A fault monitoring method and system for a domestic integrated energy storage system

Abstract

The application relates to a fault monitoring method for a household integrated energy storage system, which comprises the following steps: constructing a three-dimensional model of a building and constructing a historical light utilization dataset; generating a reference light utilization rate as a fault analysis standard by using a light utilization efficiency analysis model; acquiring real-time environmental light data and photovoltaic equipment light receiving data, calculating an actual light utilization rate, and comparing the actual light utilization rate with the reference value to judge photovoltaic equipment faults; acquiring historical photoelectric conversion efficiency data of an inverter, constructing a photoelectric conversion dataset, and generating photoelectric conversion efficiency in different states; acquiring historical output data of the inverter and power consumption demand data, constructing a power consumption-output dataset, generating a demand output factor, and calculating expected output data; acquiring actual power consumption demand data to calculate expected output data, and comparing the expected output data with actual output data of the inverter to judge inverter faults; and generating a fault monitoring report containing a preliminary fault type judgment and maintenance suggestions based on the fault judgment result. The scheme has the effect of improving the fault monitoring accuracy of the household integrated energy storage system.

Description

Technical Field

[0001] This invention relates to the technical field of intelligent monitoring of energy storage systems, and in particular to a fault monitoring method and system for a home integrated energy storage system. Background Technology

[0002] With the increasing popularity of environmental protection concepts, integrated home energy storage systems are gradually becoming a hot topic. More and more households are beginning to consider how to use energy efficiently, economically, and environmentally. Integrated home energy storage systems achieve self-sufficiency and efficient management of household energy by integrating photovoltaic modules, energy storage batteries, energy storage inverters, grid connection and metering equipment.

[0003] Existing integrated residential energy storage systems primarily rely on real-time monitoring and analysis of equipment operating data for fault detection. These systems typically collect data, such as voltage, current, and temperature, through sensors installed on the equipment. They then use this data to detect the equipment's operating status and determine whether a fault exists based on set data thresholds.

[0004] Regarding the aforementioned existing technologies, there are some problems with the fault monitoring of integrated home energy storage systems: existing systems often rely on a single data source for fault monitoring, lack the fusion and analysis of multi-dimensional data, which can easily lead to misjudgment or untimely fault detection, resulting in a decrease in the accuracy of fault judgment. Summary of the Invention

[0005] To improve the accuracy of fault monitoring in residential integrated energy storage systems, this application provides a fault monitoring method and system for residential integrated energy storage systems.

[0006] Firstly, the above-mentioned inventive objective of this application is achieved through the following technical solution:

[0007] A fault monitoring method for a residential integrated energy storage system, the method comprising the steps of:

[0008] Obtain building data and the distribution coordinates of photovoltaic devices, and construct a corresponding 3D building model, in which the distribution locations of the photovoltaic devices are set;

[0009] Historical environmental data and historical operating data of photovoltaic equipment are acquired, and the environmental data and operating data are correlated based on a pre-set public timeline to construct a historical solar utilization dataset.

[0010] A pre-set light utilization efficiency analysis model analyzes the light utilization dataset to generate reference light utilization rates corresponding to different environmental data. The reference light utilization rates are used as a standard for fault analysis of photovoltaic equipment.

[0011] Real-time ambient light data and light-receiving data of photovoltaic equipment are acquired to calculate the actual light utilization rate, and compared with the reference light utilization rate to determine whether there is a fault in the photovoltaic equipment;

[0012] Historical photoelectric conversion efficiency data of the inverter is obtained, and the photoelectric conversion efficiency data is correlated with the light utilization rate based on the common time axis to construct a photoelectric conversion dataset;

[0013] The pre-set photoelectric conversion analysis model analyzes the photoelectric conversion dataset based on a machine self-learning algorithm to generate the photoelectric conversion efficiency corresponding to different states of the photovoltaic device;

[0014] Historical inverter output data and electricity demand data are acquired, and the output data and electricity demand data are correlated based on the common time axis to construct an electricity consumption-output dataset;

[0015] The pre-set electricity consumption output analysis model analyzes the electricity consumption-output dataset based on a machine self-learning algorithm to generate demand output factors, which are used to calculate the expected output data corresponding to the electricity demand.

[0016] The actual electricity demand data is obtained, and the expected output data is calculated based on the demand output factor. The expected output data is used as a reference value for fault judgment.

[0017] The actual output data of the inverter is obtained and compared with the expected output data to determine whether the inverter has a fault.

[0018] Based on the fault diagnosis results of photovoltaic equipment and inverters, a fault monitoring report is generated for the home integrated energy storage system. The monitoring report includes a preliminary judgment of the fault type and corresponding maintenance suggestions.

[0019] By adopting the above technical solutions, and by constructing a 3D building model combined with the distribution coordinates of photovoltaic equipment, the equipment layout can be displayed more intuitively, providing more accurate basic data for fault monitoring; by acquiring historical environmental data and photovoltaic equipment operating data and correlating them based on a common time axis, a historical light utilization dataset can be constructed, providing more comprehensive data support for light utilization efficiency analysis; a reference light utilization rate can be generated through a pre-set light utilization efficiency analysis model, providing a more accurate standard for photovoltaic equipment fault analysis; by acquiring ambient light data and photovoltaic equipment light-receiving data in real time, the actual light utilization rate can be calculated and compared with the reference value, photovoltaic equipment faults can be detected more promptly; by acquiring historical photovoltaic conversion efficiency data of the inverter and correlating it with light utilization rate based on a common time axis, a photovoltaic conversion dataset can be constructed, providing more comprehensive data support for photovoltaic conversion efficiency analysis; and by using a pre-set photovoltaic conversion... The analysis model generates photoelectric conversion efficiency under different states, providing a more accurate reference for inverter fault diagnosis. By acquiring historical inverter output data and electricity demand data and linking them based on a common time axis, an electricity consumption-output dataset is constructed, providing a more comprehensive data foundation for electricity output analysis. A pre-set electricity output analysis model generates a demand output factor, providing a more accurate basis for calculating expected output data. By acquiring actual electricity demand data, calculating expected output data, and comparing it with actual inverter output data, inverter faults can be detected more promptly. Finally, a fault monitoring report is generated based on the fault diagnosis results, providing more detailed fault information and maintenance suggestions, improving fault handling efficiency. This invention can more comprehensively and accurately monitor faults in integrated residential energy storage systems, thereby improving the accuracy of fault monitoring, enhancing system reliability and stability, and reducing maintenance costs.

[0020] In a preferred embodiment, this application can be further configured such that the step of analyzing the light utilization dataset using a pre-set light utilization efficiency analysis model to generate reference light utilization rates corresponding to different environmental data includes the following steps:

[0021] Historical ambient light data and photovoltaic device light reception data are acquired, and the ambient light data and light reception data are correlated based on a preset common time axis to calculate the corresponding light utilization rate.

[0022] Historical status data of photovoltaic equipment is obtained, and the light utilization rate and status data are associated based on the common time axis to construct a status-light utilization rate dataset, wherein the status data includes solar panel angle data, solar panel temperature data and solar panel working area data;

[0023] The pre-set utilization rate analysis model analyzes the state-light utilization rate dataset based on a machine self-learning algorithm to generate photovoltaic equipment state factors. These equipment state factors are used to correct the light utilization rate of photovoltaic equipment when it is in different states.

[0024] By adopting the above technical solution and associating the solar utilization rate with the historical status data of photovoltaic equipment, a comprehensive dataset containing equipment status and solar utilization rate can be constructed. This helps to analyze the changes in solar utilization rate under different equipment statuses, provides data support for subsequent utilization rate correction, and improves the accuracy of fault monitoring. By generating photovoltaic equipment status factors, the solar utilization rate under different statuses can be accurately corrected, improving the accuracy of solar utilization efficiency analysis. This helps to more accurately judge the performance changes of photovoltaic equipment, detect potential faults in a timely manner, and improve the reliability and stability of the system.

[0025] In a preferred embodiment, this application can be further configured as follows: after the step of analyzing the state-light utilization dataset based on a machine self-learning algorithm using a pre-set utilization analysis model to generate the state factor of the photovoltaic device, the following steps are included:

[0026] Acquire real-time ambient light data, and calculate the initial predicted ambient light utilization rate based on the light utilization rate corresponding to the real-time ambient light data;

[0027] Real-time photovoltaic equipment status data is acquired to match the corresponding photovoltaic equipment status factor. The initial predicted light utilization rate is corrected based on the photovoltaic equipment status factor to generate a corrected predicted light utilization rate, and the corrected predicted light utilization rate is used as the reference light utilization rate.

[0028] By adopting the above technical solutions, the initial predicted light utilization rate can be accurately corrected, generating a reference light utilization rate that is more consistent with the actual operating conditions. This helps to improve the accuracy of fault monitoring, detect performance changes of photovoltaic equipment in a timely manner, optimize system operation, and improve energy utilization efficiency.

[0029] In a preferred example, this application can be further configured as follows: after the step of acquiring historical inverter output data and electricity demand data, and associating the output data and electricity demand data based on the common time axis to construct an electricity consumption-output dataset, the application includes the following steps:

[0030] The pre-set electricity demand model analyzes the electricity consumption-output dataset based on a machine learning algorithm to generate an electricity demand profile.

[0031] Based on the electricity demand profile, an electricity demand forecast trend is generated, which is used to predict the electricity demand at different times in the future cycle.

[0032] Based on the demand output factor, the predicted electricity demand data corresponding to the electricity demand forecast trend is generated.

[0033] By adopting the above technical solutions and generating predicted electricity demand data, users can gain a more accurate understanding of their electricity demand at different times in the future, allowing them to take proactive measures such as adjusting the operating time of electrical equipment, optimizing electricity usage plans, and reducing electricity costs. At the same time, this also helps power companies better manage grid load and improve the stability and reliability of the grid.

[0034] In a preferred embodiment, this application can be further configured as follows: In the step of analyzing the light utilization dataset using a pre-set light utilization efficiency analysis model to generate reference light utilization rates corresponding to different environmental data, the light utilization efficiency analysis model is set with a reference light utilization rate calculation formula to calculate the reference light utilization rate, and the reference light utilization rate calculation formula is as follows:

[0035]

[0036] Where η ref (t) represents the reference illumination utilization rate at different temperatures, η base ΔT(t) represents the historical maximum value under clear, cloudless conditions corresponding to different temperatures, ΔT(t) represents the difference between the real-time temperature and the standard temperature, C(t) represents the cloud cover index, α represents the temperature correction coefficient, and β represents the cloud cover correction coefficient. α and β are generated by a machine learning algorithm.

[0037] By adopting the above technical solution and introducing a dynamic compensation mechanism for temperature and cloud cover, the problem that traditional static thresholds cannot adapt to weather changes is solved, the reliability of reference light utilization is improved, and the misjudgment rate is reduced.

[0038] In a preferred embodiment, this application can be further configured as follows: In the step of analyzing the state-light utilization dataset based on a machine learning algorithm using a pre-set utilization analysis model to generate a photovoltaic device state factor, wherein the device state factor is used to correct the light utilization rate of the photovoltaic device when it is in different states, a photovoltaic device state factor calculation formula is provided:

[0039]

[0040] Where θ is the actual installation tilt angle of the photovoltaic equipment, θ opt Here, k represents the optimal tilt angle for different states, k is the temperature decay coefficient, and T is the solar panel temperature. optThe optimal operating temperature for the solar panel is given by η, where η is the area sensitivity factor and A is the area sensitivity factor. eff For the effective light-receiving area, A nom This refers to the nominal area.

[0041] By adopting the above technical solution, cos(θ-θ) opt This method calculates angular deviations, replacing the traditional fixed angle coefficient, making the calculation of angle coefficients more flexible and accurate. The calculation of temperature effects is more realistic than existing linear models, thereby improving the reliability of the photovoltaic equipment state factor and the accuracy of the correction.

[0042] Secondly, the above-mentioned inventive objective of this application is achieved through the following technical solutions:

[0043] A fault monitoring device for a residential integrated energy storage system, the device comprising: a building 3D model construction unit, used to acquire building data and distribution coordinate information of photovoltaic equipment, and construct a corresponding building 3D model, wherein the building 3D model contains the corresponding distribution locations of the photovoltaic equipment;

[0044] The historical solar utilization dataset construction unit is used to acquire historical environmental data and historical operating data of photovoltaic equipment, and to associate the environmental data and operating data based on a pre-set common time axis to construct a historical solar utilization dataset.

[0045] The reference light utilization rate generation unit is used to pre-set a light utilization efficiency analysis model to analyze the light utilization dataset and generate reference light utilization rates corresponding to different environmental data. The reference light utilization rate is used as a fault analysis standard for photovoltaic equipment.

[0046] The actual light utilization rate calculation unit is used to acquire real-time ambient light data and light-receiving data of photovoltaic equipment to calculate the actual light utilization rate, and compare it with the reference light utilization rate to determine whether there is a fault in the photovoltaic equipment;

[0047] A photoelectric conversion dataset construction unit is used to acquire historical photoelectric conversion efficiency data of the inverter and associate the photoelectric conversion efficiency data with the light utilization rate based on the common time axis to construct a photoelectric conversion dataset.

[0048] The photoelectric conversion efficiency generation unit is used to pre-set a photoelectric conversion analysis model to analyze the photoelectric conversion dataset based on a machine self-learning algorithm, so as to generate the photoelectric conversion efficiency corresponding to different states of the photovoltaic device;

[0049] The power consumption-output dataset construction unit is used to acquire historical inverter output data and power demand data, and associate the output data and power demand data based on the common time axis to construct the power consumption-output dataset.

[0050] The demand output factor generation unit is used to pre-set an electricity output analysis model to analyze the electricity consumption-output dataset based on a machine self-learning algorithm to generate demand output factors, which are used to calculate the expected output data corresponding to the electricity demand.

[0051] The expected output data calculation unit is used to acquire actual electricity demand data and calculate expected output data based on the demand output factor. The expected output data is used as a fault judgment reference value.

[0052] The actual output data acquisition unit is used to acquire the actual output data of the inverter and compare it with the expected output data to determine whether the inverter has a fault.

[0053] The fault monitoring report generation unit is used to generate a fault monitoring report for the home integrated energy storage system based on the fault judgment results of photovoltaic equipment and inverter. The monitoring report includes a preliminary judgment of the fault type and corresponding maintenance suggestions.

[0054] Thirdly, the above-mentioned objectives of this application are achieved through the following technical solutions:

[0055] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the fault monitoring method for a home integrated energy storage system described above.

[0056] Fourthly, the above-mentioned objectives of this application are achieved through the following technical solutions:

[0057] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described fault monitoring method for a home integrated energy storage system.

[0058] In summary, this application includes at least one of the following beneficial technical effects:

[0059] 1. By constructing a 3D building model and combining it with the coordinate information of photovoltaic equipment distribution, the equipment layout can be displayed more intuitively, providing more accurate basic data for fault monitoring; by acquiring historical environmental data and photovoltaic equipment operating data and correlating them based on a common time axis, a historical light utilization dataset can be constructed, providing more comprehensive data support for light utilization efficiency analysis; by generating a reference light utilization rate through a pre-set light utilization efficiency analysis model, a more accurate standard can be provided for photovoltaic equipment fault analysis; by acquiring real-time ambient light data and photovoltaic equipment light reception data to calculate the actual light utilization rate and comparing it with the reference value, photovoltaic equipment faults can be detected more promptly; by acquiring historical photovoltaic conversion efficiency data of inverters and correlating it with light utilization rate based on a common time axis, a photovoltaic conversion dataset can be constructed, providing more comprehensive data support for photovoltaic conversion efficiency analysis; by pre-... The established photoelectric conversion analysis model generates photoelectric conversion efficiency under different conditions, providing a more accurate reference for inverter fault diagnosis. By acquiring historical inverter output data and electricity demand data and linking them based on a common time axis, an electricity consumption-output dataset is constructed, providing a more comprehensive data foundation for electricity output analysis. A pre-set electricity output analysis model generates a demand output factor, providing a more accurate basis for calculating expected output data. By acquiring actual electricity demand data, calculating expected output data, and comparing it with actual inverter output data, inverter faults can be detected more promptly. Finally, a fault monitoring report is generated based on the fault diagnosis results, providing more detailed fault information and maintenance suggestions, improving fault handling efficiency. This invention can more comprehensively and accurately monitor faults in residential integrated energy storage systems, improve system reliability and stability, and reduce maintenance costs.

[0060] 2. By setting a reference illuminance utilization rate calculation formula, the impact of temperature and cloud cover on the illuminance utilization rate of photovoltaic equipment can be comprehensively considered, thereby more accurately calculating the reference illuminance utilization rate under different environmental conditions. Specifically, the temperature correction term in the formula... The difference between real-time and standard temperatures was considered, and the light utilization rate was adjusted using a temperature correction factor α to make the calculation results closer to the equipment performance under actual temperatures. Additionally, a cloud cover correction term was included. The light utilization rate is then corrected based on the cloud cover index C(t). The cloud cover correction coefficient β reflects the weakening effect of clouds on light intensity. These two correction coefficients α and β are generated by a machine learning algorithm, which can automatically optimize based on historical data, further improving the accuracy and adaptability of the calculation. Through this calculation method that comprehensively considers multiple environmental factors, this scheme can provide a more accurate reference standard for fault monitoring of photovoltaic equipment, timely detect potential faults, and improve the reliability and stability of the system.

[0061] 3. By setting a formula for calculating the state factor of photovoltaic equipment, factors such as the actual installation tilt angle, temperature, and effective light-receiving area of ​​the photovoltaic equipment can be comprehensively considered, and the light utilization rate under different conditions can be accurately corrected. Specifically, the cosine term in the formula, cos(θ-θ), is used to calculate the state factor. opt The deviation between the actual installation tilt angle and the optimal tilt angle was taken into account, and the temperature decay term was used to account for this. This reflects the impact of temperature on light utilization, while the area-sensitive item... This approach takes into account the difference between the effective light-receiving area and the nominal area. By comprehensively considering these factors, this solution can more accurately correct the light utilization rate, provide a more precise reference standard for fault monitoring of photovoltaic equipment, thereby timely detection of potential faults and improvement of system reliability and stability. Attached Figure Description

[0062] Figure 1 This is a flowchart of a fault monitoring method for a home integrated energy storage system according to an embodiment of this application;

[0063] Figure 2 This is a schematic diagram of a fault monitoring system for a home integrated energy storage system according to one embodiment of this application;

[0064] Figure 3 This is a schematic diagram of an electronic device according to an embodiment of this application.

[0065] Icon labels:

[0066] 1. Building 3D Model Construction Unit; 2. Historical Lighting Utilization Dataset Construction Unit; 3. Reference Lighting Utilization Rate Generation Unit; 4. Actual Lighting Utilization Rate Calculation Unit; 5. Photovoltaic Conversion Dataset Construction Unit; 6. Photovoltaic Conversion Efficiency Generation Unit; 7. Electricity Consumption-Output Dataset Construction Unit; 8. Demand Output Factor Generation Unit; 9. Expected Output Data Calculation Unit; 10. Actual Output Data Acquisition Unit; 11. Fault Monitoring Report Generation Unit. Detailed Implementation

[0067] The present application will be further described in detail below with reference to the accompanying drawings.

[0068] In one embodiment, such as Figure 1 As shown, this application discloses a fault monitoring method for a residential integrated energy storage system, which specifically includes the following steps:

[0069] S1: Obtain building data and the distribution coordinates of photovoltaic equipment, and construct the corresponding 3D building model, in which the distribution locations of photovoltaic equipment are set in the 3D building model;

[0070] Specifically, the project acquires the three-dimensional coordinate data of the building and the distribution coordinates of the photovoltaic (PV) equipment, constructing a three-dimensional model of the building that includes the locations of the PV panels. This model serves as a foundation for subsequent data collection on solar radiation and environmental impact. Constructing the three-dimensional model helps to visually demonstrate the distribution of PV equipment, providing fundamental data for subsequent solar radiation utilization analysis and improving the accuracy of fault monitoring.

[0071] S2: Acquire historical environmental data and historical operating data of photovoltaic equipment, and associate the environmental data and operating data based on a pre-set common time axis to construct a historical solar utilization dataset;

[0072] Specifically, assuming that environmental data such as daily light intensity, temperature, and humidity over the past week, along with historical operating data such as output power, voltage, and current of photovoltaic equipment, are correlated through a common timeline, a historical light utilization dataset can be constructed. By correlating historical environmental data and operating data, the performance of photovoltaic equipment under different environmental conditions can be analyzed, providing a reference standard for fault analysis.

[0073] S3: A pre-set light utilization efficiency analysis model analyzes the light utilization dataset to generate reference light utilization rates corresponding to different environmental data. The reference light utilization rates are used as a standard for fault analysis of photovoltaic equipment.

[0074] Generating a reference illuminance utilization rate as a fault analysis standard helps determine whether the performance of photovoltaic equipment is normal under different environmental conditions, thereby improving the accuracy of fault monitoring.

[0075] S4: Obtain real-time ambient light data and light-receiving data of photovoltaic equipment to calculate the actual light utilization rate, and compare it with the reference light utilization rate to determine whether there is a fault in the photovoltaic equipment;

[0076] Specifically, real-time data on ambient light intensity and the light received by photovoltaic devices are acquired to calculate the actual light utilization rate. Assume the current ambient light intensity is 1000 W / m². 2 The photovoltaic equipment has a light-receiving area of ​​40 square meters and an actual output power of 30kW, resulting in a calculated actual light utilization rate of 75%. Comparing this to the reference light utilization rate of 80%, a discrepancy is found, potentially indicating a fault in the photovoltaic equipment. By calculating the actual light utilization rate in real time and comparing it with the reference value, faults in the photovoltaic equipment can be detected promptly, improving the real-time nature and accuracy of fault monitoring.

[0077] S5: Obtain historical photoelectric conversion efficiency data of the inverter, and associate the photoelectric conversion efficiency data with the light utilization rate based on the common time axis to construct a photoelectric conversion dataset;

[0078] Specifically, the photovoltaic conversion efficiency data of the inverter over the past week (or other periods) is obtained, and then correlated with the light utilization rate data through a common time axis to construct a photovoltaic conversion dataset. By correlating the photovoltaic conversion efficiency data and the light utilization rate data, the performance of the inverter under different lighting conditions can be analyzed, providing more comprehensive data support for fault analysis.

[0079] S6: The pre-set photoelectric conversion analysis model analyzes the photoelectric conversion dataset based on a machine self-learning algorithm to generate the photoelectric conversion efficiency corresponding to different states of the photovoltaic equipment;

[0080] Specifically, the photoelectric conversion analysis model analyzes the photoelectric conversion dataset to generate the photoelectric conversion efficiency of photovoltaic equipment under different conditions. For example, the photoelectric conversion efficiency is 90% when the light utilization rate is 80%, and 85% when the light utilization rate is 60%. By generating the photoelectric conversion efficiency under different conditions, the performance of the inverter can be evaluated more accurately, providing a more precise reference standard for fault diagnosis.

[0081] S7: Obtain historical inverter output data and electricity demand data, and associate the output data and electricity demand data based on the common time axis to construct an electricity consumption-output dataset;

[0082] S8: The pre-set electricity consumption output analysis model analyzes the electricity consumption-output dataset based on a machine self-learning algorithm to generate demand output factors, which are used to calculate the expected output data corresponding to the electricity demand.

[0083] Specifically, the electricity consumption-output analysis model analyzes the electricity consumption-output dataset to generate a demand output factor. For example, during peak electricity demand periods, the demand output factor is 1.2, indicating that the inverter needs to provide more output power than usual.

[0084] By generating demand output factors, the expected output data corresponding to electricity demand can be calculated more accurately, providing a more precise reference standard for fault diagnosis.

[0085] S9: Obtain actual electricity demand data and calculate expected output data based on the demand output factor. The expected output data is used as a reference value for fault judgment.

[0086] Specifically, assuming the current actual power demand is 20kW and the demand output factor is 1.2, the calculated expected output data is 24kW. By calculating the expected output data, a reference value can be provided for fault diagnosis, helping to determine whether the inverter is working properly.

[0087] S10: Obtain the actual output data of the inverter and compare it with the expected output data to determine whether the inverter has a fault;

[0088] Specifically, by comparing actual output data with expected output data, inverter faults can be detected in a timely manner, improving the accuracy and timeliness of fault monitoring.

[0089] S11: Generate a fault monitoring report for the home integrated energy storage system based on the fault judgment results of photovoltaic equipment and inverters;

[0090] The monitoring report includes a preliminary assessment of the fault type and corresponding maintenance recommendations. Specifically, a fault monitoring report is generated based on the fault assessment results of the photovoltaic equipment and inverter. The report includes a preliminary assessment of the fault type, such as decreased photovoltaic panel efficiency or insufficient inverter output, as well as corresponding maintenance recommendations, such as cleaning the photovoltaic panels and checking the inverter connections. Generating a fault monitoring report helps maintenance personnel quickly understand the system fault situation, take appropriate maintenance measures, and improve the system's reliability and operating efficiency.

[0091] Furthermore, assuming that the fault assessment in steps S4 and S10 reveals that the actual solar utilization rate of the photovoltaic equipment is lower than the reference value, indicating a potential issue of reduced efficiency in the photovoltaic panels; and simultaneously, that the actual output data of the inverter is lower than the expected output data, indicating a potential issue of insufficient inverter output, a fault monitoring report is generated based on these assessments. The content of the monitoring report is as follows:

[0092] Photovoltaic equipment fault diagnosis results:

[0093] Fault type: Decreased photovoltaic panel efficiency

[0094] Possible causes: Dirt on the surface of the photovoltaic panel, damage or aging of some photovoltaic panels.

[0095] Maintenance recommendations: Clean the surface of the photovoltaic panels, inspect and replace any damaged or aging panels.

[0096] Inverter fault diagnosis results:

[0097] Fault type: Insufficient inverter output

[0098] Possible causes: Faulty internal components of the inverter, loose or damaged wiring.

[0099] Repair suggestion: Inspect the internal components of the inverter, tighten or replace the connection lines.

[0100] The beneficial effects of the fault monitoring report in this application are as follows:

[0101] Comprehensiveness: The fault monitoring report integrates the fault diagnosis results of photovoltaic equipment and inverters, providing comprehensive fault information to help operation and maintenance personnel quickly understand the overall operating status of the system.

[0102] Accuracy: The report compares actual data with preset reference values ​​to ensure the accuracy of fault diagnosis and reduce the possibility of misdiagnosis and omission.

[0103] Guidance: The report provides specific maintenance recommendations to guide maintenance personnel in taking effective maintenance measures to improve maintenance efficiency and system reliability.

[0104] Timeliness: Through real-time monitoring and analysis, fault monitoring reports can be generated in a timely manner, ensuring that maintenance personnel can respond quickly, reducing system downtime, and improving system availability and stability.

[0105] User-friendliness: The report content is clear, concise, and easy to understand, allowing even non-professionals to quickly grasp the fault situation and repair suggestions, thus improving user satisfaction.

[0106] In summary, regarding steps S1-S11, compared to existing technologies, this solution, by constructing a 3D building model and combining it with the distribution coordinates of photovoltaic equipment, can more intuitively display the equipment layout, providing more accurate basic data for fault monitoring; by acquiring historical environmental data and photovoltaic equipment operating data and correlating them based on a common time axis, a historical light utilization dataset is constructed, providing more comprehensive data support for light utilization efficiency analysis; a reference light utilization rate is generated through a pre-set light utilization efficiency analysis model, providing a more precise standard for photovoltaic equipment fault analysis; by acquiring ambient light data and photovoltaic equipment light reception data in real time to calculate the actual light utilization rate and comparing it with the reference value, photovoltaic equipment faults can be detected more promptly; by acquiring historical photovoltaic conversion efficiency data of the inverter and correlating it with light utilization rate based on a common time axis, a photovoltaic conversion dataset is constructed, providing a basis for photovoltaic conversion efficiency analysis. More comprehensive data support; by generating photoelectric conversion efficiency under different conditions through a pre-set photoelectric conversion analysis model, more accurate references are provided for inverter fault diagnosis; by acquiring historical inverter output data and electricity demand data and linking them based on a common time axis, an electricity consumption-output dataset is constructed, providing a more comprehensive data foundation for electricity output analysis; by generating demand output factors through a pre-set electricity output analysis model, more accurate basis is provided for calculating expected output data; by acquiring actual electricity demand data, calculating expected output data and comparing it with actual inverter output data, inverter faults can be detected more promptly; finally, a fault monitoring report is generated based on the fault diagnosis results, providing more detailed fault information and maintenance suggestions, improving fault handling efficiency. This invention can more comprehensively and accurately monitor faults in residential integrated energy storage systems, improve system reliability and stability, and reduce maintenance costs.

[0107] In step S3: the pre-set light utilization efficiency analysis model analyzes the light utilization dataset to generate reference light utilization rates corresponding to different environmental data, the steps include the following:

[0108] S31: Obtain historical ambient light data and photovoltaic equipment light reception data, and associate the ambient light data and the light reception data based on a preset common time axis to calculate the corresponding light utilization rate;

[0109] By correlating ambient light data with the light received by photovoltaic (PV) devices, the light utilization rate at different time points can be accurately calculated, providing fundamental data for subsequent light utilization efficiency analysis. This helps to promptly detect performance changes in PV devices under different lighting conditions and improves the accuracy of fault monitoring.

[0110] S32: Obtain historical status data of photovoltaic equipment, and associate the light utilization rate and the status data based on the common time axis to construct a status-light utilization rate dataset, wherein the status data includes solar panel angle data, solar panel temperature data and solar panel working area data;

[0111] S33: The pre-set utilization rate analysis model analyzes the state-light utilization rate dataset based on a machine self-learning algorithm to generate photovoltaic equipment state factors. The equipment state factors are used to correct the light utilization rate of photovoltaic equipment when it is in different states.

[0112] In summary, by linking solar irradiance utilization rate with historical state data of photovoltaic (PV) equipment, a comprehensive dataset containing equipment state and solar irradiance utilization rate can be constructed. This helps analyze changes in solar irradiance utilization rate under different equipment states, providing data support for subsequent utilization rate correction and improving the accuracy of fault monitoring. By generating PV equipment state factors, solar irradiance utilization rate under different states can be accurately corrected, improving the accuracy of solar utilization efficiency analysis. This helps to more accurately judge the performance changes of PV equipment, promptly detect potential faults, and improve the reliability and stability of the system.

[0113] In step S3, the light utilization efficiency analysis model is equipped with a reference light utilization rate calculation formula to calculate the reference light utilization rate. The reference light utilization rate calculation formula is as follows:

[0114]

[0115] Where η ref (t) represents the reference illumination utilization rate at different temperatures, η baseΔT(t) represents the historical maximum value under clear, cloudless conditions corresponding to different temperatures, ΔT(t) represents the difference between the real-time temperature and the standard temperature, C(t) represents the cloud cover index, α represents the temperature correction coefficient, and β represents the cloud cover correction coefficient. α and β are generated by a machine learning algorithm.

[0116] Specifically, by setting a reference illuminance utilization rate calculation formula, the impact of temperature and cloud cover on the illuminance utilization rate of photovoltaic equipment can be comprehensively considered, thereby more accurately calculating the reference illuminance utilization rate under different environmental conditions. Specifically, the temperature correction term in the formula... The difference between real-time and standard temperatures was considered, and the light utilization rate was adjusted using a temperature correction factor α to make the calculation results closer to the equipment performance under actual temperatures. Additionally, a cloud cover correction term was included. The solar utilization rate is then corrected based on the cloud cover index C(t). The cloud cover correction coefficient β reflects the weakening effect of clouds on solar intensity. These two correction coefficients α and β are generated by a machine learning algorithm, which can automatically optimize based on historical data, further improving the accuracy and adaptability of the calculation. Through this calculation method that comprehensively considers multiple environmental factors, this scheme can provide a more accurate reference standard for fault monitoring of photovoltaic equipment, timely detect potential faults, and improve the reliability and stability of the system.

[0117] Regarding step S33, in which the pre-set utilization analysis model analyzes the state-light utilization dataset based on a machine self-learning algorithm to generate a photovoltaic device state factor, and this state factor is used to correct the light utilization rate of the photovoltaic device when it is in different states, a photovoltaic device state factor calculation formula is set:

[0118]

[0119] Where θ is the actual installation tilt angle of the photovoltaic equipment, θ opt Here, k represents the optimal tilt angle for different states, k is the temperature decay coefficient, and T is the solar panel temperature. opt The optimal operating temperature for the solar panel is given by η, where η is the area sensitivity factor and A is the area sensitivity factor. eff For the effective light-receiving area, A nom This refers to the nominal area.

[0120] The beneficial effect of this scheme is that, by setting a formula for calculating the state factor of photovoltaic equipment, it can comprehensively consider factors such as the actual installation tilt angle, temperature, and effective light-receiving area of ​​the photovoltaic equipment, and accurately correct the light utilization rate under different conditions. Specifically, the cosine term cos(θ-θ) in the formula... opt The deviation between the actual installation tilt angle and the optimal tilt angle was taken into account, and the temperature decay term was used to account for this. This reflects the impact of temperature on light utilization, while the area-sensitive item... This approach takes into account the difference between the effective light-receiving area and the nominal area. By comprehensively considering these factors, this solution can more accurately correct the light utilization rate, provide a more precise reference standard for fault monitoring of photovoltaic equipment, thereby timely detection of potential faults and improving the reliability and stability of the system.

[0121] After step S33: the pre-set utilization analysis model analyzes the state-light utilization dataset based on a machine self-learning algorithm to generate the photovoltaic device state factor, the following steps are included:

[0122] S331: Obtain real-time ambient light data, and calculate the initial predicted ambient light utilization rate based on the light utilization rate corresponding to the real-time ambient light data;

[0123] For example, suppose that on a sunny midday, real-time ambient light data shows that the light intensity is 1000 W / m². 2 Based on historical data and preset models, the light utilization rate is typically 80% at this time. Therefore, the system calculates an initial predicted light utilization rate of 80%. By acquiring real-time ambient light data and calculating the initial predicted light utilization rate, the system can quickly respond to environmental changes, providing basic data for subsequent adjustments to the light utilization rate. This helps improve the real-time performance and accuracy of predictions, ensuring that the system can adjust its operating status in a timely manner and optimize energy utilization.

[0124] S332: Obtain real-time photovoltaic equipment status data to match the corresponding photovoltaic equipment status factor, correct the initial predicted light utilization rate based on the photovoltaic equipment status factor to generate a corrected predicted light utilization rate, and use the corrected predicted light utilization rate as the reference light utilization rate.

[0125] Specifically, assuming a sunny midday, real-time photovoltaic (PV) equipment status data shows that the actual installation tilt angle of the PV panels is 30 degrees, the temperature is 35℃, the effective light-receiving area is 45 square meters, and the nominal area is 50 square meters. Based on the preset PV equipment status factor calculation formula and substituting the example data from this application: assuming the optimal tilt angle is 40 degrees, the optimal operating temperature is 25℃, the temperature attenuation coefficient k is 0.1, and the area sensitivity factor η is 0.2, the calculated γ is... state =0.349, therefore, the corrected predicted light utilization rate is: Corrected predicted light utilization rate = 80% × 0.349 ≈ 27.9%, and this corrected predicted light utilization rate will be used as the reference light utilization rate.

[0126] Furthermore, in the example, 27.9% differs significantly from 80%, exceeding the generally set threshold. However, in this application's example, it precisely reflects the actual environmental and weather conditions. Therefore, it is evident that by acquiring real-time photovoltaic equipment status data and matching the corresponding photovoltaic equipment status factors, the system can accurately correct the initial predicted solar utilization rate, generating a reference solar utilization rate that better reflects the actual operating conditions. This helps improve the accuracy of fault monitoring, promptly detect performance changes in photovoltaic equipment, optimize system operation, and improve energy utilization efficiency.

[0127] After step S7: acquiring historical inverter output data and electricity demand data, and associating the output data and electricity demand data based on the common time axis to construct an electricity consumption-output dataset, the following steps are included:

[0128] S71: The pre-set electricity demand model analyzes the electricity demand-output dataset based on a machine self-learning algorithm to generate an electricity demand profile;

[0129] Specifically, suppose a household's electricity consumption-output dataset shows that peak electricity consumption on weekdays occurs between 7:00 and 9:00 AM and between 7:00 and 10:00 PM, while peak consumption on weekends is more dispersed. The electricity demand model analyzes this data using a machine learning algorithm to generate an electricity demand profile, showing the household's electricity consumption habits and patterns.

[0130] By generating electricity demand profiles, we can gain a clear understanding of users' electricity consumption habits and patterns, providing foundational data for subsequent electricity demand forecasting. This helps improve the accuracy and relevance of forecasts, and optimize energy allocation and utilization efficiency.

[0131] S72: Generate an electricity demand forecast trend based on the electricity demand profile. The electricity demand forecast trend is used to predict the electricity demand at different times in the future cycle.

[0132] Specifically, based on the above electricity demand profile, the system predicts electricity demand trends for the coming week. For example, it predicts that peak weekday electricity demand will still occur between 7:00 AM and 9:00 AM and between 7:00 PM and 10:00 PM, while weekend demand will be relatively stable. This trend prediction can help users plan their electricity usage in advance and avoid excessive electricity consumption during peak hours.

[0133] By generating electricity demand forecast trends, users can anticipate future changes in electricity demand, rationally plan their electricity usage, avoid excessive electricity consumption during peak hours, and reduce electricity costs. Simultaneously, this also helps power companies optimize grid load allocation and improve grid operating efficiency.

[0134] S73: Generate predicted electricity demand data corresponding to the electricity demand forecast trend based on the demand output factor;

[0135] Specifically, assuming a demand output factor of 1.2, this means that during peak electricity consumption periods, users' electricity demand will increase by 20% compared to normal times. Based on the electricity demand forecast trend, the system generates specific predicted electricity demand data. For example, if the predicted electricity demand from 7:00 AM to 9:00 AM on a certain weekday is 10kW, after considering the demand output factor, the predicted electricity demand data is 12kW.

[0136] By generating forecasted electricity demand data, users can gain a more accurate understanding of their electricity needs at different times in the future, allowing them to take proactive measures such as adjusting the operating time of electrical equipment, optimizing electricity usage plans, and reducing electricity costs. At the same time, this also helps power companies better manage grid load and improve grid stability and reliability.

[0137] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0138] In one embodiment, a fault monitoring device for a residential integrated energy storage system is provided, which corresponds one-to-one with the fault monitoring method for a residential integrated energy storage system described in the above embodiments. For example... Figure 2 As shown, the fault monitoring device for a home integrated energy storage system includes a building 3D model construction unit 1, which is used to acquire building data and the distribution coordinate information of photovoltaic equipment, and construct a corresponding building 3D model, wherein the building 3D model contains the corresponding distribution locations of photovoltaic equipment.

[0139] Historical solar utilization dataset construction unit 2 is used to acquire historical environmental data and historical operating data of photovoltaic equipment, and to associate the environmental data and operating data based on a pre-set common time axis to construct a historical solar utilization dataset.

[0140] The reference light utilization rate generation unit 3 is used to pre-set a light utilization efficiency analysis model to analyze the light utilization dataset and generate reference light utilization rates corresponding to different environmental data. The reference light utilization rate is used as a fault analysis standard for photovoltaic equipment.

[0141] The actual light utilization rate calculation unit 4 is used to acquire real-time ambient light data and light-receiving data of photovoltaic equipment to calculate the actual light utilization rate, and compare it with the reference light utilization rate to determine whether the photovoltaic equipment has a fault.

[0142] Photovoltaic conversion dataset construction unit 5 is used to acquire historical photovoltaic conversion efficiency data of the inverter, and associate the photovoltaic conversion efficiency data with the light utilization rate based on the common time axis to construct a photovoltaic conversion dataset;

[0143] The photoelectric conversion efficiency generation unit 6 is used to pre-set a photoelectric conversion analysis model to analyze the photoelectric conversion dataset based on a machine self-learning algorithm, so as to generate the photoelectric conversion efficiency corresponding to different states of the photovoltaic device.

[0144] The power consumption-output dataset construction unit 7 is used to acquire historical inverter output data and power demand data, and associate the output data and power demand data based on the common time axis to construct the power consumption-output dataset.

[0145] The demand output factor generation unit 8 is used to pre-set an electricity output analysis model to analyze the electricity consumption-output dataset based on a machine self-learning algorithm to generate demand output factors. The demand output factors are used to calculate the expected output data corresponding to the electricity demand.

[0146] The expected output data calculation unit 9 is used to obtain actual electricity demand data and calculate expected output data based on the demand output factor. The expected output data is used as a fault judgment reference value.

[0147] The actual output data acquisition unit 10 is used to acquire the actual output data of the inverter and compare it with the expected output data to determine whether the inverter has a fault.

[0148] The fault monitoring report generation unit 11 is used to generate a fault monitoring report for the home integrated energy storage system based on the fault judgment results of the photovoltaic equipment and inverter. The monitoring report includes a preliminary judgment of the fault type and corresponding maintenance suggestions.

[0149] Specific limitations regarding the fault monitoring device for integrated residential energy storage systems can be found in the limitations regarding the fault monitoring method for integrated residential energy storage systems described above, and will not be repeated here. Each module in the aforementioned fault monitoring device for integrated residential energy storage systems can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the electronic device, or stored in the memory of the electronic device as software, so that the processor can call and execute the corresponding operations of each module.

[0150] In one embodiment, an electronic device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3As shown, the electronic device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the database. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a fault monitoring method for a home integrated energy storage system.

[0151] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0152] Obtain building data and the distribution coordinates of photovoltaic devices, and construct a corresponding 3D building model, in which the distribution locations of the photovoltaic devices are set;

[0153] Historical environmental data and historical operating data of photovoltaic equipment are acquired, and the environmental data and operating data are correlated based on a pre-set public timeline to construct a historical solar utilization dataset.

[0154] A pre-set light utilization efficiency analysis model analyzes the light utilization dataset to generate reference light utilization rates corresponding to different environmental data. The reference light utilization rates are used as a standard for fault analysis of photovoltaic equipment.

[0155] Real-time ambient light data and light-receiving data of photovoltaic equipment are acquired to calculate the actual light utilization rate, and compared with the reference light utilization rate to determine whether there is a fault in the photovoltaic equipment;

[0156] Historical photoelectric conversion efficiency data of the inverter is obtained, and the photoelectric conversion efficiency data is correlated with the light utilization rate based on the common time axis to construct a photoelectric conversion dataset;

[0157] The pre-set photoelectric conversion analysis model analyzes the photoelectric conversion dataset based on a machine self-learning algorithm to generate the photoelectric conversion efficiency corresponding to different states of the photovoltaic device;

[0158] Historical inverter output data and electricity demand data are acquired, and the output data and electricity demand data are correlated based on the common time axis to construct an electricity consumption-output dataset;

[0159] The pre-set electricity consumption output analysis model analyzes the electricity consumption-output dataset based on a machine self-learning algorithm to generate demand output factors, which are used to calculate the expected output data corresponding to the electricity demand.

[0160] The actual electricity demand data is obtained, and the expected output data is calculated based on the demand output factor. The expected output data is used as a reference value for fault judgment.

[0161] The actual output data of the inverter is obtained and compared with the expected output data to determine whether the inverter has a fault.

[0162] Based on the fault diagnosis results of photovoltaic equipment and inverters, a fault monitoring report is generated for the home integrated energy storage system. The monitoring report includes a preliminary judgment of the fault type and corresponding maintenance suggestions.

[0163] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0164] Obtain building data and the distribution coordinates of photovoltaic devices, and construct a corresponding 3D building model, in which the distribution locations of the photovoltaic devices are set;

[0165] Historical environmental data and historical operating data of photovoltaic equipment are acquired, and the environmental data and operating data are correlated based on a pre-set public timeline to construct a historical solar utilization dataset.

[0166] A pre-set light utilization efficiency analysis model analyzes the light utilization dataset to generate reference light utilization rates corresponding to different environmental data. The reference light utilization rates are used as a standard for fault analysis of photovoltaic equipment.

[0167] Real-time ambient light data and light-receiving data of photovoltaic equipment are acquired to calculate the actual light utilization rate, and compared with the reference light utilization rate to determine whether there is a fault in the photovoltaic equipment;

[0168] Historical photoelectric conversion efficiency data of the inverter is obtained, and the photoelectric conversion efficiency data is correlated with the light utilization rate based on the common time axis to construct a photoelectric conversion dataset;

[0169] The pre-set photoelectric conversion analysis model analyzes the photoelectric conversion dataset based on a machine self-learning algorithm to generate the photoelectric conversion efficiency corresponding to different states of the photovoltaic device;

[0170] Historical inverter output data and electricity demand data are acquired, and the output data and electricity demand data are correlated based on the common time axis to construct an electricity consumption-output dataset;

[0171] The pre-set electricity consumption output analysis model analyzes the electricity consumption-output dataset based on a machine self-learning algorithm to generate demand output factors, which are used to calculate the expected output data corresponding to the electricity demand.

[0172] The actual electricity demand data is obtained, and the expected output data is calculated based on the demand output factor. The expected output data is used as a reference value for fault judgment.

[0173] The actual output data of the inverter is obtained and compared with the expected output data to determine whether the inverter has a fault.

[0174] Based on the fault diagnosis results of photovoltaic equipment and inverters, a fault monitoring report is generated for the home integrated energy storage system. The monitoring report includes a preliminary judgment of the fault type and corresponding maintenance suggestions.

[0175] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0176] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0177] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A fault monitoring method for a residential integrated energy storage system, characterized in that, The method includes the following steps: acquiring building data and the distribution coordinate information of photovoltaic equipment, and constructing a corresponding three-dimensional building model, wherein the three-dimensional building model contains the corresponding distribution locations of the photovoltaic equipment; Historical environmental data and historical operating data of photovoltaic equipment are acquired, and the environmental data and operating data are correlated based on a pre-set public timeline to construct a historical solar utilization dataset. A pre-set light utilization efficiency analysis model analyzes the light utilization dataset to generate reference light utilization rates corresponding to different environmental data. The reference light utilization rates are used as a standard for fault analysis of photovoltaic equipment. Real-time ambient light data and light-receiving data of photovoltaic equipment are acquired to calculate the actual light utilization rate, and compared with the reference light utilization rate to determine whether there is a fault in the photovoltaic equipment; Historical photoelectric conversion efficiency data of the inverter is obtained, and the photoelectric conversion efficiency data is correlated with the light utilization rate based on the common time axis to construct a photoelectric conversion dataset; The pre-set photoelectric conversion analysis model analyzes the photoelectric conversion dataset based on a machine self-learning algorithm to generate the photoelectric conversion efficiency corresponding to different states of the photovoltaic device; Historical inverter output data and electricity demand data are acquired, and the output data and electricity demand data are correlated based on the common time axis to construct an electricity consumption-output dataset; The pre-set electricity consumption output analysis model analyzes the electricity consumption-output dataset based on a machine self-learning algorithm to generate demand output factors, which are used to calculate the expected output data corresponding to the electricity demand. Obtain actual electricity demand data, calculate expected output data based on the demand output factor, and use the expected output data as a reference value for fault judgment. The actual output data of the inverter is obtained and compared with the expected output data to determine whether the inverter has a fault. Based on the fault diagnosis results of photovoltaic equipment and inverters, a fault monitoring report is generated for the home integrated energy storage system. The monitoring report includes a preliminary judgment of the fault type and corresponding maintenance suggestions.

2. The fault monitoring method for a residential integrated energy storage system according to claim 1, characterized in that, The step of analyzing the light utilization dataset using a pre-set light utilization efficiency analysis model to generate reference light utilization rates for different environmental data includes the following steps: Historical ambient light data and photovoltaic device light reception data are acquired, and the ambient light data and light reception data are correlated based on a preset common time axis to calculate the corresponding light utilization rate. Historical status data of photovoltaic equipment is obtained, and the light utilization rate and status data are associated based on the common time axis to construct a status-light utilization rate dataset, wherein the status data includes solar panel angle data, solar panel temperature data and solar panel working area data; The pre-set utilization analysis model analyzes the state-light utilization dataset based on a machine self-learning algorithm to generate photovoltaic equipment state factors. These equipment state factors are used to correct the light utilization of photovoltaic equipment when it is in different states.

3. The fault monitoring method for a residential integrated energy storage system according to claim 2, characterized in that, After the step of analyzing the state-light utilization rate dataset based on a machine self-learning algorithm using a pre-set utilization rate analysis model to generate the state factor of photovoltaic equipment, the following steps are included: Acquire real-time ambient light data, and calculate the initial predicted ambient light utilization rate based on the light utilization rate corresponding to the real-time ambient light data; Real-time photovoltaic equipment status data is acquired to match the corresponding photovoltaic equipment status factor. The initial predicted light utilization rate is corrected based on the photovoltaic equipment status factor to generate a corrected predicted light utilization rate, and the corrected predicted light utilization rate is used as the reference light utilization rate.

4. The fault monitoring method for a residential integrated energy storage system according to claim 1, characterized in that, After acquiring historical inverter output data and electricity demand data, and associating the output data and electricity demand data based on the common time axis to construct an electricity consumption-output dataset, the following steps are included: The pre-set electricity demand model analyzes the electricity consumption-output dataset based on a machine learning algorithm to generate an electricity demand profile. Based on the electricity demand profile, an electricity demand forecast trend is generated, which is used to predict the electricity demand at different times in the future cycle. Based on the demand output factor, the predicted electricity demand data corresponding to the electricity demand forecast trend is generated.

5. A fault monitoring method for a residential integrated energy storage system according to claim 2, characterized in that, In the step of analyzing the light utilization dataset using a pre-set light utilization efficiency analysis model to generate reference light utilization rates corresponding to different environmental data, the light utilization efficiency analysis model is equipped with a reference light utilization rate calculation formula to calculate the reference light utilization rate. The reference light utilization rate calculation formula is as follows: ； in Reference light utilization rate for different temperatures, These represent the historical maximum values ​​under clear, cloudless conditions at different temperatures. This is the difference between the real-time temperature and the standard temperature. Cloud cover index This is the temperature correction factor. This is the cloud cover correction factor. and Generated by a machine learning algorithm.

6. A fault monitoring method for a residential integrated energy storage system according to claim 3, characterized in that, In the step of analyzing the state-light utilization rate dataset based on a machine self-learning algorithm using a pre-set utilization rate analysis model to generate a photovoltaic equipment state factor, which is used to correct the light utilization rate of the photovoltaic equipment when it is in different states, a photovoltaic equipment state factor calculation formula is set: ； in This refers to the actual installation tilt angle of the photovoltaic equipment. Here, k represents the optimal tilt angle for different states, T is the temperature decay coefficient, and T is the solar panel temperature. The optimal operating temperature for solar panels As an area-sensitive factor, For effective light-receiving area, This refers to the nominal area.

7. A fault monitoring device for a residential integrated energy storage system, applied to the fault monitoring method for a residential integrated energy storage system as described in any one of claims 1-6, characterized in that, The device includes: a building 3D model construction unit (1), used to acquire building data and distribution coordinate information of photovoltaic equipment, and construct a corresponding building 3D model, wherein the building 3D model is set with the corresponding distribution location of photovoltaic equipment; The historical solar utilization dataset construction unit (2) is used to acquire historical environmental data and historical working data of photovoltaic equipment, and to associate the environmental data and working data based on a pre-set public time axis to construct a historical solar utilization dataset. The reference light utilization rate generation unit (3) is used to pre-set a light utilization efficiency analysis model to analyze the light utilization dataset in order to generate reference light utilization rates corresponding to different environmental data. The reference light utilization rate is used as a fault analysis standard for photovoltaic equipment. The actual light utilization rate calculation unit (4) is used to obtain real-time ambient light data and light-receiving data of photovoltaic equipment to calculate the actual light utilization rate, and compare it with the reference light utilization rate to determine whether the photovoltaic equipment has a fault. Photovoltaic conversion dataset construction unit (5) is used to obtain historical photovoltaic conversion efficiency data of inverters and associate the photovoltaic conversion efficiency data with the light utilization rate based on the common time axis to construct a photovoltaic conversion dataset; Photovoltaic conversion efficiency generation unit (6) is used to pre-set a photovoltaic conversion analysis model to analyze the photovoltaic conversion dataset based on a machine self-learning algorithm, so as to generate the photovoltaic conversion efficiency corresponding to different states of photovoltaic equipment; The power consumption-output dataset construction unit (7) is used to acquire historical inverter output data and power demand data, and associate the output data and power demand data based on the common time axis to construct the power consumption-output dataset. The demand output factor generation unit (8) is used to pre-set an electricity output analysis model to analyze the electricity consumption-output dataset based on a machine self-learning algorithm to generate demand output factors. The demand output factors are used to calculate the expected output data corresponding to the electricity demand. The expected output data calculation unit (9) is used to obtain actual electricity demand data and calculate expected output data based on the demand output factor. The expected output data is used as a fault judgment reference value. The actual output data acquisition unit (10) is used to acquire the actual output data of the inverter and compare it with the expected output data to determine whether the inverter has a fault. The fault monitoring report generation unit (11) is used to generate a fault monitoring report for the home integrated energy storage system based on the fault judgment results of photovoltaic equipment and inverter.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of a fault monitoring method for a home integrated energy storage system as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the fault monitoring method for a home integrated energy storage system as described in any one of claims 1 to 6.

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