Double-layer MPPT control strategy method and device of photovoltaic system

Abstract

The invention relates to the technical field of data processing, and discloses a double-layer MPPT control strategy method and device of a photovoltaic system. The method comprises the following steps: performing first-layer maximum power point tracking calculation on an initial voltage and current reference data matrix to obtain a component power tracking data set and a dynamic step-up ratio data set; performing characteristic analysis processing on the component power tracking data set and the dynamic step-up ratio data set under different load rates to obtain an efficiency-step-up ratio characteristic curve and an optimal step-up ratio range value; and performing second-layer optimal step-up ratio tracking operation on the efficiency-step-up ratio characteristic curve and the optimal step-up ratio range value to obtain an inverter dynamic adjustment parameter and an optimal working point control quantity, performing hierarchical cooperative processing to obtain a double-layer control priority sequence and an interactive control instruction set, and performing operation state evaluation calculation. And obtaining a control parameter correction amount and an optimization adjustment instruction. According to the invention, the efficiency of the double-layer MPPT control strategy of the photovoltaic system is improved.

Description

Technical Field

[0001] This application relates to the field of data processing, and particularly to a double-layer MPPT control strategy method and device for a photovoltaic system. Background Art

[0002] With the rapid development of photovoltaic power generation technology, the MPPT control technology, as a key technology to improve the power generation efficiency of photovoltaic systems, has been widely applied. Currently, the mainstream MPPT control methods include the perturbation observation method, the conductance increment method, etc. These methods change the output voltage of photovoltaic modules by adjusting the duty cycle of the controller, and then determine the next adjustment direction according to the power change to achieve the maximum power point tracking. At the same time, in order to further improve the system efficiency, researchers have developed various improved MPPT control strategies, such as variable step size MPPT control, fuzzy control, etc. These methods have improved the tracking speed and accuracy of MPPT to varying degrees.

[0003] However, the existing MPPT control methods mainly focus on the maximum power point tracking at the photovoltaic module level, ignoring the efficiency characteristics of the MPPT controller itself. In practical applications, the conversion efficiency of the MPPT controller will change significantly with the change of the boost ratio. Especially in the low load rate condition, a high boost ratio will lead to a significant reduction in the controller efficiency. In addition, there is a lack of a coordinated control mechanism between the MPPT controller and the inverter in the prior art, and the overall efficiency optimization of the system cannot be achieved. Summary of the Invention

[0004] This application provides a double-layer MPPT control strategy method and device for a photovoltaic system, which is used to improve the efficiency of the double-layer MPPT control strategy of the photovoltaic system.

[0005] In a first aspect, the present application provides a dual-layer MPPT control strategy method for a photovoltaic system. The dual-layer MPPT control strategy method for the photovoltaic system includes: collecting and processing the operating voltage, current parameters of the photovoltaic modules, and the input and output parameters of the controller to obtain an initial voltage-current reference data matrix and a controller basic parameter set; performing first-layer maximum power point tracking calculation on the initial voltage-current reference data matrix by the conductance increment method to obtain a component power tracking data set and a dynamic boost ratio data set; performing characteristic analysis and processing on the component power tracking data set and the dynamic boost ratio data set at different load rates to obtain an efficiency-boost ratio characteristic curve and an optimal boost ratio range value; performing second-layer optimal boost ratio tracking operation on the efficiency-boost ratio characteristic curve and the optimal boost ratio range value by a variable step-size adaptive algorithm to obtain inverter dynamic adjustment parameters and an optimal operating point control quantity; performing hierarchical collaborative processing on the inverter dynamic adjustment parameters and the optimal operating point control quantity to obtain a dual-layer control priority sequence and an interactive control instruction set; performing operating state evaluation calculation on the dual-layer control priority sequence and the interactive control instruction set to obtain a control parameter correction quantity and an optimized adjustment instruction.

[0006] In a second aspect, the present application provides a dual-layer MPPT control strategy device for a photovoltaic system. The dual-layer MPPT control strategy device for the photovoltaic system includes:

[0007] A collection module, configured to collect and process the operating voltage, current parameters of the photovoltaic modules, and the input and output parameters of the controller to obtain an initial voltage-current reference data matrix and a controller basic parameter set;

[0008] A tracking module, configured to perform first-layer maximum power point tracking calculation on the initial voltage-current reference data matrix by the conductance increment method to obtain a component power tracking data set and a dynamic boost ratio data set;

[0009] An analysis module, configured to perform characteristic analysis and processing on the component power tracking data set and the dynamic boost ratio data set at different load rates to obtain an efficiency-boost ratio characteristic curve and an optimal boost ratio range value;

[0010] An operation module, configured to perform second-layer optimal boost ratio tracking operation on the efficiency-boost ratio characteristic curve and the optimal boost ratio range value by a variable step-size adaptive algorithm to obtain inverter dynamic adjustment parameters and an optimal operating point control quantity;

[0011] A processing module, configured to perform hierarchical collaborative processing on the inverter dynamic adjustment parameters and the optimal operating point control quantity to obtain a dual-layer control priority sequence and an interactive control instruction set;

[0012] A calculation module is used to perform an operating state evaluation calculation on the double-layer control priority sequence and the interactive control instruction set to obtain a control parameter correction amount and an optimization adjustment instruction.

[0013] In the technical solution provided by this application, by collecting and processing the working voltage and current parameters of the photovoltaic module and the input and output parameters of the controller, and adopting means such as digital filtering, data calibration, and matrix dimensionality reduction, the accuracy and real-time performance of data collection are ensured, providing a reliable data basis for the implementation of subsequent control strategies; using the conductance increment method to perform the first-layer maximum power point tracking calculation on the initial voltage and current reference data matrix can not only quickly respond to changes in light intensity but also accurately locate the maximum power point, effectively improving the dynamic tracking performance of the system; performing characteristic analysis and processing on the component power tracking data set and the dynamic boost ratio data set under different load rates, and determining the optimal boost ratio range through the analysis of the efficiency-boost ratio characteristic curve, realizing the overall optimization of the system efficiency; adopting a variable step size adaptive algorithm to perform the second-layer optimal boost ratio tracking operation on the efficiency-boost ratio characteristic curve and the optimal boost ratio range value, enabling the system to automatically adjust the control step size according to the working state, ensuring both fast response and avoiding system oscillation; through hierarchical collaborative processing of the dynamic adjustment parameters of the inverter and the optimal operating point control quantity, a double-layer control priority sequence and an interactive control instruction set are established, realizing the effective cooperation between MPPT control and boost ratio optimization; finally, performing an operating state evaluation calculation on the double-layer control priority sequence and the interactive control instruction set, and dynamically correcting the control parameters according to the evaluation results, ensuring the long-term stable and efficient operation of the system. The entire control strategy realizes the multi-level optimization of the system efficiency through double-layer collaborative control. Compared with the traditional single MPPT control method, it not only improves the power generation efficiency of the system but also enhances the adaptability and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order 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 following drawings 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.

[0015] Figure 1 It is a schematic diagram of an embodiment of the double-layer MPPT control strategy method for a photovoltaic system in an embodiment of this application;

[0016] Figure 2 It is a schematic diagram of an embodiment of the double-layer MPPT control strategy device for a photovoltaic system in an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The embodiments of the present application provide a double - layer MPPT control strategy method and device for a photovoltaic system. Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims and the above - mentioned drawings of the present application are used to distinguish similar objects, and do not necessarily need to 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 different from that illustrated or described here. In addition, the term "including" or "having" and any variations thereof are intended to cover non - exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these process, method, product or device.

[0018] For ease of understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1 , an embodiment of the double - layer MPPT control strategy method for the photovoltaic system in the embodiments of the present application includes:

[0019] Step S101, collect and process the working voltage, current parameters of the photovoltaic modules and the input - output parameters of the controller to obtain an initial voltage - current reference data matrix and a controller basic parameter set;

[0020] Step S102, perform the first - layer maximum power point tracking calculation on the initial voltage - current reference data matrix by the conductance increment method to obtain a component power tracking data set and a dynamic boost ratio data set;

[0021] Step S103, perform characteristic analysis and processing on the component power tracking data set and the dynamic boost ratio data set under different load rates to obtain an efficiency - boost ratio characteristic curve and an optimal boost ratio range value;

[0022] Step S104, perform the second - layer optimal boost ratio tracking operation on the efficiency - boost ratio characteristic curve and the optimal boost ratio range value by the variable - step - size adaptive algorithm to obtain the inverter dynamic adjustment parameters and the optimal operating point control quantity;

[0023] Step S105, perform hierarchical collaborative processing on the inverter dynamic adjustment parameters and the optimal operating point control quantity to obtain a double - layer control priority sequence and an interactive control instruction set;

[0024] Step S106, perform an operating state evaluation calculation on the double - layer control priority sequence and the interactive control instruction set to obtain a control parameter correction amount and an optimized adjustment instruction.

[0025] It can be understood that the execution entity of this application can be a double-layer MPPT control strategy device of a photovoltaic system, or it can also be a terminal or a server. Specifically, it is not limited here. In this embodiment of the application, the server is used as the execution entity for illustration.

[0026] Specifically, the system uses a high-precision AD converter and an MCU controller to collect the working voltage and current data of the photovoltaic modules. The collection process is divided into four links: First is data sampling, where the collection device respectively obtains the voltage instantaneous value sequence and current instantaneous value sequence at the output end of the module; then digital filtering processing is performed, mainly to eliminate noise interference in the sampled data and obtain smoother voltage and current data; then data calibration is executed, and the sampling error is corrected through a calibration algorithm to ensure the accuracy of the data; finally, data matrix processing is carried out, where the calibrated data is quantized and encoded and organized into a data matrix in a standard format for subsequent analysis and processing. The first layer of maximum power point tracking uses the conductance increment method. The core idea of this method is to judge the position of the current operating point relative to the maximum power point by comparing the ratio of the power difference between two adjacent operating points to the voltage difference. The specific process is: The first step is to calculate the power difference between the current moment and the previous moment, the second step is to calculate the corresponding voltage difference, and the third step is to obtain the ratio of these two differences to get the conductance increment judgment coefficient. When this coefficient is positive, it means the operating point is on the left side of the maximum power point and the output duty cycle of the controller needs to be increased; when this coefficient is negative, it means the operating point is on the right side of the maximum power point and the duty cycle needs to be decreased; when this coefficient is close to zero, it means the maximum power point has been reached. In this way, the controller can continuously track the maximum power point of the photovoltaic module and form a complete power tracking data set.

[0027] The second-level optimal boost ratio tracking adopts a variable step adaptive algorithm. First, the collected power tracking data set is classified and sorted, and grouped according to different load rates to obtain power data groups under each load rate. At the same time, the corresponding boost ratio data is recorded to form a boost ratio data group. Then the conversion efficiency of different working points is calculated, and the efficiency-boost ratio characteristic curve is drawn. The maximum efficiency point data is extracted through curve analysis to determine the optimal boost ratio range. In actual operation, the system dynamically adjusts the boost ratio according to this optimal range so that the inverter always works in the best state. Take the operation data of a photovoltaic power station as an example: the system contains 16 series photovoltaic modules, and the controller sampling period is 100ms. In a certain sampling period, the controller collects the voltage of the first module 34.2V and the current 7.86A. After the conductance increment method, it is found that the working point deviates from the maximum power point, and the duty cycle is adjusted to move it to the maximum power point. At the same time, the system records that the optimal boost ratio at 75% load rate is 1.18, and the inverter parameters are optimized and adjusted accordingly. This two-layer control strategy not only achieves maximum power point tracking at the component level, but also improves the conversion efficiency of the entire system by optimizing the boost ratio. After hierarchical collaborative processing and operation status evaluation, control parameters and control instructions are reasonably allocated to each level of the system. Collaborative processing ensures tacit cooperation between the control units and avoids control conflicts; status evaluation monitors the system operation status in real time, makes necessary corrections and optimizations to the control parameters, and thus ensures the continuous, stable and efficient operation of the system. Through this multi-level and multi-dimensional control strategy, the overall power generation efficiency of the photovoltaic system is significantly improved.

[0028] In the embodiments of the present application, by collecting and processing the working voltage, current parameters of the photovoltaic module and the input and output parameters of the controller, and adopting means such as digital filtering, data calibration, and matrix dimensionality reduction, the accuracy and real-time nature of data collection are ensured, providing a reliable data basis for the implementation of subsequent control strategies. The incremental conductance method is used to perform the first-layer maximum power point tracking calculation on the initial voltage-current reference data matrix, which can not only quickly respond to changes in illumination but also accurately locate the maximum power point, effectively improving the dynamic tracking performance of the system. Characteristic analysis and processing are carried out on the component power tracking data set and the dynamic boost ratio data set under different load rates, and the optimal boost ratio range is determined through the analysis of the efficiency-boost ratio characteristic curve, realizing the overall optimization of the system efficiency. The variable step-size adaptive algorithm is used to perform the second-layer optimal boost ratio tracking operation on the efficiency-boost ratio characteristic curve and the optimal boost ratio range value, enabling the system to automatically adjust the control step size according to the working state, ensuring both fast response and avoiding system oscillation. Through hierarchical collaborative processing of the dynamic adjustment parameters of the inverter and the optimal operating point control quantity, a two-layer control priority sequence and an interactive control instruction set are established, realizing the effective cooperation between MPPT control and boost ratio optimization. Finally, the operating state evaluation calculation is performed on the two-layer control priority sequence and the interactive control instruction set, and the control parameters are dynamically corrected according to the evaluation results, ensuring the long-term stable and efficient operation of the system. The entire control strategy realizes the multi-level optimization of the system efficiency through two-layer collaborative control. Compared with the traditional single MPPT control method, it not only improves the power generation efficiency of the system but also enhances the adaptability and stability of the system.

[0029] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0030] (1) Sample and process the voltage signal at the output end of the photovoltaic module to obtain a sequence of instantaneous component voltage values, and sample and process the current signal at the output end of the photovoltaic module to obtain a sequence of instantaneous component current values;

[0031] (2) Perform digital filtering processing on the sequence of instantaneous component voltage values and the sequence of instantaneous component current values to obtain a filtered voltage data set and a filtered current data set, and perform data calibration processing on the filtered voltage data set and the filtered current data set to obtain a calibrated voltage data set and a calibrated current data set;

[0032] (3) Perform quantization and encoding processing on each voltage data in the calibrated voltage data set to obtain a voltage reference quantization value, and perform quantization and encoding processing on each current data in the calibrated current data set to obtain a current reference quantization value;

[0033] (4) Perform matrix arrangement processing on the voltage reference quantization values and current reference quantization values to obtain a voltage matrix and a current matrix, and perform dimensionality reduction processing on the voltage matrix and the current matrix to obtain a voltage dimensionality reduction data group and a current dimensionality reduction data group;

[0034] (5) Perform combined mapping processing on the voltage dimensionality reduction data group and the current dimensionality reduction data group to obtain a voltage-current correlation data group, and perform time series marking processing on the voltage-current correlation data group to obtain a reference data matrix with time series marks;

[0035] (6) Sample the input terminal parameters of the MPPT controller to obtain a controller input parameter sequence, and sample the output terminal parameters of the MPPT controller to obtain a controller output parameter sequence;

[0036] (7) Perform data normalization processing on the controller input parameter sequence and the controller output parameter sequence to obtain a normalized input data group and a normalized output data group, and perform parameter merging processing on the normalized input data group and the normalized output data group to obtain a controller basic parameter set.

[0037] Specifically, electrical parameters are collected at the output terminal of the photovoltaic module through a high-precision AD converter. The sampling frequency is set to 10 kHz, and the sampling duration is 1 second. When collecting the voltage signal, with a period of 100 ms, 100 data points are collected in each period to form a voltage instantaneous value sequence. Similarly, when collecting the current signal, a current instantaneous value sequence is obtained. In this way, 10 groups of a total of 1000 voltage-current instantaneous values can be obtained within 1 second. The collected instantaneous value sequence is processed by a Butterworth digital low-pass filter, and the cut-off frequency is set to 1 kHz. The filtered data can effectively suppress high-frequency interference and retain the effective signal. Subsequently, calibration processing is performed on the filtered data. The main calibration items include: zero drift calibration, gain calibration, and linearity calibration. Taking voltage collection as an example, the collected value is compared with the measured value of a high-precision voltmeter to establish a calibration curve, and a calibrated voltage data group is obtained. The current data is obtained in the same way to obtain a calibrated current data group.

[0038] The calibrated data is quantized and encoded through a 12-bit AD converter. Taking a voltage range of -100 V to +100 V as an example, the quantization accuracy is 0.049 V. The obtained voltage reference quantization value range after encoding is 0 - 4095. The current signal is encoded according to a range of -20 A to +20 A to obtain the corresponding current reference quantization value. The quantization data is rearranged into a matrix form in chronological order. Both the voltage matrix and the current matrix are 10×100 two-dimensional matrices, representing 100 sampling point data within 10 sampling periods. Dimensionality reduction processing is performed on the matrix through the principal component analysis method to extract the main features and compress the data volume. After dimensionality reduction, a 10×10 voltage dimensionality reduction data group and a current dimensionality reduction data group are obtained.

[0039] Perform combined mapping processing on the data group after dimensionality reduction, establish the corresponding relationship between voltage and current, and form a voltage-current correlation data group. Each group of data is marked with the acquisition time, accurate to the millisecond level, constituting a reference data matrix with time sequence marks. At the same time, collect parameters at the input and output ends of the MPPT controller. At the input end, collect parameters such as the duty cycle and switching frequency of the PWM signal, and at the output end, collect parameters such as the output voltage and current. The sampling interval is 1 ms, and the sampling duration is 1 second, respectively obtaining the controller input parameter sequence and output parameter sequence.

[0040] Finally, perform normalization processing on the controller parameter sequence to unify parameters with different dimensions into the interval [0, 1]. The normalized input and output data are combined and sorted to form a complete set of basic controller parameters. The parameter set contains important information such as the working state and control effect of the controller. The entire data processing process realizes the conversion from raw sampling to a standardized parameter set, providing a reliable data basis for subsequent MPPT control strategies. Through means such as digital filtering, data calibration, and quantization coding, the accuracy and usability of the data are ensured. By using methods such as matrix dimensionality reduction and combined mapping, the data processing efficiency is improved. The finally obtained parameter set comprehensively reflects the operating state of the photovoltaic system, providing a basis for optimizing control.

[0041] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0042] (1) Perform power calculation processing on the voltage and current data in the initial voltage-current reference data matrix to obtain the current power value and historical power value;

[0043] (2) Perform difference calculation processing on the current power value and historical power value to obtain the power change amount, and perform difference calculation processing on the voltage value corresponding to the current power value and the historical voltage value to obtain the voltage change amount;

[0044] (3) Perform conductance increment ratio calculation processing on the power change amount and voltage change amount to obtain the conductance increment judgment coefficient;

[0045] (4) Perform threshold judgment processing on the conductance increment judgment coefficient to obtain the duty cycle adjustment amount of the MPPT controller;

[0046] (5) Adjust and calculate the output parameters of the MPPT controller through the duty cycle adjustment amount to obtain the power tracking curve data and boost ratio curve data;

[0047] (6) Perform data grouping processing on the power tracking curve data and boost ratio curve data to obtain the component power tracking data set and dynamic boost ratio data set.

[0048] Specifically, process the data in the reference data matrix. For each sampling period, calculate the real-time power based on the voltage and current data. Taking the k-th sampling period as an example, multiply the voltage value of this period by the current value to obtain the current power value, and calculate the historical power value of the (k - 1)-th period in the same way. Through this calculation, the power change situation of the system within multiple consecutive sampling periods can be obtained. Then calculate the power difference and voltage difference between two adjacent sampling periods. Specifically, subtract the power value of the k-th period from the power value of the (k - 1)-th period to obtain the power change amount between these two moments. Similarly, calculate the voltage difference corresponding to these two moments to obtain the voltage change amount. These two change amounts reflect the dynamic characteristics of the system's power change with voltage.

[0049] Then, according to the ratio relationship between the power change amount and the voltage change amount, calculate the conductance increment judgment coefficient. This coefficient characterizes the position of the current operating point of the photovoltaic module relative to the maximum power point. Specifically, during the sampling period, continuously calculate this coefficient to judge the power change trend. Set positive and negative threshold intervals for the conductance increment judgment coefficient. When the coefficient is positive and greater than the positive threshold, it indicates that the operating point is on the left side of the maximum power point, and the duty cycle needs to be increased; when the coefficient is negative and less than the negative threshold, it indicates that the operating point is on the right side of the maximum power point, and the duty cycle needs to be decreased. Determine the magnitude and direction of the duty cycle adjustment amount according to the judgment result.

[0050] Use the obtained duty cycle adjustment amount to dynamically adjust the output parameters of the MPPT controller. During the adjustment process, record the power values of each operating point to form a power tracking curve. At the same time, record the corresponding input-output voltage ratio to obtain a boost ratio curve. These curve data completely record the MPPT process of the system. Finally, classify and organize the collected curve data according to different operating states. For example, group the power tracking data according to environmental parameters such as light intensity and temperature; group the boost ratio data according to the load size. The grouped data is more conducive to analyzing the operating characteristics of the system under different working conditions.

[0051] For example: For a certain photovoltaic module under standard test conditions, the sampling period of the MPPT controller is 100 ms. At the k-th period, the voltage of 34.5 V and current of 8.2 A are collected, and the calculated power is 282.9 W. The voltage and power of the previous period are 34.2 V and 280.44 W respectively. The calculated power change amount is 2.46 W, and the voltage change amount is 0.3 V. Based on this, the conductance increment judgment coefficient is 8.2, which is greater than the set threshold of 5, indicating that the duty cycle needs to be increased. The controller adjusts the duty cycle from 0.45 to 0.46 accordingly, pushing the operating point towards the maximum power point. This process continues until the system stabilizes at the maximum power point.

[0052] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0053] (1) Segment the component power tracking data set to obtain power data groups for different load rate intervals;

[0054] (2) Segment the dynamic boost ratio data set to obtain boost ratio data groups for different load rate intervals;

[0055] (3) Calculate the conversion efficiency for the power data groups and boost ratio data groups in different load rate intervals to obtain a sequence of efficiency calculation values;

[0056] (4) Perform curve fitting on the sequence of efficiency calculation values to obtain an efficiency-boost ratio characteristic curve;

[0057] (5) Extract the extreme point of the efficiency-boost ratio characteristic curve to obtain a data group of the maximum efficiency point;

[0058] (6) Perform interval statistics on the data group of the maximum efficiency point to obtain the optimal boost ratio range value.

[0059] Specifically, segment the component power tracking data according to the load rate. The data segmentation adopts an equal-spacing division method, dividing the 0-100% load rate into 10 intervals, with each interval having a span of 10%. For the power data collected during a certain operation cycle, classify it into the corresponding interval according to the current load rate to form a segmented power data group. For example, the power data with a 25% load rate is classified into the 20-30% interval, and the data with a 35% load rate is classified into the 30-40% interval. Segment the boost ratio data in the same way. Allocate the collected boost ratio data to different intervals according to the load rate at the corresponding moment to obtain a segmented boost ratio data group. In this way, each load rate interval corresponds to a group of power data and boost ratio data, reflecting the operating characteristics of the system at that load level.

[0060] Then calculate the conversion efficiency for each load rate interval. For the power data within each interval, calculate the ratio of the output power to the input power to obtain the conversion efficiency at this operating point. Repeat this calculation process to obtain a complete sequence of efficiency calculation values. This sequence reflects the energy conversion ability of the system under different operating conditions. Use the polynomial fitting method to plot the efficiency-boost ratio characteristic curve for the sequence of efficiency calculation values. Select a 5th-order polynomial as the fitting function and determine the polynomial coefficients through the least squares method. The fitting curve visually shows the law of the system efficiency changing with the boost ratio, which helps to analyze the optimal operating point.

[0061] Next, extract the extreme points on the characteristic curve. The derivative method is used to find the maximum points of the curve, and the data set of the maximum efficiency points is obtained. Each extreme point corresponds to a maximum efficiency value and the corresponding boost ratio. These points reflect the optimal operating state of the system under different load conditions. Finally, statistical analysis is performed on the data of the maximum efficiency points. By calculating the distribution range of the boost ratios corresponding to the maximum efficiency points, the optimal boost ratio range is determined. For example, in the operating data of a certain photovoltaic system, when the load rate is 80%, it is statistically found that the maximum efficiency points are mainly distributed in the range of boost ratios from 1.15 to 1.25. Therefore, this range is determined as the optimal boost ratio range at this load level.

[0062] For example: Analyze the operating data of a certain photovoltaic system continuously for 24 hours. First, divide the data into 10 groups at intervals of 10% load rate. In the load rate range of 70 - 80%, the power data shows that the input power range is 280 - 320W, and the output power range is 260 - 295W. The calculated conversion efficiency value in this range is between 92 - 96%. The corresponding boost ratio data is distributed between 1.1 and 1.3. Through curve fitting, it is found that the maximum efficiency in this load range is 95.8%, and the corresponding boost ratio is 1.18. Further statistical analysis shows that when the boost ratio is in the range of 1.15 - 1.22, the system efficiency can remain above 95%.

[0063] In a specific embodiment, the process of performing step S104 may specifically include the following steps:

[0064] (1) Perform sampling interval division processing on the efficiency-boost ratio characteristic curve to obtain a sequence of boost ratio sampling points;

[0065] (2) Perform deviation calculation processing on the sequence of boost ratio sampling points and the optimal boost ratio range value to obtain the current deviation value;

[0066] (3) Perform step size calculation processing on the current deviation value through a variable step size algorithm to obtain the boost ratio adjustment step size;

[0067] (4) Perform direction determination processing on the boost ratio adjustment step size to obtain a boost ratio adjustment instruction;

[0068] (5) Perform adjustment calculation on the inverter parameters through the boost ratio adjustment instruction to obtain the inverter dynamic adjustment parameters;

[0069] (6) Perform working point positioning processing on the inverter dynamic adjustment parameters to obtain the optimal working point control quantity.

[0070] Specifically, equally spaced sampling is performed on the efficiency-boost ratio characteristic curve. The entire boost ratio range is divided into 200 sampling points, and the sampling interval is 0.01. Each sampling point corresponds to a specific boost ratio value, forming a boost ratio sampling point sequence. This sequence contains all typical operating points from the minimum boost ratio to the maximum boost ratio. Then, the deviation between each sampling point and the optimal boost ratio range is calculated. Set the median value of the optimal boost ratio range as the reference point, calculate the distance between the current sampling point and the reference point, and obtain the current deviation value. The magnitude of the deviation value reflects the degree to which the current operating point of the system deviates from the optimal operating area.

[0071] Next, a variable step-size algorithm is used to dynamically determine the adjustment step size. The core idea of the algorithm is to use a large step size for rapid adjustment when the deviation is large and a small step size for fine adjustment when the deviation is small. Taking a certain sampling point as an example, when the deviation value is greater than the set threshold, the step size is set to 50% of the deviation value; when the deviation value is small, the step size is reduced to 10% of the deviation value. This adaptive adjustment method not only ensures the response speed but also avoids system oscillation. Determine the adjustment direction according to the positive or negative of the deviation value. A positive deviation indicates that the current boost ratio is greater than the optimal value, and the boost ratio needs to be reduced; a negative deviation indicates that the current boost ratio is less than the optimal value, and the boost ratio needs to be increased. Combine the adjustment step size to generate a specific boost ratio adjustment instruction.

[0072] Use the boost ratio adjustment instruction to modify the operating parameters of the inverter. The adjustment content includes key parameters such as the frequency of the PWM wave, dead time, and turn-on time. After each adjustment, the changes in the output parameters are monitored in real time to ensure the effectiveness of the parameter adjustment. Finally, evaluate the adjusted inverter parameters to determine the optimal operating point. The evaluation indicators include output power, conversion efficiency, voltage stability, etc. When all indicators reach the optimum, record the current control parameters as the control quantity of the optimal operating point.

[0073] For example: The efficiency-boost ratio characteristic curve of a certain photovoltaic inverter shows that the optimal boost ratio range is 1.15 - 1.25. Perform 200-point sampling and analysis on this range. The boost ratio of the current operating point is 1.32, and the deviation from the median value 1.20 of the optimal range is 0.12. The adjustment step size calculated by the variable step-size algorithm is 0.06. Since the deviation is positive, a boost ratio reduction adjustment instruction is generated. After receiving the instruction, the inverter adjusts the dead time from 2.0 microseconds to 1.8 microseconds and the PWM frequency from 18 kHz to 20 kHz. After the adjustment, the boost ratio drops to 1.26, and the system efficiency increases by 2 percentage points. After 3 similar adjustments, the system finally stabilizes at the optimal operating point with a boost ratio of 1.22. At this time, the conversion efficiency reaches 98.5%, and the output voltage volatility is less than 0.5%. The control parameters of this operating point are recorded for subsequent steady-state control.

[0074] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0075] (1) Calculate the time constants of the dynamic regulation parameters of the inverter to obtain the first-layer and second-layer control time series;

[0076] (2) Perform priority allocation processing on the first-layer and second-layer control time series to obtain the double-layer control priority sequence;

[0077] (3) Perform data caching processing on the optimal operating point control quantity to obtain the operating point control cache data;

[0078] (4) Perform filtering processing on the operating point control cache data to obtain the filtered control data;

[0079] (5) Perform instruction generation processing on the filtered control data to obtain the basic control instruction set;

[0080] (6) Perform timing combination processing on the basic control instruction set to obtain the interactive control instruction set.

[0081] Specifically, perform time-scale analysis on the dynamic regulation parameters of the inverter. Calculate the response time and regulation period of the first-layer MPPT control, which is generally in milliseconds, for quickly tracking the maximum power point of the photovoltaic module; at the same time, calculate the response time and regulation period of the second-layer boost ratio optimization control, which is usually in seconds, for optimizing the overall efficiency of the system. These two time series reflect the control characteristics of different levels of the system. Determine the priority of the double-layer control according to the control time series. Usually, set the first-layer MPPT control with a fast response speed as the high priority, responsible for real-time power tracking; set the second-layer boost ratio optimization control with a slower response as the low priority, for parameter optimization during the stable operation of the system. The priority sequence ensures the orderliness of the system control.

[0082] Adopt a double-buffer mechanism to process the optimal operating point control data. The main buffer stores the control parameters currently being executed, including the PWM duty cycle, boost ratio, etc.; the standby buffer stores the optimization parameters to be executed. When a new optimal operating point is determined, the control parameters are first written into the standby buffer and wait for the appropriate time to switch. Process the cached control data using a Butterworth digital filter. The cut-off frequency of the filter is set to 100 Hz, which can effectively suppress the high-frequency interference components in the data and retain the effective control information. The filtered data is more conducive to generating stable control instructions.

[0083] Generate basic control instructions based on the filtered control data. The instruction content includes PWM waveform parameter adjustment instructions, voltage and current limit setting instructions, protection threshold setting instructions, etc. Each instruction contains information such as an operation code, parameter values, and a check code to ensure the accurate transmission and execution of the instructions. Finally, reorganize the basic instructions according to the execution timing. Package the relevant instructions within the same control cycle to form a complete control instruction set. The execution order, priority, and response requirements of each instruction are clearly marked in the instruction set.

[0084] For example: During the operation of a certain photovoltaic system, the response time of the first-layer MPPT control is 10 milliseconds, and the response time of the second-layer boost ratio optimization is 1 second. When a change in light intensity is detected, the first-layer control is immediately activated, and the new maximum power point is tracked by adjusting the PWM duty cycle. The system parameter cache shows that the current PWM duty cycle is 0.45, and the newly calculated optimal value is 0.48. This value is output as 0.475 after being filtered by a 100Hz low-pass filter, and the system generates the corresponding PWM adjustment instruction. At the same time, the second-layer control is activated after the system stabilizes to optimize the boost ratio parameter. The final generated control instruction set includes: PWM duty cycle adjustment instruction (priority 1, execution time 0ms), dead time adjustment instruction (priority 2, execution time 5ms), boost ratio optimization instruction (priority 3, execution time 1000ms), etc. This hierarchical and collaborative control method not only ensures the fast response ability of the system but also realizes the optimization of the overall efficiency.

[0085] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0086] (1) Perform a running state scanning process on the double-layer control priority sequence to obtain a controller state parameter group;

[0087] (2) Perform an execution result acquisition process on the interactive control instruction set to obtain an instruction execution parameter group;

[0088] (3) Perform a deviation statistics process on the controller state parameter group to obtain a state deviation data group;

[0089] (4) Perform an effect evaluation process on the instruction execution parameter group to obtain an instruction execution evaluation value;

[0090] (5) Perform a correction calculation process on the state deviation data group to obtain a control parameter correction amount;

[0091] (6) Perform a threshold judgment process on the instruction execution evaluation value to obtain an optimization adjustment instruction.

[0092] Specifically, the operation status of the scanning double-layer control is monitored in real time. The system scans the first-layer MPPT control status every 100 milliseconds, mainly collecting PWM waveform parameters, output voltage and current values, etc.; scans the second-layer boost ratio control status every 1 second, collecting parameters such as boost ratio and conversion efficiency. These real-time status parameters are grouped and stored according to the control levels. The execution of the control instructions is collected synchronously. The effects of the executed control instructions are tracked, and the changes in the system parameters before and after the instruction execution are recorded. For example, the change in output power after the execution of the PWM adjustment instruction, the change in efficiency after the execution of the boost ratio adjustment instruction, etc. These change data form the instruction execution parameter group.

[0093] Analyze the deviation between the controller status parameters and the expected target. Conduct deviation analysis on the first-layer control, calculating the deviation between the current output power and the maximum power point; analyze the deviation between the current boost ratio and the optimal boost ratio for the second-layer control. Classify and statistically analyze these deviation data to form a complete status deviation data group. Evaluate the execution effect of the control instructions. Set evaluation indicators including power tracking accuracy, response speed, system stability, etc. Score the execution result of each instruction, with the score range from 0 to 100. For example, when the power tracking deviation is less than 1%, the score is above 90; when the response time exceeds the expected value by 50%, 20 points are deducted.

[0094] Calculate the correction parameters based on the status deviation. When the power tracking deviation is detected, calculate the correction amount of the PWM duty cycle that needs to be adjusted; when it is found that the boost ratio deviates from the optimal range, calculate the adjustment amount of the boost ratio. The calculation of the correction parameters comprehensively considers the deviation magnitude and the system response characteristics. Evaluate and judge the execution effect of the instructions. Set an evaluation threshold. For example, if the instruction execution score is lower than 60 points, optimization and adjustment are required. Generate corresponding optimization instructions according to the evaluation results, and the instruction content includes the parameter adjustment direction, adjustment step size, etc.

[0095] For example: During the operation of a certain photovoltaic system, the status scan shows that the current PWM duty cycle is 0.45, the output power is 280W, and the boost ratio is 1.28. Through deviation analysis, it is found that: the power is 3.4% lower than the maximum power point of 290W, and the boost ratio exceeds the optimal range of 1.15 - 1.25. The status deviation data prompts the system to generate a correction instruction: adjust the PWM duty cycle to 0.47 and reduce the boost ratio to 1.25. After executing the correction instruction, the system is evaluated again: the output power increases to 288W, the power tracking deviation drops to 0.7%, the boost ratio enters the optimal range, and the instruction execution score is 92 points. The evaluation result shows that this correction is effective, and the system records the relevant parameters for subsequent optimization. Through this continuous evaluation and optimization, the system always maintains the best working state.

[0096] The above describes the double - layer MPPT control strategy method for the photovoltaic system in the embodiments of the present application. Next, the double - layer MPPT control strategy device for the photovoltaic system in the embodiments of the present application will be described. Please refer to Figure 2 An embodiment of the double - layer MPPT control strategy device for the photovoltaic system in the embodiments of the present application includes:

[0097] The acquisition module 201 is configured to collect and process the working voltage, current parameters of the photovoltaic modules and the input - output parameters of the controller, and obtain the initial voltage - current reference data matrix and the controller basic parameter set;

[0098] The tracking module 202 is configured to perform the first - layer maximum power point tracking calculation on the initial voltage - current reference data matrix by the conductance increment method, and obtain the component power tracking data set and the dynamic boost ratio data set;

[0099] The analysis module 203 is configured to perform characteristic analysis processing on the component power tracking data set and the dynamic boost ratio data set under different load rates, and obtain the efficiency - boost ratio characteristic curve and the optimal boost ratio range value;

[0100] The operation module 204 is configured to perform the second - layer optimal boost ratio tracking operation on the efficiency - boost ratio characteristic curve and the optimal boost ratio range value by the variable - step - size adaptive algorithm, and obtain the inverter dynamic adjustment parameter and the optimal operating point control quantity;

[0101] The processing module 205 is configured to perform hierarchical collaborative processing on the inverter dynamic adjustment parameter and the optimal operating point control quantity, and obtain the double - layer control priority sequence and the interactive control instruction set;

[0102] The calculation module 206 is configured to perform the running state evaluation calculation on the double - layer control priority sequence and the interactive control instruction set, and obtain the control parameter correction quantity and the optimization adjustment instruction.

[0103] Through the collaborative cooperation of the above-mentioned various components, by collecting and processing the working voltage, current parameters of the photovoltaic module and the input and output parameters of the controller, and by means of digital filtering, data calibration, matrix dimensionality reduction, etc., the accuracy and real-time performance of data collection are ensured, providing a reliable data basis for the implementation of subsequent control strategies; using the conductance increment method to perform the first-layer maximum power point tracking calculation on the initial voltage and current reference data matrix can not only quickly respond to changes in illumination, but also accurately locate the maximum power point, effectively improving the dynamic tracking performance of the system; analyzing and processing the characteristics of the component power tracking data set and the dynamic boost ratio data set under different load rates, and determining the optimal boost ratio range through the analysis of the efficiency-boost ratio characteristic curve, realizing the overall optimization of the system efficiency; using the variable step size adaptive algorithm to perform the second-layer optimal boost ratio tracking operation on the efficiency-boost ratio characteristic curve and the optimal boost ratio range value, enabling the system to automatically adjust the control step size according to the working state, which not only ensures fast response but also avoids system oscillation; through hierarchical collaborative processing of the dynamic adjustment parameters of the inverter and the optimal operating point control quantity, a two-layer control priority sequence and an interactive control instruction set are established, realizing the effective cooperation between MPPT control and boost ratio optimization; finally, performing an operating state evaluation calculation on the two-layer control priority sequence and the interactive control instruction set, and dynamically correcting the control parameters according to the evaluation results, ensuring the long-term stable and efficient operation of the system. The entire control strategy realizes the multi-level optimization of the system efficiency through two-layer collaborative control. Compared with the traditional single MPPT control method, it not only improves the power generation efficiency of the system, but also enhances the adaptability and stability of the system.

[0104] 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 double - layer MPPT control strategy method for a photovoltaic system, characterized in that, The double - layer MPPT control strategy method of the photovoltaic system includes: Collect and process the working voltage, current parameters of the photovoltaic modules and the input - output parameters of the controller to obtain the initial voltage - current reference data matrix and the controller basic parameter set; Perform the first - layer maximum power point tracking calculation on the initial voltage - current reference data matrix by the conductance increment method to obtain the component power tracking data set and the dynamic boost ratio data set; Conduct characteristic analysis and processing on the component power tracking data set and the dynamic boost ratio data set under different load rates to obtain the efficiency - boost ratio characteristic curve and the optimal boost ratio range value; Perform the second - layer optimal boost ratio tracking operation on the efficiency - boost ratio characteristic curve and the optimal boost ratio range value by the variable - step - size adaptive algorithm to obtain the inverter dynamic adjustment parameters and the optimal operating point control quantity; Conduct hierarchical collaborative processing on the inverter dynamic adjustment parameters and the optimal operating point control quantity to obtain the double - layer control priority sequence and the interactive control instruction set; Conduct operation - state evaluation calculation on the double - layer control priority sequence and the interactive control instruction set to obtain the control parameter correction quantity and the optimization adjustment instruction.

2. The double-layer MPPT control strategy method for a photovoltaic system according to claim 1, characterized in that The collection and processing of the working voltage, current parameters of the photovoltaic modules and the input - output parameters of the controller to obtain the initial voltage - current reference data matrix and the controller basic parameter set includes: Sample the output - terminal voltage signal of the photovoltaic module to obtain the component voltage instantaneous - value sequence, and sample the output - terminal current signal of the photovoltaic module to obtain the component current instantaneous - value sequence; Perform digital filtering processing on the component voltage instantaneous - value sequence and the component current instantaneous - value sequence to obtain the filtered voltage data group and current data group, and perform data calibration processing on the filtered voltage data group and current data group to obtain the calibrated voltage data group and calibrated current data group; Perform quantization encoding processing on each voltage data in the calibrated voltage data group to obtain the voltage reference quantization value, and perform quantization encoding processing on each current data in the calibrated current data group to obtain the current reference quantization value; Perform matrix arrangement processing on the voltage reference quantization value and the current reference quantization value to obtain the voltage matrix and current matrix, and perform dimensionality reduction processing on the voltage matrix and current matrix to obtain the voltage dimensionality - reduction data group and current dimensionality - reduction data group; Perform combined mapping processing on the voltage dimensionality - reduction data group and the current dimensionality - reduction data group to obtain the voltage - current correlation data group, and perform time - series marking processing on the voltage - current correlation data group to obtain the reference data matrix with time - series marking; Sample the input - terminal parameters of the MPPT controller to obtain the controller input parameter sequence, and sample the output - terminal parameters of the MPPT controller to obtain the controller output parameter sequence; Perform data normalization processing on the controller input parameter sequence and the controller output parameter sequence to obtain the normalized input data group and normalized output data group, and perform parameter merging processing on the normalized input data group and normalized output data group to obtain the controller basic parameter set.

3. The double-layer MPPT control strategy method of the photovoltaic system according to claim 1, characterized in that, Performing the first - layer maximum power point tracking calculation on the initial voltage - current reference data matrix by the conductance increment method to obtain a component power tracking data set and a dynamic boost ratio data set, including: Performing power calculation processing on the voltage and current data in the initial voltage - current reference data matrix to obtain the current power value and the historical power value; Performing difference calculation processing on the current power value and the historical power value to obtain the power change amount, and performing difference calculation processing on the voltage value corresponding to the current power value and the historical voltage value to obtain the voltage change amount; Performing conductance increment ratio calculation processing on the power change amount and the voltage change amount to obtain the conductance increment judgment coefficient; Performing threshold judgment processing on the conductance increment judgment coefficient to obtain the duty - cycle adjustment amount of the MPPT controller; Adjusting and calculating the output parameters of the MPPT controller through the duty - cycle adjustment amount to obtain the power tracking curve data and the boost ratio curve data; Performing data grouping processing on the power tracking curve data and the boost ratio curve data to obtain a component power tracking data set and a dynamic boost ratio data set.

4. The double-layer MPPT control strategy method for a photovoltaic system according to claim 1, characterized in that, Performing characteristic analysis processing on the component power tracking data set and the dynamic boost ratio data set at different load rates to obtain an efficiency - boost ratio characteristic curve and an optimal boost ratio range value, including: Performing data segmentation processing on the component power tracking data set to obtain power data groups in different load rate intervals; Performing data segmentation processing on the dynamic boost ratio data set to obtain boost ratio data groups in different load rate intervals; Performing conversion efficiency calculation on the power data groups and the boost ratio data groups in different load rate intervals to obtain a sequence of efficiency calculation values; Performing curve fitting processing on the sequence of efficiency calculation values to obtain an efficiency - boost ratio characteristic curve; Performing extreme - point extraction processing on the efficiency - boost ratio characteristic curve to obtain a maximum - efficiency point data group; Performing interval statistical processing on the maximum - efficiency point data group to obtain an optimal boost ratio range value.

5. The double-layer MPPT control strategy method of the photovoltaic system according to claim 1, characterized in that, Performing the second - layer optimal boost ratio tracking operation on the efficiency - boost ratio characteristic curve and the optimal boost ratio range value by the variable - step - size adaptive algorithm to obtain inverter dynamic adjustment parameters and an optimal operating point control quantity, including: Performing sampling interval division processing on the efficiency - boost ratio characteristic curve to obtain a boost ratio sampling point sequence; Performing deviation calculation processing on the boost ratio sampling point sequence and the optimal boost ratio range value to obtain the current deviation value; Performing step - size calculation processing on the current deviation value by the variable - step - size algorithm to obtain the boost ratio adjustment step; Performing direction determination processing on the boost ratio adjustment step to obtain a boost ratio adjustment instruction; Adjusting and calculating the inverter parameters through the boost ratio adjustment instruction to obtain inverter dynamic adjustment parameters; Performing operating - point positioning processing on the inverter dynamic adjustment parameters to obtain an optimal operating point control quantity.

6. The double - layer MPPT control strategy method for a photovoltaic system according to claim 1, characterized in that, Performing hierarchical collaborative processing on the inverter dynamic adjustment parameters and the optimal operating point control quantity to obtain a two - layer control priority sequence and an interactive control instruction set, including: Performing time - constant calculation processing on the inverter dynamic adjustment parameters to obtain the first - layer and second - layer control time sequences; Perform priority assignment processing on the first-layer and second-layer control time series to obtain a double-layer control priority sequence; Perform data caching processing on the optimal operating point control quantity to obtain operating point control cache data; Perform filtering processing on the operating point control cache data to obtain filtered control data; Perform instruction generation processing on the filtered control data to obtain a basic control instruction set; Perform timing combination processing on the basic control instruction set to obtain an interactive control instruction set.

7. The double-layer MPPT control strategy method of the photovoltaic system according to claim 1, characterized in that The operation state evaluation calculation of the double-layer control priority sequence and the interactive control instruction set to obtain a control parameter correction amount and an optimization adjustment instruction includes: Perform operation state scanning processing on the double-layer control priority sequence to obtain a controller state parameter set; Perform execution result acquisition processing on the interactive control instruction set to obtain an instruction execution parameter set; Perform deviation statistics processing on the controller state parameter set to obtain a state deviation data set; Perform effect evaluation processing on the instruction execution parameter set to obtain an instruction execution evaluation value; Perform correction calculation processing on the state deviation data set to obtain a control parameter correction amount; Perform threshold judgment processing on the instruction execution evaluation value to obtain an optimization adjustment instruction.

8. A double-layer MPPT control strategy device for a photovoltaic system, which is used to implement the double-layer MPPT control strategy method for the photovoltaic system as described in any one of claims 1-7, characterized in that, The double-layer MPPT control strategy device of the photovoltaic system includes: An acquisition module for performing acquisition processing on the working voltage, current parameters of the photovoltaic module and the input and output parameters of the controller to obtain an initial voltage-current reference data matrix and a controller basic parameter set; A tracking module for performing first-layer maximum power point tracking calculation on the initial voltage-current reference data matrix by the conductance increment method to obtain a component power tracking data set and a dynamic boost ratio data set; An analysis module for performing characteristic analysis processing on the component power tracking data set and the dynamic boost ratio data set under different load rates to obtain an efficiency-boost ratio characteristic curve and an optimal boost ratio range value; An operation module for performing second-layer optimal boost ratio tracking operation on the efficiency-boost ratio characteristic curve and the optimal boost ratio range value by a variable step size adaptive algorithm to obtain inverter dynamic adjustment parameters and an optimal operating point control quantity; A processing module for performing hierarchical collaborative processing on the inverter dynamic adjustment parameters and the optimal operating point control quantity to obtain a double-layer control priority sequence and an interactive control instruction set; A calculation module for performing operation state evaluation calculation on the double-layer control priority sequence and the interactive control instruction set to obtain a control parameter correction amount and an optimization adjustment instruction.

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