Automatic disk monitoring method, device and equipment for photovoltaic power station and storage medium

Abstract

The invention provides an automatic disk monitoring method, device and equipment for a photovoltaic power station and a storage medium, and relates to the field of photovoltaic power station monitoring, and the method comprises the steps: obtaining environment feature data and inverter operation data, and calculating an average influence value of each environment feature on the inverter operation data; establishing an environment influence matrix of each inverter according to the average influence value, and calculating a correlation coefficient of the environment influence matrixes of adjacent inverters; based on the physical space position and the correlation coefficient of the inverter, carrying out region segmentation and anomaly identification on the power station to obtain a to-be-checked inverter; according to the correlation coefficient between the environment influence matrixes of the inverter, updating the weight of the training sample and the diagnosis model; and performing fault diagnosis on the to-be-checked inverter and generating an operation and maintenance plan. According to the invention, the method improves the accuracy of abnormality recognition through the correlation analysis of the environment characteristics and the operation parameters of the inverter, optimizes the fault diagnosis model, and improves the fault diagnosis precision.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic power station monitoring, and in particular to an automated monitoring method, device, equipment and medium for a photovoltaic power station. Background Art

[0002] Photovoltaic power station fault diagnosis technology is the key to power station automation monitoring. With the widespread application of photovoltaic power generation, fault diagnosis technology is also constantly developing. Currently, common fault diagnosis technologies include imaging technology, electrical feature analysis, and machine learning-based methods. Among them, machine learning-based diagnosis methods can identify more types of faults and can make more accurate predictions of power station power. However, photovoltaic power stations have complex equipment types and large numbers, and the operating parameters of various equipment are greatly affected by environmental changes. In some complex environments, the accuracy of machine learning-based diagnosis results is low. Summary of the invention

[0003] The purpose of the present invention is to provide a method, device, equipment and medium for automatic monitoring of photovoltaic power stations to improve the above problems. In order to achieve the above purpose, the technical solution adopted by the present invention is as follows: In a first aspect, the present application provides an automated monitoring method for a photovoltaic power station, comprising: Acquire environmental characteristic data and inverter operation data, wherein the inverter operation data includes string power characteristic data, and respectively calculate an average impact value of each environmental characteristic on each inverter operation data; According to the average impact value, an environmental impact matrix of each inverter is established, and a correlation coefficient between the environmental impact matrix of each inverter and its adjacent inverter is calculated; Based on the physical spatial location of the inverter and the correlation coefficient between the inverters, the power station is segmented into regions through a clustering algorithm to obtain multiple operating regions. The abnormal points in each operating region are found, and the inverter to be checked is determined based on the abnormal points. Calculate the correlation coefficients between the environmental impact matrices of all inverters, and draw the correlation coefficient curves of each inverter and other inverters in time series; Building training samples based on the historical operating data of all inverters in the power station, and updating the weight of each training sample based on the correlation coefficient curve between the inverter to be checked and other sample inverters, and using the training samples with updated weights to update the pre-established diagnostic model; The operating data of the inverter to be checked is obtained, and the data is input into an updated diagnostic model to obtain a fault diagnosis result. The historical maintenance strategy is searched according to the fault diagnosis result, and a list of available resources is obtained. An operation and maintenance plan is generated according to the historical maintenance strategy and the list of available resources.

[0004] In a second aspect, the present application provides an automated monitoring device for a photovoltaic power station, characterized in that it includes: A first calculation module is used to obtain environmental characteristic data and inverter operation data, wherein the inverter operation data includes string power characteristic data, and respectively calculate an average impact value of each environmental characteristic on each inverter operation data; A second calculation module, used to establish an environmental impact matrix of each inverter according to the average impact value, and calculate a correlation coefficient between the environmental impact matrix of each inverter and its adjacent inverters; A segmentation module is used to segment the power station into regions based on the physical spatial positions of the inverters and the correlation coefficients between the inverters by using a clustering algorithm to obtain multiple operating regions, find abnormal points in each operating region, and determine the inverter to be checked based on the abnormal points; The third calculation module is used to calculate the correlation coefficients between the environmental impact matrices of all inverters, and to draw the correlation coefficient curves between each inverter and other inverters in time sequence; An updating module is used to construct training samples based on the historical operation data of all inverters in the power station, and to update the weight of each training sample according to the correlation coefficient curve between the inverter to be checked and other sample inverters, and to update the pre-established diagnostic model using the training samples with updated weights; The diagnostic module is used to obtain the operating data of the inverter to be checked, input it into the updated diagnostic model, obtain the fault diagnosis result, search the historical maintenance strategy according to the fault diagnosis result, and obtain the list of available resources, and generate the operation and maintenance plan according to the historical maintenance strategy and the list of available resources.

[0005] In a third aspect, the present application provides an automated monitoring device for a photovoltaic power station, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the automated monitoring method for a photovoltaic power station as described above when executing the computer program.

[0006] In a fourth aspect, the present application provides a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the automated monitoring method for a photovoltaic power station as described above are implemented.

[0007] The beneficial effects of the present invention are: This application improves the accuracy of abnormality identification by performing correlation analysis between environmental characteristics and inverter operating parameters, reasonably dividing the power station into areas with different operating conditions, and searching for abnormal inverters by area. At the same time, based on the similarity of the inverter operating conditions, the weights of training samples are adjusted to continuously optimize the fault diagnosis model, so that the fault diagnosis model can better adapt to changes in the environment and the working performance of each device, and effectively improve the diagnosis accuracy.

[0008] Other features and advantages of the present invention will be set forth in the following description, and in part will become apparent from the description, or may be understood by practicing embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 This is a flow chart of an automated monitoring method for a photovoltaic power station in an embodiment of the present application; Figure 2 This is a schematic diagram of the structure of an automated monitoring device for a photovoltaic power station in an embodiment of the present application; Figure 3 This is a schematic diagram of the structure of the automated monitoring equipment for a photovoltaic power station in an embodiment of the present application; Markings in the figure: 100-first calculation module; 110-processing unit; 120-generation unit; 130-calculation unit; 140-elimination unit; 200-second calculation module; 300-segmentation module; 400-third calculation module; 500-update module; 600-diagnosis module; 800-automatic monitoring equipment of photovoltaic power station; 801-processor; 802-memory; 803-multimedia component; 804-I / O interface; 805-communication component. DETAILED DESCRIPTION

[0011] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0012] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings. Embodiment 1:

[0013] See also Figure 1 , an automated monitoring method for a photovoltaic power station, comprising steps S100, S200, S300, S400, S500 and S600; S100, obtaining environmental characteristic data and inverter operation data, wherein the inverter operation data includes string power characteristic data, and calculating the average impact value of each environmental characteristic on each inverter operation data; In this step, the environmental characteristic data obtained include temperature (T), humidity (R), wind speed (W), and solar radiation intensity (I); the inverter operation data include DC power, AC power, operating temperature, and electrical characteristic data of each string connected to the inverter, etc.; among them, the acquired electrical characteristic operation data include output power (P), maximum power point current (I MPP )、Maximum power point voltage(U MPP ); Each data is collected at a preset frequency, such as inverter operation data is collected every 5 minutes, and environmental characteristic data is collected every 1-2 minutes. The environmental characteristic data collected within 5 minutes are averaged, and the averaged value is used as the environmental characteristic data corresponding to the inverter operation data at that time point; after collecting data for a period of time (such as collecting for 3-4 hours), a data sequence arranged by time is obtained, and the data is processed as follows: S110, cleaning the environmental characteristic data and the inverter operation data, and performing dynamic time warping on the cleaned data; The environmental feature data can be processed for missing values, outliers, and smoothing values. For the inverter operation data, since this application needs to identify the inverter operation anomalies, outlier processing cannot be performed on the inverter operation data, but the anomalies in the data must be retained. Dynamic time warping (DTW) can match the sequence by stretching and compressing the nonlinear time axis, and can effectively handle the time offset, scaling, and distortion in the time series, so that the time series data can be aligned to the greatest extent. In this step, considering that the impact of environmental changes on the inverter and string operation may be delayed, dynamic time warping is selected to match the two time series data to improve the credibility of the subsequent calculation of the correlation coefficient.

[0014] S120. Based on the processed environmental feature data, scikit-learn is used to generate multiple variable interaction terms. Scikit-learn is a machine learning library in Python. The PolynomialFeatures class of Scikit-Learn can generate polynomial features and interaction terms between variables. These interaction terms can be the product of temperature and humidity T×R, or the second-order interaction R 2×I, etc., and then the feature selection techniques in scikit-learn can be used to preliminarily screen important variable interactions.

[0015] S130, taking the processed environmental characteristic data and variable interaction items as input, taking the processed inverter operation data as output, performing average impact value calculation, and obtaining the average impact value of each variable interaction item; The mean influence value (MIV) is a method used to quantify the impact of features on model output. MIV quantifies the average impact of each feature on the output by perturbing the input features and observing the changes in the model output.

[0016] The environmental characteristic data and the variable interaction terms are used as input features, and the corresponding inverter operation data is used as output to construct a feature matrix X (the size is N×m, N is the number of samples, and m is the number of features). It should be noted that the output of each sample in this step is only one inverter operation data, that is, the inverter DC power, AC power, operating temperature, etc. are used as outputs respectively, so as to calculate the impact values ​​of the input features on different inverter operation data.

[0017] The neural network model is trained using N samples, and then each input feature is perturbed in turn; Denote the input feature as j, j=1,2,3…m, m is the number of features; set the perturbation value β; Add disturbance to feature j: increase the value of feature j by β to get the new feature matrix X j+ ; X j+ =X+βM j； Among them, M j is an N×m matrix whose jth column is 1 and the rest of the columns are 0; Reduce the disturbance of feature j: reduce the value of feature j by β to obtain a new feature matrix X j- ; X j- =X-βM j； The disturbed samples are input into the trained neural network model, and the output changes before and after the disturbance are calculated; the output changes of each sample are averaged to obtain the average influence value MIV of feature j j ; S140. Eliminate variable interaction items according to the average impact value to obtain retained interaction items, and record each environmental characteristic data and the average impact value of the retained interaction items.

[0018] If the average impact value is very small, it proves that the variable interaction term has a low impact on the prediction result. These terms are eliminated and only the features that have a greater impact on the inverter operating status are retained.

[0019] S200, establishing an environmental impact matrix for each inverter according to the average impact value, and calculating a correlation coefficient between the environmental impact matrix of each inverter and its adjacent inverters; The environmental impact matrix has environmental characteristics and reserved interaction items as rows and different inverter operation data as columns, and the elements thereof are correlation coefficients; In a photovoltaic power station, the inverter is set according to the location of the photovoltaic panel, and is generally distributed in an array. Adjacent inverters refer to the four inverters on the top, bottom, left, and right. The inverter at the edge of the array only calculates the correlation coefficient between it and two or three adjacent inverters. The correlation coefficient can be calculated using the Pearson correlation coefficient or the Kendall correlation coefficient; S300, based on the physical spatial position of the inverter and the correlation coefficient between the inverters, the power station is segmented by a clustering algorithm to obtain multiple operating areas, abnormal points in each operating area are found, and the inverter to be checked is determined according to the abnormal points; specifically: S310, randomly selecting an inverter as a coding origin, encoding coordinate data of each inverter according to the physical spatial position of the inverter, and calculating an initial distance between adjacent inverters based on the coordinate data; the coordinate data is a two-dimensional coordinate; In this step, if the arrangement of the inverters is neat and orderly, and the spacing between the rows and columns is not much different, the inverters can be regarded as equally spaced during encoding; if the spacing between the rows and columns is significantly increased, the distance between the inverter and the adjacent inverter is increased accordingly when encoding the coordinates; at the same time, in some power stations with special terrain, the height difference between the inverters needs to be considered. If the horizontal distance between the inverters is not large, but the height difference is obvious, the distance between the inverters should also be increased. After encoding, the initial data distribution of the inverter can be obtained; S320, starting from the inverter at the encoding origin, correcting the initial distance between the inverters according to the correlation coefficient between adjacent inverters to obtain a corrected distribution data point; When the correlation coefficient value between adjacent inverters is large, it means that the operating states of the two are basically the same, and the change trends of the operating states of the two have a high similarity, so the distance between the two data points needs to be closer; if the correlation coefficient value between adjacent inverters is small, it means that the operating states of the two are different, and the distance between the two data points needs to be farther, so that the data points can be separated in subsequent clustering or identified as abnormal points; S330, clustering the corrected distribution data points using a density clustering algorithm to obtain clustering results, performing image segmentation according to the clustering results to obtain multiple operating areas.

[0020] In this step, the modified distribution data points are clustered by the DBSCAN clustering algorithm, and the clustering results are mapped back to the actual spatial position of each inverter, thereby segmenting the inverters in the power station; Specifically, different clusters after clustering are displayed in different colors to obtain the actual inverter distribution map of the entire power station. The color mark of each inverter is mapped to the inverter distribution map, and the image segmentation algorithm is used to extract the contour to form different operating areas. The inverter operating states in each operating area have similarities, and the inverter operating states in different operating areas are different. The abnormal points identified by the DBSCAN clustering algorithm are also mapped to the inverter distribution map to make it visual; the abnormal points in this step include noise points and potential isolated points. Noise points are points that are not assigned to any cluster. Potential isolated points include data points that are incorrectly divided into clusters, and when a small number of data points are divided into the same cluster, but the cluster is completely surrounded by another larger cluster, it means that the data points in the cluster may all be abnormal, and these points can be marked as potential isolated points. In this step, potential isolated points can be identified by the ODIC-DBSCAN (Outlier detection of inner-cluster based on DBSCAN) method, the local outlier factor method, or the isolation forest (IsolationForest) method.

[0021] S400, calculating the correlation coefficients between the environmental impact matrices of all inverters, and drawing correlation coefficient curves between each inverter and other inverters in time sequence; The correlation coefficient curve is drawn and saved according to a preset period. For example, a correlation coefficient curve between inverters within a day is drawn and saved in a database. The correlation coefficient curve with a continuously low correlation coefficient can be found by analyzing historical data (within the past month), and the correlation coefficient curve between the two inverters within this month can be deleted to avoid occupying computing resources.

[0022] S500, constructing training samples based on the historical operation data of all inverters in the power station, and updating the weight of each training sample according to the correlation coefficient curve between the inverter to be checked and other sample inverters, and using the training samples with updated weights to update the pre-established diagnostic model; specifically: S510. When it is necessary to check a certain inverter, first obtain the correlation coefficient curve between the inverter and other inverters from the database, obtain the preset correlation coefficient threshold, and use the correlation coefficient threshold to segment each correlation coefficient curve to obtain the correlation area greater than the correlation coefficient threshold, and obtain the corresponding correlation time domain; if the Pearson correlation coefficient is used, the range of the correlation coefficient curve is [-1,1]. In this step, the correlation coefficient threshold is set to 0.78, and the area where the curve is above 0.78 is obtained. At the same time, the time period corresponding to the area is obtained, that is, the correlation time domain, indicating that the operating states of the two inverters during this period are highly similar; It is understandable that if there are many correlation coefficient curves stored in the database, in order to improve the calculation efficiency, the correlation coefficient curves can be searched by season, or some correlation coefficient curves can be randomly selected for calculation; S520, searching for historical environmental characteristic data in a relevant time domain, and comparing the historical environmental characteristic data with the environmental characteristic data of the inverter to be checked, to obtain environmental similarity; The acquisition time of the data can be determined according to the relevant time domain, so as to find the environmental feature data of the time period, compare it with the environmental feature data of the inverter to be checked, calculate the Euclidean distance between the corresponding environmental feature data, and then add the similarity of each environmental feature to obtain the environmental similarity; S530: Acquire historical operation data of the inverter in each relevant time domain, and generate a first weight for the historical operation data according to the environmental similarity and the area of ​​the relevant region.

[0023] First, number each relevant area, and the number is the first weight A of the k relevant area k for: A k =uS k +(1-u)H k Among them, u is the first weight coefficient, S k is the area of ​​the relevant region k, H k is the environmental similarity of the relevant area k; In this step, in addition to obtaining the historical operation data within the relevant time domain, it is also necessary to search for the historical operation data before and after the relevant time domain according to the preset length, so as to ensure that the inverter failure near the relevant time domain is not missed; if there is another relevant time domain before and after the preset length of the relevant time domain, that is, the historical operation data is repeated, the repeated historical operation data are merged; the obtained historical operation data are used to construct the weighted data set E k ; Each weighted data set E k corresponds to a relevant region k; Assign the first weight of the relevant region k to the corresponding weighted dataset E k All historical operation data in the system; historical operation data includes inverter DC power, AC power, operating temperature and string operation data, etc. This step can be used to update the weights for some historical operation data to optimize the fault diagnosis model and improve the accuracy of diagnosis. However, in some special climates and special conditions, the spatial distribution of the inverter may have a greater impact on the accuracy of the model prediction (the spatial distribution of the inverter can be equivalent to the spatial distribution of the strings). Therefore, based on the above method, the spatial distribution characteristics of the inverter can be further considered to optimize the model. The steps are as follows: S540, obtaining coordinate data of each inverter, and calculating the spatial distance between the inverters based on the coordinate data; and obtaining a second weight of the inverter according to the spatial distance between the inverters; The coordinate data used in this step may be the same as the coordinate data encoded in step S310, and the second weight may directly use the spatial distance d; S550, performing linear weighted fusion on the first weight and the second weight to obtain a comprehensive weight of each training sample; The calculation formula of the comprehensive weight C is as follows: C = α·A k + (1-α) e -βd ; Among them, α is the second weight coefficient; β is the parameter that controls the attenuation speed of the distance effect. The farther the distance, the faster the influence on the weight decays; the second weight coefficient can be dynamically adjusted according to the environment or time. By deep learning of historical data, the optimal α corresponding to a certain type of environmental condition (cloudy, overcast or different seasons) can be determined.

[0024] In step S530, the first weight is generated only for part of the historical operation data. For other data for which the first weight is not generated, the first weight value A is set to k Set to 0.

[0025] The present application preferably uses the XGBoost algorithm to construct an error diagnosis test model, and updates the comprehensive weight of each sample calculated above to the XGBoost error diagnosis test model, so that the model pays more attention to certain samples, and obtains an updated diagnosis model.

[0026] S600, obtaining the operating data of the inverter to be checked, inputting it into an updated diagnosis model, obtaining a fault diagnosis result, searching for a historical maintenance strategy based on the fault diagnosis result, obtaining a list of available resources, and generating an operation and maintenance plan based on the historical maintenance strategy and the list of available resources.

[0027] The fault diagnosis results can reflect the type of fault, such as component damage, inverter failure, wiring problems, photovoltaic panel shadows, etc.; according to the type of fault, the corresponding maintenance strategy is found in the historical maintenance records and the urgency is analyzed, and keywords are extracted from the historical maintenance strategies, including spare parts keywords, such as inverters, cables, and iV curve testers; personnel keywords, that is, a list of necessary or available personnel to handle this type of fault; obtain a list of available resources, including current inventory spare parts and personnel duty rosters; filter out the resources needed to solve the current fault from the list of available resources, and then automatically generate one or more operation and maintenance plans based on the urgency of the fault, and push the operation and maintenance plans to the staff while issuing fault warnings, providing the staff with accurate fault information and recommended maintenance strategies.

[0028] The above method focuses on fault diagnosis of a single inverter or inverters in a small area, but it is difficult to reflect the abnormality of a large area. Therefore, in order to monitor the abnormality of a large area at the same time, the method of the present application also includes step S700: The correlation coefficient is updated at a preset frequency to obtain the operation area information arranged in time; the operation area information includes an operation area map; The operation area change information is obtained according to the operation area diagrams at different times, wherein the operation area change information includes movement information of the center point of the operation area and movement information of the edge of each operation area, specifically including the movement distance and movement direction of the center point of the operation area, and the movement distance and movement direction of the edge of the operation area; Acquiring cloud image information and environmental information, wherein the environmental information includes solar radiation angle, solar radiation intensity, temperature, wind speed and wind direction; Using cloud map information, environmental information, and operating area change information as training sets, the convolutional neural network model is trained to obtain an operating area prediction model; The changes in the operating area are predicted based on the operating area prediction model, and the actual change information of the operating area is compared with the prediction results to diagnose regional abnormalities. The operating area prediction model can predict the change trend of the operating area according to changes in the environment, and judge whether the actual change trend is reasonable based on the predicted change trend. If the actual value and the predicted value are significantly different, it may indicate that there is an abnormality in a certain area of ​​the power station, and an alarm will be issued to prompt the staff to conduct further inspection. Embodiment 2:

[0029] See also Figure 2 The present application also provides an automatic monitoring device for a photovoltaic power station, comprising: A first calculation module 100 is used to obtain environmental characteristic data and inverter operation data, wherein the inverter operation data includes string power characteristic data, and respectively calculate an average impact value of each environmental characteristic on each inverter operation data; A second calculation module 200 is used to establish an environmental impact matrix of each inverter according to the average impact value, and calculate a correlation coefficient between the environmental impact matrix of each inverter and its adjacent inverters; The segmentation module 300 is used to segment the power station into regions based on the physical spatial positions of the inverters and the correlation coefficients between the inverters by using a clustering algorithm to obtain multiple operating regions, find abnormal points in each operating region, and determine the inverter to be checked according to the abnormal points; The third calculation module 400 is used to calculate the correlation coefficients between the environmental impact matrices of all inverters, and to draw the correlation coefficient curves between each inverter and other inverters in time sequence; An updating module 500 is used to construct training samples based on the historical operation data of all inverters in the power station, and to update the weight of each training sample according to the correlation coefficient curve between the inverter to be checked and other sample inverters, and to update the pre-established diagnostic model using the training samples with updated weights; The diagnostic module 600 is used to obtain the operating data of the inverter to be checked, input it into the updated diagnostic model, obtain the diagnostic result, search the historical maintenance strategy according to the fault diagnosis result, and obtain the list of available resources, and generate the operation and maintenance plan according to the historical maintenance strategy and the list of available resources.

[0030] As an optional implementation, the first calculation module 100 includes: The processing unit 110 is used to clean the environmental characteristic data and the inverter operation data, and perform dynamic time warping processing on the cleaned data; A generating unit 120, configured to generate a plurality of variable interaction items using scikit-learn according to the processed environmental feature data; A calculation unit 130 is used to take the processed environmental characteristic data and the variable interaction item as input, take the processed inverter operation data as output, perform average impact value calculation, and obtain the average impact value of each variable interaction item; The elimination unit 140 is used to eliminate the variable interaction items according to the average impact value to obtain the retained interaction items, and record each environmental feature data and the average impact value of the retained interaction items. Embodiment 3:

[0031] Corresponding to the above method embodiment, this embodiment also provides an automated monitoring system for a photovoltaic power station. The automated monitoring system for a photovoltaic power station described below and the automated monitoring method for a photovoltaic power station described above can refer to each other.

[0032] Figure 3 FIG. 8 is a block diagram of an automatic monitoring panel 800 of a photovoltaic power station according to an exemplary embodiment. Figure 3As shown, the automated monitoring panel 800 of the photovoltaic power station includes a processor 801 and a memory 802. The automated monitoring panel 800 of the photovoltaic power station may also include one or more of a multimedia component 803, an input / output (I / O) interface 804, and a communication component 805. Among them, the processor 801 is used to control the overall operation of the automated monitoring panel 800 of the photovoltaic power station to complete all or part of the steps in the automated monitoring panel method of the photovoltaic power station described above. The memory 802 is used to store various types of data to support the operation of the automated monitoring panel 800 of the photovoltaic power station, and these data may include, for example, commands for any application or method used to operate on the automated monitoring panel 800 of the photovoltaic power station, and application-related data, such as contact data, messages sent and received, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0033] The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, and the microphone is used to receive external audio signals.

[0034] The received audio signal can be further stored in the memory 802 or sent through the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the automated monitoring panel 800 of the photovoltaic power station and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include: Wi-Fi module, Bluetooth module, NFC module.

[0035] In an exemplary embodiment, the device 800 for mutual signing and verification of digital files can be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, referred to as ASIC), digital signal processors (Digital Signal Processor, referred to as DSP), digital signal processing devices (Digital Signal Processing Device, referred to as DSPD), programmable logic devices (Programmable Logic Device, referred to as PLD), field programmable gate arrays (Field Programmable Gate Array, referred to as FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned automated monitoring method of photovoltaic power stations.

[0036] In another exemplary embodiment, a computer-readable storage medium including program commands is also provided, and when the program commands are executed by a processor, the steps of the above-mentioned automatic monitoring method for a photovoltaic power station are implemented. For example, the computer-readable storage medium may be the above-mentioned memory 802 including program commands, and the above-mentioned program commands may be executed by the processor 801 of the automatic monitoring 800 for a photovoltaic power station to complete the above-mentioned automatic monitoring method for a photovoltaic power station. Example 4

[0037] Corresponding to the above embodiment of the method for automated monitoring of a photovoltaic power station, this embodiment further provides a readable storage medium. The readable storage medium described below and the above-described method for automated monitoring of a photovoltaic power station can be referred to in correspondence with each other.

[0038] A readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned photovoltaic power station automatic monitoring method embodiment.

[0039] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other readable storage medium that can store program codes.

[0040] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0041] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An automated monitoring method for a photovoltaic power station, characterized in that: include: Acquire environmental characteristic data and inverter operation data, wherein the inverter operation data includes string power characteristic data, and respectively calculate an average impact value of each environmental characteristic on each inverter operation data; According to the average impact value, an environmental impact matrix of each inverter is established, and a correlation coefficient between the environmental impact matrix of each inverter and its adjacent inverter is calculated; Based on the physical spatial location of the inverter and the correlation coefficient between the inverters, the power station is segmented into regions through a clustering algorithm to obtain multiple operating regions. The abnormal points in each operating region are found, and the inverter to be checked is determined based on the abnormal points. Calculate the correlation coefficients between the environmental impact matrices of all inverters, and draw the correlation coefficient curves of each inverter and other inverters in time series; Building training samples based on the historical operating data of all inverters in the power station, and updating the weight of each training sample based on the correlation coefficient curve between the inverter to be checked and other sample inverters, and using the training samples with updated weights to update the pre-established diagnostic model; The operating data of the inverter to be checked is obtained, and the data is input into an updated diagnostic model to obtain a fault diagnosis result. The historical maintenance strategy is searched according to the fault diagnosis result, and a list of available resources is obtained. An operation and maintenance plan is generated according to the historical maintenance strategy and the list of available resources.

2. The method for automatic monitoring of a photovoltaic power station according to claim 1, characterized in that: The step of acquiring environmental characteristic data and inverter operation data, wherein the inverter operation data includes string power characteristic data, and calculating the average impact value of each environmental characteristic on each inverter operation data, includes: Cleaning the environmental characteristic data and the inverter operation data, and performing dynamic time regularization processing on the cleaned data; Based on the processed environmental feature data, scikit-learn was used to generate multiple variable interaction terms; The processed environmental characteristic data and variable interaction items are used as input, and the processed inverter operation data is used as output to calculate the average impact value and obtain the average impact value of each variable interaction item; The variable interaction terms were eliminated according to the average impact value to obtain the retained interaction terms, and the average impact value of each environmental characteristic data and the retained interaction terms was recorded.

3. The method for automatic monitoring of a photovoltaic power station according to claim 1, characterized in that: Based on the physical spatial position of the inverter and the correlation coefficient between the inverters, the power station is segmented into regions by a clustering algorithm to obtain multiple operating regions, including: Randomly select an inverter as the encoding origin, encode the coordinate data of each inverter according to the physical spatial position of the inverter, and calculate the initial distance between adjacent inverters based on the coordinate data; Starting from the inverter at the encoding origin, the initial distance between the inverters is corrected according to the correlation coefficient between adjacent inverters to obtain the corrected distribution data points; The density clustering algorithm is used to cluster the modified distribution data points to obtain clustering results. Image segmentation is performed based on the clustering results to obtain multiple operating areas.

4. The method for automatic monitoring of a photovoltaic power station according to claim 1, characterized in that: The method constructs training samples based on the historical operation data of all inverters in the power station, and updates the weight of each training sample according to the correlation coefficient curve between the inverter to be checked and other sample inverters, and uses the training samples with updated weights to update the pre-established diagnostic model, including: Obtain a preset correlation coefficient threshold, use the correlation coefficient threshold to segment each correlation coefficient curve, obtain a correlation area greater than the correlation coefficient threshold, and obtain a corresponding correlation time domain; Find the historical environmental feature data in the relevant time domain, compare it with the environmental feature data of the current inverter to be checked, and obtain the environmental similarity; According to the historical operation data of the inverter acquired in each relevant time domain, a first weight is generated for the historical operation data according to the environmental similarity and the area of ​​the relevant region.

5. The method for automatic monitoring of a photovoltaic power station according to claim 4, characterized in that: The method constructs training samples based on the historical operation data of all inverters in the power station, updates the weight of each training sample according to the correlation coefficient curve between the inverter to be checked and other sample inverters, and updates the pre-established diagnostic model using the updated training samples, including: Acquire coordinate data of each inverter, and calculate the spatial distance between the inverters based on the coordinate data; obtain a second weight of the inverter according to the spatial distance between the inverters; The first weight and the second weight are linearly weighted fused to obtain a comprehensive weight of each training sample.

6. The method for automatic monitoring of a photovoltaic power station according to claim 1, characterized in that: The method further comprises: The correlation coefficient is updated at a preset frequency to obtain the operation area information arranged in time; the operation area information includes an operation area map; Obtaining operation area change information according to the operation area diagrams at different times, wherein the operation area change information includes movement information of the center point of the operation area and movement information of the edge of each operation area; Acquiring cloud image information and environmental information, wherein the environmental information includes solar radiation angle, solar radiation intensity, temperature, wind speed and wind direction; Using cloud map information, environmental information, and operating area change information as training sets, the convolutional neural network model is trained to obtain an operating area prediction model; Based on the operating area prediction model, the changes in the operating area are predicted, and the actual change information of the operating area is compared with the prediction results to diagnose regional anomalies.

7. An automatic monitoring device for a photovoltaic power station, characterized in that: include: A first calculation module is used to obtain environmental characteristic data and inverter operation data, wherein the inverter operation data includes string power characteristic data, and respectively calculate an average impact value of each environmental characteristic on each inverter operation data; A second calculation module, used to establish an environmental impact matrix of each inverter according to the average impact value, and calculate a correlation coefficient between the environmental impact matrix of each inverter and its adjacent inverters; A segmentation module is used to segment the power station into regions based on the physical spatial positions of the inverters and the correlation coefficients between the inverters by using a clustering algorithm to obtain multiple operating regions, find abnormal points in each operating region, and determine the inverter to be checked based on the abnormal points; The third calculation module is used to calculate the correlation coefficients between the environmental impact matrices of all inverters, and to draw the correlation coefficient curves between each inverter and other inverters in time sequence; An updating module is used to construct training samples based on the historical operation data of all inverters in the power station, and to update the weight of each training sample according to the correlation coefficient curve between the inverter to be checked and other sample inverters, and to update the pre-established diagnostic model using the training samples with updated weights; The diagnostic module is used to obtain the operating data of the inverter to be checked, input it into the updated diagnostic model, obtain the fault diagnosis result, search the historical maintenance strategy according to the fault diagnosis result, and obtain the list of available resources, and generate the operation and maintenance plan according to the historical maintenance strategy and the list of available resources.

8. The automatic monitoring device for a photovoltaic power station according to claim 7, characterized in that: The first calculation module includes: A processing unit, used for cleaning the environmental characteristic data and the inverter operation data, and performing dynamic time regularization processing on the cleaned data; A generation unit, used to generate multiple variable interaction terms using scikit-learn based on the processed environmental feature data; A calculation unit, used for taking the processed environmental characteristic data and variable interaction items as inputs and taking the processed inverter operation data as outputs, performing average impact value calculation, and obtaining an average impact value of each variable interaction item; The elimination unit is used to eliminate the variable interaction items according to the average impact value, obtain the retained interaction items, and record the average impact value of each environmental characteristic data and the retained interaction items.

9. An automated monitoring device for a photovoltaic power station, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for automatic monitoring of a photovoltaic power station as claimed in any one of claims 1 to 6 when executing the computer program.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the automatic monitoring method of a photovoltaic power station as claimed in any one of claims 1 to 6 are implemented.

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