Anti-local shadow photovoltaic system based on micro inverter and power optimization method

Abstract

The invention relates to the field of photovoltaic power generation, and discloses an anti-local shadow photovoltaic system based on a micro inverter and a power optimization method, and the system comprises a photovoltaic module array which is used for converting solar energy into DC electric energy; the micro inverter array is used for converting the direct-current electric energy into alternating-current electric energy; the branch communication gateway is used for collecting and forwarding the operation data; the cloud AI platform is used for executing a shadow prediction and power scheduling strategy; the power grid interface module is used for realizing grid-connected electric energy transmission, and the method comprises the following steps: S1, predicting local shadow change in the future 10 minutes; s2, pre-adjusting an MPPT working curve; s3, power grid harmonic waves are detected, and anti-phase compensation current is injected; s4, dynamically adjusting the branch output power; and S5, identifying an abnormal attenuation component. Through cooperative work of the micro inverter array and the LSTM shadow prediction model, MPPT dynamic adjustment is realized, and the power generation stability of the photovoltaic system under a complex illumination condition is effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of photovoltaic power generation, in particular to a local shadow-resistant photovoltaic system based on micro-inverters and a power optimization method. BACKGROUND

[0002] In a photovoltaic power generation system, micro-inverter technology has become an important solution in the field of building integrated photovoltaic (BIPV) due to its component-level energy conversion characteristics. Currently, mainstream micro-inverter systems can alleviate the power mismatch problem caused by shading or aging of photovoltaic modules to some extent through independent maximum power point tracking (MPPT) control. In terms of grid connection, traditional solutions usually use centralized or string inverters in combination with passive filter circuits to achieve grid connection, while some advanced systems have begun to introduce harmonic detection and compensation functions. In addition, the operation and maintenance of photovoltaic systems mainly relies on regular manual inspection and infrared thermal imaging detection methods, and some systems have attempted to assess the health of the components by monitoring power generation data.

[0003] However, when the photovoltaic array is in a dynamic shadow condition, the MPPT algorithm of the existing micro-inverter is difficult to respond to the rapid changes in irradiance in a timely manner, resulting in a decrease in the power generation efficiency of the system. In terms of grid interaction, as the penetration rate of distributed photovoltaic increases, the adaptability of traditional harmonic suppression methods in complex grid environments is insufficient, which may cause power quality problems. At the same time, the existing operation and maintenance methods have limited ability to identify early component performance degradation and hidden faults, making it difficult to provide timely and effective decision support for system maintenance. These factors to some extent restrict the performance and economic benefits of photovoltaic systems in complex application scenarios. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a local shadow-resistant photovoltaic system based on micro-inverters and a power optimization method, which solves the problems of power generation efficiency decline, unstable grid-connected power quality, and untimely operation and maintenance fault detection of existing photovoltaic systems under local shadow conditions.

[0005] To achieve the above purpose, the present application is implemented by the following technical scheme: a local shadow-resistant photovoltaic system based on micro-inverters, comprising: a photovoltaic module array for converting solar energy into direct current power; a micro-inverter array connected to the photovoltaic module array for converting direct current power into alternating current power; a branch communication gateway connected to the micro-inverter array for collecting and forwarding operation data; a cloud AI platform connected to the branch communication gateway for executing shadow prediction and power scheduling strategies; A grid interface module connected to the micro-inverter array for grid-connected power delivery.

[0006] Preferably, the micro-inverter array is composed of multiple micro-inverters, and each micro-inverter includes: A DC input interface for connecting a single photovoltaic module; A high-frequency power conversion module for DC-AC conversion; A DSP control module for maximum power point tracking control; An AC output interface for parallel formation of an AC branch.

[0007] Preferably, the DSP control module includes: An MPPT pre-adjustment unit for receiving shadow prediction instructions and switching working curves; A harmonic coordination unit for generating anti-phase harmonic currents to offset grid harmonics; An impedance scanning unit for real-time detection of grid impedance characteristics.

[0008] Preferably, the branch communication gateway includes: A data acquisition unit for collecting module voltage, current, and temperature data; An edge computing unit for performing power curve fingerprint comparison; A communication relay unit for connecting to a cloud AI platform through 4G / optical fiber.

[0009] Preferably, the cloud AI platform includes: An LSTM prediction module for establishing a shadow spatio-temporal distribution model; A VPP scheduling engine for generating power regulation instructions; A fault diagnosis module for identifying module degradation and hot spot faults.

[0010] Preferably, in the LSTM prediction module, the establishment of the shadow spatio-temporal distribution model includes the following operations: Receiving photovoltaic module voltage fluctuation data at a 5-minute cycle; Learning tree shadow movement patterns through a long short-term memory network; Outputting a future 10-minute irradiance change probability distribution graph.

[0011] Preferably, in the VPP scheduling engine, the production of power regulation instructions includes the following operations: Accessing grid peak and valley electricity price signals; Calculating a branch total power regulation range of -15% to +10%; Generating dynamic power instructions and issuing them to the micro-inverter array.

[0012] The formula for canceling the harmonic of the power grid in the harmonic coordination unit is: ; In the formula, is the injected anti-phase harmonic current; is the detected hth harmonic component; is the hth harmonic compensation coefficient (0.9≤ ≤1.1); is the phase compensation angle.

[0013] Preferably, in the fault diagnosis module, identifying the component attenuation and hot spot fault comprises the following operations: establishing a three-dimensional mapping relationship of component power, temperature and irradiation; triggering an alarm when real-time data deviates from historical reference values by more than 5%; locating the hot spot position by matching the voiceprint feature library.

[0014] A power optimization method for a local shadow-resistant photovoltaic system based on a micro inverter, comprising the following steps: S1, predicting local shadow changes in the next 10 minutes through an LSTM model; S2, pre-adjusting the MPPT working curve based on the prediction result; S3, detecting the harmonic of the power grid and injecting an anti-phase compensation current; S4, dynamically adjusting the branch output power in response to the peak-valley electricity price signal; S5, identifying and locating the component attenuation and hot spot fault by establishing and comparing the three-dimensional mapping relationship of the component power, temperature and irradiation with the historical reference value, and combining the matching voiceprint feature library.

[0015] The application provides a local shadow-resistant photovoltaic system based on a micro inverter and a power optimization method. 1. The application realizes dynamic adjustment of component-level MPPT through the cooperative work of the micro inverter array and the LSTM shadow prediction model, solves the problem of sudden drop in system efficiency of the traditional component string type inverter under local shadow, and effectively improves the power generation stability of the photovoltaic system under complex lighting conditions; 2. The application adopts a harmonic coordination control algorithm and impedance adaptive technology, solves the harmonic pollution problem caused by distributed photovoltaic grid connection by real-time detection of the harmonic characteristics of the power grid and injection of a compensation current, and improves the system grid connection compatibility and power supply quality; 3. The application realizes real-time monitoring and accurate positioning of the health status of the photovoltaic component by establishing a component operation feature database and a voiceprint recognition technology, solves the problems of low efficiency and high omission rate of traditional manual inspection, and greatly reduces the system operation and maintenance cost and work burden. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 System module framework diagram of the present application; Figure 2 Mini-inverter schematic diagram of the present application; Figure 3 Branch communication gateway schematic diagram of the present application; Figure 4 Cloud AI platform schematic diagram of the present application; Figure 5 Method step flowchart of the present application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0018] Please refer to the accompanying drawings of the present application Figure 1 - the accompanying drawings of the present application Figure 4 The embodiments of the present application provide a micro-inverter-based anti-local shadow photovoltaic system, which comprises: A photovoltaic module array is used to convert solar energy into direct current power. A micro-inverter array is connected to the photovoltaic module array and is used to convert direct current power into alternating current power. A branch communication gateway is connected to the micro-inverter array and is used to collect and forward operation data. A cloud AI platform is connected to the branch communication gateway and is used to perform shadow prediction and power scheduling strategies. A grid interface module is connected to the micro-inverter array and is used to realize grid-connected power transmission.

[0019] The specific technical solution details of the above photovoltaic module array are as follows: Specifically, the array is composed of multiple photovoltaic modules, which are used to convert solar energy obtained from the external environment into direct current power, thereby providing continuous power support for the system.

[0020] The basic structure of the photovoltaic module includes the following parts: Photovoltaic cell: Each photovoltaic module contains multiple photovoltaic cells, and the function of the photovoltaic cell is to generate current under light conditions. Its output voltage and current are affected by shadow conditions and temperature changes, so the selection of the module needs to meet specific performance standards to improve power generation efficiency.

[0021] Encapsulation: Photovoltaic cells are encapsulated in a layer of transparent protective material, usually high-transmission glass, to prevent irreversible damage to the cells from the external environment. In the encapsulation design, it is ensured that the optical performance of the photovoltaic cells is maintained to minimize reflection losses and improve light transmission.

[0022] Support structure: Photovoltaic modules are fixed on a support made of aluminum alloy or other corrosion-resistant materials. The design of the support ensures that the photovoltaic modules can work stably under various weather conditions, while supporting the optimal inclination angle of the photovoltaic modules to maximize light reception.

[0023] In terms of connection, the photovoltaic module array can be combined in series or parallel according to the specific needs of the system. Series connection affects the increase of output voltage, while parallel connection can improve output current. The connection method should ensure that the working conditions are met and the power loss caused by the shadow effect is minimized.

[0024] The electrical output characteristics of the photovoltaic module can be represented by the formula: ; In the formula, P is the output power (unit: watt), V is the output voltage (unit: volt), and I is the output current (unit: ampere). The power generation capacity of the photovoltaic module is affected by multiple factors, including solar irradiance, module temperature, and photovoltaic conversion efficiency.

[0025] In order to deal with the impact of local shadows on power generation efficiency, the photovoltaic module array needs to have high resource utilization, and the arrangement between adjacent modules should be considered in the design to achieve the best light conditions. The module arrangement strategy should be optimized according to the actual situation of the site to optimize the overall power generation efficiency.

[0026] The working state of the photovoltaic module in the system is monitored and controlled in real time by the micro-inverter. The micro-inverter is connected to the photovoltaic module through a direct current input interface and has voltage and current collection functions. Through these measures, it is ensured that the system can realize efficient energy conversion and effective power transmission in the actual working process.

[0027] In summary, the photovoltaic module array, as the primary component of the system, has the basic conditions for efficient energy conversion and effective environmental adaptability, providing technical support for the stable operation of the overall system.

[0028] The specific technical scheme details of the above micro-inverter array are as follows: Specifically, the micro-inverter array is composed of multiple micro-inverters, and its main function is to convert the direct current power generated by the photovoltaic module into alternating current power that meets the requirements of the power grid, and to realize maximum power point tracking (MPPT).

[0029] Each micro-inverter includes the following main components: The core of the micro-inverter is the high-frequency power conversion module. This module is responsible for high-frequency switching conversion of the input DC power to achieve DC to AC conversion. Its output waveform is a sine wave, and it ensures that the output voltage and frequency meet the grid specifications. The output voltage can be represented by the formula: ; In the formula, is the output AC voltage (in volts); is the peak value of the AC voltage; is the angular frequency (in rad / s); t is the time (in seconds), is the phase angle (in degrees or radians).

[0030] The DSP control module equipped in the micro-inverter is responsible for executing the maximum power point tracking (MPPT) algorithm. This module monitors the output voltage and current of the photovoltaic module in real time, and dynamically adjusts the working mode according to the real-time data to keep the system in the best power output state. The basic principle of the MPPT algorithm is to adjust the working point of the photovoltaic module to maximize the photovoltaic output power, and the power calculation formula is: ; In the formula, is the output AC voltage (in volts); is the peak value of the AC voltage; is the angular frequency (in rad / s); t is the time (in seconds), is the phase angle (in degrees or radians).

[0031] The DSP control module is mainly used to execute the maximum power point tracking (MPPT) control. This module is composed of an MPPT pre-adjustment unit, a harmonic coordination unit and an impedance scanning unit to realize the optimization of photovoltaic module power generation efficiency and effective management of grid harmonics.

[0032] The MPPT pre-adjustment unit is responsible for receiving shadow prediction instructions from the cloud AI platform and switching the working curve according to the latest prediction information to keep the system in the best power generation state. This unit adjusts the operating point of the inverter to always maintain the maximum power output point, thereby improving the overall operating efficiency of the system.

[0033] The harmonic coordination unit is designed to generate anti-phase harmonic current to offset the harmonic interference existing in the grid. The output anti-phase harmonic current can be represented by the following formula: ; In the formula, is the injected anti-phase harmonic current; for the detected hth harmonic component; for the hth harmonic compensation factor (0.9≤ ≤1.1); for the phase compensation angle. The injection of anti-phase harmonic current will effectively reduce the harmonic distortion of the power grid and improve the power quality.

[0034] The impedance scanning unit is used to detect the impedance characteristics of the power grid in real time. By analyzing the relationship between the output voltage and current, the impedance of the power grid can be obtained. The impedance can be calculated by the following formula: ; where, is the impedance of the power grid (unit: ohm), is the output voltage of the inverter (unit: volt), is the output current of the inverter (unit: ampere). The detection result supports the inverter to dynamically adjust according to the characteristics of the power grid to optimize the power output and system stability.

[0035] The DSP control module effectively integrates the above three units, making the micro-inverter array have high adaptability and response ability when facing different light, shadow and power grid conditions. Through the implementation of these control strategies, the system can maximize the power generation efficiency of photovoltaic modules, while ensuring good coordination with the power grid and reducing unnecessary energy loss.

[0036] The micro-inverter is provided with a DC input interface connected with the photovoltaic module. The interface is used to receive the DC power transmitted by the photovoltaic module and introduce it into the high-frequency power conversion module. At the same time, the micro-inverter has an AC output interface to deliver the generated AC power to the power grid interface module. The design of the AC output interface ensures stable output under full load conditions, reduces harmonic interference, and ensures that the output power meets the power grid access requirements.

[0037] In terms of system coordination, the micro-inverter exchanges data with other inverters and cloud AI platforms through the branch communication gateway. Through high-speed communication protocols, the micro-inverter can transmit its running state information and electrical parameters in real time, so as to carry out more efficient power scheduling and fault monitoring.

[0038] The overall structure of the micro-inverter array and its connection relationship ensures the efficient and stable operation of the system. The array can work independently, while responding to changes in light conditions with other components and modules to maintain optimal power generation efficiency. When local shadow affects a certain micro-inverter, other inverters will automatically adjust their output to ensure that the overall power generation capacity of the photovoltaic module array is not significantly lost.

[0039] In summary, the micro-inverter array plays a crucial role in photovoltaic systems, ensuring stable power output by accurately converting electrical energy and tracking power, enabling the system to cope with the challenges posed by partial shading.

[0040] The specific technical solution details of the branch communication gateway are as follows: Specifically, the gateway serves as an intermediary component of the system, mainly responsible for collecting and forwarding operation data of micro-inverters, and realizing information communication between different modules.

[0041] The branch communication gateway includes the following main functional components: data acquisition unit, edge computing unit, and communication relay unit. The data acquisition unit is configured with multiple input channels, capable of real-time monitoring and recording voltage, current, and temperature data related to each micro-inverter. The data collected by this unit can be represented by the following formula: ; In the formula, D is the collected data (unit: undefined, depends on specific application); V is the output voltage of the micro-inverter (unit: volts); I is the output current of the micro-inverter (unit: amperes); T is the internal temperature of the micro-inverter (unit: degrees Celsius).

[0042] The edge computing unit performs real-time processing on the collected data, executing power curve comparison and analysis. This unit can detect potential abnormal conditions based on the received data, thereby providing basic data support for subsequent fault diagnosis modules. Through specific algorithms, this unit realizes fast calculation and processing of data, and the output result can be represented as: ; In the formula, R is the result obtained by edge computing (unit: undefined, depends on specific application); g is the processing function, integrating various analysis logic.

[0043] The communication relay unit is responsible for realizing data transmission between the branch communication gateway and the cloud AI platform. This unit connects to the cloud platform through 4G, optical fiber, or other appropriate communication protocols, ensuring efficient and secure data upload. In this process, the transmission rate and stability of data have a direct impact on the efficiency of the entire system.

[0044] The branch communication gateway is connected to each micro-inverter through a bus structure and receives the status information of each inverter. The gateway uses standardized interface protocols to realize effective communication and data exchange with micro-inverters, thereby ensuring coordinated operation of the system.

[0045] At system startup, the branch communication gateway first initializes each data acquisition channel to ensure the timeliness and accuracy of monitoring data. During operation, the state information of the micro-inverter is continuously collected and transmitted to the edge computing unit, which is processed and then uploaded to the cloud AI platform for further analysis.

[0046] The design of this gateway effectively improves the monitoring capability of the system through standardized communication protocols and multi-channel data acquisition schemes, and provides data support for power generation efficiency optimization. At the same time, the stable operation of the branch communication gateway can reduce the loss caused by information delay or loss in the system, thereby enhancing the operation efficiency of the entire photovoltaic system.

[0047] In summary, the branch communication gateway plays an important role in the operation of the photovoltaic system, ensuring that each module of the system can work efficiently and accurately through real-time data transmission and processing.

[0048] The specific technical scheme details of the above cloud AI platform are as follows: Specifically, the main functions of the platform are data analysis, shadow prediction, power scheduling, and fault diagnosis to improve the overall performance of the photovoltaic system.

[0049] The cloud AI platform includes the following key modules: LSTM prediction module, VPP scheduling engine, and fault diagnosis module. The main function of the LSTM prediction module is to predict the future 10-minute irradiance change after receiving the voltage fluctuation data of the photovoltaic module from the branch communication gateway. This prediction process can be represented by the following formula: ; In the formula, is the shadow prediction output at time t; is the photovoltaic module voltage data in the past i minutes; h is the mapping function of the LSTM model, and n is the number of historical time steps considered by the model. This module updates the shadow pattern in real time to enhance the system's adaptability in local shadow conditions.

[0050] The main function of the VPP scheduling engine is to dynamically calculate the adjustment range of the total power of the branch by accessing the peak and valley electricity price signals of the power grid, and output the adjustment instructions. This adjustment range can be represented by the following formula: ; In the formula, is the power adjustment range (unit: watt); is the maximum power output corresponding to the electricity price at the peak time of the power grid; is the minimum power output corresponding to the electricity price at the valley time of the power grid. The dynamic power instructions generated according to this range will be sent to the micro-inverter array to achieve the best economic benefit.

[0051] The fault diagnosis module is responsible for monitoring the health status of the photovoltaic module. This module conducts comprehensive analysis on power, temperature and irradiation data by establishing a three-dimensional mapping relationship model. When real-time data deviates from historical baseline values beyond a set threshold, the system will trigger an alarm and issue a fault prompt. This process can be represented by the following logical judgment: ; In the formula, is the current real-time monitoring data, is the historical baseline data, is the set deviation threshold. The fault diagnosis module can also combine sound sensor data to locate hot spot faults through a voiceprint feature library.

[0052] The cloud AI platform and branch communication gateway realize data interaction through high-speed data channels. Through standardized API interfaces, the platform can receive actual operation data from the system and output effective decision instructions to optimize the operation state of the micro-inverter.

[0053] The design of this platform ensures a complete information analysis and feedback mechanism, improving the power generation efficiency and safety of the photovoltaic system under different environmental conditions. According to real-time data, the platform can quickly respond to local shadow changes and reduce power output fluctuations caused by shadows through precise power scheduling.

[0054] In summary, the cloud AI platform plays a key supporting role in the photovoltaic system, with its intelligent data processing and analysis capabilities, it can effectively improve the overall performance of the system and ensure stable power output.

[0055] The specific technical scheme details of the above power grid interface module are as follows: Specifically, the power grid interface module is composed of multiple key components, including a bidirectional inverter, a power monitoring unit, a safety protection unit and a communication interface unit. The bidirectional inverter is responsible for converting alternating current from the micro-inverter into alternating current that meets the requirements of the power grid. In this process, the output voltage of the inverter needs to match the grid voltage, and the output voltage can be represented by the following formula: ; In the formula, is the grid voltage (unit: volts), is the inverter output voltage (unit: volts), is the angular frequency of the grid (unit: rad / s), t is the time (unit: seconds), is the phase angle (unit: degrees or radians).

[0056] The power monitoring unit monitors the power value connected to the power grid in real time to ensure the stability of the output power waveform. The monitoring data can be summarized as follows: ; where, P_in is the input power to the grid (unit: watt), I_out is the output current of the inverter (unit: ampere). By real-time monitoring of power, it can ensure that the output power does not exceed the maximum access limit of the grid.

[0057] The safety protection unit includes overvoltage protection, overcurrent protection and short circuit protection functions. Its function ensures timely disconnection with the grid in the presence of abnormal operating conditions. The safety protection logic can be represented as: ; where V is the current inverter output voltage, V_max is the maximum allowed voltage of the grid, I_max is the maximum allowed current of the grid. When the output voltage or current of the inverter exceeds the safety range, the circuit breaker is triggered through this logic module to ensure system safety.

[0058] The communication interface unit is responsible for data transmission between the cloud AI platform and the branch communication gateway. This interface supports standardized communication protocols such as RS-485 or MODBUS, ensuring that the module can real-time synchronize operating state data and power output information.

[0059] During operation, the grid interface module sends the monitored voltage and current data to the power monitoring unit and compares it with the standard values of the grid. If the grid conditions change, the module will automatically adjust the inverter output to maintain the power quality of the grid access.

[0060] Through the integration of these functions, the grid interface module ensures the efficient connection and safe operation of the photovoltaic system with the grid. Modular design enables the system to flexibly cope with different load conditions, maximizing power transmission efficiency and minimizing the impact on the grid.

[0061] In summary, the grid interface module plays a crucial role in the photovoltaic system, ensuring the effective output of alternating current energy and reliable connection with the grid, meeting the requirements of power quality and safety. Please refer to the attached Figure 5 A power optimization method for a local shadow-resistant photovoltaic system based on a micro-inverter, comprising the following steps: S1, predict the local shadow change in the next 10 minutes through the LSTM model; S2, pre-adjust the MPPT working curve based on the prediction result; S3, detect grid harmonics and inject anti-phase compensation current; S4, dynamically adjust the branch output power in response to peak-valley electricity price signals; S5, by establishing and comparing the three-dimensional mapping relationship of power, temperature and irradiation of the component and the historical reference value, and combining the matching voiceprint feature library, the recognition and positioning of the component attenuation and hot spot fault are realized; The technical details of the above method steps are the same as those of the system part scheme, and will not be described here.

[0062] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A micro-inverter based partial shading tolerant photovoltaic system, characterized in that, Comprise: a photovoltaic module array for converting solar energy into direct current power; a micro-inverter array connected to the photovoltaic module array for converting direct current power into alternating current power; a branch communication gateway connected to the micro-inverter array for collecting and forwarding operation data; a cloud AI platform connected to the branch communication gateway for executing shadow prediction and power scheduling strategies; a grid interface module connected to the micro-inverter array for realizing grid-connected power transmission; the micro-inverter array is composed of multiple micro-inverters, and each micro-inverter comprises: a direct current input interface for connecting a single photovoltaic module; a high-frequency power conversion module for realizing DC-AC conversion; a DSP control module for executing maximum power point tracking control; an alternating current output interface for forming an alternating current branch in parallel; the DSP control module comprises: an MPPT pre-adjustment unit for receiving shadow prediction instructions and switching working curves; a harmonic coordination unit for generating anti-phase harmonic current to offset grid harmonics; an impedance scanning unit for real-time detection of grid impedance characteristics; the cloud AI platform comprises: an LSTM prediction module for establishing a shadow spatio-temporal distribution model; a VPP scheduling engine for generating power regulation instructions; a fault diagnosis module for identifying component degradation and hot spot faults.

2. A micro-inverter based partial shading tolerant photovoltaic system according to claim 1, wherein, the branch communication gateway comprises: a data acquisition unit for collecting component voltage, current and temperature data; an edge computing unit for executing power curve fingerprint comparison; a communication relay unit for connecting the cloud AI platform through 4G / optical fiber.

3. A micro-inverter based partial shading tolerant photovoltaic system according to claim 1, wherein, In the LSTM prediction module, the establishment of the shadow spatio-temporal distribution model comprises the following operations: receive photovoltaic module voltage fluctuation data at a 5-minute interval; learn shadow movement patterns through a long short-term memory network; output a future 10-minute irradiance change probability distribution graph.

4. A micro-inverter based partial shade tolerant photovoltaic system according to claim 1, wherein, In the VPP scheduling engine, the production of power regulation instructions comprises the following operations: access grid peak and valley electricity price signals; calculate the total power regulation range of the branch -15% to +10%; generate dynamic power instructions and issue them to the micro-inverter array.

5. A micro-inverter based partial shade tolerant photovoltaic system according to claim 1, wherein, In the harmonic coordination unit, the formula for offsetting grid harmonics is: ； wherein is the injected anti-phase harmonic current; is the detected hth harmonic component; is the hth harmonic compensation factor; is the phase compensation angle.

6. A micro-inverter based partial shade tolerant photovoltaic system according to claim 1, wherein, In the fault diagnosis module, identifying component degradation and hot spot faults comprises the following operations: establish a three-dimensional mapping relationship of component power, temperature and irradiance; trigger an alarm when real-time data deviates from historical baseline values by >5%; match the voiceprint feature library to locate the hot spot position.

7. A method of power optimization for a micro-inverter based partial- shade tolerant photovoltaic system as claimed in any one of claims 1-6, wherein, Comprise the following steps: S1, predict local shadow changes in the next 10 minutes through an LSTM model; S2, pre-adjust the MPPT working curve based on the prediction results; S3, detect grid harmonics and inject anti-phase compensation current; S4, dynamically adjust the branch output power in response to peak and valley electricity price signals; S5, identify and locate component degradation and hot spot faults by establishing and comparing the three-dimensional mapping relationship of component power, temperature and irradiance with historical baseline values, and combining the matching voiceprint feature library.

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