Passive solar tracking and self-cleaning integrated photovoltaic system

Abstract

The present application relates to the technical field of intelligent control and health management of photovoltaic systems, in particular to a passive solar tracking and self-cleaning integrated photovoltaic system, comprising: a multi-dimensional environment perception unit sends multi-dimensional environment parameters to a catastrophe risk modeling unit and a post-disaster recovery control unit; the catastrophe risk modeling unit performs thermal-mechanical coupling catastrophe risk modeling analysis to obtain a catastrophe risk index; a system health degree evaluation unit performs structural fatigue evolution modeling analysis to obtain a cumulative fatigue damage index; an operation mode decision unit performs intelligent switching decision analysis of operation mode to generate an efficient power generation mode signal, an active risk avoidance mode signal or a survival lock mode signal; the post-disaster recovery control unit generates a recovery instruction; the present application constructs a complete technical closed loop from environment perception, risk assessment, health monitoring to decision control and post-disaster recovery, solving the problem of poor adaptability and low reliability of existing photovoltaic systems under extreme weather.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control and health management of photovoltaic systems, in particular to a passive solar tracking and self-cleaning integrated photovoltaic system. BACKGROUND

[0002] In the current photovoltaic industry, passive solar tracking systems are widely used as key technologies to improve power generation efficiency. Their stability and safety under changing or even extreme weather conditions are crucial. In order to ensure the long-term reliable operation of the system, it is necessary to effectively respond to the risk of mechanical overload and structural damage caused by adverse weather such as wet snow and freezing rain. This requires the system not only to maximize power generation efficiency, but also to have accurate perception of environmental risks, accurate assessment of its own health status, and intelligent operation mode decision-making ability under different risk levels.

[0003] Existing technologies in response to extreme weather conditions mostly use static protection strategies based on fixed thresholds, lacking comprehensive analysis capabilities for complex environmental factors. These methods generally have low modeling accuracy for thermal-mechanical coupling risks and insufficient assessment of cumulative fatigue damage caused by long-term service of the system. At the same time, existing protection mechanisms mostly ignore the dynamic characteristics of the system health evolution over time, and cannot adjust the risk response strategy according to the current actual health status of the system, resulting in overly conservative or aggressive triggering of protection modes, making it difficult to achieve the best balance between power generation benefits and structural safety.

[0004] Therefore, how to provide a photovoltaic system that can integrate multi-dimensional environmental real-time perception, structural health quantitative evaluation and operation mode dynamic decision-making to solve the problem of poor adaptability and low reliability of existing technologies in complex adverse environments is a technical problem that needs to be solved by those skilled in the art. SUMMARY

[0005] To solve the above technical problems, the present application provides a passive solar tracking and self-cleaning integrated photovoltaic system, specifically, the technical solution of the present application is:

[0006] A passive solar tracking and self-cleaning integrated photovoltaic system, comprising:

[0007] A multi-dimensional environment perception unit for real-time acquisition of multi-dimensional environmental parameters of the system and sending the multi-dimensional environmental parameters to a disaster risk modeling unit and a post-disaster recovery control unit;

[0008] A disaster risk modeling unit for thermal-mechanical coupling disaster risk modeling analysis based on the multi-dimensional environmental parameters acquired by the multi-dimensional environment perception unit, to obtain a disaster risk index;

[0009] A system health assessment unit is configured to collect stress history data of the system, perform structural fatigue evolution modeling analysis, and obtain a cumulative fatigue damage index.

[0010] An operation mode decision unit is configured to combine the catastrophe risk index and the cumulative fatigue damage index output by the system health assessment unit, perform intelligent switching decision analysis of the operation mode, and generate an efficient power generation mode signal, an active risk avoidance mode signal, or a survival lock mode signal.

[0011] A post-disaster recovery control unit is configured to, after receiving the survival lock mode signal, perform post-disaster recovery condition identification analysis based on the multi-dimensional environmental parameters, and generate a recovery instruction.

[0012] Preferably, the thermal-mechanical coupling catastrophe risk modeling analysis process is as follows:

[0013] Real-time mechanical load, real-time normal solar irradiance, real-time environmental temperature, and real-time relative humidity in the multi-dimensional environmental parameters are obtained.

[0014] Mechanical overload risk, heat source failure risk, and phase change icing risk are determined.

[0015] The mechanical overload risk, heat source failure risk, and phase change icing risk are weighted and summed to obtain the catastrophe risk index.

[0016] Preferably, the structural fatigue evolution modeling analysis process is as follows:

[0017] The actual number of cycles experienced by the system at each stress level is obtained.

[0018] The total number of cycles allowed for failure of the material at the corresponding stress level is obtained.

[0019] The actual number of cycles is divided by the total number of cycles to obtain a damage ratio.

[0020] The damage ratios for all stress levels are summed to obtain a sum value, which is set as the cumulative fatigue damage index.

[0021] Preferably, the intelligent switching decision analysis process of the operation mode is as follows:

[0022] The cumulative fatigue damage index is used to dynamically adjust a preset reference lock threshold to obtain a dynamic lock threshold.

[0023] The catastrophe risk index is compared with a preset warning threshold and the dynamic lock threshold.

[0024] When the catastrophe risk index is less than the warning threshold, an efficient power generation mode signal is generated.

[0025] When the catastrophe risk index is greater than or equal to the early warning threshold and less than the dynamic locking threshold, a proactive risk avoidance mode signal is generated;

[0026] When the catastrophe risk index is greater than or equal to the dynamic locking threshold, a survival locking mode signal is generated.

[0027] Preferably, the post-disaster recovery condition identification analysis process is as follows:

[0028] Real-time normal solar irradiance, real-time mechanical load and real-time environmental temperature in the multi-dimensional environmental parameters are obtained, and the obtained parameters are weighted and fused to obtain a recovery index;

[0029] The recovery index is compared and analyzed with a preset recovery threshold, and if the recovery index reaches the recovery threshold, a recovery instruction is generated.

[0030] Preferably, in response to the recovery instruction, a phased recovery strategy is executed; the phased recovery strategy includes load closed-loop testing, functional self-checking and recovery to normal operation.

[0031] Preferably, the load closed-loop testing process is as follows:

[0032] Based on the cumulative fatigue damage index output by the system health degree evaluation unit, a dynamic maximum test load is calculated;

[0033] The driving motor applies a driving torque, and the real-time mechanical load is monitored;

[0034] If the real-time mechanical load does not exceed the dynamic maximum test load throughout the driving process, it is determined that the test is passed, and the functional self-checking stage is entered;

[0035] If the real-time mechanical load reaches the dynamic maximum test load, it is determined that the test fails, and the system is rolled back to the initial locking position.

[0036] Preferably, when the load closed-loop testing determines that the test is passed, the system performs functional self-checking; the functional self-checking is used to verify that all transmission components are active and self-cleaning mechanisms are activated, and after the self-checking is completed, the system is switched to a high-efficiency power generation mode.

[0037] Compared with the prior art, the present application has the following beneficial effects:

[0038] 1. The technical scheme constructs a complete technical closed loop from environmental perception, risk assessment, health monitoring to decision control and post-disaster recovery, solving the problem of poor adaptability and low reliability of existing photovoltaic systems in extreme weather;

[0039] 2. The system acquires multi-dimensional environmental parameters such as mechanical load, solar irradiance, environmental temperature and humidity in real time through a multi-dimensional environment perception unit; unlike the rough judgment of existing technologies relying on a single parameter, the catastrophe risk modeling unit of the present scheme can perform thermal-mechanical coupling analysis on these multi-dimensional heterogeneous parameters, and creatively quantize various risks with different physical properties such as mechanical overload, heat source failure and phase change icing into a comprehensive catastrophe risk index; this quantitative evaluation method makes risk identification more accurate and comprehensive, providing a solid data foundation for subsequent intelligent decision-making;

[0040] 3. The technical scheme introduces a system health assessment unit; the unit quantifies the cumulative fatigue damage of the system due to long-term service through structural fatigue evolution modeling, and obtains a cumulative fatigue damage index; this enables the system not only to respond to external instantaneous risks, but also to understand its long-term health status;

[0041] 4. On this basis, the operation mode decision unit realizes a major breakthrough in decision logic; instead of using the fixed and unchanging safety threshold in existing technologies, it combines the real-time catastrophe risk index with the cumulative fatigue damage index representing long-term damage; specifically, the system will dynamically adjust the baseline lock threshold based on the cumulative fatigue damage index; a healthy new system has a relatively lenient safety threshold and can withstand higher risks to pursue power generation efficiency; while a long-serving system with high cumulative damage, the safety threshold will become more stringent, triggering proactive risk avoidance or survival lock mode in advance; this decision-making mechanism dynamically associated with the health of the system throughout its life cycle, discards the one-size-fits-all static protection strategy, and achieves the best balance between structural reliability and maximum power generation benefit;

[0042] 5. The present technical scheme exhibits unprecedented safety and intelligence in post-disaster recovery control; it establishes an independent post-disaster recovery condition identification and analysis process, scientifically judges the recovery time through a quantitative recovery index, completely replaces the traditional extensive mode relying on fixed delay or manual intervention, and avoids secondary damage caused by misjudgment; the recovery process is not a simple restart, but a rigorous phased recovery strategy; the strategy first performs a load closed-loop test, and the maximum allowed load is also dynamically associated with the cumulative fatigue damage index of the system, ensuring that the trial process for possible mechanical failures such as icing is extremely gentle and safe; only after confirming that the mechanical system has escaped, will it enter a comprehensive functional self-check and eventually recover normal operation; this design minimizes the risk of the recovery process, greatly improving the availability and reliability of the system after experiencing extreme disasters. BRIEF DESCRIPTION OF DRAWINGS

[0043] The present application will be further explained in conjunction with the accompanying drawings and examples:

[0044] Figure 1 is a system block diagram of a passive solar tracking and self-cleaning integrated photovoltaic system. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in combination with specific embodiments.

[0046] Embodiment 1:

[0047] Please refer to Figure 1 A passive solar tracking and self-cleaning integrated photovoltaic system comprises:

[0048] A multi-dimensional environment perception unit is used to collect multi-dimensional environment parameters of the system in real time and send the multi-dimensional environment parameters to a catastrophe risk modeling unit and a post-disaster recovery control unit.

[0049] The catastrophe risk modeling unit is used to perform thermal-mechanical coupling catastrophe risk modeling analysis based on the multi-dimensional environment parameters collected by the multi-dimensional environment perception unit, and obtain a catastrophe risk index.

[0050] A system health degree evaluation unit is used to collect stress historical data of the system, perform structural fatigue evolution modeling analysis, and obtain a cumulative fatigue damage index.

[0051] An operation mode decision unit is used to perform operation mode intelligent switching decision analysis in combination with the catastrophe risk index and the cumulative fatigue damage index output by the system health degree evaluation unit, and generate an efficient power generation mode signal, an active risk avoidance mode signal or a survival lock mode signal.

[0052] A post-disaster recovery control unit is used to perform post-disaster recovery condition identification analysis according to the multi-dimensional environment parameters after receiving the survival lock mode signal, and generate a recovery instruction.

[0053] The embodiment provides a passive solar tracking and self-cleaning integrated photovoltaic system, which aims to solve the problems of poor adaptability and low reliability of the prior art under extreme weather conditions by intelligent perception of a complex environment, accurate evaluation of system health degree and dynamic decision of an operation mode; the system constitutes a complete and self-consistent technical closed loop, and ensures efficient power generation and self-protection ability under a variable environment; wherein passive solar tracking refers to that, in the efficient power generation mode, the system follows a preset algorithm for solar trajectory tracking without real-time light feedback, and the environmental adaptation and self-protection function of the system is realized through active control.

[0054] The system comprises the following core units:

[0055] A multi-dimensional environment perception unit, which aims to provide real-time and accurate environmental data input for subsequent risk assessment and decision-making; in this embodiment, the unit is an integrated sensor network, which is implemented by: acquiring real-time mechanical load through strain sensors deployed in the drive mechanism ; monitoring the environmental temperature and relative humidity using integrated temperature and humidity sensors ; and monitoring the normal solar irradiance through photovoltaic array built-in or independent light-sensitive sensors ; These collected multi-dimensional environmental parameters are sent in real time to the disaster risk modeling unit and the post-disaster recovery control unit;

[0056] A disaster risk modeling unit, which aims to convert multi-dimensional and heterogeneous environmental parameters into a unified and quantifiable risk indicator; in this embodiment, the unit performs a thermal-mechanical coupling disaster risk modeling analysis based on the received multi-dimensional environmental parameters; the core of this analysis is to build a mathematical model that can unify and quantify the dual risks of thermodynamic driven failure and mechanical load surge caused by weather such as wet snow or freezing rain, and finally output a dimensionless disaster risk index ; This index is the core basis for subsequent operation mode decision-making;

[0057] A system health assessment unit, which aims to monitor and predict the structural degradation of the system due to long-term service, and to consider the historical state of the system in decision-making; in this embodiment, the unit performs a structural fatigue evolution modeling analysis by continuously collecting stress history data of the system, especially recording high stress events caused by extreme weather events; This analysis draws on the Palmgren-Miner linear cumulative damage criterion in material mechanics to calculate a cumulative fatigue damage index that quantifies the cumulative damage of the drive mechanism due to repeated load bearing ;

[0058] An operation mode decision unit, which aims to intelligently select the optimal system operation mode based on the current real-time risk and long-term system health status; in this embodiment, the unit combines the real-time disaster risk index output by the disaster risk modeling unit and the cumulative fatigue damage index output by the system health assessment unit ; Based on the cumulative fatigue damage index , the disaster risk index is compared with the dynamically adjusted threshold value, and finally the intelligent switching decision analysis of the operation mode is performed, and one of the efficient power generation mode signal, the active risk avoidance mode signal or the survival lock mode signal is generated;

[0059] ​A post-disaster recovery control unit aims to safely and reliably guide the system to recover from the self-protection state to the normal working state after extreme weather; in this embodiment, after receiving the survival lock mode signal, the unit does not immediately recover, but continuously identifies and analyzes the post-disaster recovery conditions according to the environmental parameters sent by the multi-dimensional environmental perception unit; only when the environmental parameters meet the preset safety conditions, the unit will generate a recovery instruction to start a phased recovery process associated with the system health degree;

[0060] This embodiment builds a complete closed-loop control system from environmental perception, risk modeling, health assessment to intelligent decision-making and post-disaster recovery through the cooperative work of the above-mentioned units; it not only can cope with sudden extreme weather risks, but also can include the long-term cumulative damage of the system into the decision-making model, realizing the unification of short-term risk avoidance and long-term reliability, greatly improving the survival ability, power generation efficiency and economic benefits in the whole life cycle of the photovoltaic system in complex and harsh environments;

[0061] To ensure the robustness of system decision-making, the multi-dimensional environmental perception unit is built-in with data validity checking logic, for each parameter collected, the system will check in advance whether it is within a reasonable physical range, when some sensor data is continuously abnormal or missing, the system can trigger a fault alarm, and switch to the survival lock mode according to the preset safety strategy until the fault is eliminated, thereby avoiding decision-making errors caused by sensor failure; at the same time, all calculation models will perform validity check on key parameters before execution to prevent calculation abnormalities.

[0062] Embodiment 2:

[0063] The thermal-mechanical coupling disaster risk modeling and analysis process is as follows:

[0064] Obtain real-time mechanical load, real-time normal solar irradiance, real-time environmental temperature and real-time relative humidity in the multi-dimensional environmental parameters;

[0065] Determine the mechanical overload risk, heat source failure risk and phase change icing risk;

[0066] Weighted sum processing is performed on the mechanical overload risk, heat source failure risk and phase change icing risk to obtain the disaster risk index.

[0067] This embodiment is a specific embodiment of the thermal-mechanical coupling disaster risk modeling and analysis process in the disaster risk modeling unit; its purpose is to elaborate how to normalize multiple risk factors of different physical dimensions and integrate them into a single scalar index that can be used for decision-making;

[0068] Four key real-time parameters are obtained from the multi-dimensional environmental perception unit: real-time mechanical load , real-time normal solar irradiance , real-time ambient temperature , and real-time relative humidity ;

[0069] Based on the above physical quantities, the mechanical overload risk, heat source failure risk, and phase change icing risk are quantified:

[0070] The mechanical overload risk refers to the risk that the real-time mechanical load approaches or exceeds the design maximum load of the driving mechanism ;

[0071] The heat source failure risk refers to the risk that the solar irradiance received by the photovoltaic panel drops sharply due to snow cover, affecting system function

[0072] The phase change icing risk refers to the risk that the combination of ambient temperature and humidity and reaches a condition that is prone to freezing rain or icing, which may cause the mechanical structure to freeze

[0073] The above three risks are weighted and summed to obtain the catastrophe risk index ; To achieve this integration, the calculation model of the catastrophe risk index is introduced, and its mathematical expression is defined as:

[0074] ;

[0075] wherein, : catastrophe risk index, dimensionless, as the final calculation output of this step; its role is as a direct input for system operation mode switching decision

[0076] : real-time mechanical load, unit: Newton N, collected by strain sensors in the multi-dimensional environment perception unit in real time

[0077] : design maximum load, unit: Newton N, a constant determined according to system mechanical design specifications term directly reflects the mechanical overload risk

[0078] : real-time normal solar irradiance, unit: Watt per square meter W / m², collected by photosensitive sensors in the multi-dimensional environment perception unit in real time

[0079] : reference maximum irradiance, unit: Watt per square meter W / m², a reference value determined according to historical weather data at the current geographic location and season term reflects the heat source failure risk due to shading

[0080] : Real-time ambient temperature in Celsius °C, collected by temperature sensor in multi-dimensional environmental perception unit in real time;

[0081] : Real-time relative humidity in %, collected by humidity sensor in multi-dimensional environmental perception unit in real time;

[0082] , : Reference icing temperature and humidity in °C and % respectively, which are reference values of most likely to cause phase change icing determined according to physical meteorology, and are set to = 0 °C, = 90% in this embodiment;

[0083] , : Icing condition sensitivity distribution width parameter, which has the same unit as and respectively, and is related to local climate statistical characteristics, and is obtained by statistical analysis of historical meteorological data; Gaussian exponential term describes the phase change icing risk, which significantly increases when and only when the temperature and humidity are close to and ;

[0084] : Risk component weight coefficient, dimensionless, and satisfies ; These coefficients are not runtime variables, but are calibrated by regression analysis of historical disaster event data sets; To illustrate the calibration process, a historical event data set is defined, which contains event samples; Each sample consists of a set of environmental parameters and a known binary result , where represents a disaster, represents safety; By using statistical methods such as logistic regression, the model is fitted to maximize the prediction accuracy, thereby determining the optimal value;

[0085] This embodiment creatively unifies the originally difficult to compare mechanical, thermal and meteorological risks into a single disaster risk index This quantification method not only makes risk assessment more accurate and comprehensive, but also provides a solid, quantifiable data foundation for subsequent automated and intelligent decision-making, significantly improving the scientific nature and accuracy of decision-making. It is worth noting that the linear weighted model used in this embodiment is to achieve a balance between computational efficiency and model accuracy. In future research, nonlinear coupling models between risk factors can be further explored. For example, phase change icing risk can be used as a multiplier factor for mechanical overload risk to more accurately characterize its enhancement effect.

[0086] Example 3:

[0087] The structural fatigue evolution modeling and analysis process is as follows:

[0088] Obtain the actual number of cycles the system has undergone at each stress level;

[0089] Obtain the total number of cycles that allow the material to fail at the corresponding stress level;

[0090] The damage ratio is obtained by dividing the actual number of cycles by the total number of cycles.

[0091] The damage ratios for all stress levels are summed, and the resulting sum is set as the cumulative fatigue damage index.

[0092] This embodiment is a concretization of the structural fatigue evolution modeling and analysis process in the system health assessment unit; its purpose is to accurately quantify the cumulative damage caused by long-term variable amplitude loads on key components such as drive mechanisms, thereby providing a basis for dynamically adjusting the system's safety strategy.

[0093] The analysis process draws on the Palmgren-Mainner linear cumulative damage criterion, which is widely used in the field of materials mechanics. The specific steps are as follows:

[0094] Obtain the actual number of cycles the system has undergone at each stress level. This data comes from strain sensors deployed on key transmission components. A complete stress history dataset was constructed, and algorithms such as rainflow counting were used to process the data, providing real-time statistics on the [number of stresses]. stress level The actual number of iterations already performed;

[0095] Obtain the total number of cycles that the material is allowed to fail at the corresponding stress level. This value is not measured directly, but determined through the material's SN curve; in this embodiment, This can be mathematically described using the Basquin equation:

[0096] ;

[0097] wherein, : allowable damage cycle number, unit is times, calculated by the above formula;

[0098] : the stress level, unit is Pascal Pa, obtained by stress spectrum division;

[0099] : fatigue strength coefficient of material, unit is Pascal Pa, is the inherent property of material, its value is accurately determined by consulting the corresponding material engineering manual or through standardized material fatigue test;

[0100] : fatigue strength index of material, dimensionless, is the inherent property of material, the acquisition method is the same as ;

[0101] The actual cycle number is divided by the total cycle number to obtain the damage ratio ; this ratio represents the percentage of fatigue life consumed at this stress level;

[0102] The damage ratios of all stress levels are summed to obtain the cumulative fatigue damage index , the calculation formula is as follows:

[0103] ;

[0104] wherein, : cumulative fatigue damage index, dimensionless, as the final calculation output of this step; its value range is , when tends to 1, it indicates that the component has a failure risk;

[0105] : the number of different stress levels in the stress spectrum, is an integer, determined according to stress history data analysis;

[0106] : the actual cycle number at the stress level, unit is times, obtained by real-time counting of the stress monitoring system;

[0107] : the allowable damage cycle number at the stress level, unit is times, calculated by the above Basquin equation;

[0108] This embodiment realizes the quantitative evaluation of the system structure health degree by establishing a structure fatigue evolution model; the cumulative fatigue damage index Not only can be used for predictive maintenance alarm, more importantly, it as a core state variable, feedback to the operating mode decision unit, so that the system can dynamically adjust its behavior patterns according to its age and health status, thereby maximizing the service life of the system under the premise of safety.

[0109] Embodiment 4:

[0110] The operating mode intelligent switching decision analysis process is as follows:

[0111] The preset reference locking threshold is dynamically adjusted based on the cumulative fatigue damage index to obtain a dynamic locking threshold;

[0112] The catastrophe risk index is compared and analyzed with the preset warning threshold and the dynamic locking threshold;

[0113] When the catastrophe risk index is less than the warning threshold, a high-efficiency power generation mode signal is generated;

[0114] When the catastrophe risk index is greater than or equal to the warning threshold and less than the dynamic locking threshold, an active risk avoidance mode signal is generated;

[0115] When the catastrophe risk index is greater than or equal to the dynamic locking threshold, a survival locking mode signal is generated.

[0116] This embodiment is a specific embodiment of the operating mode intelligent switching decision analysis process in the operating mode decision unit; the internal logic is to integrate real-time risk and long-term health degree to realize intelligent and adaptive mode switching from high-efficiency power generation to active risk avoidance and then to survival locking;

[0117] The preset reference locking threshold is dynamically adjusted based on the cumulative fatigue damage index to obtain a dynamic locking threshold ; the technical principle of this design is that as the system service life increases and the cumulative damage increases, its mechanical reliability will decrease, so a lower risk threshold is needed to trigger the protection mechanism, in this embodiment, calculated by the following formula:

[0118] ;

[0119] wherein, : dynamic locking threshold, dimensionless, calculated by this formula; it is the current effective decision threshold for triggering the survival locking mode;

[0120] : reference locking threshold, dimensionless, is the initial value set when the system is shipped, and its source is the safety margin calculation of mechanical design;

[0121] : cumulative fatigue damage index, dimensionless, calculated by the system health assessment unit in real time;

[0122] : risk sensitivity adjustment coefficient, dimensionless, 0 <1, is a preset parameter, used to control the adjustment range of damage degree to the decision threshold;

[0123] The catastrophe risk index is compared with the preset warning threshold and the dynamic locking threshold. The system compares the real-time calculated catastrophe risk index with two key thresholds, a fixed warning threshold and the above calculated dynamic locking threshold . The determination method of the warning threshold is: based on the statistical analysis of the catastrophe risk index of the system under a large number of normal operating conditions , the 99th percentile of the statistical distribution is taken to balance between sensitivity and false alarm rate;

[0124] According to the comparison result, different operation mode signals are generated:

[0125] When the catastrophe risk index is less than the warning threshold , the efficient power generation mode signal is generated; in this mode, the system determines that the environment is safe, and will normally execute its passive solar tracking and self-cleaning functions to maximize energy output;

[0126] When the catastrophe risk index is greater than or equal to the warning threshold and less than the dynamic locking threshold , the active risk avoidance mode signal is generated; in this mode, the system identifies potential risks, will suspend or reduce the frequency and amplitude of tracking movement, and can execute the preset micro-vibration program to try to remove the initial snow, and actively avoid the further expansion of risks;

[0127] When the catastrophe risk index is greater than or equal to the dynamic locking threshold , the survival locking mode signal is generated; in this mode, the system judges that the risk has reached a critical level, will execute the highest level of protection strategy, i.e. drive the photovoltaic panel to the preset safest angle and perform mechanical locking, stop all movements, and ensure the core components are preserved in extreme disasters;

[0128] The embodiment realizes the leap from a one-size-fits-all static security policy to a dynamic intelligent decision-making according to the situation by introducing a decision threshold dynamically associated with the system health degree; this design enables the system to be more daring in pursuing power generation efficiency when young, and to become more cautious after being old or damaged, thereby achieving the best balance between power generation benefit and structural reliability throughout the life cycle.

[0129] Embodiment 5:

[0130] The post-disaster recovery condition identification analysis process is as follows:

[0131] Real-time normal solar irradiance, real-time mechanical load and real-time ambient temperature in the multi-dimensional environmental parameters are obtained, and the obtained parameters are weighted and fused to obtain a recovery index;

[0132] The recovery index is compared and analyzed with a preset recovery threshold, and if the recovery index reaches the recovery threshold, a recovery instruction is generated.

[0133] The embodiment is a specific embodiment of the post-disaster recovery condition identification analysis process in the post-disaster recovery control unit; the purpose is to establish a scientific and quantitative decision-making mechanism to determine whether the disastrous weather has ended and whether the environment is suitable for recovery to normal operation, thereby avoiding secondary damage caused by premature or reckless recovery attempts;

[0134] Real-time normal solar irradiance , real-time mechanical load and real-time ambient temperature in the multi-dimensional environmental parameters are obtained, and the obtained parameters are weighted and fused to obtain a recovery index ; to achieve this purpose, the embodiment constructs a recovery index corresponding to the catastrophe risk index ; the index is also based on the multi-attribute decision-making theory, but its focus shifts from risk assessment to recovery condition assessment, and its mathematical expression is:

[0135] ;

[0136] Among them, : recovery index, dimensionless, as the final calculation output of this step; the higher the index, the more conducive the environmental conditions are to safe recovery;

[0137] : the parameter definition is the same as the previous embodiment;

[0138] : reference thawing temperature span, unit: °C, is a preset parameter, which is set to 5°C in this embodiment, and is used to normalize the positive effect of temperature recovery; The item ensures that only when the temperature rises above the freezing point, an active recovery contribution is generated;

[0139] : recovery component weight coefficient, dimensionless, and the sum is 1; and Similar to the weight calibration of, these coefficients are determined by regression analysis on a data set of historical successful recovery events, but the calibration target is more focused on load reduction and temperature rise to ensure that the recovery program is started under safe conditions where mechanical load has been significantly reduced and ice and snow have begun to melt;

[0140] Compare the recovery index with the preset recovery threshold, if the recovery index reaches the recovery threshold , a recovery instruction is generated; the recovery threshold is a critical value determined by a large number of simulation experiments or field tests, and the setting standard is that the success rate of starting the recovery program above this threshold is greater than 99.9%; only when the real-time calculation of lasts for a period of time above , the system will determine that the recovery condition is mature, and generate a recovery instruction to avoid false positives due to temporary fluctuations in environmental parameters;

[0141] This embodiment realizes accurate and quantitative decision-making of post-disaster recovery timing by constructing an independent recovery index oriented to the recovery scene; this completely changes the traditional recovery method which relies on fixed delay or manual intervention, ensuring that the system always starts the recovery process within a safe window period when the physical conditions are fully confirmed, greatly improving the safety and success rate of the recovery process; in addition, to further improve the recovery safety, a wind speed sensor can be integrated into the multi-dimensional environment perception unit. The calculation model of the recovery index can introduce a wind speed term and set a safe wind speed threshold to ensure that the system will only execute the recovery instruction when the wind speed is below the threshold, thereby avoiding the risk of wind-induced structural damage.

[0142] Embodiment 6:

[0143] In response to the recovery instruction, a phased recovery strategy is executed; the phased recovery strategy includes load closed-loop testing, functional self-checking, and recovery to normal operation;

[0144] The load closed-loop testing process is as follows:

[0145] Based on the cumulative fatigue damage index output by the system health assessment unit, the dynamic maximum test load is calculated;

[0146] The driving motor applies a driving torque, and the real-time mechanical load is monitored;

[0147] If the real-time mechanical load does not exceed the dynamic maximum test load at any time during the entire driving process, the test is determined to be passed, and the system enters the functional self-check phase;

[0148] If the real-time mechanical load reaches the dynamic maximum test load, the test is determined to be failed, and the system reverts to the initial locked position;

[0149] If the real-time mechanical load reaches the dynamic maximum test load, the test is determined to be failed, and the system reverts to the initial locked position;

[0150] If the load closed-loop test is determined to be passed, the system performs a functional self-check. The functional self-check is used to verify that all transmission components are active and to activate the self-cleaning mechanism, and after the self-check is completed, the system switches to the high-efficiency power generation mode.

[0151] The embodiment is a further deepening and refinement of the post-disaster recovery process, and its purpose is to ensure the safety, controllability and reliability of the recovery process itself through a dynamic and progressive recovery strategy that is dynamically associated with the system health status, and to achieve a system function restart that is absolutely safe;

[0152] Load closed-loop test

[0153] The core of this phase is to perform a small-angle driving test, which aims to confirm whether the mechanical system has escaped from physical lock such as ice and snow freezing with minimal risk. To ensure the absolute safety of the test, the system will first calculate a dynamic maximum test load according to the current cumulative fatigue damage index The formula for calculating a dynamic maximum test load is as follows:

[0154] ;

[0155] Wherein, : dynamic maximum test load, unit: Newton N, calculated by the formula; it sets a dynamic safety line for this test;

[0156] : safety test load reference value, unit: Newton N, is a very conservative load value set for a new system, which is much smaller than the design maximum load ;

[0157] : damage sensitivity coefficient, dimensionless, is a pre-set parameter, used to adjust the degree of damage to the test load reduction; this formula ensures that as the system ages, the recovery test process will become more gentle;

[0158] ​The load closed-loop test process is as follows: the motor is driven to apply and gradually increase the driving torque at a very slow speed, while the real-time mechanical load is monitored at high frequency ; the control system follows strict logical judgment:

[0159] If the value of the real-time mechanical load does not exceed the dynamic maximum test load during the whole driving process, it is determined that the test is passed, indicating that the main mechanical obstruction has been eliminated, and the system can safely enter the next recovery phase;

[0160] If the value of the real-time mechanical load has reached the dynamic maximum test load before reaching the predetermined angle, it is determined that the test fails, the system immediately stops applying torque and retreats to the initial locked position, re-enters the waiting state, and attempts again after the recovery index is further raised;

[0161] Functional self-test

[0162] The system performs a functional self-test only when the load closed-loop test determines that the test is passed. The purpose of this phase is to comprehensively verify whether the motion and cleaning functions of the system are intact. The system will perform a complete motion range self-test procedure to verify that all transmission components are moving freely. At the same time, the self-cleaning mechanism can be activated to remove any loose contaminants that may be left on the photovoltaic panel;

[0163] Return to normal operation

[0164] After the functional self-test is successfully completed, the system confirms that all functional units are working properly, and automatically switches to the efficient power generation mode, re-starting the passive sun tracking task. Thus, the entire post-disaster recovery closed-loop control process ends;

[0165] This embodiment constructs an unprecedented, highly intelligent and safety-redundant post-disaster recovery system. It is not simply restarted, but first explores the road through a load closed-loop test that is linked to the health of the system and has controllable torque, to eliminate potential hard failure risks at minimal cost. On this basis, a comprehensive functional self-test is performed to ensure that all functions are complete. The coordinated design of this phased recovery strategy minimizes the risk of the recovery process, greatly improving the usability and reliability of the system after experiencing extreme disasters, and ensuring the safety of the entire photovoltaic system asset.

[0166] The above is only a preferred embodiment of the present application, and is not intended to limit the protection scope of the present application; any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0167] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced, without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A passive sun-tracking and self-cleaning integrated photovoltaic system, characterized by, The application relates to a system health degree evaluation unit for collecting stress history data of the system and performing structural fatigue evolution modeling analysis to obtain a cumulative fatigue damage index. The application relates to a running mode decision unit for combining the catastrophe risk index and the cumulative fatigue damage index output by the system health degree evaluation unit to perform running mode intelligent switching decision analysis and generate an efficient power generation mode signal, an active risk avoidance mode signal or a survival locking mode signal. The application relates to a post-disaster recovery control unit for performing post-disaster recovery condition identification analysis according to the multi-dimensional environment parameters after receiving the survival locking mode signal and generating a recovery instruction. The application relates to a thermal-mechanical coupling catastrophe risk modeling analysis process as follows: real-time mechanical load, real-time normal solar irradiance, real-time environmental temperature and real-time relative humidity in the multi-dimensional environment parameters are obtained. Mechanical overload risk, heat source failure risk and phase change icing risk are determined. The mechanical overload risk, the heat source failure risk and the phase change icing risk are weighted and summed to obtain the catastrophe risk index. The application relates to a structural fatigue evolution modeling analysis process as follows: actual cycle times experienced by the system under each stress level are obtained. The total cycle times allowed for material destruction under the corresponding stress level are obtained. The actual cycle times are divided by the total cycle times to obtain a damage ratio. The damage ratios of all stress levels are summed to obtain a cumulative fatigue damage index. The application relates to an intelligent running mode switching decision analysis process as follows: a dynamic locking threshold is obtained by dynamically adjusting a preset reference locking threshold based on the cumulative fatigue damage index. The catastrophe risk index is compared with a preset early warning threshold and the dynamic locking threshold. When the catastrophe risk index is less than the early warning threshold, an efficient power generation mode signal is generated. When the catastrophe risk index is greater than or equal to the early warning threshold and less than the dynamic locking threshold, an active risk avoidance mode signal is generated. When the catastrophe risk index is greater than or equal to the dynamic locking threshold, a survival locking mode signal is generated. The application relates to a post-disaster recovery condition identification analysis process as follows: real-time normal solar irradiance, real-time mechanical load and real-time environmental temperature in the multi-dimensional environment parameters are obtained, and the obtained parameters are weighted and fused to obtain a recovery index. The recovery index is compared with a preset recovery threshold, and if the recovery index reaches the recovery threshold, a recovery instruction is generated. In response to the recovery instruction, a phased recovery strategy is performed, which includes load closed-loop testing, functional self-checking and recovery to normal operation. The application relates to a load closed-loop testing process as follows: a dynamic maximum test load is calculated based on the cumulative fatigue damage index output by the system health degree evaluation unit. A driving torque is applied to a motor, and real-time mechanical load is monitored. ​ 2. The integrated passive solar tracking and self-cleaning photovoltaic system according to claim 1, wherein, ​ ​ ​ 3. The integrated passive solar tracking and self-cleaning photovoltaic system according to claim 2, wherein, ​ 4. The integrated passive solar tracking and self-cleaning photovoltaic system according to claim 3, wherein, ​ ​ ​ If the real-time mechanical load does not exceed the dynamic maximum test load throughout the entire drive, the test is determined to pass and enters the functional self-check phase; If the real-time mechanical load reaches the dynamic maximum test load, the test is determined to fail and reverts to the initial locked position.

5. The integrated passive solar tracking and self-cleaning photovoltaic system according to claim 4, wherein, When the load closed-loop test is determined to pass, the system performs a functional self-check; the functional self-check is used to verify that all transmission components are active and to activate the self-cleaning mechanism, and switches to the high-efficiency power generation mode after the self-check is completed.

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