A method, device, equipment and medium for predicting wind power ramp events

Abstract

The embodiment of the present invention discloses a method, device, equipment, and medium for predicting wind power ramping events. The method comprises: obtaining wind speed information and power information of an offshore wind farm; inputting the wind speed information and power information into a preset ramping event prediction model, and obtaining attribute information of the ramping event output by the preset ramping event prediction model; the preset ramping event prediction model comprises an encoding sub-model, an attention mechanism sub-model, and multiple parallel decoding sub-models, wherein the output end of the encoding sub-model is connected to the input end of the attention mechanism sub-model; the output end of the attention mechanism sub-model is respectively connected to the input end of each decoding sub-model, and different decoding sub-models are used to output different types of attribute information of the ramping event. This solution can accurately obtain the attribute information of the ramping event, improve the prediction accuracy of offshore wind power ramping events, and thereby ensure the safe and stable operation of the power system.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of offshore wind power prediction, and in particular to a method, device, equipment, and medium for predicting wind power ramping events. Background Art

[0002] With the continuous development of offshore wind power generation technology, changes in wind speed during wind power generation may cause wind power to fluctuate significantly in a short period of time. This phenomenon is called a wind power ramp event. Therefore, to ensure the safe and stable operation of the power system, it is urgent to predict offshore wind power ramp events.

[0003] Existing wind power ramp event prediction methods are mainly used to predict wind power ramp events in onshore wind farms, and are not suitable for predicting wind power ramp events in offshore wind farms with more complex weather systems, resulting in low prediction accuracy for offshore wind power ramp events. Summary of the Invention

[0004] The embodiments of the present invention provide a method, device, equipment and medium for predicting wind power ramping events, which can predict offshore wind power ramping events, improve prediction accuracy, and thus ensure the safe and stable operation of the power system.

[0005] In a first aspect, an embodiment of the present invention provides a method for predicting a wind power ramp event, comprising:

[0006] Obtain wind speed and power information of offshore wind farms;

[0007] The wind speed information and the power information are input into a preset climbing event prediction model to obtain the attribute information of the climbing event output by the preset climbing event prediction model, wherein the attribute information includes the climbing event category; the preset climbing event prediction model includes an encoding sub-model, an attention mechanism sub-model and multiple parallel decoding sub-models, wherein the output end of the encoding sub-model is connected to the input end of the attention mechanism sub-model; the output end of the attention mechanism sub-model is respectively connected to the input end of each decoding sub-model, and different decoding sub-models are used to output different types of attribute information of the climbing event.

[0008] In a second aspect, an embodiment of the present invention further provides a device for predicting wind power ramp events, the device comprising:

[0009] An information acquisition module is used to obtain wind speed information and power information of an offshore wind farm;

[0010] A climbing event prediction module is used to input the wind speed information and the power information into a preset climbing event prediction model, and obtain the attribute information of the climbing event output by the preset climbing event prediction model, wherein the attribute information includes the climbing event category; the preset climbing event prediction model includes an encoding sub-model, an attention mechanism sub-model and multiple parallel decoding sub-models, wherein the output end of the encoding sub-model is connected to the input end of the attention mechanism sub-model; the output end of the attention mechanism sub-model is respectively connected to the input end of each decoding sub-model, and different decoding sub-models are used to output different types of attribute information of the climbing event.

[0011] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising:

[0012] at least one processor; and

[0013] a memory communicatively connected to the at least one processor; wherein,

[0014] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for predicting wind power ramping events according to an embodiment of the present invention.

[0015] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a processor to implement the method for predicting wind power ramping events described in an embodiment of the present invention when executed.

[0016] In a fifth aspect, an embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the method for predicting wind power ramping events described in an embodiment of the present invention.

[0017] An embodiment of the present invention discloses a method, device, equipment and medium for predicting wind power ramping events, including: obtaining wind speed information and power information of an offshore wind farm; inputting the wind speed information and the power information into a preset ramping event prediction model, and obtaining attribute information of the ramping event output by the preset ramping event prediction model, wherein the attribute information includes a ramping event category; the preset ramping event prediction model includes an encoding sub-model, an attention mechanism sub-model and multiple parallel decoding sub-models, wherein the output end of the encoding sub-model is connected to the input end of the attention mechanism sub-model; the output end of the attention mechanism sub-model is respectively connected to the input end of each decoding sub-model, and different decoding sub-models are used to output different types of attribute information of the ramping event. The above technical solution can predict offshore wind power ramping events, specifically considering the impact of wind speed and power on ramping events, thereby obtaining wind speed information and power information of the offshore wind farm. At the same time, it also considers inputting the obtained wind speed information and power information into a pre-trained preset ramping event prediction model to obtain attribute information of the ramping event. Among them, the preset ramping event prediction model composed of an encoding sub-model, an attention mechanism sub-model, and multiple parallel decoding sub-models has a high feature extraction capability and can extract feature information about the ramping event from the wind speed information and power information of the offshore wind farm, thereby accurately obtaining attribute information of the ramping event and improving the prediction accuracy of offshore wind power ramping events. Compared with the existing technology, the technical solution provided by this embodiment can accurately obtain attribute information of the ramping event, improve the prediction accuracy of offshore wind power ramping events, and thus ensure the safe and stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a method for predicting wind power ramping events in the first embodiment of the present invention;

[0019] Figure 2 A schematic diagram of a hill climbing event provided by an embodiment of the present invention;

[0020] Figure 3 A schematic diagram of the structure of a preset hill climbing event prediction model provided by an embodiment of the present invention;

[0021] Figure 4 This is a flow chart of a method for predicting wind power ramping events in the second embodiment of the present invention;

[0022] Figure 5 This is a schematic structural diagram of a device for predicting wind power ramp events in a third embodiment of the present invention;

[0023] Figure 6 It is a structural diagram of an electronic device in embodiment 4 of the present invention. DETAILED DESCRIPTION

[0024] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0025] Example 1

[0026] Figure 1 This is a flow chart of a method for predicting wind power ramp events provided in Example 1 of the present invention. This embodiment is applicable to the prediction of offshore wind power ramp events. The method can be executed by a wind power ramp event prediction device, which can be implemented in the form of software and / or hardware. Optionally, it can be implemented by an electronic device, which can be a mobile terminal, PC, or server. Specifically, it includes the following steps:

[0027] S110: Obtain wind speed information and power information of the offshore wind farm.

[0028] In this embodiment, an offshore wind farm can be understood as an area in a marine environment where wind turbines are installed. Multiple wind farms are located offshore, each of which generates wind energy through wind turbines. The wind speed information refers to the wind speed at the offshore wind farm, and the power information refers to the power converted from wind energy to electrical energy by the wind turbines. Specifically, wind speed information and power information can be obtained for multiple offshore wind farms.

[0029] S120: Input the wind speed information and the power information into a preset slope climbing event prediction model, and obtain attribute information of the slope climbing event output by the preset slope climbing event prediction model.

[0030] In this embodiment, the preset ramp event prediction model is a pre-trained model used to predict ramp events. A ramp event can be understood as a phenomenon in which the power of an offshore wind farm fluctuates significantly due to changes in wind speed. The attribute information can be understood as key information about the ramp event, including the ramp event category and at least one of the initial power, start time, ramp amplitude, ramp direction, and duration of the ramp event.

[0031] Continuing with the above description, the ramp event category can be understood as the type of ramp event, such as a large-amplitude ramp event, a small-amplitude ramp event, or a long-duration ramp event. The initial power can be understood as the power corresponding to the start of the ramp event; the start time can be understood as the time when the ramp event begins; the ramp amplitude can be understood as the amplitude of the power change after the ramp event occurs; the ramp direction can be understood as the direction of power change during the duration of the ramp event, with an increase in power being represented as an up-ramp and a decrease in power being represented as a down-ramp; and the duration can be understood as the total time from the onset to the end of the ramp event.

[0032] Optionally, a ramp event can be characterized by the initial power, start time, ramp amplitude, ramp direction and duration when the ramp event occurs. Figure 2 A schematic diagram of a hill climbing event provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the power curve is used to represent the power change of the ramp event, and the initial power of the ramp event is recorded as P0, the start time is recorded as t0, and the ramp amplitude is recorded as P A , the climbing direction is recorded as P F And it is climbing uphill, the duration is recorded as t A .

[0033] In this embodiment, Figure 3 A schematic diagram of the structure of a preset climbing event prediction model provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the preset hill climbing event prediction model includes an encoding sub-model, an attention mechanism sub-model, and multiple parallel decoding sub-models. The output of the encoding sub-model is connected to the input of the attention mechanism sub-model; the output of the attention mechanism sub-model is connected to the input of each decoding sub-model, and different decoding sub-models are used to output different types of attribute information of the hill climbing event.

[0034] Continuing with the above description, the encoding sub-model is composed of a three-layer graph convolutional neural network (GCN); the attention mechanism sub-model can be a neural network model; each decoding sub-model is composed of a three-layer convolutional neural network (CNN), and different decoding sub-models are used to output the climbing event category, initial power when the climbing event occurs, start time, climbing amplitude, climbing direction and duration of the climbing event.

[0035] Specifically, for inputting wind speed information and power information into a preset climbing event prediction model and obtaining the attribute information of the climbing event output by the preset climbing event prediction model, one implementation method can be described as: inputting the wind speed information and power information of multiple offshore wind farms into the encoding sub-model to obtain the encoding information corresponding to the wind speed information and power information; inputting the encoding information into the attention mechanism sub-model to obtain the characteristic information of the climbing event; inputting the characteristic information of the climbing event into multiple parallel decoding sub-models to obtain the climbing event category, the initial power when the climbing event occurs, the start time, the climbing amplitude, the climbing direction and the duration respectively.

[0036] The technical solution of this embodiment obtains wind speed information and power information of an offshore wind farm; inputs the wind speed information and power information into a preset ramping event prediction model, and obtains attribute information of the ramping event output by the preset ramping event prediction model. The above technical solution takes into account the impact of wind speed and power on ramping events, thereby obtaining wind speed information and power information of an offshore wind farm; at the same time, it also considers inputting the obtained wind speed information and power information into a pre-trained preset ramping event prediction model to obtain attribute information of the ramping event; wherein, the preset ramping event prediction model, which is composed of an encoding sub-model, an attention mechanism sub-model, and multiple parallel decoding sub-models, has a high feature extraction capability and can extract feature information about the ramping event from the wind speed information and power information of the offshore wind farm, thereby accurately obtaining attribute information of the ramping event, thereby improving the prediction accuracy of offshore wind power ramping events. The technical solution provided by this embodiment can accurately obtain attribute information of the ramping event, improve the prediction accuracy of offshore wind power ramping events, and thus ensure the safe and stable operation of the power system.

[0037] Example 2

[0038] Figure 4 This is a flow chart of a method for predicting wind power ramp events provided in the second embodiment of the present invention. Based on the above embodiment, the method includes the following steps:

[0039] S210. Collect power sequence samples of the offshore wind farm.

[0040] In this embodiment, the power sequence samples can be understood as power data arranged in chronological order. Moreover, the power sequence samples arranged in chronological order may contain a situation where the power changes significantly within a certain period of time, that is, a wind power ramp event.

[0041] S220. Decompose the power sequence sample using a variational mode decomposition method to obtain multiple power components arranged from high frequency to low frequency.

[0042] In this embodiment, the variational modal decomposition method is a signal processing method, which can decompose the original signal to obtain multiple sub-signal components of different frequencies; the power component can be understood as a power signal curve that fluctuates according to a certain period, the high-frequency power component is a power signal curve with a shorter period, and the low-frequency power component is a power signal curve with a longer period.

[0043] Exemplarily, the collected power sequence samples are decomposed using a variational mode decomposition method to obtain k power components arranged from high frequency to low frequency.

[0044] S230: Construct a hill climbing event sample set based on multiple power components.

[0045] In this embodiment, the ramp event sample set is used to train a preset ramp event prediction model, so that the preset ramp event prediction model can accurately predict offshore wind power ramp events. The ramp event sample set includes historical wind speed samples and historical power samples, as well as attribute information samples of actual ramp events corresponding to the historical wind speed samples and historical power samples.

[0046] Furthermore, the historical wind speed sample is the wind speed information of the offshore wind farm within the historical time period, and the historical power sample is the power information of the offshore wind farm within the historical time period; the attribute information sample of the real climbing event can be understood as the attribute information of the actual climbing event corresponding to the historical wind speed and historical power.

[0047] In this embodiment, one implementation of constructing a hill climbing event sample set based on multiple power components can be described as the following steps:

[0048] a1) extracting at least one of an initial power sample, a start time sample, a climbing amplitude sample, a climbing direction sample, and a duration sample of each real climbing event in the power sequence sample based on the multiple power components;

[0049] In this embodiment, since the power sequence samples may contain multiple instances of significant power changes, these instances can be understood as power ramp events, and can simply be understood as the presence of multiple power ramp events in the power sequence samples. Therefore, the initial power sample, start time sample, ramp amplitude sample, ramp direction sample, and duration sample of each actual ramp event can be extracted from the power sequence samples.

[0050] In this embodiment, for extracting at least one of the initial power sample, start time sample, climbing amplitude sample, climbing direction sample and duration sample of each real climbing event in the power sequence sample based on multiple power components, one implementation method can be described as: according to the arrangement order of multiple power components, a set number of power components are obtained from back to front as the power components to be fused; the power components to be fused are fused to obtain the fused power components; the first time point corresponding to the local maximum value and the second time point corresponding to the local minimum value in the fused power components are obtained; and at least one of the initial power sample, start time sample, climbing amplitude sample, climbing direction sample and duration sample of the real climbing event is determined based on the power data between the adjacent first time point and the second time point in the power sequence sample.

[0051] Continuing with the above description, the set number is a value set in advance, and the set number must be less than the number of power components. For example, obtaining 2 power components from back to front means obtaining the last two power components. The power components to be fused are the acquired set number of power components; the fusion can be understood as addition, and the fused power component can be understood as the power component obtained by adding the set number of power components. The power component is a power signal curve that fluctuates according to a certain period, then the local maximum value in the fused power component can be understood as the value of a certain power signal in the power signal curve being greater than the power signal value on the left and the power signal value on the right of the power signal; the local minimum value can be understood as the value of a certain power signal in the power signal curve being less than the power signal value on the left and the power signal value on the right of the power signal.

[0052] Furthermore, the first time point can be understood as the time corresponding to the local maximum value in the fused power component, and the second time point can be understood as the time corresponding to the local minimum value in the fused power component; and there are multiple local maxima and multiple local minima in the fused power component, so multiple first time points and multiple second time points can be obtained. The power data between adjacent first time points and second time points in the power sequence sample is a power that changes significantly. Since there are multiple first time points and multiple second time points, there are multiple groups of power data between adjacent first time points and second time points in the power sequence sample, that is, multiple groups of power that changes significantly; and the power that changes significantly between the adjacent first time points and second time points can be regarded as a wind power ramp event.

[0053] Exemplarily, the implementation method of extracting at least one of the initial power sample, start time sample, climbing amplitude sample, climbing direction sample and duration sample of each real climbing event in the power sequence sample based on multiple power components can be: from multiple power components arranged from high frequency to low frequency, two power components are obtained from back to front as power components to be fused; the two power components to be fused are added to obtain the added power component; in the added power component, a first time point corresponding to multiple local maxima and a second time point corresponding to multiple local minima are obtained; in the power sequence sample, power data between multiple groups of adjacent first time points and second time points are extracted, and the initial power sample, start time sample, climbing amplitude sample, climbing direction sample and duration sample of each group of corresponding real climbing events are determined according to each group of power data. For example, for a certain set of power data, assuming that the adjacent first time point and second time point are t1 and t2 respectively, the power corresponding to time t1 is p1, and the power corresponding to time t2 is p2, then the initial power sample of the actual climbing event corresponding to this group is p1, the start time sample is t1, the climbing amplitude sample is (p2-p1), the climbing direction sample is downward, and the duration sample is (t2-t1).

[0054] b1) Obtaining historical wind speed samples and historical power samples corresponding to each actual climbing event;

[0055] In this embodiment, after obtaining the initial power sample, start time sample, climbing amplitude sample, climbing direction sample and duration sample of each real climbing event, for each real climbing event, it is also necessary to obtain the historical wind speed sample and historical power sample corresponding to the real climbing event.

[0056] c1) classifying each real climbing event to obtain a real category sample of each real climbing event;

[0057] In this embodiment, the true category samples can be understood as the categories of the actual hill climbing events. Optionally, a K-means clustering algorithm can be used to classify the actual hill climbing events to obtain true category samples for each actual hill climbing event. Other clustering algorithms can also be used for classification, which are not limited here.

[0058] d1) The historical wind speed samples, historical power samples, and attribute information samples of real climbing events constitute a climbing event sample set.

[0059] In this embodiment, the attribute information samples include at least one of an initial power sample, a start time sample, a climbing amplitude sample, a climbing direction sample, and a duration sample, as well as a true category sample.

[0060] In this embodiment, the climbing event sample set includes multiple historical wind speed samples, historical power samples and attribute information samples of real climbing events; for each historical wind speed sample and historical power sample, there is a corresponding attribute information sample of the real climbing event.

[0061] S240: Training a preset hill climbing event prediction model based on the hill climbing event sample set.

[0062] In this embodiment, before using the preset ramping event prediction model to predict the future ramping events of the offshore wind farm, the preset ramping event prediction model needs to be trained first so that the trained preset ramping event prediction model can accurately predict the ramping events.

[0063] In this embodiment, one implementation of training a preset hill climbing event prediction model based on a hill climbing event sample set can be described as the following steps:

[0064] a2) inputting historical wind speed samples and historical power samples into a preset ramp event prediction model to obtain attribute information of the predicted ramp event output by the preset ramp event prediction model;

[0065] In this embodiment, historical wind speed samples and historical power samples are input into a preset climbing event prediction model to obtain attribute information of the predicted climbing event, namely, predicted initial power, predicted start time, predicted climbing amplitude, predicted climbing direction, predicted duration and predicted category.

[0066] b2) determining a loss function based on attribute information of the predicted climbing event and attribute information samples of the actual climbing event;

[0067] In this embodiment, the attribute information of the predicted climbing event can be predicted initial power, predicted start time, predicted climbing amplitude, predicted climbing direction, predicted duration and predicted category, and the attribute information samples of the real climbing event can be initial power samples, start time samples, climbing amplitude samples, climbing direction samples, duration samples and real category samples.

[0068] Furthermore, the loss function can be a mean square error loss function, a binary cross entropy loss function, or a cross entropy loss function. The loss function for initial power, start time, climbing amplitude, and duration is a mean square error loss function; the loss function for climbing direction is a binary cross entropy loss function; and the loss function for climbing event category is a cross entropy loss function.

[0069] Specifically, one implementation method for determining a loss function based on the attribute information of the predicted climbing event and the attribute information samples of the actual climbing event can be described as follows: constructing a first mean square error loss function between the predicted initial power and the initial power sample; constructing a second mean square error loss function between the predicted start time and the start time sample; constructing a third mean square error loss function between the predicted climbing amplitude and the climbing amplitude sample; constructing a fourth mean square error loss function between the predicted duration and the duration sample; constructing a binary cross entropy loss function between the predicted climbing direction and the climbing direction sample; constructing a cross entropy loss function between the predicted category and the actual category sample; and performing a weighted summation of the above-mentioned first mean square error loss function, second mean square error loss function, third mean square error loss function, fourth mean square error loss function, binary cross entropy loss function, and cross entropy loss function to obtain a weighted summation loss function. The weight corresponding to each loss function is a pre-set value.

[0070] For example, assuming that the first mean square error loss function, the second mean square error loss function, the third mean square error loss function, the fourth mean square error loss function, the binary cross entropy loss function, and the cross entropy loss function are represented as f1, f2, f3, f4, f5, and f6, respectively, and their corresponding weights are w1, w2, w3, w4, w5, and w6, respectively. Then the loss function after weighted summation is (f1*w1+f2*w2+f3*w3+f4*w4+f5*w5+f6*w6).

[0071] c2) adjusting model parameters of a preset hill climbing event prediction model based on the loss function.

[0072] In this embodiment, the model parameters may be weights and bias values in the model.

[0073] In this embodiment, the model parameters in the preset climbing event prediction model are reversely adjusted according to the above-mentioned weighted summation loss function to obtain the preset climbing event prediction model after parameter adjustment; the historical wind speed samples and the historical power samples are continued to be input into the preset climbing event prediction model after parameter adjustment to obtain the attribute information of the predicted climbing event output by the preset climbing event prediction model after parameter adjustment; then a new loss function is determined based on the attribute information of the predicted climbing event and the attribute information sample of the actual climbing event, and the model parameters are continued to be reversely adjusted according to the new loss function; the above steps are repeated until the preset climbing event prediction model converges, indicating that the training is completed.

[0074] S250: Obtain wind speed information and power information of the offshore wind farm.

[0075] In this embodiment, after the preset ramp event prediction model is trained, wind speed information and power information of the offshore wind farm are obtained in real time.

[0076] S260: Input the wind speed information and the power information into a preset slope climbing event prediction model, and obtain attribute information of the slope climbing event output by the preset slope climbing event prediction model.

[0077] In this embodiment, the acquired wind speed information and power information are input into a trained preset climbing event prediction model, so that the attribute information of the climbing event can be accurately obtained, namely, the climbing event category, the initial power when the climbing event occurs, the start time, the climbing amplitude, the climbing direction and the duration.

[0078] The technical solution of this embodiment is to collect power sequence samples of offshore wind farms; decompose the power sequence samples using a variational mode decomposition method to obtain multiple power components arranged from high frequency to low frequency; construct a ramping event sample set based on the multiple power components; train a preset ramping event prediction model based on the ramping event sample set; obtain wind speed information and power information of the offshore wind farm; input the wind speed information and power information into the preset ramping event prediction model to obtain attribute information of the ramping event output by the preset ramping event prediction model. The above technical solution takes into account the need to train a preset ramp event prediction model in advance, so as to collect power sequence samples of offshore wind farms; at the same time, the variational mode decomposition method is used to decompose the power sequence samples to obtain multiple power components, and a ramp event sample set is constructed based on the multiple power components, which can quickly and accurately obtain the ramp event sample data; then, the preset ramp event prediction model is trained based on the constructed ramp event sample set, so that the preset ramp event prediction model can accurately predict the ramp event; finally, the wind speed information and power information are input into the trained preset ramp event prediction model, so that the attribute information of the ramp event can be accurately obtained. The technical solution provided by this embodiment can quickly and accurately obtain the ramp event sample data, so that the preset ramp event prediction model can accurately predict the ramp event, so that the attribute information of the ramp event can be accurately obtained, thereby improving the prediction accuracy of offshore wind power ramp events, and thus ensuring the safe and stable operation of the power system.

[0079] Example 3

[0080] Figure 5 This is a schematic diagram of a wind power ramp event prediction device provided by the third embodiment of the present invention. Figure 5 As shown, the device includes:

[0081] The information acquisition module 310 is used to obtain wind speed information and power information of the offshore wind farm;

[0082] The climbing event prediction module 320 is used to input wind speed information and power information into a preset climbing event prediction model, and obtain attribute information of the climbing event output by the preset climbing event prediction model, wherein the attribute information includes the climbing event category; the preset climbing event prediction model includes an encoding sub-model, an attention mechanism sub-model and multiple parallel decoding sub-models, wherein the output end of the encoding sub-model is connected to the input end of the attention mechanism sub-model; the output end of the attention mechanism sub-model is respectively connected to the input end of each decoding sub-model, and different decoding sub-models are used to output different types of attribute information of climbing events.

[0083] The technical solution of this embodiment obtains wind speed and power information of the offshore wind farm through an information acquisition module; inputs the wind speed and power information into a preset ramping event prediction model through a ramping event prediction module to obtain attribute information of the ramping event output by the preset ramping event prediction model. The above technical solution takes into account the impact of wind speed and power on ramping events, thereby obtaining wind speed and power information of the offshore wind farm through the information acquisition module; at the same time, it also considers inputting the acquired wind speed and power information into a pre-trained preset ramping event prediction model through the ramping event prediction module to obtain attribute information of the ramping event; wherein the preset ramping event prediction model has a high feature extraction capability and can extract feature information about the ramping event from the wind speed and power information of the offshore wind farm, thereby accurately obtaining attribute information of the ramping event, thereby improving the prediction accuracy of offshore wind power ramping events. The technical solution provided by this embodiment can accurately obtain attribute information of the ramping event, improve the prediction accuracy of offshore wind power ramping events, and thus ensure the safe and stable operation of the power system.

[0084] Optionally, the attribute information further includes at least one of the initial power, start time, climbing amplitude, climbing direction and duration when the climbing event occurs.

[0085] Optionally, the wind power ramp event prediction device may further include:

[0086] Power sequence sample collection module, used to collect power sequence samples of offshore wind farms;

[0087] A power component acquisition module is used to decompose the power sequence sample using a variational mode decomposition method to obtain multiple power components arranged from high frequency to low frequency;

[0088] A slope climbing event sample set construction module is used to construct a slope climbing event sample set based on multiple power components; wherein the slope climbing event sample set includes historical wind speed samples and historical power samples, as well as attribute information samples of real slope climbing events corresponding to the historical wind speed samples and historical power samples;

[0089] The model training module is used to train a preset climbing event prediction model based on a climbing event sample set.

[0090] Optionally, the hill climbing event sample set construction module may specifically include:

[0091] a sample extraction unit, configured to extract at least one of an initial power sample, a start time sample, a climbing amplitude sample, a climbing direction sample, and a duration sample of each real climbing event in the power sequence sample based on the multiple power components;

[0092] A historical wind speed sample and historical power sample acquisition unit, used to acquire historical wind speed samples and historical power samples corresponding to each real climbing event;

[0093] A climbing event classification unit is used to classify each real climbing event and obtain a real category sample of each real climbing event;

[0094] The sample set acquisition unit is used to form a climbing event sample set by combining historical wind speed samples, historical power samples and attribute information samples of real climbing events; the attribute information samples include at least one of the initial power sample, start time sample, climbing amplitude sample, climbing direction sample and duration sample, as well as real category samples.

[0095] Optionally, the sample extraction unit may be specifically used to:

[0096] According to the arrangement order of multiple power components, a set number of power components are obtained from back to front as power components to be fused; the power components to be fused are fused to obtain fused power components; a first time point corresponding to a local maximum value and a second time point corresponding to a local minimum value in the fused power components are obtained; and at least one of an initial power sample, a start time sample, a climbing amplitude sample, a climbing direction sample and a duration sample of a real climbing event is determined based on power data between adjacent first and second time points in the power sequence samples.

[0097] Optionally, the model training module can be used to:

[0098] Historical wind speed samples and historical power samples are input into a preset climbing event prediction model to obtain the attribute information of the predicted climbing event output by the preset climbing event prediction model; a loss function is determined based on the attribute information of the predicted climbing event and the attribute information samples of the actual climbing event; and the model parameters of the preset climbing event prediction model are adjusted based on the loss function.

[0099] The above device can execute the methods provided by all the above embodiments of the present invention, and has the corresponding functional modules and beneficial effects of executing the above methods. For technical details not fully described in this embodiment, please refer to the methods provided by all the above embodiments of the present invention.

[0100] Example 4

[0101] Figure 6 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0102] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0103] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0104] Processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any other suitable processors, controllers, microcontrollers, etc. Processor 11 executes the various methods and processes described above, such as the method for predicting wind power ramping events.

[0105] In some embodiments, the method for predicting wind power ramping events may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for predicting wind power ramping events described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for predicting wind power ramping events in any other appropriate manner (e.g., by means of firmware).

[0106] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0107] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0108] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0109] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0110] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0111] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0112] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the method for predicting wind power ramping events as provided in any embodiment of the present application.

[0113] The computer program product may be implemented by writing computer program code for performing the operations of the present invention in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0114] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0115] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for predicting wind power ramp events, characterized in that: include: Obtain wind speed and power information of offshore wind farms; Inputting the wind speed information and the power information into a preset climbing event prediction model, obtaining attribute information of the climbing event output by the preset climbing event prediction model, wherein the attribute information includes a climbing event category, and the attribute information also includes at least one of the initial power, start time, climbing amplitude, climbing direction, and duration when the climbing event occurs; the preset climbing event prediction model includes an encoding sub-model, an attention mechanism sub-model, and multiple parallel decoding sub-models, wherein the output end of the encoding sub-model is connected to the input end of the attention mechanism sub-model; the output end of the attention mechanism sub-model is respectively connected to the input end of each decoding sub-model, and different decoding sub-models are used to output different types of attribute information of the climbing event; Before obtaining the wind speed information and power information of the offshore wind farm, the method further includes: collecting power series samples of the offshore wind farm; Decomposing the power sequence sample by using a variational mode decomposition method to obtain a plurality of power components arranged from high frequency to low frequency; Constructing a hill climbing event sample set based on the multiple power components; wherein the hill climbing event sample set includes historical wind speed samples and historical power samples, and attribute information samples of real hill climbing events corresponding to the historical wind speed samples and historical power samples; Training the preset hill climbing event prediction model based on the hill climbing event sample set; Inputting the wind speed information and the power information into a preset hill climbing event prediction model, and obtaining attribute information of the hill climbing event output by the preset hill climbing event prediction model, includes: The wind speed information and power information of multiple offshore wind farms are input into the encoding sub-model to obtain the encoding information corresponding to the wind speed information and the power information; the encoding information is input into the attention mechanism sub-model to obtain the characteristic information of the climbing event; the characteristic information of the climbing event is input into multiple parallel decoding sub-models to respectively obtain the climbing event category, the initial power when the climbing event occurs, the start time, the climbing amplitude, the climbing direction and the duration.

2. The method according to claim 1, characterized in that The constructing a hill climbing event sample set based on the multiple power components includes: Extracting at least one of an initial power sample, a start time sample, a climbing amplitude sample, a climbing direction sample, and a duration sample of each real climbing event in the power sequence samples based on the multiple power components; Obtaining historical wind speed samples and historical power samples corresponding to each of the actual climbing events; Classifying each of the real climbing events to obtain a real category sample of each of the real climbing events; The historical wind speed samples, the historical power samples and the attribute information samples of the real climbing events constitute the climbing event sample set; the attribute information samples include at least one of the initial power sample, the start time sample, the climbing amplitude sample, the climbing direction sample and the duration sample, as well as the real category sample.

3. The method according to claim 2, characterized in that The extracting at least one of an initial power sample, a start time sample, a climbing amplitude sample, a climbing direction sample, and a duration sample of each real climbing event in the power sequence samples based on the multiple power components includes: According to the arrangement order of the multiple power components, a set number of power components are obtained from back to front as the power components to be fused; Fusing the power components to be fused to obtain fused power components; Obtaining a first time point corresponding to a local maximum value and a second time point corresponding to a local minimum value in the fused power component; At least one of an initial power sample, a start time sample, a climbing amplitude sample, a climbing direction sample and a duration sample of a real climbing event is determined based on power data between the adjacent first time point and the second time point in the power sequence samples.

4. The method according to claim 1, wherein The training of the preset hill climbing event prediction model based on the hill climbing event sample set includes: Inputting the historical wind speed samples and the historical power samples into the preset climbing event prediction model, and obtaining attribute information of the predicted climbing event output by the preset climbing event prediction model; Determining a loss function based on the attribute information of the predicted hill climbing event and the attribute information sample of the actual hill climbing event; Model parameters of the preset hill climbing event prediction model are adjusted based on the loss function.

5. A device for predicting wind power ramp events, characterized in that: include: An information acquisition module is used to obtain wind speed information and power information of an offshore wind farm; A climbing event prediction module is configured to input the wind speed information and the power information into a preset climbing event prediction model, and obtain attribute information of the climbing event output by the preset climbing event prediction model, wherein the attribute information includes the climbing event category and at least one of the initial power, start time, climbing amplitude, climbing direction, and duration when the climbing event occurs; the preset climbing event prediction model includes an encoding sub-model, an attention mechanism sub-model, and multiple parallel decoding sub-models, wherein the output end of the encoding sub-model is connected to the input end of the attention mechanism sub-model; the output end of the attention mechanism sub-model is respectively connected to the input end of each decoding sub-model, and different decoding sub-models are used to output different types of attribute information of the climbing event; The device for predicting wind power ramp events further includes: A power sequence sample collection module, used for collecting power sequence samples of the offshore wind farm; A power component acquisition module is used to decompose the power sequence sample using a variational mode decomposition method to obtain multiple power components arranged from high frequency to low frequency; a hill climbing event sample set construction module, configured to construct a hill climbing event sample set based on the multiple power components; wherein the hill climbing event sample set includes historical wind speed samples and historical power samples, and attribute information samples of real hill climbing events corresponding to the historical wind speed samples and historical power samples; A model training module, configured to train the preset hill climbing event prediction model based on the hill climbing event sample set; The ramping event prediction module is specifically used to input the wind speed information and power information of multiple offshore wind farms into the encoding sub-model to obtain the encoding information corresponding to the wind speed information and the power information; input the encoding information into the attention mechanism sub-model to obtain the characteristic information of the ramping event; input the characteristic information of the ramping event into multiple parallel decoding sub-models to respectively obtain the ramping event category, the initial power when the ramping event occurs, the start time, the ramping amplitude, the ramping direction and the duration.

6. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for predicting wind power ramping events according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for predicting wind power ramping events according to any one of claims 1 to 4 when executed.

8. A computer program product, comprising a computer program, wherein when executed by a processor, the computer program implements the method for predicting wind power ramping events according to any one of claims 1 to 4.

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