Actual data management method and system for new energy photovoltaic power station

Abstract

The invention discloses an actual data management method and system for a new energy photovoltaic power station, and relates to the related field of data processing, and the method comprises the steps: deploying a generating capacity collection device, an environment state collection device and an equipment state collection device at a photovoltaic power station; collecting generating capacity data through a generating capacity collection device; acquiring illumination intensity, temperature, humidity and wind speed information through an environment state acquisition device to obtain environment state data; the method comprises the following steps: acquiring inverter efficiency, photovoltaic panel performance, energy storage system state and equipment fault record information through an equipment state acquisition device to obtain equipment state data; and performing operation and maintenance management based on the generating capacity data, the environment state data and the equipment state data. The technical problems of one-sided operation and maintenance decision and low operation and maintenance efficiency caused by lack of an effective integration and analysis mechanism in existing photovoltaic power station data management are solved, and the technical effects of improving the accuracy of the operation and maintenance decision and the operation and maintenance efficiency through comprehensive integration and association of the data are achieved.

Description

Technical Field

[0001] The present application relates to data processing related fields, and in particular to a method and system for managing real-time data for a new energy photovoltaic power station. Background Art

[0002] As the global demand for renewable energy continues to grow, the importance of new energy photovoltaic power stations as a clean and renewable energy generation method has become increasingly prominent. The operation and maintenance management of photovoltaic power stations is directly related to their power generation efficiency, stability and economy. In the daily operation of photovoltaic power stations, power generation, environmental conditions and equipment conditions are the three core elements, which directly affect the overall performance and operation and maintenance decisions of photovoltaic power stations. In the operation and maintenance management of photovoltaic power stations, traditional data management methods often focus on the collection and analysis of a single data source, such as only focusing on power generation data or equipment failure records, while ignoring the comprehensive impact of environmental conditions on power generation and equipment status. Due to the lack of an effective integration and analysis mechanism, it is impossible to form effective associations and complementarities between data, making it difficult to comprehensively and accurately evaluate the actual operating status and potential problems of photovoltaic power stations, which in turn leads to one-sided operation and maintenance decisions and low operation and maintenance efficiency.

[0003] Among the current related technologies, the data management of new energy photovoltaic power stations lacks an effective integration and analysis mechanism, which leads to technical problems such as one-sided operation and maintenance decision-making and low operation and maintenance efficiency. Summary of the invention

[0004] The present application provides a method and system for managing actual data of new energy photovoltaic power stations, and deploys a power generation collection device, an environmental status collection device and an equipment status collection device in the photovoltaic power station to achieve comprehensive collection of power generation data, environmental status data (including light intensity, temperature, humidity and wind speed information) and equipment status data (including inverter efficiency, photovoltaic panel performance, energy storage system status and equipment fault record information). Based on these data, technical means such as operation and maintenance management of photovoltaic power stations are performed, achieving the technical effect of improving the accuracy of operation and maintenance decisions and operation and maintenance efficiency through comprehensive integration and association of power generation, environmental status and equipment status data.

[0005] This application provides a method for managing real-time data of a new energy photovoltaic power station, including:

[0006] A power generation collection device, an environmental status collection device and an equipment status collection device are deployed in the photovoltaic power station; power generation data is collected through the power generation collection device; light intensity, temperature, humidity and wind speed information are collected through the environmental status collection device to obtain environmental status data; inverter efficiency, photovoltaic panel performance, energy storage system status and equipment fault record information are collected through the equipment status collection device to obtain equipment status data; operation and maintenance management of the photovoltaic power station is performed based on the power generation data, the environmental status data and the equipment status data.

[0007] This application also provides a real-time data management system for new energy photovoltaic power stations, including:

[0008] A data acquisition device deployment module, the data acquisition device deployment module is used to deploy a power generation acquisition device, an environmental status acquisition device and an equipment status acquisition device in a photovoltaic power station; a power generation data acquisition module, the power generation data acquisition module is used to collect power generation data through the power generation acquisition device; an environmental status data acquisition module, the environmental status data acquisition module is used to collect light intensity, temperature, humidity and wind speed information through the environmental status acquisition device to obtain environmental status data; an equipment status data acquisition module, the equipment status data acquisition module is used to collect inverter efficiency, photovoltaic panel performance, energy storage system status and equipment fault record information through the equipment status acquisition device to obtain equipment status data; a photovoltaic power station management module, the photovoltaic power station management module is used to perform operation and maintenance management of the photovoltaic power station based on the power generation data, the environmental status data and the equipment status data.

[0009] The actual data management method and system for new energy photovoltaic power stations proposed in this application are intended to first deploy a power generation collection device, an environmental status collection device and an equipment status collection device in the photovoltaic power station, and then collect power generation data through the power generation collection device, collect light intensity, temperature, humidity and wind speed information through the environmental status collection device to obtain environmental status data, collect inverter efficiency, photovoltaic panel performance, energy storage system status and equipment fault record information through the equipment status collection device to obtain equipment status data, and finally perform operation and maintenance management of the photovoltaic power station based on the power generation data, environmental status data and equipment status data, thereby achieving the technical effect of improving the accuracy of operation and maintenance decisions and the efficiency of operation and maintenance through the comprehensive integration and association of power generation, environmental status and equipment status data. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0011] Figure 1 A flow chart of a method for managing actual data of a new energy photovoltaic power station provided in an embodiment of the present application.

[0012] Figure 2 A schematic diagram of the structure of a real-time data management system for a new energy photovoltaic power station provided in an embodiment of the present application.

[0013] Explanation of the reference numerals: data collection device deployment module 10 , power generation data collection module 20 , environment status data collection module 30 , equipment status data collection module 40 , photovoltaic power station management module 50 . DETAILED DESCRIPTION

[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0015] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0016] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0017] The present application embodiment provides a method for managing real-time data of a new energy photovoltaic power station, such as Figure 1 As shown, the method includes:

[0018] Step S100, deploying power generation collection devices, environmental status collection devices and equipment status collection devices in the photovoltaic power station. Specifically, according to the layout and scale of the photovoltaic power station, plan the installation locations of the power generation collection devices, environmental status collection devices and equipment status collection devices. Among them, the power generation collection device is used to measure and record the power generation of the photovoltaic power station in real time, including an electric meter or a data collector, etc., which is installed at the main output point of the photovoltaic power station, such as a junction box or an inverter outlet; the environmental status collection device includes a light intensity sensor, a temperature sensor, a humidity sensor and a wind speed sensor, which are used to monitor the environmental conditions during the operation of the photovoltaic power station. The environmental status collection device is distributed in different areas of the photovoltaic power station to obtain comprehensive environmental data; the equipment status collection device is used to monitor the operating status and performance parameters of key equipment in the photovoltaic power station, and is directly connected to key equipment, such as inverters, photovoltaic panels and energy storage systems. According to the planned location, install various collection devices to ensure that the equipment is stable, the wiring is correct, and the necessary protective measures are in place, such as waterproofing, dustproofing, lightning protection, etc. Configure the installed collection device, set parameters such as data collection frequency, storage method, transmission protocol, and then debug the equipment to ensure that the data is accurately and stably transmitted to the data center.

[0019] Step S200, collecting power generation data through the power generation collection device. Specifically, the power generation collection device automatically collects power generation data according to a preset frequency, stores it in a local or remote data center, verifies the collected power generation data to ensure the accuracy and completeness of the data, and summarizes the verified power generation data according to dimensions such as time and power station area for subsequent analysis and management.

[0020] Step S300, collect light intensity, temperature, humidity and wind speed information through the environmental state acquisition device to obtain environmental state data. Specifically, each environmental state sensor synchronously collects the current light intensity, temperature, humidity and wind speed information, performs pre-processing operations such as filtering and denoising on the collected raw environmental data to improve data quality, stores the processed environmental state data in a database, and shares it with the operation and maintenance management system or data analysis platform through the network.

[0021] Step S400, the device status acquisition device collects information about inverter efficiency, photovoltaic panel performance, energy storage system status, and equipment fault record information to obtain device status data. Specifically, the device status acquisition device monitors key parameters such as inverter efficiency, photovoltaic panel output power, and energy storage system charge and discharge status in real time, automatically detects equipment faults, and generates fault records. When a serious fault is detected, an alarm message is sent to the operation and maintenance personnel.

[0022] Step S500, based on the power generation data, the environmental status data and the equipment status data, the operation and maintenance management of the photovoltaic power station is performed. Specifically, data analysis technology (such as data mining, machine learning, etc.) is used to deeply analyze the power generation, environmental status and equipment status data, find potential problems and optimization space, make operation and maintenance decisions according to the analysis results, including equipment maintenance plans, power generation forecasts, energy scheduling, etc., execute operation and maintenance decisions, and continuously monitor the execution effect, adjust the operation and maintenance strategy according to the feedback results, and form a closed-loop management. The embodiment of the present application adopts the deployment of power generation collection devices, environmental status collection devices and equipment status collection devices in photovoltaic power stations to realize the comprehensive collection of power generation data, environmental status data (including light intensity, temperature, humidity and wind speed information) and equipment status data (including inverter efficiency, photovoltaic panel performance, energy storage system status and equipment fault record information), and the operation and maintenance management of photovoltaic power stations based on these data and other technical means, so as to achieve the technical effect of improving the precision of operation and maintenance decisions and operation and maintenance efficiency through the comprehensive integration and association of power generation, environmental status and equipment status data.

[0023] In a possible implementation, the operation and maintenance management of the photovoltaic power station is performed based on the power generation data, the environmental status data and the equipment status data, and step S500 further includes step S510, analyzing the power generation data to determine the power generation fluctuation law of the photovoltaic power station. Specifically, extract the historical power generation data from the database, clean and organize it, and remove abnormal values ​​and missing values. Use time series analysis methods (such as ARIMA, LSTM, etc.) to perform trend analysis, seasonal decomposition, etc. on the power generation data to identify the periodic fluctuation and random fluctuation of the power generation. According to the analysis results, determine the power generation fluctuation law of the photovoltaic power station, including daily fluctuation, weekly fluctuation, seasonal fluctuation, etc. Step S520, analyze the impact of light intensity, temperature, humidity and wind speed on power generation, and establish an environment-power generation impact mapping. Specifically, the power generation data is associated with the environmental status data (light intensity, temperature, humidity, wind speed) of the same period, and the degree and direction of the impact of environmental variables on power generation are analyzed using statistical methods such as multivariate linear regression and nonlinear regression. According to the results of regression analysis, a mapping relationship between environmental variables and power generation is established, that is, the environment-power generation impact mapping. Step S530, analyze the impact of inverter efficiency, photovoltaic panel performance and energy storage system status on power generation efficiency, and establish a device-power generation efficiency impact mapping. Specifically, extract relevant data such as inverter efficiency, photovoltaic panel performance (such as output power, conversion efficiency) and energy storage system status (such as charging and discharging efficiency, remaining capacity), evaluate the performance of each device, and identify the key factors affecting power generation efficiency. Analyze the specific impact of equipment performance on power generation efficiency using statistical analysis or machine learning methods, and establish a mapping relationship between equipment performance and power generation efficiency based on the analysis results, that is, the equipment-power generation efficiency impact mapping. Step S540, establish a power generation prediction model based on the power generation fluctuation law, the environment-power generation impact mapping and the equipment-power generation efficiency impact mapping. Specifically, a prediction model (such as neural network, support vector machine, etc.) is selected according to data characteristics and prediction requirements. According to the environment-generation impact mapping, the light intensity, temperature, humidity and wind speed environmental variables are used as input features of the model. According to the equipment-generation efficiency impact mapping, the inverter efficiency, photovoltaic panel performance and energy storage system status equipment performance parameters are used as input features of the model. According to the law of power generation fluctuation, time-related features (such as date, timestamp, seasonal indicator, etc.) are also used as inputs of the model. Special layers or modules are designed in the model architecture to handle different types of features (such as environmental features, equipment features and time features) to ensure that they are effectively integrated and utilized. Using historical power generation data as the target variable (i.e., the value to be predicted), the above features are used as input variables. During the training process, the model learns the complex relationship between these features and the target variable, which is guided by the established environment-generation impact mapping, equipment-generation efficiency impact mapping and power generation fluctuation law.By adjusting the model parameters (such as learning rate, batch size, number of iterations, etc.) and optimization algorithms (such as gradient descent, Adam, etc.), the model can minimize the prediction error and improve the generalization ability. Use an independent validation set to verify the trained power generation prediction model, evaluate its prediction accuracy and generalization ability, and make necessary adjustments and optimizations to the model (such as adding / reducing features, adjusting the model structure, etc.) based on the validation results.

[0024] Step S550, the operation and maintenance management of the photovoltaic power station is performed based on the power generation prediction model. Specifically, the trained power generation prediction model is used to predict the power generation in the future, and the operation and maintenance decisions are made according to the prediction results and the operation and maintenance goals (such as maximizing power generation, reducing operation and maintenance costs, etc.), and the operation and maintenance decisions are executed. The operation status and power generation changes of the photovoltaic power station are continuously monitored to ensure the effectiveness of the decision, and the operation and maintenance decisions are fed back and adjusted according to the monitoring results and actual conditions to form a closed-loop management. This implementation method establishes a power generation prediction model by deeply analyzing the complex relationship between power generation data, environmental status data and equipment status data, and makes corresponding operation and maintenance decisions based on the prediction results, thereby achieving the technical effect of improving the scientificity and rationality of operation and maintenance decisions.

[0025] In a possible implementation, the operation and maintenance management of the photovoltaic power station is performed based on the power generation prediction model, and step S550 further includes step S551, presetting a first time window and a second time window, wherein the length of the first time window is smaller than the second time window. Specifically, according to the operation and maintenance requirements of the photovoltaic power station and the characteristics of the power generation prediction model, two time windows of different lengths are defined, namely the first time window and the second time window. The two time windows can be fixed time periods (such as hours, days, weeks, etc.) or dynamically adjusted. Ensure that the length of the first time window is smaller than the length of the second time window. The first time window is used to capture rapid changes in power generation in a short period of time, and the second time window is used to predict the changing trend of power generation over a longer period of time. Step S552, based on the power generation prediction model, respectively perform power generation prediction for the first time window and the second time window to obtain a first prediction result and a second prediction result. Specifically, according to the preset first time window and second time window, prepare corresponding input data (including historical power generation data, environmental status data and equipment status data), use the power generation prediction model to predict the power generation in the first time window and the second time window respectively, record the prediction result of the first time window as the first prediction result, and record the prediction result of the second time window as the second prediction result. Step S553, if the first prediction result is greater than the first preset power generation, and the second prediction result is less than the second preset power generation, store surplus power in the first time window. Specifically, compare the first prediction result with the first preset power generation, and compare the second prediction result with the second preset power generation. These two preset power generation are thresholds set according to the operation and maintenance objectives and actual conditions of the photovoltaic power station. If the first prediction result is greater than the first preset power generation (indicating that there is expected to be surplus power in the first time window), and the second prediction result is less than the second preset power generation (indicating that power generation will decrease or be insufficient to meet demand on a longer time scale), then in the first time window, the energy storage system (such as battery packs, pumped storage power stations, etc.) is used to store the surplus power so as to provide power support when power generation is insufficient and ensure the stable operation of the photovoltaic power station. This implementation method presets time windows of different lengths and uses the power generation prediction model to perform power generation prediction on multiple time scales. By comparing the prediction results of the two time windows, the use of the energy storage system is reasonably arranged, which avoids waste or shortage of resources and achieves the technical effect of optimizing resource utilization.

[0026] In a possible implementation, the operation and maintenance management of the photovoltaic power station is performed based on the power generation prediction model, and step S550 further includes step S554, obtaining real-time power generation data, and judging whether the real-time power generation data is less than the predicted power generation data output by the power generation prediction model. Specifically, the power generation data at the current moment is obtained in real time through the monitoring system or data acquisition system (such as SCADA system) of the photovoltaic power station. At the same time, the predicted power generation data corresponding to the current moment is obtained from the power generation prediction model. The real-time power generation data is compared with the predicted power generation data, the difference or ratio between them is calculated, and a threshold value (which can be a percentage or an absolute value) is set to judge whether the real-time power generation is significantly lower than the predicted power generation. If the real-time power generation data is less than the predicted power generation data output by the power generation prediction model, and the difference exceeds the set threshold, it is judged that the power generation is insufficient. Step S555, if so, generate a photovoltaic panel cleaning strategy, and manage the photovoltaic panels based on the photovoltaic panel cleaning strategy. Specifically, once it is determined that the power generation is insufficient, and this deficiency may be caused by the accumulation of dirt on the surface of the photovoltaic panel (because dirt will reduce the conversion efficiency of the photovoltaic panel), a photovoltaic panel cleaning strategy is automatically generated. The cleaning strategy may include the cleaning schedule, cleaning method (such as manual cleaning, mechanical cleaning, water cleaning, etc.), cleaning priority (determined according to the degree of reduction in the power generation efficiency of the photovoltaic panel, location and other factors), etc. The generated cleaning strategy is communicated to the operation and maintenance team or cleaning equipment to guide the cleaning of the photovoltaic panel. During the cleaning process, the cleaning effect and power generation recovery of the photovoltaic panel are monitored in real time, and the cleaning strategy is adjusted or other measures are taken in time. The dirt on the surface of the photovoltaic panel will significantly reduce its conversion efficiency, resulting in a decrease in power generation. This implementation method achieves the technical effect of improving the overall power generation efficiency by timely cleaning the photovoltaic panel and restoring its power generation capacity.

[0027] In a possible implementation, the operation and maintenance management of the photovoltaic power station is performed based on the power generation data, the environmental status data and the equipment status data, and step S500 further includes step S560, establishing a photovoltaic equipment-fault type correspondence table, wherein each photovoltaic equipment corresponds to multiple fault types. Specifically, information of all photovoltaic equipment (such as photovoltaic panels, inverters, transformers, etc.) in the photovoltaic power station is collected, including equipment models, specifications, installation locations, etc. Based on historical experience, equipment manuals and industry standards, the possible fault types of each photovoltaic equipment are defined, such as photovoltaic panel damage, inverter overheating, cable aging, etc. A correspondence is established between each photovoltaic equipment and its multiple possible fault types, and stored in a database to form a photovoltaic equipment-fault type correspondence table. Step S570, based on the equipment fault record information and the environmental status data, a first mapping relationship between the environmental type and the fault type of the photovoltaic equipment is established. Specifically, based on the historical equipment fault records, the types and frequencies of photovoltaic equipment faults under different environmental conditions are analyzed, and based on the environmental status data and the fault record information, a mapping relationship between the environmental type (such as high temperature, high humidity, strong wind, etc.) and the photovoltaic equipment fault type is established and stored in the database. Step S580, based on the equipment fault record information and the power generation data, establish a second mapping relationship between the power generation level and the fault type of the photovoltaic device. Specifically, the power generation data and the equipment fault record are associated and analyzed to find the association between the power generation level drop or fluctuation and the equipment fault type. Based on the power generation data and the fault record information, establish a mapping relationship between the power generation level (such as a sharp drop in power generation, large power generation fluctuations, etc.) and the photovoltaic device fault type, and store it in the database. Step S590, based on the first mapping relationship and the second mapping relationship, match the real-time fault type according to the real-time environment type and the real-time power generation level, perform reverse matching of the photovoltaic device according to the real-time fault type and the photovoltaic device-fault type correspondence table, and perform maintenance and management of the photovoltaic device according to the matching result. Specifically, obtain the current environment type data and power generation data in real time, match the possible fault type according to the real-time environment type and the power generation level using the first and second mapping relationships, and reversely match the photovoltaic device that may fail according to the matched fault type and the photovoltaic device-fault type correspondence table. Based on the matching result, formulate a targeted maintenance plan, and arrange the operation and maintenance team to maintain and repair the photovoltaic device that may fail. This implementation method predicts possible failures of photovoltaic equipment in advance through correlation analysis of real-time data and historical data, and performs preventive maintenance in advance, avoiding the impact of failures on power generation. It achieves the technical effect of improving the accuracy and efficiency of photovoltaic power station operation and maintenance management through preventive maintenance.

[0028] In a possible implementation, a first mapping relationship between the environment type and the fault type of the photovoltaic device is established based on the device fault record information and the environmental status data, and step S570 further includes step S571, clustering the device fault record information to obtain multiple fault type clusters. Specifically, the device fault record information is cleaned to remove duplicate, missing or abnormal data records. Features useful for clustering analysis, such as fault description, fault code, equipment type, etc., are extracted from the fault records. Clustering algorithms, such as K-means, hierarchical clustering, etc., are used to cluster according to the features of the fault records to form multiple fault type clusters, each cluster representing one or a class of similar fault types. Step S572, according to the timestamp information of each fault record in each fault type cluster, the corresponding environmental information is filtered from the environmental status data to obtain multiple environmental condition sets corresponding to multiple fault type clusters. Specifically, for each fault record in each fault type cluster, extract its timestamp information, and according to the timestamp information, filter out the environmental information (such as temperature, humidity, wind speed, etc.) corresponding to the fault record time from the environmental state data, and group the filtered environmental information according to the fault type cluster to form multiple environmental condition sets corresponding to multiple fault type clusters. Step S573, extract environmental features from the multiple environmental condition sets to obtain multiple initial environmental types, and according to the similarity analysis results of the initial environmental types, aggregate the multiple initial environmental types to obtain multiple aggregated environmental types, wherein the number of aggregated environmental types is less than the number of initial environmental types. Specifically, extract key environmental features from each environmental condition set, such as temperature range, humidity level, wind speed range, etc., and define an initial environmental type for each environmental condition set according to the extracted environmental features. Calculate the similarity or distance between different initial environmental types, such as using cosine similarity, Euclidean distance, etc., and according to the similarity analysis results, aggregate similar initial environmental types to form a smaller number of aggregated environmental types. Step S574, based on the correspondence between the fault type cluster and the environmental condition set, establish a first mapping relationship between the aggregated environmental type and the fault type cluster. Specifically, associate each fault type cluster with the corresponding aggregated environmental type to form a mapping relationship. Since the same or similar environmental conditions are aggregated together, fault types that originally belonged to different fault type clusters but had the same environmental conditions are also aggregated together. Therefore, one aggregated environmental type can correspond to multiple fault types, and the mapping relationship is stored in a database or data structure for subsequent query and use.This implementation method groups similar fault records into fault type clusters through cluster analysis, and establishes mapping relationships with environmental conditions based on these clusters, reducing the impact of noise and abnormal data on the mapping relationship and improving the accuracy of the mapping relationship; by aggregating the same or similar environmental conditions and aggregating the corresponding fault types, when it is known that a certain aggregated environmental type will cause multiple fault types, operation and maintenance personnel can allocate operation and maintenance resources more reasonably, take preventive and response measures in advance, and reduce the impact of faults on the power generation of photovoltaic power stations.

[0029] In a possible implementation, the method further includes step S600, using a cloud platform to remotely store and access photovoltaic power station data. Specifically, real-time data, including power generation, equipment status, environmental parameters, etc., are collected from various sensors, monitoring systems, etc. of the photovoltaic power station, and the data is transmitted to a data center or edge computing node through the Internet of Things technology, and preliminary cleaning and formatting are performed to remove outliers and duplicate data. The preprocessed data is uploaded to a data storage service (such as Amazon S3, Google Cloud Storage, etc.) of the cloud platform through a secure channel (such as HTTPS), access rights and security policies are configured, and the data is transmitted to the cloud platform using an API or SDK. On the cloud platform, storage formats and policies are selected according to data types and access requirements, such as relational databases, NoSQL databases, or object storage, and at the same time, data management tools of the cloud platform are used for data governance and lifecycle management. Data access rights and authentication mechanisms are configured to ensure data security and privacy protection. Using the API or SDK provided by the cloud platform, data access applications are developed or integrated into existing systems to achieve remote access and sharing of data and support data interaction between different users or systems. This implementation method ensures the security and reliability of photovoltaic power station data through the data backup, recovery and disaster recovery capabilities of the cloud platform. Even if the local system fails, the data will not be lost and can be quickly restored from the cloud platform. Through the cloud platform, operation and maintenance personnel can access the data of the photovoltaic power station anytime and anywhere without being restricted by geographical location and network environment. At the same time, the cloud platform supports data sharing between multiple users and multiple systems, improving data utilization and collaborative work efficiency.

[0030] In the above, refer to Figure 1 The actual data management method for a new energy photovoltaic power station according to an embodiment of the present invention is described in detail. Figure 2 The present invention describes a real-time data management system for a new energy photovoltaic power station according to an embodiment of the present invention.

[0031] The real-time data management system for a new energy photovoltaic power station according to an embodiment of the present invention is used to solve the technical problem that the existing data management of new energy photovoltaic power stations lacks an effective integration and analysis mechanism, resulting in one-sided operation and maintenance decision-making and low operation and maintenance efficiency, and achieves the technical effect of improving the accuracy of operation and maintenance decision-making and operation and maintenance efficiency through comprehensive integration and association of power generation, environmental status and equipment status data. The real-time data management system for a new energy photovoltaic power station includes: a data acquisition device deployment module 10, a power generation data acquisition module 20, an environmental status data acquisition module 30, an equipment status data acquisition module 40, and a photovoltaic power station management module 50.

[0032] The data acquisition device deployment module 10 is used to deploy a power generation acquisition device, an environmental status acquisition device and an equipment status acquisition device in a photovoltaic power station; the power generation data acquisition module 20 is used to collect power generation data through the power generation acquisition device; the environmental status data acquisition module 30 is used to collect light intensity, temperature, humidity and wind speed information through the environmental status acquisition device to obtain environmental status data; the equipment status data acquisition module 40 is used to collect inverter efficiency, photovoltaic panel performance, energy storage system status and equipment fault record information through the equipment status acquisition device to obtain equipment status data; the photovoltaic power station management module 50 is used to perform operation and maintenance management of the photovoltaic power station based on the power generation data, the environmental status data and the equipment status data.

[0033] The specific configuration of the photovoltaic power station management module 50 will be described in detail below. As described above, the photovoltaic power station operation and maintenance management is performed based on the power generation data, the environmental status data and the equipment status data. The photovoltaic power station management module 50 may further include: a power generation fluctuation law determination unit for analyzing the power generation data and determining the power generation fluctuation law of the photovoltaic power station; an environment-power generation impact mapping establishment unit for analyzing the impact of light intensity, temperature, humidity and wind speed on power generation and establishing an environment-power generation impact mapping; an equipment-power generation efficiency impact mapping establishment unit for analyzing the impact of inverter efficiency, photovoltaic panel performance and energy storage system status on power generation efficiency and establishing an equipment-power generation efficiency impact mapping; a power generation prediction model establishment unit for establishing a power generation prediction model based on the power generation fluctuation law, the environment-power generation impact mapping and the equipment-power generation efficiency impact mapping; and an operation and maintenance management unit for performing operation and maintenance management of the photovoltaic power station based on the power generation prediction model.

[0034] Among them, the operation and maintenance management of the photovoltaic power station is performed based on the power generation prediction model, and the operation and maintenance management unit may further include: a time window preset subunit is used to preset a first time window and a second time window, wherein the length of the first time window is smaller than the second time window; a power generation prediction subunit is used to perform power generation prediction for the first time window and the second time window respectively based on the power generation prediction model to obtain a first prediction result and a second prediction result; a surplus power storage subunit is used to store surplus power in the first time window if the first prediction result is greater than the first preset power generation and the second prediction result is less than the second preset power generation.

[0035] Among them, the operation and maintenance management of the photovoltaic power station is carried out based on the power generation prediction model, and the operation and maintenance management unit may further include: a judgment subunit is used to obtain real-time power generation data, and judge whether the real-time power generation data is less than the predicted power generation data output by the power generation prediction model; the photovoltaic panel management subunit is used to generate a photovoltaic panel cleaning strategy if so, and manage the photovoltaic panels based on the photovoltaic panel cleaning strategy.

[0036] Among them, the operation and maintenance management of the photovoltaic power station is performed based on the power generation data, the environmental status data and the equipment status data, and the photovoltaic power station management module 50 may further include: a photovoltaic equipment-fault type correspondence table establishing unit is used to establish a photovoltaic equipment-fault type correspondence table, wherein each photovoltaic equipment corresponds to multiple fault types; a first mapping relationship establishing unit is used to establish a first mapping relationship between the environmental type and the fault type of the photovoltaic equipment according to the equipment fault record information and the environmental status data; a second mapping relationship establishing unit is used to establish a second mapping relationship between the power generation level and the fault type of the photovoltaic equipment according to the equipment fault record information and the power generation data; a photovoltaic equipment maintenance management unit is used to match the real-time fault type according to the real-time environment type and the real-time power generation level based on the first mapping relationship and the second mapping relationship, perform reverse matching of the photovoltaic equipment according to the real-time fault type and the photovoltaic equipment-fault type correspondence table, and perform maintenance management of the photovoltaic equipment according to the matching result.

[0037] Among them, according to the equipment fault record information and the environmental status data, a first mapping relationship between the environmental type and the fault type of the photovoltaic equipment is established, and the first mapping relationship establishing unit may further include: a clustering subunit is used to cluster the equipment fault record information to obtain multiple fault type clusters; a screening subunit is used to screen the corresponding environmental information from the environmental status data according to the timestamp information of each fault record in each fault type cluster, and obtain multiple environmental condition sets corresponding to the multiple fault type clusters one by one; an aggregated environmental type acquisition subunit is used to extract environmental features from the multiple environmental condition sets to obtain multiple initial environmental types, and according to the similarity analysis results of the initial environmental types, the multiple initial environmental types are aggregated to obtain multiple aggregated environmental types, wherein the number of aggregated environmental types is less than the number of initial environmental types; the first mapping relationship establishing subunit is used to establish a first mapping relationship between the aggregated environmental type and the fault type cluster according to the correspondence between the fault type cluster and the environmental condition set.

[0038] Wherein, the system may further include: a data management module for remotely storing and accessing photovoltaic power station data using a cloud platform.

[0039] The actual data management system for a new energy photovoltaic power station provided by an embodiment of the present invention can execute the actual data management method for a new energy photovoltaic power station provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0040] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0041] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for managing real-time data of a new energy photovoltaic power station, characterized in that: The method comprises: Deploy power generation collection devices, environmental status collection devices and equipment status collection devices in photovoltaic power stations; Collecting power generation data through the power generation collection device; The environmental state acquisition device collects light intensity, temperature, humidity and wind speed information to obtain environmental state data; The device status acquisition device acquires the inverter efficiency, photovoltaic panel performance, energy storage system status and equipment fault record information to obtain the equipment status data; The operation and maintenance of the photovoltaic power station is performed based on the power generation data, the environmental status data and the equipment status data.

2. The method for managing real-time data of a new energy photovoltaic power station according to claim 1, characterized in that: The operation and maintenance management of the photovoltaic power station is performed based on the power generation data, the environmental status data and the equipment status data, including: Analyze the power generation data to determine the power generation fluctuation law of the photovoltaic power station; Analyze the impact of light intensity, temperature, humidity and wind speed on power generation, and establish environment-power generation impact mapping; Analyze the impact of inverter efficiency, photovoltaic panel performance and energy storage system status on power generation efficiency, and establish equipment-power generation efficiency impact mapping; Establishing a power generation prediction model based on the power generation fluctuation law, the environment-power generation impact mapping and the equipment-power generation efficiency impact mapping; The operation and maintenance management of the photovoltaic power station is performed based on the power generation prediction model.

3. The method for managing the actual data of a new energy photovoltaic power station according to claim 2, characterized in that: The operation and maintenance management of the photovoltaic power station is performed based on the power generation prediction model, including: Preset a first time window and a second time window, wherein the length of the first time window is shorter than the second time window; Based on the power generation prediction model, respectively predicting the power generation in the first time window and the second time window to obtain a first prediction result and a second prediction result; If the first prediction result is greater than the first preset power generation, and the second prediction result is less than the second preset power generation, surplus power is stored within the first time window.

4. The method for managing real-time data of a new energy photovoltaic power station according to claim 2, characterized in that: The operation and maintenance management of the photovoltaic power station is performed based on the power generation prediction model, including: Acquire real-time power generation data, and determine whether the real-time power generation data is less than the predicted power generation data output by the power generation prediction model; If yes, a photovoltaic panel cleaning strategy is generated, and photovoltaic panel management is performed based on the photovoltaic panel cleaning strategy.

5. The method for managing real-time data of a new energy photovoltaic power station according to claim 1, characterized in that: The operation and maintenance management of the photovoltaic power station is performed based on the power generation data, the environmental status data and the equipment status data, including: Establishing a photovoltaic equipment-fault type correspondence table, wherein each photovoltaic equipment corresponds to multiple fault types; Establishing a first mapping relationship between an environment type and a fault type of a photovoltaic device according to the device fault record information and the environmental status data; Establishing a second mapping relationship between a power generation level and a fault type of a photovoltaic device according to the device fault record information and the power generation data; Based on the first mapping relationship and the second mapping relationship, the real-time fault type is matched according to the real-time environment type and the real-time power generation level, the photovoltaic equipment is reversely matched according to the real-time fault type and the photovoltaic equipment-fault type correspondence table, and the photovoltaic equipment is maintained and managed according to the matching results.

6. The method for managing the actual data of a new energy photovoltaic power station according to claim 5, characterized in that: Establishing a first mapping relationship between an environment type and a fault type of a photovoltaic device according to the device fault record information and the environmental status data includes: Clustering the equipment fault record information to obtain multiple fault type clusters; According to the timestamp information of each fault record in each fault type cluster, corresponding environmental information is filtered from the environmental status data to obtain multiple environmental condition sets corresponding to multiple fault type clusters one by one; Extracting environmental features from the multiple environmental condition sets to obtain multiple initial environmental types, and aggregating the multiple initial environmental types according to similarity analysis results of the initial environmental types to obtain multiple aggregated environmental types, wherein the number of aggregated environmental types is less than the number of initial environmental types; According to the corresponding relationship between the fault type cluster and the environmental condition set, a first mapping relationship between the aggregated environmental type and the fault type cluster is established.

7. The method for managing real-time data of a new energy photovoltaic power station according to claim 1, characterized in that: Use cloud platforms for remote storage and access of PV power plant data.

8. A real-time data management system for a new energy photovoltaic power station, characterized in that: The system is used to implement the actual data management method for a new energy photovoltaic power station according to any one of claims 1 to 7, and the system includes: A data acquisition device deployment module, the data acquisition device deployment module is used to deploy a power generation acquisition device, an environmental status acquisition device and an equipment status acquisition device in a photovoltaic power station; A power generation data collection module, the power generation data collection module is used to collect power generation data through the power generation collection device; An environmental status data acquisition module, the environmental status data acquisition module is used to collect light intensity, temperature, humidity and wind speed information through the environmental status acquisition device to obtain environmental status data; An equipment status data acquisition module, wherein the equipment status data acquisition module is used to acquire equipment status data by collecting information on inverter efficiency, photovoltaic panel performance, energy storage system status, and equipment fault records through the equipment status acquisition device; A photovoltaic power station management module is used to perform operation and maintenance management of the photovoltaic power station based on the power generation data, the environmental status data and the equipment status data.

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