A Method, Device, and Storage Medium for Data Classification and Update of a New Energy System

Abstract

The present invention discloses a method, device and storage medium for classifying and updating new energy system data, relating to the technical field of new energy data classification and updating, and comprising the following steps: obtaining 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 the 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; the present invention is used to solve the problems that the existing new energy data classification and updating technology still has too basic updating methods and does not identify abnormal data, resulting in users being unable to discover new energy data anomalies in the first time.

Description

Technical Field

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

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

[0003] The existing new energy data classification and update technologies usually directly update the monitored new energy data on the system display interface. Fundamentally, it is the real-time display of data, and the method is too basic. At present, what is needed is the update after fine classification of new energy data, so that users can more clearly see the categories to which the new energy data belongs. At the same time, there will inevitably be some abnormal data during the monitoring and update of existing new energy data. If they are directly updated into the interface without identification, it will cause users to be unable to detect the abnormality of new energy data in a timely manner. At the same time, the anomaly detection module in the new energy system also needs to comprehensively monitor the new energy data, increasing the consumption of computing resources. The existing new energy data classification and update technologies also have problems such as too basic update methods and failure to identify abnormal data, resulting in users being unable to discover the abnormality of new energy data in the first time. Summary of the Invention

[0004] The present invention aims to at least partly solve one of the technical problems in the existing technology. By obtaining new energy system data, classifying the new energy system data in two dimensions, then obtaining the historical data of the new energy system data, analyzing the wind power plant data based on the historical data to construct a wind power reference diagram, and at the same time analyzing the photovoltaic power station data based on the historical data to construct a photovoltaic reference diagram, and then analyzing the judgment rules of the wind power plant data and the photovoltaic power station data in the abnormal classification based on the wind power reference diagram and the photovoltaic reference diagram, and finally further classifying and updating the new energy system data based on the judgment rules, to solve the problems that the existing new energy data classification and update technologies have too basic update methods and do not identify abnormal data, resulting in users being unable to discover the abnormality of new energy data in the first time.

[0005] To achieve the above object, in the first aspect, the present application provides a new energy system data classification and update method, including the following steps:

[0006] Obtain new energy system data and classify the new energy system data in two dimensions;

[0007] Set up anomaly classification and dynamically update the judgment rules for anomaly classification based on the historical data of new energy system data;

[0008] Further classify and update the new energy system data based on the judgment rules.

[0009] Furthermore, obtain the new energy system data and perform two-dimensional grouping on the new energy system data, including the following sub-steps:

[0010] 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;

[0011] 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;

[0012] A spatial dimension is set under the time dimension, and it 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;

[0013] The new energy system data included in the photovoltaic station data is the photovoltaic panel temperature and the photovoltaic panel power generation. The new energy system data included in the wind power station data is the wind turbine power generation and the wind turbine speed. The new energy system data included in the energy storage system data is the voltage and the current. The new energy system data included in the environmental data is the wind speed, wind direction, light intensity, ambient temperature, and humidity;

[0014] 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 directories.

[0015] Furthermore, set up anomaly classification, and dynamically update the judgment rules for anomaly classification based on the historical data of new energy system data, including the following sub-steps:

[0016] Obtain the historical data of the new energy system data;

[0017] Analyze the wind power station data based on the historical data and construct a wind power reference diagram;

[0018] Analyze the photovoltaic station data based on the historical data and construct a photovoltaic reference diagram;

[0019] Analyze the judgment rules for the wind power station data and the photovoltaic station data in the anomaly classification based on the wind power reference diagram and the photovoltaic reference diagram.

[0020] Further, the steps for obtaining historical data of the new energy system data include the following sub-steps:

[0021] Construct a historical database for the new energy system and record the historical data of the new energy system data;

[0022] In addition to the fan power generation, fan speed, photovoltaic panel power generation, photovoltaic panel temperature, voltage, current, wind speed, wind direction, light intensity, ambient temperature, and humidity, the historical data recorded in the historical database of the new energy system records the recording time of this set of historical data, and one recording time corresponds to one piece of historical data;

[0023] The historical database of the new energy system is a table. Mark the known abnormal historical data as historical abnormal data, change the font color of the historical abnormal data to red for marking, and when screening historical abnormal data, it can be screened by color. The historical abnormal data includes historical abnormal fan 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;

[0024] Name the fan power generation, fan speed, photovoltaic panel power generation, photovoltaic panel temperature, voltage, current, wind speed, wind direction, light intensity, ambient temperature, and humidity in the historical data as historical fan 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 in sequence.

[0025] Further, based on the historical data analysis of the wind power station data, the steps for constructing a wind power reference diagram include the following sub-steps:

[0026] Obtain historical fan power, historical speed, historical wind speed, and historical wind direction, including historical abnormal fan power and historical abnormal speed;

[0027] Calculate u wind through the formula u = wspd × sin(wdir), and calculate v wind through the formula v = wspd × cos(wdir), where u is the u wind, representing the wind in the longitude direction of the historical wind speed and historical wind direction, v is the v wind, representing the wind in the latitude direction of the historical wind speed and historical wind direction, wspd is the wind speed, and wdir is the wind direction;

[0028] Obtain the direction the fan faces. If the fan faces due east or due west, mark v wind as the reference wind force. If the fan faces due north or due south, mark u wind as the reference wind force;

[0029] Taking the reference wind power as the X-axis, and taking the historical fan power and the historical rotational speed as the Y-axis respectively, a plane rectangular coordinate system is established, named as the wind power reference coordinate system and the rotational speed reference coordinate system respectively. The reference wind power obtained by the historical fan power according to the historical wind speed and the historical wind direction is input into the wind power reference coordinate system, and the reference wind power obtained by the historical rotational speed according to the historical wind speed and the historical wind direction is input into the rotational speed reference coordinate system;

[0030] The wind power reference coordinate system and the rotational speed reference coordinate system are collectively referred to as the wind power reference diagram.

[0031] Further, based on the historical data analysis of the photovoltaic station data, constructing a photovoltaic reference diagram includes the following sub-steps:

[0032] Obtain the historical photovoltaic power, the historical photovoltaic temperature, and the historical illumination, including the historical abnormal photovoltaic power and the historical abnormal photovoltaic temperature;

[0033] Taking the historical illumination as the horizontal axis, and taking the historical photovoltaic power and the historical photovoltaic temperature as the vertical axis respectively, a plane rectangular coordinate system is established, named as the photovoltaic power reference coordinate system and the temperature reference coordinate system respectively. The historical photovoltaic power and the historical photovoltaic temperature are input into the photovoltaic power reference coordinate system and the temperature reference coordinate system according to the historical illumination;

[0034] The photovoltaic power reference coordinate system and the temperature reference coordinate system are collectively referred to as the photovoltaic reference diagram.

[0035] Further, based on the wind power reference diagram and the photovoltaic reference diagram, the judgment rules for the wind power station data and the photovoltaic station data in the abnormal classification include the following sub-steps:

[0036] The wind power reference diagram and the photovoltaic reference diagram are uniformly marked as the rule reference diagram. For any rule reference diagram, the coordinate points therein are named as reference points, and the coordinate points corresponding to the historical abnormal fan power, the historical abnormal rotational speed, the historical abnormal photovoltaic power, and the historical abnormal photovoltaic temperature are uniformly marked as abnormal points;

[0037] The abnormal points are removed from the rule reference diagram, and then a regression analysis is performed on the rule reference diagram, and the curve with the smallest standard deviation is selected as the rule reference curve;

[0038] The abnormal points are added to the rule reference diagram, and the rule reference curve is moved upward and downward respectively along the vertical direction. If there are abnormal points on the rule reference curve during the movement, the movement is stopped, and two curves with the same change trend as the rule reference curve are obtained, named as the rule upward curve and the rule downward curve from top to bottom;

[0039] The said regular ascending curves include the fan power ascending curve, the rotational speed ascending curve, the photovoltaic power ascending curve, and the photovoltaic temperature ascending curve, and the said regular descending curves include the fan power descending curve, the rotational speed descending curve, the photovoltaic power descending curve, and the photovoltaic temperature descending curve;

[0040] Monitor the fan power generation, fan rotational speed, wind speed, and wind direction in real time. Obtain the reference wind force based on the wind speed and wind direction. Mark the Y-axis values corresponding to the fan power ascending curve and the fan power descending curve when the X-axis is equal to the reference wind force as the fan power reference upper limit and the fan power reference lower limit respectively, and mark the Y-axis values corresponding to the rotational speed ascending curve and the rotational speed descending curve when the X-axis is equal to the reference wind force as the rotational speed reference upper limit and the rotational speed reference lower limit respectively;

[0041] If the fan power generation is not within the range from the fan power reference lower limit to the fan power reference upper limit, output a fan abnormal power signal; if the fan rotational speed is not within the range from the rotational speed reference lower limit to the rotational speed reference upper limit, output a rotational speed abnormal signal;

[0042] Monitor the photovoltaic panel power generation, photovoltaic panel temperature, and light intensity in real time. Mark the Y-axis values corresponding to the photovoltaic power ascending curve and the photovoltaic power descending 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, and mark the Y-axis values corresponding to the photovoltaic temperature ascending curve and the photovoltaic temperature descending 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;

[0043] If the photovoltaic panel power generation is not within the range from the photovoltaic power reference lower limit to the photovoltaic power reference upper limit, output a photovoltaic abnormal power signal; if the photovoltaic panel temperature is not within the range from the photovoltaic temperature reference lower limit to the photovoltaic temperature reference upper limit, output a photovoltaic temperature abnormal signal.

[0044] Further, further classify and update the new energy system data based on the judgment rule, including the following sub-steps:

[0045] Classify and update the new energy system data according to the structure corresponding to the first-level directory and the second-level directory;

[0046] For the new energy system data that outputs fan abnormal power signals, rotational speed abnormal signals, photovoltaic abnormal power signals, and photovoltaic temperature abnormal signals, mark them as real-time abnormal data;

[0047] Highlight and display the real-time abnormal data in red in the first-level directory and the second-level directory;

[0048] Add an abnormal classification in the first-level directory and centrally display the real-time abnormal data in the abnormal classification.

[0049] In a second aspect, the present application provides an electronic device, including a processor and a memory. The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the above method are run.

[0050] In a third aspect, 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 method are run.

[0051] Advantages of the present invention: By obtaining new energy system data and classifying the new energy system data in two dimensions, the new energy data is classified in detail from the time dimension and the space dimension. The advantage is that users can more clearly observe the changes in new energy data at different times, and at the same time, the new energy data can be classified and observed based on the source of the new energy data, improving the effectiveness and rationality of the classification and update of the new energy data.

[0052] By obtaining historical data of the new energy system data, analyzing wind power plant data based on the historical data to construct a wind power reference diagram, analyzing photovoltaic power plant data based on the historical data to construct a photovoltaic reference diagram, then analyzing the judgment rules of the wind power plant data and the photovoltaic power plant data in the abnormal classification based on the wind power reference diagram and the photovoltaic reference diagram, and finally further classifying and updating the new energy system data based on the judgment rules. The advantage is that the judgment rules are obtained through historical data analysis, and the judgment rules reveal the normal range of the new energy data under the corresponding wind force, wind speed, and light intensity conditions. If it exceeds this range, it means that the new energy data is suspected of being abnormal data. Marking it can allow users to clearly see the abnormality of the data. Then, through a professional anomaly detection system, targeted analysis of the new energy data suspected of being abnormal data can effectively save the computing resources of the anomaly detection system, improve the effectiveness of the classification and update of the new energy data and the economy of the computing resources, and at the same time make the anomaly detection system targeted. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flowchart of the steps of the method of the present invention;

[0054] Figure 2 is a schematic diagram of the relationship between the time dimension and the space dimension classification of the present invention;

[0055] Figure 3 is the rotational speed reference coordinate system of the present invention;

[0056] Figure 4 is a schematic diagram of the rule reference curve of the present invention;

[0057] Figure 5 is a schematic diagram of the rotational speed upward curve and the rotational speed downward curve of the present invention;

[0058] Figure 6 It is a schematic diagram of the display interface for data classification and update of the new energy system of the present invention;

[0059] Figure 7 It is a schematic diagram of the structure of the electronic device of the present invention. Specific embodiments

[0060] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0061] Embodiment 1, please refer to Figure 1 As shown, the present application provides a method for classifying and updating new energy system data, including the following steps:

[0062] Step S1, obtain new energy system data and perform two-dimensional classification on the new energy system data; Step S1 includes the following sub-steps:

[0063] 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, environmental temperature, and humidity;

[0064] Please refer to Figure 2 As shown, in step S102, 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;

[0065] In the time dimension, a space dimension is set, and it 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;

[0066] The new energy system data included in the photovoltaic station data is the photovoltaic panel temperature and the photovoltaic panel power generation. The new energy system data included in the wind power station data is the wind turbine power generation and the wind turbine speed. The new energy system data included in the energy storage system data is the voltage and the current. The new energy system data included in the environmental data is the wind speed, wind direction, light intensity, environmental temperature, and humidity;

[0067] Step S105: Real-time data, hourly data, and daily data are the first-level directories, and photovoltaic power station data, wind power station data, energy storage system data, and environmental data are the second-level directories under the first-level directories.

[0068] In specific implementation, the relationships among real-time data, hourly data, daily data, photovoltaic power station data, wind power station data, energy storage system data, and environmental data classified based on the time dimension and space dimension are as Figure 2 shown.

[0069] Step S2: Set up anomaly classification and dynamically update the judgment rules of anomaly classification based on the historical data of new energy system data. Step S2 includes the following sub-steps:

[0070] Step S201: Obtain the historical data of new energy system data.

[0071] Step S201 includes the following sub-steps:

[0072] Step S201.1: Build a historical database of the new energy system and record the historical data of the new energy system data.

[0073] In addition to the fan power generation power, fan speed, photovoltaic panel power generation power, photovoltaic panel temperature, voltage, current, wind speed, wind direction, light intensity, ambient temperature, and humidity in the historical data recorded in the historical database of the new energy system, the recording time of this set of historical data is recorded, and one recording time corresponds to one piece of historical data.

[0074] The historical database of the new energy system is a table. Mark the known abnormal historical data as historical abnormal data, change the font color of the historical abnormal data to red for marking, and the historical abnormal data can be screened by color. The historical abnormal data includes historical abnormal fan 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.

[0075] Name the fan power generation power, fan speed, photovoltaic panel power generation power, photovoltaic panel temperature, voltage, current, wind speed, wind direction, light intensity, ambient temperature, and humidity in the historical data as historical fan 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 in turn.

[0076] In specific implementation, some data of the historical database of the new energy system are shown in Table 1 below:

[0077] Table 1 Part of the data in the historical database of the new energy system

[0078]

[0079] The font color of ordinary historical data is uniformly black, while the font color of historical abnormal data is uniformly red;

[0080] Step S202, analyze the wind farm data based on historical data and construct a wind power reference diagram;

[0081] Step S202 includes the following sub-steps:

[0082] Step S202.1, obtain historical fan power, historical speed, historical wind speed, and historical wind direction, including historical abnormal fan power and historical abnormal speed;

[0083] In specific implementation, since historical abnormal fan power and historical abnormal speed are distinguished by font color in the historical database of the new energy system and are not stored separately, historical abnormal fan power and historical abnormal speed can be obtained while obtaining historical fan power and historical speed;

[0084] Step S202.2, calculate the wind speed u in the longitude direction by the formula u = wspd × sin(wdir), and calculate the wind speed v in the latitude direction by the formula v = wspd × cos(wdir), where u is the wind speed u in the longitude direction representing the historical wind speed and historical wind direction, v is the wind speed v in the latitude direction representing the historical wind speed and historical wind direction, wspd is the wind speed, and wdir is the wind direction;

[0085] Step S202.3, obtain the direction the fan faces. If the direction the fan faces is due east or due west, mark the wind speed v as the reference wind power. If the direction the fan faces is due north or due south, mark the wind speed u as the reference wind power;

[0086] In specific implementation, since the front of the fan blades of the wind turbine is most affected by the wind force during rotation, that is, the wind direction parallel to the direction faced by the wind turbine, which can also be expressed as the wind direction perpendicular to the plane where the fan blades are located. However, in real life, the wind direction is variable and not necessarily due east, due west, due north, or due south. Therefore, it is necessary to calculate the components of the wind speed in the longitude direction and the latitude direction based on the wind direction and wind speed, so as to analyze whether the power generation power and the rotational speed of the wind turbine are abnormal based on the influence of the wind speed on the wind turbine. The wind direction usually takes due north as 0°, rotates clockwise, takes due east as 90°, and so on. For example, if the historical wind speed is obtained as 6.8 m / s and the corresponding historical wind direction is 38°, substituting into the calculation gives u = 6.8 m / s × sin(38°) = 4.19 m / s, v = 6.8 m / s × cos(38°) = 5.36 m / s. That is, the wind in the longitude direction of the historical wind speed and historical wind direction is 4.19 m / s, and the wind in the latitude direction of the historical wind speed and historical wind direction is 5.36 m / s. When the wind turbine is installed, it usually faces any one of due east, due south, due west, and due north. If the direction faced by the wind turbine is obtained as due east, and the wind in the latitude direction is parallel to due east, the rotation of the fan blades is most affected by the v wind. Therefore, the v wind is used as the reference wind force, that is, the reference wind force is 5.36 m / s;

[0087] Please refer to Figure 3 As shown, in step S202.4, taking the reference wind force as the X-axis and the historical wind turbine power and historical rotational speed as the Y-axis respectively, establish a plane rectangular coordinate system, which are named the wind power reference coordinate system and the rotational speed reference coordinate system respectively. Enter the reference wind force obtained from the historical wind speed and historical wind direction of the historical wind turbine power into the wind power reference coordinate system, and enter the reference wind force obtained from the historical wind speed and historical wind direction of the historical rotational speed into the rotational speed reference coordinate system;

[0088] In step S202.5, the wind power reference coordinate system and the rotational speed reference coordinate system are collectively referred to as the wind power reference diagram;

[0089] In specific implementation, since the wind power reference coordinate system and the rotational speed reference coordinate system both belong to the wind power reference diagram and the subsequent analysis processes are the same, only the rotational speed reference coordinate system is given in this embodiment for reference, providing materials for the description of the subsequent analysis of the judgment rules. The rotational speed reference coordinate system is as Figure 3 shown;

[0090] In step S203, analyze the photovoltaic power station data based on historical data and construct a photovoltaic reference diagram;

[0091] Step S203 includes the following sub-steps:

[0092] Step S203.1: Obtain historical photovoltaic power, historical photovoltaic temperature, and historical light intensity, including historical abnormal photovoltaic power and historical abnormal photovoltaic temperature;

[0093] Step S203.2: Taking historical light intensity as the horizontal axis, and historical photovoltaic power and historical photovoltaic temperature as the vertical axes respectively, establish a rectangular coordinate system, named photovoltaic power reference coordinate system and temperature reference coordinate system respectively. Input historical photovoltaic power and historical photovoltaic temperature into the photovoltaic power reference coordinate system and temperature reference coordinate system according to historical light intensity;

[0094] Step S203.3: The photovoltaic power reference coordinate system and the temperature reference coordinate system are collectively referred to as the photovoltaic reference diagram;

[0095] In specific implementation, since the power generation power of a photovoltaic panel is usually only related to the light intensity, the judgment rules can be analyzed through the relationship between the light intensity and historical photovoltaic power and historical photovoltaic temperature. Moreover, 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 the same. Therefore, specific display is not carried out in this embodiment;

[0096] Step S204: Analyze the judgment rules for wind power station data and photovoltaic station data in abnormal classification based on the wind power reference diagram and the photovoltaic reference diagram;

[0097] Step S204 includes the following sub-steps:

[0098] Step S204.1: Uniformly mark the wind power reference diagram and the photovoltaic reference diagram as the rule reference diagram. For any rule reference diagram, name the coordinate points in it as reference points, and uniformly mark the coordinate points corresponding to historical abnormal wind turbine power, historical abnormal rotational speed, historical abnormal photovoltaic power, and historical abnormal photovoltaic temperature as abnormal points;

[0099] Please refer to Figure 4 As shown in the figure, Step S204.2: Remove the abnormal points from the rule reference diagram, and then perform regression analysis on the rule reference diagram. Select the curve with the smallest standard deviation as the rule reference curve;

[0100] In specific implementation, taking Figure 3 the rotational speed reference coordinate system as an example, remove the abnormal points in it and then perform regression analysis. The regression analysis includes 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 the lowest degree of dispersion of the reference points and the best effect. The method of selecting the discrete function through the standard deviation in regression analysis is a prior art, and no specific example is given in this embodiment; The rule reference curve obtained through regression analysis is as Figure 4 shown;

[0101] Please refer to Figure 5 As shown in Figure Figure 5 , in step S204.3, add the abnormal points to the rule reference graph, move the rule reference curve upward and downward respectively along the vertical direction. If there are abnormal points on the rule reference curve during the movement, stop the movement to obtain two curves with the same change trend as the rule reference curve, which are named the rule upward curve and the rule downward curve from top to bottom;

[0102] Step S204.4, the rule upward curve includes the fan power upward curve, the rotation speed upward curve, the photovoltaic power upward curve, and the photovoltaic temperature upward curve, and the rule downward curve includes the fan power downward curve, the rotation speed downward curve, the photovoltaic power downward curve, and the photovoltaic temperature downward curve;

[0103] In specific implementation, usually, the discrete degree between the abnormal points and the rule reference curve is relatively large, and there is an obvious difference from the reference points. The rule reference curve obtained by removing the abnormal points reflects the change trend of the fan rotation speed changing with the reference wind force under normal conditions. After adding the abnormal points, through the vertical movement up and down, the normal range of the fan rotation speed can be determined on the premise of retaining the change trend of the fan rotation speed. If the rule reference curve touches the abnormal points, it means that after exceeding the rule reference curve, the reference points may belong to the abnormal points. Although there are a small number of reference points with the same discrete degree as the abnormal points from the rule reference curve, the proportion is very small. And this embodiment is a classification update method, aiming to conduct a preliminary screening before the fault detection system detects the data of the new energy system, list the data of suspected abnormal points for users to refer to, and at the same time enable the abnormal detection system to detect the data of the new energy system targeted; The rule upward curve and the rule downward curve obtained by moving are as Figure 5 shown Figure 5 The rule upward curve and the rule downward curve in Figure 5 are the rotation speed upward curve and the rotation speed downward curve respectively;

[0104] Step S204.5, monitor the fan power generation power, fan rotation speed, wind speed, and wind direction in real time, obtain the reference wind force based on the wind speed and wind direction, mark the Y-axis values corresponding to the fan power upward curve and the fan power downward curve when the X-axis is equal to the reference wind force as the fan power reference upper limit and the fan power reference lower limit respectively, and mark the Y-axis values corresponding to the rotation speed upward curve and the rotation speed downward curve when the X-axis is equal to the reference wind force as the rotation speed reference upper limit and the rotation speed reference lower limit respectively;

[0105] Step S204.6, if the fan power generation power is not within the range from the fan power reference lower limit to the fan power reference upper limit, output a fan abnormal power signal; if the fan rotation speed is not within the range from the rotation speed reference lower limit to the rotation speed reference upper limit, output a rotation speed abnormal signal;

[0106] Step S204.7, monitor the power generation power of the photovoltaic panel, the temperature of the photovoltaic panel, and the light intensity in real time. Mark the Y-axis values corresponding to the upward and downward curves of the photovoltaic power when the X-axis is equal to the light intensity as the upper reference limit and the lower reference limit of the photovoltaic power respectively. Mark the Y-axis values corresponding to the upward and downward curves of the photovoltaic temperature when the X-axis is equal to the light intensity as the upper reference limit and the lower reference limit of the photovoltaic temperature respectively;

[0107] Step S204.8, if the power generation power of the photovoltaic panel is not within the range from the lower reference limit to the upper reference limit of the photovoltaic power, output a photovoltaic abnormal power signal; if the temperature of the photovoltaic panel is not within the range from the lower reference limit to the upper reference limit of the photovoltaic temperature, output a photovoltaic temperature abnormal signal;

[0108] In specific implementation, Figure 5 for example, assume that the reference wind force v_wind calculated from the monitored wind speed and wind direction is 6.72 m / s, and the monitored motor speed is 26 r / min. Enter the coordinates (6.72, 26) into Figure 5 and observe whether the coordinates (6.72, 26) are between the upward and downward curves of the rotational speed. If they are between the upward and downward curves of the rotational speed, it means that the monitored motor speed is normal; otherwise, there may be an abnormality. The analysis processes of the fan power generation power, the photovoltaic power generation power, and the photovoltaic temperature are the same as that of the fan rotational speed, so no specific description will be given in this embodiment.

[0109] Please refer to Figure 6 as shown. Step S3, further classify and update the new energy system data based on the judgment rules; Step S3 includes the following sub-steps:

[0110] Step S301, classify and update the new energy system data according to the structure corresponding to the first-level directory and the second-level directory;

[0111] Step S302, for the new energy system data that outputs the fan abnormal power signal, the rotational speed abnormal signal, the photovoltaic abnormal power signal, and the photovoltaic temperature abnormal signal, mark it as real-time abnormal data;

[0112] Step S303, mark and highlight the real-time abnormal data in red in the first-level directory and the second-level directory;

[0113] Step S304, add an abnormal classification in the first-level directory, and centrally display the real-time abnormal data in the abnormal classification;

[0114] In specific implementation, Figure 6The display interface for classification update in this embodiment is exemplified in a relatively simple manner. It is not the display interface in actual use. Moreover, this embodiment is only a method for classifying and updating data, not a display method, and it can be applied to any new energy monitoring. It can conduct primary screening on new energy system data, automatically mark suspected abnormal data, and the abnormal classification is at the same first-level directory as real-time data, hourly data, and daily data.

[0115] Embodiment 2. Please refer to Figure 7 as shown in Figure 7 It exemplifies a schematic structural diagram of an electronic device. The electronic device may include: a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication 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, it runs the steps in a method for classifying and updating new energy system data to achieve the following functions: obtaining new energy system data and performing two-dimensional classification on the new energy system data; setting abnormal classification and dynamically updating the judgment rules for 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.

[0116] In addition, when the logical instructions in the above-mentioned memory are implemented in the form of software functional units and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical discs and other various media that can store program codes.

[0117] Embodiment 3. This application also provides a computer program product. The computer program product includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute a method for classifying and updating new energy system data provided by the above-mentioned various methods. The method includes: obtaining new energy system data and performing two-dimensional classification on the new energy system data; setting abnormal classification and dynamically updating the judgment rules for 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.

[0118] Example 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 method for classifying and updating new energy system data are run to achieve the following functions: obtaining 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 the 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.

[0119] Through the description of the above embodiments, the embodiments of the present invention can be provided as a method, a system or a computer program product. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions for causing 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.

[0120] In the embodiments provided by the present application, it should be understood that the disclosed system or method can be implemented in other ways. The above-described embodiments are merely illustrative. For example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation. For another 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 displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces. The indirect coupling or communication connection of systems, modules and units can be in an electrical, mechanical or other form.

[0121] 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 them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment 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; 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 the wind power reference map and the photovoltaic reference map, including 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; Real-time monitoring of wind turbine power generation, wind turbine speed, wind speed and wind direction; 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 classifying the new energy system data in two dimensions 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: 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.

4. A new energy system data classification and updating method according to claim 3, 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.

5. A new energy system data classification and updating method according to claim 4, 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.

6. A new energy system data classification and updating method according to claim 5, 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 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 reference wind force is obtained based on the wind speed and wind direction, and 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.

7. A new energy system data classification and updating method according to claim 6, 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.

8. 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.

9. 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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