New energy system data classification updating method and device and storage medium

Abstract

The invention discloses a new energy system data classification updating method and device and a storage medium, and relates to the technical field of new energy data classification updating, and the method comprises the following steps: obtaining new energy system data, and carrying out the two-dimensional classification of the new energy system data; setting exception classification, and dynamically updating a judgment rule of the exception classification based on historical data of the new energy system data; further classifying and updating the new energy system data based on the judgment rule; the method is used for solving the problem that in an existing new energy data classification updating technology, an updating method is too basic, abnormal data are not recognized, and consequently a user cannot find that new energy data are abnormal at the first time.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy data classification and updating, and in particular to a new energy system data classification and updating method, device and storage medium. Background Art

[0002] New energy data classification and updating technology refers to the method of classifying and updating new energy related data in the new energy field by using various data processing and machine learning technologies. These technologies are designed to effectively classify, organize and update new energy data to improve the performance, efficiency and reliability of new energy systems.

[0003] The existing new energy data classification and updating technology usually directly updates the monitored new energy data on the display interface of the system. At its core, it is the real-time display of data, and the method is too basic. What is needed at this stage is to update the new energy data after fine classification, so that users can see the category to which the new energy data belongs more clearly. At the same time, some abnormal data will inevitably appear in the existing new energy data during monitoring and updating. If they are directly updated to the interface without identifying them, users will not be able to discover the abnormalities of the new energy data in time. At the same time, the abnormal detection module in the new energy system also needs to conduct comprehensive monitoring of the new energy data, which increases the consumption of computing resources. The existing new energy data classification and updating technology also has the problem that the updating method is too basic and the abnormal data is not identified, which makes it impossible for users to discover the abnormalities of the new energy data in the first time. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the prior art to a certain extent, by acquiring new energy system data, classifying the new energy system data in two dimensions, then acquiring historical data of the new energy system data, analyzing wind power station data based on historical data, constructing a wind power reference map, and analyzing photovoltaic station data based on historical data, constructing a photovoltaic reference map, and then analyzing the judgment rules of wind power station data and photovoltaic station data in abnormal classification based on the wind power reference map and the photovoltaic reference map, and finally further classifying and updating the new energy system data based on the judgment rules, so as to solve the problem that the existing new energy data classification and updating technology still has the problem that the updating method is too basic and the abnormal data is not identified, resulting in the user being unable to discover the abnormality of the new energy data in the first time.

[0005] To achieve the above objectives, in a first aspect, the present application provides a new energy system data classification and updating method, comprising the following steps: Obtain new energy system data and classify the new energy system data in two dimensions; Set abnormal classification and dynamically update the judgment rules of abnormal classification based on the historical data of new energy system data; The new energy system data is further classified and updated based on the judgment rules.

[0006] Furthermore, obtaining the new energy system data and performing two-dimensional grouping on the new energy system data includes the following sub-steps: The new energy system data includes wind turbine power generation, wind turbine speed, photovoltaic panel power generation, photovoltaic panel temperature, voltage, current, wind speed, wind direction, light intensity, ambient temperature and humidity; Classify the new energy system data based on the time dimension, and divide the new energy system data into real-time data, hourly data and daily data. The real-time data is the new energy system data at the current moment, the hourly data is the change of the new energy system data within one hour, and the daily data is the change of the new energy system data within one day; A spatial dimension is set under the time dimension, and the new energy system data is divided according to the area where the data is located. For the new energy system data, photovoltaic station data, wind power station data, energy storage system data and environmental data are set; The new energy system data included in the photovoltaic station data are photovoltaic panel temperature and photovoltaic panel power generation, the new energy system data included in the wind power station data are wind turbine power generation and wind turbine speed, the new energy system data included in the energy storage system data are voltage and current, and the new energy system data included in the environmental data are wind speed, wind direction, light intensity, ambient temperature and humidity; The real-time data, hourly data and daily data are first-level directories, and the photovoltaic station data, wind power station data, energy storage system data and environmental data are second-level directories under the first-level directory.

[0007] Furthermore, setting abnormal classification and dynamically updating the judgment rules of abnormal classification based on the historical data of the new energy system data includes the following sub-steps: Obtain historical data on new energy system data; Analyze wind power station data based on historical data and build a wind power reference map; Analyze PV station data based on historical data and build PV reference maps; The judgment rules of wind power station data and photovoltaic station data in abnormal classification are analyzed based on wind power reference map and photovoltaic reference map.

[0008] Furthermore, obtaining historical data of new energy system data includes the following sub-steps: Build a new energy system historical database to record the historical data of the new energy system data; The historical data recorded in the new energy system historical database includes the time when this set of historical data was recorded, in addition to the wind turbine power generation, wind turbine speed, photovoltaic panel power generation, photovoltaic panel temperature, voltage, current, wind speed, wind direction, light intensity, ambient temperature and humidity. One recording time corresponds to one historical data. The new energy system historical database is a table, and the known abnormal historical data are marked as historical abnormal data, and the font color of the historical abnormal data is changed to red for marking. When filtering the historical abnormal data, it can be filtered by color. The historical abnormal data includes historical abnormal wind turbine power, historical abnormal speed, historical abnormal photovoltaic power, historical abnormal photovoltaic temperature, historical abnormal voltage, historical abnormal current, historical abnormal wind speed, historical abnormal wind direction, historical abnormal light, historical abnormal ambient temperature and historical abnormal humidity; The wind turbine power generation, wind turbine speed, photovoltaic panel power generation, photovoltaic panel temperature, voltage, current, wind speed, wind direction, light intensity, ambient temperature and humidity in the historical data are named historical wind power, historical speed, historical photovoltaic power, historical photovoltaic temperature, historical voltage, historical current, historical wind speed, historical wind direction, historical light, historical ambient temperature and historical humidity respectively.

[0009] Furthermore, analyzing the wind power station data based on historical data and constructing a wind power reference map includes the following sub-steps: Obtain historical wind turbine power, historical speed, historical wind speed, and historical wind direction, including historical abnormal wind turbine power and historical abnormal speed; The u wind is calculated by the formula u=wspd×sin(wdir), and the v wind is calculated by the formula v=wspd×cos(wdir), where u is the u wind, representing the historical wind speed and historical wind direction in the longitude direction, v is the v wind, representing the historical wind speed and historical wind direction in the latitude direction, wspd is the wind speed, and wdir is the wind direction; Get the direction of the wind turbine. If the wind turbine is facing due east or due west, mark v wind as the reference wind force. If the wind turbine is facing due north or due south, mark u wind as the reference wind force. With the reference wind force as the X-axis, and the historical wind turbine power and the historical speed as the Y-axis, a plane rectangular coordinate system is established, which are named the wind power reference coordinate system and the speed reference coordinate system respectively. The reference wind force obtained by the historical wind turbine power according to the historical wind speed and the historical wind direction is entered into the wind power reference coordinate system, and the reference wind force obtained by the historical speed according to the historical wind speed and the historical wind direction is entered into the speed reference coordinate system; The wind power reference coordinate system and the speed reference coordinate system are called wind power reference graphs.

[0010] Furthermore, based on the historical data analysis of the photovoltaic station data, the construction of the photovoltaic reference map includes the following sub-steps: Obtain historical photovoltaic power, historical photovoltaic temperature, and historical light, including historical abnormal photovoltaic power and historical abnormal photovoltaic temperature; With historical light intensity as the horizontal axis, and historical photovoltaic power and historical photovoltaic temperature as the vertical axis, a plane rectangular coordinate system is established, which are named photovoltaic power reference coordinate system and temperature reference coordinate system respectively. The historical photovoltaic power and historical photovoltaic temperature are recorded into the photovoltaic power reference coordinate system and the temperature reference coordinate system according to the historical light intensity. The photovoltaic power reference coordinate system and the temperature reference coordinate system are referred to as photovoltaic reference graphs.

[0011] Furthermore, the judgment rules for analyzing the wind power station data and the photovoltaic station data in the abnormal classification based on the wind power reference map and the photovoltaic reference map include the following sub-steps: The wind power reference map and the photovoltaic reference map are uniformly marked as regular reference maps. For any regular reference map, the coordinate points therein are named reference points, and the coordinate points corresponding to the historical abnormal wind turbine power, historical abnormal speed, historical abnormal photovoltaic power, and historical abnormal photovoltaic temperature are uniformly marked as abnormal points. Remove the abnormal points from the rule reference graph, then perform regression analysis on the rule reference graph, and select the curve with the smallest standard deviation as the rule reference curve; Add the abnormal point to the regular reference graph, and move the regular reference curve upward and downward along the vertical direction. If there is an abnormal point on the regular reference curve during the movement, stop moving, and obtain two curves with the same change trend as the regular reference curve, which are named the regular upward curve and the regular downward curve from top to bottom; The regular upward curve includes a wind turbine power upward curve, a speed upward curve, a photovoltaic power upward curve and a photovoltaic temperature upward curve, and the regular downward curve includes a wind turbine power downward curve, a speed downward curve, a photovoltaic power downward curve and a photovoltaic temperature downward curve; The wind turbine power generation, wind turbine speed, wind speed and wind direction are monitored in real time, and the reference wind force is obtained based on the wind speed and wind direction. The Y-axis values ​​corresponding to the wind turbine power upward curve and the wind turbine power downward curve when the X-axis is equal to the reference wind force are marked as the wind turbine power reference upper limit and the wind turbine power reference lower limit respectively, and the Y-axis values ​​corresponding to the speed upward curve and the speed downward curve when the X-axis is equal to the reference wind force are marked as the speed reference upper limit and the speed reference lower limit respectively; If the fan power generation power is not within the range of the fan power reference lower limit to the fan power reference upper limit, the fan abnormal power signal is output; if the fan speed is not within the range of the speed reference lower limit to the speed reference upper limit, the speed abnormal signal is output; The photovoltaic panel power generation, photovoltaic panel temperature and light intensity are monitored in real time. When the X-axis is equal to the light intensity, the Y-axis values ​​corresponding to the photovoltaic power upward curve and the photovoltaic power downward curve are marked as the photovoltaic power reference upper limit and the photovoltaic power reference lower limit respectively. When the X-axis is equal to the light intensity, the Y-axis values ​​corresponding to the photovoltaic temperature upward curve and the photovoltaic temperature downward curve are marked as the photovoltaic temperature reference upper limit and the photovoltaic temperature reference lower limit respectively. If the photovoltaic panel power generation power is not within the range of the photovoltaic power reference lower limit to the photovoltaic power reference upper limit, a photovoltaic abnormal power signal is output; if the photovoltaic panel temperature is not within the range of the photovoltaic temperature reference lower limit to the photovoltaic temperature reference upper limit, a photovoltaic temperature abnormal signal is output.

[0012] Furthermore, further classifying and updating the new energy system data based on the judgment rule includes the following sub-steps: Update the new energy system data according to the corresponding structures of the primary directory and the secondary directory; The new energy system data that outputs abnormal wind turbine power signals, abnormal speed signals, abnormal photovoltaic power signals, and abnormal photovoltaic temperature signals are marked as real-time abnormal data; Mark the real-time abnormal data in red and highlight it in the primary and secondary directories; Add anomaly categories in the first-level directory to display real-time anomaly data in the anomaly categories.

[0013] In a second aspect, the present application provides an electronic device, including a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the above method are performed.

[0014] In a third aspect, the present application provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the above method are performed.

[0015] Beneficial effects of the present invention: The present invention obtains new energy system data, classifies the new energy system data in two dimensions, and classifies the new energy data in a refined manner from the time dimension and the space dimension. The advantage is that the user can more clearly observe the changes in the new energy data at different times, and can classify and observe the new energy data based on the source of the new energy data, thereby improving the effectiveness and rationality of the classification and update of the new energy data; The present invention acquires historical data of new energy system data, analyzes wind power station data based on historical data, constructs a wind power reference map, analyzes photovoltaic station data based on historical data, constructs a photovoltaic reference map, and then analyzes judgment rules for wind power station data and photovoltaic station data in abnormal classification based on the wind power reference map and the photovoltaic reference map. Finally, the new energy system data is further classified and updated based on the judgment rules. The advantage is that the judgment rules are obtained based on the analysis of historical data, and the judgment rules reveal the normal range of new energy data under corresponding wind force, wind speed and light intensity conditions. If this range is exceeded, it represents that the new energy data is suspected to be abnormal data. Marking it can allow users to clearly see the abnormality of the data, and then a professional anomaly detection system is used to perform targeted analysis on the new energy data suspected to be abnormal data, which can effectively save the computing resources of the anomaly detection system, improve the effectiveness of the classification and update of new energy data and the saving of computing resources, and make the anomaly detection system targeted. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of the steps of the method of the present invention; Figure 2 A schematic diagram of the relationship between the time dimension and the space dimension classification of the present invention; Figure 3 is the speed reference coordinate system of the present invention; Figure 4 is a schematic diagram of a regular reference curve of the present invention; Figure 5 It is a schematic diagram of the speed upward curve and the speed downward curve of the present invention; Figure 6 A schematic diagram of a display interface for classifying and updating data of a new energy system of the present invention; Figure 7 It is a schematic structural diagram of the electronic device of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example 1, please refer to Figure 1 As shown, the present application provides a new energy system data classification and updating method, comprising the following steps: Step S1, acquiring new energy system data, and classifying the new energy system data in two dimensions; Step S1 includes the following sub-steps: Step S101, the new energy system data includes wind turbine power generation, wind turbine speed, photovoltaic panel power generation, photovoltaic panel temperature, voltage, current, wind speed, wind direction, light intensity, ambient temperature and humidity; See also Figure 2 As shown, step S102, classifying the new energy system data based on the time dimension, and dividing the new energy system data into real-time data, hourly data and daily data, the real-time data is the new energy system data at the current moment, the hourly data is the change of the new energy system data within one hour, and the daily data is the change of the new energy system data within one day; Step S103, a space dimension is set under the time dimension, and the new energy system data is divided according to the area where the new energy system data is located. For the new energy system data, photovoltaic station data, wind power station data, energy storage system data and environmental data are set; Step S104, the new energy system data included in the photovoltaic station data are the photovoltaic panel temperature and the photovoltaic panel power generation, the new energy system data included in the wind power station data are the wind turbine power generation and the wind turbine speed, the new energy system data included in the energy storage system data are the voltage and current, and the new energy system data included in the environmental data are the wind speed, wind direction, light intensity, ambient temperature and humidity; Step S105, real-time data, hourly data and daily data are first-level directories, and photovoltaic station data, wind power station data, energy storage system data and environmental data are second-level directories under the first-level directory; In the specific implementation, the relationship between the real-time data, hourly data, daily data, photovoltaic station data, wind power station data, energy storage system data and environmental data obtained by classification based on the time dimension and space dimension is as follows: Figure 2 shown.

[0019] Step S2, setting abnormal classification, dynamically updating the judgment rules of abnormal classification based on the historical data of the new energy system data; Step S2 includes the following sub-steps: Step S201, obtaining historical data of new energy system data; Step S201 includes the following sub-steps: Step S201.1, constructing a new energy system historical database to record the historical data of the new energy system data; Step S201.2, the historical data recorded in the new energy system history database includes the recording time of this group of historical data, in addition to the wind turbine power generation, wind turbine speed, photovoltaic panel power generation, photovoltaic panel temperature, voltage, current, wind speed, wind direction, light intensity, ambient temperature and humidity. One recording time corresponds to one historical data; Step S201.3, the new energy system historical database is a table, and the known abnormal historical data are marked as historical abnormal data, and the font color of the historical abnormal data is changed to red for marking. When filtering the historical abnormal data, it can be filtered by color. The historical abnormal data includes historical abnormal wind turbine power, historical abnormal speed, historical abnormal photovoltaic power, historical abnormal photovoltaic temperature, historical abnormal voltage, historical abnormal current, historical abnormal wind speed, historical abnormal wind direction, historical abnormal light, historical abnormal ambient temperature and historical abnormal humidity; Step S201.4, the wind turbine power generation, wind turbine speed, photovoltaic panel power generation, photovoltaic panel temperature, voltage, current, wind speed, wind direction, light intensity, ambient temperature and humidity in the historical data are named historical wind power, historical speed, historical photovoltaic power, historical photovoltaic temperature, historical voltage, historical current, historical wind speed, historical wind direction, historical light intensity, historical ambient temperature and historical humidity in sequence; In the specific implementation, some data of the new energy system historical database is shown in Table 1 below: Table 1 Partial data of the new energy system historical database

[0020] The font color of normal historical data is unified in black, while the font color of historical abnormal data is unified in red; Step S202, analyzing wind power station data based on historical data to construct a wind power reference map; Step S202 includes the following sub-steps: Step S202.1, obtaining historical wind turbine power, historical speed, historical wind speed and historical wind direction, including historical abnormal wind turbine power and historical abnormal speed; In the specific implementation, since the historical abnormal wind turbine power and the historical abnormal speed are distinguished by font color in the new energy system history database and are not stored separately, the historical abnormal wind turbine power and the historical abnormal speed can also be obtained when the historical wind turbine power and the historical speed are obtained; Step S202.2, calculate u wind by formula u=wspd×sin(wdir), calculate v wind by formula v=wspd×cos(wdir), where u is u wind, representing the historical wind speed and historical wind direction in the longitude direction, v is v wind, representing the historical wind speed and historical wind direction in the latitude direction, wspd is wind speed, and wdir is wind direction; Step S202.3, obtaining the direction of the wind turbine. If the wind turbine is facing due east or due west, v wind is marked as the reference wind force. If the wind turbine is facing due north or due south, u wind is marked as the reference wind force. In specific implementation, since the fan blades are most affected by the wind force on the front side during rotation, that is, the wind direction parallel to the direction facing the fan, it can also be expressed as the wind direction perpendicular to the surface where the fan blades are located. However, in real life, the wind direction is changeable, and the wind direction is not necessarily due east, due west, due north and due south. Therefore, it is necessary to calculate the components of the wind speed in the longitude and latitude directions based on the wind direction and wind speed, so as to analyze whether the wind turbine power generation and wind turbine speed are abnormal based on the influence of the wind speed on the wind turbine. The wind direction usually takes due north as 0°, rotates in a clockwise direction, takes due east as 90°, and so on. For example, the historical wind speed is 6.8m / s, corresponding to The historical wind direction is 38°. Substituting it into the calculation, we get u=6.8m / s×sin(38°)=4.19m / s, v=6.8m / s×cos(38°)=5.36m / s, that is, the historical wind speed and historical wind direction in the longitude direction are 4.19m / s, and the historical wind speed and historical wind direction in the latitude direction are 5.36m / s. When the fan is installed, it usually faces any direction of the east, south, west and north. The direction of the fan is east, and the wind in the latitude direction is parallel to the east. The rotation of the fan blades is most affected by the v wind, so the v wind is used as the reference wind force, that is, the reference wind force is 5.36m / s. See also Figure 3 As shown, step S202.4, with the reference wind force as the X-axis, and the historical wind turbine power and the historical speed as the Y-axis, a plane rectangular coordinate system is established, which are named the wind power reference coordinate system and the speed reference coordinate system respectively, and the reference wind force obtained by the historical wind turbine power according to the historical wind speed and the historical wind direction is entered into the wind power reference coordinate system, and the reference wind force obtained by the historical speed according to the historical wind speed and the historical wind direction is entered into the speed reference coordinate system; Step S202.5, the wind power reference coordinate system and the speed reference coordinate system are called wind power reference graph; In the specific implementation, since the wind power reference coordinate system and the speed reference coordinate system belong to the wind power reference map and the subsequent analysis process is the same, in this embodiment, only the speed reference coordinate system is given for reference to provide materials for the subsequent analysis of the judgment rules. Figure 3 As shown; Step S203, analyzing the photovoltaic station data based on historical data and constructing a photovoltaic reference map; Step S203 includes the following sub-steps: Step S203.1, obtaining historical photovoltaic power, historical photovoltaic temperature and historical light, including historical abnormal photovoltaic power and historical abnormal photovoltaic temperature; Step S203.2, establish a plane rectangular coordinate system with historical illumination as the horizontal axis and historical photovoltaic power and historical photovoltaic temperature as the vertical axis, respectively named photovoltaic power reference coordinate system and temperature reference coordinate system, and record the historical photovoltaic power and historical photovoltaic temperature into the photovoltaic power reference coordinate system and the temperature reference coordinate system according to the historical illumination; Step S203.3, the photovoltaic power reference coordinate system and the temperature reference coordinate system are called photovoltaic reference graphs; In a specific implementation, since the power generation of a photovoltaic panel is usually only related to the light intensity, the judgment rule can be obtained by analyzing the relationship between the light intensity and the historical photovoltaic power and the historical photovoltaic temperature, and the photovoltaic power reference coordinate system and the temperature reference coordinate system are similar to the wind power reference diagram, and the subsequent analysis process is also the same, so it will not be specifically demonstrated in this embodiment; Step S204, analyzing the judgment rules of wind power station data and photovoltaic station data in abnormal classification based on the wind power reference map and the photovoltaic reference map; Step S204 includes the following sub-steps: Step S204.1, uniformly marking the wind power reference map and the photovoltaic reference map as regular reference maps, naming the coordinate points in any regular reference map as reference points, and uniformly marking the coordinate points corresponding to historical abnormal wind turbine power, historical abnormal speed, historical abnormal photovoltaic power, and historical abnormal photovoltaic temperature as abnormal points; See also Figure 4 As shown, in step S204.2, the abnormal points are removed from the rule reference graph, and then regression analysis is performed on the rule reference graph to select the curve with the smallest standard deviation as the rule reference curve; In specific implementation, Figure 3 The speed reference coordinate system is taken as an example, and the abnormal points are removed before performing regression analysis. The regression analysis is divided into linear regression, exponential regression, logarithmic regression, power regression and polynomial regression. The standard deviation reflects the degree of dispersion of the reference points when using different regression curves. The curve with the smallest standard deviation indicates that the degree of dispersion of the reference points is the lowest and the effect is the best. The method of selecting a discrete function by standard deviation in regression analysis is a prior art, and this embodiment will not give a specific example. The regular reference curve obtained by regression analysis is as follows Figure 4 As shown; See also Figure 5 As shown, step S204.3, the abnormal point is added to the regular reference graph, and the regular reference curve is moved upward and downward along the vertical direction respectively. If there is an abnormal point on the regular reference curve during the movement, the movement is stopped, and two curves with the same change trend as the regular reference curve are obtained, which are named as the regular upward curve and the regular downward curve from top to bottom; Step S204.4, the regular upward curve includes a wind turbine power upward curve, a speed upward curve, a photovoltaic power upward curve, and a photovoltaic temperature upward curve, and the regular downward curve includes a wind turbine power downward curve, a speed downward curve, a photovoltaic power downward curve, and a photovoltaic temperature downward curve; In specific implementation, under normal circumstances, the degree of discreteness between the abnormal point and the regular reference curve is large, and there is a clear difference from the reference point. The regular reference curve obtained by excluding the abnormal point analysis reflects the changing trend of the fan speed with the change of the reference wind force under normal circumstances. After adding the abnormal point, the normal range of the fan speed can be determined by vertical movement up and down while retaining the changing trend of the fan speed. If the regular reference curve touches the abnormal point, it means that after exceeding the regular reference curve, the reference point may be an abnormal point. Although there are a few reference points that are the same as the abnormal point and have a large degree of discreteness from the regular reference curve, the proportion they occupy is very small. In addition, this embodiment is a classification update method, which aims to perform preliminary screening before the fault detection system detects the new energy system data, and lists the data of suspected abnormal points for user reference, and at the same time enables the abnormal detection system to detect the new energy system data in a targeted manner; the regular upward curve and the regular downward curve obtained by movement are as follows: Figure 5 As shown, Figure 5 The regular upward curve and the regular downward curve in are the speed upward curve and the speed downward curve respectively; Step S204.5, real-time monitoring of the wind turbine power generation, wind turbine speed, wind speed and wind direction, obtaining a reference wind force based on the wind speed and wind direction, marking the Y-axis values ​​corresponding to the wind turbine power upward curve and the wind turbine power downward curve when the X-axis is equal to the reference wind force as the wind turbine power reference upper limit and the wind turbine power reference lower limit, respectively, marking the Y-axis values ​​corresponding to the speed upward curve and the speed downward curve when the X-axis is equal to the reference wind force as the speed reference upper limit and the speed reference lower limit, respectively; Step S204.6: if the wind turbine power generation power is not within the range of the wind turbine power reference lower limit to the wind turbine power reference upper limit, then an abnormal wind turbine power signal is output; if the wind turbine speed is not within the range of the speed reference lower limit to the speed reference upper limit, then an abnormal speed signal is output; Step S204.7, real-time monitoring of photovoltaic panel power generation, photovoltaic panel temperature and light intensity, marking the Y-axis values ​​corresponding to the photovoltaic power up curve and the photovoltaic power down curve when the X-axis is equal to the light intensity as the photovoltaic power reference upper limit and the photovoltaic power reference lower limit, respectively, marking the Y-axis values ​​corresponding to the photovoltaic temperature up curve and the photovoltaic temperature down curve when the X-axis is equal to the light intensity as the photovoltaic temperature reference upper limit and the photovoltaic temperature reference lower limit, respectively; Step S204.8, if the photovoltaic panel power generation power is not within the range of the photovoltaic power reference lower limit to the photovoltaic power reference upper limit, then output a photovoltaic abnormal power signal; if the photovoltaic panel temperature is not within the range of the photovoltaic temperature reference lower limit to the photovoltaic temperature reference upper limit, then output a photovoltaic temperature abnormal signal; In specific implementation, Figure 5 For example, assuming that the reference wind force vwind is 6.72m / s calculated by monitoring the wind speed and wind direction, and the motor speed is 26r / min, the coordinates (6.72,26) are entered into Figure 5 In the example, observe whether the coordinate (6.72, 26) is between the upward speed curve and the downward speed curve. If it is between the upward speed curve and the downward speed curve, it means that the monitored motor speed is normal, otherwise it is suspected to be abnormal. The analysis process of the wind turbine power generation, photovoltaic power generation and photovoltaic temperature is the same as the analysis process of the wind turbine speed, so it will not be described in detail in this embodiment.

[0021] See also Figure 6 As shown, step S3 further classifies and updates the new energy system data based on the judgment rules; step S3 includes the following sub-steps: Step S301, classify and update the new energy system data according to the structures corresponding to the primary directory and the secondary directory; Step S302, for the new energy system data outputting abnormal wind turbine power signals, abnormal speed signals, abnormal photovoltaic power signals, and abnormal photovoltaic temperature signals, marking them as real-time abnormal data; Step S303, marking the real-time abnormal data in red and highlighting it in the primary directory and the secondary directory; Step S304, adding an abnormal category in the primary directory, and centrally displaying the real-time abnormal data in the abnormal category; In specific implementation, Figure 6 The display interface updated by classification in this embodiment is illustrated in a relatively simple way, and it is not the display interface during actual use. Moreover, this embodiment is only a classification and update method of data, not a display method. It can be applied to any new energy monitoring, and can perform primary screening of new energy system data, and automatically mark suspected abnormal data. The abnormal classification is in the same first-level directory as the real-time data, hourly data, and daily data.

[0022] Example 2, please refer to Figure 7 As shown, Figure 7The structural diagram of an electronic device is illustrated, and the electronic device may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in a new energy system data classification and updating method are executed to achieve the following functions: obtaining new energy system data, and classifying the new energy system data in two dimensions; setting abnormal classification, and dynamically updating the judgment rules of abnormal classification based on the historical data of the new energy system data; and further classifying and updating the new energy system data based on the judgment rules.

[0023] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0024] Embodiment 3. The present application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute a new energy system data classification and update method provided by the above methods, and the method includes: acquiring new energy system data, and performing two-dimensional classification on the new energy system data; setting abnormal classification, and dynamically updating the judgment rules of abnormal classification based on the historical data of the new energy system data; and further classifying and updating the new energy system data based on the judgment rules.

[0025] Embodiment 4, the present application also provides a computer-readable storage medium, the present application provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned new energy system data classification and updating method are executed to achieve the following functions: obtaining new energy system data, and classifying the new energy system data in two dimensions; setting abnormal classification, and dynamically updating the judgment rules of abnormal classification based on the historical data of the new energy system data; further classifying and updating the new energy system data based on the judgment rules.

[0026] Through the description of the above implementation methods, the embodiments of the present invention can be provided as methods, systems or computer program products. Based on such an understanding, the above technical solutions can be essentially or partly contributed to the prior art in the form of software products, which can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and include several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0027] In the embodiments provided in the present application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of systems, modules and units can be electrical, mechanical or other forms.

[0028] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A new energy system data classification and updating method, characterized in that: The steps include: Obtain new energy system data and classify the new energy system data in two dimensions; Set abnormal classification and dynamically update the judgment rules of abnormal classification based on the historical data of new energy system data; The new energy system data is further classified and updated based on the judgment rules.

2. A new energy system data classification and updating method according to claim 1, characterized in that: Acquiring new energy system data and performing two-dimensional grouping of the new energy system data includes the following sub-steps: The new energy system data includes wind turbine power generation, wind turbine speed, photovoltaic panel power generation, photovoltaic panel temperature, voltage, current, wind speed, wind direction, light intensity, ambient temperature and humidity; Classify the new energy system data based on the time dimension, and divide the new energy system data into real-time data, hourly data and daily data. The real-time data is the new energy system data at the current moment, the hourly data is the change of the new energy system data within one hour, and the daily data is the change of the new energy system data within one day; A spatial dimension is set under the time dimension, and the new energy system data is divided according to the area where the data is located. For the new energy system data, photovoltaic station data, wind power station data, energy storage system data and environmental data are set; The new energy system data included in the photovoltaic station data are photovoltaic panel temperature and photovoltaic panel power generation, the new energy system data included in the wind power station data are wind turbine power generation and wind turbine speed, the new energy system data included in the energy storage system data are voltage and current, and the new energy system data included in the environmental data are wind speed, wind direction, light intensity, ambient temperature and humidity; The real-time data, hourly data and daily data are first-level directories, and the photovoltaic station data, wind power station data, energy storage system data and environmental data are second-level directories under the first-level directory.

3. A new energy system data classification and updating method according to claim 2, characterized in that: Setting abnormal classification and dynamically updating the judgment rules of abnormal classification based on the historical data of new energy system data includes the following sub-steps: Obtain historical data on new energy system data; Analyze wind power station data based on historical data and build a wind power reference map; Analyze PV station data based on historical data and build PV reference maps; The judgment rules of wind power station data and photovoltaic station data in abnormal classification are analyzed based on wind power reference map and photovoltaic reference map.

4. A new energy system data classification and updating method according to claim 3, characterized in that: Obtaining historical data of new energy system data includes the following sub-steps: Build a new energy system historical database to record the historical data of the new energy system data; The historical data recorded in the new energy system historical database includes the time when this set of historical data was recorded, in addition to the wind turbine power generation, wind turbine speed, photovoltaic panel power generation, photovoltaic panel temperature, voltage, current, wind speed, wind direction, light intensity, ambient temperature and humidity. One recording time corresponds to one historical data. The new energy system historical database is a table, and the known abnormal historical data are marked as historical abnormal data, and the font color of the historical abnormal data is changed to red for marking. When filtering the historical abnormal data, it can be filtered by color. The historical abnormal data includes historical abnormal wind turbine power, historical abnormal speed, historical abnormal photovoltaic power, historical abnormal photovoltaic temperature, historical abnormal voltage, historical abnormal current, historical abnormal wind speed, historical abnormal wind direction, historical abnormal light, historical abnormal ambient temperature and historical abnormal humidity; The wind turbine power generation, wind turbine speed, photovoltaic panel power generation, photovoltaic panel temperature, voltage, current, wind speed, wind direction, light intensity, ambient temperature and humidity in the historical data are named historical wind power, historical speed, historical photovoltaic power, historical photovoltaic temperature, historical voltage, historical current, historical wind speed, historical wind direction, historical light, historical ambient temperature and historical humidity respectively.

5. A new energy system data classification and updating method according to claim 4, characterized in that: Analyzing wind power station data based on historical data and constructing a wind power reference map includes the following sub-steps: Obtain historical wind turbine power, historical speed, historical wind speed, and historical wind direction, including historical abnormal wind turbine power and historical abnormal speed; The u wind is calculated by the formula u=wspd×sin(wdir), and the v wind is calculated by the formula v=wspd×cos(wdir), where u is the u wind, representing the historical wind speed and historical wind direction in the longitude direction, v is the v wind, representing the historical wind speed and historical wind direction in the latitude direction, wspd is the wind speed, and wdir is the wind direction; Get the direction of the wind turbine. If the wind turbine is facing due east or due west, mark v wind as the reference wind force. If the wind turbine is facing due north or due south, mark u wind as the reference wind force. With the reference wind force as the X-axis, and the historical wind turbine power and the historical speed as the Y-axis, a plane rectangular coordinate system is established, which are named the wind power reference coordinate system and the speed reference coordinate system respectively. The reference wind force obtained by the historical wind turbine power according to the historical wind speed and the historical wind direction is entered into the wind power reference coordinate system, and the reference wind force obtained by the historical speed according to the historical wind speed and the historical wind direction is entered into the speed reference coordinate system; The wind power reference coordinate system and the speed reference coordinate system are called wind power reference graphs.

6. A new energy system data classification and updating method according to claim 5, characterized in that: Analyzing PV station data based on historical data and constructing a PV reference map includes the following sub-steps: Obtain historical photovoltaic power, historical photovoltaic temperature, and historical light, including historical abnormal photovoltaic power and historical abnormal photovoltaic temperature; With historical light intensity as the horizontal axis, and historical photovoltaic power and historical photovoltaic temperature as the vertical axis, a plane rectangular coordinate system is established, which are named photovoltaic power reference coordinate system and temperature reference coordinate system respectively. The historical photovoltaic power and historical photovoltaic temperature are recorded into the photovoltaic power reference coordinate system and the temperature reference coordinate system according to the historical light intensity. The photovoltaic power reference coordinate system and the temperature reference coordinate system are referred to as photovoltaic reference graphs.

7. A new energy system data classification and updating method according to claim 6, characterized in that: The judgment rules for analyzing wind power station data and photovoltaic station data in abnormal classification based on wind power reference map and photovoltaic reference map include the following sub-steps: The wind power reference map and the photovoltaic reference map are uniformly marked as regular reference maps. For any regular reference map, the coordinate points therein are named reference points, and the coordinate points corresponding to the historical abnormal wind turbine power, historical abnormal speed, historical abnormal photovoltaic power, and historical abnormal photovoltaic temperature are uniformly marked as abnormal points. Remove the abnormal points from the rule reference graph, then perform regression analysis on the rule reference graph, and select the curve with the smallest standard deviation as the rule reference curve; Add the abnormal point to the regular reference graph, and move the regular reference curve upward and downward along the vertical direction. If there is an abnormal point on the regular reference curve during the movement, stop moving, and obtain two curves with the same change trend as the regular reference curve, which are named the regular upward curve and the regular downward curve from top to bottom; The regular upward curve includes a wind turbine power upward curve, a speed upward curve, a photovoltaic power upward curve and a photovoltaic temperature upward curve, and the regular downward curve includes a wind turbine power downward curve, a speed downward curve, a photovoltaic power downward curve and a photovoltaic temperature downward curve; The wind turbine power generation, wind turbine speed, wind speed and wind direction are monitored in real time, and the reference wind force is obtained based on the wind speed and wind direction. The Y-axis values ​​corresponding to the wind turbine power upward curve and the wind turbine power downward curve when the X-axis is equal to the reference wind force are marked as the wind turbine power reference upper limit and the wind turbine power reference lower limit respectively, and the Y-axis values ​​corresponding to the speed upward curve and the speed downward curve when the X-axis is equal to the reference wind force are marked as the speed reference upper limit and the speed reference lower limit respectively; If the fan power generation power is not within the range of the fan power reference lower limit to the fan power reference upper limit, the fan abnormal power signal is output; if the fan speed is not within the range of the speed reference lower limit to the speed reference upper limit, the speed abnormal signal is output; The photovoltaic panel power generation, photovoltaic panel temperature and light intensity are monitored in real time. When the X-axis is equal to the light intensity, the Y-axis values ​​corresponding to the photovoltaic power upward curve and the photovoltaic power downward curve are marked as the photovoltaic power reference upper limit and the photovoltaic power reference lower limit respectively. When the X-axis is equal to the light intensity, the Y-axis values ​​corresponding to the photovoltaic temperature upward curve and the photovoltaic temperature downward curve are marked as the photovoltaic temperature reference upper limit and the photovoltaic temperature reference lower limit respectively. If the photovoltaic panel power generation power is not within the range of the photovoltaic power reference lower limit to the photovoltaic power reference upper limit, a photovoltaic abnormal power signal is output; if the photovoltaic panel temperature is not within the range of the photovoltaic temperature reference lower limit to the photovoltaic temperature reference upper limit, a photovoltaic temperature abnormal signal is output.

8. A new energy system data classification and updating method according to claim 7, characterized in that: Further classifying and updating the new energy system data based on the judgment rules includes the following sub-steps: Update the new energy system data according to the corresponding structures of the first-level directory and the second-level directory; The new energy system data that outputs abnormal fan power signals, abnormal speed signals, abnormal photovoltaic power signals, and abnormal photovoltaic temperature signals are marked as real-time abnormal data; Mark the real-time abnormal data in red and highlight it in the primary and secondary directories; Add anomaly categories in the first-level directory to display real-time anomaly data in the anomaly categories.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the method according to any one of claims 1 to 7 are executed.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are executed.

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