Power generation information processing method and device applied to wind power generation and electronic equipment

Abstract

The embodiment of the invention discloses a power generation information processing method and device applied to wind power generation and electronic equipment. A specific embodiment of the method comprises the following steps: inputting a preprocessed wind power generation information set into a wind power generation information calculation model to obtain actual power generation information of a corresponding target wind power plant; generating wind power resource query information corresponding to the target wind power plant in response to determining that the power generation information of the actual power plant meets the preset adjustment condition; inputting the wind power resource query information into a wind power resource library; in response to the queried wind power resource information sets corresponding to the wind power resource query information, sorting the wind power resource information sets to obtain a wind power resource information sequence; and sending the actual power generation information of the power plant and the wind power resource information sequence to an associated wind power plant data analysis end. According to the implementation mode, the inquired wind power resource information is sorted, so that important wind power resource information can be marked, and further data analysis is facilitated.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the computer field, and in particular to a power generation information processing method, device and electronic device applied to wind power generation. Background Art

[0002] With the continuous development of wind power generation, effective control of wind power generation equipment has become one of the main development directions. At present, the calculation and query of power generation data corresponding to wind power generation equipment are usually carried out in the following ways: calculating the collected power generation data through modeling and simulation (CFD model or linear model); and simply querying through public third-party software (Fremeso).

[0003] However, when using the above method to calculate and query power generation data, the following technical problems often occur:

[0004] The collected power generation data is calculated through modeling and simulation, which is technically complex and time-consuming; simple query through third-party software is inefficient.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0006] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0007] Some embodiments of the present disclosure propose a power generation information processing method, device, electronic device and computer-readable medium applied to wind power generation to solve one or more of the technical problems mentioned in the above background technology section.

[0008] In a first aspect, some embodiments of the present disclosure provide a method for processing power generation information applied to wind power generation, the method comprising: in response to receiving a wind power generation information set corresponding to a target wind farm, preprocessing the wind power generation information set to obtain a preprocessed wind power generation information set, wherein the wind power generation information in the wind power generation information set corresponds to a wind power generation device in the target wind farm; inputting the preprocessed wind power generation information set into a pre-trained wind power generation information calculation model to obtain actual power generation information corresponding to the target wind farm, wherein the actual power farm The power generation information includes: total technical reduction information, wind power wake information and actual power generation; in response to determining that the above-mentioned actual power plant power generation information meets the preset adjustment conditions, generating wind resource query information corresponding to the above-mentioned target wind farm; inputting the above-mentioned wind resource query information into the wind resource library to perform wind resource query; in response to querying the wind resource information set corresponding to the above-mentioned wind resource query information, sorting the above-mentioned wind resource information set to obtain a wind resource information sequence; sending the above-mentioned actual power plant power generation information and the above-mentioned wind resource information sequence to the associated wind farm data analysis terminal.

[0009] In a second aspect, some embodiments of the present disclosure provide a power generation information processing device for wind power generation, the device comprising: a preprocessing unit, configured to, in response to receiving a wind power generation information set corresponding to a target wind farm, preprocess the wind power generation information set to obtain a preprocessed wind power generation information set, wherein the wind power generation information in the wind power generation information set corresponds to a wind power generation device in the target wind farm; a first input unit, configured to input the preprocessed wind power generation information set into a pre-trained wind power generation information calculation model to obtain actual power generation information corresponding to the target wind farm, wherein the actual power generation information includes: a generating unit, configured to generate wind resource query information corresponding to the target wind farm in response to determining that the actual power generation information of the power plant meets the preset adjustment conditions; a second input unit, configured to input the wind resource query information into a wind resource library to perform a wind resource query; a sorting unit, configured to sort the wind resource information set in response to querying a wind resource information set corresponding to the wind resource query information to obtain a wind resource information sequence; a sending unit, configured to send the actual power generation information of the power plant and the wind resource information sequence to an associated wind farm data analysis terminal.

[0010] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner of the above-mentioned first aspect.

[0011] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner of the above-mentioned first aspect is implemented.

[0012] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the power generation information processing method applied to wind power generation of some embodiments of the present disclosure, it is convenient to calculate the collected power generation data and shorten the calculation time; in addition, by sorting the queried wind resource information, important wind resource information can be marked to facilitate further data analysis. Specifically, the reason for the low query efficiency is that the collected power generation data is calculated by modeling and simulation, the technical route is complex and takes a long time; simple query through third-party software has low query efficiency. Based on this, the power generation information processing method applied to wind power generation of some embodiments of the present disclosure, first, in response to receiving the wind power generation information set corresponding to the target wind farm, the wind power generation information set is pre-processed to obtain the pre-processed wind power generation information set. Among them, the wind power generation information in the wind power generation information set corresponds to a wind power generation equipment in the target wind farm. Thus, data support is provided for calculating the actual power generation information of the power farm. Secondly, the pre-processed wind power generation information set is input into the pre-trained wind power generation information calculation model to obtain the actual power generation information of the target wind farm. Among them, the actual power generation information of the power farm includes: total technical reduction information, wind power generation wake information and actual power generation. Therefore, the power generation data can be calculated by the pre-trained wind power generation information calculation model, which speeds up the data calculation efficiency and shortens the calculation time. Then, in response to determining that the actual power generation information of the power farm meets the preset adjustment conditions, the wind resource query information corresponding to the target wind farm is generated. The wind resource query information is input into the wind resource library to query the wind resources. Therefore, the corresponding wind resource information can be queried through the pre-built wind resource library. Then, in response to querying the wind resource information set corresponding to the wind resource query information, the wind resource information set is sorted to obtain a wind resource information sequence. Finally, the actual power generation information of the power farm and the wind resource information sequence are sent to the associated wind farm data analysis terminal. Therefore, by sorting the queried wind resource information, important wind resource information can be marked to facilitate further data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0014] Figure 1 is a flow chart of some embodiments of a method for processing power generation information applied to wind power generation according to the present disclosure;

[0015] Figure 2 is a schematic structural diagram of some embodiments of a power generation information processing device applied to wind power generation according to the present disclosure;

[0016] Figure 3 is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure;

[0017] Figure 4 It is a schematic scenario diagram of a wind resource information sequence in a power generation information processing method for wind power generation according to the present disclosure. DETAILED DESCRIPTION

[0018] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0019] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0020] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0021] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0022] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0023] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0024] Figure 1 The flowchart of some embodiments of the power generation information processing method applied to wind power generation according to the present disclosure is shown. The process 100 of some embodiments of the power generation information processing method applied to wind power generation according to the present disclosure is shown. The power generation information processing method applied to wind power generation includes the following steps:

[0025] Step 101 : in response to receiving a wind power generation information set corresponding to a target wind farm, preprocessing the wind power generation information set to obtain a preprocessed wind power generation information set.

[0026] In some embodiments, the execution subject (for example, a computing device) of the power generation information processing method applied to wind power generation can pre-process the wind power generation information set in response to receiving the wind power generation information set corresponding to the target wind farm, and obtain the pre-processed wind power generation information set. Among them, the wind power generation information in the wind power generation information set corresponds to a wind power generation device in the target wind farm. The wind power generation equipment can include: a wind wheel, a generator; the wind wheel contains blades, a hub, a reinforcement, etc.; it has the functions of blades rotating by wind power to generate electricity, and the generator head rotating. The wind power generation power source is composed of a wind turbine generator set, a tower supporting the generator set, a battery charging controller, an inverter, a load unloader, a grid-connected controller, a battery pack, etc. The wind power generation equipment can also include: a speed-increasing gearbox, a yaw device, a control system, a battery pack, a main transformer and system equipment, a collector line, a control cabinet and a distribution device, a power cable, a substation monitoring system equipment, a DC system equipment, a communication system equipment, an air conditioning system equipment, and a power station relay protection system equipment. The target wind farm may be a farm where various wind power generation equipment are arranged. For example, preprocessing may include: supplementing missing values ​​and removing null values. Wind power generation information includes a sequence of wind power generation data of wind power generation equipment within a preset time period. Wind power generation data may include, but are not limited to: wind power generation equipment information, geographic location data, conversion efficiency of wind power generation equipment, open circuit voltage, short circuit current, maximum output power, operating temperature range, rated power, rotor diameter, hub height, cut-in wind speed, rated wind speed, cut-out wind speed, survival wind speed, and operating temperature.

[0027] Step 102: input the pre-processed wind power generation information set into a pre-trained wind power generation information calculation model to obtain actual power generation information of the target wind power plant.

[0028] In some embodiments, the execution subject may input the pre-processed wind power generation information set into a pre-trained wind power generation information calculation model to obtain the actual power generation information of the target wind farm. The actual power generation information of the target wind farm includes: total technical reduction information, wind power generation wake information and actual power generation. The wind power generation information calculation model may be a pre-trained time series model for calculating the actual power generation information of the target wind farm corresponding to the wind power generation data sequence included in the wind power generation information. The actual power generation information of the target wind farm may include multiple actual power generation information. One actual power generation information corresponds to one wind power generation data sequence. The actual power generation information of the target wind farm may include: the cumulative probability density distribution of wind speed, the power curve (wind speed-power curve), the power of each wind speed segment, and the cumulative sum. The total technical reduction information may represent the ratio of the actual power generation of the target wind farm to the preset power generation. The wind power generation wake information may represent the wake of the wind turbine. For example, the wind power generation information calculation model can be an autoregressive moving average model (ARMA), an autoregressive integrated moving average model (ARIMA), a seasonal autoregressive integrated moving average model (SARIMA), or a vector autoregressive model (VAR). For another example, the wind power generation information calculation model can also be a CNN convolutional neural network model or an LSTM model.

[0029] The wind power generation information calculation model can be trained through the following steps:

[0030] The first step is to obtain a power generation monitoring data set corresponding to each wind power generation equipment. Each power generation monitoring data in the above power generation monitoring data set can be data collected and recorded for power generation of various wind power generation equipment within a preset time period. The above power generation monitoring data can be data used to determine the type of power generation abnormality. The above power generation monitoring data can include various power generation monitoring sub-data. Each power generation monitoring sub-data in the above power generation monitoring sub-data can be: an electric quantity power generation data group sequence, a power generation loss sequence.

[0031] The second step is to determine the initial wind power generation information calculation model. The above-mentioned initial wind power generation information calculation model includes: an initial power generation information calculation model and an initial power generation anomaly type detection model. The initial power generation information calculation model can be an untrained autoregressive moving average model (ARMA), an autoregressive integrated moving average model (ARIMA), a seasonal autoregressive integrated moving average model (SARIMA), and a vector autoregressive model (VAR). The initial power generation anomaly type detection model may refer to an untrained recurrent neural network (RNN). For example, the network structure of the initial wind power generation information calculation model, which network layers it contains, and the loss function involved in the initial wind power generation information calculation model can be determined.

[0032] The third step is to perform power generation abnormality type identification processing on each power generation monitoring data in the above power generation monitoring data set to generate a power generation abnormality type identifier and obtain a power generation abnormality type identifier set. For example, the above execution subject can perform power generation abnormality type identification processing on each power generation monitoring data in the above power generation monitoring data set through a multi-category power generation abnormal behavior detection method of a CNN-LSTM hybrid model to generate a power generation abnormality type identifier and obtain a power generation abnormality type identifier set. Among them, the above power generation abnormality type identifier can be a name representing the type of power generation abnormal behavior. The above power generation abnormality type identifier can be: a bearing fault type identifier, a gearbox fault type identifier, a blade fault type identifier, a generator winding fault type identifier, a control system fault type identifier, and a converter fault type identifier.

[0033] The fourth step is to train the initial power generation anomaly type detection model based on the above power generation monitoring data set and the power generation anomaly type identification set to obtain a trained power generation anomaly type detection model.

[0034] The fourth step may include the following sub-steps:

[0035] The first sub-step is to perform the following processing steps for each power generation monitoring data in the power generation monitoring data set:

[0036] First, the power generation abnormality type identification set corresponding to the power generation abnormality type identification of the power generation monitoring data is determined as a comparison power generation abnormality type identification.

[0037] Second, the power generation monitoring data and the compared power generation abnormality type identifiers are determined as power generation abnormality type training samples.

[0038] The second sub-step is to determine the determined power generation abnormality type training samples as a power generation abnormality type training sample set.

[0039] The third sub-step is to perform the following training steps based on the power generation abnormality type training sample set:

[0040] First, the power generation monitoring data of at least one power generation abnormality type training sample in the power generation abnormality type training sample set is input into the initial power generation abnormality type detection model to obtain a detected power generation abnormality type identifier corresponding to each power generation abnormality type training sample in the at least one power generation abnormality type training sample.

[0041] Second, the detection power generation abnormality type identifier corresponding to each power generation abnormality type training sample in at least one power generation abnormality type training sample is compared with the corresponding comparison power generation abnormality type identifier. For example, the difference between the detection power generation abnormality type identifier corresponding to each power generation abnormality type training sample and the corresponding comparison power generation abnormality type identifier can be determined by a hinge loss function or a cross entropy loss function.

[0042] Third, according to the comparison result, determine whether the initial power generation abnormality type detection model reaches the preset training target. The comparison result can represent a difference value / loss value. The training target can mean that the difference value / loss value is less than or equal to a preset loss value.

[0043] In a fourth sub-step, in response to determining that the initial power generation abnormality type detection model reaches the training target, the initial power generation abnormality type detection model is determined as a trained power generation abnormality type detection model.

[0044] The fifth step is to train the initial power generation information calculation model according to the power generation monitoring data set to obtain a trained power generation information calculation model. Here, you can refer to the training method of the power generation anomaly type detection model, which will not be repeated here. For another example, you can also refer to the training method of the deep neural network model.

[0045] The sixth step is to integrate the above power generation abnormality type detection model and the above power generation information calculation model into a wind power generation information calculation model.

[0046] As a result, the processing of data corresponding to abnormal identifications with smaller power generation abnormality factors is reduced, reducing the waste of computer computing resources.

[0047] Step 103: in response to determining that the actual power generation information of the power plant meets the preset adjustment condition, generating wind resource query information corresponding to the target wind farm.

[0048] In some embodiments, the execution entity may generate wind resource query information corresponding to the target wind farm in response to determining that the actual power generation information of the power plant meets the preset adjustment condition. The preset adjustment condition may be that the actual power generation included in the power generation information of the actual power plant is less than the preset minimum power generation. The wind resource query information may be an automatically generated query instruction for querying various wind resource information of the target wind farm within a preset time period, as well as the power generation data and operation data of each wind power generation equipment. The wind resource information may represent the weather information of the target wind farm at a certain time node within the preset time period, and may include: wind speed, whether it is raining, and other information. The time node may refer to a certain day.

[0049] Step 104: input the wind resource query information into a wind resource library to perform a wind resource query.

[0050] In some embodiments, the execution entity may input the wind resource query information into a wind resource library to query the wind resource. The wind resource library may be a pre-built database storing wind resource information of each wind farm and power generation data and operation data of each wind power generation equipment.

[0051] Step 105 , in response to finding the wind resource information set corresponding to the wind resource query information, sorting the wind resource information set to obtain a wind resource information sequence.

[0052] In some embodiments, the execution entity may sort the wind resource information set in response to querying the wind resource information set corresponding to the wind resource query information to obtain a wind resource information sequence. The wind resource information sorting model may be a pre-trained neural network model for sorting. For example, the wind resource information sorting model may be a BERT model or a RoBERTa model.

[0053] In practice, the execution entity may input the wind resource query information and the wind resource information set into a pre-trained wind resource information sorting model to generate a wind resource information sequence.

[0054] The wind resource information ranking model can be trained through the following steps:

[0055] The first step is to obtain a target wind resource information sample set and a wind resource query information sample set. The target wind resource information sample in the target wind resource information sample set is a wind resource information sample that supports an associated query. The associated query may be to query the target wind resource information sample set that is most associated with the query content corresponding to the wind resource query information sample from the target wind resource information sample set. The wind resource query information sample may be information that has an associated relationship with the target wind resource information sample. The target wind resource information sample set and the wind resource query information sample set are pre-set and have a content associated relationship.

[0056] In the second step, for each wind resource query information sample in the wind resource query information sample set, the following processing steps are performed:

[0057] 1. According to the target wind resource information sample set, determine the initial sorting result corresponding to the wind resource query information sample. The initial sorting result may be the result of sorting the target wind resource information samples with text content relevance based on the wind resource query information sample. The initial sorting result may be the result of sorting the target wind resource information samples based on the content relevance between the target wind resource information sample and the wind resource query information sample. For example, the initial sorting result may be the result of sorting the target wind resource information samples from large to small based on the content relevance.

[0058] 2. Adjust the sorting results of the above initial sorting results to generate initial adjusted sorting results.

[0059] The step of adjusting the initial sorting result to generate an initial adjusted sorting result includes:

[0060] First, according to the initial sorting result, sort the target wind resource information samples in the target wind resource information sample set to obtain a target wind resource information sample sequence. For example, the target wind resource information samples in the target wind resource information sample set can be sorted in descending order of matching degree to obtain a target wind resource information sample sequence.

[0061] Second, according to the preset text combination window, each target wind resource information sample in the target wind resource information sample sequence is window combined to obtain a target wind resource information sample set, wherein the window combination can be in the form of combining adjacent samples according to the target number to form a sample group.

[0062] Third, sample prompt information representing the sample sorting of each target wind resource information sample group in the target wind resource information sample group set is generated.

[0063] Fourth, the sample prompt information and the wind resource query information sample are input into a pre-trained sorting model to obtain a group sorting result corresponding to each target wind resource information sample group in the target wind resource information sample group set, thereby obtaining a group sorting result set.

[0064] Fifth, based on the above group sorting result set, an initial adjusted sorting result is generated. For example, the group sorting results can be merged to obtain the initial adjusted sorting result.

[0065] In the third step, according to each initial adjustment sorting result, model training is performed on at least one initial wind resource information sorting reference model to obtain at least one wind resource information sorting reference model. Among them, the initial wind resource information sorting reference model may be a teacher model that has not yet been trained. The teacher model is a teacher model in a teacher-student model (Teacher-StudentModel). For example, the model structures corresponding to each initial wind resource information sorting reference model in at least one initial wind resource information sorting reference model are different. For example, at least one initial wind resource information sorting reference model may be a plurality of different BERT-based teacher models. At least one initial wind resource information sorting reference model may include: a BERT model, a RoBERTa model.

[0066] The third step may include the following sub-steps:

[0067] In the first sub-step, for each of the above-mentioned initial adjustment sorting results, the following processing steps are performed:

[0068] First, according to the above-mentioned initial adjustment sorting result, the initial positive and negative sample sets are determined. Among them, the text order difference between the positive sample and the negative sample included in the initial positive and negative samples meets the preset condition. The initial positive and negative samples may include: positive samples and negative samples. The preset condition may be that the sorting position corresponding to the positive sample is separated from the sorting position corresponding to the negative sample by a preset number of positions. And the sorting position of the positive sample is higher than the preset sorting position, and the sorting position of the negative sample is lower than the preset sorting position. For example, the preset sorting position may be the middle position corresponding to the initial adjustment sorting result.

[0069] Then, according to the initial positive and negative sample sets and the wind resource query information samples corresponding to the initial adjustment sorting results, multiple initial wind resource query information training samples are generated. For example, the execution entity may combine each initial positive and negative sample in the initial positive and negative sample sets with the wind resource query information sample to generate an initial wind resource query information training sample, thereby obtaining multiple initial wind resource query information training samples.

[0070] In a second sub-step, based on each initial wind resource query information training sample, the at least one initial wind resource information ranking reference model is trained to obtain at least one wind resource information ranking reference model. For example, the at least one initial wind resource information ranking reference model can be trained to obtain at least one wind resource information ranking reference model by updating the model parameters through back propagation.

[0071] In the fourth step, the initial sorting results corresponding to the wind resource query information sample set are adjusted according to the at least one wind resource information sorting reference model to generate a target adjusted sorting result set. For example, first, the execution entity may randomly select a wind resource information sorting reference model from at least one wind resource information sorting reference model as the target wind resource information sorting reference model. Then, each of the initial sorting results and the wind resource query information sample in the initial sorting results may be input into the target wind resource information sorting reference model to generate a target adjusted sorting result and obtain a target adjusted sorting result set.

[0072] The fifth step is to adjust the sorting result set according to the above-mentioned goal, perform model training on the initial wind resource information sorting model, and obtain a trained wind resource information sorting model. The initial wind resource information sorting model may be a student model that has not yet been trained. The student model may be a student model in a teacher-student model. The student model is used for real-time online information retrieval sorting. For example, the wind resource information sorting model may be a text sorting model. For example, the wind resource information sorting model may be a BERT model.

[0073] The fifth step may include the following sub-steps:

[0074] In the first sub-step, for each target adjustment sorting result in the target adjustment sorting result set, the following processing steps are performed:

[0075] First, according to the above target adjustment sorting results, the target positive and negative sample sets are determined. Among them, the order difference between the positive samples and the negative samples included in the target positive and negative samples meets the preset conditions. Please refer to the generation method of the initial positive and negative samples.

[0076] Then, a plurality of target wind resource query information training samples are generated according to the target positive and negative sample sets and the wind resource query information samples corresponding to the target adjustment sorting results.

[0077] In the second sub-step, the initial wind resource information sorting model is trained according to each target wind resource query information training sample to obtain a trained wind resource information sorting model. For example, the initial wind resource information sorting model can be trained with reference to the training method of the deep neural network model to obtain a trained wind resource information sorting model.

[0078] Thus, the model training of the wind resource information sorting reference model (teacher model) and the wind resource information sorting model (student model) is effectively realized, and a student model that can accurately sort wind resource information is generated. This effectively solves the problem that the sample information length usually exceeds the upper limit of the input information length that the model can accept, and improves the efficiency of generating sorting results.

[0079] Step 106: Send the actual power generation information of the power plant and the wind resource information sequence to the associated wind power plant data analysis terminal.

[0080] In some embodiments, the execution entity may send the actual power generation information of the power plant and the wind resource information sequence to an associated wind power plant data analysis terminal. The wind power plant data analysis terminal may refer to a terminal for further data analysis of the actual power generation information of the power plant and the wind resource information sequence. For example, the wind power plant data analysis terminal may be a terminal operated by a technician. Thus, the wind power generation information may be further analyzed.

[0081] like Figure 4 In the example shown, the wind resource information sequence can be displayed in the form of a table, which may include: wind parameter input, wind turbine selection, and evaluation results; wind parameter input, wind turbine selection, and evaluation results all correspond to content, input, unit, and remarks / recommended values. The content corresponding to wind parameter input includes: average wind speed, air density, and K value. The content corresponding to wind turbine selection includes: wind turbine model, single unit capacity, and power curve source. The content corresponding to the evaluation results includes: total technical reduction, wake, total reduction, and annual equivalent utilization hours.

[0082] It should be noted that Figure 4 The text and numerical values ​​displayed are merely illustrative descriptions and do not represent fixed limitations on the wind resource information involved in this application.

[0083] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a power generation information processing device applied to wind power generation. These embodiments of the power generation information processing device applied to wind power generation are similar to Figure 1 Corresponding to the method embodiments shown, the power generation information processing device applied to wind power generation can be specifically applied to various electronic devices.

[0084] like Figure 2 As shown, some embodiments of the power generation information processing device 200 applied to wind power generation include: a preprocessing unit 201, a first input unit 202, a generating unit 203, a second input unit 204, a sorting unit 205 and a sending unit 206. The preprocessing unit 201 is configured to, in response to receiving a wind power generation information set corresponding to a target wind farm, preprocess the wind power generation information set to obtain a preprocessed wind power generation information set, wherein the wind power generation information in the wind power generation information set corresponds to a wind power generation device in the target wind farm; the first input unit 202 is configured to input the preprocessed wind power generation information set into a pre-trained wind power generation information calculation model to obtain actual power generation information corresponding to the target wind farm, wherein the actual power generation information includes: total technical reduction information, wind power wake information and actual power generation; the generating unit 206 is configured to input the preprocessed wind power generation information set into a pre-trained wind power generation information calculation model to obtain actual power generation information corresponding to the target wind farm, wherein the actual power generation information includes: total technical reduction information, wind power wake information and actual power generation; The generating unit 203 is configured to generate wind resource query information corresponding to the target wind farm in response to determining that the actual power generation information of the power plant meets the preset adjustment conditions; the second input unit 204 is configured to input the wind resource query information into the wind resource library to perform wind resource query; the sorting unit 205 is configured to sort the wind resource information set in response to querying the wind resource information set corresponding to the wind resource query information to obtain a wind resource information sequence; the sending unit 206 is configured to send the actual power generation information of the power plant and the wind resource information sequence to the associated wind farm data analysis terminal.

[0085] It can be understood that the units described in the power generation information processing device 200 for wind power generation are similar to those described in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the power generation information processing device 200 for wind power generation and the units contained therein, and will not be described in detail here.

[0086] Reference below Figure 3 , which shows a schematic diagram of the structure of an electronic device 300 (e.g., a computing device) suitable for implementing some embodiments of the present disclosure. The electronic devices in some embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0087] like Figure 3As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0088] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0089] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0090] It should be noted that the computer-readable medium recorded in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0091] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with 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"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0092] The computer-readable medium may be included in the electronic device; or it may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: in response to receiving a wind power generation information set corresponding to a target wind farm, pre-process the wind power generation information set to obtain a pre-processed wind power generation information set, wherein the wind power generation information in the wind power generation information set corresponds to a wind power generation device in the target wind farm; input the pre-processed wind power generation information set into a pre-trained wind power generation information calculation model to obtain actual power generation information corresponding to the target wind farm, wherein the actual wind power generation information in the wind power generation information set corresponds to a wind power generation device in the target wind farm; input the pre-processed wind power generation information set into a pre-trained wind power generation information calculation model to obtain actual power generation information corresponding to the target wind farm, The actual power plant power generation information includes: total technical reduction information, wind power wake information and actual power generation; in response to determining that the actual power plant power generation information meets the preset adjustment conditions, generating wind resource query information corresponding to the target wind plant; inputting the wind resource query information into the wind resource library to perform wind resource query; in response to querying the wind resource information set corresponding to the wind resource query information, sorting the wind resource information set to obtain a wind resource information sequence; sending the actual power plant power generation information and the wind resource information sequence to the associated wind plant data analysis terminal.

[0093] Computer program code for performing the operations of some embodiments of the present disclosure may be written 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 separate 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).

[0094] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0095] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The units described may also be provided in a processor, for example, may be described as: a processor comprising: a preprocessing unit, a first input unit, a generating unit, a second input unit, a sorting unit, and a sending unit. The names of these units do not, in some cases, constitute limitations on the units themselves, for example, the sending unit may also be described as "a unit that sends the above-mentioned actual power plant power generation information and the above-mentioned wind resource information sequence to the associated wind farm data analysis terminal".

[0096] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0097] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A method for processing power generation information applied to wind power generation, comprising: In response to receiving a wind power generation information set corresponding to a target wind farm, preprocessing the wind power generation information set to obtain a preprocessed wind power generation information set, wherein the wind power generation information in the wind power generation information set corresponds to a wind power generation device in the target wind farm; Inputting the pre-processed wind power generation information set into a pre-trained wind power generation information calculation model to obtain actual power generation information of the target wind power plant; In response to determining that the actual power generation information of the power plant meets the preset adjustment condition, generating wind resource query information corresponding to the target wind farm; Inputting the wind resource query information into a wind resource library to perform wind resource query; In response to querying a wind resource information set corresponding to the wind resource query information, sorting the wind resource information set to obtain a wind resource information sequence; The actual power generation information of the power plant and the wind resource information sequence are sent to an associated wind power plant data analysis terminal.

2. The method according to claim 1, wherein: The step of sorting the wind resource information set to obtain a wind resource information sequence includes: The wind resource query information and the wind resource information set are input into a pre-trained wind resource information sorting model to generate a wind resource information sequence.

3. The method according to claim 2, wherein: Before inputting the wind resource query information and the wind resource information set into a pre-trained wind resource information sorting model, the method further includes: Obtaining a target wind resource information sample set and a wind resource query information sample set; For each wind resource query information sample in the wind resource query information sample set, the following processing steps are performed: Determining, according to the target wind resource information sample set, an initial sorting result corresponding to the wind resource query information sample; Performing sorting result adjustment on the initial sorting result to generate an initial adjusted sorting result; According to each initial adjustment sorting result, at least one initial wind resource information sorting reference model is trained to obtain at least one wind resource information sorting reference model; according to the at least one wind resource information sorting reference model, each initial sorting result corresponding to the wind resource query information sample set is adjusted to generate a target adjustment sorting result set; According to the target adjustment sorting result set, the initial wind resource information sorting model is trained to obtain a trained wind resource information sorting model.

4. The method according to claim 3, wherein: The method of performing model training on at least one initial wind resource information sorting reference model according to each initial adjustment sorting result to obtain at least one wind resource information sorting reference model includes: For each of the initial adjusted sorting results, the following processing steps are performed: Determining an initial positive and negative sample set according to the initial adjustment and sorting result; Generate a plurality of initial wind resource query information training samples according to the initial positive and negative sample sets and the wind resource query information samples corresponding to the initial adjustment sorting results; According to each initial wind resource query information training sample, model training is performed on the at least one initial wind resource information sorting reference model to obtain at least one wind resource information sorting reference model.

5. The method according to claim 1, wherein: Before inputting the pre-processed wind power generation information set into the pre-trained wind power generation information calculation model to obtain actual power generation information corresponding to the target wind power plant, the method further includes: Obtaining a power generation monitoring data set corresponding to each wind power generation device; Determine an initial wind power generation information calculation model, wherein the initial wind power generation information calculation model includes: an initial power generation information calculation model and an initial power generation abnormality type detection model; Performing power generation abnormality type identification processing on each power generation monitoring data in the power generation monitoring data set to generate a power generation abnormality type identifier to obtain a power generation abnormality type identifier set; Based on the power generation monitoring data set and the power generation abnormality type identification set, an initial power generation abnormality type detection model is trained to obtain a trained power generation abnormality type detection model; According to the power generation monitoring data set, the initial power generation information calculation model is trained to obtain a trained power generation information calculation model; The power generation anomaly type detection model and the power generation information calculation model are integrated into a wind power generation information calculation model.

6. A power generation information processing device for wind power generation, comprising: a preprocessing unit configured to, in response to receiving a wind power generation information set corresponding to a target wind farm, preprocess the wind power generation information set to obtain a preprocessed wind power generation information set, wherein the wind power generation information in the wind power generation information set corresponds to a wind power generation device in the target wind farm; a first input unit configured to input the pre-processed wind power generation information set into a pre-trained wind power generation information calculation model to obtain actual power generation information of the target wind power plant; a generating unit configured to generate wind resource query information corresponding to the target wind farm in response to determining that the actual power generation information of the power farm meets the preset adjustment condition; a second input unit, configured to input the wind resource query information into a wind resource library to perform a wind resource query; a sorting unit, configured to sort the wind resource information set in response to finding the wind resource information set corresponding to the wind resource query information, to obtain a wind resource information sequence; The sending unit is configured to send the actual power generation information of the power plant and the wind resource information sequence to an associated wind power plant data analysis terminal.

7. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 5.

8. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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