CN113177187A - Equivalent laser radar wind speed calculation method based on long-term and short-term memory neural network - Google Patents
Equivalent laser radar wind speed calculation method based on long-term and short-term memory neural network Download PDFInfo
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Abstract
The invention discloses an equivalent laser radar wind speed calculation method based on a long-term and short-term memory neural network, which comprises the following two parts: the first part is that related operation data are sorted and counted in a wind turbine generator provided with laser radar equipment, and a final model structure and specific parameters are obtained through long-term and short-term memory neural network model training; and the second part is to adapt the trained model structure and specific parameters to a control system of a wind turbine generator without laser radar equipment, and the generator inputs the real-time running state of the generator into the model to obtain the equivalent laser radar wind speed for the control system of the generator, so as to realize the specific control function. The invention uses the long-short term memory neural network, generates an equivalent wind speed signal which is the same as the wind speed measured by the laser radar after training, is used for the control technologies of feedforward, correction and the like, and reduces the number of the laser radars used in the project, thereby saving the cost of sensor equipment, effectively reducing the load of a unit and improving the generating capacity.
Description
Technical Field
The invention relates to the technical field of wind turbines, in particular to an equivalent laser radar wind speed calculation method based on a long-term and short-term memory neural network.
Background
In the prior art, a cabin type laser radar wind meter measures wind speed and related environmental parameters of a wind turbine generator by using a Doppler frequency shift principle, and is rapidly applied and popularized as an advanced sensing device in the wind power industry in recent years. At present, the mature control technology based on laser radar data mainly comprises the application of a feedforward control technology for collecting wind speed by using the laser radar, yaw correction based on collected wind direction data, cabin wind speed transfer function correction and the like.
The wind power generation set can reduce the fluctuation amplitude of components such as a generator, effectively reduce the load of the set and improve the generated energy through controlling the wind power generation set by measuring the front wind speed signal of the wind direction through the laser radar, but is limited by the cost of hardware equipment of the laser radar, and the laser radar equipment is rarely installed for each machine position in the domestic existing running wind power field project, so that the application of related control strategies is limited, and the popularization is not universal in a large area. At present, in order to obtain higher generated energy, better generating efficiency and lower electricity consumption cost, advanced intelligent technology of the unit is strongly required.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art, and provides an equivalent laser radar wind speed calculation method based on a long-short term memory neural network.
In order to achieve the purpose, the technical scheme provided by the invention is as follows: the equivalent laser radar wind speed calculation method based on the long-term and short-term memory neural network comprises two parts: the first part is that related operation data are sorted and counted in a wind turbine generator provided with laser radar equipment, and a final model structure and specific parameters are obtained through long-term and short-term memory neural network model training; and the second part is to adapt the trained model structure and specific parameters to a control system of a wind turbine generator without laser radar equipment, and the generator inputs the real-time running state of the generator into the model to obtain the equivalent laser radar wind speed for the control system of the generator, so as to realize the specific control function.
Further, the equivalent laser radar wind speed calculation method based on the long-term and short-term memory neural network has the following specific scheme:
firstly, a wind turbine generator set of the cabin type laser radar equipment needs to be installed, and the wind turbine generator set statistics comprises the generator rotating speed, the blade pitch angle, the generator power of the wind turbine generator set, the cabin wind speed, the cabin wind direction and the wind speed data of the wind turbine generator set measured by a laser radar used for feedforward or other related control, wherein the generator rotating speed, the blade pitch angle, the generator power of the wind turbine generator set, the cabin wind speed and the cabin wind direction are cut into a cut-out wind speed interval;
in the process of arranging data, operating parameters are kept coherent, numerical values with abnormal fluctuation are eliminated, and because the operating state data of some units are coupled with the fluctuation of different frequencies, the arranged operating parameters need to be processed by low-pass filtering, high-frequency interference in signals is eliminated, and basic operating state change conditions are kept;
various parameters input in the long-short term memory neural network model often have different dimensions and dimension units, the condition can affect the result of data analysis, and in order to eliminate the dimension influence among indexes, data standardization processing is needed to solve the comparability among data indexes; after the raw data are subjected to data standardization processing, all indexes are in the same order of magnitude and are suitable for comprehensive comparison and evaluation; wherein, each input variable is linearly changed by using a dispersion standardization method, the original numerical value is converted into a [0,1] interval, and the specific conversion function is as follows:
wherein X is a value after the conversion, X is a value before the conversion, XminIs the minimum value of sample data in the statistical data, XmaxThe maximum value of the sample data in the statistical data is obtained;
after filtering and standard normalization processing, taking the rotating speed of a generator, the blade pitch angle, the generating power of a unit, the wind speed of an engine room and the wind direction parameters of the engine room as input parameters of long-short term memory neural network training, and taking upwind direction wind speed data measured by a laser radar for feedforward or other related control as a target output value of the long-short term memory neural network training; the input parameters are sequence data with time sequence as change, the front data and the back data have relevance, and the long-short term memory neural network is suitable for processing and predicting important events with long intervals and delays in the time sequence, so the structure is used as a training model of equivalent laser radar wind speed;
after the training of the long-term and short-term memory neural network, a complete model structure and specific parameters including internal weights and offsets can be obtained, the trained model structure and specific parameters are adapted to a control system of a wind turbine generator without laser radar equipment, the generator can obtain real-time generator rotating speed, blade pitch angle and generator power in an operating state, real-time wind speed and wind direction information can be obtained through a wind speed and direction indicator, normalization is carried out through the deviation standardization method, and X is carried out at the momentminAnd XmaxThe maximum value and the minimum value of sample data in the statistical data are required to be kept consistent;
the data are normalized and then input into a software model of a control system to obtain an equivalent laser radar wind speed signal with the amplitude value between 0 and 1 in a standardized state, and the equivalent wind speed with the same dimension as the laser radar wind speed can be obtained through reverse normalization, so that the real-time equivalent wind speed output by the model can replace the wind speed signal obtained by actual measurement of the laser radar to perform specific control operation.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the method uses the long-short term memory neural network, generates an equivalent wind speed signal which is the same as the wind speed measured by the laser radar after training, is used for control technologies such as feedforward and correction, and reduces the number of the laser radars used in the project, thereby saving the cost of sensor equipment, effectively reducing the load of a unit, and improving the generated energy.
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FIG. 1 is a flow chart of model training of the method of the present invention.
FIG. 2 is a logic flow diagram of the method of the present invention.
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.
The embodiment discloses an equivalent laser radar wind speed calculation method based on a long-term and short-term memory neural network, which mainly comprises the following two parts: the first part is that related operation data are sorted and counted in a wind turbine generator provided with laser radar equipment, and a final model structure and specific parameters are obtained through long-term and short-term memory neural network model training, wherein the final model structure and the specific parameters are shown in figure 1; the second part is that the trained model structure and specific parameters are adapted to a control system of a wind turbine generator without laser radar equipment, the generator inputs the real-time running state of the generator into the model, and the equivalent laser radar wind speed is obtained and used for the control system of the generator, which is shown in figure 2; the detailed design scheme of the two parts is as follows:
firstly, a wind turbine generator set of the nacelle type laser radar equipment needs to be installed, and the wind turbine generator set statistics comprises the rotating speed of a generator, the blade pitch angle, the generating power of the wind turbine generator set, the wind speed of the nacelle, the wind direction of the nacelle and the wind speed data of the wind turbine generator set measured by a laser radar used for feedforward or other related control, wherein the rotating speed of the generator is cut into a cut-out wind speed interval.
The operation parameters are kept coherent as much as possible in the process of sorting the data, numerical values with abnormal fluctuation are rejected, and the sorted operation parameters need to be processed by low-pass filtering to reject high-frequency interference in signals and keep the basic operation state change condition because the operation state data of the generator, such as the rotating speed of the generator, the blade pitch angle and the like, are coupled with the fluctuation of different frequencies.
Various parameters input in the long-short term memory neural network model often have different dimensions and dimension units, the condition can influence the result of data analysis, and in order to eliminate the dimension influence among indexes, data standardization processing is needed to solve the comparability among data indexes; after the raw data are subjected to data standardization processing, all indexes are in the same order of magnitude, and the method is suitable for comprehensive comparison and evaluation. The invention uses the deviation standardization method to carry out linear change on each input variable, converts the original numerical value into a [0,1] interval, and the specific conversion function is as follows:
in the formula, X*Is a value after the conversion, X is a value before the conversion, XminIs the minimum value of sample data in the statistical data, XmaxIs the maximum value of the sample data in the statistical data.
After filtering and standard normalization processing, taking parameters such as the rotating speed of a generator, the blade pitch angle, the generating power of a unit, the wind speed of an engine room, the wind direction of the engine room and the like as input parameters of long-short term memory neural network training, and taking upwind wind speed data measured by a laser radar used for feedforward or other related control as a target output value of the long-short term memory neural network training; because the input parameters are sequence data with time sequence as change, the front data and the back data have relevance, the long-term and short-term memory neural network has a unique design structure and is particularly suitable for processing and predicting important events with very long interval and delay in the time sequence, and therefore the structure is used as a training model of the equivalent laser radar wind speed.
After training of the long-term and short-term memory neural network, a complete model structure and specific parameters such as internal weights and offsets can be obtained, the trained model structure and specific parameters are adapted to a control system of a unit without laser radar equipment, the unit can obtain real-time rotating speed of a generator, blade pitch angle and unit generating power in an operating state, real-time wind speed and wind direction information can be obtained through a wind speed and wind direction indicator, normalization is carried out through the deviation standardization method, and at the moment, X is XminAnd XmaxThe data needs to be consistent with the maximum and minimum values of the sample data in the statistical data.
And after normalization processing, the data are input into a software model of the control system to obtain an equivalent laser radar wind speed signal with the amplitude value between 0 and 1 in a standardized state, and after reverse normalization conversion, the equivalent wind speed with the same dimension as the laser radar wind speed is obtained, so that the real-time equivalent wind speed output by the model can replace the wind speed signal obtained by actual measurement of the laser radar to perform specific control 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 (2)
1. The equivalent laser radar wind speed calculation method based on the long-term and short-term memory neural network is characterized by comprising two parts: the first part is that related operation data are sorted and counted in a wind turbine generator provided with laser radar equipment, and a final model structure and specific parameters are obtained through long-term and short-term memory neural network model training; and the second part is to adapt the trained model structure and specific parameters to a control system of a wind turbine generator without laser radar equipment, and the generator inputs the real-time running state of the generator into the model to obtain the equivalent laser radar wind speed for the control system of the generator, so as to realize the specific control function.
2. The equivalent lidar wind speed calculation method based on the long-short term memory neural network according to claim 1, wherein: firstly, a wind turbine generator set of the cabin type laser radar equipment needs to be installed, and the wind turbine generator set statistics comprises the generator rotating speed, the blade pitch angle, the generator power of the wind turbine generator set, the cabin wind speed, the cabin wind direction and the wind speed data of the wind turbine generator set measured by a laser radar used for feedforward or other related control, wherein the generator rotating speed, the blade pitch angle, the generator power of the wind turbine generator set, the cabin wind speed and the cabin wind direction are cut into a cut-out wind speed interval;
in the process of arranging data, operating parameters are kept coherent, numerical values with abnormal fluctuation are eliminated, and because the operating state data of some units are coupled with the fluctuation of different frequencies, the arranged operating parameters need to be processed by low-pass filtering, high-frequency interference in signals is eliminated, and basic operating state change conditions are kept;
various parameters input in the long-short term memory neural network model often have different dimensions and dimension units, the condition can affect the result of data analysis, and in order to eliminate the dimension influence among indexes, data standardization processing is needed to solve the comparability among data indexes; after the raw data are subjected to data standardization processing, all indexes are in the same order of magnitude and are suitable for comprehensive comparison and evaluation; wherein, each input variable is linearly changed by using a dispersion standardization method, the original numerical value is converted into a [0,1] interval, and the specific conversion function is as follows:
wherein X is a value after the conversion, X is a value before the conversion, XminIs the minimum value of sample data in the statistical data, XmaxThe maximum value of the sample data in the statistical data is obtained;
after filtering and standard normalization processing, taking the rotating speed of a generator, the blade pitch angle, the generating power of a unit, the wind speed of an engine room and the wind direction parameters of the engine room as input parameters of long-short term memory neural network training, and taking upwind direction wind speed data measured by a laser radar for feedforward or other related control as a target output value of the long-short term memory neural network training; the input parameters are sequence data with time sequence as change, the front data and the back data have relevance, and the long-short term memory neural network is suitable for processing and predicting important events with long intervals and delays in the time sequence, so the structure is used as a training model of equivalent laser radar wind speed;
after the training of the long-term and short-term memory neural network, the complete model structure and specific parameters including internal weights and offsets can be obtained, and the trained model structure and specific parameters are adapted to the control of the wind turbine generator without the laser radar equipmentIn the system, the unit can obtain the real-time rotating speed of the generator, the blade pitch angle and the generating power of the unit in the running state, the real-time wind speed and wind direction information can be obtained through the anemorumbometer, the normalization is carried out through the deviation standardization method, and at the moment, X isminAnd XmaxThe maximum value and the minimum value of sample data in the statistical data are required to be kept consistent;
the data are normalized and then input into a software model of a control system to obtain an equivalent laser radar wind speed signal with the amplitude value between 0 and 1 in a standardized state, and the equivalent wind speed with the same dimension as the laser radar wind speed can be obtained through reverse normalization, so that the real-time equivalent wind speed output by the model can replace the wind speed signal obtained by actual measurement of the laser radar to perform specific control operation.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116357522A (en) * | 2023-01-03 | 2023-06-30 | 蒙东协合开鲁风力发电有限公司 | Blower system integrated with airborne laser wind-finding radar and operation control method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106979126A (en) * | 2017-04-12 | 2017-07-25 | 浙江大学 | Wind power generating set high wind speed section effective wind speed method of estimation based on SVR |
| KR101853122B1 (en) * | 2017-09-27 | 2018-04-30 | 제주대학교 산학협력단 | GROUNDED-BASED LiDAR, APPARATUS AND METHOD FOR CORRECTING MEASUREMENT ERROR FOR LIDAR |
| CN110929963A (en) * | 2019-12-16 | 2020-03-27 | 深圳智润新能源电力勘测设计院有限公司 | Wind speed prediction method, wind speed prediction device, and storage medium |
| CN111120205A (en) * | 2019-12-30 | 2020-05-08 | 明阳智慧能源集团股份公司 | Wind power plant regionalization control method based on laser radar |
| CN111666716A (en) * | 2020-06-05 | 2020-09-15 | 中南大学 | Large wind turbine blade surface equivalent wind speed prediction method |
| WO2020254514A1 (en) * | 2019-06-20 | 2020-12-24 | Wobben Properties Gmbh | Method for making an energy yield forecast, and wind farm |
-
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- 2021-03-04 CN CN202110237742.6A patent/CN113177187A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106979126A (en) * | 2017-04-12 | 2017-07-25 | 浙江大学 | Wind power generating set high wind speed section effective wind speed method of estimation based on SVR |
| KR101853122B1 (en) * | 2017-09-27 | 2018-04-30 | 제주대학교 산학협력단 | GROUNDED-BASED LiDAR, APPARATUS AND METHOD FOR CORRECTING MEASUREMENT ERROR FOR LIDAR |
| WO2020254514A1 (en) * | 2019-06-20 | 2020-12-24 | Wobben Properties Gmbh | Method for making an energy yield forecast, and wind farm |
| CN110929963A (en) * | 2019-12-16 | 2020-03-27 | 深圳智润新能源电力勘测设计院有限公司 | Wind speed prediction method, wind speed prediction device, and storage medium |
| CN111120205A (en) * | 2019-12-30 | 2020-05-08 | 明阳智慧能源集团股份公司 | Wind power plant regionalization control method based on laser radar |
| CN111666716A (en) * | 2020-06-05 | 2020-09-15 | 中南大学 | Large wind turbine blade surface equivalent wind speed prediction method |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116357522A (en) * | 2023-01-03 | 2023-06-30 | 蒙东协合开鲁风力发电有限公司 | Blower system integrated with airborne laser wind-finding radar and operation control method |
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