Optical storage alternating current and direct current hybrid micro-grid system and method

Abstract

The invention relates to the technical field of power grid operation, in particular to an optical storage alternating current and direct current hybrid micro-grid system and method. A data acquisition module is used for acquiring operation parameters of each working module; the parameter correction module is used for correcting the operation parameters by adopting the weather data to obtain corrected operation parameters; the aging model generation module is used for generating a line aging prediction model based on the corrected operation parameters and the line parameters; the aging grade generation module is used for generating a current line aging degree grade by adopting the corrected line aging prediction model; the inspection strategy matching module is used for matching a corresponding maintenance inspection strategy based on the line aging degree grade; and the aging grade correction module is used for correcting the aging degree grade of the line based on the line feature information obtained by routing inspection. Therefore, the intelligent level and the operation and maintenance efficiency of the optical storage alternating current and direct current hybrid micro-grid system can be improved, and the operation and maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid operation, and particularly to a photovoltaic-storage AC-DC hybrid microgrid system and method. Background Art

[0002] A photovoltaic-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 and renewable energy production of the photovoltaic system, the energy storage and management capabilities of the energy storage system, and the advantages of AC-DC hybrid power distribution to achieve efficient, reliable, and flexible power supply. In this system, solar panels convert sunlight into direct current (DC), and then an inverter can convert the direct current into alternating current (AC) to meet the needs of local loads or feed into the public power grid. Energy storage devices, such as battery packs, can store electrical energy when power production is excessive and release electrical energy when needed, helping to balance supply and demand and improve the self-sufficiency rate and stability of the system.

[0003] During the operation of the photovoltaic-storage AC-DC hybrid microgrid system, faults may occur in the lines due to operational reasons and external environmental reasons. Therefore, regular inspections are required, but regular inspections consume a large amount of manpower and material resources, thus increasing the operation and maintenance costs. Summary of the Invention

[0004] The purpose of the present invention is to provide a photovoltaic-storage AC-DC hybrid microgrid system and method, aiming to improve the intelligent level and operation and maintenance efficiency of the photovoltaic-storage AC-DC hybrid microgrid system, thereby reducing the operation and maintenance costs.

[0005] To achieve the above purpose, in the first aspect, the present invention provides a photovoltaic-storage AC-DC hybrid microgrid system, including 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 operation parameters of each working module;

[0007] The parameter correction module is used to correct the operation parameters with weather data to obtain corrected operation parameters;

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

[0009] The aging level generation module is used to generate the current line aging degree level by 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 degree level;

[0011] 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 tour.

[0012] Among them, the data acquisition module includes a parameter acquisition unit, an acquisition frequency setting unit, and a data processing unit;

[0013] The parameter acquisition unit is used to acquire the operating parameters of each working module, and the operating parameters include voltage, current, and temperature;

[0014] The acquisition frequency setting unit is used to set the acquisition frequency of the parameter acquisition unit;

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

[0016] Among them, the parameter correction module includes a historical data acquisition unit, a feature extraction unit, and a correction unit;

[0017] The historical data acquisition unit is used to collect historical weather data;

[0018] The feature extraction unit is used to extract correction feature values from the historical weather data, and the correction feature values include average temperature, daily average sunlight duration, and humidity;

[0019] The correction unit is used to correct the temperature data in the operating parameters based on the average temperature and daily average sunlight duration.

[0020] Among them, 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;

[0021] The aging feature calculation unit is used to extract aging prediction features from the corrected operating parameters, and the aging prediction features include average temperature, temperature standard deviation, maximum and minimum temperature values, average humidity, humidity standard deviation, maximum and minimum humidity values;

[0022] The aggregation statistics unit is used to calculate the cumulative sum temperature data and cumulative sum humidity data within a preset time period, and the preset time period is from the last maintenance time to the current time;

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

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

[0025] Among them, 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;

[0026] The target line data acquisition unit is used to acquire target line parameter data;

[0027] The target data aggregation unit is used to calculate the current cumulative sum temperature data and the current cumulative sum humidity data based on the target line parameter data;

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

[0029] The level matching unit is used to match the current line aging degree level based on the target line aging degree value.

[0030] Among them, the inspection strategy matching module includes a strategy library unit, a mapping unit, a level acquisition unit, and a strategy matching unit;

[0031] The strategy library unit is used to set an inspection strategy library, and the training strategy library contains multiple inspection strategies;

[0032] The mapping unit is used to set a mapping table from the line aging degree level to the inspection strategy;

[0033] The level acquisition unit is used to receive the output from the aging level generation module, that is, the aging degree level of the current line;

[0034] The strategy matching unit is used to match the corresponding inspection strategy based on the aging level according to the mapping table.

[0035] Among them, the aging level correction module includes an inspection data acquisition unit, an inspection feature marking unit, and an inspection aging level matching unit

[0036] The inspection data acquisition unit is used to collect inspection data, and the inspection data includes appearance images and electrical performance test data

[0037] The inspection feature marking unit is used to mark key features reflecting the line aging state based on the inspection data, and the key features include the resistance change rate, the insulation resistance drop amplitude, and the mechanical damage degree;

[0038] The inspection aging level matching unit is used to match the inspection aging level based on the key features;

[0039] The correction unit is used to replace the current line aging degree level based on the inspection aging level, and initialize the corresponding current cumulative sum temperature data and the current cumulative sum humidity data.

[0040] Among them, the described AC-DC hybrid microgrid energy storage system further includes a data storage module, and the data storage module is used to store operation parameter data and inspection data.

[0041] In a second aspect, the present invention also provides an AC-DC hybrid microgrid energy storage control method, including:

[0042] Collect the operation parameters of each working module;

[0043] Use weather data to correct the operation parameters to obtain corrected operation parameters;

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

[0045] Used to generate the current line aging degree level by using the corrected line aging prediction model;

[0046] Used to match the corresponding maintenance and inspection strategies based on the line aging degree level;

[0047] Used to correct the line aging degree level based on the line characteristic information obtained from the inspection.

[0048] For the AC-DC hybrid microgrid energy storage system and method of the present invention, the data acquisition module monitors and collects the operation parameters of each working module in the microgrid in real time. These parameters include but are not limited to temperature, humidity, voltage, current, and 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 collected original operation parameters. This step ensures that the data for 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 itself (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. Doing so helps to intuitively understand the aging degree of each part and facilitates the adoption of targeted measures. The inspection strategy matching module selects the most suitable maintenance and inspection plan automatically according to different aging levels. This means that those areas with more serious aging will receive more frequent or in-depth attention, ensuring the effective use of resources while reducing the failure risk. After actually performing the inspection task, the aging level correction module re-evaluates and adjusts the previously determined aging level according to 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, from data acquisition to analysis and processing, then to decision support and final result verification, the present invention forms a closed-loop management process, greatly improving the intelligent level and operation and maintenance efficiency of the AC-DC hybrid microgrid system with energy storage and photovoltaic power generation, thereby reducing the operation and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0051] Figure 1 It is a structural diagram of an AC-DC hybrid microgrid system with energy storage and photovoltaic power generation according to the present invention.

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

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

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

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

[0056] Figure 6 It is a structural diagram of the inspection strategy matching module of the present invention.

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

[0058] Figure 8 It is a flowchart of a control method for an AC-DC hybrid microgrid system with energy storage and photovoltaic power generation according to the present invention.

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

[0060] The embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0061] First embodiment

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

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

[0064] In this embodiment, the data acquisition module 101 monitors and collects the operation parameters of each working module in the microgrid in real time. These parameters include, but are not limited to, temperature, humidity, voltage, current, and 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 meteorological data to adjust the collected original operation parameters. This step ensures that the data for 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 itself (i.e., line parameters), the aging model generation module 103 constructs 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 classify 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 105 automatically selects the most suitable maintenance and inspection plan according to different aging levels. This means that those areas with more severe aging will receive more frequent or in-depth attention, ensuring the effective use of resources while reducing the failure risk. After the actual inspection task is executed, the aging level correction module 106 re-evaluates and adjusts the previously determined aging level according to 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.

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

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

[0067] The parameter acquisition unit 107 is used to acquire the operation parameters of each working module, and the operation parameters include voltage, current, and temperature; the acquisition frequency setting unit 108 is used to set the acquisition frequency of the parameter acquisition unit 107;

[0068] The data processing unit 109 is used to filter and clean the acquired operation parameters.

[0069] The parameter acquisition unit 107 collects the key operating parameters of each working module in the microgrid in real time. These parameters are crucial for evaluating the immediate status 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 for measuring the quality of power transmission, the load situation, and the working environment of the equipment, respectively.

[0070] The acquisition frequency setting unit 108 provides flexibility, allowing users or system administrators to adjust the time interval of parameter acquisition according to the actual situation. For example, in the case of large fluctuations in grid load or expected extreme weather, the acquisition frequency can be increased to obtain more detailed operation data; during stable operation, the acquisition frequency can be appropriately reduced to reduce the amount of data and save resources. By setting a reasonable frequency, both the timeliness and accuracy of the data can be ensured without causing unnecessary resource waste.

[0071] The data processing unit 109 performs preliminary processing on the original operating parameters obtained by the parameter acquisition unit 107, including but not limited to filtering and cleaning. This is because the actually collected data contains noise, outliers, or incomplete records, and directly using this unprocessed data will lead to distorted analysis results. Therefore, this unit uses advanced algorithms and technologies to identify and exclude invalid or incorrect data points to ensure that the information on which subsequent analysis is based is both accurate and reliable. In addition, the data processing unit 109 also performs some basic statistical operations, such as mean calculation, trend analysis, etc., to prepare 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 from the historical weather data, and the correction feature values include average temperature, daily average sunshine duration, and humidity; the correction unit 112 is used to correct the temperature data in the operating parameters based on the average temperature and daily average sunshine duration.

[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 natural environmental changes in the current and future periods. Specifically, this unit can obtain various meteorological parameter records including but not limited to temperature, light intensity, humidity, wind speed, etc. Through the long-term accumulated data set, a solid foundation can be provided for subsequent feature extraction and parameter correction. In addition, this unit also supports a data update mechanism to ensure that the information used is always the latest and can adapt to the impact of climate change.

[0074] The feature extraction unit 111 extracts eigenvalue directly related to the correction of device operation parameters from a large amount of historical weather data. In the present invention, special emphasis is placed on three key indicators, namely average temperature, daily average sunshine duration, and humidity, as the correction eigenvalues. The average temperature reflects the thermal environment change experienced by the device within a certain period; the daily average sunshine 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] Based on the key correction eigenvalues (i.e., average temperature and daily average sunshine duration) obtained from the feature extraction unit 111, the correction unit 112 finely adjusts the temperature data in the originally collected operation parameters. Considering that the impact of temperature on the power system is particularly significant, for example, the wire resistance increases with the increase in temperature, which will directly affect the current and voltage levels, it is particularly important to accurately correct it. This unit uses a mathematical model or empirical formula, combines the real-time collected temperature data and the extracted historical eigenvalues, and calculates a corrected temperature value that is closer to the actual situation. This can not only improve the accuracy of the power grid state assessment but also provide 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 and 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 operation parameters, and the aging prediction features include average temperature, temperature standard deviation, maximum and minimum temperature values, average humidity, humidity standard deviation, and maximum and minimum humidity values; the aggregation and statistics unit 114 is used to calculate the cumulative sum temperature data and cumulative sum humidity data within a preset time period, and the preset time period is from the last maintenance time to the current time; the model parameter setting unit 115 is used to set the parameters of the seasonal ARIMA aging prediction model; the model training unit 116 is used to apply the cumulative sum temperature data and 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 in the entire aging model generation process. It extracts a series of feature values closely related to device aging from the calibrated operating parameters. These aging prediction features include, but are not limited to, the average temperature, temperature standard deviation, maximum and minimum temperature values, average humidity, humidity standard deviation, and maximum and minimum humidity values. 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 temperature fluctuation (i.e., standard deviation) can reflect the stress on the device more than the simple average temperature, and the maximum and minimum temperature or humidity values can help identify the impact on the device under extreme conditions.

[0078] The aggregation 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 here refers to the time from the last maintenance moment to the current moment, and this design takes into account the impact of the actual maintenance cycle on the device aging process. By means of cumulative summation, the total change in temperature and humidity 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 capture the impact brought by 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, that is, the SARIMA (Seasonal ARIMA) model. Considering that the operation of the power system is significantly affected by natural seasonal changes, introducing seasonal components can improve the prediction accuracy of the model. This unit allows users to adjust parameters such as the lag order, differencing times, moving average order, etc. according to specific application scenarios, and can also set the specific period of the seasonal pattern (such as annually, quarterly, or monthly). Reasonable parameter selection is crucial for constructing a prediction model that can reflect both short-term dynamics and capture long-term trends.

[0080] The model training unit 116 is the core link in the aging model generation. It takes the cumulative sum of temperature data and the cumulative sum of humidity data provided by the aggregation statistics unit 114 as inputs and applies them to the already configured SARIMA model for training. During the training process, the model will learn the patterns and rules in these data, thereby establishing a mathematical model that can predict the aging degree of the power line. To ensure the effectiveness of the model, methods such as cross-validation are used to optimize the model performance, and it is continuously iteratively improved until a satisfactory prediction effect is achieved. The finally obtained line aging prediction model can not only help the operation and maintenance personnel understand the current state of the device but also give early warnings 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 sum temperature data and current cumulative sum 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 by using a line aging prediction model based on the current cumulative sum temperature data and the current cumulative sum humidity data; the level matching unit 120 is configured to match the 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, operating parameters such as voltage, current, temperature, and humidity collected in real time, 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 also has flexibility 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 and process the data from the target line data acquisition unit 117, especially to calculate the current cumulative sum temperature data and the current cumulative sum 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 moment), so as to obtain an index reflecting the total impact of environmental conditions during this period. In this way, not only can the long-term environmental factors' impact on 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 by using the current cumulative sum temperature data and the current cumulative sum humidity data based on a pre-trained line aging prediction model. This link is the most critical part of the entire assessment process because it directly determines the quantification result of the aging degree. The aging prediction model usually combines statistical methods and machine learning algorithms to 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 comprehensively reflecting the line aging state, providing a basis for subsequent level division.

[0085] The level matching unit 120 compares the target line aging value obtained by the aging calculation unit 119 with the predefined aging level standard to determine the aging level of the current line. This process involves establishing a scientific and reasonable grading system, which usually takes into account factors in multiple dimensions, such as the severity of aging, the impact on grid stability, etc. By setting clear thresholds or intervals, different aging values can be mapped to corresponding levels. This not only makes the aging assessment results more intuitive and easy to understand, but also provides clear guidance for operation and maintenance decisions. For example, for lines with a higher degree of aging, maintenance or replacement can be arranged as a priority; for lines with a lower degree of aging, the maintenance cycle can be appropriately extended while maintaining monitoring.

[0086] The inspection strategy matching module 105 includes a strategy library unit 121, a mapping unit 122, a level 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 line aging level to inspection strategy;

[0087] The level acquisition unit 123 is used to receive the output from the aging level generation module 104, that is, the aging level of the current line; the strategy matching unit 124 is used to match the corresponding inspection strategy based on the mapping table according to aging and the like.

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

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

[0090] The level acquisition unit 123 receives the output result 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, thus supporting the subsequent decision-making process. It can not only process the single-line aging level data, but also manage the information of multiple lines simultaneously, so as to coordinate the inspection work within a larger scope. By seamlessly 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. It automatically selects the most suitable 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 address the current aging problems without causing waste of resources. The strategy matching unit 124 also has a certain intelligent learning ability, which can continuously optimize the matching algorithm over time to 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, thus 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, and the inspection data includes appearance images and electrical performance test data; the inspection feature marking unit 126 is used to mark the key features reflecting the line aging state based on the inspection data, and the key features include the resistance change rate, the insulation resistance drop amplitude, and the 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 sum temperature data and current cumulative sum 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 display the physical state of the equipment, such as whether there are mechanical damages or corrosion phenomena; while the electrical performance test data cover indicators such as the resistance change rate and the insulation resistance decline amplitude, directly reflecting the health status inside the circuit. By comprehensively applying 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 inspection feature marking unit 126 is tasked with deeply analyzing the inspection data to extract key features reflecting the aging state of the circuit. Specifically, it marks three main key features based on the inspection data: the resistance change rate, the insulation resistance decline amplitude, and the degree of mechanical damage. The resistance change rate can reveal the aging condition of the wire material; the insulation resistance decline amplitude indicates the reduction in the effectiveness of the insulation layer, which is crucial for the safety of power transmission; the degree of mechanical damage involves physical damages such as cracks and deformations, affecting the structural integrity. By applying advanced image recognition technologies and data analysis algorithms, the inspection feature marking unit 126 can accurately identify and quantify these key features, thus providing strong support for the aging degree assessment.

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

[0096] As the last link of the aging level correction module 106, the correction unit 112 replaces the aging degree level of the current circuit according to the inspection aging level and accordingly initializes the cumulative sum temperature data and the cumulative sum humidity data. This means that once the new aging level is confirmed, all relevant historical data will be reset to ensure that subsequent aging prediction and assessment work can start from the latest base point. Doing so not only ensures the consistency and coherence of the data but also provides a more accurate basis for future maintenance decisions. In addition, the correction unit 112 also has a certain degree of flexibility and can adjust the initialized parameter settings according to the actual situation to adapt to changes in different environmental conditions.

[0097] The described AC-DC hybrid microgrid system with energy storage and photovoltaic power generation further includes a data storage module, which is used to store operation parameter data and inspection data.

[0098] The data storage module receives and saves real-time operation parameters from each working module, such as voltage, current, temperature, etc. These data are the basis for evaluating the system health status, optimizing operation efficiency, and conducting fault diagnosis. By accumulating these operation parameters over a long period, it can provide rich data support for historical data analysis, trend prediction, and performance evaluation.

[0099] In addition, this module is also used to store the data collected in 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 formulating reasonable maintenance plans. By combining inspection data with operation parameters, a more comprehensive understanding of the overall system condition can be achieved, thus making more scientific and reasonable operation and maintenance decisions.

[0100] Second Embodiment

[0101] Please refer to Figure 8 , the present invention also provides a control method for an AC-DC hybrid microgrid system with energy storage and photovoltaic power generation, including:

[0102] S201: Collect the operation parameters of each working module;

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

[0104] S202: Use weather data to correct the operation parameters to obtain corrected operation parameters;

[0105] Considering that natural environmental factors (such as weather changes) will have a significant impact on the performance of power equipment, this step introduces external meteorological data to adjust the original operation parameters. Specifically, by analyzing characteristic values such as average temperature, daily average sunshine duration, and humidity in historical weather data, combined with real-time weather forecast information, the collected operation parameters are corrected. This step ensures that the data for subsequent analysis is closer to the actual situation and improves the prediction accuracy.

[0106] S203: Generate a line aging prediction model based on the corrected operation parameters and line parameters;

[0107] Using the calibrated operating parameters and the inherent physical characteristics of the line itself (i.e., line parameters), a mathematical model that can predict the aging of the line over time is constructed. This model not only considers static attributes but also incorporates the influence 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 by adopting the corrected line aging prediction model;

[0109] Based on the aging prediction model established in the previous step, this step quantifies and evaluates 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 the adoption of targeted measures. The determination of the aging level is calculated by inputting the cumulative temperature and humidity data into the model, thus reflecting the environmental impact over a long period.

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

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

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

[0113] After actually performing the inspection task, 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 characteristics (such as the resistance change rate, the decline amplitude of insulation resistance, the degree of mechanical damage) and comparing them with the predefined aging level standards to determine the new aging level.

[0114] The above-disclosed is only a preferred embodiment of the present invention. Of course, it cannot be used to limit the scope of the rights of the present invention. Those of ordinary skill in the art can understand the entire or partial processes of implementing the above embodiments, and the equivalent changes made according to the claims of the present invention still fall within the scope covered by the invention.

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 acquire the operating parameters of each working module; The parameter correction module is used to correct the operating parameters with weather data to obtain corrected operating parameters; The aging model generation module generates a line aging prediction model based on the corrected operating parameters and line parameters; The aging level generation module is used to generate the current line aging degree level by using the corrected line aging prediction model; The inspection strategy matching module is used to match the corresponding maintenance inspection strategy based on the line aging degree level; The aging level correction module is used to correct the line aging degree level based on the line feature information obtained by inspection.

2. The photovoltaic-storage AC-DC hybrid microgrid system according to 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, and the operating parameters include 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 acquired operating parameters.

3. The photovoltaic-storage AC-DC hybrid microgrid system according to 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 the correction feature values from the historical weather data, and the correction feature values include average temperature, daily average sunshine duration, and humidity; The correction unit is used to correct the temperature data in the operating parameters based on the average temperature and daily average sunshine duration.

4. The photovoltaic-storage AC-DC hybrid microgrid system according to claim 3, characterized in that 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 corrected operating parameters, and the aging prediction features include average temperature, temperature standard deviation, maximum and minimum temperature values, average humidity, humidity standard deviation, maximum and minimum humidity values; The aggregation statistics unit is used to calculate the cumulative sum temperature data and cumulative sum humidity data within a preset time period, and 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 sum temperature data and cumulative sum humidity data to the SARIMA model for training to obtain a line aging prediction model.

5. The photovoltaic-storage AC-DC hybrid microgrid system according to claim 4, characterized in that 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 configured to acquire target line parameter data; The target data aggregation unit is configured to calculate the current cumulative sum temperature data and the current cumulative sum humidity data based on the target line parameter data; The aging degree calculation unit is configured to calculate the target line aging degree value based on the current cumulative sum temperature data and the current cumulative sum humidity data by using a line aging prediction model; The level matching unit is configured to match the current line aging degree level based on the target line aging degree value.

6. The hybrid AC-DC microgrid system for optical storage as claimed in claim 5, wherein 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 configured to set an inspection strategy library, and the training strategy library includes multiple inspection strategies; The mapping unit is configured to set a mapping table from the line aging degree level to the inspection strategy; The level acquisition unit is configured to receive the output from the aging level generation module, that is, the current line aging degree level; The strategy matching unit is configured to match the corresponding inspection strategy based on the mapping table according to the aging level.

7. The hybrid AC-DC microgrid system for optical storage as claimed in claim 6, wherein The aging level correction module includes an inspection data acquisition unit, an inspection feature marking unit, and an inspection aging level matching unit The inspection data acquisition unit is configured to collect inspection data, and the inspection data includes appearance images and electrical performance test data The inspection feature marking unit is configured to mark key features reflecting the line aging state based on the inspection data, and the key features include the resistance change rate, the insulation resistance drop amplitude, and the mechanical damage degree; The inspection aging level matching unit is configured to match the inspection aging level based on the key features; The correction unit is configured to replace the current line aging degree level based on the inspection aging level, and initialize the corresponding current cumulative sum temperature data and the current cumulative sum humidity data.

8. The hybrid AC-DC microgrid system for optical storage as claimed in claim 7, wherein The hybrid AC-DC microgrid system for optical storage further includes a data storage module, and the data storage module is configured to store operation parameter data and inspection data.

9. A control method for an optical storage AC-DC hybrid microgrid, which is applied to an optical storage AC-DC hybrid microgrid system according to any one of claims 1 to 8, characterized in that, Including: Collecting the operation parameters of each working module; Correcting the operation parameters by using weather data to obtain corrected operation parameters; Generating a line aging prediction model based on the corrected operation parameters and line parameters; For generating the current line aging degree level by using the corrected line aging prediction model; For matching the corresponding maintenance inspection strategy based on the line aging degree level; For correcting the line aging degree level based on the line feature information obtained by inspection.

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