CN111520282A - Wind turbine measurement and control system and measurement and control method based on edge calculation and deep learning - Google Patents
Wind turbine measurement and control system and measurement and control method based on edge calculation and deep learning Download PDFInfo
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- CN111520282A CN111520282A CN202010435579.XA CN202010435579A CN111520282A CN 111520282 A CN111520282 A CN 111520282A CN 202010435579 A CN202010435579 A CN 202010435579A CN 111520282 A CN111520282 A CN 111520282A
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- 238000004364 calculation method Methods 0.000 title claims abstract description 33
- 238000005259 measurement Methods 0.000 title claims abstract description 29
- 238000000034 method Methods 0.000 title claims abstract description 24
- 238000013135 deep learning Methods 0.000 title claims abstract description 23
- 238000012545 processing Methods 0.000 claims abstract description 35
- 238000013136 deep learning model Methods 0.000 claims abstract description 25
- 238000004891 communication Methods 0.000 claims description 11
- 230000001133 acceleration Effects 0.000 claims description 3
- 238000009434 installation Methods 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 239000012636 effector Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000010977 unit operation Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
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Classifications
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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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- 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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- 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
Abstract
The invention discloses a wind turbine measurement and control system and a measurement and control method based on edge calculation and deep learning, wherein the system comprises a main server, and an end sensor, an end controller, an end server and a fan main control which are arranged on a fan, wherein the end sensor is positioned at the position of a part to be monitored on the fan; the end controller is connected with the end sensor and the end server, the end controller stores a deep learning model, and is used for acquiring the acquired data of the end sensor, performing edge calculation processing on the acquired data through the deep learning model, storing the processing result in the end server and generating a fan control instruction; the end server is connected with the main server, and the main server is used for acquiring a processing result through the end server and issuing a deep learning model to the end controller; and the fan master control connecting end controller or the end server acquires a fan control instruction and controls the fan according to the instruction. The invention can control each fan in real time, rapidly and independently, and the control of the wind turbine generator is more efficient and intelligent.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to a wind turbine generator system measurement and control system and a measurement and control method based on edge calculation and deep learning.
Background
In the process of developing the wind generating set towards the intelligent direction, the state monitoring requirement of the wind generating set is continuously improved. Compared with the prior art, the types and the number of the current wind turbine state monitoring points are increased more, and the blade, the tower, the foundation, the bolt and other parts are installed and monitored very commonly.
The existing wind turbine monitoring system mainly collects data and displays real-time states, and makes a next decision by manually reading the running state, so that the remote operation can be carried out, but the real-time and quick operation cannot be realized. In addition, the same and single strategy is used for each unit through manual operation, and the operation of the whole wind farm cannot be coordinated by combining with other wind turbines, so that certain resource loss is easily caused.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide the wind turbine measurement and control system based on edge calculation and deep learning, the system can control each fan rapidly and independently in real time, and the control of the wind turbine is more efficient and intelligent.
The second purpose of the invention is to provide a wind turbine measurement and control method based on edge calculation and deep learning, which can more intelligently realize measurement and control of the wind turbine.
The first purpose of the invention is realized by the following technical scheme: a wind turbine measurement and control system based on edge calculation and deep learning comprises: a main server, and an end sensor, an end controller, an end server and a fan master controller which are arranged on each fan of the wind turbine generator, wherein,
the end sensor is positioned at the position of the part to be monitored on the fan and used for acquiring data of the part to be monitored;
the end controller is connected with the end sensor and the end server, the end controller stores a corresponding deep learning model, and is used for acquiring the acquired data of the end sensor, performing edge calculation processing on the acquired data through the deep learning model, storing a processing result in the end server and generating a corresponding fan control instruction based on the processing result;
the end server is connected with the main server and uploads the processing result to the main server, and the main server is used for acquiring the processing result uploaded by the end server and issuing a corresponding deep learning model to the end controller through the end server;
the fan master control is connected with the end controller or the end server, when the fan master control is connected with the end controller, the fan master control directly obtains a fan control instruction from the end controller and controls the fan according to the control instruction; when the fan master control is connected with the end server, the fan master control is used for acquiring a fan control instruction generated by the end controller from the end server and controlling the fan according to the control instruction.
Preferably, each fan is provided with a corresponding end server and at least one end controller, the end controllers are positioned around the part to be monitored, and the number of the end sensors monitored by one end controller is 1 or more than 1;
the end servers of the fans are connected with the same main server, and each end server is installed on a tower bottom platform corresponding to the fan and is connected with an end controller on the fan.
Preferably, the end sensor types comprise an acceleration sensor, a strain sensor, a temperature sensor and a wind speed sensor; the monitored components on the wind turbine comprise blades and tower bolts.
Preferably, the data stored by the end server includes raw end sensor data, edge computed feature data, and historical data of the fan control.
Preferably, the end sensor is connected with the end controller through a communication cable, the fan main control is connected with the end controller or the end server through the communication cable, the end controller is connected with the end server through the communication cable, and the end server is connected with the main server through a wired connection or a wireless connection mode.
The second purpose of the invention is realized by the following technical scheme: a wind turbine measurement and control method based on edge calculation and deep learning is applied to a wind turbine measurement and control system based on edge calculation and deep learning, and specifically comprises the following steps:
the main server issues the corresponding deep learning model to the end controller of each fan through the end server of each fan;
when the fan runs, an end sensor on the fan collects data of the monitored component and sends the data to an end controller;
the method comprises the steps that an end controller receives acquired data of a sensor, then edge calculation processing is carried out on the acquired data through a deep learning model to obtain a processing result and the processing result is stored in an end server, and a corresponding fan control instruction is sent to a fan master control directly or through the end server based on the processing result;
the fan master control slave controller or the end server acquires a fan control instruction, controls the fan according to the fan control instruction, and meanwhile, uploads a processing result to the main server.
Preferably, the method further comprises: the main server periodically acquires the data stored in the main server from the end server, optimizes the corresponding deep learning model based on the data, and then sends the optimized deep learning model to the end controller through the end server, so that the model in the end controller can be updated.
Compared with the prior art, the invention has the following advantages and effects:
(1) in the wind turbine measurement and control system based on edge calculation and deep learning, the end controller on each fan can perform edge calculation processing on the collected data through the deep learning model to generate a corresponding fan control instruction, so that independent control and stable operation of each fan of the wind turbine are realized, the fans can process the collected data of the end sensor and make feedback immediately and quickly, compared with a remote control mode, resource and economic losses caused by manual processing delay and negligence can be avoided, and the working efficiency of the fans is higher. The system has high independence and matching performance, each wind turbine end independently collects and processes data, and the master server can coordinate the operation of the whole wind farm by considering the operation conditions of a plurality of fans in the wind turbines so as to enable the operation of the wind farm to be more intelligent and efficient.
(2) The invention realizes the sustainable optimization of the unit operation strategy by applying an intelligent algorithm, the master server can acquire the latest data at regular time to optimize the intelligent model, the state characteristic learning of the wind farm level is realized, and the intelligent model of the end controller is uniformly updated by the end server, so that the optimization of the unit operation strategy is realized, and the method is very favorable for improving the availability of the wind turbine and the power generation efficiency.
Drawings
FIG. 1 is a structural block diagram of a wind turbine measurement and control system based on edge calculation and deep learning.
Fig. 2 is a schematic view of a first installation of the measurement and control system of fig. 1.
FIG. 3 is a schematic view of a second installation of the measurement and control system of FIG. 1.
FIG. 4 is a flow chart of the wind turbine measurement and control method based on edge calculation and deep learning.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
Examples
The embodiment discloses a wind turbine measurement and control system based on edge calculation and deep learning, and the wind turbine has a plurality of fans (fan 1, fan 2, fan 3, … …, fan n), as shown in fig. 1, includes: the system comprises a main server 1, and an end sensor 2, an end controller 3, an end server 4 and a fan master control 5 which are arranged on each fan of the wind turbine.
Each fan is provided with a corresponding end server and at least one end controller, and the number of the end sensors monitored by one end controller is 1 or more than 1. And the end servers of the plurality of fans are connected with the same main server.
The end sensor is positioned at the position of the part to be monitored on the fan and used for collecting the data of the part to be monitored. End sensor types include: acceleration sensors, strain sensors, temperature sensors, wind speed sensors, etc. The monitored components on the fan comprise blades and tower bolts, and when the monitored components are the tower bolts, the end sensors correspondingly acquire deformation data, pretightening force and the like of the bolts.
Fig. 2 and 3 show two installation modes of the system of the embodiment. As shown in fig. 2, the end sensors are located at the blades or hub and the end controllers are located at the nacelle. As shown in fig. 3, the end sensor and the end controller are both located on the tower. The end sensor is connected with the end controller through a communication cable, and the end controller is connected with the end server through the communication cable. Of course, a wireless communication method may be adopted. The end effectors are located around the part to be monitored, for example, to monitor the tower bolts with the end sensors in place, and the end effectors may be mounted near the flange.
Each end server is connected with a corresponding end controller on the fan, and each end server is installed on a tower bottom platform of the corresponding fan for conveniently checking and maintaining the end servers from time to time.
The end controller stores a corresponding deep learning model, and is used for acquiring the acquired data of the end sensor, performing edge calculation processing on the acquired data through the deep learning model, storing a processing result in the end server and generating a corresponding fan control instruction based on the processing result. The edge calculation refers to that data is processed in real time at a data source (an end controller on a fan), and the data can be directly processed without being transmitted to a wind farm main server through the edge calculation.
The data stored by the end server includes raw end sensor data, edge computed feature data, and historical data for the controls of the wind turbine. The end server is connected with the main server through a communication cable, and uploads the stored data including the processing result to the main server. The end server and the main server can not only transmit data, but also update the original software of the end server.
The main server is used for acquiring the processing result uploaded by the end server and issuing a corresponding deep learning model to the end controller through the end server. Deep learning is an intelligent identification method widely researched at present, characteristics of a research object are decomposed in multiple aspects through a simulated neural network, and the method has high identification accuracy. The overall server of the embodiment can also periodically acquire data in the end server to enable the model to continue learning and optimizing.
The fan master control is connected with the end controller or the end server through a communication cable. When the fan master control is connected with the end controller (namely, the connection mode 1), the fan master control directly obtains a fan control instruction from the end controller, and then controls the fan according to the fan control instruction.
When the fan master control is connected with the end server (namely, the connection mode 2), the fan master control acquires a fan control instruction generated by the end controller from the end server and controls the fan according to the control instruction.
The embodiment also discloses a wind turbine measurement and control method based on edge calculation and deep learning, which is applied to the wind turbine measurement and control system and specifically comprises the following steps as shown in fig. 4:
the main server issues the corresponding deep learning model to the end controller of each fan through the end server of each fan;
when the fan runs, an end sensor on the fan collects data of the monitored component and sends the data to an end controller;
the method comprises the steps that an end controller receives acquired data of a sensor, then edge calculation processing is carried out on the acquired data through a deep learning model to obtain a processing result and the processing result is stored in an end server, and a corresponding fan control instruction is sent to a fan master control directly or through the end server based on the processing result;
the fan master control slave controller or the end server acquires a fan control instruction, controls the fan according to the fan control instruction, and meanwhile, uploads a processing result to the main server.
In the process, the main server also acquires the data stored by the main server from the end server periodically, optimizes the corresponding deep learning model based on the data, and then sends the optimized deep learning model to the end controller through the end server, so that the model in the end controller can be updated.
Therefore, by the method, each fan can independently operate and can immediately and quickly make feedback. The main server can also coordinate the operation of the whole wind farm by considering the operation conditions of a plurality of fans in the wind turbine generator, and the wind farm is more intelligent and efficient in operation.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (7)
1. The utility model provides a wind turbine generator system observes and controls system based on edge calculation and deep learning which characterized in that includes: a main server, and an end sensor, an end controller, an end server and a fan master controller which are arranged on each fan of the wind turbine generator, wherein,
the end sensor is positioned at the position of the part to be monitored on the fan and used for acquiring data of the part to be monitored;
the end controller is connected with the end sensor and the end server, the end controller stores a corresponding deep learning model, and is used for acquiring the acquired data of the end sensor, performing edge calculation processing on the acquired data through the deep learning model, storing a processing result in the end server and generating a corresponding fan control instruction based on the processing result;
the end server is connected with the main server and uploads the processing result to the main server, and the main server is used for acquiring the processing result uploaded by the end server and issuing a corresponding deep learning model to the end controller through the end server;
the fan master control is connected with the end controller or the end server, when the fan master control is connected with the end controller, the fan master control directly obtains a fan control instruction from the end controller and controls the fan according to the control instruction; when the fan master control is connected with the end server, the fan master control is used for acquiring a fan control instruction generated by the end controller from the end server and controlling the fan according to the control instruction.
2. The edge calculation and deep learning-based wind turbine measurement and control system according to claim 1, wherein each wind turbine has a corresponding end server and at least one end controller, the end controllers are located around the component to be monitored, and the number of end sensors monitored by one end controller is 1 or more than 1;
the end servers of the fans are connected with the same main server, and each end server is installed on a tower bottom platform corresponding to the fan and is connected with an end controller on the fan.
3. The wind turbine measurement and control system based on edge calculation and deep learning of claim 1, wherein the end sensor types comprise an acceleration sensor, a strain sensor, a temperature sensor, and a wind speed sensor; the monitored components on the wind turbine comprise blades and tower bolts.
4. The wind turbine measurement and control system based on edge calculation and deep learning of claim 1, wherein the data stored by the end server comprises raw end sensor data, feature data after edge calculation, and historical data of wind turbine control.
5. The wind turbine measurement and control system based on edge calculation and deep learning of claim 1, wherein the end sensor is connected with the end controller through a communication cable, the fan master control is connected with the end controller or the end server through the communication cable, the end controller is connected with the end server through the communication cable, and the end server is connected with the main server through a wired connection or a wireless connection.
6. A wind turbine measurement and control method based on edge calculation and deep learning is characterized in that the method is applied to the wind turbine measurement and control system based on edge calculation and deep learning of any one of claims 1-5, and specifically comprises the following steps:
the main server issues the corresponding deep learning model to the end controller of each fan through the end server of each fan;
when the fan runs, an end sensor on the fan collects data of the monitored component and sends the data to an end controller;
the method comprises the steps that an end controller receives acquired data of a sensor, then edge calculation processing is carried out on the acquired data through a deep learning model to obtain a processing result and the processing result is stored in an end server, and a corresponding fan control instruction is sent to a fan master control directly or through the end server based on the processing result;
the fan master control slave controller or the end server acquires a fan control instruction, controls the fan according to the fan control instruction, and meanwhile, uploads a processing result to the main server.
7. The wind turbine measurement and control method based on edge calculation and deep learning of claim 6, characterized by further comprising: the main server periodically acquires the data stored in the main server from the end server, optimizes the corresponding deep learning model based on the data, and then sends the optimized deep learning model to the end controller through the end server, so that the model in the end controller can be updated.
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| CN112215397A (en) * | 2020-09-08 | 2021-01-12 | 中广核风电有限公司 | Wind turbine generator intelligent management method and system based on big data and storage medium |
| CN112483334A (en) * | 2020-12-11 | 2021-03-12 | 重庆科凯前卫风电设备有限责任公司 | Intelligent control method of wind turbine generator based on edge calculation |
| CN113357083A (en) * | 2021-08-09 | 2021-09-07 | 东方电气风电有限公司 | Intelligent control system and method for wind generating set |
| CN113982854A (en) * | 2021-10-15 | 2022-01-28 | 上海电气风电集团股份有限公司 | Wind field system |
| WO2023050930A1 (en) * | 2021-09-29 | 2023-04-06 | 新疆金风科技股份有限公司 | Auxiliary control method and system for wind turbine generator set, and wind turbine generator set |
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