CN113883009A - Wind turbine generator system anemometer angle self-optimization method - Google Patents
Wind turbine generator system anemometer angle self-optimization method Download PDFInfo
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- CN113883009A CN113883009A CN202111133671.1A CN202111133671A CN113883009A CN 113883009 A CN113883009 A CN 113883009A CN 202111133671 A CN202111133671 A CN 202111133671A CN 113883009 A CN113883009 A CN 113883009A
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- 238000001514 detection method Methods 0.000 description 6
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- 238000012423 maintenance Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 239000006185 dispersion Substances 0.000 description 2
- 238000005192 partition Methods 0.000 description 2
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/04—Automatic control; Regulation
- F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
- F03D7/043—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic
- F03D7/045—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic with model-based controls
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- 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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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/0204—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor for orientation in relation to wind direction
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/04—Automatic control; Regulation
- F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
- F03D7/048—Automatic control; Regulation by means of an electrical or electronic controller controlling wind farms
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/30—Control parameters, e.g. input parameters
- F05B2270/32—Wind speeds
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/30—Control parameters, e.g. input parameters
- F05B2270/321—Wind directions
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/30—Control parameters, e.g. input parameters
- F05B2270/335—Output power or torque
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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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- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Wind Motors (AREA)
Abstract
The invention discloses a wind turbine angle self-optimization method for a wind turbine of a wind turbine generator system, which comprises the following steps: based on the sample data, acquiring the relation among the wind speed outside the engine room, the wind angle of the engine room relative to the wind direction and the active power output by the generator set; establishing a median model and a standard deviation model of the wind angle-output power under different wind speed intervals based on the obtained relation; acquiring current real-time data, and respectively substituting the current real-time data into a median model and a standard deviation model; judging whether the current real-time data simultaneously meet reference data in the median model and the standard deviation model; if the current real-time data simultaneously meet the reference data in the median model and the standard deviation model, the current real-time data collected by the anemoscope is normal, otherwise, the current real-time data collected by the anemoscope is abnormal, and the position of the engine room needs to be adjusted according to the comparison result. Therefore, whether the current wind generating set is in the working state with the minimized power loss can be judged.
Description
Technical Field
The invention relates to the technical field of wind power plant operation control, in particular to a wind meter angle self-optimization method for a wind generating set.
Background
Wind power has surpassed nuclear power to become the third largest main power source following coal power and hydropower. With the increase of the newly added installed capacity of wind power, how to improve the generated energy under the limited condition is the goal pursued by designers in the wind power industry. A wind wheel of the wind generating set is driven by wind power to drive a gear box and a generator on a transmission chain, and wind energy is converted into electric energy. The main function of the yaw system of the unit is that the nose is opposite to the wind direction, and the wind wheel faces the wind in the front direction, so that the captured energy can be the maximum.
At present, in a yaw control system of a large wind turbine, a data source of yaw wind alignment angle mainly comes from a wind meter device installed at the tail of a cabin. During field debugging, in order to ensure the accuracy of data, the initial positions of the cabin and the anemometer need to be calibrated, so that the direction of the head of the cabin is consistent with the direction of the wind vane of the anemometer. In the debugging and operation process of the wind turbine generator, the accuracy of a yaw angle can be influenced by two conditions: one is zero calibration deviation of the anemometer after long-term operation, and the other is individual subjective difference caused by visual calibration of operation and maintenance personnel. In the traditional mode, when the output power of the wind turbine generator is obviously not matched with the wind speed, the anemoscope is recalibrated by operation and maintenance personnel through visual inspection, so that the mode is not only delayed, but also the accuracy cannot be guaranteed, the wind energy utilization rate is reduced, and the generated energy loss is caused.
Disclosure of Invention
In order to solve part or all of the technical problems in the prior art, the invention provides a wind meter angle self-optimization method for a wind generating set.
The technical scheme of the invention is as follows:
a wind turbine angle self-optimization method of a wind generating set is characterized by comprising the following steps:
step S1: based on the sample data, acquiring the relation among the wind speed outside the engine room, the wind angle of the engine room relative to the wind direction and the active power output by the generator set;
step S2: based on the relationship obtained in step S1, a median model of "angle to wind-output power" at different wind speed intervals is established, and a standard deviation model of "angle to wind-output power" at different wind speed intervals is established;
step S3: acquiring current real-time data, and respectively substituting the current real-time data into the median model and the standard deviation model in the step S2;
step S4: judging whether the current real-time data simultaneously meet reference data in the median model and the standard deviation model;
step S5: if the current real-time data simultaneously meet the reference data in the median model and the standard deviation model, the current real-time data collected by the anemoscope is normal, otherwise, the current real-time data collected by the anemoscope is abnormal, and the position of the engine room needs to be adjusted according to the comparison result.
Optionally, in the method, in the step S5, the method further includes:
if the current real-time data does not simultaneously meet the reference data in the median model and the standard deviation model, detecting turbulence parameters where the anemoscope is located;
when the turbulence parameter reaches a preset critical value, judging that the current real-time data is invalid and needing to be observed continuously; and when the turbulence parameter is smaller than a preset critical value, comparing the current real-time data with the reference data in the median model and the standard deviation model, and adjusting the angle of the cabin according to the comparison result.
Optionally, in the method, in step S5, when the turbulence parameter is smaller than a preset critical value, comparing the current real-time data with reference data in a median model and a standard deviation model, and adjusting an angle of the nacelle according to a comparison result therebetween, the method includes:
and acquiring the offset direction and the offset angle of the anemometer according to the comparison result, calculating the offset value of the cabin, adjusting the angle of the cabin according to the offset value, and then performing loop iteration and self-optimization verification.
The technical scheme of the invention has the following main advantages:
the wind meter angle self-optimization method of the wind generating set can be used for large wind generating sets, and two different data models can be established in different wind speed intervals based on standard sample data to judge whether the current wind generating set is in a working state with minimized power loss. And the turbulence state at the wind meter can be judged, the influence of turbulence on the detection data of the wind meter is avoided, the influence of the environment on the detection data is reduced, and the detection accuracy is improved. In addition, when the current wind generating set is judged not to be in the optimal working state, the wind direction of the cabin can be timely adjusted, and the cabin can automatically rotate to the optimal direction by means of a closed-loop control mode.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic flow chart of a wind turbine generator set anemometer angle self-optimization method according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the specific embodiments of the present invention and the accompanying drawings. It is to be understood that the described embodiments are merely a few embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
The technical scheme provided by the embodiment of the invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, according to an embodiment of the present invention, a wind meter angle self-optimization method for a wind turbine generator system is provided, which can be used for a large wind turbine generator system, and by using the method, data collected by the wind meter can be quickly corrected during power generation of the wind turbine generator system, so that a nacelle is directly facing a wind direction, and power loss is minimized. It will be appreciated that anemometers include anemometers and anemometers, with the anemometers being capable of detecting wind speed and the anemometers being capable of detecting wind direction.
The method comprises the following steps:
step S1: based on the sample data, acquiring the relation among the wind speed outside the engine room, the wind angle of the engine room relative to the wind direction and the active power output by the generator set;
step S2: based on the relationship obtained in step S1, a median model of "angle to wind-output power" at different wind speed intervals is established, and a standard deviation model of "angle to wind-output power" at different wind speed intervals is established;
step S3: acquiring current real-time data, and respectively substituting the current real-time data into the median model and the standard deviation model in the step S2;
step S4: judging whether the current real-time data simultaneously meet reference data in the median model and the standard deviation model;
step S5: if the current real-time data simultaneously meet the reference data in the median model and the standard deviation model, the current real-time data collected by the anemoscope is normal, otherwise, the current real-time data collected by the anemoscope is abnormal, and the position of the engine room needs to be adjusted according to the comparison result.
It can be understood that, in the present embodiment, sample data corresponding to the current generator set may be acquired based on the current generator set, where the sample data is acquired standard data of the wind turbine generator set which is debugged and confirmed to be capable of operating normally. The sample data acquisition mode is as follows: after the debugging of the wind generating set is finished, the wind generating set is enabled to enter a normal operation state, the wind speed, the wind angle and the output power of the wind generating set during operation are collected, the collection is continued for a period of time, and the collected data are used as sample data. Thus, the sample data includes wind speed outside the nacelle, wind angle of the nacelle relative to the wind direction, and active power output by the generator set. In the initial database, the relation among the wind speed outside the nacelle, the wind angle of the nacelle relative to the wind direction, and the active power output by the generator set can be obtained based on the sample data.
Then, the division is performed based on the different wind speed intervals. In the present embodiment, based on the relationship obtained previously, two different data models are established in the partitions of different wind speed intervals, that is, a median model of "wind angle-output power" in different wind speed intervals and a standard deviation model of "wind angle-output power" in different wind speed intervals. Specifically, for sample data, the wind speed may be first divided into a plurality of continuous wind speed intervals at predetermined interval intervals, and the smaller the interval of the wind speed intervals, the more accurate the data is, for example, the predetermined interval intervals may be 0.5 or 1, and the wind speed corresponding to each wind speed interval is a fixed quantity. At this time, curve fitting may be performed on the scatter data of the wind angle and the output power in different wind speed intervals, that is, the wind speed is determined as a fixed quantity by adopting a variable control manner, and a corresponding relationship between the two variables of the wind angle and the output power is obtained.
The median model is based on scatter data, the fitting method of the median model is to partition the wind angle, for example, the wind angle can be divided into a plurality of continuous intervals at preset interval intervals, similarly, the smaller the interval of the wind angle intervals is, the more accurate the data is, then the output power in the same wind angle interval is averaged, so that each wind angle interval corresponds to an average output power, and then all the corresponding average output powers are connected according to the sequence of the wind angle intervals, so that the median model can be obtained, and the median model can reflect the average level of the data. The standard deviation model is based on scatter data and median, and can reflect the dispersion degree of the data points relative to the median.
Then, obtaining current real-time data, such as real-time wind speed outside the nacelle, wind angle of the nacelle relative to the wind direction, and real-time power output by the generator set, comparing and calculating the real-time data with reference data in the median model and the standard deviation model, and judging whether the current real-time data is normal, that is, judging whether the current real-time data meets standard data in the median model and the standard deviation model.
If the current real-time data simultaneously meet the reference data in the median model and the standard deviation model, the real-time data collected by the wind meter is normal, otherwise, the real-time data collected by the wind meter is abnormal, and the position of the engine room needs to be adjusted according to the comparison result.
Further, step S5 includes:
if the current real-time data does not simultaneously meet the reference data in the median model and the standard deviation model, detecting turbulence parameters where the anemoscope is located;
when the turbulence parameter reaches a preset critical value, judging that the current real-time data is invalid and needing to be observed continuously; and when the turbulence parameter is smaller than a preset critical value, comparing the current real-time data with the reference data in the median model and the standard deviation model, and adjusting the angle of the cabin according to the comparison result.
It can be understood that, in the present embodiment, it is necessary to determine whether the data collected by the anemometer is normal in combination with the turbulence condition at the anemometer, so as to improve the accuracy. The preset critical value can be calculated by means of a calculation method for turbulence level in IEC61400-1 standard, and turbulence parameters can acquire wind speed data of the wind turbine generator set for 10min in real time, so that the influence of turbulence is judged by comparing the wind speed data with the preset critical value.
When the turbulence parameter is detected to reach the critical value, the current turbulence influence is large, the current real-time data is judged to be invalid, and the observation is required to be continued; when the detected turbulence parameter is smaller than the critical value, it is indicated that the current turbulence influence is small, the current real-time data is effective, and the active power output by the current generator set is not matched with the standard output power at the current wind speed, so that the wind angle of the engine room needs to be adjusted.
In contrast, the offset direction and the offset angle of the wind direction indicator in the wind meter can be obtained according to the current comparison result, the offset value of the cabin is calculated, and the angle of the cabin is adjusted according to the offset value, so that the output power of the generator set can be adjusted conveniently. For example, after the real-time data is acquired, a real-time median curve may be established, and then the real-time median curve is compared with the power peak in the median model curve, the offset angle is determined according to the offset amount therebetween, and the offset direction is determined according to the positional relationship therebetween. And a real-time standard deviation curve can be established based on real-time data to assist in judging the dispersion degree of the anemometer at the new median.
After the offset direction and the offset angle of the anemoscope are judged according to the calculation result between the current real-time data and the reference data in the median model and the standard deviation model, a new offset vector can be calculated by means of a program, and the offset vector is used for controlling the yaw correction of the nacelle, so that the nacelle can be ensured to be over against the wind direction. And then, adjusting the wind angle of the cabin in real time according to the offset vector, and in the adjusting process, detecting the offset direction and the offset angle, calculating the offset vector and adjusting the cabin direction in a circulating and iterative manner to realize self-optimization and verification. Eventually enabling the nacelle to be in an optimal orientation at the current wind speed.
In addition, when the operation and maintenance personnel maintain the fan, the equipment can be adjusted, and the offset vector needs to be reset.
Therefore, the wind turbine angle self-optimization method of the wind turbine generator set provided by the invention has the following advantages:
the method can establish two different data models in different wind speed intervals based on standard sample data to judge whether the current wind generating set is in a working state with minimized power loss. And the turbulence state at the wind meter can be judged, the influence of turbulence on the detection data of the wind meter is avoided, the influence of the environment on the detection data is reduced, and the detection accuracy is improved. In addition, when the current wind generating set is judged not to be in the optimal working state, the wind direction of the cabin can be timely adjusted, and the cabin can automatically rotate to the optimal direction by means of a closed-loop control mode.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. In addition, "front", "rear", "left", "right", "upper" and "lower" in this document are referred to the placement states shown in the drawings.
Finally, it should be noted that: the above examples are only for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (3)
1. A wind turbine angle self-optimization method of a wind generating set is characterized by comprising the following steps:
step S1: based on the sample data, acquiring the relation among the wind speed outside the engine room, the wind angle of the engine room relative to the wind direction and the active power output by the generator set;
step S2: based on the relationship obtained in step S1, a median model of "angle to wind-output power" at different wind speed intervals is established, and a standard deviation model of "angle to wind-output power" at different wind speed intervals is established;
step S3: acquiring current real-time data, and respectively substituting the current real-time data into the median model and the standard deviation model in the step S2;
step S4: judging whether the current real-time data simultaneously meet reference data in the median model and the standard deviation model;
step S5: if the current real-time data simultaneously meet the reference data in the median model and the standard deviation model, the current real-time data collected by the anemoscope is normal, otherwise, the current real-time data collected by the anemoscope is abnormal, and the position of the engine room needs to be adjusted according to the comparison result.
2. The wind turbine generator system anemometer angle self-optimization method according to claim 1, further comprising, in step S5:
if the current real-time data does not simultaneously meet the reference data in the median model and the standard deviation model, detecting turbulence parameters where the anemoscope is located;
when the turbulence parameter reaches a preset critical value, judging that the current real-time data is invalid and needing to be observed continuously; and when the turbulence parameter is smaller than a preset critical value, comparing the current real-time data with the reference data in the median model and the standard deviation model, and adjusting the angle of the cabin according to the comparison result.
3. The method for self-optimizing the wind meter angle of the wind turbine generator system according to claim 2, wherein in step S5, when the turbulence parameter is smaller than the predetermined critical value, the method compares the current real-time data with the reference data in the median model and the standard deviation model, and adjusts the angle of the nacelle according to the comparison result therebetween, and comprises:
and acquiring the offset direction and the offset angle of the anemometer according to the comparison result, calculating the offset value of the cabin, adjusting the angle of the cabin according to the offset value, and then performing loop iteration and self-optimization verification.
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| CN113048016A (en) * | 2019-12-27 | 2021-06-29 | 新疆金风科技股份有限公司 | Method and device for correcting wind deviation of wind generating set on line |
| CN112031996A (en) * | 2020-08-28 | 2020-12-04 | 山东中车风电有限公司 | Method and system for optimizing over-limit of cabin vibration during yaw motion of wind turbine generator |
| KR20210006874A (en) * | 2020-12-30 | 2021-01-19 | 정인우 | Kalman Filter and Deep Reinforcement Learning based Wind Turbine Yaw Misalignmnet Control Method |
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