Component-level power optimization method and device for photovoltaic system

Abstract

The invention relates to the technical field of power optimization, and discloses a component-level power optimization method and device of a photovoltaic system. The method comprises the following steps: carrying out classification processing on working state evaluation results, and carrying out maximum power point tracking control on different types of components through a conductance increment method and a three-point comparison method to obtain optimal output power parameters of each component; carrier communication transmission is carried out on the optimal output power parameters of all the assemblies, and working state information of all the assemblies is transmitted to an integrated controller in a sequential relay mode to obtain group cascade optimization control parameters; and performing performance calculation on the group cascade stage optimization control parameters and the component working parameters, and evaluating a system operation state through tracking efficiency calculation and conversion efficiency calculation to obtain a system control parameter optimization result. According to the invention, the efficiency and accuracy of module-level power optimization of the photovoltaic system are improved.

Description

Technical Field

[0001] This application relates to the field of power optimization, and particularly to a component-level power optimization method and device for a photovoltaic system. Background Art

[0002] As an important part of renewable energy, photovoltaic systems have received extensive attention and applications in recent years. In a photovoltaic system, component-level power optimization is one of the key means to improve the overall power generation efficiency and system stability. Traditional photovoltaic systems usually adopt centralized inverters for power conversion and control, but this method has problems such as power mismatch between components and low system conversion efficiency. To solve these problems, the industry has gradually developed Component-Level Power Electronics (CLPE) technology. By connecting independent power electronic devices behind each photovoltaic component or string, precise control of each component or string can be achieved. These power electronic devices can collect the operating parameters of the components, perform operations such as anomaly detection, power calculation, and Maximum Power Point Tracking (MPPT), so as to optimize the output power of the components.

[0003] Although component-level power electronics technology provides a new way for the optimization of photovoltaic systems, it still faces some challenges in practical applications. First, there are problems with synchronization and accuracy in the acquisition and processing of the operating parameters of photovoltaic components, which may lead to errors in power calculation and MPPT control. Second, the communication and data transmission methods between components limit the system scalability and flexibility, increasing the system complexity and cost. In addition, for components in different operating states, there is a lack of an effective classification and processing strategy to achieve precise control and optimization of various components. Finally, the evaluation of the overall system performance and the parameter optimization method are not yet perfect, and it is difficult to accurately reflect the actual operating state and potential optimization space of the system. Summary of the Invention

[0004] This application provides a component-level power optimization method and device for a photovoltaic system, which is used to improve the efficiency and accuracy of component-level power optimization of the photovoltaic system.

[0005] In a first aspect, the present application provides a method for component-level power optimization of a photovoltaic system. The method for component-level power optimization of the photovoltaic system includes: synchronously collecting voltage and current data of each component in a photovoltaic string, obtaining the operating parameters of each component through a master-slave multi-channel synchronous acquisition system to obtain the operating parameter data of each component; performing outlier detection and filtering processing on the operating parameter data, calculating the component power and component matching degree of the processed data to obtain the operating state evaluation result of each component; setting a resonant conversion controller at the output end of each component, converting direct current into alternating current through an integrated switching tube, and converting the alternating current through a resonant network and a synchronous rectification circuit to obtain direct current that meets a preset voltage range; classifying the operating state evaluation results, performing maximum power point tracking control on different types of components through the conductance increment method and the three-point comparison method to obtain the optimal output power parameters of each component; transmitting the optimal output power parameters of each component through carrier communication, and transmitting the operating state information of each component to a centralized controller in a sequential relay manner to obtain string-level optimization control parameters; calculating the performance of the string-level optimization control parameters and the component operating parameters, and evaluating the operating state of the system through tracking efficiency calculation and conversion efficiency calculation to obtain the optimized result of the system control parameters.

[0006] In a second aspect, the present application provides a device for component-level power optimization of a photovoltaic system. The device for component-level power optimization of the photovoltaic system includes:

[0007] An acquisition module, configured to synchronously collect voltage and current data of each component in a photovoltaic string, obtain the operating parameters of each component through a master-slave multi-channel synchronous acquisition system to obtain the operating parameter data of each component;

[0008] A filtering module, configured to perform outlier detection and filtering processing on the operating parameter data, calculate the component power and component matching degree of the processed data to obtain the operating state evaluation result of each component;

[0009] A setting module, configured to set a resonant conversion controller at the output end of each component, convert direct current into alternating current through an integrated switching tube, and convert the alternating current through a resonant network and a synchronous rectification circuit to obtain direct current that meets a preset voltage range;

[0010] A classification module, configured to classify the operating state evaluation results, perform maximum power point tracking control on different types of components through the conductance increment method and the three-point comparison method to obtain the optimal output power parameters of each component;

[0011] A transmission module, configured to perform carrier communication transmission on the optimal output power parameters of each component, and transmit the working state information of each component to a centralized controller in a sequential relay manner to obtain string-level optimization control parameters;

[0012] A calculation module, configured to perform performance calculations on the string-level optimization control parameters and component working parameters, evaluate the system operation state through tracking efficiency calculation and conversion efficiency calculation, and obtain an optimization result of system control parameters.

[0013] In the technical solution provided by this application, by adopting a master-slave multi-channel synchronous acquisition system to synchronously collect the voltage and current of each component in a photovoltaic string, the problems of synchronization and accuracy of parameter acquisition are effectively solved. This improvement not only improves the accuracy of data acquisition, but also ensures the reliability of subsequent power calculation and component matching degree calculation, providing a solid foundation for the precise control and optimization of the system. Outlier detection and filtering processing are performed in the data preprocessing stage to further improve the quality of the data. By performing component power calculation and component matching degree calculation on the processed data, the working state of each component can be accurately evaluated, providing strong support for subsequent power optimization control. At the same time, this processing method also helps to detect and solve potential fault problems in a timely manner, improving the stability and reliability of the system. By setting a resonant conversion controller at the output end of each component, the conversion from direct current to alternating current is realized, and the alternating current is transformed through a resonant network and a synchronous rectification circuit to finally obtain direct current that meets the preset voltage range. This improvement not only improves the energy conversion efficiency of the system, but also enhances the flexibility and adaptability of the system, enabling it to better adapt to different lighting conditions and load requirements.

[0014] In addition, the conductance increment method and the three-point comparison method are also adopted to perform maximum power point tracking control on different types of components to obtain the optimal output power parameters of each component. This classification processing strategy not only improves the accuracy and efficiency of MPPT control, but also helps to achieve the global optimization of the system, improving the overall power generation efficiency. Finally, the working state information of each component is transmitted to the centralized controller in a sequential relay manner through carrier communication transmission to obtain string-level optimization control parameters. This improvement not only simplifies the system structure, reduces costs, but also improves the scalability and flexibility of the system. At the same time, by performing performance calculations on the string-level optimization control parameters and component working parameters, the operation state of the system can be accurately evaluated. Description of the Drawings

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0016] Figure 1 FIG. is a schematic diagram of an embodiment of the component-level power optimization method for a photovoltaic system in an embodiment of the present application;

[0017] Figure 2 FIG. is a schematic diagram of an embodiment of the component-level power optimization device for a photovoltaic system in an embodiment of the present application. Detailed implementation manners

[0018] The embodiments of the present application provide a component-level power optimization method and device for a photovoltaic system. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and drawings of the present application are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily limit to the clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0019] For ease of understanding, the following describes the specific process of the embodiments of the present application. Please refer to Figure 1 An embodiment of the component-level power optimization method for a photovoltaic system in an embodiment of the present application includes:

[0020] Step S101: Perform synchronous data acquisition of voltage and current for each component in the photovoltaic string, obtain the operating parameters of each component through a master-slave multi-channel synchronous acquisition system, and obtain the operating parameter data of each component;

[0021] Step S102: Perform outlier detection and filtering processing on the operating parameter data, calculate the component power and component matching degree for the processed data, and obtain the operating state evaluation results of each component;

[0022] Step S103: Set a resonant transformation controller for the output end of each component, convert direct current into alternating current through an integrated switching tube, and perform transformation on the alternating current through a resonant network and a synchronous rectification circuit to obtain direct current that meets the preset voltage range;

[0023] Step S104: Classify the evaluation results of the working state, and perform maximum power point tracking control on different types of components through the conductance increment method and the three-point comparison method to obtain the optimal output power parameters of each component.

[0024] Step S105: Transmit the optimal output power parameters of each component through carrier communication, and transmit the working state information of each component to the centralized controller in a relay manner to obtain the string-level optimization control parameters.

[0025] Step S106: Calculate the performance of the string-level optimization control parameters and the component working parameters, and evaluate the system operation state through tracking efficiency calculation and conversion efficiency calculation to obtain the optimization result of the system control parameters.

[0026] It can be understood that the execution subject of this application can be the component-level power optimization device of the photovoltaic system, or it can also be a terminal or a server, and specific limitations are not made here. In this embodiment of the application, the server is used as the execution subject for illustration.

[0027] Specifically, in the data acquisition link, a master-slave multi-channel synchronous acquisition system is used to obtain data. This system includes a voltage acquisition module and a current acquisition module. The voltage acquisition module is connected in parallel at the output end of each photovoltaic component, and the current acquisition module is connected in series at the output end of the string. The sampling accuracy reaches 0.5%. The acquisition modules are connected in series into a network through the POWERBUS bus, and the IEEE1588 clock synchronization protocol is used to achieve synchronous data acquisition, and the synchronous time difference is controlled within 1 millisecond. The master station triggers the slave stations to synchronously sample by sending a broadcast command, and reads the data one by one according to the slave station number. When detecting and filtering the outliers in the acquired data, set the effective ranges of voltage and current, and the data points outside the range are marked as outliers. Perform a moving average filter on the valid data, calculate the weighted average of adjacent data points to suppress data noise. Then calculate the real-time output power of each component, and calculate the component matching degree through the deviation percentage from the average power of the string. According to the matching degree, the component state is divided into three categories: normal (deviation less than 5%), abnormal (deviation 5%-20%), and faulty (deviation greater than 20%).

[0028] The resonant conversion controller set at the output end of the component adopts an LLC topology structure, which includes an H-bridge circuit composed of four gallium nitride switching tubes, a resonant capacitor, and a high-frequency transformer. The H-bridge circuit converts the input direct current into alternating current, and the resonant network consists of the leakage inductance (1.5 μH), the magnetizing inductance (5.5 μH), and the resonant capacitor (0.44 μF) of the transformer. By adjusting the switching frequency (within the range of 90 - 200 kHz), soft switching is achieved to reduce losses. The output end adopts a synchronous rectification method, and the conversion efficiency reaches more than 98%. Different control strategies are adopted for components in different working states: for components in normal working conditions, the conductance increment method is used for maximum power point tracking, and the voltage adjustment direction is determined by calculating the ratio of the power change amount to the voltage change amount; for components in abnormal working conditions, the three-point comparison method is adopted, and power comparison is carried out by sampling at the current voltage point and its two sides, and the point with the maximum power is selected as the new working point; for faulty components, voltage clamping protection is carried out.

[0029] The controllers of each component communicate through power line carrier mode, and the carrier frequency is selected within the range of 3 - 4 MHz through testing. The "handshake" data transmission mode is adopted, and the data packet starts from the terminal component and is forwarded to the centralized controller through the intermediate components level by level. The centralized controller calculates the optimal string working voltage according to the working states of each component and issues voltage range constraints. Finally, the system performance is evaluated, and the system operation status is evaluated by calculating the tracking efficiency (the ratio of the actual output power to the theoretical maximum power) and the conversion efficiency. For example, in a certain test, the current and voltage sampling values of 16 components in the string are obtained through synchronous acquisition. After outlier detection and filtering processing, 12 components with a power matching degree between 95% - 102% are determined to be in normal working conditions, 3 components between 85% - 95% are in abnormal working conditions, and a component below 80% is determined to be in a faulty state. After differential MPPT control and system-level optimization, the overall conversion efficiency of the system is increased from the original 95% to 97.7%, and the tracking efficiency reaches 99.8%.

[0030] In the embodiments of the present application, by using a master-slave multi-channel synchronous acquisition system to synchronously acquire the voltage and current of each component in the photovoltaic string, the problems of synchronization and accuracy of parameter acquisition are effectively solved. This improvement not only improves the accuracy of data acquisition, but also ensures the reliability of subsequent power calculation and component matching degree calculation, providing a solid foundation for the precise control and optimization of the system. Outlier detection and filtering processing are carried out in the data preprocessing stage to further improve the quality of the data. By calculating the component power and component matching degree of the processed data, the working state of each component can be accurately evaluated, providing strong support for subsequent power optimization control. At the same time, this processing method also helps to detect and solve potential fault problems in a timely manner, improving the stability and reliability of the system. By setting a resonant conversion controller at the output end of each component, the conversion from direct current to alternating current is realized, and the alternating current is converted through a resonant network and a synchronous rectification circuit to finally obtain direct current that meets the preset voltage range. This improvement not only improves the energy conversion efficiency of the system, but also enhances the flexibility and adaptability of the system, enabling it to better adapt to different lighting conditions and load requirements.

[0031] In addition, the conductance increment method and the three-point comparison method are also used to perform maximum power point tracking control on different types of components, and the optimal output power parameters of each component are obtained. This classification processing strategy not only improves the accuracy and efficiency of MPPT control, but also helps to achieve the global optimization of the system and improve the overall power generation efficiency. Finally, the working state information of each component is transmitted to the centralized controller through carrier communication transmission and sequential relaying to obtain the string-level optimization control parameters. This improvement not only simplifies the system structure, reduces costs, but also improves the scalability and flexibility of the system. At the same time, by calculating the performance of the string-level optimization control parameters and the component working parameters, the operating state of the system can be accurately evaluated.

[0032] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0033] (1) Connect the voltage acquisition modules in parallel at the output end of the photovoltaic components, and connect the current acquisition modules in series at the output end of the string. Sample the voltage signal and current signal through a high-precision analog-to-digital converter to obtain component voltage data and string current data;

[0034] (2) Connect the voltage acquisition modules and current acquisition modules in series through a fieldbus, and transmit the data and power supply signal through the power line transmission method to obtain an acquisition module network;

[0035] (3) Perform clock synchronization processing on each acquisition module in the acquisition module network, and calibrate the sampling time of each acquisition module through the IEEE1588 clock synchronization protocol to obtain a synchronous sampling timing;

[0036] (4) Conduct master - slave communication control on the acquisition module network, and obtain synchronous acquisition data by the master station sending a synchronous acquisition broadcast command and reading data one by one according to the slave station numbers.

[0037] (5) Conduct time - series recording on the synchronous acquisition data, and store the voltage and current data of each component through cyclic sampling at a fixed time interval to obtain the working parameter data of each component.

[0038] Specifically, a voltage acquisition module needs to be connected in parallel at the output end of the photovoltaic module, and a current acquisition module needs to be connected in series at the output end of the entire string. Then, the sampling of voltage signals and current signals is realized through a high - precision analog - to - digital converter (ADC). This analog - to - digital converter converts analog voltage and current signals into digital signals, and these digital signals are the voltage data of the components and the current data of the string. Since the process of analog - to - digital conversion involves signal digitization, its accuracy directly affects the subsequent data - processing effect. Therefore, it is crucial to select a high - precision analog - to - digital converter, which can effectively ensure the signal quality of the collected voltage and current. When implementing the construction of the component - level acquisition module network, by connecting the voltage acquisition module and the current acquisition module in series through a fieldbus, data and power transmission can be shared among modules. At this time, the power - line transmission method is adopted, enabling data and power - supply signals to share a communication link, reducing the complexity and cost of traditional cable wiring. In addition, this fieldbus series mode can effectively improve the stability of data transmission, ensuring the reliable operation of the photovoltaic system in a complex environment.

[0039] Next, to ensure the consistency of the acquisition time of each acquisition module, the clocks of the acquisition modules are synchronized through the IEEE1588 clock synchronization protocol. The IEEE1588 protocol is an accurate network time synchronization protocol that can achieve sub - microsecond - level time calibration through network devices, ensuring that the data - acquisition timings of all acquisition modules are exactly the same, and avoiding data - acquisition errors caused by clock deviations of different modules. Under the synchronized sampling timing, each acquisition module can ensure that voltage and current signals are acquired at the same moment, thus realizing synchronous data acquisition of the entire photovoltaic string at the system level. To further obtain synchronous acquisition data, master - slave communication control needs to be carried out in the entire acquisition module network. By the master station sending a broadcast command for synchronous acquisition, all slave - station modules respond in sequence and send the data back to the master station. The master station reads the data from each slave station one by one according to the number sequence to ensure the accuracy and integrity of data transmission. This master - slave communication structure can reduce data conflicts, improve communication efficiency, and ensure that each module can complete data acquisition orderly.

[0040] After the data acquisition is completed, the synchronously acquired data is recorded in a time series, so that each piece of acquired data has a corresponding timestamp, ensuring good time continuity for subsequent data processing. By performing cyclic sampling at fixed time intervals, the voltage and current data of each component can be stored as time series data, thus constituting the working parameter data of each component. These time series data record the voltage and current conditions at each moment, enabling the system to understand the dynamic power changes of the components within a certain time range.

[0041] For example, assume that a photovoltaic string consists of 10 components. The voltage acquisition module is connected in parallel to the output terminal of each component, and the current acquisition module is connected in series to the total output terminal of the string. Assume that the acquisition frequency is 1000 times per second and the accuracy of the analog-to-digital converter is 16 bits. Then, each acquisition can obtain the voltage value of each component and the current value of the string. Through IEEE1588 clock synchronization, the acquisition moments of all components are kept consistent, avoiding asynchronous acquisition of current and voltage caused by time differences between different components. In the master-slave communication structure, the master station sends acquisition commands at fixed intervals, and the slave stations respond in sequence, transmitting the acquired voltage and current data back to the master station in turn. The master station records and stores these data.

[0042] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0043] (1) Perform data range detection on the working parameter data, mark abnormal data points through a preset voltage and current threshold range, and obtain an effective data set;

[0044] (2) Perform moving average filtering on the effective data set, suppress data noise by calculating the weighted average of adjacent data points, and obtain the parameter data after smoothing processing;

[0045] (3) Calculate the component power for the parameter data after smoothing processing, calculate the output power of each component through the multiplication operation of the voltage value and the current value, and obtain the component power data;

[0046] (4) Calculate the power matching degree for the component power data, quantify the power matching degree by calculating the deviation percentage between the component power and the average power of the string, and obtain the power matching degree data;

[0047] (5) Classify the working states for the power matching degree data, divide the component states into normal, abnormal, and faulty through a preset matching degree threshold, and obtain the working state evaluation results of each component.

[0048] Specifically, during the data range detection process, the system identifies and marks abnormal data points through preset voltage and current threshold ranges. These thresholds are set based on the normal operating characteristics of the photovoltaic modules and are usually derived from the electrical specifications of the equipment or historical data analysis. If the collected data exceeds this range, the system will consider these data points as abnormal to exclude noise that may be caused by external interference or equipment anomalies. The resulting "effective data set" after such processing can more accurately reflect the true operating state of the modules, avoiding interference from abnormal data in subsequent calculations and analyses. The "moving average filtering" process smooths the data in the effective data set. This filtering method weakens random noise during the acquisition process by calculating the weighted average of each group of adjacent data points, making the data curve smoother and facilitating analysis. In a photovoltaic system, due to external factors such as sunlight intensity and temperature changes, voltage and current signals may exhibit short-term fluctuations, and this filtering process can effectively suppress such fluctuations. For example, within a 10-second sampling period, the system calculates the weighted average of each data point with its adjacent data points before and after. The smoothed data can better reflect the current power trend, laying a foundation for subsequent power calculations and status assessments.

[0049] After obtaining the smoothed data, the system enters the "module power calculation" step. The power calculation is based on the voltage and current data of the module, and the product result represents the instantaneous power output value of the module. This calculation helps the system obtain the output power level of each photovoltaic module, providing key data for the overall power generation efficiency and the health status of the modules. For example, if the voltage of module A is 30V and the current is 8A, then the power of this module is 240W. The system will regularly record this value to provide a basis for the analysis of the long-term performance of the module. Subsequently, the system performs "power matching degree calculation". The power matching degree reflects the consistency of power output among the modules. Usually, with the average power of the string as the reference value, the percentage of power deviation of each module is calculated. The power matching degree data can show whether a certain module has a performance decline or a tendency to fail. In a photovoltaic string, the outputs of the modules should be close. If the power of a certain module deviates significantly from the average value, for example, the deviation reaches more than 20%, it may mean that there is a problem with this module or it is shaded. Through the quantified matching degree data, the system can more intuitively grasp the working states of the modules within the string.

[0050] Finally, the "working state classification" is performed on the power matching degree data to complete the state assessment. The system classifies the component states into three categories: normal, abnormal, and faulty according to the preset matching degree threshold. The output power of normal components is relatively close to the string average value, and the deviation is generally within 5%; abnormal components may have a deviation of 10% to 20%; while components with a deviation exceeding 20% are marked as faulty. For example, if the power of component B is 200W, but the average power of the string is 240W, the matching degree deviation is about 16.7%, which belongs to the abnormal state and needs further inspection. Through this classification, the system can effectively identify and locate potential problem components in the string, providing data support for maintenance and optimization.

[0051] In a specific embodiment, the process of performing step S103 may specifically include the following steps:

[0052] (1) Connect a gallium nitride switch tube bridge circuit to the output end of the component, and perform full-bridge inversion on the input direct current through four gallium nitride devices to obtain an alternating current signal;

[0053] (2) Process the alternating current signal through a resonant network. A resonant circuit is formed by the leakage inductance, exciting inductance of the high-frequency transformer and the resonant capacitor to obtain the alternating current after resonant transformation;

[0054] (3) Adjust the switching frequency of the alternating current after resonant transformation. Control the resonant frequency by adjusting the conduction timing of the switch tube to obtain a soft-switching transformation signal;

[0055] (4) Perform synchronous rectification transformation on the soft-switching transformation signal. Synchronously rectify the alternating current through a half-bridge rectification circuit to obtain the target direct current;

[0056] (5) Perform closed-loop control on the target direct current. Real-time adjust the switching frequency by detecting the deviation between the output voltage and the target value to obtain the direct current that meets the preset voltage range.

[0057] Specifically, the output end of the component is connected through a gallium nitride switch tube bridge circuit. This bridge circuit consists of four gallium nitride switch tubes. Through the precise control of these switch tubes, the full-bridge inversion operation of the input direct current is completed, and finally an alternating current signal is obtained. Gallium nitride (GaN) devices have the characteristics of high switching speed and low loss, enabling the inversion process to operate stably at high frequencies, while maintaining low thermal loss and higher conversion efficiency, laying a foundation for the efficient operation of subsequent steps. Secondly, the obtained alternating current signal is processed by a resonant network. This process mainly relies on the leakage inductance, magnetizing inductance of the high-frequency transformer, and the resonant capacitor connected in series with it to jointly form a resonant circuit. In this circuit, the alternating current signal realizes the effective transfer and transformation of energy through the high-frequency processing of the resonant network. This resonant transformation enables the system to operate under high-frequency conditions, effectively reducing the volume and increasing the power density. At the same time, the resonant network also has the function of reducing switching losses, avoiding the increase of non-harmonic components, and ensuring better signal purity.

[0058] On this basis, the system adjusts the switching frequency of the alternating current after resonant transformation. By precisely adjusting the conduction timing of the gallium nitride switch tubes, the resonant frequency of the resonant circuit is controlled. This frequency control realizes soft-switching operation, that is, the switch tubes conduct at the moment when the current is zero, thereby minimizing the switching losses and extending the service life of the devices. The soft-switching transformation signal not only reduces the switching losses but also further improves the stability and reliability of the system. Especially in high-power photovoltaic systems, this soft-switching regulation is of great significance for improving the system efficiency. Next, the system performs synchronous rectification transformation on the soft-switching transformation signal. Through a half-bridge rectifier circuit, the alternating current signal is converted into direct current after synchronous rectification. Compared with the traditional diode rectification method, the synchronous rectification technology utilizes the high-efficiency conduction characteristics of the switch tubes, further reducing the losses in the rectification process and improving the rectification efficiency. Through synchronous rectification, the system obtains a more stable and efficient target direct current signal, which is suitable for direct application in photovoltaic energy storage or power supply systems.

[0059] Finally, to ensure that the output direct current meets the set voltage range, the system implements closed-loop control. By continuously monitoring the deviation between the output voltage and the preset target voltage, the system can immediately adjust the switching frequency to keep the output voltage within the predetermined range. Suppose the target output voltage of the system is 400V. When it is detected in real time that the output voltage is higher or lower than the target value, the system will automatically adjust the frequency of the switch tubes to correct the deviation, thereby quickly adjusting the voltage back to the target value. Closed-loop control ensures the stability and accuracy of the output direct current, enabling stable power supply of the system even under environmental voltage fluctuations or load changes.

[0060] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0061] (1) Allocate control strategies based on the evaluation results of the working state, select control methods for components according to the working state categories, and obtain the component control schemes;

[0062] (2) Calculate the conductance increment for components in the normal working state, determine the voltage regulation direction by the ratio of the power change amount to the voltage change amount, and obtain the voltage regulation command;

[0063] (3) Conduct a three-point power comparison for components in the abnormal working state, search for the maximum power point based on the power magnitudes of the current voltage point and its two adjacent voltage points, and obtain the power search results;

[0064] (4) Perform voltage clamping on components in the fault state, limit the output voltage of the components by setting a safe voltage range, and obtain the fault protection parameters;

[0065] (5) Aggregate the voltage regulation commands, power search results, and fault protection parameters to obtain the optimal output power parameters for each component.

[0066] Specifically, allocate control strategies based on the evaluation results of the working state of each component. This step divides the components into three states: normal, abnormal, or faulty based on the working state categories of the components, and selects appropriate control methods according to different states to generate the control scheme for each component. This allocation strategy ensures that the system can adopt targeted control strategies for components in different states, maximizing the power output and operating stability of the components. The system performs conductance increment calculation on components in the normal working state. The conductance increment calculation method determines the voltage regulation direction by detecting the change amount of the component power and the change amount of the voltage. For example, when the photovoltaic component is operating near the maximum power point, increasing the conductance can enable the system to correct the voltage in a timely manner, thereby achieving the optimal control of voltage regulation and obtaining the corresponding voltage regulation command. The advantage of this method lies in its fast response, which can track the maximum power point of the component in real time, enabling the component to continuously output the best power under normal working conditions.

[0067] For components in an abnormal state, the system uses a three-point power comparison method to search for the maximum power point. This method quickly locks the maximum power output point of the component by comparing the power at the current voltage point with the power at the voltage points on its front and back sides. In practical applications, if the power of a certain component suddenly drops, the system will move the voltage point, calculate the power magnitudes at the two adjacent voltage points, and then determine the optimal operating voltage range. The three-point power comparison method avoids complex calculations and can quickly find the maximum power point through only local power comparisons, making it suitable for power tracking of components in abnormal states. For components determined to be in a faulty state, the system will perform voltage clamping. Voltage clamping is achieved by setting a safe voltage range to limit the output voltage of the component within the preset safe interval. Specifically, when a component fails, such as overheating or short-circuiting, the system will immediately adjust the voltage of the component to the safe range to ensure that it does not affect other parts of the system and at the same time protect the component itself from further damage.

[0068] Finally, the voltage regulation instructions, power search results, and fault protection parameters generated in the above steps are summarized to form the optimal output power parameters for each component. This summarization step comprehensively analyzes the data results of the previous steps to ensure that each component can achieve maximum power output. During the summarization process, the system will uniformly adjust the output power according to the specific parameter conditions of different components to achieve the power optimization effect of the overall system. For example, in a certain photovoltaic array, assuming that the voltage regulation instructions for normal components guide their voltage to increase to 320V, the maximum power search for abnormal components determines that the best output is achieved at 310V, and the voltage clamping for faulty components limits their voltage within 300V, then the system will form the final power parameters based on these results to ensure that each component reaches the optimal output in its respective state.

[0069] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0070] (1) Test and select the carrier communication frequency, determine the carrier frequency through signal strength testing, and obtain the optimal carrier frequency;

[0071] (2) Construct a communication network for each component controller, establish a communication link between the controllers through power line carrier, and obtain a component communication network;

[0072] (3) Perform segmented transmission processing on component data, and transmit the data step by step through data packaging and forwarding between adjacent components to obtain a string data stream;

[0073] (4) Perform centralized processing on the string data stream, parse and integrate the working state information of each component through a centralized controller, and obtain system operation data;

[0074] (5) Calculate the series string voltage for the system operation data, determine the optimal operating voltage of the series string based on the working states of each component, and obtain the series string level optimization control parameters.

[0075] Specifically, test and select the frequency of carrier communication through signal strength testing. In this step, the system analyzes the signal transmission effects at different frequencies to find the frequency with the optimal signal strength. This method ensures that signals can be transmitted stably and effectively in a complex photovoltaic system environment, and the obtained optimal carrier frequency serves as the basis for subsequent communication. In the construction step of the communication network, establish the communication links between each component controller through power line carrier mode to form a component communication network. The power line carrier mode here utilizes the existing power lines to transmit data and power signals together, avoiding the complexity and high cost of traditional wiring. This network ensures that information can be interchanged between each component controller, providing a reliable path for data collection and transmission of control instructions.

[0076] The system performs segmented transmission processing on the component data. This step realizes segmented transmission through data packing and forwarding of adjacent components. Each component receives the data packet from the adjacent component and then forwards it, forming a series string data stream that is transmitted step by step. This data processing method is particularly important in a photovoltaic system because step-by-step transmission reduces the risk of data congestion. Especially when the data volume is large, through the step-by-step transmission method of each component, the stability and efficiency of data transmission can be improved. The system converges all the series string data streams to the central controller for centralized processing. The central controller analyzes and integrates the working state information of each component to generate the system operation data. The centralized control method can obtain the operating conditions of the entire system at a single control node, including parameter information such as the voltage and current of each component, thus providing basic data support for subsequent optimization.

[0077] Finally, the system calculates the series string voltage based on the centrally obtained operation data. By analyzing the working states of each component, the system determines the optimal operating voltage of the series string to obtain the series string level optimization control parameters. In this step, the system considers the voltage requirements of different components to ensure that the entire series string is within the optimal voltage range, thereby improving the stability and efficiency of the overall power output of the system. For example, in a series string, if the voltage requirements of each component are slightly different, the system will calculate the comprehensive voltage to achieve overall optimal control on the premise that each component can perform optimally.

[0078] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0079] (1) Calculate the maximum power point tracking efficiency of each component, quantify the tracking performance through the ratio of the actual output power to the theoretical maximum power, and obtain the tracking efficiency data;

[0080] (2) Calculate the energy conversion efficiency at all levels of the system, quantify the conversion performance by the ratio of output power to input power, and obtain the conversion efficiency data;

[0081] (3) Analyze the rationality of the series string working voltage configuration, evaluate the series string voltage setting through the distribution of the working points of each component, and obtain the voltage configuration evaluation result;

[0082] (4) Perform calibration calculations on the system control parameters, adjust the control parameters through the tracking efficiency and conversion efficiency, and obtain the corrected control parameters;

[0083] (5) Record and update the corrected control parameters, update the system operation parameters through data storage, and obtain the optimized result of the system control parameters.

[0084] Specifically, calculate the maximum power point tracking efficiency of each component. In this step, calculate the ratio of the actual output power to the theoretical maximum power to obtain the tracking efficiency data. This method of quantifying the tracking efficiency can accurately reflect the performance level of the current control strategy in maximum power point tracking. For example, when the actual output power of a certain component is close to the theoretical maximum value, it means that the tracking strategy of this component is effective, and the efficiency value is also close to the full value. Such tracking efficiency data provides a basic evaluation criterion for subsequent optimization. Calculate the energy conversion efficiency at each level of the system. Quantify the conversion efficiency by the ratio of the output power and input power at each level to obtain an accurate evaluation result of the conversion performance. The energy conversion efficiency here reflects the energy loss situation in each link of the system, specifically for different modules and conversion steps. For example, if the efficiency of a certain level is significantly low, it indicates that there is a large amount of energy loss there, providing an improvement direction for the subsequent optimization of the system.

[0085] Then, the system analyzes the rationality of the series string working voltage configuration. In this step, comprehensively evaluate the distribution of the working points of each component to determine the rationality of the current series string voltage configuration. By analyzing the working voltage distribution of the components under different load conditions, it can be detected whether the configuration is suitable for the current system operation conditions. Especially under the condition of load variation, the optimized voltage setting can make all components in the series string operate at a voltage close to the maximum power point, ensuring the optimal overall performance. Perform calibration calculations on the system control parameters. Here, combine the aforementioned tracking efficiency and conversion efficiency data, and adjust the control parameters by analyzing the data deviation at each level to optimize the overall performance of the system. The process of parameter calibration ensures that the system can dynamically adapt to environmental changes and load fluctuations, thereby improving the output stability and energy utilization rate of the photovoltaic system.

[0086] Record and update the corrected control parameters and store them in the system. This process realizes the persistent update of the control parameters through data storage, enabling the system to continue the current optimization results during subsequent operations. As the control parameters are continuously updated and optimized, the system can accumulate empirical data and gradually improve the overall control level. For example, after a certain string has been running continuously for a period of time, the system updates its optimal control parameters and stores the new parameters as the best operating values under the current state, ensuring that these optimization results can be automatically applied under similar environmental conditions.

[0087] The above describes the component-level power optimization method of the photovoltaic system in the embodiments of the present application. Next, the component-level power optimization device of the photovoltaic system in the embodiments of the present application will be described. Please refer to Figure 2 , an embodiment of the component-level power optimization device of the photovoltaic system in the embodiments of the present application includes:

[0088] The acquisition module 201 is used to synchronously collect voltage and current data of each component in the photovoltaic string, obtain the working parameter data of each component by acquiring the working parameters of each component through a master-slave multi-channel synchronous acquisition system;

[0089] The filtering module 202 is used to detect and filter out outliers from the working parameter data, calculate the component power and the component matching degree for the processed data, and obtain the working state evaluation results of each component;

[0090] The setting module 203 is used to set the resonant transformation controller at the output end of each component, convert direct current into alternating current through an integrated switch tube, and transform the alternating current through a resonant network and a synchronous rectification circuit to obtain direct current that meets the preset voltage range;

[0091] The classification module 204 is used to classify the working state evaluation results, and perform maximum power point tracking control on different categories of components through the conductance increment method and the three-point comparison method to obtain the optimal output power parameters of each component;

[0092] The transmission module 205 is used to transmit the optimal output power parameters of each component through carrier communication, and transmit the working state information of each component to the centralized controller in a relay manner to obtain the string-level optimization control parameters;

[0093] The calculation module 206 is used to calculate the performance of the string-level optimization control parameters and the component working parameters, evaluate the system operation state through tracking efficiency calculation and conversion efficiency calculation, and obtain the system control parameter optimization results.

[0094] Through the collaborative cooperation of the above-mentioned various components, by adopting a master-slave multi-channel synchronous acquisition system to synchronously collect the voltage and current of each component in the photovoltaic string, the problems of synchronization and accuracy of parameter acquisition are effectively solved. This improvement not only improves the accuracy of data acquisition, but also ensures the reliability of subsequent power calculation and component matching degree calculation, providing a solid foundation for the precise control and optimization of the system. Outlier detection and filtering processing are carried out in the data preprocessing stage, further improving the quality of the data. By calculating the component power and component matching degree of the processed data, the working status of each component can be accurately evaluated, providing strong support for subsequent power optimization control. At the same time, this processing method also helps to timely detect and solve potential fault problems, improving the stability and reliability of the system. By setting a resonant conversion controller at the output end of each component, the conversion from direct current to alternating current is realized, and the alternating current is converted through a resonant network and a synchronous rectifier circuit, and finally the direct current that meets the preset voltage range is obtained. This improvement not only improves the energy conversion efficiency of the system, but also enhances the flexibility and adaptability of the system, enabling it to better adapt to different lighting conditions and load requirements.

[0095] In addition, the conductance increment method and the three-point comparison method are also used to perform maximum power point tracking control on different types of components, and the optimal output power parameters of each component are obtained. This classification processing strategy not only improves the accuracy and efficiency of MPPT control, but also helps to achieve the global optimization of the system, improving the overall power generation efficiency. Finally, the working status information of each component is transmitted to the centralized controller through carrier communication transmission and sequential relaying, and the string-level optimization control parameters are obtained. This improvement not only simplifies the system structure, reduces the cost, but also improves the scalability and flexibility of the system. At the same time, by calculating the performance of the string-level optimization control parameters and the component working parameters, the operating status of the system can be accurately evaluated.

[0096] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing component-level power of a photovoltaic system, characterized in that: The component-level power optimization method of the photovoltaic system comprises: Perform voltage and current synchronous data collection on each component in the photovoltaic string, obtain the working parameters of each component through the master-slave multi-channel synchronous acquisition system, and obtain the working parameter data of each component; Performing abnormal value detection and filtering processing on the working parameter data, performing component power calculation and component matching degree calculation on the processed data, and obtaining working status evaluation results of each component; A resonant conversion controller is set at the output end of each component, DC power is converted into AC power through an integrated switch tube, and the AC power is converted through a resonant network and a synchronous rectification circuit to obtain DC power that meets the preset voltage range; Classify the working status evaluation results, perform maximum power point tracking control on components of different categories by using a conductance increment method and a three-point comparison method, and obtain the optimal output power parameters of each component; The optimal output power parameters of each component are transmitted by carrier communication, and the working status information of each component is transmitted to the centralized controller by sequential relay to obtain the group string level optimization control parameters; The performance calculation is performed on the group of cascade optimization control parameters and component operating parameters, the system operation status is evaluated through tracking efficiency calculation and conversion efficiency calculation, and the system control parameter optimization result is obtained.

2. The component-level power optimization method for a photovoltaic system according to claim 1, characterized in that: The voltage and current synchronous data acquisition is performed on each component in the photovoltaic string, and the working parameters of each component are acquired through a master-slave multi-channel synchronous acquisition system to obtain the working parameter data of each component, including: The voltage acquisition module is connected in parallel to the output end of the photovoltaic module, and the current acquisition module is connected in series to the output end of the string. The voltage signal and current signal are sampled by a high-precision analog-to-digital converter to obtain the module voltage data and string current data; Connect the voltage acquisition module and the current acquisition module in series via a field bus, and transmit data and power supply signals via power line transmission to obtain an acquisition module network; Performing clock synchronization processing on each acquisition module in the acquisition module network, calibrating the sampling time of each acquisition module through the IEEE1588 clock synchronization protocol, and obtaining a synchronous sampling timing; The acquisition module network is controlled by master-slave communication, and the synchronous acquisition data is obtained by sending a synchronous acquisition broadcast command through the master station and reading the data one by one according to the slave station number; The synchronously collected data is recorded in time series, and the voltage and current data of each component are stored through cyclic sampling at fixed time intervals to obtain the working parameter data of each component.

3. The component-level power optimization method for a photovoltaic system according to claim 1, characterized in that: The abnormal value detection and filtering processing are performed on the working parameter data, and the component power calculation and component matching degree calculation are performed on the processed data to obtain the working status evaluation results of each component, including: Performing data range detection on the working parameter data, marking abnormal data points by using a preset voltage and current threshold range, and obtaining a valid data set; Performing sliding average filtering on the valid data set, suppressing data noise by calculating the weighted average of adjacent data points, and obtaining smoothed parameter data; Calculating the component power of the smoothed parameter data, calculating the output power of each component by multiplying the voltage value by the current value, and obtaining component power data; Calculating the power matching degree of the component power data, quantifying the power matching degree by calculating the deviation percentage between the component power and the average power of the string, and obtaining the power matching degree data; The power matching data is classified into working states, and the component states are divided into normal, abnormal and faulty states according to a preset matching threshold, so as to obtain working state evaluation results of each component.

4. The component-level power optimization method for a photovoltaic system according to claim 1, characterized in that: The resonant conversion controller is set at the output end of each component, the DC power is converted into AC power through the integrated switch tube, and the AC power is converted through the resonant network and the synchronous rectification circuit to obtain the DC power that meets the preset voltage range, including: Connect the output end of the module with a GaN switch tube bridge circuit, and use four GaN devices to perform full-bridge inversion on the input DC power to obtain an AC signal. The AC power signal is subjected to resonant network processing, and a resonant circuit is formed by the leakage inductance, the exciting inductance and the resonant capacitor of the high-frequency transformer to obtain AC power after resonant conversion; The switching frequency of the alternating current after the resonant conversion is adjusted, and the resonant frequency is controlled by adjusting the conduction timing of the switch tube to obtain a soft switching conversion signal; Performing synchronous rectification transformation on the soft switch conversion signal, and performing synchronous rectification on the alternating current through a half-bridge rectifier circuit to obtain a target direct current; The target direct current is controlled in a closed loop, and the switching frequency is adjusted in real time by detecting the deviation between the output voltage and the target value, so as to obtain the direct current that meets the preset voltage range.

5. The component-level power optimization method for a photovoltaic system according to claim 1, characterized in that: The working state evaluation results are classified and processed, and the maximum power point tracking control is performed on components of different categories by the conductance increment method and the three-point comparison method to obtain the optimal output power parameters of each component, including: Allocating a control strategy to the working state evaluation result, selecting a control method for the component according to the working state category, and obtaining a component control plan; Conductance increment calculation is performed on components in normal working state, and the voltage regulation direction is determined by the ratio of power change to voltage change to obtain voltage regulation instructions; Compare the power of three points of the abnormal working state components, search for the maximum power point through the power of the current voltage point and the voltage points on both sides, and obtain the power search result; Perform voltage clamping on faulty components, limit component output voltage by setting a safe voltage range, and obtain fault protection parameters; The voltage regulation instructions, power search results and fault protection parameters are aggregated to obtain optimal output power parameters of each component.

6. The component-level power optimization method for a photovoltaic system according to claim 1, characterized in that: The optimal output power parameters of each component are transmitted by carrier communication, and the working status information of each component is transmitted to the centralized controller by sequential relaying to obtain the group string level optimization control parameters, including: Test and select the carrier communication frequency, determine the carrier frequency through signal strength test, and obtain the optimal carrier frequency; A communication network is constructed for each component controller, and a communication link between the controllers is established through a power line carrier to obtain a component communication network; The component data is transmitted in segments, and the data is transmitted step by step through data packaging and forwarding between adjacent components to obtain a group data stream; The string data stream is centrally processed, and the working status information of each component is analyzed and integrated through a centralized controller to obtain system operation data; The string voltage is calculated for the system operation data, and the optimal string operating voltage is determined according to the working status of each component to obtain the string-level optimization control parameters.

7. The component-level power optimization method for a photovoltaic system according to claim 1, characterized in that: The performance calculation of the group cascade optimization control parameters and component operating parameters is performed, and the system operation status is evaluated through tracking efficiency calculation and conversion efficiency calculation to obtain the system control parameter optimization result, including: Calculate the maximum power point tracking efficiency of each component, quantify the tracking performance through the ratio of actual output power to theoretical maximum power, and obtain tracking efficiency data; Calculate the energy conversion efficiency of each level of the system, quantify the conversion performance through the ratio of output power to input power, and obtain conversion efficiency data; Conduct rationality analysis on the string operating voltage configuration, evaluate the string voltage setting based on the distribution of each component's operating point, and obtain the voltage configuration evaluation result; Correction calculation is performed on the system control parameters, and the control parameters are adjusted by tracking efficiency and conversion efficiency to obtain the corrected control parameters; The modified control parameters are recorded and updated, and the system operation parameters are updated through data storage to obtain the system control parameter optimization results.

8. A photovoltaic system component-level power optimization device, used to implement the photovoltaic system component-level power optimization method according to any one of claims 1 to 7, characterized in that: The component-level power optimization device of the photovoltaic system comprises: The acquisition module is used to synchronously acquire voltage and current data of each component in the photovoltaic string, and obtain the working parameters of each component through a master-slave multi-channel synchronous acquisition system to obtain the working parameter data of each component; A filtering module is used to perform abnormal value detection and filtering on the working parameter data, and to perform component power calculation and component matching calculation on the processed data to obtain the working status evaluation results of each component; A setting module is used to set the resonant conversion controller at the output end of each component, convert direct current into alternating current through an integrated switch tube, and convert the alternating current through a resonant network and a synchronous rectification circuit to obtain direct current that meets a preset voltage range; A classification module is used to classify the working state evaluation results, perform maximum power point tracking control on components of different categories through a conductance increment method and a three-point comparison method, and obtain the optimal output power parameters of each component; The transmission module is used to transmit the optimal output power parameters of each component through carrier communication, and transmit the working status information of each component to the centralized controller by sequential relay to obtain the group string level optimization control parameters; The calculation module is used to perform performance calculation on the group of cascade optimization control parameters and component operating parameters, evaluate the system operation status through tracking efficiency calculation and conversion efficiency calculation, and obtain the system control parameter optimization result.

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