Dynamic optimization system and method for wiring mode of photovoltaic group string

Abstract

The invention discloses a dynamic optimization system and method for a photovoltaic string connection mode, and the system comprises a data collection module which comprises an environmental parameter collection sub-module which collects the parameters of solar irradiance, environment temperature and assembly temperature in real time; the electrical parameter acquisition sub-module is used for monitoring voltage, current and power parameters of each photovoltaic string in real time; the space-time parameter calculation sub-module is used for calculating sun position parameters including a solar altitude angle, an azimuth angle and an incident angle based on GPS positioning information and a real-time clock; by dynamically optimizing a wiring mode in real time, the system can always keep a better operation state under different illumination conditions, and actually measured data shows that under a typical shadow shielding scene, the power generation efficiency can be improved, and the improvement effect is more obvious under a complex terrain condition; based on accurate geometric shadow calculation and actual electrical parameter verification, the system can accurately identify various types of shadow shielding.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic power generation technology, specifically relating to a dynamic optimization system and method for photovoltaic module string connection. Background Technology

[0002] With the rapid development of renewable energy technologies, the proportion of photovoltaic power generation in the global energy structure continues to increase. However, traditional photovoltaic power plants face many technical challenges in actual operation, among which the shading problem is particularly prominent. In centralized photovoltaic power plants, factors such as mutual shading between front and rear photovoltaic strings, cloud drift shading, and shading by nearby buildings or vegetation can lead to a serious mismatch in the output characteristics of photovoltaic strings, resulting in a significant decrease in power generation efficiency.

[0003] Currently, the most commonly used photovoltaic string wiring methods in the industry mainly include two basic forms: straight-line wiring and C-shaped detour wiring. Traditional fixed wiring methods have the following technical defects: Fixed wiring lacks adaptability. Straight-line wiring has low line loss under no-shade conditions, but significant power generation loss occurs when shaded. C-shaped wiring can balance the differences between strings under shaded conditions, but line loss is high under uniform lighting. There is a lack of intelligent mechanisms to dynamically adjust the wiring method according to the actual operating environment.

[0004] Shadow recognition accuracy is insufficient. Existing technologies mostly use simple voltage and current thresholds for judgment, which cannot accurately distinguish shadow types, assess the degree of occlusion, or predict the duration and trend of shadow changes.

[0005] Switching decisions lack comprehensiveness. Traditional switching decisions often only consider instantaneous power generation and ignore comprehensive factors such as equipment losses, system stability impact, and long-term operating benefits caused by switching wiring methods. To address this, this invention proposes a dynamic optimization system and method for photovoltaic string wiring methods. Summary of the Invention

[0006] The purpose of this invention is to provide a dynamic optimization system and method for photovoltaic string wiring, which calculates the power generation performance gain value by collecting environmental and electrical data in real time, and dynamically switches the wiring method of photovoltaic strings in combination with shading determination, thereby improving power generation efficiency.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a dynamic optimization system for photovoltaic module string connection method, comprising... The data acquisition module includes: Environmental parameter acquisition submodule: Real-time acquisition of solar irradiance, ambient temperature, and component temperature parameters; Electrical parameter acquisition submodule: Real-time monitoring of voltage, current, and power parameters of each photovoltaic string; Spatiotemporal parameter calculation submodule: Based on GPS positioning information and a real-time clock, it calculates the solar position parameters, including solar altitude angle, azimuth angle, and incident angle.

[0008] A shadow recognition module, comprising: Geometric Shadow Calculation Submodule: Calculates theoretical shadow distribution based on the geometric layout of the photovoltaic array and the position of the sun; Actual shadow detection submodule: Identifies actual shadow occlusion through current and voltage characteristic analysis; Shadow type determination submodule: distinguishes between static shadows and dynamic shadows, and determines the duration of shadows.

[0009] The optimization decision-making module includes: Performance prediction submodule: Predicts power generation performance under different wiring methods based on current operating conditions; The revenue assessment submodule calculates the power generation gain and switching losses resulting from the switching of wiring methods. Decision execution submodule: Determines the optimal wiring method after comprehensive evaluation and generates control commands.

[0010] Wiring switching module, the wiring switching module includes: Switch control submodule: controls the on / off states of multiple intelligent switching devices; Wiring reconfiguration submodule: Enables dynamic reconfiguration of electrical connection relationships between strings; Status monitoring submodule: Real-time monitoring of the safety and reliability of the wiring switching process.

[0011] A communication coordination module, comprising: Data interaction submodule: Enables data sharing and command transmission between modules; Timing coordination submodule: Ensures the time synchronization and logical consistency of operations across all modules; Exception handling submodule: Initiates backup communication mechanism in case of communication failure.

[0012] Preferably, the data acquisition module further includes: The historical data storage submodule is used to store historical operating data and meteorological data, and to establish a photovoltaic power station operation database; The data preprocessing submodule filters, calibrates, and handles outliers in the collected raw data to ensure data quality.

[0013] Preferably, in the shadow recognition module, the geometric shadow calculation submodule is specifically used to: establish a three-dimensional shadow model based on the size of the photovoltaic module, array layout, support tilt angle parameters, and combined with the sun position calculation; and calculate the accurate shadow coverage range and degree of occlusion considering the influence of terrain undulations and surrounding obstacles.

[0014] A method for dynamically optimizing the string connection method of photovoltaic modules includes the following steps: Step S1: System Initialization and Parameter Setting Read the basic parameters of the photovoltaic power station, including geographical location, component specifications, and array layout; Set the optimization cycle, data sampling frequency, and control parameter thresholds; Initialize all switching devices to the reference wiring state.

[0015] Step S2: Real-time acquisition and processing of multi-source data Simultaneously collect environmental parameters: solar irradiance, ambient temperature, wind speed and direction; Synchronously collect electrical parameters: voltage and current of each branch, and total DC power; Calculate solar position parameters: Based on date, time and geographical location, calculate solar altitude angle h, azimuth angle ψ, and incident angle θ.

[0016] Step S3: Accurate Identification and Evaluation of Shadow Occlusion Calculate theoretical shading: Based on the geometric model of the photovoltaic array and the position of the sun, calculate the shading situation of the front and rear rows; Detecting actual shadows: Analyzing current-voltage characteristic curves to identify the location and extent of actual shading; Determine shadow characteristics: differentiate between temporary cloud cover and fixed object cover, and assess the duration of cover.

[0017] Step S4: Performance Prediction of Multiple Wiring Methods Establish a C-shaped wiring performance model to predict power generation performance under shaded conditions.

[0018] Establish a performance model for a linear wiring configuration to predict power generation performance under uniform illumination conditions.

[0019] Calculate switching benefits: Evaluate the theoretical gain of switching from the current wiring method to the target method.

[0020] Step S5: Dynamic Optimization Decision Generation A comprehensive assessment of power generation gain, switching losses, and equipment lifespan factors was conducted. Determine the optimal wiring configuration: C-type or straight-line; Generate specific switch control sequences and operation timings.

[0021] Step S6: Perform wiring switching safely and reliably. Send control commands to each switching device; Real-time monitoring of changes in electrical parameters during the switching process; Verify the stability and security of the system operation after the switch.

[0022] Step S7: Performance Evaluation and Parameter Optimization Monitor the actual power generation performance after the switch; Compare predicted values ​​with actual values ​​and optimize performance model parameters; Update the system's learning database to improve the accuracy of subsequent decisions.

[0023] Preferably, in step S3, the accurate identification and evaluation of shadow occlusion specifically includes: Step S31: Accurate Calculation of Solar Position Based on the latitude and longitude coordinates of the photovoltaic power station and real-time time, an astronomical algorithm is used for calculation: Solar altitude angle h: sinh = sin(phi) + cos(phi)cos(delta) Solar azimuth ψ: cos\psi=frac{sin\phi\sin h- \sin\delta}{cos\phi\cos h} The angle of incidence of the sun, θ, is given by: cosθ = sin h cos alpha + cos h sin alpha cos(psi - A). Where φ is the geographic latitude, δ is the declination angle, ω is the hour angle, α is the tilt angle of the component, and A is the azimuth angle of the component.

[0024] Step S32: Determining the shadow boundary conditions. For planar photovoltaic power plants, calculate the solar incident boundary angle γ: gamma=arctan\left(frac{L\sin\alpha}{D- L\cos\alpha}\right); For non-planar photovoltaic power plants, considering the height difference ΔZ, the solar incident boundary angle γ is calculated as: gamma=arctan\left(frac{L\sin\alpha+DeltaZ}{D- L\cos\alpha}\right); Where L is the component length and D is the spacing between the front and rear rows.

[0025] Step S33, Actual Shadow Verification Theoretical current values ​​are calculated based on solar irradiance and module temperature. Measuring actual current value: acquired in real time via a current sensor; Comparative analysis: When the actual current is significantly lower than the theoretical current, the presence of shading is confirmed.

[0026] Preferably, in step S5, the dynamic optimization decision generation specifically includes: Step S51: Calculate the power generation gain value ΔP_gen and the line loss power improvement value ΔP_loss when switching between C-type and straight wiring. ΔP_gen = P_{C-type} - P_{straight} ΔP_loss = P_{loss,C-type} - P_{loss,straight}. Step S52: Comprehensive benefit assessment considering the various impacts of the switch: Revenue from electricity generation: ΔP_gen × electricity price × expected duration; Switching costs: power generation losses during the switching process + equipment wear and tear; Risk assessment: System failures that may result from a failed switchover.

[0027] Step S53: Optimal decision-making is based on the following criteria to determine the target wiring method: When there is shadow occlusion and ΔP_gen > ΔP_loss, choose C-type wiring; When there is shadow occlusion and ΔP_gen≤ΔP_loss, choose a straight-line connection; When there is no shadow obstruction, C-type wiring is preferred.

[0028] Preferably, in step S6, the safe and reliable execution of the wiring switch specifically includes: Step S61: Safety check before switching; Step S62, Step-by-step sequence control; Step S63: Verify after switching.

[0029] Preferably, in step S61, the pre-switching security check specifically includes: Confirm that all switching equipment is in an operable state; Verify that the communication link is normal; The system check showed no fault alarms.

[0030] Preferably, in step S62, the step-by-step sequence control specifically includes: First, disconnect all switches that are to be operated; Then the target switch is turned on according to the predetermined sequence; After each step of the operation, verify that the electrical parameters meet the expectations.

[0031] Preferably, in step S63, the post-switch verification specifically includes: Measure the electrical parameters under the new wiring method; Verify that the system is operating stably; Update system status indicators.

[0032] Compared with the prior art, the beneficial effects of the present invention are: 1. Significantly improved power generation efficiency: By dynamically optimizing the wiring method in real time, the system can always maintain a better operating state under different lighting conditions. The measured data shows that the present invention can improve power generation efficiency in typical shadow occlusion scenarios, and the improvement effect is more obvious in complex terrain conditions.

[0033] 2. Enhanced Shadow Adaptability: Based on accurate geometric shadow calculations and actual electrical parameter verification, the system can accurately identify various types of shadow occlusion and adjust wiring strategies accordingly, effectively mitigating the "weakest link effect".

[0034] 3. Improved system reliability: Intelligent fault prevention monitors the health status of switchgear in real time through the status monitoring submodule and performs safety checks before switching to effectively prevent electrical faults; Equipment life extension optimization decision module comprehensively considers the impact of switching frequency on equipment life, avoids unnecessary frequent switching, and extends the service life of switchgear.

[0035] 4. Reduced operation and maintenance costs: The fully automated system reduces manual intervention and eliminates the need for manual intervention in wiring adjustments, significantly reducing operation and maintenance labor costs; Improved power generation revenue: Through accurate revenue assessment and optimized decision-making, it ensures that each wiring switch brings positive power generation revenue, shortening the investment payback period.

[0036] 5. Strong environmental adaptability: The system is suitable for various complex environments, including special application scenarios such as mountain photovoltaic, water surface photovoltaic, and building photovoltaic, and has good universality. Attached Figure Description

[0037] Figure 1 This is a system framework diagram of the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0039] Example Please see Figure 1 As an embodiment of the present invention, this embodiment provides the following technical solution: a dynamic optimization system for photovoltaic module string connection method, comprising... The data acquisition module includes: Environmental parameter acquisition submodule: Real-time acquisition of solar irradiance, ambient temperature, and component temperature parameters, using a distributed sensor network: Irradiance sensor: installed at different locations in the array, sampling frequency 1Hz; Temperature sensor: platinum resistance thermometer PT100, accuracy ±0.1℃; Data acquisition unit: 16-bit ADC, RS485 communication interface.

[0040] Electrical parameter acquisition submodule: Real-time monitoring of voltage, current, and power parameters of each photovoltaic string, configured as follows: Voltage sensor: range 0-1500VDC, accuracy class 0.5; DC current sensor: Hall effect principle, range 0-15A, accuracy class 0.5; Power calculation unit: DSP processor, real-time calculation of power of each branch.

[0041] Spatiotemporal parameter calculation submodule: Based on GPS positioning information and a real-time clock, it calculates the solar position parameters, including solar altitude angle, azimuth angle, and incident angle.

[0042] The shadow recognition module includes: Geometric Shadow Calculation Submodule: Calculates theoretical shadow distribution based on the geometric layout of the photovoltaic array and the position of the sun; Actual shadow detection submodule: Identifies actual shadow occlusion through current and voltage characteristic analysis; Shadow type determination submodule: distinguishes between static shadows and dynamic shadows, and determines the duration of shadows.

[0043] The decision-making optimization module includes: Performance prediction submodule: Predicts power generation performance under different wiring methods based on current operating conditions; The revenue assessment submodule calculates the power generation gain and switching losses resulting from the switching of wiring methods. Decision execution submodule: Determines the optimal wiring method after comprehensive evaluation and generates control commands.

[0044] Wiring switching module, the wiring switching module includes: Switch control submodule: Controls the on and off states of multiple intelligent switching devices. The switching devices adopt intelligent circuit breakers specifically designed for photovoltaic DC applications: rated voltage: 1500VDC, rated current: 20A, switch life: ≥10000 cycles, communication interface: CAN bus or wireless LoRa; The main controller adopts an industrial-grade embedded system: processor: ARM Cortex-A53 quad-core, memory: 2GB DDR4, storage: 16GB eMMC, communication interface: Ethernet, 4G, Wi-Fi multi-mode communication.

[0045] Wiring reconfiguration submodule: Enables dynamic reconfiguration of electrical connection relationships between strings; Status monitoring submodule: Real-time monitoring of the safety and reliability of the wiring switching process.

[0046] The communication coordination module includes: Data interaction submodule: Enables data sharing and command transmission between modules; Timing coordination submodule: Ensures the time synchronization and logical consistency of operations across all modules; Exception handling submodule: Initiates backup communication mechanism in case of communication failure.

[0047] In this embodiment, preferably, the data acquisition module further includes: The historical data storage submodule is used to store historical operating data and meteorological data, and to establish a photovoltaic power station operation database; The data preprocessing submodule filters, calibrates, and handles outliers in the collected raw data to ensure data quality.

[0048] In this embodiment, preferably, the geometric shadow calculation submodule in the shadow recognition module is specifically used to: establish a three-dimensional shadow model based on the size of the photovoltaic module, array layout, support tilt angle parameters, and combined with the sun position calculation; and calculate the accurate shadow coverage range and degree of occlusion considering the influence of terrain undulations and surrounding obstacles.

[0049] A method for dynamically optimizing the string connection method of photovoltaic modules includes the following steps: Step S1: System Initialization and Parameter Setting Read the basic parameters of the photovoltaic power station, including geographical location, component specifications, and array layout; Set the optimization cycle, data sampling frequency, and control parameter thresholds; Initialize all switching devices to the reference wiring state.

[0050] Step S2: Real-time acquisition and processing of multi-source data Simultaneously collect environmental parameters: solar irradiance, ambient temperature, wind speed and direction; Synchronously collect electrical parameters: voltage and current of each branch, and total DC power; Calculate solar position parameters: Based on date, time and geographical location, calculate solar altitude angle h, azimuth angle ψ, and incident angle θ.

[0051] Step S3: Accurate Identification and Evaluation of Shadow Occlusion Calculate theoretical shading: Based on the geometric model of the photovoltaic array and the position of the sun, calculate the shading situation of the front and rear rows; Actual shading detection: Analyze current and voltage characteristic curves to identify the location and extent of actual shading. Actual shading detection algorithm: Shading detection method based on IV characteristic analysis: Establish IV curve database under standard operating conditions; collect actual IV characteristic data in real time; use curve fitting algorithm to identify anomalies; combine environmental parameters to eliminate the influence of temperature and irradiance changes; determine the specific string location and severity of shading.

[0052] Determine shadow characteristics: differentiate between temporary cloud cover and fixed object cover, and assess the duration of cover.

[0053] Step S4: Performance Prediction of Multiple Wiring Methods Establish a C-shaped wiring performance model to predict power generation performance under shaded conditions.

[0054] Establish a performance model for a linear wiring configuration to predict power generation performance under uniform illumination conditions.

[0055] Calculate switching benefits: Evaluate the theoretical gain of switching from the current wiring method to the target method.

[0056] Step S5: Dynamic Optimization Decision Generation A comprehensive assessment of power generation gain, switching losses, and equipment lifespan factors was conducted. Determine the optimal wiring configuration: C-type or straight-line; Generate specific switch control sequences and operation timings.

[0057] Step S6: Perform wiring switching safely and reliably. Send control commands to each switching device; Real-time monitoring of changes in electrical parameters during the switching process; Verify the stability and security of the system operation after the switch.

[0058] Step S7: Performance Evaluation and Parameter Optimization Monitor the actual power generation performance after the switch; Compare predicted values ​​with actual values ​​and optimize performance model parameters; Update the system's learning database to improve the accuracy of subsequent decisions.

[0059] In this embodiment, preferably, step S3, the accurate identification and evaluation of shadow occlusion, specifically includes: Step S31: Accurate Calculation of Solar Position Based on the latitude and longitude coordinates of the photovoltaic power station and real-time time, an astronomical algorithm is used for calculation: Solar altitude angle h: sinh = sin(phi) + cos(phi)cos(delta) Solar azimuth ψ: cos\psi=frac{sin\phi\sin h- \sin\delta}{cos\phi\cos h} The angle of incidence of the sun, θ, is given by: cosθ = sin h cos alpha + cos h sin alpha cos(psi - A). Where φ is the geographic latitude, δ is the declination angle, ω is the hour angle, α is the tilt angle of the component, and A is the azimuth angle of the component.

[0060] Step S32: Determining the shadow boundary conditions. For planar photovoltaic power plants, calculate the solar incident boundary angle γ: gamma=arctan\left(frac{L\sin\alpha}{D- L\cos\alpha}\right); For non-planar photovoltaic power plants, considering the height difference ΔZ, the solar incident boundary angle γ is calculated as: gamma=arctan\left(frac{L\sin\alpha+DeltaZ}{D- L\cos\alpha}\right); Where L is the component length and D is the spacing between the front and rear rows.

[0061] Step S33, Actual Shadow Verification Theoretical current values ​​are calculated based on solar irradiance and module temperature. Measuring actual current value: acquired in real time via a current sensor; Comparative analysis: When the actual current is significantly lower than the theoretical current, the presence of shading is confirmed.

[0062] In this embodiment, preferably, step S5, the dynamic optimization decision generation specifically includes: Step S51: Calculate the power generation gain value ΔP_gen and the line loss power improvement value ΔP_loss when switching between C-type and straight wiring. ΔP_gen = P_{C-type} - P_{straight} ΔP_loss = P_{loss,C-type} - P_{loss,straight}. Step S52: Comprehensive benefit assessment considering the various impacts of the switch: Revenue from electricity generation: ΔP_gen × electricity price × expected duration; Switching costs: power generation losses during the switching process + equipment wear and tear; Risk assessment: System failures that may result from a failed switchover.

[0063] Step S53: Optimal decision-making is based on the following criteria to determine the target wiring method: When there is shadow occlusion and ΔP_gen > ΔP_loss, choose C-type wiring; When there is shadow occlusion and ΔP_gen≤ΔP_loss, choose a straight-line connection; When there is no shadow obstruction, C-type wiring is preferred.

[0064] Based on long-term operational data statistics, key threshold parameters for optimization decisions are set: Power generation gain threshold: ΔP_gen ≥ 3% × rated power Line loss power enhancement threshold: ΔP_loss ≤ 1.5% × rated power Shadow duration threshold: continuous occlusion time ≥ 30 minutes.

[0065] To ensure a safe and reliable handover process, a step-by-step timing control strategy is adopted: Preparation phase (t0-t1): Disconnect all switches to be operated for 100ms. Transition phase (t1-t2): All switches are in the open state for 50ms. Setup phase (t2-t3): The target switch is turned on according to a predetermined sequence, lasting for 150ms; Stable phase (t3-t4): Verify the operating status of the new wiring method, lasting 200ms.

[0066] In this embodiment, preferably, step S6, the safe and reliable execution of the wiring switch specifically includes: Step S61: Safety check before switching; Step S62, Step-by-step sequence control; Step S63: Verify after switching.

[0067] In this embodiment, preferably, the pre-switching security check in step S61 specifically includes: Confirm that all switching equipment is in an operable state; Verify that the communication link is normal; The system check showed no fault alarms.

[0068] In this embodiment, preferably, step S62, the step sequence control specifically includes: First, disconnect all switches that are to be operated; Then the target switch is turned on according to the predetermined sequence; After each step of the operation, verify that the electrical parameters meet the expectations.

[0069] In this embodiment, preferably, step S63, the post-switch verification specifically includes: Measure the electrical parameters under the new wiring method; Verify that the system is operating stably; Update system status indicators.

[0070] Although embodiments of the invention have been shown and described (see the detailed description above), it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A dynamic optimization system for photovoltaic module string connection, characterized in that: include The data acquisition module includes: Environmental parameter acquisition submodule: Real-time acquisition of solar irradiance, ambient temperature, and component temperature parameters; Electrical parameter acquisition submodule: Real-time monitoring of voltage, current, and power parameters of each photovoltaic string; Spatiotemporal parameter calculation submodule: Based on GPS positioning information and a real-time clock, it calculates the solar position parameters, including solar altitude angle, azimuth angle, and incident angle. A shadow recognition module, comprising: Geometric Shadow Calculation Submodule: Calculates theoretical shadow distribution based on the geometric layout of the photovoltaic array and the position of the sun; Actual shadow detection submodule: Identifies actual shadow occlusion through current and voltage characteristic analysis; Shadow type determination submodule: distinguishes between static shadows and dynamic shadows, and determines the duration of shadows. The optimization decision-making module includes: Performance prediction submodule: Predicts power generation performance under different wiring methods based on current operating conditions; The revenue assessment submodule calculates the power generation gain and switching losses resulting from the switching of wiring methods. Decision execution submodule: Determines the optimal wiring method after comprehensive evaluation and generates control commands. Wiring switching module, the wiring switching module includes: Switch control submodule: controls the on / off states of multiple intelligent switching devices; Wiring reconfiguration submodule: Enables dynamic reconfiguration of electrical connection relationships between strings; Status monitoring submodule: Real-time monitoring of the safety and reliability of the wiring switching process. A communication coordination module, comprising: Data interaction submodule: Enables data sharing and command transmission between modules; Timing coordination submodule: Ensures the time synchronization and logical consistency of operations across all modules; Exception handling submodule: Initiates backup communication mechanism in case of communication failure.

2. The photovoltaic module string connection dynamic optimization system according to claim 1, characterized in that: The data acquisition module also includes: The historical data storage submodule is used to store historical operating data and meteorological data, and to establish a photovoltaic power station operation database; The data preprocessing submodule filters, calibrates, and handles outliers in the collected raw data to ensure data quality.

3. The dynamic optimization system for photovoltaic module string connection method according to claim 1, characterized in that: In the shadow recognition module, the geometric shadow calculation submodule is specifically used to: establish a three-dimensional shadow model based on the size of the photovoltaic module, array layout, support tilt angle parameters, and combined with the sun position calculation; and calculate the accurate shadow coverage range and degree of occlusion considering the influence of terrain undulations and surrounding obstacles.

4. A method for dynamically optimizing the string connection method of a photovoltaic module applied to the dynamic optimization system for the string connection method of a photovoltaic module as described in any one of claims 1-3, characterized in that: Includes the following steps: Step S1: System Initialization and Parameter Setting Read the basic parameters of the photovoltaic power station, including geographical location, component specifications, and array layout; Set the optimization cycle, data sampling frequency, and control parameter thresholds; Initialize all switching devices to the reference wiring state. Step S2: Real-time acquisition and processing of multi-source data Simultaneously collect environmental parameters: solar irradiance, ambient temperature, wind speed and direction; Synchronously collect electrical parameters: voltage and current of each branch, and total DC power; Calculate solar position parameters: Based on date, time and geographical location, calculate solar altitude angle h, azimuth angle ψ, and incident angle θ. Step S3: Accurate Identification and Evaluation of Shadow Occlusion Calculate theoretical shading: Based on the geometric model of the photovoltaic array and the position of the sun, calculate the shading situation of the front and rear rows; Detecting actual shadows: Analyzing current-voltage characteristic curves to identify the location and extent of actual shading; Determine shadow characteristics: differentiate between temporary cloud cover and fixed object cover, and assess the duration of cover. Step S4: Performance Prediction of Multiple Wiring Methods Establish a C-shaped wiring performance model to predict power generation performance under shaded conditions. Establish a performance model for a linear wiring configuration to predict power generation performance under uniform illumination conditions. Calculate switching benefits: Evaluate the theoretical gain of switching from the current wiring method to the target method. Step S5: Dynamic Optimization Decision Generation A comprehensive assessment of power generation gain, switching losses, and equipment lifespan factors was conducted. Determine the optimal wiring configuration: C-type or straight-line; Generate specific switch control sequences and operation timings. Step S6: Perform wiring switching safely and reliably. Send control commands to each switching device; Real-time monitoring of changes in electrical parameters during the switching process; Verify the stability and security of the system operation after the switch. Step S7: Performance Evaluation and Parameter Optimization Monitor the actual power generation performance after the switch; Compare predicted values ​​with actual values ​​and optimize performance model parameters; Update the system's learning database to improve the accuracy of subsequent decisions.

5. The method for dynamic optimization of photovoltaic module string connection according to claim 4, characterized in that: In step S3, the accurate identification and evaluation of shadow occlusion specifically includes: Step S31: Accurate Calculation of Solar Position Based on the latitude and longitude coordinates of the photovoltaic power station and real-time time, an astronomical algorithm is used for calculation: Solar altitude angle h: sinh = sin\phi\sin\delta + \cos\phi\cos\delta\cos\omega; Solar azimuth ψ: cos\psi=frac{sin\phi\sin h- \sin\delta}{cos\phi\cos h}; The angle of incidence of the sun θ is: cosθ = sin h cos alpha + cos h sin alpha cos(psi - A); Where φ is the geographic latitude, δ is the declination angle, ω is the hour angle, α is the tilt angle of the component, and A is the azimuth angle of the component. Step S32: Determining the shadow boundary conditions. For planar photovoltaic power plants, calculate the solar incident boundary angle γ: gamma=arctan\left(frac{L\sin\alpha}{D- L\cos\alpha}\right); For non-planar photovoltaic power plants, considering the height difference ΔZ, the solar incident boundary angle γ is calculated as: gamma=arctan\left(frac{L\sin\alpha+DeltaZ}{D- L\cos\alpha}\right); Where L is the component length and D is the spacing between the front and rear rows. Step S33, Actual Shadow Verification Theoretical current values ​​are calculated based on solar irradiance and module temperature. Measuring actual current value: acquired in real time via a current sensor; Comparative analysis: When the actual current is significantly lower than the theoretical current, the presence of shading is confirmed.

6. The method for dynamic optimization of photovoltaic module string connection according to claim 4, characterized in that: In step S5, the dynamic optimization decision generation specifically includes: Step S51: Calculate the power generation gain value ΔP_gen and the line loss power improvement value ΔP_loss when switching between C-type and straight wiring. ΔP_gen = P_{C-type} - P_{straight} ΔP_loss = P_{loss,C-type} - P_{loss,straight}. Step S52: Comprehensive benefit assessment considering the various impacts of the switch: Revenue from electricity generation: ΔP_gen × electricity price × expected duration; Switching costs: power generation losses during the switching process + equipment wear and tear; Risk assessment: System failures that may result from a failed switchover. Step S53: Optimal decision-making is based on the following criteria to determine the target wiring method: When there is shadow occlusion and ΔP_gen > ΔP_loss, choose C-type wiring; When there is shadow occlusion and ΔP_gen≤ΔP_loss, choose a straight-line connection; When there is no shadow obstruction, C-type wiring is preferred.

7. The method for dynamic optimization of photovoltaic module string connection according to claim 4, characterized in that: In step S6, the safe and reliable execution of the wiring switch specifically includes: Step S61: Safety check before switching; Step S62, Step-by-step sequence control; Step S63: Verify after switching.

8. The method for dynamic optimization of photovoltaic module string connection according to claim 7, characterized in that: In step S61, the pre-switching security check specifically includes: Confirm that all switching equipment is in an operable state; Verify that the communication link is normal; The system check showed no fault alarms.

9. The method for dynamic optimization of photovoltaic module string connection according to claim 7, characterized in that: In step S62, the step-by-step sequence control specifically includes: First, disconnect all switches that are to be operated; Then the target switch is turned on according to the predetermined sequence; After each step of the operation, verify that the electrical parameters meet the expectations.

10. The method for dynamic optimization of photovoltaic module string connection according to claim 7, characterized in that: In step S63, the post-switch verification specifically includes: Measure the electrical parameters under the new wiring method; Verify that the system is operating stably; Update system status indicators.

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