CN117614023A - Wind power plant operation control method, device, equipment and storage medium - Google Patents
Wind power plant operation control method, device, equipment and storage medium Download PDFInfo
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- 238000004088 simulation Methods 0.000 claims abstract description 114
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- 238000005259 measurement Methods 0.000 claims description 71
- 238000013528 artificial neural network Methods 0.000 claims description 17
- 238000012549 training Methods 0.000 claims description 13
- 238000000605 extraction Methods 0.000 claims description 8
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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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
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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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 disclosure relates to a wind farm operation control method, a device, equipment and a storage medium. The method comprises the following steps: acquiring current actually measured influence data and current simulation influence data of a target wind power plant in a current time period, wherein the current actually measured influence data and the current simulation influence data both comprise wind condition data; processing the current actually measured influence data and the current simulation influence data by utilizing a pre-trained control parameter prediction model to obtain target control parameters of a target wind power plant in a future time period after the current time period, wherein the target control parameters are working condition data of the target wind power plant when the target wind power plant outputs the maximum power generation in the future time period; and controlling each unit in the target wind power plant to operate based on the target control parameters. By the method, the problem that the control strategy is error in the process of determining the predicted wind condition based on the actual wind condition is avoided, the effect of eliminating the wake effect is improved, and the practical application value of the wake control technology of the wind power plant is improved.
Description
Technical Field
The disclosure relates to the technical field of new energy power generation, in particular to a wind farm operation control method, a device, equipment and a storage medium.
Background
The unit in the wind power plant is a power generation device for converting wind energy in the air into electric energy. The upstream unit can form a wake flow area with the wind speed reduced at the downstream of the upstream unit when acquiring wind energy from wind, so that wake flow effect is generated, the wind speed of the downstream unit can be reduced due to the wake flow effect, and the overall power generation of the wind power plant is finally reduced.
In order to eliminate wake effects, in the related art, the wind power plant generally utilizes the actual measurement wind condition of the wind power plant in the current time period to determine the predicted wind condition, and then determines the control parameters of the wind power plant based on the predicted wind condition, so that each unit in the wind power plant is controlled to operate based on the control parameters. However, the actually measured wind condition is not matched with the control parameter determined by the predicted wind condition, so that the generated energy loss caused by the wake effect cannot be effectively reduced and the generated energy loss caused by the wake effect cannot be solved when the actually measured wind condition of the wind power plant and the control parameter corresponding to the predicted wind condition are used for generating power.
Disclosure of Invention
In order to solve the technical problems described above or at least partially solve the technical problems described above, the present disclosure provides a wind farm operation control method, apparatus, device and storage medium.
In a first aspect, the present disclosure provides a method for controlling operation of a wind farm, the method comprising:
acquiring current actually measured influence data and current simulation influence data of a target wind power plant in a current time period, wherein the current actually measured influence data and the current simulation influence data both comprise wind condition data;
processing the current actual measurement influence data and the current simulation influence data by using a pre-trained control parameter prediction model to obtain target control parameters of the target wind power plant in a future time period after the current time period, wherein the target control parameters are working condition data of the target wind power plant when the target wind power plant outputs maximum power generation in the future time period;
and controlling each unit in the target wind power plant to operate based on the target control parameters.
In a second aspect, the present disclosure provides a wind farm operation control device, the device comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring current actually measured influence data and current simulation influence data of a target wind power plant in a current time period, and the current actually measured influence data and the current simulation influence data both comprise wind condition data;
the determining module is used for processing the current actually measured influence data and the current simulation influence data by utilizing a pre-trained control parameter prediction model to obtain target control parameters of the target wind power plant in a future time period after the current time period, wherein the target control parameters are working condition data of the target wind power plant when the target wind power plant outputs the maximum power generation in the future time period;
and the operation control module is used for controlling each unit in the target wind power plant to operate based on the target control parameters.
In a third aspect, embodiments of the present disclosure also provide an apparatus, the apparatus comprising:
one or more processors;
storage means for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method provided by the first aspect.
In a fourth aspect, embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method provided by the first aspect.
Compared with the prior art, the technical scheme provided by the embodiment of the disclosure has the following advantages:
according to the wind power plant operation control method, the wind power plant operation control equipment and the storage medium, current actual measurement influence data and current simulation influence data of a target wind power plant in a current time period are obtained, wherein the current actual measurement influence data and the current simulation influence data comprise wind condition data; processing the current actually measured influence data and the current simulation influence data by utilizing a pre-trained control parameter prediction model to obtain target control parameters of a target wind power plant in a future time period after the current time period, wherein the target control parameters are working condition data of the target wind power plant when the target wind power plant outputs the maximum power generation in the future time period; and controlling each unit in the target wind power plant to operate based on the target control parameters. By the method, the target control parameters for controlling the operation of each unit in the wind power plant can be directly determined by using the current actually measured influence data, the current simulation influence data and the pre-trained control parameter prediction model, so that the problem that errors exist in a control strategy in the process of determining the predicted wind condition based on the actual wind condition is avoided, the effect of eliminating the wake effect is improved, and the practical application value of the wake control technology of the wind power plant is finally improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments of the present disclosure or the solutions in the prior art, the drawings that are required for the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic flow chart of a method for controlling operation of a wind farm according to an embodiment of the disclosure;
FIG. 2 is a flow chart of a method for controlling a parametric prediction model according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a wind farm operation control device according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a wind farm operation control device according to an embodiment of the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, a further description of aspects of the present disclosure will be provided below. It should be noted that, without conflict, the embodiments of the present disclosure and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced otherwise than as described herein; it will be apparent that the embodiments in the specification are only some, but not all, embodiments of the disclosure.
The embodiment of the disclosure provides a wind farm operation control method, device and storage medium capable of eliminating wake effects.
Next, a method for controlling operation of a wind farm according to an embodiment of the present disclosure will be described with reference to fig. 1 to 2.
Fig. 1 shows a flowchart of a wind farm operation control method according to an embodiment of the present disclosure.
In an embodiment of the present disclosure, the wind farm operation control method shown in fig. 1 may be performed by a wind farm operation control device. The wind farm operation control device may be an electronic device or a server. The electronic device may include, but is not limited to, a stationary terminal such as a smart phone, a notebook computer, a desktop computer, and the like. The server may be a cloud server or a server cluster, or other devices with storage and computing functions. The embodiment of the disclosure uses the electronic device as an execution body for detailed explanation.
As shown in fig. 1, the wind farm operation control method may include the following steps.
S110, acquiring current actually measured influence data and current simulation influence data of the target wind power plant in a current time period, wherein the current actually measured influence data and the current simulation influence data comprise wind condition data.
When the method is actually applied, when the effect of eliminating the wake effect is required to be improved, real-time data and simulation data which influence working conditions in the target wind power plant are collected, and control parameters of the target wind power plant are determined by the real-time data and the simulation data.
The target wind power plant can be any large wind power plant needing to eliminate wake effects.
The current actually measured influence data refer to actual influence data of the target wind power plant in a current time period. Optionally, the current measured impact data includes one or more of the following combinations: current measured wind condition data, current measured temperature data, current measured humidity data, and current measured air pressure data.
The current simulation influence data are simulation data for running the target wind power plant in the current time period. Optionally, the current simulation influence data includes one or more of the following combinations: current simulation wind condition data, current wake flow influence relation among all units, current simulation temperature data, current simulation humidity data and current simulation air pressure data.
Specifically, the method for determining the wake influence relationship includes, but is not limited to, the following ways:
acquiring a power generation power influence relation of each unit in a target wind power plant, and taking the power generation power influence relation as a wake flow influence relation;
acquiring the relative position relation of each unit in the target wind power plant under the current actual measurement wind condition in the current actual measurement influence data, and taking the relative position relation as a wake flow influence relation;
and acquiring the impeller areas of each unit in wake areas of other units in the target wind power plant, and taking the impeller areas as wake influence relations.
The generated power influence relationship is understood as a constraint relationship that influences the generated power of each unit.
The relative positional relationship may be a coordinate relative value of each generator set.
The impeller area can be used for evaluating the influence of each unit on wake flow areas of other units.
S120, processing the current actually measured influence data and the current simulation influence data by using a pre-trained control parameter prediction model to obtain target control parameters of a target wind power plant in a future time period after the current time period, wherein the target control parameters are working condition data of the target wind power plant when the target wind power plant outputs the maximum power generation in the future time period.
In order to avoid the problem that wake effects cannot be effectively solved because the actual measured wind conditions are not matched with the control parameters determined by using the predicted wind conditions. In this embodiment, the current actually measured influence data and the current simulation influence data are input into a pre-trained control parameter prediction model, so that a target control parameter when the target wind farm outputs the maximum power is determined by using the control parameter prediction model.
Specific implementations of S120 include, but are not limited to, the following: based on a space-time feature extraction network in the control parameter prediction model, extracting current actual measurement space-time features from current actual measurement influence data and extracting current simulation space-time features from current simulation influence data; respectively carrying out attention processing on the current actually measured space-time characteristics and the current simulation space-time characteristics based on an attention mechanism in the control parameter prediction model; fitting the current measured space-time characteristics after the attention processing and the current simulation space-time characteristics after the attention processing based on a fully connected network in the control parameter prediction model, and taking the obtained fitting data as target control parameters.
The current actually measured space-time characteristics comprise actually measured characteristics in the time dimension and the space dimension, and the current simulation space-time characteristics comprise predicted characteristics in the time dimension and the space dimension.
The target control parameters refer to the predicted working conditions when all units in the target wind power plant output the maximum power. Optionally, the target control parameters include, but are not limited to, yaw angle, rotational speed, pitch angle of each unit.
The maximum generated power may be determined based on historical data of a reference wind farm corresponding to the target wind farm.
Therefore, the space-time characteristic extraction network, the attention mechanism and the full-connection network in the control parameter prediction model are utilized to process the current actually measured influence data and the current simulation influence data, so that the working condition data of each unit when outputting the maximum power in the future time period is accurately determined.
S130, controlling each unit in the target wind power plant to operate based on the target control parameters.
It can be understood that, because the target control parameter is working condition data when each unit outputs the maximum power in a future time period, when each unit in the target wind power plant is controlled to operate by using the target control parameter, the target wind power plant can output the maximum power, thereby effectively solving the problem of power generation loss caused by wake effect.
In the embodiment of the disclosure, current actually measured influence data and current simulation influence data of a target wind power plant in a current time period are obtained, wherein the current actually measured influence data and the current simulation influence data both comprise wind condition data; processing the current actually measured influence data and the current simulation influence data by utilizing a pre-trained control parameter prediction model to obtain target control parameters of a target wind power plant in a future time period after the current time period, wherein the target control parameters are working condition data of the target wind power plant when the target wind power plant outputs the maximum power generation in the future time period; and controlling each unit in the target wind power plant to operate based on the target control parameters. By the method, the target control parameters for controlling the operation of each unit in the wind power plant can be directly determined by using the current actually measured influence data, the current simulation influence data and the pre-trained control parameter prediction model, so that the problem that errors exist in a control strategy in the process of determining the predicted wind condition based on the actual wind condition is avoided, the effect of eliminating the wake effect is improved, and the practical application value of the wake control technology of the wind power plant is finally improved.
In another embodiment of the present disclosure, a training method of the control parameter prediction model is specifically explained.
Fig. 2 is a flowchart illustrating a training method of a control parameter prediction model according to an embodiment of the disclosure.
As shown in fig. 2, the training method of the control parameter prediction model includes the following steps.
S210, acquiring historical actual measurement influence data, historical simulation influence data of the reference wind power plant in a historical time period and actual measurement control parameters of the reference wind power plant in a future time period after the historical time period.
The reference wind power plant is any wind power plant close to the influence data of the target wind power plant.
Wherein, the historical time period may be 3 days, 5 days, 7 days, etc. before the current time.
The historical actual measurement influence data refer to actual influence data of the reference wind power plant in a historical time period. Optionally, the historical measured impact data includes one or more of the following combinations: historical measured wind condition data, historical measured temperature data, historical measured humidity data and historical measured air pressure data.
The historical simulation influence data are simulation data of the reference wind power plant running in a historical time period. Optionally, the historical simulation impact data includes one or more of the following combinations: historical simulation wind condition data, historical wake influence relation among all units, historical simulation temperature data, historical simulation humidity data and historical simulation air pressure data.
Specifically, the method for determining the influence relationship of the historical wake includes, but is not limited to, the following ways:
acquiring a power generation power influence relation of each unit in a reference wind power plant, and taking the power generation power influence relation as a historical wake flow influence relation;
acquiring the relative position relation of each unit in the reference wind power plant under the history actual measurement wind condition in the history actual measurement influence data, and taking the relative position relation as a history wake flow influence relation;
and acquiring the impeller areas of each unit in the wake areas of other units in the reference wind power plant, and taking the impeller areas as the historical wake influence relation.
The actually measured control parameters refer to actually measured working conditions of each unit in the reference wind power plant in a historical time period. Optionally, the measured control parameters include, but are not limited to, yaw angle, rotational speed, pitch angle of each unit.
S220, obtaining the actual measurement unit power of each unit in the reference wind power plant in a historical time period, wherein the sum of the actual measurement unit powers of each wind power unit is the actual measurement power of the wind power plant.
The measured unit power is the actual power generated when each unit in the reference wind power plant operates according to the measured control parameters under the historical measured influence data. And adding the measured unit power generated by each unit in the reference wind power plant to obtain the measured power of the wind power plant.
S230, performing iterative training on a preset neural network by using the historical actual measurement influence data, the historical simulation influence data, the actual measurement control parameters, the actual measurement power of the wind power plant and the maximum generation power to obtain a control parameter prediction model.
In this embodiment, specific implementation manners of S230 include, but are not limited to, the following manners:
s2301, processing historical actual measurement influence data and historical simulation influence data by using a preset neural network to obtain prediction control parameters when the actual measurement power of the wind power plant is output by the reference wind power plant;
and S2302, if the measured power of the wind power plant is not equal to the maximum power generation power, performing iterative training on a preset neural network based on the measured control parameter and the predicted control parameter until the measured power of the wind power plant output by the reference wind power plant is equal to the maximum power generation power, and obtaining a control parameter prediction model.
The preset neural network may include a spatiotemporal feature extraction network, an attention mechanism, and a fully connected network, among others. Accordingly, S2301 may specifically include the following steps: based on a space-time feature extraction network in a preset neural network, extracting historical actual measurement space-time features from historical actual measurement influence data and extracting historical simulation space-time features from historical simulation influence data; respectively carrying out attention processing on the historical actual measurement space-time characteristics and the historical simulation space-time characteristics based on an attention mechanism in a preset neural network; fitting the historical actual measurement space-time characteristics after the attention processing and the historical simulation space-time characteristics after the attention processing based on a fully connected network in a preset neural network, and taking the obtained fitting data as a prediction control parameter.
The historical actual measurement space-time characteristics comprise actual measurement characteristics in the time dimension and the space dimension, and the historical simulation space-time characteristics comprise prediction characteristics in the time dimension and the space dimension.
It can be understood that if the measured power of the wind farm is not equal to the maximum generated power, it is indicated that the wake effect of the target wind farm is not solved effectively, and the preset neural network is trained iteratively based on the measured control parameter and the predicted control parameter until the measured power of the wind farm output by the reference wind farm is equal to the maximum generated power, and a control parameter prediction model is obtained, and at this time, the maximum generated power of the reference wind farm can be output by using the control parameter prediction model.
The method comprises the steps of processing historical actual measurement influence data and historical simulation influence data by utilizing a space-time characteristic extraction network, an attention mechanism and a full-connection network of a preset neural network, so as to accurately determine the prediction control parameters when the reference wind power plant outputs actual measurement power of the wind power plant, and carrying out iterative training on the preset neural network when the actual measurement power of the wind power plant is unequal to the maximum generation power, so as to train out a control parameter prediction model capable of outputting the maximum generation power of the target wind power plant.
In the present embodiment, the specific determination modes of the maximum generated power include, but are not limited to, the following modes: acquiring actual measurement wind conditions of a reference wind power plant in a future time period after a historical time period, and acquiring actual measurement control parameters corresponding to the actual measurement wind conditions; performing simulation calculation on the actually measured control parameters under the actually measured wind condition by using a power simulation model to obtain a plurality of simulation powers; the simulation power having the largest power value is selected from the plurality of simulation powers as the maximum generated power.
Wherein the future time period after the historical time period is equal to the future time period after the current time period.
The power simulation model can process the actually measured wind conditions and the actually measured control parameters to calculate a plurality of simulation powers of the reference wind power plant. Further, after the plurality of simulation powers are sequenced, the simulation power with the largest power value is selected, so that the largest power generation power is obtained.
Therefore, the power simulation model can be utilized to determine a plurality of simulation powers, and then the maximum power value is selected from the plurality of simulation powers, so that the maximum generated power can be accurately determined.
The embodiment of the disclosure further provides a wind farm operation control device for implementing the wind farm operation control method, and the description is made below with reference to fig. 3. In an embodiment of the disclosure, the wind farm operation control device may be an electronic device. The electronic device may include a mobile terminal, a tablet computer, and other devices with a communication function.
Fig. 3 shows a schematic structural diagram of a wind farm operation control device according to an embodiment of the present disclosure.
As shown in fig. 3, the wind farm operation control device 300 may include:
an obtaining module 310, configured to obtain current actually measured impact data and current simulation impact data of a target wind farm in a current time period, where the current actually measured impact data and the current simulation impact data both include wind condition data;
a determining module 320, configured to process the current actually measured influence data and the current simulation influence data by using a pre-trained control parameter prediction model, so as to obtain a target control parameter of the target wind farm in a future time period after the current time period, where the target control parameter is working condition data of the target wind farm when the target wind farm outputs the maximum power in the future time period;
and the operation control module 330 is configured to control each unit operation in the target wind farm based on the target control parameter.
In the embodiment of the disclosure, current actually measured influence data and current simulation influence data of a target wind power plant in a current time period are obtained, wherein the current actually measured influence data and the current simulation influence data both comprise wind condition data; processing the current actually measured influence data and the current simulation influence data by utilizing a pre-trained control parameter prediction model to obtain target control parameters of a target wind power plant in a future time period after the current time period, wherein the target control parameters are working condition data of the target wind power plant when the target wind power plant outputs the maximum power generation in the future time period; and controlling each unit in the target wind power plant to operate based on the target control parameters. By the method, the target control parameters for controlling the operation of each unit in the wind power plant can be directly determined by using the current actually measured influence data, the current simulation influence data and the pre-trained control parameter prediction model, so that the problem that errors exist in a control strategy in the process of determining the predicted wind condition based on the actual wind condition is avoided, the effect of eliminating the wake effect is improved, and the practical application value of the wake control technology of the wind power plant is finally improved.
In some embodiments, the determining module 320 includes:
the extraction unit is used for extracting current actual measurement space-time characteristics from the current actual measurement influence data and extracting current simulation space-time characteristics from the current simulation influence data based on a space-time characteristic extraction network in the control parameter prediction model;
the attention processing unit is used for respectively carrying out attention processing on the current actual measurement space-time characteristic and the current simulation space-time characteristic based on an attention mechanism in the control parameter prediction model;
and the fitting unit is used for fitting the current actually measured space-time characteristics after the attention processing and the current simulation space-time characteristics after the attention processing based on the fully connected network in the control parameter prediction model, and the obtained fitting data are used as the target control parameters.
In some embodiments, the current measured impact data includes one or more of the following combinations: current measured wind condition data, current measured temperature data, current measured humidity data and current measured air pressure data;
the current simulation influence data includes one or more of the following combinations: current simulation wind condition data, current wake flow influence relation among all units, current simulation temperature data, current simulation humidity data and current simulation air pressure data.
In some embodiments, the apparatus further comprises: a wake impact relationship determination module; the wake impact relationship determination module includes:
the first determining unit is used for obtaining the power generation power influence relation of each unit in the target wind power plant and taking the power generation power influence relation as the wake flow influence relation;
the second determining unit is used for obtaining the relative position relation of each unit in the target wind power plant under the current actual measurement wind condition in the current actual measurement influence data, and taking the relative position relation as the wake flow influence relation;
and the third determining unit is used for acquiring the impeller areas of each unit in the wake areas of other units in the target wind power plant, and taking the impeller areas as the wake influence relation.
In some embodiments, the apparatus further comprises:
the first acquisition module is used for acquiring historical actual measurement influence data and historical simulation influence data of the reference wind power plant in a historical time period and actual measurement control parameters of the reference wind power plant in a future time period after the historical time period;
the second acquisition module is used for acquiring the actual measurement unit power of each unit in the reference wind power plant in the historical time period, wherein the sum of the actual measurement unit powers of each wind power unit is the actual measurement power of the wind power plant;
and the iterative training module is used for carrying out iterative training on the preset neural network by utilizing the historical actual measurement influence data, the historical simulation influence data, the actual measurement control parameters, the actual measurement power of the wind power plant and the maximum power generation power to obtain the control parameter prediction model.
In some embodiments, the iterative training module is specifically configured to:
processing the historical actual measurement influence data and the historical simulation influence data by using the preset neural network to obtain a prediction control parameter when the reference wind power plant outputs the actual measurement power of the wind power plant;
and if the measured power of the wind power plant is not equal to the maximum power generation power, performing iterative training on the preset neural network based on the measured control parameter and the predicted control parameter until the measured power of the wind power plant output by the reference wind power plant is equal to the maximum power generation power, so as to obtain the control parameter prediction model.
In some embodiments, the apparatus comprises: a maximum generated power determining module; the maximum generated power determining module includes:
the historical data acquisition unit is used for acquiring actual measurement wind conditions of the reference wind power plant in a future time period after the historical time period and acquiring actual measurement control parameters corresponding to the actual measurement wind conditions;
the simulation calculation unit is used for performing simulation calculation on the actual measurement control parameters under the actual measurement wind condition by using a power simulation model to obtain a plurality of simulation powers;
and a maximum power generation power determination unit configured to select, as the maximum power generation power, a simulation power having a maximum power value from the plurality of simulation powers.
It should be noted that, the wind farm operation control device 300 shown in fig. 3 may perform the steps in the method embodiments shown in fig. 1 to 2, and implement the processes and effects in the method or system embodiments shown in fig. 1 to 2, which are not described herein.
Fig. 4 shows a schematic structural diagram of a wind farm operation control device according to an embodiment of the present disclosure.
As shown in fig. 4, the wind farm operation control device may comprise a processor 401 and a memory 402 storing computer program instructions.
In particular, the processor 401 described above may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured to implement one or more integrated circuits of embodiments of the present application.
Memory 402 may include mass storage for information or instructions. By way of example, and not limitation, memory 402 may comprise a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, magnetic tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of these. Memory 402 may include removable or non-removable (or fixed) media, where appropriate. The memory 402 may be internal or external to the integrated gateway device, where appropriate. In a particular embodiment, the memory 402 is a non-volatile solid state memory. In a particular embodiment, the Memory 402 includes Read-Only Memory (ROM). The ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (Electrical Programmable ROM, EPROM), electrically erasable PROM (Electrically Erasable Programmable ROM, EEPROM), electrically rewritable ROM (Electrically Alterable ROM, EAROM), or flash memory, or a combination of two or more of these, where appropriate.
The processor 401 reads and executes the computer program instructions stored in the memory 402 to perform the steps of the wind farm operation control method provided by the embodiments of the present disclosure.
In one example, the wind farm operation control device may further comprise a transceiver 403 and a bus 404. As shown in fig. 4, the processor 401, the memory 402, and the transceiver 403 are connected by a bus 404 and perform communication with each other.
Bus 404 includes hardware, software, or both. By way of example, and not limitation, the buses may include an accelerated graphics port (Accelerated Graphics Port, AGP) or other graphics BUS, an enhanced industry standard architecture (Extended Industry Standard Architecture, EISA) BUS, a Front Side BUS (FSB), a HyperTransport (HT) interconnect, an industry standard architecture (Industrial Standard Architecture, ISA) BUS, an InfiniBand interconnect, a Low Pin Count (LPC) BUS, a memory BUS, a micro channel architecture (Micro Channel Architecture, MCa) BUS, a peripheral control interconnect (Peripheral Component Interconnect, PCI) BUS, a PCI-Express (PCI-X) BUS, a serial advanced technology attachment (Serial Advanced Technology Attachment, SATA) BUS, a video electronics standards association local (Video Electronics Standards Association Local Bus, VLB) BUS, or other suitable BUS, or a combination of two or more of these. Bus 404 may include one or more buses, where appropriate. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.
The following are embodiments of a computer readable storage medium provided in the embodiments of the present disclosure, where the computer readable storage medium and the wind farm operation control method of the foregoing embodiments belong to the same inventive concept, and details of the computer readable storage medium are not described in detail in the embodiments of the computer readable storage medium, and reference may be made to the embodiments of the foregoing wind farm operation control method.
The present embodiments provide a storage medium containing computer executable instructions for performing a wind farm operation control method when executed by a computer processor.
Of course, the storage medium containing the computer executable instructions provided by the embodiments of the present disclosure is not limited to the above method operations, but may also perform the related operations in the wind farm operation control method provided by any embodiment of the present disclosure.
From the above description of embodiments, it will be apparent to those skilled in the art that the present disclosure may be implemented by means of software and necessary general purpose hardware, but may of course also be implemented by means of hardware, although in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present disclosure may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a FLASH Memory (FLASH), a hard disk, or an optical disk of a computer, where the instructions include a computer cloud platform (which may be a personal computer, a server, or a network cloud platform, etc.) to execute the wind farm operation control method provided by the embodiments of the present disclosure.
Note that the above is only a preferred embodiment of the present disclosure and the technical principle applied. Those skilled in the art will appreciate that the present disclosure is not limited to the particular embodiments described herein, and that various obvious changes, rearrangements and substitutions can be made by those skilled in the art without departing from the scope of the disclosure. Therefore, while the present disclosure has been described in connection with the above embodiments, the present disclosure is not limited to the above embodiments, but may include many other equivalent embodiments without departing from the spirit of the present disclosure, the scope of which is determined by the scope of the appended claims.
Claims (10)
1. A method for controlling operation of a wind farm, comprising:
acquiring current actually measured influence data and current simulation influence data of a target wind power plant in a current time period, wherein the current actually measured influence data and the current simulation influence data both comprise wind condition data;
processing the current actual measurement influence data and the current simulation influence data by using a pre-trained control parameter prediction model to obtain target control parameters of the target wind power plant in a future time period after the current time period, wherein the target control parameters are working condition data of the target wind power plant when the target wind power plant outputs maximum power generation in the future time period;
and controlling each unit in the target wind power plant to operate based on the target control parameters.
2. The method according to claim 1, wherein processing the current measured impact data and the current simulation impact data using a pre-trained control parameter prediction model to obtain a target control parameter for a future time period of the target wind farm after the current time period comprises:
based on a space-time feature extraction network in the control parameter prediction model, extracting current actual measurement space-time features from the current actual measurement influence data and extracting current simulation space-time features from the current simulation influence data;
respectively carrying out attention processing on the current actually measured space-time characteristics and the current simulation space-time characteristics based on an attention mechanism in the control parameter prediction model;
fitting the current actually measured space-time characteristics after the attention processing and the current simulation space-time characteristics after the attention processing based on the fully connected network in the control parameter prediction model, and taking the obtained fitting data as the target control parameters.
3. The method of claim 1, wherein the current measured impact data comprises one or more of the following combinations: current measured wind condition data, current measured temperature data, current measured humidity data and current measured air pressure data;
the current simulation influence data includes one or more of the following combinations: current simulation wind condition data, current wake flow influence relation among all units, current simulation temperature data, current simulation humidity data and current simulation air pressure data.
4. A method according to claim 3, wherein the method of determining wake impact relationships comprises:
acquiring a power generation power influence relation of each unit in the target wind power plant, and taking the power generation power influence relation as the wake flow influence relation;
acquiring the relative position relation of each unit in the target wind power plant under the current actual measurement wind condition in the current actual measurement influence data, and taking the relative position relation as the wake flow influence relation;
and acquiring the impeller areas of each unit in wake areas of other units in the target wind power plant, and taking the impeller areas as wake influence relations.
5. The method according to claim 1, wherein the method further comprises:
acquiring historical actual measurement influence data, historical simulation influence data of a reference wind power plant in a historical time period and actual measurement control parameters of the reference wind power plant in a future time period after the historical time period;
obtaining the actual measurement unit power of each unit in the reference wind power plant in the historical time period, wherein the sum of the actual measurement unit powers of each wind power unit is the actual measurement power of the wind power plant;
and performing iterative training on the preset neural network by using the historical actual measurement influence data, the historical simulation influence data, the actual measurement control parameters, the actual measurement power of the wind power plant and the maximum power generation power to obtain the control parameter prediction model.
6. The method of claim 5, wherein iteratively training the predetermined neural network to obtain the control parameter prediction model using the historical measured impact data, the historical simulation impact data, the measured control parameter, the wind farm measured power, and the maximum generated power comprises:
processing the historical actual measurement influence data and the historical simulation influence data by using the preset neural network to obtain a prediction control parameter when the reference wind power plant outputs the actual measurement power of the wind power plant;
and if the measured power of the wind power plant is not equal to the maximum power generation power, performing iterative training on the preset neural network based on the measured control parameter and the predicted control parameter until the measured power of the wind power plant output by the reference wind power plant is equal to the maximum power generation power, so as to obtain the control parameter prediction model.
7. The method according to claim 5, wherein the method for determining the maximum generated power includes:
acquiring an actual measurement wind condition of the reference wind power plant in a future time period after the historical time period, and acquiring an actual measurement control parameter corresponding to the actual measurement wind condition;
using a power simulation model to perform simulation calculation on the actually measured control parameters under the actually measured wind condition to obtain a plurality of simulation powers;
and selecting the simulation power with the largest power value from the simulation powers as the largest power generation power.
8. A wind farm operation control device, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring current actually measured influence data and current simulation influence data of a target wind power plant in a current time period, and the current actually measured influence data and the current simulation influence data both comprise wind condition data;
the determining module is used for processing the current actually measured influence data and the current simulation influence data by utilizing a pre-trained control parameter prediction model to obtain target control parameters of the target wind power plant in a future time period after the current time period, wherein the target control parameters are working condition data of the target wind power plant when the target wind power plant outputs the maximum power generation in the future time period;
and the operation control module is used for controlling each unit in the target wind power plant to operate based on the target control parameters.
9. An apparatus, comprising:
a processor;
a memory for storing executable instructions;
wherein the processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the method of any of the preceding claims 1-7.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the storage medium stores a computer program, which, when executed by a processor, causes the processor to implement the method of any of the preceding claims 1-7.
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