of the collected wind direction data is guaranteed, and effective data support is provided for guaranteeing a wind direction prediction result.
Step S104: and determining the current wind direction of the target fan based on the relation between the wind direction prediction result and the first wind direction data.
By executing the above steps, according to the wind direction prediction method for the wind turbine generator provided by the embodiment of the present invention, the first wind direction data of the target fan and the second wind direction data of the first fan closest to the target fan are obtained, the second wind direction data, the target fan and the position data of the first fan are input into the wind direction prediction model, so that the wind direction prediction result of the target fan can be obtained, and the current wind direction of the target fan is finally determined by comparing the relationship between the wind direction prediction result and the first wind direction data. By introducing the second wind direction data corresponding to the first fan nearest to the target fan, even if the anemoscope corresponding to the target fan fails under a special working condition, the wind direction data of the target fan can be effectively predicted through the second wind direction data, the accuracy of wind direction prediction is greatly improved, and meanwhile, the accurate yaw wind control of the target fan is realized by utilizing a wind direction prediction result.
Specifically, in an embodiment, the construction of the wind direction prediction model specifically includes the following steps:
step S201: and acquiring climate data of the target area and position data of each fan in the target area.
Step S202: and analyzing and calculating the influence of wind directions among the fans in different wind directions based on the climate data and the position data of each fan to obtain a fan correlation calculation result.
Step S203: and constructing a wind turbine influence topological graph in different wind directions based on the wind turbine correlation calculation result.
Step S204: and constructing a wind direction prediction model based on the influence topological graph and the climate data of the fans in different wind directions.
Specifically, in practical application, in order to determine a weight relationship of influence between fans, the embodiment of the invention respectively calculates correlation coefficients between every two fans according to historical climate data, geographical position data of a target area and an original wind direction in historical data of the fans and the wind direction of the first 30 s; since the influence weight of the wind turbines under different wind directions is influenced, and in addition, the influence weight may be influenced by the wind directions of a plurality of wind turbines, it is necessary to establish a topological graph of the influence of the wind turbines under different wind directions as shown in fig. 2 according to the wind directions and the calculation result of the correlation coefficient.
The fan correlation calculation result can be obtained by calculation through a correlation coefficient method, and the calculation process refers to the correlation calculation process in the prior art and is not repeated herein.
Wherein r (X, Y) is a correlation calculation result of the X fan and the Y fan; cov (X, Y) is covariance of the X fan and the Y fan; var [ X ] is the variance of the X fan; var [ Y ] is the variance of the Y fan.
According to the embodiment of the invention, the strength relation of wind direction influences among fans can be measured by analyzing and calculating the wind direction influences among the fans in different wind directions, wherein when the fan correlation calculation result is 1, the positive correlation between the X fan and the Y fan is shown, namely the X fan can enhance the wind direction and the wind force at the Y fan when the current wind direction is downward; when the fan correlation calculation result is positive, the X fan and the Y fan are negatively correlated, namely the current wind direction is downward, and the X fan can weaken the wind direction and the wind power of the Y fan; when the fan correlation calculation result is closer to 0, the correlation between the X fan and the Y fan is weaker.
As shown in fig. 3, it can be seen from the fan influence topological diagram under the working condition of wind directions of 20 ° to 50 °, that the 4# fan is influenced most by the 2# fan, and the wind directions of the 5# fan and the 6# fan are influenced, and then the two fans influence the wind direction of the 8# fan together. In the fan influence topological diagram under the wind direction condition of 300-330 ° shown in fig. 4, the 4# fan is influenced most by the 3# fan.
According to the embodiment of the invention, the wind direction influence relation among the fans is intuitively reflected by constructing the wind direction fan influence topological graphs with different wind directions, so that the accuracy of the prediction result is further improved while the data processing difficulty of the fan prediction model is greatly reduced.
Specifically, in practical application, the embodiment of the invention also calculates the maximum likelihood estimator between fans according to the constructed wind direction fan influence topological graphs, and effectively analyzes the wind direction influence result between fans, thereby further ensuring the accuracy of the prediction result. The calculation process and the necessary formula for calculating the maximum likelihood estimator between the fans can refer to the related description in the prior art, and are not described in detail herein.
Specifically, in an embodiment, the step S204 of building a wind direction prediction model based on the wind turbine influence topological graph and the weather data in different wind directions includes the following steps:
step S301: and calculating the wind direction lag time between the current fan and the adjacent fan under different wind direction data based on the wind fan influence topological graphs under different wind directions.
Step S302: and constructing a wind direction prediction model based on wind direction lag time, wind turbine influence topological graphs in different wind directions and weather data.
Specifically, in practical application, after constructing the wind turbine influence topological graphs in different wind directions, the embodiment of the present invention obtains parameters such as wind field terrain α (high slope, canyon, plain, etc.), season β, inter-wind turbine distance d, wind speed signal v, wind direction signal γ, and number n of associated units, and constructs a wind direction prediction model based on the parameters. In addition, considering that the interval distance between the fans is long, and there may be a case where the time to reach different fans is different for the same gust, the embodiment of the present invention constructs a wind direction prediction model together with the wind field terrain α (high slope, canyon, plain, etc.), the season β, the inter-fan distance d, the wind speed signal v, the wind direction signal γ, and the number of associated units n parameters, using the wind direction lag time t as an influence factor.
Specifically, in practical application, in order to be able to quickly and accurately judge wind direction data, the embodiment of the present invention constructs a wind direction prediction model based on a BP neural network model, and optimizes the wind direction prediction model by using a grey wolf optimization algorithm, or constructs a wind direction prediction model by using optimization algorithms such as particle swarm and related classification models of a support vector machine, but the actual situation is not limited thereto, and the type or number of the wind direction prediction model is changed to ensure the accuracy of the wind direction prediction result, which is also within the protection range of the wind direction prediction method of the wind turbine generator set provided by the embodiment of the present invention.
Specifically, the embodiment of the invention needs to determine nodes of an input layer, a hidden layer and an output layer of a wind direction prediction model, after the number of nodes corresponding to each layer is determined, the collected input set information is used as the input of a classification model, the wind direction information of a fan is used as an output result to train and predict the model, the wind direction prediction model is trained, the accuracy of the wind direction prediction model is verified by using historical wind direction data, and finally the wind direction prediction model for wind direction prediction is obtained.
Specifically, the wind direction information of the fan comprises machine position information, real-time wind direction of the fan and other fan and wind direction data.
The specific training and verification process of the wind direction prediction model can refer to the related description in the prior art, and will not be described herein again.
Specifically, in an embodiment, the step S104 of determining the current wind direction of the target fan based on the relationship between the wind direction prediction result and the first wind direction data specifically includes the following steps:
step S401: and calculating the difference between the wind direction prediction result and the first wind direction data.
Step S402: and judging whether the difference value is larger than a preset threshold value.
Step S403: and when the difference value is larger than the preset threshold value, correcting the wind direction prediction result according to the second wind direction data, and determining the current wind direction of the target fan based on the corrected wind direction prediction result.
Step S404: and when the difference value is not greater than the preset threshold value, determining the current wind direction of the target fan based on the wind direction prediction result.
Specifically, in practical application, when the difference between the wind direction prediction result and the first wind direction data is greater than a preset threshold, it indicates that a failure may exist in a anemoscope corresponding to the target fan, at this time, the wind direction of the position of the target fan is determined according to the second wind direction data and a wind direction influence topological graph in the wind direction prediction model under different wind directions, the wind direction prediction result is corrected according to the wind direction, and the current wind direction of the target fan is determined based on the corrected wind direction prediction result. For example, the preset threshold may be 10 °.
Specifically, when the difference is greater than the preset threshold, the embodiment of the invention acquires the position information of the target fan and makes an early warning prompt to feed back the position information to the maintenance department, so that a technician can master the fault condition of the target fan at the first time, the fault troubleshooting efficiency is greatly improved, and the stable operation of the fan is ensured.
According to the embodiment of the invention, the preset threshold value is set, and when the difference value exceeds the preset threshold value, the predicted value can be corrected according to the second wind direction result, so that the accurate current wind direction of the target fan can be obtained through the wind direction prediction model even if the anemoscope corresponding to the target fan fails. When the situation that wind direction data cannot be accurately acquired due to the fact that a anemoscope fails under special working conditions is avoided, the influence of the fans with different wind directions on a topological graph and the wind direction data acquired by other fans corresponding to the anemoscope are ingeniously utilized to obtain the current wind direction of the target fan, normal operation of the fans is guaranteed, and accuracy of wind direction prediction is further improved.
An embodiment of the present invention provides a wind turbine generator direction prediction apparatus, as shown in fig. 5, the wind turbine generator direction prediction apparatus includes:
the acquisition module 101 is configured to acquire position data of each fan in a target area, a target fan to be monitored, and first wind direction data acquired by a target fan corresponding to a anemoscope, where the target fan is located in the target area. For details, refer to the related description of step S101 in the above method embodiment, and no further description is provided here.
The first processing module 102 is configured to determine, based on the fan position data, a first fan closest to the target fan, and acquire second wind direction data acquired by a anemoscope corresponding to the first fan. For details, refer to the related description of step S102 in the above method embodiment, and no further description is provided here.
And the second processing module 103 is configured to input the second wind direction data, the target fan and the position data of the first fan into the wind direction prediction model to perform wind direction prediction, so as to obtain a wind direction prediction result of the target fan. For details, refer to the related description of step S103 in the above method embodiment, and no further description is provided here.
And the third processing module 104 is configured to determine the current wind direction of the target fan based on a relationship between the wind direction prediction result and the first wind direction data. For details, refer to the related description of step S104 in the above method embodiment, and no further description is provided here.
For further description of the wind direction prediction device of the wind turbine generator, reference is made to the related description of the wind direction prediction method embodiment of the wind turbine generator, which is not described herein again.
Through the cooperative cooperation of the above components, the wind direction prediction apparatus for a wind turbine generator provided in the embodiment of the present invention obtains first wind direction data of a target fan and second wind direction data of a first fan closest to the target fan, inputs the second wind direction data, the target fan, and position data of the first fan into a wind direction prediction model, so as to obtain a wind direction prediction result of the target fan, and finally determines the current wind direction of the target fan by comparing a relationship between the wind direction prediction result and the first wind direction data. By introducing the second wind direction data corresponding to the first fan nearest to the target fan, even if the anemoscope corresponding to the target fan fails under a special working condition, the wind direction data of the target fan can be effectively predicted through the second wind direction data, the accuracy of wind direction prediction is greatly improved, and meanwhile, the accurate yaw wind control of the target fan is realized by utilizing a wind direction prediction result.
The embodiment of the invention provides a wind turbine generator yaw control method, and as shown in fig. 6, the wind turbine generator yaw control method specifically comprises the following steps:
step S501: by adopting the wind direction prediction method of the wind turbine generator, the current wind direction of a target fan in a target area is determined, and the target area comprises a plurality of fans.
Step S502: and controlling each fan in the target area to yaw and align wind based on the current wind direction.
Specifically, in practical application, after a target fan prediction result is obtained, the embodiment of the invention can obtain the current wind direction corresponding to each fan based on the fan influence topological graph under different wind directions, and control each fan to yaw and align wind according to the current wind direction corresponding to each fan.
By executing the steps, the wind turbine yaw control method provided by the embodiment of the invention effectively predicts the current wind direction by using the wind turbine wind direction prediction method, and timely performs yaw control on the wind turbine according to the current wind direction, so that the wind direction data is ensured to be accurate, and the yaw control efficiency and accuracy are further improved.
Specifically, in an embodiment, the method specifically includes the following steps:
step S601: and acquiring the running state of the current fan, wherein the current fan is a fan in the target area.
Step S602: and when the current fan is in a shutdown state and the difference value between the wind direction prediction result and the first wind direction data does not exceed a preset threshold value, utilizing the wind direction prediction result to yaw in advance to align the wind for the current fan.
Specifically, in practical application, considering that a sudden shutdown condition of the fan may occur, the embodiment of the invention acquires the running state of the current fan, and if the current fan is in the shutdown state, the current wind direction of the current fan can be determined according to the fan prediction result, so that the current fan is subjected to yaw wind alignment in advance, the yaw wind alignment time is greatly shortened, and the generating efficiency of the fan is improved.
Specifically, in an embodiment, before performing the step S602 to advance yaw of the current wind turbine by using the wind direction prediction result, the method further includes the following steps:
step S701: the duration of the current wind direction is obtained.
Step S702: and judging whether the current wind is gust or not based on the duration of the current wind direction.
Step S703: and when the current wind is gust, carrying out advanced yawing on the current fan by utilizing the current wind direction.
Specifically, in practical application, the embodiment of the invention can also judge whether the current wind is gust according to the duration of the current wind direction, so that the current fan is yawed in advance according to the judgment result, and the prediction efficiency and the yaw control efficiency are greatly improved.
The wind direction prediction accuracy is improved by establishing the wind influence topological graph of the maximum correlation unit under different wind directions and topographic conditions, the yaw control of the wind machine under the failure condition of the anemoscope is realized by utilizing the predicted wind direction value, and the wind machine is controlled to wind in advance by protruding gust, the power generation efficiency is improved, the yaw control of each wind machine can be realized under the condition of ensuring severe weather, the advantages of a wind direction prediction model are exerted to the maximum extent, the running condition of the anemoscope is synchronously monitored, the anemoscope data with faults are timely early-warning and prompted, the auxiliary maintenance department carries out fault troubleshooting, and the power generation efficiency of the wind machine is greatly improved.
An embodiment of the present invention provides a wind turbine yaw control device, and as shown in fig. 7, the wind turbine yaw control device includes:
the acquisition module 501 is configured to determine the current wind direction of a target fan in a target area by using the wind direction prediction apparatus of the wind turbine generator, where the target area includes multiple fans. For details, refer to the related description of step S501 in the above method embodiment, and no further description is provided here.
And a fourth processing module 502, configured to control each fan in the target area to yaw and align wind based on the current wind direction. For details, refer to the related description of step S502 in the above method embodiment, and no further description is provided herein.
For further description of the yaw control device of the wind turbine generator, reference is made to the related description of the embodiments of the yaw control method of the wind turbine generator, and details are not repeated here.
Through the cooperative cooperation of the components, the wind turbine yaw control device provided by the embodiment of the invention effectively predicts the current wind direction by using the wind direction prediction method of the wind turbine, and timely performs yaw control on the wind turbine according to the current wind direction, so that the wind direction data is ensured to be accurate, and the yaw control efficiency and accuracy are further improved.
An embodiment of the present invention provides a wind turbine generator, as shown in fig. 8, the wind turbine generator includes a wind turbine 100 and a controller 900 connected to the wind turbine, as shown in fig. 9, the controller 900 includes a processor 901 and a memory 902, and the memory 902 and the processor 901 are connected in communication with each other, where the processor 901 and the memory 902 may be connected by a bus or in another manner, and fig. 9 takes the connection by a bus as an example.
The memory 902, which is a non-transitory computer-readable storage medium, may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the methods of the embodiments of the present invention. The processor 901 executes various functional applications and data processing of the processor 901 by executing non-transitory software programs, instructions and modules stored in the memory 902, that is, implements the methods in the above-described method embodiments.
The memory 902 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 901, and the like. Further, the memory 902 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 902 may optionally include memory located remotely from the processor 901, which may be connected to the processor 901 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
One or more modules are stored in the memory 902, which when executed by the processor 901 performs the methods in the above-described method embodiments.
The specific details of the wind turbine generator set can be understood by referring to the corresponding relevant description and effects in the method embodiments, and are not described herein again.
Those skilled in the art will understand that all or part of the processes in the methods of the embodiments described above may be implemented by instructing the relevant hardware through a computer program, and the implemented program may be stored in a computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk Drive (Hard Disk Drive, abbreviated as HDD), or a Solid State Drive (SSD); the storage medium may also comprise a combination of memories of the kind described above.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications derived therefrom are intended to be within the scope of the invention.
Claims (10)
1. A wind turbine generator wind direction prediction method is characterized by comprising the following steps:
acquiring position data of each fan in a target area, a target fan to be monitored and first wind direction data acquired by a wind direction instrument corresponding to the target fan, wherein the target fan is positioned in the target area;
determining a first fan closest to the target fan based on the fan position data, and acquiring second wind direction data acquired by a corresponding wind direction indicator of the first fan;
inputting the second wind direction data and the position data of the target fan and the first fan into a wind direction prediction model for wind direction prediction to obtain a wind direction prediction result of the target fan;
and determining the current wind direction of the target fan based on the relation between the wind direction prediction result and the first wind direction data.
2. The method of claim 1, wherein the wind direction prediction model is constructed by:
acquiring climate data of the target area and position data of each fan in the target area;
analyzing and calculating the influence of wind directions among the fans in different wind directions based on the climate data and the position data of each fan to obtain a fan correlation calculation result;
constructing a wind turbine influence topological graph under different wind directions based on the wind turbine correlation calculation result;
and constructing a wind direction prediction model based on the wind turbine influence topological graph under different wind directions and the climate data.
3. The method of claim 2, wherein constructing a wind direction prediction model based on the wind turbine influence topology map and the climate data for different wind directions comprises:
calculating wind direction lag time between the current fan and the adjacent fan under different wind direction data based on the wind direction influence topological graph under different wind directions;
and constructing a wind direction prediction model based on the wind direction lag time, the wind turbine influence topological graph under different wind directions and the climate data.
4. The method of claim 1, wherein determining the current wind direction of the target wind turbine based on the relationship of the wind direction prediction result and the first wind direction data comprises:
calculating a difference between the wind direction prediction result and the first wind direction data;
judging whether the difference value is larger than a preset threshold value or not;
when the difference value is larger than the preset threshold value, correcting the wind direction prediction result according to the second wind direction data, and determining the current wind direction of the target fan based on the corrected wind direction prediction result;
and when the difference value is not larger than the preset threshold value, determining the current wind direction of the target fan based on the wind direction prediction result.
5. A wind turbine generator yaw control method is characterized by comprising the following steps:
determining the current wind direction of a target fan in a target area by adopting the wind turbine generator wind direction prediction method according to any one of claims 1 to 4, wherein the target area comprises a plurality of fans;
and controlling each fan in the target area to yaw and align wind based on the current wind direction.
6. The method of claim 5, further comprising:
acquiring the running state of a current fan, wherein the current fan is a fan in a target area;
and when the current fan is in a shutdown state and the difference value between the wind direction prediction result and the first wind direction data does not exceed a preset threshold value, performing advanced yaw wind alignment on the current fan by using the wind direction prediction result.
7. The method of claim 6, wherein prior to yawing the current wind turbine ahead of time with the wind direction prediction, the method further comprises:
obtaining the duration of the current wind direction;
judging whether the current wind is gust or not based on the duration of the current wind direction;
and when the current wind is gust, carrying out advanced yawing on the current fan by utilizing the current wind direction.
8. A wind turbine wind direction prediction device is characterized by comprising:
the system comprises an acquisition module, a monitoring module and a monitoring module, wherein the acquisition module is used for acquiring position data of each fan in a target area, a target fan to be monitored and first wind direction data acquired by a wind direction indicator corresponding to the target fan, and the target fan is positioned in the target area;
the first processing module is used for determining a first fan closest to the target fan based on the fan position data and acquiring second wind direction data acquired by a corresponding wind direction indicator of the first fan;
the second processing module is used for inputting the second wind direction data, the target fan and the position data of the first fan into a wind direction prediction model for wind direction prediction to obtain a wind direction prediction result of the target fan;
and the third processing module is used for determining the current wind direction of the target fan based on the relation between the wind direction prediction result and the first wind direction data.
9. The utility model provides a wind turbine generator system yaw controlling means which characterized in that includes:
an acquisition module, configured to determine a current wind direction of a target wind turbine in a target area by using the wind turbine wind direction prediction apparatus according to claim 8, where the target area includes a plurality of wind turbines;
and the fourth processing module is used for controlling each fan in the target area to yaw and align wind based on the current wind direction.
10. A wind turbine, comprising: the fan and with the controller that the fan is connected, wherein, the controller includes: a memory and a processor, the memory and the processor being communicatively coupled to each other, the memory having stored therein computer instructions, the processor performing the method of any of claims 1-4 or performing the method of any of claims 5-7 by executing the computer instructions.
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| CN116954169A (en) * | 2023-07-19 | 2023-10-27 | 天津市易控科技发展有限公司 | Information security control method, device, equipment and medium based on DCS control |
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| CN116954169A (en) * | 2023-07-19 | 2023-10-27 | 天津市易控科技发展有限公司 | Information security control method, device, equipment and medium based on DCS control |
| CN116954169B (en) * | 2023-07-19 | 2024-06-07 | 天津市易控科技发展有限公司 | Information security control method, device, equipment and medium based on DCS control |
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