CN112231976A - Method for establishing equivalent model of wind power plant - Google Patents
Method for establishing equivalent model of wind power plant Download PDFInfo
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- CN112231976A CN112231976A CN202011099920.5A CN202011099920A CN112231976A CN 112231976 A CN112231976 A CN 112231976A CN 202011099920 A CN202011099920 A CN 202011099920A CN 112231976 A CN112231976 A CN 112231976A
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Abstract
The invention discloses a method for establishing an equivalent model of a wind power plant, which comprises the following steps: A. setting a partition distance threshold value of adjacent fans, and setting an area percentage threshold value of a single fan area in the total area of the wind power plant; B. dividing the whole wind power plant into a plurality of fan group subareas; C. for each fan group partition, different fan areas are divided by taking each fan as a center, each fan area is provided with only one fan, and the fan areas which do not belong to the fan areas are divided into an integral non-fan area; D. establishing a neural network prediction model of wind speed, wind direction and fan power in a fan area; E. establishing a first bidirectional association function set between a non-fan area and different fan areas; F. a second set of bi-directional correlation functions for non-fan zones in adjacent fan group zones is established. The method can improve the defects of the prior art, simplify the equivalent model of the wind power plant, and improve the timeliness of the grid-connected power prediction of the wind power plant.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to a method for establishing an equivalent model of a wind power plant.
Background
Wind energy is a green renewable resource, and wind power generation is a main form of utilizing the green energy. Because a large wind farm needs to be incorporated into a power grid for power transmission, in order to keep the load balance of the power grid, the grid-connected power of the wind farm needs to be predicted, and the premise of predicting the grid-connected power of the wind farm is to establish an equivalent model of the wind farm. In the prior art, equivalent model parameters of a wind power plant are multiple, complexity is high, calculation amount is large in a prediction process, and grid-connected power generation power of the wind power plant cannot be predicted in time.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method for establishing an equivalent model of a wind power plant, which can overcome the defects of the prior art, simplify the equivalent model of the wind power plant and improve the timeliness of grid-connected power prediction of the wind power plant.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows.
A method for establishing an equivalent model of a wind power plant comprises the following steps:
A. setting a partition distance threshold value of adjacent fans, and setting an area percentage threshold value of a single fan area in the total area of the wind power plant;
B. dividing the whole wind power plant into a plurality of fan group subareas, wherein the distance between all fans in the same fan group subarea and the fan closest to the fan group subarea is smaller than a subarea distance threshold value, and the area ratio of all fan group subareas to the wind power plant is smaller than an area percentage threshold value;
C. for each fan group partition, different fan areas are divided by taking each fan as a center, each fan area is provided with only one fan, and the fan areas which do not belong to the fan areas are divided into an integral non-fan area;
D. establishing a neural network prediction model of wind speed, wind direction and fan power in a fan area;
E. establishing a first bidirectional association function set between a non-fan area and different fan areas;
F. a second set of bi-directional correlation functions for non-fan zones in adjacent fan group zones is established.
Preferably, in the step D, the axial direction of the rotating shaft of the fan blade is taken as a center line, the fan area is divided into equal number of sub-areas on two sides of the center line, the wind speed and wind direction data of each sub-area are used as a group of input data of the neural network prediction model, and the wind speed and wind direction data of different sub-areas are input into the neural network prediction model for training according to the sequence from far to near from the fan until the fan power result output by the neural network prediction model is stable.
Preferably, a transformation function of wind speed and wind direction between all the sub-areas and the sub-areas adjacent to the sub-areas is established, when the neural network prediction model is used for fan power prediction, the sub-area with the most stable wind field state is selected as a reference area, wind speed and wind direction data of other sub-areas are combined into the reference area by using the transformation function, and then the data of the reference area is directly input into the neural network prediction model for prediction operation.
Preferably, in the step E, according to the distribution state of the fans, a three-dimensional trend graph of airflow disturbance caused by the existence of the fans is established in the non-fan region, then the airflow disturbance is decomposed into disturbance components corresponding to the sub-regions one by one, a first bidirectional correlation function is established according to each disturbance component, and then all the first bidirectional correlation functions form a first bidirectional correlation function set.
Preferably, in the step F, according to the airflow disturbance three-dimensional trend graphs of different non-fan areas, an airflow transmission three-dimensional state graph of the non-fan area in the adjacent fan group partition is established, then the airflow transmission three-dimensional state graph is converted into a plurality of binary probability density functions of wind speed and wind direction, and all the binary probability density functions form a second bidirectional correlation function set.
Adopt the beneficial effect that above-mentioned technical scheme brought to lie in: the method partitions the wind power plant, and then predicts the fan power of each partition by adopting a neural network model. In order to simplify the calculation amount of the prediction process of the neural network model, the fan area is reasonably partitioned according to the installation direction of the fan, and the wind speed and wind direction data of different sub-areas are combined by using a transformation function to serve as the input data of the neural network model, so that the repeated calculation of the prediction process can be effectively reduced. In order to obtain a grid-connected generating power predicted value of the whole wind power plant, the invention uses non-fan areas to establish the airflow flowing relation of fan areas in different fan unit partitions, and further obtains the real-time output total power of all fans of the whole wind power plant.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
Detailed Description
Referring to fig. 1, one embodiment of the present invention includes the steps of:
A. setting a partition distance threshold value of adjacent fans, and setting an area percentage threshold value of a single fan area in the total area of the wind power plant;
B. dividing the whole wind power plant into a plurality of fan group subareas, wherein the distance between all fans in the same fan group subarea and the fan closest to the fan group subarea is smaller than a subarea distance threshold value, and the area ratio of all fan group subareas to the wind power plant is smaller than an area percentage threshold value;
C. for each fan group partition, different fan areas are divided by taking each fan as a center, each fan area is provided with only one fan, and the fan areas which do not belong to the fan areas are divided into an integral non-fan area;
D. establishing a neural network prediction model of wind speed, wind direction and fan power in a fan area;
E. establishing a first bidirectional association function set between a non-fan area and different fan areas;
F. a second set of bi-directional correlation functions for non-fan zones in adjacent fan group zones is established.
And D, taking the axial direction of a rotating shaft of the fan blade as a central line, dividing a fan area into equal number of sub-areas on two sides of the central line, taking the wind speed and wind direction data of each sub-area as a group of input data of the neural network prediction model, and inputting the wind speed and wind direction data of different sub-areas into the neural network prediction model for training according to the sequence from far to near of the fan until the fan power result output by the neural network prediction model is stable.
And establishing a transformation function of wind speed and wind direction between all the sub-areas and the adjacent sub-areas, selecting the sub-area with the most stable wind field state as a reference area when the neural network prediction model is used for predicting the power of the fan, merging the wind speed and wind direction data of other sub-areas into the reference area by using the transformation function, and then directly inputting the data of the reference area into the neural network prediction model for prediction operation.
And step E, establishing an airflow disturbance three-dimensional trend graph caused by existence of the fan in a non-fan area according to the distribution state of the fan, decomposing the airflow disturbance into disturbance components corresponding to the sub-areas one by one, establishing a first bidirectional association function according to each disturbance component, and forming a first bidirectional association function set by all the first bidirectional association functions.
And step F, establishing an airflow transmission three-dimensional state diagram of the non-fan areas in the adjacent fan component areas according to the airflow disturbance three-dimensional trend diagrams of the different non-fan areas, converting the airflow transmission three-dimensional state diagram into a plurality of binary probability density functions of wind speed and wind direction, and forming a second bidirectional associated function set by all the binary probability density functions.
After the equivalent model is established, only a small number of wind speed and direction sensors are installed in the peripheral area of the wind power plant (the wind power plant is not required to be installed inside the wind power plant), the output power of the fans at the edge of the wind power plant is predicted according to the wind speed and direction data of the periphery of the wind power plant, then the wind power field parameters around the fans adjacent to the predicted area are fitted according to the corresponding functions in the first bidirectional correlation function set and the second bidirectional correlation function set, then the output power of the fans is predicted, and the like, so that the grid-connected power of the whole wind power plant can.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, are merely for convenience of description of the present invention, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (5)
1. A method for establishing an equivalent model of a wind power plant is characterized by comprising the following steps:
A. setting a partition distance threshold value of adjacent fans, and setting an area percentage threshold value of a single fan area in the total area of the wind power plant;
B. dividing the whole wind power plant into a plurality of fan group subareas, wherein the distance between all fans in the same fan group subarea and the fan closest to the fan group subarea is smaller than a subarea distance threshold value, and the area ratio of all fan group subareas to the wind power plant is smaller than an area percentage threshold value;
C. for each fan group partition, different fan areas are divided by taking each fan as a center, each fan area is provided with only one fan, and the fan areas which do not belong to the fan areas are divided into an integral non-fan area;
D. establishing a neural network prediction model of wind speed, wind direction and fan power in a fan area;
E. establishing a first bidirectional association function set between a non-fan area and different fan areas;
F. a second set of bi-directional correlation functions for non-fan zones in adjacent fan group zones is established.
2. The method for establishing the wind farm equivalent model according to claim 1, characterized in that: and D, taking the axial direction of a rotating shaft of the fan blade as a central line, dividing a fan area into equal number of sub-areas on two sides of the central line, taking the wind speed and wind direction data of each sub-area as a group of input data of the neural network prediction model, and inputting the wind speed and wind direction data of different sub-areas into the neural network prediction model for training according to the sequence from far to near of the fan until the fan power result output by the neural network prediction model is stable.
3. The method for establishing the wind farm equivalent model according to claim 2, characterized in that: and establishing a transformation function of wind speed and wind direction between all the sub-areas and the adjacent sub-areas, selecting the sub-area with the most stable wind field state as a reference area when the neural network prediction model is used for predicting the power of the fan, merging the wind speed and wind direction data of other sub-areas into the reference area by using the transformation function, and then directly inputting the data of the reference area into the neural network prediction model for prediction operation.
4. The method for establishing the wind farm equivalent model according to claim 3, characterized in that: and step E, establishing an airflow disturbance three-dimensional trend graph caused by existence of the fan in a non-fan area according to the distribution state of the fan, decomposing the airflow disturbance into disturbance components corresponding to the sub-areas one by one, establishing a first bidirectional association function according to each disturbance component, and forming a first bidirectional association function set by all the first bidirectional association functions.
5. The method for establishing the wind farm equivalent model according to claim 4, characterized in that: and step F, establishing an airflow transmission three-dimensional state diagram of the non-fan areas in the adjacent fan component areas according to the airflow disturbance three-dimensional trend diagrams of the different non-fan areas, converting the airflow transmission three-dimensional state diagram into a plurality of binary probability density functions of wind speed and wind direction, and forming a second bidirectional associated function set by all the binary probability density functions.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113239646A (en) * | 2021-05-25 | 2021-08-10 | 华能新能源股份有限公司 | Wind power plant modeling method, medium and equipment based on equivalent roughness |
| CN116819025A (en) * | 2023-07-03 | 2023-09-29 | 中国水利水电科学研究院 | Water quality monitoring system and method based on Internet of things |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103268366A (en) * | 2013-03-06 | 2013-08-28 | 辽宁省电力有限公司电力科学研究院 | Combined wind power prediction method suitable for distributed wind power plant |
| CN106849066A (en) * | 2017-03-07 | 2017-06-13 | 云南电网有限责任公司电力科学研究院 | A kind of regional wind power prediction method |
| CN108616139A (en) * | 2016-12-12 | 2018-10-02 | 中国电力科学研究院 | A kind of wind power cluster prediction technique and device |
| CN111525552A (en) * | 2020-04-22 | 2020-08-11 | 大连理工大学 | Three-stage short-term wind power plant group power prediction method based on characteristic information |
-
2020
- 2020-10-15 CN CN202011099920.5A patent/CN112231976B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103268366A (en) * | 2013-03-06 | 2013-08-28 | 辽宁省电力有限公司电力科学研究院 | Combined wind power prediction method suitable for distributed wind power plant |
| CN108616139A (en) * | 2016-12-12 | 2018-10-02 | 中国电力科学研究院 | A kind of wind power cluster prediction technique and device |
| CN106849066A (en) * | 2017-03-07 | 2017-06-13 | 云南电网有限责任公司电力科学研究院 | A kind of regional wind power prediction method |
| CN111525552A (en) * | 2020-04-22 | 2020-08-11 | 大连理工大学 | Three-stage short-term wind power plant group power prediction method based on characteristic information |
Non-Patent Citations (3)
| Title |
|---|
| MUDAN LI 等: "Research on Frequency Fuzzy Adaptive Additional Inertial Control Strategy for D-PMSGWind Turbine", 《SUSTAINABILITY》 * |
| 李牡丹 等: "基于ASW-FCM算法的风电场动态等效建模与仿真", 《***仿真学报》 * |
| 王增平 等: "基于特征影响因子和改进BP算法的直驱风机风电场建模方法", 《中国电机工程学报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113239646A (en) * | 2021-05-25 | 2021-08-10 | 华能新能源股份有限公司 | Wind power plant modeling method, medium and equipment based on equivalent roughness |
| CN113239646B (en) * | 2021-05-25 | 2023-08-22 | 华能新能源股份有限公司 | Wind farm modeling method, medium and device based on equivalent roughness |
| CN116819025A (en) * | 2023-07-03 | 2023-09-29 | 中国水利水电科学研究院 | Water quality monitoring system and method based on Internet of things |
| CN116819025B (en) * | 2023-07-03 | 2024-01-23 | 中国水利水电科学研究院 | Water quality monitoring system and method based on Internet of things |
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