A method and device for simulating and generating the output curve of a wind farm under typhoon disasters

Abstract

The present invention discloses a method and device for simulating and generating a wind farm output curve under typhoon disasters. The method evaluates the types of all wind farms to be evaluated based on typhoon data obtained, and obtains the corresponding wind farm types for each wind farm to be evaluated; according to the typhoon data and the wind farm types, obtains the corresponding wind power output pattern sequences for each wind farm to be evaluated from a pre-constructed model sequence sample library; according to the wind power output pattern sequences, randomly selects the corresponding basic wind power curve form segments from a pre-constructed basic wind power curve form segment library, so as to obtain the wind power output curves of each evaluated wind farm based on the basic wind power curve form segments. Compared with the prior art, the technical solution of the present invention can accurately simulate the wind power output curves of wind farms in different regions during typhoons.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid planning, and particularly to a method and device for simulating and generating the output curve of a wind farm under typhoon disasters. Background Art

[0002] Driven by the dual-carbon goal and energy transformation, the construction of wind power bases in the northwest region and offshore wind farms in the southeast coast of China has been accelerating continuously; evaluating the impact of extreme meteorological events such as typhoons on the power supply capacity of power grids with a high proportion of wind power is a new requirement for the new power system. A core link in this problem is how to model the impact of meteorological disasters on numerous wind farms in a large area, simulate and generate the time series of wind farm power generation power that conforms to the typhoon impact law, and create conditions for power grid analysis and evaluation.

[0003] The daily power generation power of wind farms varies greatly, with strong uncertainty and no clear peak-valley pattern of the daily power generation curve. For daily and hourly predictions, the wind speed change information provided by numerical weather forecasts can also be used for prediction. If it is necessary to evaluate the risk tolerance of the power grid to extreme meteorological events such as typhoons within a larger time span, there is currently a lack of effective methods for generating wind power curves.

[0004] Typhoon is one of the extreme meteorological events that has the greatest impact on power systems with a high proportion of new energy. At present, the research on the generation technology of the change scenarios of large-scale wind power curves under typhoon impact is insufficient, and it is impossible to accurately simulate the change scenarios of the time series of wind farm power generation power in different regions during typhoon periods, facing the lack of models and methods. Summary of the Invention

[0005] The technical problem to be solved by the present invention is: to provide a method and device for simulating and generating the output curve of a wind farm under typhoon disasters, and to achieve accurate simulation of the wind power output curves of wind farms in different regions during typhoons.

[0006] To solve the above technical problem, the present invention provides a method for simulating and generating the output curve of a wind farm under typhoon disasters, including:

[0007] Obtain and, based on typhoon data, evaluate the types of all wind farms to be evaluated to obtain the corresponding wind farm types of each wind farm to be evaluated;

[0008] According to the typhoon data and the wind farm types, obtain the corresponding wind power output mode sequences of each wind farm to be evaluated from a pre-constructed model sequence sample library;

[0009] According to the wind power output mode sequences, randomly select the corresponding basic segments of the wind power curve from a pre-constructed basic segment library of the wind power curve, so as to obtain the wind power output curves of each evaluated wind farm based on the basic segments of the wind power curve.

[0010] In a possible implementation manner, the pre-constructed basic wind power curve form fragment library specifically includes:

[0011] Obtain the historical power generation curve, perform normalization processing on the historical power generation curve to obtain a normalized power generation curve, and perform 0-1 processing on the normalized power generation curve to obtain a first power generation curve;

[0012] Perform curve form recognition on the first power generation curve, and construct a basic wind power curve form fragment library based on curve basic form fragments of multiple forms.

[0013] In a possible implementation manner, performing curve form recognition on the first power generation curve specifically includes:

[0014] Extract a first array from the first power generation curve, and identify the first array with a preset sequence, where the preset sequence includes an H-type form fragment sequence, a V-type form fragment sequence, a T-type form fragment sequence, and an N-type form fragment sequence;

[0015] When the preset sequence exists in the first array, it is considered that the first power generation curve contains curve basic form fragments.

[0016] In a possible implementation manner, obtain and perform wind farm type evaluation on all wind farms to be evaluated based on typhoon data, and obtain the wind farm type corresponding to each wind farm to be evaluated, specifically including:

[0017] Obtain typhoon data, where the typhoon data includes typhoon path, 7-level wind circle radius, and 10-level wind circle radius;

[0018] Calculate the first vertical distance from each wind farm to be evaluated to the typhoon center path, compare the first vertical distance with the 7-level wind circle radius and the 10-level wind circle radius, and obtain the wind farm type corresponding to each wind farm to be evaluated, where the wind farm type includes the first type of wind farm, the second type of wind farm, and the third type of wind farm.

[0019] In a possible implementation manner, according to the typhoon data and the wind farm type, obtain the wind power output mode sequence corresponding to each wind farm to be evaluated from the pre-constructed model sequence sample library, specifically including:

[0020] Obtain the typhoon data, where the typhoon data further includes the typhoon intensity level;

[0021] According to the typhoon intensity level, a typhoon category is obtained, where the typhoon category includes tropical depression typhoon, tropical storm typhoon, severe tropical storm typhoon, typhoon, and severe typhoon;

[0022] According to the typhoon category and the type of wind farm, a pre-constructed model sequence sample library corresponding to the wind farm to be evaluated is obtained, and any model sequence sample is selected from the pre-constructed model sequence sample library, and the any model sequence sample is used as the wind power output pattern sequence corresponding to the wind farm to be evaluated.

[0023] In a possible implementation manner, the pre-constructed model sequence sample library specifically includes:

[0024] According to the typhoon intensity level in historical typhoon records, the typhoon category is set;

[0025] According to the historical radius of the 7-level wind circle and the historical radius of the 10-level wind circle in historical typhoon records, the historical first vertical distance from the wind farm to the typhoon center path is calculated, and based on the historical first vertical distance, the type of wind farm is set;

[0026] Perform morphological recognition on the wind farm power generation curve of each wind farm during historical typhoons, so as to divide the wind farm power generation curve into multiple basic curve morphological segments, and sort the multiple basic curve morphological segments to obtain the pattern sequence sample corresponding to each wind farm;

[0027] By obtaining the typhoon category during the typhoon period and the type of each wind farm, classify the model sequence samples corresponding to each wind farm to generate a model sequence sample library.

[0028] The present invention also provides a device for simulating and generating a wind farm output curve under typhoon disasters, including: a wind farm type acquisition module, a wind power output pattern sequence acquisition module, and a wind power output curve simulation and generation module,

[0029] Among them, the wind farm type acquisition module is used to obtain and evaluate the type of each wind farm to be evaluated based on typhoon data, and obtain the type of wind farm corresponding to each wind farm to be evaluated;

[0030] The wind power output pattern sequence acquisition module is used to obtain the wind power output pattern sequence corresponding to each wind farm to be evaluated from the pre-constructed model sequence sample library according to the typhoon data and the type of wind farm;

[0031] The wind power output curve simulation generation module is used to randomly select corresponding basic wind power curve form segments from a pre-constructed basic wind power curve form segment library according to the wind power output mode sequence, so as to obtain the wind power output curves of each evaluated wind farm based on the basic wind power curve form segments.

[0032] A wind farm output curve simulation generation device under typhoon disasters provided by the present invention further includes: a basic wind power curve form segment library pre-construction module;

[0033] The basic wind power curve form segment library pre-construction module is used to obtain the historical power generation curve, perform normalization processing on the historical power generation curve to obtain a normalized power generation curve, perform 0-1 processing on the normalized power generation curve to obtain a first power generation curve, perform curve form recognition on the first power generation curve, and construct a basic wind power curve form segment library based on basic curve form segments of various forms.

[0034] In a possible implementation manner, the basic wind power curve form segment library pre-construction module is used to perform curve form recognition on the first power generation curve, specifically including:

[0035] Extract a first array from the first power generation curve, and identify the first array with a preset sequence, where the preset sequence includes an H-type form segment sequence, a V-type form segment sequence, a T-type form segment sequence, and an N-type form segment sequence;

[0036] When the preset sequence exists in the first array, it is considered that the first power generation curve contains basic curve form segments.

[0037] In a possible implementation manner, the wind farm type acquisition module is used to obtain and evaluate the wind farm types of all wind farms to be evaluated based on typhoon data, and obtain the corresponding wind farm types of each wind farm to be evaluated, specifically including:

[0038] Obtain typhoon data, where the typhoon data includes typhoon path, 7-level wind circle radius, and 10-level wind circle radius;

[0039] Calculate the first vertical distance from each wind farm to be evaluated to the typhoon center path, compare the first vertical distance with the 7-level wind circle radius and the 10-level wind circle radius, and obtain the corresponding wind farm types of each wind farm to be evaluated, where the wind farm types include the first type of wind farm, the second type of wind farm, and the third type of wind farm.

[0040] In a possible implementation, the wind power output pattern sequence acquisition module is configured to obtain the wind power output pattern sequence corresponding to each wind farm to be evaluated from a pre-constructed model sequence sample library according to the typhoon data and the wind farm type, specifically including:

[0041] Obtain the typhoon data, where the typhoon data further includes the typhoon intensity level;

[0042] According to the typhoon intensity level, obtain the typhoon category, where the typhoon category includes tropical depression typhoon, tropical storm typhoon, severe tropical storm typhoon, typhoon and severe typhoon;

[0043] According to the typhoon category and the wind farm type, obtain the pre-constructed model sequence sample library corresponding to the wind farm to be evaluated, select any model sequence sample from the pre-constructed model sequence sample library, and use the any model sequence sample as the wind power output pattern sequence corresponding to the wind farm to be evaluated.

[0044] A device for simulating and generating the output curve of a wind farm under typhoon disasters provided by the present invention further includes: a pre-construction module for a model sequence sample library;

[0045] The pre-construction module for the model sequence sample library is configured to set the typhoon category according to the typhoon intensity level of historical typhoon records;

[0046] The pre-construction module for the model sequence sample library is configured to calculate the historical first vertical distance from the wind farm to the typhoon center path according to the historical 7-level wind circle radius and the historical 10-level wind circle radius of historical typhoon records, and set the wind farm type based on the historical first vertical distance;

[0047] The pre-construction module for the model sequence sample library is configured to perform morphological recognition on the wind farm power generation curve of each wind farm during historical typhoons, so as to divide the wind farm power generation curve into multiple curve basic morphological segments, and sort the multiple curve basic morphological segments to obtain the model sequence sample corresponding to each wind farm;

[0048] The pre-construction module for the model sequence sample library is configured to classify the model sequence samples corresponding to each wind farm by obtaining the typhoon category during the typhoon and the wind farm type corresponding to each wind farm, and generate a model sequence sample library.

[0049] The present invention also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for simulating and generating the output curve of a wind farm under typhoon disasters as described in any one of the above is implemented.

[0050] The present invention also provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the method for simulating and generating the output curve of a wind farm under typhoon disasters as described in any one of the above.

[0051] The method and device for simulating and generating the output curve of a wind farm under typhoon disasters according to an embodiment of the present invention have the following beneficial effects compared with the prior art:

[0052] By obtaining and based on typhoon data, evaluating the types of all wind farms to be evaluated to obtain the corresponding wind farm types for each wind farm to be evaluated; according to the typhoon data and the wind farm types, obtaining the wind power output pattern sequences corresponding to each wind farm to be evaluated from a pre-constructed model sequence sample library; according to the wind power output pattern sequences, randomly selecting the corresponding basic segments of the wind power curve from a pre-constructed basic segment library of the wind power curve, so as to obtain the wind power output curves of each evaluated wind farm based on the basic segments of the wind power curve. Compared with the prior art, the technical solution of the present invention can simulate and generate the wind power output curves of different wind farm types under typhoon data for pre-evaluated wind farms by pre-constructing the basic segment library of the wind power curve and the model sequence sample library, improving the accuracy of simulating the wind power output curves of wind farms. Description of the Drawings

[0053] Figure 1 is a schematic flow chart of an embodiment of a method for simulating and generating the output curve of a wind farm under typhoon disasters provided by the present invention;

[0054] Figure 2 is a schematic structural diagram of an embodiment of a device for simulating and generating the output curve of a wind farm under typhoon disasters provided by the present invention;

[0055] Figure 3 is a schematic diagram of the simulation result of the wind power output curve of an embodiment provided by the present invention;

[0056] Figure 4 is a schematic structural diagram of another embodiment of a device for simulating and generating the output curve of a wind farm under typhoon disasters provided by the present invention. Detailed Embodiments

[0057] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0058] Example 1

[0059] See Figure 1 , Figure 1 , which is a schematic flowchart of an embodiment of a method for simulating and generating a wind farm output curve under typhoon disasters provided by the present invention. As Figure 1 shown, the method includes steps 101 - 103, specifically as follows:

[0060] In one embodiment, before performing step 101, it further includes: pre - constructing a basic form fragment library of wind power curves.

[0061] In one embodiment, curve form recognition is performed on a large number of historical power generation curves of existing wind farms, and curve fragments of four forms, namely T - type, H - type, V - type, and N - type, are selected.

[0062] Specifically, the historical power generation curve is obtained, and the historical power generation curve is normalized to obtain a normalized power generation curve.

[0063] The historical power generation curves of existing wind farms are obtained, where the historical power generation curves include historical power generation curves during typhoon periods and historical discharge power curves during non - typhoon periods; the following normalization processing is performed on the historical power generation curves during typhoon periods and the historical discharge power curves during non - typhoon periods to obtain a non - typhoon period normalized power generation curve S i (t), a typhoon period power generation normalization curve H i (t), (t = 1, 2,..., n), where the normalization calculation formula is as follows:

[0064]

[0065] Specifically, the 0 - 1 processing is performed on the normalized power generation curve to obtain a first power generation curve.

[0066] The 0 - 1 processing is performed on the non - typhoon period normalized curve S i (t) and the typhoon period normalized curve H i (t) as follows to obtain a non - typhoon period first power generation curve denoted as U i (t) and a typhoon period first power generation curve V i (t):

[0067]

[0068]

[0069] Specifically, perform curve shape recognition on the first power generation curve during non-typhoon periods and the first power generation curve during typhoon periods, and screen out the curve segments of four shapes, namely T-shaped, H-shaped, V-shaped, and N-shaped, in the first power generation curve during non-typhoon periods, as well as the curve segments of four shapes, namely T-shaped, H-shaped, V-shaped, and N-shaped, in the first power generation curve during typhoon periods.

[0070] For the first power generation curve during non-typhoon periods: Delete all adjacent repeated values in the first power generation curve U i (t) to extract the first array during non-typhoon periods from the first power generation curve during non-typhoon periods. Identify the first array during non-typhoon periods and a preset sequence according to a preset criterion, and perform curve segment screening.

[0071] For the first power generation curve during typhoon periods: Delete all adjacent repeated values in the first power generation curve V i (t) to extract the first array during typhoon periods from the first power generation curve during typhoon periods. Identify the first array during typhoon periods and a preset sequence according to a preset criterion, and perform curve segment screening.

[0072] The preset sequence includes the H-shaped form segment sequence, the V-shaped form segment sequence, the T-shaped form segment sequence, and the T-shaped form segment sequence. The set preset criteria are as follows:

[0073] a), Recognition of the basic form segment of the H-shaped curve:

[0074] Criterion 1: Denote the H-shaped form segment sequence A = "0, 0.5, 1, 0.5, 0". If two consecutive A sequences appear in the first array, and the "0" at the end of the A sequence has a continuous duration ≥ 1h in the corresponding U i (t), V i (t), then the original curve S i (t), H i (t) segment is determined to be the basic form segment of the H-shaped curve.

[0075] Criterion 2: Denote the H-shaped form segment sequence B = "0, 0.5, 1, 0, 1, 0.5, 0". If the sequence B appears in the first array, then the original curve S i (t), H i (t) segment is determined to be the basic form segment of the H-shaped curve.

[0076] b), Recognition of the basic form segment of the V-shaped curve:

[0077] Record the V-shaped morphological fragment sequence C = "1, 0.5, 0, 0.5, 1, 0.5, 0". If the sequence C appears in the first array and its starting point "1" is not located in the above H-shaped fragment, then determine the original curve S in the corresponding time period i (t), H i (t) fragment is the basic V-shaped curve morphological fragment.

[0078] c), Recognition of the basic T-shaped curve morphological fragment:

[0079] Record the T-shaped morphological fragment sequence D = S "0, 0.5, 1, 0.5, 0". If the sequence D appears in the first array and its starting point "0" is not located in the above H-shaped and V-shaped curves, then determine the curve S in the corresponding time period i (t), H i (t) fragment is the basic T-shaped curve morphological fragment.

[0080] d), Judgment of the basic N-shaped curve morphological fragment:

[0081] U i (t) The basic curve morphological fragments other than the H-shaped, V-shaped, and T-shaped are judged as N-shaped. If the duration of a basic N-shaped curve morphological fragment is too long, it can be truncated into multiple basic N-shaped curve morphological fragments. It is required that the duration of 1 N-shaped fragment does not exceed 4 hours.

[0082] In one embodiment, since the basic N-shaped curve morphological fragment corresponds to the wind farm power sequence morphology in the normal working wind speed section; to further distinguish the differences in wind speed magnitude and wind power output, the basic N-shaped curve morphological fragment is further classified according to the average power.

[0083] Specifically: For the N-shaped curve basic morphological fragments obtained in V i (t), calculate their average power one by one. Among them, the average power calculation formula is as follows:

[0084]

[0085] Using Q i as the characteristic quantity to cluster the fragment samples; preferably, use the k-nearest neighbor method (k-NN) for clustering, set the number of classifications to 3, calculate the central value of each cluster respectively, and correspond to N I 0], N II , N III three types of fragments.

[0086] Classify all the basic N-shaped curve morphological fragments obtained in U i (t) according to their average power and N I , N II , NIII Classify according to the principle that the distance to the clustering center is the closest.

[0087] In one embodiment, the obtained basic shape segments of T-shaped, H-shaped, V-shaped, N I ,N II ,N III -shaped curve are all switched to the corresponding original curve S i (t), H i (t) segments, and generate a basic shape segment library of T-shaped wind power curves, a basic shape segment library of H-shaped wind power curves, a basic shape segment library of V-shaped wind power curves, N I -shaped wind power curve basic shape segment library, N II -shaped wind power curve basic shape segment library and N III -shaped wind power curve basic shape segment library.

[0088] As an example in this embodiment: Obtain the historical power generation curve of a wind farm in western Guangdong on a certain day in June. After normalizing the historical power generation curve and identifying the curve shape, if it is determined to be a basic shape segment of a T-shaped wind power curve, then classify the basic shape segment of the T-shaped wind power curve into the basic shape segment library of T-shaped wind power curves.

[0089] As another example in this embodiment: Obtain the historical power generation curve of a wind farm in western Guangdong on a certain day in June. After normalizing the historical power generation curve and identifying the curve shape, if it is determined to be a basic shape segment of an N-shaped wind power curve, then calculate the equivalent average power of the basic shape segment of the N-shaped wind power curve to be 0.47. When identifying the historical power generation curves of all sub-wind farms in the province during historical typhoons, cluster all the basic shape segment samples of the N-shaped curves. The sample is clustered into the N III -shaped and classified into the N III -shaped wind power curve basic shape segment library.

[0090] In one embodiment, before performing step 101, it further includes: pre-constructing a model sequence sample library.

[0091] In one embodiment, set the typhoon category according to the typhoon intensity level recorded in the historical typhoon records.

[0092] Specifically, divide the typhoon category according to the intensity level recorded in the historical typhoon records. The typhoon intensity is classified according to the following criteria: The typhoon intensity level is based on the average wind speed from 2 minutes before the hour to within the hour, and is classified into 5 categories according to the national standard of "Tropical Cyclone Grades" (GB / T 19201-2006): tropical depression, tropical storm, severe tropical storm, typhoon, and severe typhoon.

[0093] In one embodiment, according to the historical radius of the 7th-level wind circle and the historical radius of the 10th-level wind circle recorded in the historical typhoon records, calculate the historical first vertical distance D from the wind farm to the typhoon center path x , and compare the first vertical distance D x with the historical radius of the 7th-level wind circle and the historical radius of the 10th-level wind circle, and set the type of the wind farm, where the type of the wind farm includes the first type of wind farm, the second type of wind farm, and the third type of wind farm.

[0094] Specifically, based on the first vertical distance D x , set the type of the wind farm as follows:

[0095]

[0096] In the formula, R7 and R 10 are the radii of the 7th-level and 10th-level wind circles of the typhoon respectively.

[0097] In one embodiment, obtain the wind farm power generation power curve of each wind farm every day during the historical typhoon, and correspondingly transform the wind farm power generation power curve into a model sequence sample of the daily output.

[0098] Specifically, perform normalization processing and curve shape recognition on the wind farm power generation power curve of each wind farm during the historical typhoon, where the normalization processing and curve shape recognition are the same as the steps in the above pre-constructed basic wind power curve shape fragment library.

[0099] Based on the normalization processing and curve shape recognition, divide the wind farm power generation power curve into multiple curve basic shape fragments {T j (t), j = 1,..., k}, obtain the curve pattern M ∈ (T, H, V, N I , N II , N III ) of each curve basic shape fragment; at the same time, obtain the time corresponding to each curve basic shape fragment, sort the multiple curve basic shape fragments according to the chronological order, and record the sorted curve pattern name to obtain the model sequence sample corresponding to each wind farm, such as "N I N I THN I H".

[0100] In one embodiment, by obtaining the typhoon category during the typhoon and the type of the wind farm corresponding to each wind farm, classify the model sequence sample corresponding to each wind farm to generate a model sequence sample library.

[0101] Specifically, 15 model sequence sample libraries are set up according to three wind farm types and five typhoon categories, wherein the 15 model sequence sample libraries include the first type of wind farm-tropical depression model sequence sample library, the second type of wind farm-tropical depression model sequence sample library, the third type of wind farm-tropical depression model sequence sample library, the first type of wind farm-tropical storm model sequence sample library, the second type of wind farm-tropical storm model sequence sample library, the third type of wind farm-tropical storm model sequence sample library, the first type of wind farm-strong tropical storm model sequence sample library, the second type of wind farm-strong tropical storm model sequence sample library, the third type of wind farm-strong tropical storm model sequence sample library, the first type of wind farm-typhoon model sequence sample library, the second type of wind farm-typhoon model sequence sample library, the third type of wind farm-typhoon model sequence sample library, the first type of wind farm-strong typhoon model sequence sample library, the second type of wind farm-strong typhoon model sequence sample library, and the third type of wind farm-strong typhoon model sequence sample library.

[0102] Based on the wind farm power generation curves corresponding to each wind farm during the existing typhoon period, a correlation analysis is performed between the typhoon category and the wind farm type, so that the model sequence samples corresponding to each wind farm are divided into 15 model sequence sample libraries according to the typhoon type and wind farm category.

[0103] As an example of this embodiment: take Typhoon Ewiniar that landed in Guangdong in June 2018 as an example to illustrate, obtain the central path of Typhoon Ewiniar, the maximum wind speed is 20m / s, the typhoon intensity is tropical storm, the radius of the level 7 wind circle is 450km, and the radius of the level 10 wind circle is 180km; still taking the above-mentioned western Guangdong wind farm as an example, calculate the vertical distance between the wind farm and the central path of Typhoon Ewiniar to be 224.14km, and judge that the wind farm is a second-class wind farm. Perform morphological recognition on the normalized curve of the power generation of the wind farm on June 6, the day when Typhoon Ewiniar landed, and obtain 5 curve segments. Use category labels to replace the curve segments to obtain the corresponding model sequence: N I THTN II This model sequence is classified as a model sequence sample into the second type of wind farm-tropical storm model sequence sample library.

[0104] Step 101: Obtain and evaluate the wind farm type of all wind farms to be evaluated based on typhoon data to obtain the wind farm type corresponding to each wind farm to be evaluated.

[0105] In one embodiment, the wind farm to be evaluated may be a wind farm in an operating power grid, or may be a wind farm to be built in a planned power grid.

[0106] In one embodiment, typhoon data is obtained, where the typhoon data includes a typhoon path, a radius of the 7-level wind circle, and a radius of the 10-level wind circle; a first vertical distance from each wind farm to be evaluated to the typhoon center path is calculated, and the first vertical distance is respectively compared with the radius of the 7-level wind circle and the radius of the 10-level wind circle. When the first vertical distance is less than or equal to the radius of the 10-level wind circle, the wind farm type of the wind farm to be evaluated is obtained as the first type of wind farm type. When the first vertical distance is less than or equal to the radius of the 7-level wind circle and the first vertical distance is greater than the radius of the 10-level wind circle, the wind farm type of the wind farm to be evaluated is obtained as the second type of wind farm type; when the first vertical distance is greater than the radius of the 7-level wind circle, the wind farm type of the wind farm to be evaluated is obtained as the third type of wind farm type.

[0107] In one embodiment, the typhoon data can be based on manual setting or can be the typhoon path, typhoon intensity level, radius of the seven-level wind circle, and radius of the ten-level wind circle randomly sampled from historical typhoon samples.

[0108] Step 102: According to the typhoon data and the wind farm type, obtain the wind power output pattern sequence corresponding to each wind farm to be evaluated from a pre-constructed model sequence sample library.

[0109] In one embodiment, the typhoon data is obtained, where the typhoon data further includes a typhoon intensity level; according to the typhoon intensity level, a typhoon category is obtained.

[0110] In one embodiment, according to the typhoon category and the wind farm type corresponding to each wind farm to be evaluated, traverse 15 pre-constructed model sequence sample libraries to obtain the pre-constructed model sequence sample library corresponding to the wind farm to be evaluated, select any model sequence sample from the pre-constructed model sequence sample library, and use the any model sequence sample as the wind power output pattern sequence corresponding to the wind farm to be evaluated.

[0111] Specifically, for each wind farm, according to the typhoon category and the wind farm type, locate the corresponding model sequence sample library, randomly select a model sequence sample from the pre-constructed model sequence sample library as the wind power output pattern sequence, denoted as: M i (j), j = 1,..., k; M ∈ (T, H, V, N I , N II , N III ).

[0112] Step 103: According to the wind power output pattern sequence, randomly select a corresponding basic shape segment of the wind power curve from a pre-constructed basic shape segment library of the wind power curve, so as to obtain the wind power output curve of each evaluated wind farm based on the basic shape segment of the wind power curve.

[0113] In one embodiment, for the model sequence sample M i in each curve pattern M i (j), j = 1, …, k, according to the curve pattern M ∈ (T, H, V, N I , N II , N III ), a curve basic form segment is randomly selected from a pre - constructed wind power curve basic form segment library to achieve a random mapping from the curve pattern to the curve - based form segment.

[0114] In one embodiment, for all the randomly selected curve basic form segments, they are spliced in the order of the wind power output pattern sequence to obtain a first curve. It is judged whether the curve length of the first curve is greater than 24 hours. If so, the part of the curve exceeding 24 hours is cut off. If the curve length of the first curve is less than 24 hours, the corresponding model sequence sample library is re - located according to the typhoon category and the wind farm type. A model sequence sample is randomly selected from the pre - constructed model sequence sample library, and a second curve is generated. The second curve is spliced behind the first curve to obtain a third curve, and the third curve is iterated as the new first curve, and it returns to the step of "judging whether the curve length of the first curve is greater than 24 hours" until the curve length of the first curve is 24 hours.

[0115] In one embodiment, the first curve with a curve length of 24 hours obtained is a 24 - hour normalized wind power generation power curve of the wind farm to be evaluated during the typhoon. After multiplying the 24 - hour normalized wind power generation power curve by the wind farm capacity of the wind farm to be evaluated, the 24 - hour normalized wind power generation power curve can be converted into a nominal value power curve, and the nominal value power curve is set as the wind power output curve of the wind farm to be evaluated, so as to realize the simulation of the wind power output curve for one day during the typhoon.

[0116] In one embodiment, for the simulation time interval during the typhoon: Let D0 represent the day when the typhoon makes landfall, and D1 represent the day when the typhoon departs; The period from D0 - 1 to D1+1 days is regarded as the typhoon period.

[0117] In one embodiment, the above steps are repeated until the wind power output curves of all wind farms to be evaluated during the entire typhoon landfall period are simulated. Preferably, by superimposing all the wind power output curves, the total wind power output time series of the whole network during the typhoon can be obtained, and the generation of a typhoon - time scenario is completed.

[0118] As an example of this embodiment, consider a 90MW wind farm A planned for construction in 2025 in the same region of western Guangdong. A simulation is conducted to study the operational risks of the power grid in a typhoon in 2025. First, a typhoon path is randomly selected from a historical typhoon path database. Next, a random sampling method is used to determine a typhoon intensity of Level 3, a Level 7 wind circle radius of 200km, and a Level 10 wind circle radius of 40km. The vertical distance between Wind Farm A and the typhoon's center path is calculated to be 259.20km, placing it in the third category.

[0119] According to the above information, the third type of wind farm-severe tropical storm model sequence sample library is selected from the pre-built multiple model sequence sample libraries, and the samples in the library are randomly sampled to obtain a sample sequence: N III THN III , which is used as the output mode of the wind farm on a certain day during a typhoon.

[0120] According to the above output mode, the pre-built multiple wind power curve basic form fragment libraries (N Ⅲ )、(H)、(T)、(N Ⅲ ) randomly extracts a normalized wind power curve segment, and splices the above curves in sequence to obtain a 20-hour curve F1(t); since the curve is less than 24 hours; a new sequence is again extracted from the third type wind farm-severe tropical storm model sequence sample library; the first mode of the new sequence is N Ⅲ ; So continue from (N Ⅲ ) and spliced it at the end of F1(t) to get the 26-hour curve F2(t). Intercept the curve F2(t) 24 hours before to get the curve F3(t), and multiply it by the installed capacity of the site 90MW to form the wind power output curve of wind farm A on a certain day during the typhoon, as shown in the figure below: Figure 3 As shown, Figure 3 It is a schematic diagram of the simulation results of wind power output curve.

[0121] In summary, the present invention provides a method for simulating and generating wind farm output curves under typhoon disasters. The method adopts a data-driven modeling approach and can establish a model library through the historical output data of some stations during the typhoon season. It can cope with a large number of newly added wind farms in the planned grid and simulate the generation of power time series of wind farms in different regions under different combinations of typhoon intensities, paths and wind circle radii. It well reflects the impact of typhoons on the changes in wind power output of planned large power grids and is suitable for carrying out typhoon disaster carrying capacity assessments on planned power grids with a high proportion of wind power or in operating power grids.

[0122] Example 2

[0123] See also Figure 2 , Figure 2This is a schematic structural diagram of an embodiment of a device for simulating and generating a wind farm output curve under typhoon disasters provided by the present invention. As Figure 2 shown, the device includes a wind farm type acquisition module 201, a wind power output pattern sequence acquisition module 202, and a wind power output curve simulation and generation module 203, which are specifically as follows:

[0124] The wind farm type acquisition module 201 is configured to acquire and evaluate the types of all wind farms to be evaluated based on typhoon data, and obtain the corresponding wind farm types for each wind farm to be evaluated.

[0125] The wind power output pattern sequence acquisition module 202 is configured to obtain the corresponding wind power output pattern sequence for each wind farm to be evaluated from a pre-constructed model sequence sample library according to the typhoon data and the wind farm type.

[0126] The wind power output curve simulation and generation module 203 is configured to randomly select the corresponding basic wind power curve form segments from a pre-constructed basic wind power curve form segment library according to the wind power output pattern sequence, so as to obtain the wind power output curves of each evaluated wind farm based on the basic wind power curve form segments.

[0127] In one embodiment, a device for simulating and generating a wind farm output curve under typhoon disasters provided by an embodiment of the present invention further includes: a basic wind power curve form segment library pre-construction module 204, as Figure 4 shown, Figure 4 This is a schematic structural diagram of another embodiment of a device for simulating and generating a wind farm output curve under typhoon disasters provided by the present invention.

[0128] In one embodiment, the basic wind power curve form segment library pre-construction module 204 is configured to acquire a historical power generation curve, perform normalization processing on the historical power generation curve to obtain a normalized power generation curve, perform 0-1 processing on the normalized power generation curve to obtain a first power generation curve, perform curve form recognition on the first power generation curve, and construct a basic wind power curve form segment library based on curve basic form segments of multiple forms.

[0129] In one embodiment, the basic wind power curve form segment library pre-construction module 204 is configured to perform curve form recognition on the first power generation curve; specifically, extract a first array from the first power generation curve, and identify the first array with a preset sequence, where the preset sequence includes an H-type form segment sequence, a V-type form segment sequence, a T-type form segment sequence, and an N-type form segment sequence; when the preset sequence exists in the first array, it is considered that the first power generation curve contains curve basic form segments.

[0130] In one embodiment, the wind farm type acquisition module 201 is configured to acquire and evaluate the wind farm types of all wind farms to be evaluated based on typhoon data, and obtain the wind farm type corresponding to each wind farm to be evaluated. Specifically, typhoon data is acquired, where the typhoon data includes typhoon path, 7 - level wind circle radius, and 10 - level wind circle radius; the first vertical distance from each wind farm to be evaluated to the typhoon center path is calculated, and the first vertical distance is compared with the 7 - level wind circle radius and the 10 - level wind circle radius to obtain the wind farm type corresponding to each wind farm to be evaluated. The wind farm types include the first - type wind farm, the second - type wind farm, and the third - type wind farm.

[0131] In one embodiment, the wind power output mode sequence acquisition module 202 is configured to acquire the wind power output mode sequence corresponding to each wind farm to be evaluated from a pre - constructed model sequence sample library according to the typhoon data and the wind farm type. Specifically, the typhoon data is acquired, where the typhoon data further includes typhoon intensity level; according to the typhoon intensity level, typhoon categories are obtained, where the typhoon categories include tropical depression typhoon, tropical storm typhoon, severe tropical storm typhoon, typhoon, and severe typhoon; according to the typhoon category and the wind farm type, the pre - constructed model sequence sample library corresponding to the wind farm to be evaluated is obtained, and any model sequence sample is selected from the pre - constructed model sequence sample library and used as the wind power output mode sequence corresponding to the wind farm to be evaluated.

[0132] In one embodiment, a device for simulating and generating a wind farm output curve under typhoon disasters provided by an embodiment of the present invention further includes: a model sequence sample library pre - construction module 205, as Figure 4 shown.

[0133] In one embodiment, the model sequence sample library pre - construction module 205 is configured to set typhoon categories according to the typhoon intensity levels of historical typhoon records; calculate the historical first vertical distance from the wind farm to the typhoon center path according to the historical 7 - level wind circle radius and historical 10 - level wind circle radius of historical typhoon records, and set the wind farm type based on the historical first vertical distance; perform morphological recognition on the wind farm power generation curve of each wind farm during historical typhoons, so as to divide the wind farm power generation curve into multiple curve basic morphological segments, and sort the multiple curve basic morphological segments to obtain the model sequence sample corresponding to each wind farm; classify the model sequence samples corresponding to each wind farm by obtaining the typhoon category during the typhoon and the wind farm type corresponding to each wind farm, and generate a model sequence sample library.

[0134] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process of the device described above can refer to the corresponding process in the foregoing method embodiments, and will not be described herein again.

[0135] It should be noted that the embodiments of the wind farm output curve simulation generation device under the above typhoon disaster are merely illustrative. The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0136] Based on the embodiments of the method for simulating and generating the wind farm output curve under the above typhoon disaster, another embodiment of the present invention provides a terminal device for simulating and generating the wind farm output curve under the typhoon disaster. The terminal device for simulating and generating the wind farm output curve under the typhoon disaster includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for simulating and generating the wind farm output curve under any embodiment of the present invention is implemented.

[0137] Exemplarily, in this embodiment, the computer program can be divided into one or more modules. The one or more modules are stored in the memory and executed by the processor to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device for simulating and generating the wind farm output curve under the typhoon disaster.

[0138] The terminal device for simulating and generating the wind farm output curve under the typhoon disaster can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device for simulating and generating the wind farm output curve under the typhoon disaster may include, but is not limited to, a processor and a memory.

[0139] The so-called processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the terminal device for simulating and generating the output curve of the wind farm under typhoon disasters, and connects various parts of the terminal device for simulating and generating the output curve of the wind farm under typhoon disasters through various interfaces and lines.

[0140] The memory can be used to store the computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory, the processor realizes various functions of the terminal device for simulating and generating the output curve of the wind farm under typhoon disasters. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, Flash Card, at least one magnetic disk storage device, flash device, or other volatile solid-state storage devices.

[0141] Based on the embodiments of the method for simulating and generating the output curve of the wind farm under typhoon disasters described above, another embodiment of the present invention provides a storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the storage medium is located to execute the method for simulating and generating the output curve of the wind farm under typhoon disasters according to any embodiment of the present invention.

[0142] In this embodiment, the above storage medium is a computer-readable storage medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice within the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0143] In summary, the present invention provides a method and device for simulating and generating the output curve of a wind farm under typhoon disasters. By obtaining and based on typhoon data, the wind farm type evaluation is carried out for all wind farms to be evaluated, and the wind farm type corresponding to each wind farm to be evaluated is obtained; according to the typhoon data and the wind farm type, the wind power output mode sequence corresponding to each wind farm to be evaluated is obtained from a pre-constructed model sequence sample library; according to the wind power output mode sequence, the corresponding basic form segment of the wind power curve is randomly selected from the pre-constructed basic form segment library of the wind power curve, so that based on the basic form segment of the wind power curve, the wind power output curve of each evaluated wind farm is obtained. Compared with the prior art, the technical solution of the present invention can simulate and generate the wind power output curves of different wind farm types under typhoon data for the pre-evaluated wind farms by pre-constructing the basic form segment library of the wind power curve and the model sequence sample library, improving the accuracy of simulating the wind power output curve of the wind farm.

[0144] The above are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and replacements can still be made, and these improvements and replacements should also be regarded as the protection scope of the present invention.

Claims

1. A method for simulating and generating the output curve of a wind farm under typhoon disasters, characterized in that Including: Obtain typhoon data and conduct a wind farm type assessment on all wind farms to be evaluated based on the typhoon data, so as to obtain the wind farm type corresponding to each wind farm to be evaluated; among them, obtain typhoon data, and the typhoon data includes typhoon path, radius of the 7-level wind circle, and radius of the 10-level wind circle; calculate the first vertical distance from each wind farm to be evaluated to the typhoon center path, and compare the first vertical distance with the radius of the 7-level wind circle and the radius of the 10-level wind circle to obtain the wind farm type corresponding to each wind farm to be evaluated, and obtain the wind farm type corresponding to the wind farm to be evaluated, where the wind farm type includes the first type of wind farm, the second type of wind farm, and the third type of wind farm; According to the typhoon data and the wind farm type, obtain the wind power output pattern sequence corresponding to each wind farm to be evaluated from the pre-constructed model sequence sample library; among them, the typhoon data also includes the typhoon intensity level; according to the typhoon intensity level, obtain the typhoon category, where the typhoon category includes tropical depression typhoon, tropical storm typhoon, severe tropical storm typhoon, typhoon, and severe typhoon; according to the typhoon category and the wind farm type, obtain the pre-constructed model sequence sample library corresponding to the wind farm to be evaluated, select any model sequence sample from the pre-constructed model sequence sample library, and use the any model sequence sample as the wind power output pattern sequence corresponding to the wind farm to be evaluated; According to the wind power output pattern sequence, randomly select the corresponding basic wind power curve form segment from the pre-constructed basic wind power curve form segment library, so as to obtain the wind power output curve of each evaluated wind farm based on the basic wind power curve form segment; among them, the pre-constructed basic wind power curve form segment library specifically includes: obtain the historical power generation curve, perform normalization processing on the historical power generation curve to obtain the normalized power generation curve, and perform 0-1 processing on the normalized power generation curve to obtain the first power generation curve; perform curve form recognition on the first power generation curve, and construct a basic wind power curve form segment library based on the curve basic form segments of multiple forms obtained; 2. The method for simulating and generating the output curve of a wind farm under typhoon disasters according to claim 1, wherein Performing curve form recognition on the first power generation curve specifically includes: Extract the first array from the first power generation curve, and identify the first array with a preset sequence, where the preset sequence includes an H-type form segment sequence, a V-type form segment sequence, a T-type form segment sequence, and an N-type form segment sequence; When the preset sequence exists in the first array, it is considered that the first power generation curve contains a curve basic form segment.

3. The method for simulating and generating the output curve of a wind farm under typhoon disasters according to claim 1, wherein, The pre-constructed model sequence sample library specifically includes: Set the typhoon category according to the typhoon intensity level of the historical typhoon record; Calculate the historical first vertical distance from the wind farm to the typhoon center path according to the historical 7-level wind circle radius and historical 10-level wind circle radius of the historical typhoon record, and set the wind farm type based on the historical first vertical distance; Perform morphological recognition on the wind farm power generation curves of each wind farm during historical typhoons, so as to divide the wind farm power generation curves into multiple basic curve morphological segments, and sort the multiple basic curve morphological segments to obtain a pattern sequence sample corresponding to each wind farm; By obtaining the typhoon category during the typhoon and the wind farm type corresponding to each wind farm, classify the model sequence samples corresponding to each wind farm to generate a model sequence sample library.

4. A wind farm output curve simulation and generation device under typhoon disasters, characterized in that, It includes: A wind farm type acquisition module, a wind power output pattern sequence acquisition module, and a wind power output curve simulation generation module, Among them, the wind farm type acquisition module is used to obtain and evaluate the wind farm type of all wind farms to be evaluated based on typhoon data, and obtain the wind farm type corresponding to each wind farm to be evaluated; among them, obtain typhoon data, and the typhoon data includes typhoon path, 7-level wind circle radius, and 10-level wind circle radius; calculate the first vertical distance from each wind farm to be evaluated to the typhoon center path, and compare the first vertical distance with the 7-level wind circle radius and the 10-level wind circle radius to obtain the wind farm type corresponding to each wind farm to be evaluated, and obtain the wind farm type corresponding to the wind farm to be evaluated, where the wind farm type includes the first type of wind farm, the second type of wind farm, and the third type of wind farm; The wind power output pattern sequence acquisition module is used to obtain the wind power output pattern sequence corresponding to each wind farm to be evaluated from the pre-constructed model sequence sample library according to the typhoon data and the wind farm type; among them, the typhoon data also includes the typhoon intensity level; according to the typhoon intensity level, obtain the typhoon category, where the typhoon category includes tropical depression typhoon, tropical storm typhoon, severe tropical storm typhoon, typhoon, and severe typhoon; according to the typhoon category and the wind farm type, obtain the pre-constructed model sequence sample library corresponding to the wind farm to be evaluated, and select any model sequence sample from the pre-constructed model sequence sample library, and use the any model sequence sample as the wind power output pattern sequence corresponding to the wind farm to be evaluated; The wind power output curve simulation generation module is used to randomly select the corresponding wind power curve basic morphological segments from the pre-constructed wind power curve basic morphological segment library according to the wind power output pattern sequence, so as to obtain the wind power output curve of each evaluated wind farm based on the wind power curve basic morphological segments; The wind farm output curve simulation generation device further includes: a wind power curve basic morphological segment library pre-construction module; the wind power curve basic morphological segment library pre-construction module is used to obtain the historical power generation curve, perform normalization processing on the historical power generation curve to obtain a normalized power generation curve, perform 0-1 processing on the normalized power generation curve to obtain a first power generation curve, perform curve morphological recognition on the first power generation curve, and construct a wind power curve basic morphological segment library based on the curve basic morphological segments of multiple morphologies.

5. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the method for simulating and generating the output curve of a wind farm under typhoon disasters as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the method for simulating and generating the output curve of a wind farm under typhoon disasters as described in any one of claims 1 to 3.

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