Optical storage ac-dc hybrid micro-grid system and method

Abstract

The application relates to the technical field of power grid operation, in particular to a kind of photovoltaic storage alternating current and direct current hybrid microgrid system and method, the data acquisition module is used to collect the operating parameter of each working module;The parameter correction module is used to correct operating parameter using weather data, to obtain corrected operating parameter;The aging model generation module generates line aging prediction model based on corrected operating parameter and line parameter;The aging grade generation module is used to generate the aging degree grade of current line using the modified line aging prediction model;The inspection strategy matching module is used to match the corresponding maintenance inspection strategy based on the line aging degree grade;The aging grade correction module is used to correct the line aging degree grade based on the line feature information obtained by inspection. Thus the intelligent level and operation and maintenance efficiency of the photovoltaic storage alternating current and direct current hybrid microgrid system can be improved, thereby reducing the operation and maintenance cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid operation, in particular to a kind of light storage AC-DC hybrid microgrid system and method. BACKGROUND

[0002] Light storage AC-DC hybrid microgrid system is an intelligent power network integrating solar photovoltaic power generation, energy storage devices and AC and DC power transmission and distribution. This system combines the clean renewable energy production of photovoltaic systems, the energy storage and management capabilities of energy storage systems, and the advantages of AC-DC hybrid distribution to achieve efficient, reliable and flexible power supply. In this system, solar panels convert sunlight into direct current (DC), which can then be converted into alternating current (AC) by an inverter to meet local load demand or feed into the public grid. Energy storage devices such as battery packs can store electricity when power production is surplus and release electricity when needed, helping to balance supply and demand and improve system self-sufficiency and stability.

[0003] During the operation of the light storage AC-DC hybrid microgrid system, line faults may occur due to operational reasons and external environmental reasons, so regular inspection is needed, but regular inspection requires a lot of manpower and resources, increasing the cost of operation and maintenance. SUMMARY

[0004] The purpose of the present application is to provide a light storage AC-DC hybrid microgrid system and method, which aims to improve the intelligent level and operation and maintenance efficiency of the light storage AC-DC hybrid microgrid system, thereby reducing the cost of operation and maintenance.

[0005] To achieve the above purpose, in a first aspect, the present application provides a light storage AC-DC hybrid microgrid system, comprising a data acquisition module, a parameter correction module, an aging model generation module, an aging level generation module, an inspection strategy matching module and an aging level correction module.

[0006] The data acquisition module is used to acquire the operating parameters of each working module.

[0007] The parameter correction module is used to correct the operating parameters using weather data to obtain corrected operating parameters.

[0008] The aging model generation module generates a line aging prediction model based on the corrected operating parameters and line parameters.

[0009] The aging level generation module is used to generate the current line aging level using the corrected line aging prediction model.

[0010] The inspection strategy matching module is used to match the corresponding maintenance inspection strategy based on the line aging level.

[0011] The aging level correction module is configured to correct the line aging level based on the line feature information obtained through the inspection.

[0012] The data acquisition module comprises a parameter acquisition unit, an acquisition frequency setting unit and a data processing unit.

[0013] The parameter acquisition unit is configured to acquire the operating parameters of each working module, wherein the operating parameters comprise voltage, current and temperature.

[0014] The acquisition frequency setting unit is configured to set the acquisition frequency of the parameter acquisition unit.

[0015] The data processing unit is configured to filter and clean the acquired operating parameters.

[0016] The parameter correction module comprises a historical data acquisition unit, a feature extraction unit and a correction unit.

[0017] The historical data acquisition unit is configured to collect historical weather data.

[0018] The feature extraction unit is configured to extract correction feature values in the historical weather data, wherein the correction feature values comprise average temperature, daily average illumination time and humidity.

[0019] The correction unit is configured to correct the temperature data in the operating parameters based on the average temperature and the daily average illumination time.

[0020] The aging model generation module comprises an aging feature calculation unit, an aggregation and statistics unit, a model parameter setting unit and a model training unit.

[0021] The aging feature calculation unit is configured to extract aging prediction features from the corrected operating parameters, wherein the aging prediction features comprise temperature average value, temperature standard deviation, temperature maximum and minimum value, humidity average value, humidity standard deviation and humidity maximum and minimum value.

[0022] The aggregation and statistics unit is configured to calculate cumulative summation temperature data and cumulative summation humidity data in a preset time period, wherein the preset time period is from the last maintenance time to the current time.

[0023] The model parameter setting unit is configured to set the parameters of the ARIMA aging prediction model with seasonality.

[0024] The model training unit is configured to apply the cumulative summation temperature data and the cumulative summation humidity data to the SARIMA model for training to obtain the line aging prediction model.

[0025] The aging grade generation module comprises a target line data acquisition unit, a target data aggregation unit, an aging degree calculation unit and a grade matching unit.

[0026] The target line data acquisition unit is configured to acquire target line parameter data.

[0027] The target data aggregation unit is configured to calculate current cumulative summation temperature data and current cumulative summation humidity data based on the target line parameter data.

[0028] The aging degree calculation unit is configured to calculate a target line aging degree value based on the current cumulative summation temperature data and the current cumulative summation humidity data by using a line aging prediction model.

[0029] The grade matching unit is configured to match a current line aging degree grade based on the target line aging degree value.

[0030] The patrol strategy matching module comprises a strategy library unit, a mapping unit, a grade acquisition unit and a strategy matching unit.

[0031] The strategy library unit is configured to set a patrol strategy library, and the training strategy library comprises a plurality of patrol strategies.

[0032] The mapping unit is configured to set a mapping table from a line aging degree grade to a patrol strategy.

[0033] The grade acquisition unit is configured to receive an output from the aging grade generation module, i.e., an aging degree grade of a current line.

[0034] The strategy matching unit is configured to match a corresponding patrol strategy based on the aging degree grade and the mapping table.

[0035] The aging grade correction module comprises a patrol data acquisition unit, a patrol feature marking unit and a patrol aging grade matching unit.

[0036] The patrol data acquisition unit is configured to collect patrol data, and the patrol data comprises appearance images and electrical performance test data.

[0037] The patrol feature marking unit is configured to mark key features reflecting a line aging state based on the patrol data, and the key features comprise a resistance change rate, an insulation resistance drop amplitude and a mechanical damage degree.

[0038] The patrol aging grade matching unit is configured to match a patrol aging grade based on the key features.

[0039] The correction unit is configured to replace a current line aging degree grade based on the patrol aging grade, and initialize corresponding current cumulative summation temperature data and current cumulative summation humidity data.

[0040] The one light storage AC / DC hybrid micro-grid system further comprises a data storage module for storing operation parameter data and inspection data.

[0041] In a second aspect, the application further provides a light storage AC / DC hybrid micro-grid control method, comprising:

[0042] Collecting operation parameters of each working module;

[0043] Correcting the operation parameters using weather data to obtain corrected operation parameters;

[0044] Generating a line aging prediction model based on the corrected operation parameters and line parameters;

[0045] Using the corrected line aging prediction model to generate a current line aging level;

[0046] Matching a corresponding maintenance inspection strategy based on the line aging level;

[0047] Correcting the line aging level based on the line feature information obtained by inspection.

[0048] The light storage AC / DC hybrid micro-grid system and method of the application, the data acquisition module real-time monitors and collects the operation parameters of each working module in the micro-grid. These parameters include but are not limited to temperature, humidity, voltage, current, power factor. Considering that natural environmental factors such as weather changes will affect the performance of power equipment, the parameter correction module introduces external meteorological data to adjust the original operation parameters collected. This step ensures that the data relied on subsequent analysis is closer to the actual situation, thereby improving the prediction accuracy. Based on the corrected operation parameters and the inherent physical characteristics of the line (i.e. line parameters), the aging model generation module constructs a mathematical model that can predict the aging of the line over time. The aging level generation module uses the aging prediction model established in the previous step. This module can quantitatively evaluate the specific aging state of the current line and divide it into different levels. This helps to intuitively understand the aging degree of each part and facilitates the adoption of targeted measures. The inspection strategy matching module automatically selects the most suitable maintenance and inspection scheme according to different aging levels. This means that areas with more serious aging will receive more frequent or more in-depth attention, ensuring that resources are used effectively while reducing the risk of failure. The aging level correction module re-evaluates and adjusts the previously determined aging level after the actual inspection task based on the specific information obtained on site (such as directly observed problems, test results, etc.). This feedback mechanism ensures that the entire system always operates based on the latest and most accurate information, minimizing the gap between prediction and reality.

[0049] In summary, the present application forms a closed-loop management process from data acquisition to analysis processing, to decision support and final result verification, greatly improves the intelligent level and operation and maintenance efficiency of the optical storage AC / DC hybrid micro-grid system, and reduces the operation and maintenance cost. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0051] Figure 1 is a structural diagram of an optical storage AC / DC hybrid micro-grid system of the present application.

[0052] Figure 2 is a structural diagram of a data acquisition module of the present application.

[0053] Figure 3 is a structural diagram of a parameter correction module of the present application.

[0054] Figure 4 is a structural diagram of an aging model generation module of the present application.

[0055] Figure 5 is a structural diagram of an aging level generation module of the present application.

[0056] Figure 6 is a structural diagram of a patrol strategy matching module of the present application.

[0057] Figure 7 is a structural diagram of an aging level correction module of the present application.

[0058] Figure 8 is a flowchart of an optical storage AC / DC hybrid micro-grid control method of the present application.

[0059] The data acquisition module 101, the parameter correction module 102, the aging model generation module 103, the aging level generation module 104, the inspection strategy matching module 105, the aging level correction module 106, the parameter acquisition unit 107, the acquisition frequency setting unit 108, the data processing unit 109, the historical data acquisition unit 110, the feature extraction unit 111, the correction unit 112, the aging feature calculation unit 113, the aggregation statistical unit 114, the model parameter setting unit 115, the model training unit 116, the target line data acquisition unit 117, the target data aggregation unit 118, the aging degree calculation unit 119, the level matching unit 120, the strategy library unit 121, the mapping unit 122, the level acquisition unit 123, the strategy matching unit 124, the inspection data acquisition unit 125, the inspection feature marking unit 126, and the inspection aging level matching unit 127. DETAILED DESCRIPTION

[0060] Embodiments of the present application are described in detail below with reference to examples illustrated in the accompanying drawings, in which the same or similar components or components having the same or similar functions are denoted by the same or similar reference numerals throughout. The embodiments described below by reference to the accompanying drawings are exemplary and are intended to explain the present application, and cannot be understood as limiting the present application.

[0061] First embodiment

[0062] Please refer to Figures 1-7 The present application provides a kind of optical storage AC-DC hybrid micro grid system, including data acquisition module 101, parameter correction module 102, aging model generation module 103, aging level generation module 104, inspection strategy matching module 105 and aging level correction module 106;The data acquisition module 101 is used to acquire the operating parameter of each working module;The parameter correction module 102 is used to correct operating parameter using weather data, to obtain corrected operating parameter;The aging model generation module 103 generates line aging prediction model based on corrected operating parameter and line parameter;

[0063] The aging level generation module 104 is used to generate current line aging degree level using the line aging prediction model after correction;The inspection strategy matching module 105 is used to match corresponding maintenance inspection strategy based on line aging degree level;The aging level correction module 106 is used to correct line aging degree level based on line feature information obtained by inspection.

[0064] In this embodiment, the data acquisition module 101 monitors and collects the operating parameters of each working module in the micro-grid in real time. These parameters include but are not limited to temperature, humidity, voltage, current, power factor. Considering that natural environmental factors such as weather changes will affect the performance of power equipment, the parameter correction module 102 introduces external weather data to adjust the original operating parameters collected. This step ensures that the data relied on subsequent analysis is closer to the actual situation, thereby improving the prediction accuracy. Based on the corrected operating parameters and the inherent physical characteristics of the line (i.e. line parameters), the aging model generation module 103 builds a mathematical model that can predict the aging of the line over time. The aging level generation module 104 uses the aging prediction model established in the previous step. This module can quantitatively evaluate the specific aging state of the current line and divide it into different levels. This helps to intuitively understand the aging degree of each part and facilitate targeted measures. The inspection strategy matching module 105 automatically selects the most suitable maintenance and inspection scheme according to different aging levels. This means that areas with more serious aging will receive more frequent or more in-depth attention, ensuring that resources are used effectively while reducing the risk of failure. The aging level correction module 106 re-evaluates and adjusts the aging level determined previously after the actual implementation of the inspection task based on specific information obtained on site (such as directly observed problems, test results, etc.). This feedback mechanism ensures that the entire system always operates based on the latest and most accurate information, minimizing the gap between prediction and reality.

[0065] In summary, the present application forms a closed-loop management process from data acquisition to analysis and processing, to decision support and final result verification, greatly improving the intelligent level and operation and maintenance efficiency of the light storage AC-DC hybrid micro-grid system, thereby reducing operation and maintenance costs.

[0066] The data acquisition module 101 includes a parameter acquisition unit 107, a collection frequency setting unit 108, and a data processing unit 109.

[0067] The parameter acquisition unit 107 is configured to acquire operating parameters of each working module, wherein the operating parameters include voltage, current, and temperature.

[0068] The data processing unit 109 is configured to filter and clean the collected operating parameters.

[0069] The parameter collection unit 107 collects key operational parameters of each module in the microgrid in real time. These parameters are crucial for assessing the immediate state and long-term trends of the system. Specifically, this unit can measure and record voltage (V), current (I), and temperature (T), which are important indicators of power transmission quality, load conditions, and equipment operating environment, respectively.

[0070] The collection frequency setting unit 108 provides flexibility, allowing users or system administrators to adjust the time interval of parameter collection according to actual conditions. For example, in the case of large fluctuations in grid load or extreme weather conditions, the collection frequency can be increased to obtain more detailed operational data; while during stable operation, the collection frequency can be appropriately reduced to reduce data volume and save resources. Through reasonable frequency setting, both the timeliness and accuracy of the data can be ensured, and unnecessary resource waste can be avoided.

[0071] The data processing unit 109 performs preliminary processing on the raw operational parameters obtained by the parameter collection unit 107, including but not limited to filtering and cleaning. This is because the actual collected data contains noise, outliers or incomplete records, and direct use of these unprocessed data will lead to distorted analysis results. Therefore, this unit uses advanced algorithms and techniques to identify and exclude invalid or erroneous data points, ensuring that the information relied upon for subsequent analysis is both accurate and reliable. In addition, the data processing unit 109 also performs some basic statistical operations, such as mean value calculation, trend analysis, etc., to provide preparation for higher-level data analysis and decision support.

[0072] The parameter correction module 102 includes a historical data acquisition unit 110, a feature extraction unit 111, and a correction unit 112; the historical data acquisition unit 110 is used to collect historical weather data; the feature extraction unit 111 is used to extract correction feature values in the historical weather data, the correction feature values include average temperature, daily average illumination time and humidity; the correction unit 112 is used to correct temperature data in the operation parameters based on the average temperature and the daily average illumination time.

[0073] The historical data acquisition unit 110 is the starting point of the parameter correction process, collecting past weather data from reliable databases or external resources. These historical weather data are crucial for understanding and predicting changes in the natural environment in the current and future period of time. Specifically, this unit can obtain a variety of meteorological parameter records including but not limited to temperature, light intensity, humidity, wind speed, etc. Through long-term accumulation of data sets, a solid foundation can be provided for subsequent feature extraction and parameter correction. In addition, this unit also supports data update mechanism, ensuring that the used data is always up-to-date, and can adapt to the impact of climate change.

[0074] The feature extraction unit 111 extracts feature values directly related to the correction of device operating parameters from a large amount of historical weather data. In this invention, the average temperature, daily average light duration and humidity are highlighted as key indicators as correction feature values. The average temperature reflects the thermal environment changes experienced by the device within a certain period; the daily average light duration directly affects the power generation efficiency of the photovoltaic system; while humidity not only affects the power transmission efficiency, but also has a significant impact on the aging rate of electrical equipment. Through advanced algorithms and technical means, such as feature selection methods in machine learning, the feature extraction unit 111 can identify and quantify these factors, providing a scientific basis for subsequent parameter correction work.

[0075] The correction unit 112 makes fine adjustments to the temperature data in the originally collected operating parameters based on the key correction feature values (i.e. average temperature and daily average light duration) obtained from the feature extraction unit 111. Considering the significant impact of temperature on the power system, such as the increase in wire resistance with temperature, which will directly affect the current and voltage levels, it is particularly important to accurately correct it. This unit uses mathematical models or empirical formulas, combined with real-time collected temperature data and extracted historical feature values, to calculate a more realistic corrected temperature value. This not only improves the accuracy of the power grid state assessment, but also provides more reliable data input for other related modules (such as the aging model generation module 103), thereby optimizing the prediction ability and maintenance strategy of the entire system.

[0076] The aging model generation module 103 includes an aging feature calculation unit 113, an aggregation statistics unit 114, a model parameter setting unit 115 and a model training unit 116; the aging feature calculation unit 113 is used to extract aging prediction features from the corrected operating parameters, the aging prediction features including temperature average, temperature standard deviation, temperature maximum and minimum, humidity average, humidity standard deviation, humidity maximum and minimum; the aggregation statistics unit 114 is used to calculate the cumulative sum temperature data and the cumulative sum humidity data within a preset time period, the preset time period being from the last maintenance time to the current time; the model parameter setting unit 115 is used to set the parameters of the ARIMA aging prediction model with seasonality; the model training unit 116 is used to apply the cumulative sum temperature data and the cumulative sum humidity data to the SARIMA model for training to obtain a line aging prediction model.

[0077] The aging feature calculation unit 113 is the first step of the entire aging model generation process, which extracts a series of feature values closely related to device aging from the corrected operating parameters. These aging prediction features include but are not limited to temperature average, temperature standard deviation, temperature maximum and minimum, humidity average, humidity standard deviation, and humidity maximum and minimum. By calculating these feature values, the working state of the device under different environmental conditions can be quantitatively described, providing basic data for subsequent aging assessment. For example, the fluctuation of temperature (i.e. standard deviation) can better reflect the stress on the device than the simple temperature average, while the maximum and minimum values of temperature or humidity can help identify the impact on the device under extreme conditions.

[0078] The aggregated statistics unit 114 calculates the cumulative sum of temperature data and the cumulative sum of humidity data within a preset time period. The preset time period referred to here is from the last maintenance time to the current time, which takes into account the impact of actual maintenance period on the device aging process. By cumulative summation, the total amount of temperature and humidity change over a period of time can be more intuitively seen, which is crucial for understanding the impact of long-term environmental factors on the device. This cumulative data not only helps to capture the impact of seasonal changes, but also provides strong support for predicting future trends.

[0079] The model parameter setting unit 115 is used to configure the parameters of the ARIMA (Autoregressive Integrated Moving Average) model with seasonality, i.e. the SARIMA (Seasonal ARIMA) model. Considering that the operation of the power system is significantly affected by natural seasonal changes, the introduction of seasonal components can improve the prediction accuracy of the model. This unit allows users to adjust parameters such as lag order, difference order, moving average order, etc. according to specific application scenarios, while also allowing the user to set the specific period of the seasonal pattern (such as annually, quarterly, or monthly). Reasonable parameter selection is crucial for building a prediction model that can reflect both short-term dynamics and long-term trends.

[0080] The model training unit 116 is the core link of the aging model generation, which takes the cumulative sum of temperature data and the cumulative sum of humidity data provided by the aggregated statistics unit 114 as input and applies it to the SARIMA model that has been configured for training. During the training process, the model learns the patterns and rules in these data, thereby establishing a mathematical model that can predict the degree of aging of the power line. To ensure the effectiveness of the model, cross-validation and other methods are used to optimize the model performance, and the model is continuously iterated and improved until satisfactory prediction results are achieved. The final line aging prediction model not only helps operation and maintenance personnel understand the current state of the device, but also can provide early warning of potential problems and guide future maintenance plans.

[0081] The aging level generation module 104 includes a target line data acquisition unit 117, a target data aggregation unit 118, an aging degree calculation unit 119, and a level matching unit 120; the target line data acquisition unit 117 is configured to acquire target line parameter data; the target data aggregation unit 118 is configured to calculate current cumulative summation temperature data and current cumulative summation humidity data based on the target line parameter data; the aging degree calculation unit 119 is configured to calculate a target line aging degree value based on the current cumulative summation temperature data and the current cumulative summation humidity data using a line aging prediction model; and the level matching unit 120 is configured to match a current line aging degree level based on the target line aging degree value.

[0082] The target line data acquisition unit 117 is the starting point of the entire aging assessment process, extracting parameter data related to a specific power line from multiple data sources. These data include but are not limited to real-time collected operating parameters such as voltage, current, temperature, humidity, as well as historical maintenance records and environmental condition information. By integrating multiple data sources, a comprehensive understanding of the target line status is ensured. In addition, this unit is flexible and can adjust the content and frequency of data collection according to the characteristics of different lines to adapt to diverse application scenarios.

[0083] The task of the target data aggregation unit 118 is to integrate the data from the target line data acquisition unit 117, especially to calculate the current cumulative summation temperature data and the current cumulative summation humidity data. This process involves accumulating and summing the temperature and humidity data over a period of time (such as from the last maintenance to the current time), thereby obtaining an index reflecting the total impact of environmental conditions during this period. In this way, not only can the long-term environmental factors affecting the line be captured, but also a solid foundation can be provided for subsequent aging degree calculation. At the same time, the aggregated data can better reflect seasonal changes and periodic trends, improving the applicability and accuracy of the prediction model.

[0084] The aging degree calculation unit 119 calculates the specific aging degree value of the target line based on the pre-trained line aging prediction model using the current cumulative summation temperature data and the current cumulative summation humidity data. This step is the most critical part of the entire assessment process, as it directly determines the quantification result of the aging degree. The aging prediction model usually combines statistical methods and machine learning algorithms, which can identify and quantify the impact of environmental factors on line aging. By inputting the accumulated temperature and humidity data into the model, the calculation unit can obtain a value that comprehensively reflects the aging state of the line, providing a basis for subsequent level division.

[0085] The grade matching unit 120 compares the target line aging degree value obtained by the aging degree calculation unit 119 with the pre-defined aging grade standard, and determines the aging degree grade of the current line. This process involves establishing a scientific and reasonable grading system, which usually considers multiple dimensions such as the severity of aging and the impact on grid stability. By setting clear thresholds or intervals, different aging degree values can be mapped to corresponding grades. This makes the aging assessment results more intuitive and easy to understand, and provides clear guidance for operation and maintenance decisions. For example, for lines with high aging degree, maintenance or replacement can be prioritized; for lines with low aging degree, maintenance period can be appropriately extended while monitoring is maintained.

[0086] The inspection strategy matching module 105 includes a strategy library unit 121, a mapping unit 122, a grade acquisition unit 123, and a strategy matching unit 124; the strategy library unit 121 is used to set an inspection strategy library, and the training strategy library contains multiple inspection strategies; the mapping unit 122 is used to set a mapping table from the line aging degree grade to the inspection strategy;

[0087] The grade acquisition unit 123 receives the output from the aging grade generation module 104, i.e. the aging degree grade of the current line; the strategy matching unit 124 matches the corresponding inspection strategy based on the mapping table according to the aging grade.

[0088] The strategy library unit 121 is the basic framework of the inspection strategy matching module 105, which establishes and manages a comprehensive inspection strategy library. This library contains multiple types of inspection strategies, each designed for specific aging conditions, aiming to optimize maintenance efficiency and ensure long-term stable operation of equipment. The strategy library not only includes routine inspection items (such as appearance inspection, temperature monitoring, etc.), but also covers more complex detection methods and technical means (such as unmanned aerial vehicle inspection, infrared thermal imaging analysis, etc.). In addition, the strategy library has a dynamic updating function, which can be continuously adjusted and expanded according to actual application feedback and technological progress, to maintain its advanced nature and applicability.

[0089] The mapping unit 122 functions to establish a correspondence between the line aging degree level and the specific inspection strategy, that is, to create a detailed mapping table. This mapping table is constructed based on the understanding of the problems existing in the line under different aging levels and the assessment of the corresponding maintenance requirements. By defining clear rules or logic, the mapping unit 122 can accurately associate each aging level to the most suitable inspection strategy. For example, for lines with lighter aging, a more basic inspection plan is selected; while for severely aged lines, more in-depth and frequent inspection measures are recommended. This one-to-one or many-to-one mapping relationship provides a scientific basis for subsequent strategy matching, while also enhancing the flexibility and response speed of the system.

[0090] The level acquisition unit 123 receives the output results from the aging level generation module 104, that is, the specific aging degree level of the current line. This unit ensures that the latest and most accurate aging assessment information can be timely transmitted to the inspection strategy matching module 105, thereby supporting the subsequent decision-making process. It can not only process single line aging level data, but also manage the information of multiple lines at the same time, in order to coordinate the inspection work in a larger range. Through seamless docking with the aging level generation module 104, the level acquisition unit 123 ensures the coherence and efficiency of the entire process.

[0091] The strategy matching unit 124 is the core execution part of the inspection strategy matching module 105, which automatically selects the most appropriate specific inspection strategy according to the received aging degree level information and in combination with the pre-set mapping table. To ensure that the selected strategy can effectively deal with the current aging problems, and will not cause resource waste. The strategy matching unit 124 also has certain intelligent learning ability, which can continuously optimize the matching algorithm over time and improve the matching accuracy. Finally, it will generate a detailed inspection task list to guide the on-site operation and maintenance personnel to operate according to the established strategy, thereby ensuring the safe and reliable operation of the power grid.

[0092] The aging level correction module 106 includes an inspection data acquisition unit 125, an inspection feature marking unit 126, and an inspection aging level matching unit 127. The inspection data acquisition unit 125 is used to collect inspection data, including appearance images and electrical performance test data. The inspection feature marking unit 126 is used to mark key features reflecting the line aging state based on the inspection data, including resistance change rate, insulation resistance drop amplitude, and mechanical damage degree. The inspection aging level matching unit 127 is used to match the inspection aging level based on the key features. The correction unit 112 is used to replace the current line aging degree level based on the inspection aging level, and initialize the corresponding current cumulative summation temperature data and current cumulative summation humidity data.

[0093] The inspection data acquisition unit 125 is the starting point of the entire correction process, collecting various data from on-site inspections. These data include but are not limited to appearance images and electrical performance test data, which provide a rich source of information for subsequent analysis. Appearance images can visually show the physical state of the equipment, such as whether there are mechanical damage or corrosion phenomena; while electrical performance test data covers indicators such as resistance change rate and insulation resistance drop amplitude, directly reflecting the internal health status of the line. By comprehensively using various detection means, the inspection data acquisition unit 125 ensures that the collected data is comprehensive and detailed, laying a solid foundation for subsequent processing.

[0094] The task of the inspection feature labeling unit 126 is to conduct in-depth analysis on the inspection data and extract key features reflecting the aging state of the line. Specifically, it will label three main key features based on the inspection data: resistance change rate, insulation resistance drop amplitude, and mechanical damage degree. Resistance change rate can reveal the aging of the conductor material; insulation resistance drop amplitude indicates the reduction of the effectiveness of the insulation layer, which is crucial for the safety of power transmission; mechanical damage degree involves physical damage such as cracks, deformation, etc., affecting the structural integrity. By applying advanced image recognition technology and data analysis algorithms, the inspection feature labeling unit 126 can accurately identify and quantify these key features, thus providing strong support for aging degree assessment.

[0095] The inspection aging grade matching unit 127 compares the inspection results with the pre-defined aging grade standards based on the key feature information provided by the inspection feature labeling unit 126, to determine the actual aging degree of the current line. This unit can find the most suitable aging grade according to different combinations of key features by establishing a detailed mapping table. For example, if a line shows a high resistance change rate and significant mechanical damage, it will be rated as a higher level of aging state. This aging grade assessment based on actual inspection data makes the results closer to the real situation, enhancing the credibility and practicality of the assessment.

[0096] The correction unit 112, as the last link of the aging grade correction module 106, replaces the aging degree grade of the current line according to the inspection aging grade, and initializes the cumulative summation temperature data and cumulative summation humidity data accordingly. This means that once the new aging grade is confirmed, all related historical data will be reset to ensure that subsequent aging prediction and evaluation work can start from the latest base point. This not only ensures the consistency and continuity of the data, but also provides a more accurate basis for future maintenance decisions. In addition, the correction unit 112 also has a certain flexibility, which can adjust the initialization parameter settings according to the actual situation to adapt to the changes of different environmental conditions.

[0097] The optical storage AC-DC hybrid micro-grid system also comprises a data storage module for storing operating parameter data and inspection data.

[0098] The data storage module receives and saves real-time operating parameters such as voltage, current, temperature, etc. from various working modules. These data are the basis for evaluating system health, optimizing operating efficiency, and diagnosing faults. By accumulating these operating parameters over a long period of time, rich data support can be provided for historical data analysis, trend prediction, and performance evaluation.

[0099] In addition, this module is also used to store the data collected during each inspection activity, including appearance images, electrical performance test results, etc. Inspection data is crucial for understanding the actual state of equipment, identifying potential problems, and developing reasonable maintenance plans. By combining inspection data with operating parameters, a more comprehensive understanding of the overall condition of the system can be achieved, enabling more scientific and reasonable operation and maintenance decisions.

[0100] Second embodiment

[0101] Please refer to Figure 8 The present application also provides a kind of optical storage AC-DC hybrid micro-grid control method, comprising:

[0102] S201 collects the operating parameters of each working module;

[0103] This step is the basis of the entire control method, and real-time collection of key operating parameters of each working module in the micro-grid. These parameters include but are not limited to voltage, current, temperature, humidity, etc., which are crucial for assessing the immediate state and long-term trends of the system. Through high-precision sensors and other monitoring equipment, it is ensured that the collected data is both comprehensive and accurate.

[0104] S202 corrects the operating parameters using weather data to obtain corrected operating parameters;

[0105] Considering that natural environmental factors such as weather changes can significantly affect the performance of electrical equipment, this step introduces external meteorological data to adjust the original operating parameters. Specifically, by analyzing the average temperature, daily average light duration and humidity, etc. characteristic values in historical weather data, combined with real-time weather forecast information, the collected operating parameters are corrected. This step ensures that the data relied upon for subsequent analysis is more realistic, improving prediction accuracy.

[0106] S203 generates a line aging prediction model based on the corrected operating parameters and line parameters;

[0107] A mathematical model is constructed to predict the aging of the line over time using the corrected operating parameters and the physical characteristics inherent to the line itself (i.e. line parameters). This model not only considers static properties but also incorporates the effects of dynamic operating conditions, providing a scientific basis for subsequent aging degree assessment. By applying machine learning algorithms or statistical methods, the model can identify and quantify the impact of environmental factors on line aging.

[0108] S204 is used to generate the current line aging degree level using the corrected line aging prediction model;

[0109] Based on the aging prediction model established in the previous step, this step quantitatively assesses the specific aging state of the current line and divides it into different levels. This helps to intuitively understand the aging degree of each part and facilitates targeted measures. The determination of the aging level is calculated by inputting the cumulative sum of temperature and humidity data into the model, thus reflecting the environmental impact over a long period of time.

[0110] S205 is used to match the corresponding maintenance and inspection strategy based on the line aging degree level;

[0111] According to different aging levels, the system automatically selects the most suitable maintenance and inspection scheme. This means that areas with more severe aging will receive more frequent or in-depth attention, ensuring that resources are used effectively while reducing the risk of failure. Through a pre-set mapping table, the aging level can be directly mapped to a specific inspection strategy, guiding the operation and maintenance personnel to operate according to the established scheme.

[0112] S206 is used to correct the line aging degree level based on the line feature information obtained through inspection.

[0113] After the actual inspection task is performed, based on the specific information obtained on site (such as directly observed problems, test results, etc.), the previously determined aging level is re-evaluated and adjusted. This feedback mechanism ensures that the entire system always operates based on the latest and most accurate information, minimizing the gap between prediction and reality. The correction process involves recalculating key features such as resistance change rate, insulation resistance drop amplitude, and mechanical damage degree and comparing them with pre-defined aging level standards to determine the new aging level.

[0114] The above only discloses one preferred embodiment of the present application, and of course cannot limit the scope of the present application. Those skilled in the art can understand that the entire or partial processes of the above-mentioned embodiments can be implemented, and equivalent changes made in accordance with the claims of the present application still fall within the scope of the present application.

Claims

1. A photovoltaic-storage AC / DC hybrid microgrid system, characterized in that, It includes a data acquisition module, a parameter correction module, an aging model generation module, an aging level generation module, an inspection strategy matching module, and an aging level correction module; The data acquisition module is used to collect the operating parameters of each working module; The parameter correction module is used to correct the operating parameters using weather data to obtain the corrected operating parameters; The aging model generation module generates a line aging prediction model based on the calibration operating parameters and line parameters. The aging level generation module is used to generate the current aging level of the line using the modified line aging prediction model. The inspection strategy matching module is used to match the corresponding maintenance inspection strategy based on the aging level of the line. The aging level correction module is used to correct the aging level of the line based on the line feature information obtained from the inspection; the aging model generation module includes an aging feature calculation unit, an aggregation statistics unit, a model parameter setting unit, and a model training unit. The aging feature calculation unit is used to extract aging prediction features from the calibration operation parameters. The aging prediction features include average temperature, standard deviation of temperature, maximum and minimum temperature, average humidity, standard deviation of humidity, and maximum and minimum humidity. The aggregation and statistics unit is used to calculate the cumulative summation of temperature data and cumulative summation of humidity data within a preset time period, wherein the preset time period is from the last maintenance time to the current time. The model parameter setting unit is used to set the parameters of the ARIMA aging prediction model with seasonality. The model training unit is used to apply the cumulative summation temperature data and cumulative summation humidity data to the SARIMA model for training, so as to obtain the line aging prediction model; the aging level generation module includes a target line data acquisition unit, a target data aggregation unit, an aging degree calculation unit, and a level matching unit. The target line data acquisition unit is used to acquire target line parameter data; The target data aggregation unit is used to calculate the current cumulative summation temperature data and the current cumulative summation humidity data based on the target line parameter data; The aging degree calculation unit is used to calculate the target line aging degree value based on the current cumulative summation temperature data and the current cumulative summation humidity data using a line aging prediction model; The grade matching unit is used to match the current line aging grade based on the target line aging grade value.

2. The photovoltaic-storage AC / DC hybrid microgrid system as described in claim 1, characterized in that, The data acquisition module includes a parameter acquisition unit, an acquisition frequency setting unit, and a data processing unit; The parameter acquisition unit is used to acquire the operating parameters of each working module, including voltage, current and temperature; The acquisition frequency setting unit is used to set the acquisition frequency of the parameter acquisition unit; The data processing unit is used to filter and clean the collected operating parameters.

3. The photovoltaic-storage AC / DC hybrid microgrid system as described in claim 2, characterized in that, The parameter correction module includes a historical data acquisition unit, a feature extraction unit, and a correction unit; The historical data acquisition unit is used to collect historical weather data; The feature extraction unit is used to extract corrected feature values ​​from historical weather data, including average temperature, average daily sunshine duration, and humidity. The correction unit is used to correct the temperature data in the operating parameters based on the average temperature and the average daily sunshine duration.

4. The photovoltaic-storage AC / DC hybrid microgrid system as described in claim 3, characterized in that, The inspection strategy matching module includes a strategy library unit, a mapping unit, a level acquisition unit, and a strategy matching unit. The strategy library unit is used to set up an inspection strategy library, which contains multiple inspection strategies. The mapping unit is used to set up a mapping table from the line aging level to the inspection strategy; The grade acquisition unit is used to receive the output from the aging grade generation module, that is, the aging degree grade of the current line. The strategy matching unit is used to match the corresponding inspection strategy based on the aging level and a mapping table.

5. The photovoltaic-storage AC / DC hybrid microgrid system as described in claim 4, characterized in that, The aging level correction module includes an inspection data acquisition unit, an inspection feature marking unit, and an inspection aging level matching unit. Inspection data acquisition unit is used to collect inspection data, which includes appearance images and electrical performance test data; The inspection feature marking unit is used to mark key features reflecting the aging status of the line based on the inspection data. The key features include resistance change rate, insulation resistance decrease, and mechanical damage degree. The inspection aging level matching unit is used to match the inspection aging level based on key features. The correction unit is used to replace the current line aging level based on the inspection aging level, and initialize the corresponding current cumulative summation temperature data and current cumulative summation humidity data.

6. The photovoltaic-storage AC / DC hybrid microgrid system as described in claim 5, characterized in that, The aforementioned photovoltaic-storage AC / DC hybrid microgrid system also includes a data storage module, which is used to store operating parameter data and inspection data.

7. A control method for a photovoltaic-storage AC / DC hybrid microgrid, applied to a photovoltaic-storage AC / DC hybrid microgrid system as described in any one of claims 1 to 6, characterized in that, include: Collect the operating parameters of each working module; The operating parameters are corrected using weather data to obtain the corrected operating parameters; A line aging prediction model is generated based on the corrected operating parameters and line parameters. Used to generate the current line aging level using the modified line aging prediction model; Used to match corresponding maintenance and inspection strategies based on the level of line aging; Used to correct the aging level of lines based on line characteristic information obtained from inspections.

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