Electric energy meter and concentrator control method based on distributed photovoltaics

Abstract

The present invention discloses a method for controlling an electric energy meter and a concentrator based on distributed photovoltaics, and relates to the field of electric power equipment. The method includes: collecting environmental characteristic information through a sensor array; predicting based on the environmental characteristic information to obtain a theoretical photovoltaic power generation array; receiving the actual photovoltaic power generation array through a concentrator; performing error compensation to obtain a first compensated photovoltaic power generation array; analyzing and collecting error parameter arrays, performing error compensation to obtain a second compensated photovoltaic power generation array; collecting the original photovoltaic power generation array, calculating to obtain a transmission accuracy parameter array, and calculating to obtain a collection accuracy parameter array based on the second compensated photovoltaic power generation array and the theoretical photovoltaic power generation array, and performing maintenance decision control. The method solves the technical problem of errors caused by environmental factors and self-factors in the process of data collection and communication, and achieves the technical effect of realizing the accuracy of data in a distributed photovoltaic system and the stability and reliability of system operation.

Description

Technical Field

[0001] The present application relates to the field of power equipment, and in particular to a method for controlling an electric energy meter and a concentrator based on distributed photovoltaics. Background Art

[0002] In the distributed photovoltaic system scenario, the demand for accurate control of photovoltaic power generation data and stable operation of the system is extremely prominent, and the optimization and improvement of related technologies have become crucial aspects. Traditional distributed photovoltaic management often lacks attention to environmental characteristic information and insufficient analysis and utilization. It only focuses on the power generation data itself, and the data processing between the power meter and the concentrator is relatively simple. There is a lack of effective response to errors in the data transmission and collection process, and there is a lack of scientific and reasonable basis for the evaluation and maintenance control of the equipment status in the system. There are many deficiencies in data accuracy and system reliability assurance, and it is difficult to adapt well to the actual application needs of the ever-changing distributed photovoltaic.

[0003] In the current related technologies, there are technical problems in distributed photovoltaic systems, where errors are caused by environmental factors and their own factors during data collection and communication between electricity meters and concentrators. Summary of the invention

[0004] The present application provides a method for controlling an electric energy meter and a concentrator based on distributed photovoltaics, adopts a distributed sensor array to collect environmental feature information, obtains the distribution of environmental feature information, predicts photovoltaic power generation data through a concentrator, obtains a theoretical photovoltaic power generation data array, receives an actual photovoltaic power generation data array sent by an electric energy meter array through a concentrator, analyzes to obtain a communication error parameter array, performs error compensation on the actual photovoltaic power generation data array, obtains a first compensated photovoltaic power generation data array, analyzes to obtain a collection error parameter array for an electric energy meter array, performs error compensation on the first compensated photovoltaic power generation data array, obtains a second compensated photovoltaic power generation data array, collects an original photovoltaic power generation data array, calculates to obtain a transmission accuracy parameter array in combination with the first compensated photovoltaic power generation data array, calculates to obtain a collection accuracy parameter array based on the second compensated photovoltaic power generation data array and the theoretical photovoltaic power generation data array, makes maintenance control decisions for the concentrator and the electric energy meter array, performs maintenance control on the concentrator and the electric energy meter array, realizes improving the accuracy and reliability of data through analysis and compensation measures, and provides a basis for the maintenance control of equipment, thereby achieving the technical effect of realizing the accuracy of data in a distributed photovoltaic system and the stability and reliability of system operation.

[0005] The present application provides a distributed photovoltaic based electric energy meter and concentrator control method, including:

[0006] By means of a distributed sensor array arranged in the distributed photovoltaic system, environmental characteristic information of multiple photovoltaic module locations is collected to obtain the distribution of environmental characteristic information, wherein the environmental characteristic information includes dust characteristic information, temperature characteristic information, humidity characteristic information and light characteristic information; by means of the distributed photovoltaic concentrator, photovoltaic power generation data of multiple photovoltaic modules are predicted according to the distribution of environmental characteristic information to obtain a theoretical photovoltaic power generation data array; by means of the concentrator, an actual photovoltaic power generation data array sent by an electric energy meter array arranged at the multiple photovoltaic module locations is received; according to the distribution of environmental characteristic information, an array of communication error parameters for communication between the electric energy meter array and the concentrator is obtained by analysis, and the actual photovoltaic power generation data is predicted. According to the array, error compensation is performed to obtain a first compensated photovoltaic power generation data array; according to the distribution of the environmental characteristic information, an acquisition error parameter array for photovoltaic power generation data acquisition by the electric energy meter array is analyzed and obtained, and the error compensation is performed on the first compensated photovoltaic power generation data array to obtain a second compensated photovoltaic power generation data array; the original photovoltaic power generation data array locally collected by the electric energy meter array is collected, and combined with the first compensated photovoltaic power generation data array, a transmission accuracy parameter array is calculated to obtain a collection accuracy parameter array according to the second compensated photovoltaic power generation data array and the theoretical photovoltaic power generation data array, and maintenance control decisions are made on the concentrator and the electric energy meter array, and maintenance control is performed on the concentrator and the electric energy meter array.

[0007] The distributed photovoltaic-based electric energy meter and concentrator control method proposed in this application first collects environmental feature information through the deployed distributed sensor array to obtain the distribution of environmental feature information, predicts photovoltaic power generation data through the concentrator to obtain a theoretical photovoltaic power generation data array, receives the actual photovoltaic power generation data array sent by the electric energy meter array through the concentrator, analyzes to obtain a communication error parameter array, performs error compensation on the actual photovoltaic power generation data array to obtain a first compensated photovoltaic power generation data array, analyzes to obtain a collection error parameter array for the electric energy meter array, performs error compensation on the first compensated photovoltaic power generation data array to obtain a second compensated photovoltaic power generation data array, collects the original photovoltaic power generation data array, combines the first compensated photovoltaic power generation data array to calculate the transmission accuracy parameter array, calculates the collection accuracy parameter array based on the second compensated photovoltaic power generation data array and the theoretical photovoltaic power generation data array, makes maintenance control decisions for the concentrator and the electric energy meter array, and performs maintenance control on the concentrator and the electric energy meter array, thereby achieving the technical effect of realizing the accuracy of data in the distributed photovoltaic system and the stability and reliability of system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] 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.

[0009] Figure 1 A flow chart of a method for controlling an electric energy meter and a concentrator based on distributed photovoltaics provided in an embodiment of the present application.

[0010] Figure 2 A schematic diagram of the maintenance control flow of the distributed photovoltaic-based electric energy meter and concentrator control method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] 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.

[0012] 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.

[0013] 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 thereof 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 that are 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.

[0014] The present application embodiment provides a method for controlling an electric energy meter and a concentrator based on distributed photovoltaics, such as Figure 1As shown, the method includes:

[0015] Step S100, collecting environmental characteristic information of multiple photovoltaic module locations through a distributed sensor array arranged in the distributed photovoltaic system to obtain the distribution of environmental characteristic information, wherein the environmental characteristic information includes dust characteristic information, temperature characteristic information, humidity characteristic information and light characteristic information.

[0016] Specifically, in the distributed photovoltaic system, distributed sensor arrays are pre-deployed at various key locations. The sensors have the ability to detect a variety of environmental characteristics. During the operation of the system, the sensors responsible for detecting dust characteristic information will accurately measure the dust concentration, particle size and other related data around the photovoltaic module to reflect the impact of dust on the photovoltaic module. The temperature characteristic information sensor continuously monitors the temperature changes of the environment in which the photovoltaic module is located, including real-time temperature values ​​and temperature fluctuations. The humidity characteristic information sensor focuses on measuring the humidity level of the surrounding environment to understand the potential impact of humidity conditions on the operation of the photovoltaic module. The light characteristic information sensor is responsible for capturing light. The intensity, angle, duration and other important parameters of illumination are measured, because illumination is one of the key driving factors of photovoltaic power generation. Sensors collect their corresponding environmental characteristic information and transmit the data back to the data processing center in real time. The data processing center summarizes, organizes and analyzes a large amount of data from various sensors, and constructs a detailed environmental characteristic information distribution model through comprehensive processing of different types of environmental characteristic information. The model can clearly display the specific conditions of environmental characteristics at different locations in the distributed photovoltaic system, including spatial differences and changing trends in dust, temperature, humidity and illumination, providing basic data support for subsequent various analyses and decisions based on this information.

[0017] In one possible implementation, environmental characteristic information of multiple photovoltaic module locations is collected by a distributed sensor array arranged in a distributed photovoltaic system to obtain the distribution of environmental characteristic information, wherein the environmental characteristic information includes dust characteristic information, temperature characteristic information, humidity characteristic information and light characteristic information. Step S100 further includes step S110, wherein dust concentration information, temperature information, humidity information and light information in the environment where multiple photovoltaic modules are located in the distributed photovoltaic system are collected by a distributed sensor array arranged in the distributed photovoltaic system as dust characteristic information, temperature characteristic information, humidity characteristic information and light characteristic information.

[0018] Specifically, in a distributed photovoltaic system, a distributed sensor array is arranged at each key position. The sensors can effectively monitor multiple photovoltaic modules. Among them, the sensor that collects dust concentration information measures the dust particles in the surrounding environment, obtains the dust concentration, and determines it as dust characteristic information. Through continuous monitoring, the dynamic changes of dust concentration are reflected in real time. The sensor that collects temperature information senses the ambient temperature through thermistors, thermocouples and other components, and accurately records the real-time temperature data to form temperature characteristic information. The humidity sensor detects the amount of water vapor in the environment, obtains the specific value of humidity, forms humidity characteristic information, and accurately reflects Reflecting the level of ambient humidity and possible fluctuations, the light information sensor obtains key information such as light intensity, direction, duration, etc. by sensing and measuring light, and summarizes it as light characteristic information, accurately quantifies various light parameters, and provides an important basis for subsequent analysis and application. Through the collaborative work of distributed sensors, it can comprehensively and accurately collect dust concentration information, temperature information, humidity information and light information of the location environment of multiple photovoltaic modules in distributed photovoltaics, and define it as dust characteristic information, temperature characteristic information, humidity characteristic information and light characteristic information, laying the foundation for subsequent processing and application based on this information.

[0019] Step S120, integrating the dust characteristic information, temperature characteristic information, humidity characteristic information and light characteristic information to obtain a plurality of environmental characteristic information, and combining the position information of the plurality of photovoltaic modules to construct a distribution of the environmental characteristic information.

[0020] Specifically, for dust characteristic information, specially designed dust monitoring sensors or equipment are used to continuously and accurately measure the dust conditions in the environment, including detailed information such as dust concentration level, the proportion of dust with different particle sizes, and the uniformity of dust distribution. High-precision temperature sensors are used to perform real-time monitoring at various key locations, record accurate temperature values ​​and temperature change trends over time, and obtain temperature characteristic information. Humidity characteristic information is obtained through tools such as hygrometers, covering the specific percentage of environmental humidity and humidity fluctuations. Light characteristic information is collected through special light meters, including key data such as light intensity, the proportion of light with different wavelengths, and the direction and angle of light. After collecting dust characteristic information, temperature characteristic information, humidity characteristic information, and light characteristic information, a data association model is established. For dust characteristic information, the potential association between dust concentration and data such as particle size distribution and other characteristic information is analyzed. For example, the specific relationship equation that high dust concentration may cause light intensity attenuation is determined. For temperature characteristic information, the possible thermal equilibrium relationship between it and humidity characteristic information is studied, as well as the effect of temperature changes on dust suspension state and light. The influence law of absorption is analyzed, and data preprocessing is performed. The data of each feature information is cleaned, deduplicated and normalized to ensure the quality and consistency of the data. For example, abnormal dust concentration values ​​or unreasonable temperature fluctuation data are removed. The mathematical model between dust, temperature, humidity and light characteristics is established by using the statistical method of multivariate regression analysis. The weight coefficient and interaction relationship between each feature are determined through a large number of data sample training. For example, it may be found that each temperature increase to a certain extent will cause a corresponding change in humidity. At the same time, the influence coefficient of dust concentration on light penetration is a specific value. In the actual data fusion process, each data point is comprehensively calculated according to the established model and relationship. When new dust feature information is input, the integrated environmental feature information is calculated according to the model in combination with the existing temperature, humidity and light feature information. The model is continuously verified and adjusted to adapt to the complex relationship under different scenes and environmental conditions. For example, when a higher dust concentration is detected, the light feature information is automatically adjusted according to the model to consider the weakening of its penetration; and when the temperature and humidity change significantly, the evaluation of dust status and light reception is updated synchronously.Through this iterative processing and optimization, it is ensured that the integrated environmental feature information can accurately reflect the relationship and influence between various features, obtain the location information of multiple photovoltaic modules, and achieve it through geographic positioning system, coordinate marking or other precise positioning technology. Each photovoltaic module is accurately assigned a specific location identification to clarify its specific coordinates or relative layout in the entire system or specific area. Based on the integrated multiple environmental feature information and the location information of each photovoltaic module, the distribution of environmental feature information is constructed, and various environmental features are matched and labeled according to the location of the photovoltaic module. In the distribution model, the unique environmental conditions faced by photovoltaic modules in different locations can be clearly seen. For example, photovoltaic modules in a certain area may be in an environment with more dust and higher temperature. The PV modules in one area may be in an environment with strong light but low humidity, while the PV modules in another area may be in an environment with strong light but low humidity. By constructing the distribution of environmental characteristic information, we can fully understand the characteristic distribution pattern of the environment in which the entire PV system is located from macro to micro, providing a solid data foundation for subsequent in-depth analysis, helping decision makers and technicians to better understand the impact of the environment on PV modules. For example, we can formulate targeted cleaning plans based on the distribution of environmental characteristic information, focusing on cleaning areas with more dust; or optimize the heat dissipation design and angle adjustment of PV modules based on temperature and light distribution to maximize energy conversion efficiency. We can also predict environmental change trends based on long-term monitoring and constructed distribution of environmental characteristic information, prepare countermeasures in advance, and ensure stable operation and efficient power generation of PV modules.

[0021] Step S200, using the distributed photovoltaic concentrator, according to the distribution of the environmental characteristic information, photovoltaic power generation data of multiple photovoltaic modules is predicted to obtain a theoretical photovoltaic power generation data array.

[0022] Specifically, as the core hub of the entire system, the concentrator of distributed photovoltaics first obtains the constructed environmental characteristic information distribution, which includes rich and detailed data such as dust characteristic information, temperature characteristic information, humidity characteristic information and light characteristic information of the location of each photovoltaic module. After receiving the environmental information, the concentrator conducts in-depth analysis of multiple photovoltaic modules. For each photovoltaic module, it accurately locates its specific location and related environmental conditions in the environmental characteristic information distribution. For example, a photovoltaic module may be in a specific area with high dust concentration, moderate temperature, low humidity and good light. The concentrator combines the principles of physics, the characteristics of photovoltaic power generation and historical experience data to analyze in detail the specific impact of each environmental characteristic on photovoltaic power generation. For example, high concentrations of dust may significantly reduce the effective amount of incident light, thereby affecting the efficiency of photovoltaic materials in absorbing light energy; changes in temperature may cause changes in the electrical properties of photovoltaic materials, thereby affecting power generation output; differences in humidity may affect the heat dissipation and electrical insulation of the equipment. When making prediction calculations, the concentrator adjusts various calculation parameters at the same time, flexibly optimizes the weights and coefficients in the algorithm according to the subtle changes in the actual environmental characteristics, refers to the actual power generation data of each photovoltaic module under similar environmental conditions in the past, explores the laws and trends therein, and refers to the photovoltaic power generation theory and empirical formulas widely recognized in the industry, and integrates them into the prediction process. In the continuous iteration and optimization calculation process, the concentrator generates highly accurate theoretical photovoltaic power generation data for each photovoltaic module. The data fully considers the comprehensive impact of environmental factors and the characteristics of the photovoltaic module itself. The data of each photovoltaic module are combined together to form a complete theoretical photovoltaic power generation data array. The concentrator makes accurate and reliable predictions of the future power generation of multiple photovoltaic modules based on detailed environmental feature information, which provides a valuable basis for energy planning, equipment maintenance decisions, and coordinated operation of the power grid, and can help us better tap the potential of distributed photovoltaic power generation and improve the efficiency and reliability of the entire energy system.

[0023] In one possible implementation, photovoltaic power generation data of multiple photovoltaic modules are predicted through the distributed photovoltaic concentrator according to the distribution of the environmental characteristic information to obtain a theoretical photovoltaic power generation data array, and step S200 further includes step S210, based on the photovoltaic power generation data record log of the distributed photovoltaic, multiple sample environmental characteristic information sets of multiple photovoltaic modules are collected, and multiple sample photovoltaic power generation data sets are collected.

[0024] Specifically, the work is carried out based on the photovoltaic power generation data recording log of distributed photovoltaics. The log records in detail the power generation data information of the photovoltaic system in the past period of time. For multiple photovoltaic modules, the system conducts large-scale data collection work. Through environmental monitoring equipment and sensors, the environment in which each photovoltaic module is located is comprehensively and carefully monitored to obtain multiple sample environmental characteristic information sets. The environmental characteristic information includes temperature, humidity, light intensity, wind speed, wind direction, dust concentration and other factors. The sample photovoltaic power generation data of multiple photovoltaic modules are collected simultaneously to accurately record the actual power generation data of each photovoltaic module under different environmental conditions, including key parameters such as power generation, voltage, and current. The sample photovoltaic power generation data set will be matched one by one with the corresponding environmental characteristic information set. The collection process needs to ensure the accuracy and completeness of the data. The collection work will continue at a certain time interval to timely reflect the dynamic changes of the environment and power generation conditions. The collected multiple sample environmental characteristic information sets and multiple sample photovoltaic power generation data sets will be sorted and stored by the system to form a large and orderly database. The database will become an important basis for subsequent analysis, modeling and prediction.

[0025] Step S220, using the multiple sample environmental feature information sets and the multiple sample photovoltaic power generation data sets as training supervision data, respectively, training to obtain multiple photovoltaic power generation prediction branches for predicting multiple photovoltaic module power generation data, and integrating to obtain a photovoltaic power generation predictor.

[0026] Specifically, a set of environmental characteristic information and a set of photovoltaic power generation data of multiple photovoltaic modules are collected and obtained through sensors, monitoring equipment or other data collection methods. The environmental characteristic information may include but is not limited to light intensity, temperature, humidity, weather conditions, etc., and the photovoltaic power generation data is the corresponding power generation or power generation. The collected data is preprocessed, including data cleaning, outlier processing, data normalization, etc., to improve the quality and reliability of the data and reduce the impact of noise and outliers on model training. Meaningful features are extracted from the environmental characteristic information through methods such as feature selection, feature construction or feature transformation. Important features related to photovoltaic power generation are selected and converted into a form suitable for model input. An optimal straight line is constructed to associate environmental features with power generation data. A set of environmental characteristic data (such as light intensity) and the corresponding photovoltaic power generation data are calculated by the least squares method to minimize the sum of squares of the errors between the actual photovoltaic power generation data value and the photovoltaic power generation data value predicted by the regression line, and the slope and intercept of the regression line are determined. Similarly, a large number of environmental characteristic information such as light intensity, temperature, humidity, etc. are used as input vectors, and the corresponding photovoltaic power generation data is used as the input vector. The electric data is taken as the expected output value. During the training process, the regression coefficient is continuously optimized to better fit the data. This is repeated until the model achieves good performance and avoids overfitting of the model to the data. Regularization methods are introduced to constrain the parameters to ensure that the model has a certain generalization ability. The trained model is evaluated using a test set or a validation set. Evaluation indicators such as mean square error and mean absolute error are calculated to evaluate the performance of the model. According to the evaluation results, the model is optimized and adjusted, such as selecting a more appropriate model structure, adjusting parameters, increasing training data, etc. Multiple models are trained and integrated or fused to improve the accuracy and reliability of the prediction. The prediction results of multiple models are combined using an integrated learning method. The trained model is applied to the actual photovoltaic power generation system. Environmental feature information is input in real time to predict photovoltaic power generation. The model is updated and adjusted according to the actual power generation data to adapt to environmental changes and the dynamic characteristics of the system. The prediction results are monitored and analyzed and compared with the actual photovoltaic power generation data. According to the monitoring results, deviations and problems of the model are discovered in time, and corresponding measures are taken to adjust and improve them.

[0027] Step S230: Based on the photovoltaic power generation predictor, photovoltaic power generation prediction is performed on the environmental characteristic information distribution to obtain a plurality of predicted photovoltaic power generation data, and a theoretical photovoltaic power generation data array is constructed.

[0028] Specifically, the distribution of environmental characteristic information is input into the predictor, and the predictor will perform calculations and inferences based on the relationship pattern between environmental characteristics and photovoltaic power generation, taking into account the specific values ​​and combinations of various environmental characteristics. For example, environmental factors such as light intensity, temperature, and humidity will be analyzed and processed one by one. Through comprehensive consideration of the information, the predictor uses its internal algorithms and parameters to calculate the corresponding photovoltaic power generation prediction value. With the input of different environmental characteristic information distributions, the predictor will output multiple predicted photovoltaic power generation data in turn. The data represents the amount of electricity that the photovoltaic power generation system may generate under different environmental conditions, and constructs a theoretical photovoltaic power generation data array. The array stores this data in chronological order and in different combinations of environmental characteristics. By constructing a theoretical photovoltaic power generation data array, the potential situation of photovoltaic power generation can be observed and analyzed more systematically and clearly. For example, this array can be used to evaluate the impact of different environmental conditions on photovoltaic power generation, or to plan the optimal photovoltaic power generation system operation strategy, which provides important basic data for subsequent research and improvement, so that we can continuously optimize and improve the photovoltaic power generation predictor to improve its prediction accuracy and reliability.

[0029] Step S300: receiving, through the concentrator, an array of actual photovoltaic power generation data sent by an array of electric energy meters arranged at the locations of the plurality of photovoltaic modules.

[0030] Specifically, the concentrator receives data from various important nodes. An array of electric energy meters is deployed at the locations of multiple photovoltaic modules. The electric energy meters measure and record the actual electric energy data generated by each photovoltaic module. Each electric energy meter corresponds to a specific photovoltaic module and monitors the power generation of the module in real time. The electric energy meter continuously collects actual photovoltaic power generation data and transmits it in a specific format and signal. The data carries the power generation performance of the photovoltaic module at different times and under different environmental conditions. When the signal sent by the electric energy meter reaches the concentrator, the concentrator efficiently connects and communicates with many electric energy meters through the communication interface. The concentrator identifies and parses to ensure the integrity and accuracy of the data, and successfully receives and processes the data from each electric energy meter. The actual photovoltaic power generation data is integrated into an orderly array of actual photovoltaic power generation data. The array arranges the power generation data of each photovoltaic module according to the position sequence and time sequence of the photovoltaic modules, so as to clearly understand the power generation conditions of modules in different positions and the changing trend of power generation data over time. The actual photovoltaic power generation data array becomes an important basis for subsequent analysis, evaluation and decision-making, helping to understand the actual operating conditions of the entire photovoltaic power generation system, discover the power generation differences between different modules, and promptly discover possible problems or abnormal situations, providing a basis for comparing and analyzing theoretical photovoltaic power generation data, so as to further optimize and improve the performance and efficiency of the photovoltaic power generation system, and ensure the accurate collection and effective integration of actual photovoltaic power generation data.

[0031] Step S400, analyzing and obtaining a communication error parameter array between the electric energy meter array and the concentrator according to the distribution of the environmental characteristic information, performing error compensation on the actual photovoltaic power generation data array, and obtaining a first compensated photovoltaic power generation data array.

[0032] Specifically, based on the distribution of environmental characteristic information, the possible impact of environmental factors on the communication process between the electric energy meter array and the concentrator is studied. Considering that signal transmission may be interfered, attenuated or have other uncertainties under different environmental conditions, the potential effect of each environmental characteristic factor on communication is evaluated one by one, and combined with past empirical data and theoretical knowledge, the communication error parameter array is calculated. Each element in the array represents the communication error parameter that may appear under a specific combination of environmental characteristics. This communication error parameter array is corresponded to the actual photovoltaic power generation data array. According to the specific value of the error parameter, each data in the actual photovoltaic power generation data array is accurately compensated for the error. In the compensation process, we make corresponding adjustments to the actual power generation data according to the nature and size of the communication error. Through the compensation operation, we strive to eliminate or reduce the impact of the communication error on the actual power generation data. After processing and compensation one by one, the first compensated photovoltaic power generation data array is finally obtained. Compared with the original actual photovoltaic power generation data array, it is closer to the actual power generation situation, reduces the deviation and uncertainty caused by the communication error, and effectively improves the accuracy and reliability of the actual photovoltaic power generation data.

[0033] In one possible implementation, based on the distribution of the environmental characteristic information, a communication error parameter array for communication between the electric energy meter array and the concentrator is analyzed and obtained, error compensation is performed on the actual photovoltaic power generation data array to obtain a first compensated photovoltaic power generation data array, and step S400 further includes step S410, collecting multiple sample environmental characteristic information sets and multiple communication error parameter sets based on the data transmission history between the concentrator and the electric energy meter array.

[0034] Specifically, the data transmission history between the concentrator and the electric energy meter array is screened and sorted. The historical records cover the data transmission situations at different time nodes and environmental conditions in the past. Typical time periods and scenes are selected from them to obtain multiple samples. Each sample contains a specific set of environmental feature information and a corresponding set of communication error parameters. For the collection of each sample environmental feature information set, it is necessary to deeply explore various environmental factors related to the data transmission history, including the weather conditions at that time, such as temperature, humidity, light intensity, air pressure, etc., as well as other characteristics of the surrounding environment, such as terrain, building shielding conditions, etc. These environmental feature information are sorted and summarized one by one to form a A specific set of sample environmental characteristic information is obtained. For each sample, the communication error parameters presented under the environmental characteristic information are obtained, involving aspects such as signal strength attenuation, data transmission delay, bit error rate, etc. With the help of in-depth analysis of the data transmission history, the communication error parameter set corresponding to each sample is accurately determined. To ensure the diversity and representativeness of the samples, efforts are made to cover different environmental conditions, different data transmission loads and different time stages, so that the multiple samples collected can comprehensively and accurately reflect the various actual conditions of data transmission between the concentrator and the electric energy meter array, and finally multiple sample environmental characteristic information sets and corresponding multiple communication error parameter sets are obtained.

[0035] Step S420, using the multiple sample environment feature information sets and the multiple communication error parameter sets, training multiple communication error analysis branches, and integrating to obtain a communication error analyzer.

[0036] Specifically, the acquired multiple sample environmental feature information sets and multiple communication error parameter sets constitute data resources, covering the specific manifestations of communication errors corresponding to various combinations of environmental features. For the training of multiple communication error analysis branches, for each branch, a part of the sample environmental feature information set and the corresponding communication error parameter set are used as input data, and the branch gradually learns the potential relationship pattern between environmental feature information and communication error. In the training process, each branch continuously adjusts its own parameters and model structure to achieve the purpose of better fitting the input data, thereby improving its analysis and prediction capabilities for communication errors. After data iterative optimization, each branch gradually forms different analysis logics and capabilities for communication errors. After training multiple communication error analysis branches, an integration operation is performed, and the characteristics and advantages of each branch are comprehensively considered. Integration strategies such as weighted average and voting mechanism are used. The communication error analyzer finally obtained has the significant advantage of integrating the capabilities of multiple branches. According to the input new environmental feature information, the possible communication error situations are quickly and accurately analyzed, and a powerful communication error analyzer is constructed, which provides reliable tools and technical support for the subsequent accurate prediction and effective response to communication errors in actual application scenarios.

[0037] Step S430: Based on the communication error analyzer, communication error analysis is performed on the distribution of the environmental characteristic information to obtain a plurality of communication error parameters, and a communication error parameter array is constructed.

[0038] Specifically, based on the constructed communication error analyzer, a specific environmental characteristic information distribution is input into the communication error analyzer, and the analyzer analyzes and processes the environmental characteristic information. The analyzer uses historical data experience based on the combination and characteristics of the environmental characteristic information to infer the communication error situation and obtain communication error parameters. The parameters reflect the various error manifestations that occur in communication under a given environmental characteristic information distribution, such as the degree of signal attenuation, the size of transmission delay, the bit error rate, etc. The obtained communication error parameters are arranged and combined in chronological order to construct a communication error parameter array. The communication error parameter array presents the communication error under a specific environmental characteristic information distribution, contains specific error parameter values, and reflects the mutual relationship and overall characteristics between the parameters. The communication error parameter array that can comprehensively reflect the communication status is obtained, which provides an important data foundation and basis for evaluating communication quality, formulating optimization strategies, and performing fault diagnosis.

[0039] Step S440 , performing one-to-one error compensation processing on the actual photovoltaic power generation data array according to the communication error parameter array to obtain a first compensated photovoltaic power generation data array.

[0040] Specifically, the communication error parameter array obtained explicitly includes a series of communication error parameters obtained by analyzing the distribution of information of specific environmental characteristics. The parameters accurately reflect various error situations that may occur in the communication process, and the actual photovoltaic power generation data array is placed in the analysis field of view. The actual photovoltaic power generation data array is a set of original data collected through the actual power generation process, and a one-to-one error compensation process is performed. For each data element in the actual photovoltaic power generation data array, an accurate compensation operation is performed according to the corresponding communication error parameter in the communication error parameter array. In the compensation process, it is necessary to use a corresponding algorithm or calculation formula to determine the amount and method of compensation according to the specific error type and degree. For example, the data error caused by signal attenuation is enhanced according to a certain proportion. The problem caused by transmission delay needs to be adjusted and corrected in time according to the delay time. By performing such a detailed error compensation process on each data element in the actual photovoltaic power generation data array one by one, a first compensated photovoltaic power generation data array is gradually constructed. After correction and adjustment, the influence of communication errors on photovoltaic power generation data is eliminated or reduced. The first compensated photovoltaic power generation data array finally obtained reflects the actual situation of photovoltaic power generation more truly and accurately than the original actual photovoltaic power generation data array.

[0041] In a possible implementation, according to the communication error parameter array, the actual photovoltaic power generation data array is subjected to one-to-one error compensation processing to obtain a first compensated photovoltaic power generation data array, and step S440 further includes step S441, obtaining a mapping relationship between the communication error parameter array and the actual photovoltaic power generation data array.

[0042] Specifically, the communication error parameter array and the actual photovoltaic power generation data array are analyzed and studied to obtain the characteristics, structure and mutual correlation of the elements in the two arrays. Through the comparison and correlation analysis of a large number of data points, the potential mapping law between the two is calculated, and the position, value and other relevant attributes of each element in the two arrays are compared and matched one by one. The iterations and optimizations are continuously performed, and the preliminary determined mapping relationship is verified and adjusted. The mapping relationship is further corrected and improved through testing and feedback of actual data, and finally a clear and accurate mapping relationship between the communication error parameter array and the actual photovoltaic power generation data array is clarified and obtained.

[0043] Step S442: According to the mapping relationship, using the communication error parameter, a compensation calculation is performed on the actual photovoltaic power generation data array to obtain the first compensated photovoltaic power generation data array.

[0044] Specifically, based on the mapping relationship, the communication error parameters corresponding to each element in the actual photovoltaic power generation data array are accurately selected from the communication error parameter array. The communication error parameters cover various types of error information that may be generated in the communication process, such as signal attenuation degree and delay amount. For each element in the actual photovoltaic power generation data array, compensation calculation is carried out according to the corresponding selected communication error parameters. For the type of data loss caused by signal attenuation, it is assumed that the communication error parameter indicates that the signal attenuation rate is x%, and the value of an element in the actual photovoltaic power generation data array is A. The formula A×(1+x%) is used to perform numerical increment operations. For the data loss caused by transmission delay If the delay time is t seconds, the timestamp corresponding to the element in the actual photovoltaic power generation data array is T, and the time dimension is adjusted and corrected according to the delay time, and the timestamp corresponding to the element is corrected to T+t seconds. For different types of communication errors, an appropriate compensation method should be adopted to obtain a more reliable first compensated photovoltaic power generation data array. The compensation calculation operation is performed on all elements in the actual photovoltaic power generation data array one by one, and finally the first compensated photovoltaic power generation data array is obtained. Compared with the original actual photovoltaic power generation data array, the array has effectively eliminated or reduced the impact of the communication error and is closer to the real and accurate photovoltaic power generation data.

[0045] Step S500, analyzing and obtaining a collection error parameter array for photovoltaic power generation data collection by the electric energy meter array according to the distribution of the environmental characteristic information, performing error compensation on the first compensated photovoltaic power generation data array, and obtaining a second compensated photovoltaic power generation data array.

[0046] Specifically, based on the distribution of environmental characteristic information, the key factors and rules that may affect the photovoltaic power generation data collection process implemented by the electric energy meter array are analyzed. For example, through analysis, it is found that every time the light intensity decreases to a certain extent, the collection error will increase by a certain proportion. Based on the analysis results of the environmental impact, the collection error parameter array is further calculated and established. Each element in the array represents the error amount or error type that occurs when the electric energy meter collects photovoltaic power generation data under specific environmental conditions. The first compensated photovoltaic power generation data array and the collection error parameter array are corresponded to each other. For each data point in the first compensated photovoltaic power generation data array, according to the relative The corresponding acquisition error parameters are used to carry out precise error compensation calculations. Assuming a certain element value in the first compensated photovoltaic power generation data array and the corresponding acquisition error parameter, the compensated value is the element value plus the acquisition error parameter. In the compensation process, appropriate compensation strategies are adopted according to different error parameter types. For example, for an error of fixed size caused by a certain environmental factor, the corresponding value is directly increased or decreased. The compensation operation is performed one by one for each data in the first compensated photovoltaic power generation data array. Through calculation and adjustment, the influence of the acquisition error caused by environmental factors is eliminated or reduced, and finally a more accurate and reliable second compensated photovoltaic power generation data array is obtained.

[0047] In one possible implementation, based on the distribution of the environmental characteristic information, an array of collection error parameters for collecting photovoltaic power generation data by the electric energy meter array is analyzed and obtained, error compensation is performed on the first compensated photovoltaic power generation data array to obtain a second compensated photovoltaic power generation data array, and step S500 further includes step S510, collecting multiple sets of sample environmental characteristic information based on the photovoltaic power generation data collection history of multiple electric energy meters in the electric energy meter array, and processing to obtain multiple sets of sample collection error parameters based on the errors between the photovoltaic power generation data collected under different sample environmental characteristic information and the actual photovoltaic power generation data.

[0048] Specifically, multiple electric energy meters in the electric energy meter array are analyzed one by one. Assuming there are N electric energy meters, for each electric energy meter, M representative time periods are selected from its photovoltaic power generation data collection history to obtain N×M data sample points. The sample points are classified according to environmental characteristics. For example, according to factors such as weather conditions (sunny, cloudy, rainy, etc.), time intervals (morning, afternoon, evening, etc.), etc., they are divided into multiple sample environmental characteristic information sets. For each sample environmental characteristic information set, the error between the photovoltaic power generation data collected under the environmental characteristics and the actual photovoltaic power generation data is calculated. Assuming a certain sample environment The photovoltaic power generation data collected in the feature information set is Pi (i=1,2,3,...,n), and the corresponding actual photovoltaic power generation data is Qi (i=1,2,3,...,n). Then the error Ei=Pi-Qi. All the data in the set are calculated to get a series of error values. The error values ​​are further processed and analyzed, the average value of the error is calculated, the variance of the error is calculated, and the characteristics of the error are described from multiple aspects, so as to get multiple sample collection error parameter sets, which help to understand the accuracy and deviation of data collection under the environmental characteristics and provide a basis for subsequent improvement and optimization.

[0049] Step S520: respectively use the multiple sample environment feature information sets and the multiple sample acquisition error parameter sets to train multiple acquisition error identification branches to obtain an acquisition error identifier.

[0050] Specifically, for each sample environment feature information set, it is regarded as a set of input data. Different sample environment feature information sets correspond to multiple sample acquisition error parameter sets, and the acquisition error recognition branch is trained. Taking the first branch as an example, the sample environment feature information is used as input, and the various error parameters in the sample acquisition error parameters are used as expected outputs. During the training process, the parameters of the branch model are continuously adjusted so that the model can output the error characteristics corresponding to the sample acquisition error parameters as accurately as possible for the input sample environment feature information. Specifically, the branch model adopts a neural network structure. In each training iteration, the difference between the model output and the actual error parameter is calculated. Through the back propagation algorithm, this error value is back-propagated back to the model to update the parameters of the model so that the next output is closer to the actual value. Similar training is performed on each branch. After a large amount of training data and iterations, each branch gradually learns the mapping relationship between different sample environment features and acquisition error parameters, and finally combines to form an acquisition error identifier. The acquisition error identifier can quickly and accurately identify the corresponding acquisition error parameters through the synergy of each branch, thereby realizing effective identification and judgment of acquisition errors, providing an important basis for subsequent analysis and processing.

[0051] Step S530: Based on the acquisition error identifier, acquisition error identification is performed on the distribution of the environmental feature information to obtain a plurality of acquisition error parameters, and an acquisition error parameter array is constructed.

[0052] Specifically, the distribution of environmental feature information is obtained, which includes different specific environmental feature information. The environmental feature information is input into the acquisition error identifier one by one. The acquisition error identifier performs calculations based on its internally trained model and parameters. The identifier obtains the acquisition error parameters for the specific environmental feature information through calculation, which involves multiple values, such as the size of the error and the direction of the error. This process is repeated until all the environmental feature information is processed, and all the acquired acquisition error parameters are arranged in a certain order and structure to form an acquisition error parameter array. The array reflects the acquisition error corresponding to each point in the distribution of environmental feature information, thereby realizing accurate acquisition error identification of the distribution of environmental feature information and constructing an acquisition error parameter array with practical significance.

[0053] Step S540: Based on the acquisition error parameter array, compensation processing is performed on a plurality of first compensated photovoltaic power generation data in the first compensated photovoltaic power generation data array to obtain the second compensated photovoltaic power generation data array.

[0054] Specifically, an acquisition error parameter array is obtained, which includes a series of acquisition error parameters related to environmental characteristics, and a first compensated photovoltaic power generation data array is obtained, in which there are multiple first compensated photovoltaic power generation data. For each data in the first compensated photovoltaic power generation data array, according to the corresponding environmental characteristics, the acquisition error parameters matching therewith in the acquisition error parameter array are found, and the compensated data is the data in the first compensated photovoltaic power generation data array plus the corresponding acquisition error parameters. Each first compensated photovoltaic power generation data is processed one by one to obtain a series of compensated second compensated photovoltaic power generation data, and this process is repeated to traverse all the data in the first compensated photovoltaic power generation data array to finally obtain a complete second compensated photovoltaic power generation data array. The first compensated photovoltaic power generation data array is successfully compensated based on the acquisition error parameter array to obtain a more accurate second compensated photovoltaic power generation data array that is more in line with the actual situation, thereby providing a more reliable data basis for subsequent analysis, application or further processing.

[0055] Step S600, collect the original photovoltaic power generation data array collected locally by the electric energy meter array, combine it with the first compensated photovoltaic power generation data array, calculate to obtain the transmission accuracy parameter array, calculate to obtain the collection accuracy parameter array according to the second compensated photovoltaic power generation data array and the theoretical photovoltaic power generation data array, make maintenance control decisions for the concentrator and the electric energy meter array, and perform maintenance control on the concentrator and the electric energy meter array.

[0056] Specifically, the original photovoltaic power generation data array locally collected by the electric energy meter array is collected to obtain the first compensated photovoltaic power generation data array. By comparing the original photovoltaic power generation data array and the first compensated photovoltaic power generation data array, the difference of corresponding elements, etc., the transmission accuracy parameter array is calculated, wherein each parameter reflects the accuracy-related index in the data transmission process. The second compensated photovoltaic power generation data array and the theoretical photovoltaic power generation data array are obtained. By comparing the difference, similarity, etc. between the second compensated photovoltaic power generation data array and the theoretical photovoltaic power generation data array, the acquisition accuracy parameter array is calculated. The elements of the array reflect the degree of conformity between the actual collected data and the theoretical data. After obtaining the transmission accuracy parameter array and the acquisition accuracy parameter array, the maintenance control decision of the concentrator and the electric energy meter array is made according to the values ​​of these parameters. If some parameters in the transmission accuracy parameter array indicate that there is a large deviation or abnormality in the transmission process, or the parameters in the acquisition accuracy parameter array show that there is a problem in the acquisition link, it is necessary to perform maintenance and control on the concentrator and the electric energy meter array. The specific maintenance includes inspection and maintenance of the communication line, data processing module, etc. of the concentrator, and testing and calibration of the sensor accuracy and data acquisition circuit of the electric energy meter array.

[0057] In one possible implementation, Figure 2 As shown, the original photovoltaic power generation data array locally collected by the electric energy meter array is collected, combined with the first compensated photovoltaic power generation data array, and the transmission accuracy parameter array is calculated. According to the second compensated photovoltaic power generation data array and the theoretical photovoltaic power generation data array, the collection accuracy parameter array is calculated to make maintenance control decisions for the concentrator and the electric energy meter array, and the concentrator and the electric energy meter array are maintained and controlled. Step S600 further includes step S610, collecting the original photovoltaic power generation data array locally collected by the electric energy meter array.

[0058] Specifically, an array of electric energy meters that need to be collected data is determined. The electric energy meters are distributed in a specific area or system and are used to measure and record photovoltaic power generation data. A connection is established with the electric energy meter array through an interface. The data collection operation can be started through a wired connection, such as a data line, a communication cable, etc. The collection device sends a command to the electric energy meter array to transmit the original photovoltaic power generation data collected locally. After the electric energy meter receives the command, the original photovoltaic power generation data stored therein is sorted and prepared for transmission. The original photovoltaic power generation data is stored in a specific format, such as a digital signal or a specific data encoding. The electric energy meter transmits the original data to the collection device in sequence according to the prescribed protocol and format. The collection device receives the original photovoltaic power generation data from the electric energy meter in real time, stores and records it, and combines it into a complete original photovoltaic power generation data array. The collected data is preliminarily verified and checked to ensure its validity and integrity, thereby completing the collection process of the original photovoltaic power generation data array collected locally by the electric energy meter array.

[0059] Step S620: calculating and obtaining a transmission accuracy parameter array of photovoltaic power generation data transmission according to the original photovoltaic power generation data array and the first compensated photovoltaic power generation data array.

[0060] Specifically, an original photovoltaic power generation data array and a first compensated photovoltaic power generation data array are obtained. The original photovoltaic power generation data array contains unprocessed original data collected locally by the electric energy meter array, while the first compensated photovoltaic power generation data array is related data after a certain compensation process. Each pair of corresponding elements in the original photovoltaic power generation data array and the first compensated photovoltaic power generation data array is analyzed, and the difference or correlation between the two is evaluated by difference calculation, ratio calculation or other specific algorithms. After further processing and statistics, a series of parameters reflecting the accuracy of photovoltaic power generation data transmission are calculated. The parameters include the degree of deviation of the data, consistency index, and error range. For example, for each corresponding original data and compensated data, the absolute difference between them is calculated, and then all these differences are summarized and analyzed to obtain parameters such as average difference and maximum difference. The parameters reflect the possible fluctuation of data during transmission. The ratio of original data to compensated data is calculated, and the distribution of the ratio is observed, so as to determine whether there are problems such as data scaling or distortion during transmission. By continuously performing such detailed comparison and calculation on the original photovoltaic power generation data array and the first compensated photovoltaic power generation data array, a transmission accuracy parameter array is finally formed.

[0061] Step S630: Calculate and obtain a collection accuracy parameter array for photovoltaic power generation data collection according to the second compensated photovoltaic power generation data array and the theoretical photovoltaic power generation data array.

[0062] Specifically, the second compensated photovoltaic power generation data array and the theoretical photovoltaic power generation data array are clearly defined, and each data in the second compensated photovoltaic power generation data array is compared with the data at the corresponding position in the theoretical photovoltaic power generation data array in turn, and the difference, relative error, absolute error, etc. between the two are calculated, and the comparison results are further analyzed and processed, and these differences or errors are converted into specific parameters that can reflect the accuracy of the acquisition, for example, the average error, maximum error, standard deviation of the error, etc. are calculated, and all these parameters are sorted and combined to form an acquisition accuracy parameter array, and each element in the array represents a measure of the accuracy of photovoltaic power generation data acquisition in a certain aspect or dimension, and different weights or importance factors are considered to more accurately reflect the actual acquisition accuracy. For example, a higher weight is given to the accuracy of certain key data points or time periods, and the above steps are repeated to comprehensively compare and calculate all data in the second compensated photovoltaic power generation data array and the theoretical photovoltaic power generation data array, and finally a complete acquisition accuracy parameter array is obtained.

[0063] Step S640: making communication transmission maintenance control decisions for the concentrator and the electric energy meter array according to the transmission accuracy parameter array, and obtaining a communication maintenance control strategy set.

[0064] Specifically, the transmission accuracy parameter array is analyzed. Each parameter in the array reflects the specific accuracy index of photovoltaic power generation data during the transmission process. The communication transmission status between the current concentrator and the electric energy meter array is judged according to the specific value and change trend of the transmission accuracy parameter. If some parameters exceed the preset normal range or show abnormal fluctuations, it indicates that there may be problems with the communication transmission. Based on the evaluation of the transmission accuracy parameters, maintenance control decisions are made. For the concentrator, consider checking the communication line between it and the electric energy meter array to see if the line is loose, damaged or interfered. Check whether the communication module of the concentrator itself is working properly, including aspects such as signal sending and receiving capabilities. The decision process In the process, various factors are comprehensively considered, such as the severity of the problem, the possible scope of impact, etc., to determine the specific maintenance measures and priorities. According to the decision, a communication maintenance control strategy set is formed. The strategy set includes specific maintenance action plans for the concentrator and the electric energy meter array, such as when to carry out maintenance, which methods and tools to use, which personnel are required to participate, etc., including strategies such as testing and repairing communication lines, inspecting and maintaining concentrator and electric energy meter hardware, and updating or optimizing software. The transmission accuracy parameter array is used to guide the communication transmission maintenance control decision of the concentrator and the electric energy meter array, so as to obtain a comprehensive and effective communication maintenance control strategy set to ensure the communication transmission quality and reliability of the entire system.

[0065] Step S650: performing collection detection control decisions for the electric energy meter array according to the collection accuracy array to obtain a collection maintenance control strategy set.

[0066] Specifically, the acquisition accuracy-related information represented by each element in the acquisition accuracy array is analyzed, including the degree of data deviation, the difference from the expected value, etc. Based on the interpretation of the acquisition accuracy array, the actual acquisition situation of the electric energy meter array is evaluated. If it is found that some elements show large deviations or anomalies, it indicates that there may be problems with the acquisition of the electric energy meter array. The acquisition detection control decision is made, which involves a separate analysis of each electric energy meter in the electric energy meter array. For example, for an electric energy meter that shows an abnormality, consider checking whether the accuracy of its sensor has decreased, whether there is a fault in the measurement circuit, or whether it is affected by external interference and other factors. According to the problem, Determine the priority and focus of maintenance based on the specific situation. If the problems of some electricity meters are more serious and may have a greater impact on the overall data, they will be listed as priority maintenance objects. In the decision-making process, other relevant factors should also be considered comprehensively, such as the service life of the electricity meter, the working environment, etc., in order to formulate a more reasonable maintenance strategy. Through the above analysis and decision-making, a collection and maintenance control strategy set is formed. The strategy set contains specific maintenance action plans for each electricity meter in the electricity meter array, including specific measures such as calibration of specific electricity meters, replacement of sensors, and repair of circuits. At the same time, the time nodes, tools and technical resources required for the implementation of these measures are clearly defined.

[0067] Step S660: Use the communication maintenance control strategy set and the maintenance control strategy set to perform maintenance control on the concentrator and the electric energy meter array.

[0068] Specifically, obtain the communication maintenance control strategy set and the collection maintenance control strategy set. According to the relevant strategies in the communication maintenance control strategy set, if the strategy indicates that the communication line needs to be checked, then arrange for professionals to carefully check the lines connecting the concentrator and the electric energy meter array to check whether there are physical damage, poor contact or signal interference, and make repairs or adjustments in time. If it involves the problem of the concentrator's own communication module, such as software failure or hardware performance degradation, perform corresponding software updates, module debugging or replacement operations in accordance with the strategy requirements. For the maintenance control of the electric energy meter array, it is executed according to the collection maintenance control strategy set. If the strategy stipulates that certain electric energy meters must be calibrated, use professional calibration equipment and technology to accurately adjust the measurement parameters of the electric energy meters to restore them to an accurate collection state. If a key component of an electricity meter needs to be replaced, such as a sensor or circuit element, the operation must be done carefully to ensure that the replaced electricity meter can work normally and collect accurate data. For each maintenance task, detailed records must be kept, including the time, location, specific operation content and results of the maintenance. In the process of implementing maintenance control, the maintenance effect must be monitored and evaluated in real time. If new problems or situations are found during the maintenance process that are not in line with expectations, timely adjustments must be made to the strategy or additional measures must be taken to resolve them.

[0069] In one possible implementation, communication transmission maintenance control decisions are made for the concentrator and the electric energy meter array based on the transmission accuracy parameter array to obtain a communication maintenance control strategy set. Step S640 further includes step S641, collecting a sample transmission accuracy parameter set based on the photovoltaic power generation data transmission history of the electric energy meter array and the concentrator, and collecting communication transmission detection schemes formulated under different sample transmission accuracy parameters to obtain a sample communication maintenance control strategy set. The sample communication maintenance control strategy includes a scheme for maintenance control of the communication modules of the concentrator and the electric energy meter.

[0070] Specifically, we conducted an in-depth study of the photovoltaic power generation data transmission history between the electric energy meter array and the concentrator. By combing and analyzing a large amount of historical data, we extracted representative sample data segments. For each sample data segment, we carefully calculated and determined its transmission accuracy parameters, which involved quantitative calculations of evaluation indicators such as data integrity, consistency, and timeliness. For each different sample transmission accuracy parameter, we reviewed and organized the communication transmission detection plan formulated at that time, including various measures such as transmission line detection, signal strength testing, and communication protocol verification. Based on the detection plan and transmission accuracy parameters, we comprehensively formed a set of sample communication maintenance control strategies. For the communication module maintenance control plan of the concentrator, we checked whether its hardware connection was firm, whether there was hardware failure or aging, and its internal communication chip, Circuits are inspected and maintained. If problems are found, relevant parts are repaired or replaced in time. Similar maintenance and control are also carried out on the communication module of the electric energy meter to check whether the interface connected to the concentrator is normal, whether the software settings of the communication module are reasonable, and whether software updates or optimizations are needed. Transmission accuracy parameters and maintenance control strategies under different samples are constantly compared and analyzed to summarize the commonalities and rules, and form standardized maintenance control processes and methods so that similar situations can be handled quickly and effectively in the future. An effective monitoring and feedback mechanism is established to track and evaluate the implementation effect of these communication maintenance and control strategies at any time. If deficiencies are found in the strategies, they are adjusted and improved in time to ensure that the photovoltaic power generation data transmission between the electric energy meter array and the concentrator always maintains high accuracy and reliability.

[0071] Step S642, using the sample transmission accuracy parameter set and the sample communication maintenance control strategy set, based on a decision tree, construct a communication maintenance control decision maker.

[0072] Specifically, the acquired sample transmission accuracy parameter set is analyzed, the transmission conditions and characteristics represented by different parameter values ​​are clarified, the sample communication maintenance control strategy set is studied in depth, the specific problems and measures targeted by each strategy are solved, and the communication maintenance control decision maker is constructed using the decision tree algorithm. The sample transmission accuracy parameters are used as the input nodes of the decision tree. According to the combination of different parameter values, different paths are gradually branched out. In the branching process, the corresponding sample communication maintenance control strategy is associated with each path. For example, if a certain parameter combination indicates the existence of a specific type of transmission problem, then the branch points to the corresponding maintenance control strategy. By continuously adding nodes and branches, a complete decision tree structure is constructed. The decision tree can quickly find the corresponding communication maintenance control strategy along the corresponding path according to the input transmission accuracy parameters. In the construction During the process, various possible situations and parameter changes should be fully considered to ensure the comprehensiveness and accuracy of the decision tree, and the decision tree should be optimized and adjusted. For example, some unnecessary branches can be removed or similar strategies can be merged to improve the efficiency and ease of use of the decision maker. After the decision tree is built, it can be applied to actual communication maintenance control. When new transmission accuracy parameters are input, the decision maker can quickly output appropriate communication maintenance control strategies based on the structure and rules of the decision tree. In actual use, it is also necessary to continuously monitor and evaluate the decision maker. If new situations or problems are found that are not covered, the decision tree should be supplemented and improved in time. Using the sample transmission accuracy parameter set and the sample communication maintenance control strategy set, an efficient and accurate communication maintenance control decision maker is constructed based on the decision tree to provide strong support for the realization of effective communication maintenance control.

[0073] Step S643, based on the communication maintenance control decision maker, make a decision on the transmission accuracy parameter array to obtain a communication maintenance control strategy set.

[0074] Specifically, the transmission accuracy parameter array is input into the established communication maintenance control decision maker. The transmission accuracy parameters include various key information about the communication transmission status between the current electric energy meter array and the concentrator. After receiving these parameters, the decision maker will analyze them according to its internal rules and logic built on the decision tree, and proceed step by step along the branches of the decision tree according to the specific values ​​of the parameters to find the matching path and the corresponding communication maintenance control strategy. In the decision-making process, the decision maker will comprehensively consider multiple factors, such as the relationship between different parameters, historical experience, and predefined strategy associations. As the decision is made, one or more communication maintenance control strategies suitable for the current transmission accuracy parameter status will be determined. The strategy may involve specific maintenance actions on the concentrator or electric energy meter communication module, such as adjusting certain settings, replacing components, and repairing Complex line connection, etc. Once the communication maintenance control strategy set is obtained, it can be put into practice. The relevant technical personnel or system will carry out communication maintenance work on the concentrator and the electric energy meter in accordance with the specific requirements and steps of the strategy set. During the implementation process, it is necessary to strictly follow the guidance of the strategy to ensure that each maintenance measure is accurately executed. At the same time, it is necessary to monitor the progress and effect of the maintenance work in real time to timely discover problems and make adjustments. In addition, it is necessary to establish a feedback mechanism to feed back the actual situation during the implementation process to the decision maker in order to continuously optimize and improve the decision-making ability of the decision maker. Through this process of making decisions on the transmission accuracy parameter array based on the communication maintenance control decision maker, the communication maintenance control strategy set suitable for specific transmission conditions can be obtained efficiently and accurately, thereby ensuring the reliability and stability of communication transmission between the electric energy meter array and the concentrator.

[0075] The embodiment of the present application adopts a distributed sensor array to collect environmental characteristic information, obtain the distribution of environmental characteristic information, predict photovoltaic power generation data through a concentrator, obtain a theoretical photovoltaic power generation data array, receive the actual photovoltaic power generation data array sent by the electric energy meter array through the concentrator, analyze and obtain a communication error parameter array, perform error compensation on the actual photovoltaic power generation data array, obtain a first compensated photovoltaic power generation data array, analyze and obtain a collection error parameter array for the electric energy meter array, perform error compensation on the first compensated photovoltaic power generation data array, obtain a second compensated photovoltaic power generation data array, collect the original photovoltaic power generation data array, combine the first compensated photovoltaic power generation data array, calculate and obtain the transmission accuracy parameter array, calculate and obtain the collection accuracy parameter array based on the second compensated photovoltaic power generation data array and the theoretical photovoltaic power generation data array, make maintenance control decisions for the concentrator and the electric energy meter array, and perform maintenance control on the concentrator and the electric energy meter array, thereby achieving the technical effect of realizing the accuracy of data in a distributed photovoltaic system and the stability and reliability of system operation.

[0076] 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 controlling electric energy meters and concentrators based on distributed photovoltaics, characterized in that: The method comprises: By means of a distributed sensor array arranged in the distributed photovoltaic, environmental characteristic information of multiple photovoltaic module locations is collected to obtain the distribution of environmental characteristic information, wherein the environmental characteristic information includes dust characteristic information, temperature characteristic information, humidity characteristic information and light characteristic information; Through the distributed photovoltaic concentrator, according to the distribution of the environmental characteristic information, photovoltaic power generation data of multiple photovoltaic modules are predicted to obtain a theoretical photovoltaic power generation data array; Receiving, through the concentrator, an array of actual photovoltaic power generation data sent by an array of electric energy meters arranged at the locations of the plurality of photovoltaic modules; According to the distribution of the environmental characteristic information, analyzing and obtaining a communication error parameter array for communication between the electric energy meter array and the concentrator, performing error compensation on the actual photovoltaic power generation data array, and obtaining a first compensated photovoltaic power generation data array; According to the distribution of the environmental characteristic information, analyzing and obtaining the collection error parameter array of the electric energy meter array for collecting photovoltaic power generation data, performing error compensation on the first compensated photovoltaic power generation data array, and obtaining the second compensated photovoltaic power generation data array; The original photovoltaic power generation data array locally collected by the electric energy meter array is collected, combined with the first compensated photovoltaic power generation data array, to calculate and obtain a transmission accuracy parameter array; based on the second compensated photovoltaic power generation data array and the theoretical photovoltaic power generation data array, a collection accuracy parameter array is calculated to make maintenance control decisions for the concentrator and the electric energy meter array, and to perform maintenance control on the concentrator and the electric energy meter array.

2. The method for controlling electric energy meters and concentrators based on distributed photovoltaics according to claim 1, characterized in that: By deploying a distributed sensor array in the distributed photovoltaic system, the environmental characteristic information of multiple photovoltaic module locations is collected to obtain the distribution of environmental characteristic information, including: By means of a distributed sensor array arranged in the distributed photovoltaic system, dust concentration information, temperature information, humidity information and light information in the environment where multiple photovoltaic modules in the distributed photovoltaic system are located are collected as dust characteristic information, temperature characteristic information, humidity characteristic information and light characteristic information; The dust characteristic information, temperature characteristic information, humidity characteristic information and light characteristic information are integrated to obtain a plurality of environmental characteristic information, and the distribution of the environmental characteristic information is constructed by combining the position information of the plurality of photovoltaic modules.

3. The method for controlling electric energy meters and concentrators based on distributed photovoltaics according to claim 1, characterized in that: The photovoltaic power generation data of multiple photovoltaic modules is predicted by the distributed photovoltaic concentrator according to the distribution of the environmental characteristic information, including: Based on the photovoltaic power generation data record log of the distributed photovoltaic, multiple sample environmental feature information sets of multiple photovoltaic modules are collected, and multiple sample photovoltaic power generation data sets are collected; The plurality of sample environmental feature information sets and the plurality of sample photovoltaic power generation data sets are respectively used as training supervision data, a plurality of photovoltaic power generation prediction branches for predicting power generation data of a plurality of photovoltaic modules are obtained through training, and a photovoltaic power generation predictor is obtained through integration; Based on the photovoltaic power generation predictor, the photovoltaic power generation prediction is performed on the distribution of the environmental characteristic information, a plurality of predicted photovoltaic power generation data are obtained, and a theoretical photovoltaic power generation data array is constructed.

4. The method for controlling electric energy meters and concentrators based on distributed photovoltaics according to claim 1, characterized in that: According to the distribution of the environmental characteristic information, an array of communication error parameters for the communication between the electric energy meter array and the concentrator is analyzed and obtained, and error compensation is performed on the actual photovoltaic power generation data array, including: Collecting a plurality of sample environment characteristic information sets and a plurality of communication error parameter sets according to the data transmission history between the concentrator and the electric energy meter array; Using the plurality of sample environment feature information sets and the plurality of communication error parameter sets, training a plurality of communication error analysis branches, and integrating to obtain a communication error analyzer; Based on the communication error analyzer, performing communication error analysis on the distribution of the environmental characteristic information, obtaining a plurality of communication error parameters, and constructing a communication error parameter array; According to the communication error parameter array, a one-to-one error compensation process is performed on the actual photovoltaic power generation data array to obtain a first compensated photovoltaic power generation data array.

5. The method for controlling electric energy meters and concentrators based on distributed photovoltaics according to claim 4, characterized in that: According to the communication error parameter array, one-to-one error compensation processing is performed on the actual photovoltaic power generation data array, including: Acquire a mapping relationship between the communication error parameter array and the actual photovoltaic power generation data array; According to the mapping relationship, the communication error parameter is used to perform compensation calculation on the actual photovoltaic power generation data array to obtain the first compensated photovoltaic power generation data array.

6. The method for controlling electric energy meters and concentrators based on distributed photovoltaics according to claim 1, characterized in that: According to the distribution of the environmental characteristic information, an acquisition error parameter array of the photovoltaic power generation data acquisition by the electric energy meter array is analyzed and obtained, and the error compensation is performed on the first compensated photovoltaic power generation data array to obtain a second compensated photovoltaic power generation data array, including: According to the photovoltaic power generation data collection history of multiple electric energy meters in the electric energy meter array, multiple sample environmental characteristic information sets are collected, and according to the errors between the photovoltaic power generation data collected under different sample environmental characteristic information and the actual photovoltaic power generation data, multiple sample collection error parameter sets are processed to obtain; Using the multiple sample environment feature information sets and the multiple sample acquisition error parameter sets respectively, training multiple acquisition error identification branches to obtain an acquisition error identifier; Based on the acquisition error identifier, the environmental feature information distribution is subjected to acquisition error identification to obtain a plurality of acquisition error parameters, and an acquisition error parameter array is constructed; Based on the acquisition error parameter array, compensation processing is performed on a plurality of first compensated photovoltaic power generation data in the first compensated photovoltaic power generation data array to obtain the second compensated photovoltaic power generation data array.

7. The method for controlling electric energy meters and concentrators based on distributed photovoltaics according to claim 1, characterized in that: The original photovoltaic power generation data array locally collected by the electric energy meter array is collected, combined with the first compensated photovoltaic power generation data array, to calculate and obtain a transmission accuracy parameter array, and according to the second compensated photovoltaic power generation data array and the theoretical photovoltaic power generation data array, to calculate and obtain a collection accuracy parameter array, and to make maintenance control decisions for the concentrator and the electric energy meter array, including: Collecting the original photovoltaic power generation data array locally collected by the electric energy meter array; Calculating and obtaining a transmission accuracy parameter array of photovoltaic power generation data transmission according to the original photovoltaic power generation data array and the first compensated photovoltaic power generation data array; Calculating and obtaining an array of acquisition accuracy parameters for photovoltaic power generation data acquisition according to the second compensated photovoltaic power generation data array and the theoretical photovoltaic power generation data array; According to the transmission accuracy parameter array, communication transmission maintenance control decisions of the concentrator and the electric energy meter array are made to obtain a communication maintenance control strategy set; According to the acquisition accuracy array, the acquisition detection control decision of the electric energy meter array is made to obtain the acquisition maintenance control strategy set; The communication maintenance control strategy set and the maintenance control strategy set are used to perform maintenance control on the concentrator and the electric energy meter array.

8. The method for controlling electric energy meters and concentrators based on distributed photovoltaics according to claim 7, characterized in that: According to the transmission accuracy parameter array, a communication transmission maintenance control decision is made for the concentrator and the electric energy meter array to obtain a communication maintenance control strategy set, including: According to the photovoltaic power generation data transmission history of the electric energy meter array and the concentrator, a set of sample transmission accuracy parameters is collected, and communication transmission detection schemes formulated under different sample transmission accuracy parameters are collected to obtain a set of sample communication maintenance control strategies. The sample communication maintenance control strategy includes a scheme for maintenance control of the communication modules of the concentrator and the electric energy meter; Using the sample transmission accuracy parameter set and the sample communication maintenance control strategy set, a communication maintenance control decision maker is constructed based on a decision tree; Based on the communication maintenance control decision maker, a decision is made on the transmission accuracy parameter array to obtain a communication maintenance control strategy set.

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