Photovoltaic inverter real-time monitoring system and method based on intelligent gateway

Abstract

The invention discloses a photovoltaic inverter real-time monitoring system and method based on an intelligent gateway, and relates to the technical field of equipment monitoring, and the system comprises an environmental stress field modeling module, a performance baseline prediction module, a coupling analysis early warning module, and an active intervention and feedback optimization module. According to the method, a comprehensive stress index is generated through multi-source environment data acquisition and dynamic stress field calculation; the accumulated stress and the aging factor are analyzed, and each stress value is compensated; fusing the data to generate a dynamic performance baseline and predicting; the performance deviation degree is calculated, and fault early warning is triggered in a graded mode; and a multi-parameter utility function is constructed to generate an optimal intervention strategy, and after digital twinborn verification, system parameters are executed and optimized in a closed-loop manner, so that accurate and efficient monitoring, operation and maintenance of the photovoltaic inverter are realized.

Description

Technical Field

[0001] This invention relates to the field of equipment monitoring technology, specifically to a real-time monitoring system and method for photovoltaic inverters based on a smart gateway. Background Technology

[0002] Photovoltaic inverters operate in complex natural environments for extended periods, and environmental stresses such as temperature and humidity fluctuations and mechanical vibrations have a gradual impact on device aging. Existing monitoring solutions primarily focus on the inverter's own electrical parameters, lacking systematic means to perceive the internal microenvironment of the cabinet (such as heat sink temperature distribution and humidity changes in enclosed spaces) and external geographical and climatic factors (such as high altitude and low air pressure, and differences in irradiance due to slope aspect). Especially for regional environmental characteristics such as sandstorms and salt spray, traditional systems struggle to quantify their stress accumulation effects. Numerous failure cases demonstrate that device degradation caused by environmental factors often undergoes a long, insidious process, with irreversible damage occurring only when electrical parameters become abnormal.

[0003] Fault determination methods based on fixed thresholds are ill-suited to address the natural degradation of equipment performance. Inverters that have been in operation for many years will inevitably experience a gradual decline in output power due to material aging. Simply applying new equipment threshold standards can easily trigger false alarms; conversely, genuine faults (such as insulation degradation) may be masked by environmental interference in their early stages. While existing technologies attempt to introduce predictive models, their analysis of the coupling relationship between "environment-aging-performance" is insufficient, leading to a lag in early warning windows. Although some systems possess auxiliary means such as infrared scanning, they largely rely on post-incident manual intervention and cannot achieve automatic tracing of the root cause.

[0004] Current operation and maintenance decisions often focus on a single technical objective: reducing load when the temperature exceeds a certain level, and shutting down the plant when vibration exceeds a certain limit. This simplistic response model fails to consider the economic needs of power plant operation. For example, reducing load during peak electricity price periods amplifies revenue loss, while over-exploiting aging equipment accelerates its lifespan decline. Maintenance strategies also fail to link spare parts costs and inventory status, often resulting in excessive expenditures for emergency replacements. The industry urgently needs a control framework that can synergistically optimize power generation revenue, equipment lifespan, and operation and maintenance costs, but existing systems lack multi-objective collaborative decision-making capabilities.

[0005] To address the aforementioned shortcomings, a technical solution is provided. Summary of the Invention

[0006] The purpose of this invention is to solve existing problems and to propose a real-time monitoring system and method for photovoltaic inverters based on a smart gateway.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A real-time monitoring system for photovoltaic inverters based on a smart gateway includes:

[0009] The environmental stress field modeling module is used to collect multi-source environmental data, dynamically calculate thermal, wet, and mechanical stresses and generate cumulative stress indices, improve model accuracy through stress field compensation, and output power plant-level stress thermal maps to locate damage risk areas.

[0010] The performance baseline prediction module is used to integrate standard performance models and environmental stress data, dynamically generate compensation baselines by combining equipment aging factors, and use LSTM to predict future states; it also calculates performance deviation in real time and distinguishes between natural aging and real anomalies.

[0011] The coupling analysis and early warning module is used to analyze the time-delay correlation between environmental parameters and equipment anomalies, and to locate the root cause of the fault based on a dynamic decision tree; it triggers early warnings according to the degree of deviation and generates diagnostic reports through infrared scanning.

[0012] The active intervention and feedback optimization module is used to construct a multi-objective utility function to generate the optimal strategy, and execute control commands after verification in multiple scenarios using digital twins; it compares the predicted and actual utility values ​​for closed-loop optimization and dynamically adjusts system parameters.

[0013] Furthermore, the execution process of the environmental stress field modeling module is as follows:

[0014] Real-time data collection from weather stations, including irradiance, temperature, humidity, wind speed, and precipitation, with a sampling rate of 1Hz; microenvironmental data inside the inverter cabinet, including heat sink temperature, humidity, and vibration, with a sampling rate of 10Hz; and geographic information data, including altitude, slope, and orientation.

[0015] Stress components are calculated based on the thermal-humid-mechanical stress matrix formula:

[0016] ;in Due to the temperature difference between the heat sink and the environment, The root mean square of the vibration acceleration. For thermal stress, For humidity stress, For vibration stress, Solar irradiance, Here, t represents relative humidity, and t represents time.

[0017] Calculate the overall stress index: ,in The comprehensive stress index, , , The weights are dynamically adjusted as the equipment ages;

[0018] Cumulative stress index calculation: ,in Let be the cumulative stress exponent at time t. Let be the comprehensive stress index at time t. The cumulative stress exponent at time t-1 , is the attenuation factor;

[0019] Generation of aging-accelerating factors: ,in As an aging acceleration factor, The maximum acceleration coefficient, This is the stress accumulation threshold;

[0020] Dynamically compensated stress field:

[0021]

[0022]

[0023]

[0024] in , , These are the thermal stress, humidity stress, and vibration stress after dynamic compensation, respectively.

[0025] A power plant-level stress thermal map is generated using the inverter as a grid node. High-stress areas with CSI>0.8 lasting for 10 minutes are marked and pushed to the performance baseline prediction module.

[0026] Furthermore, the specific operation steps of the performance baseline prediction module are as follows:

[0027] Collect PU characteristic curves of various inverter models under standard testing conditions;

[0028] Construct the environmental compensation function: ;in The reference power after environmental compensation. Power under standard conditions This is the actual temperature. This represents the actual irradiance.

[0029] Receive the CSI index output from the environmental stress field modeling module, and calculate the adjusted power baseline when CSI > 0.7: ,in The adjusted power baseline;

[0030] Aging-Environment Coupling Compensation: Based on Equipment Operating Time t and Cumulative Stress Index Generate a two-factor compensated baseline;

[0031] Input CSI and weather forecast data, and output the performance baseline forecast for the next 2 hours;

[0032] Real-time calculation of performance deviation: ;in For performance deviation, These are the actual output power of the device and the adjusted power baseline, respectively. When δ>5% and continues for 3 cycles, the coupling analysis early warning module is triggered.

[0033] Furthermore, the specific operational steps of the aging-environment coupling compensation are as follows:

[0034] Calculate the aging degradation factor: ;in As an aging degradation factor, For equipment runtime, Due to natural aging and degradation, Stress accelerates aging;

[0035] Two-factor compensation baseline generation: Baseline compensation is performed using the calculated aging degradation factor λ, where The adjusted power baseline after two-factor compensation. This is the Sigmoid function, used to control the compensation amplitude;

[0036] when Additional compensation is triggered when the value is greater than 0.8.

[0037] Furthermore, the specific operation steps of the coupling analysis early warning module are as follows:

[0038] Multidimensional correlation analysis: Calculating the time-delay cross-correlation function of environmental and equipment parameters: , among which when Let n be the time-delay cross-correlation coefficient between CSI and power change, and n be the number of data points. Let be the change in power at time t+τ. As a time lag, when τ=5 minutes, if the value of Rxy is greater than 0.6, it is determined to be an environment-related fault;

[0039] Based on evidence set E, specifically CSI>0.8, δ>8%, Calculate the failure probability: ,in Let E be the failure probability given the environmental evidence set E. The prior probability of failure is obtained through statistics from historical databases. For conditional probability tables;

[0040] Triggered based on performance deviation δ:

[0041] When δ=5%-8%, the marking performance deviates slightly, and the sampling frequency is increased to 500ms;

[0042] When δ>8% and CSI>0.7, infrared scanning is initiated and an alarm is pushed.

[0043] When δ>10% and the correlation map matches, a diagnostic report is generated and active intervention is triggered.

[0044] Furthermore, the specific execution process of the active intervention and feedback optimization module is as follows:

[0045] Multi-objective cooperative control strategy generation: First, construct a multi-parameter utility function: ,in: The utility value of multi-objective coordinated control; Real-time power generation; The electricity price for the current period; This refers to the maximum allowable power of the equipment. This represents the change in the health index. To estimate maintenance costs; , , These are respectively the economic weight, the reliability weight, and the cost weight;

[0046] The same environmental stress parameters were injected into the twin, and the simulation ran for 72 hours to predict potential failure paths;

[0047] Output the optimal control scheme and calculate its predicted utility value. ;

[0048] Calculate the recovery rate: ,in The performance recovery rate after intervention. These are the equipment power after intervention, the equipment power before intervention, and the baseline power, respectively.

[0049] Calculate the actual utility value When the predicted utility value With actual utility value When the absolute difference is greater than 0.2, the strategy library parameter update will be automatically initiated;

[0050] When η < 70%, the stress weight and baseline compensation coefficient are automatically corrected.

[0051] Furthermore, the multi-objective cooperative control strategy also includes:

[0052] Candidate strategy set generation rules:

[0053] Strategy 1: Reduce load to 80% of rated power and activate the secondary cooling system;

[0054] Strategy 2: Reduce load to 70% of rated power and activate the three-stage cooling system;

[0055] Strategy 3: Switch to standby inverter operation;

[0056] Utility value execution threshold:

[0057] High efficiency When this happens, the policy will be executed automatically;

[0058] when The message will be sent to the operations and maintenance personnel for confirmation before execution.

[0059] Economic parameter binding:

[0060] Price uses time-of-use electricity price data, and its update frequency is synchronized with the grid electricity price during the same period.

[0061] Real-time unit price of spare parts inventory database.

[0062] A real-time monitoring method for photovoltaic inverters based on a smart gateway includes the following steps:

[0063] S1. Multi-source environmental data acquisition and dynamic stress field calculation: Collect meteorological station data, inverter cabinet internal microenvironment data and geographic information data, calculate each stress value through the thermal-humid-mechanical stress matrix formula, and generate a comprehensive stress index.

[0064] S2. Cumulative stress and aging factor analysis: The cumulative stress index is calculated based on the comprehensive stress index, the influence of historical stress is considered in combination with the attenuation factor, and the aging acceleration factor is generated through the aging acceleration factor formula to dynamically compensate for each stress value.

[0065] S3. Dynamic generation and prediction of performance baseline: Integrate standard performance model and environmental stress data, construct environmental compensation function to obtain baseline power, combine equipment aging factor to generate two-factor compensation baseline, and use LSTM to predict the performance baseline for the next 2 hours.

[0066] S4. Performance Deviation Calculation and Fault Warning: Real-time calculation of the performance deviation between the actual output power of the equipment and the adjusted power baseline. Through time-delay cross-correlation analysis and Bayesian network fault probability calculation, different warnings are triggered according to the deviation level.

[0067] S5. Proactive Intervention and Feedback Optimization: Based on the early warning situation, a multi-parameter utility function is constructed to generate the optimal intervention strategy. After verification by digital twin, the strategy is executed. Data after intervention is collected to calculate the recovery rate and actual utility value, and the system parameters are optimized in a closed loop.

[0068] Compared with the prior art, the beneficial effects of the present invention are:

[0069] (1) This invention collects multi-source environmental data (meteorological station data, micro-environment sensor data, and geographic information data) through an environmental stress field modeling module, constructs a thermal-humid-mechanical stress matrix and a cumulative stress index model, and accurately quantifies the long-term cumulative impact of environmental stress. At the same time, through dynamic compensation of the stress field and power plant-level stress thermal map, high-risk damage areas can be located in advance to avoid the failure from developing to an irreversible stage, and significantly improve the ability to predict potential damage to equipment;

[0070] (2) This invention integrates standard performance models and environmental stress data through a performance baseline prediction module, and generates a two-factor compensation baseline by combining equipment operating time and cumulative stress index, dynamically distinguishing between the gradual performance decline caused by natural aging and the abnormal fluctuations caused by actual faults. At the same time, it uses an LSTM model to predict the performance baseline for the next 2 hours, and combines performance deviation calculation with Bayesian network fault probability analysis to achieve early and accurate warning of faults. Furthermore, it automatically traces the environmental causes through time-delay cross-correlation analysis, solving the shortcomings of traditional warning windows that are lagging and rely on manual intervention.

[0071] (3) This invention constructs a multi-parameter utility function through an active intervention and feedback optimization module to balance power generation revenue (real-time power and electricity price), equipment lifespan (health index changes) and operation and maintenance costs, and dynamically generates the optimal control strategy (such as load reduction in conjunction with the cooling system and switching to a standby inverter). At the same time, the effectiveness of the strategy is verified through digital twin simulation, and closed-loop optimization is achieved based on the comparison of recovery rate and utility value, maximizing operating revenue while ensuring equipment lifespan, thus solving the problem of the disconnect between technical and economic goals in traditional decision-making models. Attached Figure Description

[0072] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0073] Figure 1 This is the overall system block diagram of the present invention;

[0074] Figure 2 This is the dynamic correlation map in this invention. Detailed Implementation

[0075] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] It should be understood that the terms “comprising” and “including” used in this disclosure and claims indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0077] It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure. As used in this disclosure and claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this disclosure and claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations.

[0078] like Figure 1 As shown, a real-time monitoring system for photovoltaic inverters based on a smart gateway includes an environmental stress field modeling module, a performance baseline prediction module, a coupling analysis and early warning module, and an active intervention and feedback optimization module.

[0079] The environmental stress field modeling module collects multi-source environmental data, dynamically calculates thermal / humid / mechanical stress and generates a cumulative stress index, and improves the model accuracy through stress field compensation; it outputs a power plant-level stress thermal map to accurately locate high-damage-risk areas and provide a quantitative basis for environmental damage for performance prediction.

[0080] Multi-source environmental data acquisition: Real-time acquisition of meteorological station data (irradiance / temperature and humidity / wind speed / precipitation, sampling rate 1Hz); deployment of micro-environment sensors inside the inverter cabinet (heat sink temperature / humidity / vibration, sampling rate 10Hz); acquisition of geographic information data (altitude / slope / orientation, from GIS database).

[0081] Dynamic stress field calculation: Constructing the thermal-humid-mechanical stress matrix: ;

[0082] Due to the temperature difference between the heat sink and the environment, The root mean square of the vibration acceleration. For thermal stress, For humidity stress, For vibration stress, Solar irradiance, Here, t represents relative humidity, and t represents time.

[0083] Calculate the overall stress index: ,in The comprehensive stress index, , , The weights are set to 0.6, 0.3, and 0.1 respectively, and are dynamically adjusted as the equipment ages.

[0084] Stress history cumulative compensation calculation: Cumulative stress index calculation: ,in Let be the cumulative stress exponent at time t. Let be the comprehensive stress index at time t. The cumulative stress index at time t-1 , is the attenuation factor. (Initial value);

[0085] Generation of aging-accelerating factors: ,in As an aging acceleration factor, (Maximum acceleration coefficient) (Stress accumulation threshold);

[0086] Dynamically compensated stress field:

[0087]

[0088]

[0089]

[0090] , , These are the dynamically compensated thermal stress, humidity stress, and vibration stress; measured by the cumulative stress index (...). ) and aging-accelerating factors ( The model dynamically compensates for thermal stress, humidity stress, and vibration stress, improving the accuracy of equipment performance prediction.

[0091] Stress spatiotemporal distribution map generation: Power plant-level stress thermal map is generated with inverters as grid nodes; high stress areas (CSI>0.8 for 10 minutes) are marked and pushed to the performance baseline prediction module.

[0092] The performance baseline prediction module integrates standard performance models and environmental stress data, dynamically generates compensation baselines by combining equipment aging factors, and uses LSTM to predict future states; it calculates performance deviation in real time, distinguishes between natural aging and real anomalies, and provides a scientific judgment benchmark for the early warning module.

[0093] Benchmark performance modeling: PU characteristic curves of various inverter models were collected under a standard test environment (25℃, 1000W / m²); an environmental compensation function was constructed. ;in The reference power after environmental compensation. The power output is under standard conditions (25℃, 1000W / m²). This is the actual temperature. This represents the actual irradiance.

[0094] Adaptive baseline adjustment: Receives the real-time CSI index output from the environmental stress field modeling module; baseline compensation is initiated when CSI > 0.7. ,in The adjusted power baseline;

[0095] Aging-environment coupling compensation calculation:

[0096] Receive accumulated stress data: Obtain the output of the environmental stress field modeling module. (Cumulative stress index); Obtain equipment operating time (Unit: Month); Calculate the aging degradation factor: ;in As an aging degradation factor, For equipment runtime, The natural aging process reduces the rate of decay (0.3% per month). For stress-accelerated aging (0.2% acceleration per 1000 stress integral points);

[0097] Generate a two-factor compensation baseline: Baseline compensation is performed using the calculated aging degradation factor λ, where The adjusted power baseline after two-factor compensation. This is the Sigmoid function, used to control the compensation amplitude. ;when Additional compensation is triggered when the value is >0.8 (maximum compensation 10%); this is achieved through the aging degradation factor (λ) and the two-factor compensation baseline ( The performance baseline is dynamically adjusted, which improves the ability of the performance baseline to represent the true state of the equipment.

[0098] Based on LSTM prediction of the performance baseline for the next 2 hours (input: CSI + weather forecast);

[0099] Anomaly Calculation: Real-time calculation of performance deviation: ;in For performance deviation, These are the actual output power of the device and the adjusted power baseline, respectively. When δ>5% and continues for 3 cycles, the coupling analysis early warning module is triggered.

[0100] The coupling analysis and early warning module analyzes the time-delay correlation between environmental parameters and equipment anomalies, and realizes the root cause location of the fault based on the dynamic decision tree; it triggers early warning according to the degree of deviation, and generates a diagnostic report through infrared scanning.

[0101] Multi-dimensional association analysis: Constructing a dynamic association graph, see... Figure 2 ;

[0102] Calculate the time-delay cross-correlation function of the environment and equipment parameters: , among which when This represents the time-delay cross-correlation coefficient between CSI and power change. As a time lag, when τ = 5 minutes, if the value of Rxy is greater than 0.6, it is determined to be an environment-related fault; n is the number of data points. Let be the change in power at time t+τ;

[0103] Fault probability calculation based on Bayesian networks:

[0104] Failure probability quantification model: ,in Let E be the failure probability given the environmental evidence set E. For environmental evidence sets (such as CSI>0.8, δ>8%), wait), This represents the prior probability of failure (obtained through statistics from historical databases). The conditional probability table is obtained by training with 1000+ fault cases.

[0105] Early warning strategies: Level 1 warning (δ=5%-8%): Mark "slight performance deviation" and record it in the historical database; increase the data acquisition frequency of the device to 500ms; Level 2 warning (δ>8% and CSI>0.7): Start the infrared thermal imager to automatically scan the target device; push "environment-related performance degradation" alarm to the operation and maintenance terminal; Level 3 warning (δ>10% and there is a correlation spectrum match): automatically generate a fault diagnosis report (including environmental cause analysis); trigger the active intervention and feedback optimization module.

[0106] The active intervention and feedback optimization module constructs a multi-objective utility function to generate the optimal strategy, and executes control commands after verification in multiple scenarios using digital twins; it compares the predicted and actual utility values ​​for closed-loop optimization, dynamically adjusts system parameters, and improves power generation revenue while ensuring equipment lifespan;

[0107] Multi-objective cooperative control strategy generation: First, construct a multi-parameter utility function: ,in: The utility value of multi-objective coordinated control; Real-time power generation (unit: kW); Electricity price for the current period (unit: yuan / kWh); The maximum allowable power of the equipment (unit: kW); This represents the change in the health index (from the performance baseline prediction module). For estimated maintenance costs (unit: RMB 10,000); weighting coefficient: =0.6 (economic weight) =0.3 (reliability weight) =0.1 (cost weight);

[0108] When the comprehensive stress index (CSI) > 0.9 or the performance deviation δ > 15%, three candidate strategies are dynamically generated:

[0109] Strategy 1: Reduce load to 80% of rated power and start the secondary cooling system; Strategy 2: Reduce load to 70% of rated power and start the tertiary cooling system; Strategy 3: Switch to standby inverter operation;

[0110] Calculate the utility value U for each strategy, and select the strategy with the highest utility value to execute:

[0111] When the most efficient value When, the policy is executed automatically; when The command will be pushed to the operations and maintenance personnel for confirmation before execution.

[0112] Preventive control strategy library: Environmental stress type control: When CSI>0.9: Automatically reduce load to 85% and activate the backup cooling system; Vibration S vibration >4: Switch to flexible operation mode; Performance degradation control: δ continuously >7% for 24 hours: Switch to redundant inverter operation;

[0113] Digital twin verification: Inject the same environmental stress parameters into the twin; simulate for 72 hours to predict potential failure paths; output the optimal control scheme (e.g., suggest cleaning the radiator within 48 hours); and simultaneously calculate the predicted utility value of the scheme. And this should be noted in the diagnostic report;

[0114] Feedback optimization closed loop: collecting equipment performance recovery curves after intervention;

[0115] Calculate the recovery rate: ,in The performance recovery rate after intervention. The equipment power after intervention, the equipment power before intervention, and the baseline power are respectively used to calculate the actual utility value. When the predicted utility value With actual utility value When the absolute difference is greater than 0.2, the strategy library parameter update will be started automatically; when η < 70%, it will be automatically corrected: update the stress weight of module one (e.g., increase α to 0.65), and adjust the baseline compensation coefficient of module two (0.15 → 0.18).

[0116] A real-time monitoring method for photovoltaic inverters based on a smart gateway, comprising the following steps:

[0117] Step 1: Multi-source environmental data acquisition and dynamic stress field calculation: Collect meteorological station data (irradiance, temperature and humidity, etc.), inverter cabinet internal microenvironment data (heat sink temperature, vibration, etc.) and geographic information data, calculate each stress value through the thermal-humid-mechanical stress matrix formula, and generate a comprehensive stress index.

[0118] Step 2, Cumulative Stress and Aging Factor Analysis: The cumulative stress index is calculated based on the comprehensive stress index, and the influence of historical stress is considered in combination with the attenuation factor. Then, the aging acceleration factor is generated through the aging acceleration factor formula to dynamically compensate for each stress value.

[0119] Step 3: Dynamic generation and prediction of performance baseline: By integrating the standard performance model and environmental stress data, an environmental compensation function is constructed to obtain the baseline power. A two-factor compensation baseline is generated by combining the equipment aging factor. LSTM is then used to predict the performance baseline for the next 2 hours.

[0120] Step 4: Performance Deviation Calculation and Fault Warning: Calculate the performance deviation between the actual output power of the equipment and the adjusted power baseline in real time. Through time-delay cross-correlation analysis and Bayesian network fault probability calculation, trigger different warnings according to the deviation level.

[0121] Step 5: Proactive Intervention and Feedback Optimization: Based on the early warning situation, construct a multi-parameter utility function to generate the optimal intervention strategy, execute it after digital twin verification, collect post-intervention data to calculate the recovery rate and actual utility value, and optimize system parameters in a closed loop.

[0122] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A real-time monitoring system for photovoltaic inverters based on a smart gateway, characterized in that, include; The environmental stress field modeling module is used to collect multi-source environmental data, dynamically calculate thermal, wet and mechanical stresses and generate cumulative stress indexes, and improve model accuracy through stress field compensation. Output a power plant-level stress thermal map to pinpoint areas of damage risk. The performance baseline prediction module is used to integrate standard performance models and environmental stress data, dynamically generate compensation baselines by combining equipment aging factors, and use LSTM to predict future conditions. Real-time calculation of performance deviation to distinguish between natural aging and actual anomalies; The coupling analysis and early warning module is used to analyze the time-delay correlation between environmental parameters and equipment anomalies, and to locate the root cause of the fault based on a dynamic decision tree; it triggers early warnings according to the degree of deviation and generates diagnostic reports through infrared scanning. The active intervention and feedback optimization module is used to construct a multi-objective utility function to generate the optimal strategy, and execute control commands after verification in multiple scenarios using digital twins; it compares the predicted and actual utility values ​​for closed-loop optimization and dynamically adjusts system parameters.

2. The real-time monitoring system for photovoltaic inverters based on a smart gateway according to claim 1, characterized in that, The execution process of the environmental stress field modeling module is as follows: Real-time data collection from weather stations, including irradiance, temperature, humidity, wind speed, and precipitation, with a sampling rate of 1Hz; microenvironmental data inside the inverter cabinet, including heat sink temperature, humidity, and vibration, with a sampling rate of 10Hz; and geographic information data, including altitude, slope, and orientation. Stress components are calculated based on the thermal-humid-mechanical stress matrix formula: ;in Due to the temperature difference between the heat sink and the environment, The root mean square of the vibration acceleration. For thermal stress, For humidity stress, For vibration stress, Solar irradiance, Here, t represents relative humidity, and t represents time. Calculate the overall stress index: ,in The comprehensive stress index, , , The weights are dynamically adjusted as the equipment ages; Cumulative stress index calculation: ,in Let be the cumulative stress exponent at time t. Let be the comprehensive stress index at time t. The cumulative stress exponent at time t-1 , is the attenuation factor; Generation of aging-accelerating factors: ,in As an aging acceleration factor, The maximum acceleration coefficient, This is the stress accumulation threshold; Dynamically compensated stress field: in , , These are the thermal stress, humidity stress, and vibration stress after dynamic compensation, respectively. A power plant-level stress thermal map is generated using the inverter as a grid node. High-stress areas with CSI>0.8 lasting for 10 minutes are marked and pushed to the performance baseline prediction module.

3. The real-time monitoring system for photovoltaic inverters based on a smart gateway according to claim 1, characterized in that, The specific operation steps of the performance baseline prediction module are as follows: Collect PU characteristic curves of various inverter models under standard testing conditions; Construct the environmental compensation function: ;in The reference power after environmental compensation. Power under standard conditions This is the actual temperature. This represents the actual irradiance. Receive the CSI index output from the environmental stress field modeling module, and calculate the adjusted power baseline when CSI > 0.7: ,in The adjusted power baseline; Aging-Environment Coupling Compensation: Based on Equipment Operating Time t and Cumulative Stress Index Generate a two-factor compensated baseline; Input CSI and weather forecast data, and output the performance baseline forecast for the next 2 hours; Real-time calculation of performance deviation: ;in For performance deviation, These are the actual output power of the device and the adjusted power baseline, respectively. When δ>5% and continues for 3 cycles, the coupling analysis early warning module is triggered.

4. The real-time monitoring system for photovoltaic inverters based on a smart gateway according to claim 3, characterized in that, The specific operational steps for the aging-environment coupling compensation are as follows: Calculate the aging degradation factor: ;in As an aging degradation factor, For equipment runtime, Due to natural aging and degradation, Stress accelerates aging; Two-factor compensation baseline generation: Baseline compensation is performed using the calculated aging degradation factor λ, where The adjusted power baseline after two-factor compensation. This is the Sigmoid function, used to control the compensation amplitude; when Additional compensation is triggered when the value is greater than 0.

8.

5. The real-time monitoring system for photovoltaic inverters based on a smart gateway according to claim 1, characterized in that, The specific operation steps of the coupling analysis early warning module are as follows: Multidimensional correlation analysis: Calculating the time-delay cross-correlation function of environmental and equipment parameters: , among which when Let n be the time-delay cross-correlation coefficient between CSI and power change, and n be the number of data points. Let be the change in power at time t+τ. As a time lag, when τ=5 minutes, if the value of Rxy is greater than 0.6, it is determined to be an environment-related fault; Based on evidence set E, specifically CSI>0.8, δ>8%, Calculate the failure probability: ,in Let E be the failure probability given the environmental evidence set E. The prior probability of failure is obtained through statistics from historical databases. For conditional probability tables; Triggered based on performance deviation δ: When δ=5%-8%, the marking performance deviates slightly, and the sampling frequency is increased to 500ms; When δ>8% and CSI>0.7, infrared scanning is initiated and an alarm is pushed. When δ>10% and the correlation map matches, a diagnostic report is generated and active intervention is triggered.

6. The real-time monitoring system for photovoltaic inverters based on a smart gateway according to claim 1, characterized in that, The specific execution process of the active intervention and feedback optimization module is as follows: Multi-objective cooperative control strategy generation: First, construct a multi-parameter utility function: ,in: The utility value of multi-objective coordinated control; Real-time power generation; The electricity price for the current period; This refers to the maximum allowable power of the equipment. This represents the change in the health index. To estimate maintenance costs; , , These are respectively the economic weight, the reliability weight, and the cost weight; The same environmental stress parameters were injected into the twin, and the simulation ran for 72 hours to predict potential failure paths; Output the optimal control scheme and calculate its predicted utility value. ; Calculate the recovery rate: ,in The performance recovery rate after intervention. These are the equipment power after intervention, the equipment power before intervention, and the baseline power, respectively. Calculate the actual utility value When the predicted utility value With actual utility value When the absolute difference is greater than 0.2, the strategy library parameter update will be automatically initiated; When η < 70%, the stress weight and baseline compensation coefficient are automatically corrected.

7. A real-time monitoring system for photovoltaic inverters based on a smart gateway according to claim 6, characterized in that, The multi-objective cooperative control strategy also includes: Candidate strategy set generation rules: Strategy 1: Reduce load to 80% of rated power and activate the secondary cooling system; Strategy 2: Reduce load to 70% of rated power and activate the three-stage cooling system; Strategy 3: Switch to standby inverter operation; Utility value execution threshold: High efficiency When this happens, the policy will be executed automatically; when The message will be sent to the operations and maintenance personnel for confirmation before execution. Economic parameter binding: Price uses time-of-use electricity price data, and its update frequency is synchronized with the grid electricity price during the same period. Real-time unit price of spare parts inventory database.

8. A method applied to a real-time monitoring system for a photovoltaic inverter based on a smart gateway as described in any one of claims 1-7, characterized in that, Includes the following steps: S1. Multi-source environmental data acquisition and dynamic stress field calculation: Collect meteorological station data, inverter cabinet internal microenvironment data and geographic information data, calculate each stress value through the thermal-humid-mechanical stress matrix formula, and generate a comprehensive stress index. S2. Cumulative stress and aging factor analysis: The cumulative stress index is calculated based on the comprehensive stress index, the influence of historical stress is considered in combination with the attenuation factor, and the aging acceleration factor is generated through the aging acceleration factor formula to dynamically compensate for each stress value. S3. Dynamic generation and prediction of performance baseline: Integrate standard performance model and environmental stress data, construct environmental compensation function to obtain benchmark power, combine equipment aging factor to generate two-factor compensation baseline, and use LSTM to predict the performance baseline for the next 2 hours. S4. Performance Deviation Calculation and Fault Warning: Real-time calculation of the performance deviation between the actual output power of the equipment and the adjusted power baseline. Through time-delay cross-correlation analysis and Bayesian network fault probability calculation, different warnings are triggered according to the deviation level. S5. Proactive Intervention and Feedback Optimization: Based on the early warning situation, a multi-parameter utility function is constructed to generate the optimal intervention strategy. After verification by digital twin, the strategy is executed. Data after intervention is collected to calculate the recovery rate and actual utility value, and the system parameters are optimized in a closed loop.

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