A flexible perovskite battery-based wind-solar integrated generator control method

Abstract

The present application relates to the application technical field of perovskite photovoltaic cell, in particular to a wind-solar integrated generator control method based on flexible perovskite cell, through fixing master-slave flexible perovskite cell pieces in the blade rigidity, the master cell piece collects open circuit voltage, measures the rotation period and establishes MPPT model, the fixed phase difference of the slave cell piece and the master cell piece is obtained by using cross-correlation analysis, and the best working point of the slave cell piece is deduced; the model adaptation environment and cell attenuation are calibrated regularly, and fault anomaly detection alarm is realized synchronously. The present application only needs to run 1 group of master MPPT algorithm, reduces the calculation amount, and is fast in response, high in stability, ensures that all cell pieces always work at the maximum power point, and improves the power generation efficiency.

Description

Technical Field

[0001] This invention belongs to the field of perovskite photovoltaic cell application technology, specifically relating to a control method for a wind-solar integrated generator based on flexible perovskite cells. Background Technology

[0002] In the field of new energy power generation, photovoltaic power generation is widely used due to its clean and sustainable characteristics, and maximum power point tracking (MPPT) control technology is one of the core technologies to ensure the power generation efficiency of photovoltaic systems. In existing photovoltaic power generation systems, battery modules are usually installed with a fixed orientation. The rate of change of incident irradiance power caused by sunrise and sunset is relatively slow. This change process gives traditional MPPT controllers sufficient response time, enabling them to match the output impedance of the battery modules with the input impedance of the energy storage device by adjusting the output impedance of the battery modules, thereby capturing the maximum power point of the battery modules and ensuring stable and efficient system output.

[0003] With the development of integrated new energy utilization technologies, wind-solar integrated generators have gradually become a research hotspot. These devices integrate flexible perovskite solar cell modules onto wind-driven rotors. While the rotor is driven by wind energy, photovoltaic power generation is achieved through the flexible perovskite solar cell modules on the rotor, simultaneously utilizing wind and solar energy to improve energy efficiency. However, the structural characteristics of wind-solar integrated generators present key challenges to the MPPT control of their photovoltaic components, which cannot be solved by existing technologies. Because the rotor rotates at high speed driven by wind, the solar irradiance received by the flexible perovskite solar cell module on the rotor changes rapidly with the rotation cycle. According to actual operating condition tests, this rate of irradiance change can reach 5-120 cycles per minute, depending on the wind strength. This rate of change far exceeds the response capability of traditional MPPT controllers. If the traditional MPPT control scheme continues to be used, the controller will be unable to capture the dynamic maximum power point generated by the rotation of the flexible perovskite solar cell module on the rotor in a timely manner. This leads to a lag in control parameter adjustment, ultimately causing system output imbalance and an inability to maintain operation at the maximum power point, severely limiting the overall power generation efficiency of the wind-solar integrated generator.

[0004] In view of this, the present invention is hereby proposed. Summary of the Invention

[0005] To address the aforementioned technical problems in existing technologies, this invention provides a control method for a wind-solar integrated generator based on flexible perovskite solar cells. This method solves the problems of existing wind-solar integrated generators where multiple perovskite solar cells attached to the wind turbine blades are subject to the combined effects of periodic changes in output power caused by blade rotation, spatial phase differences between the cells, fluctuations in blade speed, and changes in the perovskite solar cell's maximum power point voltage with varying light intensity, exhibiting both long-term attenuation and short-term hysteresis. Traditional independent MPPT control of multiple cells requires the simultaneous execution of multiple algorithms, resulting in high computational load, slow response, and potential conflicts between algorithmic disturbances and light intensity changes, making it unable to stably adapt to dynamic operating conditions.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows: A control method for a wind-solar integrated generator based on flexible perovskite solar cells, comprising: S1. At least two sets of flexible perovskite solar cells are attached to the surface of the wind turbine blade, one set is defined as the reference solar cell and the rest are slave solar cells. All solar cells are rigidly fixed to the blade and their relative spatial positions remain unchanged. S2. During system initialization, the main battery cell disconnects from the load to collect the open-circuit voltage, calculates the initial operating voltage based on a preset coefficient, and simultaneously measures the blade rotation period. S3. The main battery cell establishes an MPPT operating point model within the rotation cycle through mapping. The MPPT operating point model reflects the functional relationship between the maximum power point voltage and the blade rotation angle. S4. By comparing the power output curve of the reference cell with the power output curve of each slave cell through the cross-correlation analysis algorithm, the fixed spatial phase difference between each slave cell and the reference cell is calculated. Based on the phase difference and the MPPT operating point model of the reference cell, the current optimal operating point of the slave cell is derived in real time. S5. Periodically trigger the calibration mechanism. At the optimal phase point of illumination, disconnect the reference cell from the load and remeasure. Compare the newly measured maximum voltage with the original reference value in the model and scale the entire value accordingly. The model curves are used to simultaneously correct the control parameters of all cells to adapt to the degradation characteristics of perovskite cells and changes in ambient light, so that all cells can work at their respective maximum power points at the same time.

[0007] Furthermore, it also includes: S6. Automatic Fault and Anomaly Detection and Control: Single-point initial screening: Multiple measurements are performed on a single solar cell, and the extreme values ​​in the measurements are eliminated using the 3σ principle to obtain preliminary effective values; Lateral comparison: Define a neighbor group for the cell to be calibrated and calculate the power change rate of the cell. The specific formula is as follows:

[0008] The average power change rate of the neighboring group is given by the following formula:

[0009] The specific formula for calculating the difference in the rates of change between the two is as follows:

[0010] like Exceeding the preset threshold If the measurement value of the cell is affected by local interference, it is determined that the cell is removed; if the change rate of the neighboring groups is abnormal but the change rate of the current cell is consistent with that of the neighboring groups, it is determined to be a systemic environmental change. Integrated decision-making and alarm: When a certain solar cell is identified as having abnormal data in the horizontal comparison three times in a periodic calibration, a precise alarm is triggered, indicating the location information of the faulty solar cell.

[0011] Furthermore, the method for establishing the MPPT operating point model based on the reference solar cell in step S3 is one or a combination of the following: Independent sensor mode is adopted: a part of the reference cell is independently connected to the computing module. The blade rotation period T and open circuit voltage Voc are measured in real time through sensors. Combined with the corresponding list established in advance, the MPPT operating point of the reference cell is controlled. Periodic mapping mode is adopted: When the blade speed is low and the output power changes slowly, a preset rotation period is used, and the reference cell is mapped using an improved perturbation observation method. curve; A real-time dynamic perturbation mode is adopted: when the blade speed is extremely high and the output power fluctuation frequency exceeds the response capability of the MPPT circuit, a simplified MPPT model is established based on the constant voltage method of the reference cell with average illumination, or a small-step perturbation observation method is run and the results are shared with the slave cell in real time.

[0012] Furthermore, when using a periodic mapping mode without independent sensors, the startup operation of the reference cell includes: The module begins acquiring voltage parameters. At this time, the output is turned off to acquire the open-circuit voltage. According to the test list, the reference cell output operating voltage is controlled to be the MPPT maximum power point voltage. After running a complete rotation cycle, the highest point voltage within the cycle and the voltage value of the entire cycle are obtained. This is used as the basic data for establishing the MPPT operating point model.

[0013] Furthermore, step S4, which derives the optimal operating point of the solar cell based on the phase difference and MPPT operating point model, includes: The processing module monitors the current moment and corresponding optimal operating voltage of the reference cell in real time. Based on the fixed spatial phase difference, it calculates the power cycle stage of the slave cell at that moment. By querying the variation pattern of the MPPT voltage of the reference cell within a cycle, it directly outputs the preset optimal control point for the slave cell.

[0014] Furthermore, the triggering conditions and functions of the mode include: Start-up and initialization mode: Suitable for system power-on or wake-up at any speed. The initial operating point is determined by acquiring the open-circuit voltage of the reference cell and the blade rotation period is measured. Normal operating mode: Select any or a combination of the above-mentioned model establishment methods according to the blade speed to realize the synchronous execution of MPPT tracking of the reference cell and derivation from the cell operating point; Periodic recalibration mode: Triggered every 30 minutes or when a significant step change in ambient light is detected, this mode synchronously calibrates all control references from the solar cells by updating the MPPT operating point model of the reference cell.

[0015] Furthermore, when the blade rotation speed changes, the system only needs to measure the current rotation speed and adjust the scaling of the time axis, without having to recalculate the fixed spatial phase difference between each slave cell and the reference cell, thus maintaining the phase relationship and the effectiveness of the MPPT operating point model. At extremely low rotational speeds, the reference cell is mapped to the MPPT operating point model in a near-static manner to accommodate the large differences in output power between cells; at extremely high rotational speeds, a simplified MPPT model is established for the reference cell based on the average output power.

[0016] Furthermore, the optimal phase point for illumination in step S5 is the point of strongest sunlight within the output power cycle of the reference solar cell, and the calibration process also includes: The newly measured maximum open-circuit voltage is compared with the standard light intensity voltage list to calibrate the parameters of the current control algorithm and ensure that the MPPT operating point model matches the actual lighting environment.

[0017] Furthermore, when establishing the MPPT operating point model in step S3, the characteristics adapted to perovskite solar cells include: Based on the correlation that the maximum power point voltage Vmpp of perovskite solar cells increases slightly with increasing light intensity G, a correlation compensation parameter between Vmpp and G is added to the model. To address the long-term degradation characteristics and short-term hysteresis effect of perovskite solar cells, the baseline values ​​of the MPPT operating point model are periodically offset during the recalibration phase to ensure the long-term effectiveness of the model.

[0018] Compared with existing technologies, the present invention provides a method that rigidly fixes at least two sets of flexible perovskite solar cells to the wind turbine blades, defining one set as the reference cell and the rest as slave cells. During initialization, the reference cell collects the open-circuit voltage and calculates the initial operating voltage, while simultaneously measuring the blade rotation period. The reference cell establishes an MPPT model reflecting the relationship between Vmpp and rotation angle within the rotation period using independent sensors, periodic mapping, or real-time dynamic disturbance mode. Cross-correlation analysis is used to obtain the fixed spatial phase difference between the slave cells and the reference cell, and the optimal operating point of the slave cells is derived based on this phase difference and the model. Simultaneously, fault and anomaly detection and accurate alarm are achieved through single-point initial screening and lateral comparison. This invention only requires running one set of master MPPT algorithms, reducing computational load and providing fast response and high stability, ensuring that all cells always operate at their maximum power point, thus improving power generation efficiency. Attached Figure Description

[0019] Figure 1 The battery cells attached to the wind turbine impeller are provided in the embodiments of the present invention; Figure 2 This is a schematic diagram of the control principle of the integrated wind and solar generator provided in an embodiment of the present invention. Detailed Implementation

[0020] The technical solution of the present invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0021] It should be noted that, unless otherwise specifically stated, the relative arrangement and numerical expressions of the components and steps described in these embodiments should not be construed as limiting the scope of the invention.

[0022] The following description of exemplary embodiments is merely illustrative and is not intended to limit the invention or its application or use in any way. Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail herein, but where applicable, such techniques, methods, and apparatus should be considered part of this specification.

[0023] Example 1 This invention proposes a control method for a wind-solar integrated generator based on flexible perovskite solar cells, see reference [link to relevant documentation]. Figure 2 Specific steps may include: S1, see reference Figure 1 At least two sets of flexible perovskite solar cells are attached to the surface of the wind turbine blades, one set being defined as the reference cell and the others as slave cells. All cells are rigidly fixed to the blades and their relative spatial positions remain unchanged; specifically including: Hardware preparation: Four sets of flexible perovskite solar cells were selected. The output terminals of each set of perovskite solar cells were connected to one independent AD sampling module (current), one independent AD sampling module (voltage), and one independent output control module. The output terminals of all output control modules were ultimately connected to the output interface for transmitting electrical energy. The signal acquisition terminals of the four sets of perovskite solar cells were all connected to the same processing module to ensure the consistency of data acquisition and control command transmission.

[0024] Attachment and Definition: Four sets of flexible perovskite solar cells are fixed to the surfaces of three wind turbine blades using a high-temperature resistant rigid adhesive. Specifically, two sets are attached to blade 1, one set to blade 2, and one set to blade 3. A high-temperature resistant adhesive is chosen to accommodate potential temperature changes on the blade surface during wind turbine operation, preventing cell detachment or displacement due to temperature variations and ensuring that the relative positions of all cells and blades remain fixed throughout wind turbine operation. The set of cells at the root of blade 1 is defined as the reference cell, also known as the main cell; the remaining three sets are designated as secondary cell 1 (located at the tip of blade 1), secondary cell 2 (located on blade 2), and secondary cell 3 (located on blade 3).

[0025] S2. During system initialization, the main battery cell disconnects from the load and the open-circuit voltage is collected. Based on preset coefficients Calculate the initial operating voltage and simultaneously measure the blade rotation period; specifically including: Once the system completes power-on or is awakened from hibernation, the processing module will automatically trigger the startup and initialization mode. This mode is applicable to any current speed, eliminating the need to pre-determine the speed range and ensuring the system can complete initialization normally under any startup scenario.

[0026] S21. Load Disconnection and Voc Acquisition: The processing module sends a load disconnection command to the output control module corresponding to the reference cell, causing the reference cell to disconnect from the entire power generation circuit. Simultaneously, the processing module sends an acquisition command to the AD sampling module (voltage) corresponding to the reference cell. The AD sampling module (voltage) continuously acquires the open-circuit voltage of the reference cell multiple times, taking the average value as the current instantaneous open-circuit voltage, denoted as Voc_instant. In this embodiment, the Voc_instant value obtained after acquisition and calculation is 1.82 volts.

[0027] S22. Initial Operating Voltage Calculation: The processing module pre-stores a "k=Vmpp / Voc coefficient table", where k represents the ratio of the maximum power point voltage Vmpp to the open-circuit voltage Voc. This coefficient table was established by conducting multiple tests on the same type of flexible perovskite solar cell under standard light intensity conditions (1000 watts per square meter). In this embodiment, the preset k value is 0.78. The initial operating voltage is calculated according to the formula Vinit=Voc_instant×k. Substituting Voc_instant=1.82 volts and k=0.78 into the formula, Vinit is approximately 1.42 volts.

[0028] S23. Rotation Cycle Measurement: The processing module simultaneously sends synchronous acquisition commands to the AD sampling modules (current and voltage) corresponding to the reference cell. The two AD sampling modules continuously acquire data for a sufficient time (ensuring coverage of at least three rotation cycles) and transmit the acquired current value (denoted as I) and voltage value (denoted as V) to the processing module in real time. The processing module calculates the output power (denoted as P) corresponding to each sampling point according to the formula P=V×I. By analyzing the obtained power curve, the time interval between two adjacent power peaks is found; this time interval is the current blade rotation cycle, denoted as T. In this embodiment, the calculated rotation cycle T is 8.5 seconds.

[0029] S24. Initial Operating Point Setting: The arithmetic processing module pre-stores a "k=Vmpp / Voc coefficient table", where k represents the ratio of the maximum power point voltage Vmpp to the open-circuit voltage Voc. This coefficient table was established by conducting multiple tests on the same type of flexible perovskite solar cell under standard light intensity conditions (1000 watts per square meter). In this embodiment, the preset k value is 0.78. The initial operating voltage is calculated according to the formula Vinit=Voc_instant×k. Substituting Voc_instant=1.82 volts and k=0.78 into the formula, Vinit is approximately 1.42 volts.

[0030] S3. The main battery cell establishes an MPPT operating point model within the rotation cycle through mapping. The MPPT operating point model reflects the functional relationship between the maximum power point voltage and the blade rotation angle. S31, Independent Sensor Mode S311. A separate signal acquisition line is drawn from the non-power generation area of ​​the reference solar cell. This non-power generation area does not participate in energy output and is only used for data acquisition. The signal acquisition line is connected to the arithmetic processing module to form an independent detection loop. This loop is equipped with one light intensity sensor (used to detect the light intensity of the current environment in real time, denoted as G) and one rotation speed sensor (used to monitor the rotation angle of the blade in real time, denoted as θ). S312, the processing module controls the sensor in the independent detection loop to continuously collect data, and at the same time controls the AD sampling module (current) and AD sampling module (voltage) corresponding to the reference cell to synchronously collect the current value I and voltage value V data; S313. Within one complete rotation cycle (i.e., T = 8.5 seconds), for each set of data ("light intensity G, rotation angle θ, voltage V, current I") collected, the processing module calculates the corresponding power P according to the formula P = V × I, and determines the maximum power point voltage Vmpp under the current θ by combining the rotation angle θ in the data set, gradually establishing the Vmpp(θ) model. Example of model data obtained in this embodiment: Vmpp = 1.45 volts when θ = 0 degrees (directly facing the sun), Vmpp = 1.12 volts when θ = 90 degrees, Vmpp = 1.43 volts when θ = 180 degrees, and Vmpp = 1.10 volts when θ = 270 degrees.

[0031] S32, Periodic Mapping Mode S321. When the system determines that the blade speed is low and the output power changes slowly, the model is triggered to be re-mapped at each preset rotation cycle (e.g., 10 rotation cycles). S322. If the periodic mapping mode without independent sensors is adopted, the startup operation is as follows: The module starts to collect voltage parameters. At this time, the output is turned off to collect the open circuit voltage Voc. According to the test list, the reference cell output working voltage is controlled to be the MPPT maximum power point voltage. After running a complete rotation cycle, the highest point voltage in the cycle (corresponding to the strongest point of sunlight, which can determine the relationship ratio with the standard light intensity) and the voltage value of the entire cycle are obtained as the basic data for model establishment. S323. The reference cell uses an improved perturbation observation method to plot the Vmpp(θ) curve. The perturbation step size and period of this method are synchronized with the blade rotation angle (rather than a fixed time interval) to ensure plotting accuracy.

[0032] S33, Real-time Dynamic Disturbance Mode S331. When the blade speed is extremely high and the output power fluctuation frequency exceeds the response capability of the MPPT circuit, the battery output tends to be averaged. S332. A simplified MPPT model is established for the reference cell based on the constant voltage method (CV) of average illumination, or the average value of Voc measured by the main cell is used as the set benchmark; a small-step perturbation observation method can also be run, which requires extremely fast decision speed and real-time sharing of results with the slave cells.

[0033] S32, Perovskite Properties Adaptation S321. Regarding the characteristic of perovskite solar cells that "Vmpp increases slightly with increasing light intensity G": the calculation and processing module adds "the correlation compensation parameter between Vmpp and G" to the Vmpp(θ) model. If the real-time G is 10% higher than the standard light intensity (1000 watts per square meter), then the Vmpp at the corresponding θ is corrected by a ratio of 1.02 (e.g., when G=1100 watts per square meter, the Vmpp at θ=0 degrees is corrected from 1.45 volts to 1.48 volts). S322. Regarding the "long-term degradation characteristics and short-term hysteresis effect" of perovskite solar cells: The computational processing module reserves a "degradation correction factor" in the Vmpp(θ) model, with an initial value of 1.0. During subsequent periodic calibration stages, this factor is updated based on the difference between the actual measured value and the theoretical value of the model to correct the Vmpp offset.

[0034] S4. By comparing the power output curve of the reference cell with the power output curve of each slave cell through the cross-correlation analysis algorithm, the fixed spatial phase difference between each slave cell and the reference cell is calculated. Based on the phase difference and the MPPT operating point model of the reference cell, the current optimal operating point of the slave cell is derived in real time. S41. Calculation of fixed spatial phase difference: S411, the arithmetic processing module controls 3 sets of AD sampling modules (current) and AD sampling modules (voltage) corresponding to each battery cell to synchronously collect data with the AD sampling module of the reference battery cell, and continuously collect data for one complete rotation cycle (i.e. 8.5 seconds).

[0035] S412 The arithmetic processing module calculates the power output curve of the reference cell (denoted as Pmaster(t), where t represents time) and the power output curves of the three slave cells (denoted as Pslave1(t), Pslave2(t), and Pslave3(t)) based on the collected current and voltage data.

[0036] S413. The arithmetic processing module calls the "cross-correlation analysis algorithm" to compare the power output curve of the reference cell with the power output curves of each group of slave cells, determine the time difference between the peak value of the slave cell's power curve and the peak value of the reference cell's power curve, and then convert the time difference into an angle difference based on the rotation period T. This angle difference is the fixed spatial phase difference between the slave cell and the reference cell. In this embodiment, the calculated phase differences are: Δφ1 = 90 degrees between slave cell 1 and the reference cell, Δφ2 = 180 degrees between slave cell 2 and the reference cell, and Δφ3 = 270 degrees between slave cell 3 and the reference cell. This phase difference data is stored in the cache of the arithmetic processing module and is valid indefinitely.

[0037] S42. Speed ​​Adaptation: During system operation, if the fan speed changes (e.g., the rotation period T changes from 8.5 seconds to 10 seconds), the processing module only needs to measure the current rotation period through the speed sensor and then adjust the time axis accordingly. There is no need to recalculate the fixed spatial phase difference between the solar cells and the reference solar cell, and the validity of the Vmpp(θ) model remains unaffected. Specifically, at extremely low speeds, the reference solar cell is mapped to the MPPT operating point model in a near-static manner to accommodate the large differences in output power between cells; at extremely high speeds, the reference solar cell establishes a simplified MPPT model based on the average output power.

[0038] S43, Real-time control from battery cells: S431 The computation and processing module uses a high-performance MPPT algorithm (such as the adaptive conductivity incremental method) to fully track the maximum power point of the reference cell. This process is real-time and involves a large amount of computation.

[0039] S432. The processing module monitors the current time t of the reference solar cell in real time (t=4.25 seconds in this embodiment) and determines the current rotation angle θ of the reference solar cell through the rotation sensor. Substituting t=4.25 seconds and T=8.5 seconds into the angle calculation formula θ=(t / T)×360 degrees, we get θ=180 degrees. The processing module queries the pre-established Vmpp(θ) model to find the maximum power point voltage corresponding to θ=180 degrees, that is, the current optimal operating voltage of the reference solar cell Vmpp_master(t)=1.43 volts.

[0040] S433. Based on the previously calculated fixed spatial phase difference, calculate the current rotation angle of each group of solar cells: θ1 = 180 degrees + Δφ1 = 270 degrees from solar cell 1, θ2 = 180 degrees + Δφ2 = 360 degrees (corresponding to 0 degrees) from solar cell 2, and θ3 = 180 degrees + Δφ3 = 450 degrees (corresponding to 90 degrees) from solar cell 3.

[0041] S434. The calculation and processing module queries the Vmpp(θ) model again to find the maximum power point voltage corresponding to θ1=270 degrees, θ2=0 degrees, and θ3=90 degrees, respectively, and obtains the optimal operating voltage of the battery cells: Vmpp_slave1=1.10 volts for battery cell 1, Vmpp_slave2=1.45 volts for battery cell 2, and Vmpp_slave3=1.12 volts for battery cell 3.

[0042] S445, the arithmetic processing module sends instructions to the output control modules corresponding to the three sets of slave cells to control each slave cell to work at its corresponding optimal operating voltage, thereby realizing MPPT control of the slave cells (the slave cells do not need to run the complete MPPT algorithm, but can do so by looking up a table and phase prediction).

[0043] S5. Periodically trigger the calibration mechanism. At the optimal phase point of illumination, disconnect the load from the reference cell and remeasure. Compare the newly measured maximum open-circuit voltage with the original reference value in the model. Scale the entire Vmpp(θ) model curve proportionally and synchronously correct all control parameters of the cells to adapt to the degradation characteristics of perovskite cells and changes in ambient illumination, so that all cells can work at their respective maximum power points simultaneously.

[0044] The maximum open-circuit voltage is the maximum voltage value measured without any load.

[0045] S51, Calibration trigger conditions: S511, Timed Trigger: The arithmetic processing module integrates a timer to trigger a calibration operation every 30 minutes.

[0046] S512, Event Trigger: When the change in the open-circuit voltage of the reference cell collected by the AD sampling module (voltage) exceeds the preset range in a short period of time (determined as a significant step change in illumination), the calculation and processing module immediately triggers the calibration operation.

[0047] S52. Identification of the optimal phase point of illumination: The processing module calls the reference cell power curve Pmaster(t) stored in the most recent rotation cycle, analyzes the peak position of the power curve, and the rotation angle corresponding to the power peak is the "point of strongest sunlight", also known as the "optimal phase point of illumination". In this embodiment, this angle is θ = 0 degrees.

[0048] S53. Open-circuit maximum voltage measurement and model scaling: S531. The processing module sends a command to the output control module corresponding to the reference battery cell to briefly disconnect the load. During the load disconnection period, the module controls the AD sampling module (voltage) corresponding to the reference battery cell to collect the open-circuit voltage at this time, which is recorded as the maximum open-circuit voltage (Voc_max). In this embodiment, the collected Voc_max = 1.88 volts.

[0049] S532, the computation and processing module retrieves the initial reference open-circuit voltage (denoted as Voc_base=1.82 volts) used when establishing the Vmpp(θ) model, and calculates the model scaling ratio according to the formula kscale=Voc_max / Voc_base, which yields kscale to be approximately 1.033.

[0050] S533. The calculation and processing module updates all data in the Vmpp(θ) model according to the calculated scaling ratio, thus completing the scaling adjustment of the entire Vmpp(θ) model.

[0051] S54. Algorithm Parameter Calibration: The computational processing module retrieves data from the internally stored "Standard Light Intensity Voltage List," which records the open-circuit voltage values ​​corresponding to different standard light intensities. The computational processing module compares the maximum open-circuit voltage obtained in this measurement with the data in the list to determine the current ambient light intensity and adjusts the "correlation compensation parameter between Vmpp and G" to ensure that the Vmpp(θ) model is consistent with the actual lighting environment.

[0052] S55. Synchronization of cell parameters: The computation and processing module synchronously sends the updated Vmpp(θ) model data to the corresponding output control modules of the three sets of cells. Each output control module automatically updates its own voltage control reference and completes the synchronous correction of all cell control parameters.

[0053] S6. Automatic detection and control of faults and anomalies, specifically including: S61. Single-point initial screening: The processing module controls the continuous acquisition of voltage values ​​from the AD sampling module (voltage) corresponding to battery cell 2. The 3σ principle is used to eliminate extreme values ​​from the acquired data. The specific steps are: calculate the average value μ and standard deviation σ of the data, determine the 3σ range, identify values ​​outside the range as extreme values ​​and eliminate them, and take the average of the remaining data to obtain the preliminary valid value Vvalid_pre. In this embodiment, Vvalid_pre = 1.45 volts.

[0054] S62, Lateral Comparison: Define a "neighbor group" for the slave cell 2 to be calibrated, and select the reference cell and slave cell 1 that are physically adjacent to slave cell 2 as the neighbor group (if the total number of cells is small, all cells can be selected). The specific formula for calculating the power change from cell 2 is as follows:

[0055] The formula for calculating the average power change rate of neighboring groups is as follows:

[0056] The formula for calculating the difference in power change rate between cell 2 and its neighboring group is as follows:

[0057] The preset consistency threshold ε=2%. If δ<ε, the measurement value from cell 2 is considered valid. If δ>ε, the measurement value is considered to be affected by local interference (such as dust accumulation or shading) and is removed. If the power change rate of all cells in the neighboring group is abnormal, but the change rate of cell 2 is consistent with that of the neighboring group, it is considered to be a systemic environmental change (such as a large cloud passing by). The calculation and processing module suspends the current calibration task and waits for the environment to stabilize before re-execution.

[0058] S63. Comprehensive Decision-Making and Alarm: The calculation and processing module records the judgment results of each calibration of battery cell 2. If three consecutive calibrations are judged as "measurement value is affected by local interference", an abnormal alarm mechanism is triggered. The calculation and processing module sends an alarm message through the output interface, specifically "Warning: Battery cell 2 of wind turbine blade 2 is suspected of failure or severe dust accumulation, inspection recommended", clearly indicating the location of the faulty battery cell and the possible cause of the failure, providing a clear direction for operation and maintenance work.

[0059] In summary, the present invention has the following advantages: 1. Optimization of computational load and hardware cost: There is no need to run multiple sets of high-speed MPPT algorithms for all solar cells. Only one set of high-performance MPPT algorithm (such as the adaptive conductance incremental method) needs to be run for the reference solar cell. The control of the remaining solar cells is achieved through "table lookup + phase prediction", which greatly reduces the performance requirements of the processor and optimizes the system cost, power consumption and operational reliability. 2. Real-time tracking and high-efficiency response: The speed of looking up the table and outputting the preset optimal control point is much faster than completing a full MPPT calculation cycle. It can easily adapt to the high-speed rotation of the blades, with extremely low control delay, and achieve true real-time maximum power point tracking. 3. Improved control stability: Avoids the mutual interference between perturbation operations and the periodic changes in illumination itself in traditional independent MPPT algorithms. By adopting a passive and deterministic predictive control method, the inherent conflicts between algorithms are completely eliminated, and the system operates more stably. 4. Convenient calibration operation: Only the reference cell needs to be calibrated periodically and its MPPT voltage change law model updated. The predictive control benchmark of all slave cells will be automatically updated synchronously. There is no need to calibrate the slave cells separately, making it more adaptable. 5. Precise and efficient fault maintenance: Through the anomaly detection process of "single-point initial screening + horizontal comparison + comprehensive decision-making", false alarms caused by accidental local interference can be avoided. When an alarm is triggered, the location information of the faulty battery cell can be accurately indicated, providing a clear direction for maintenance work.

[0060] The above specific embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A control method for a wind-solar integrated generator based on a flexible perovskite battery, characterized in that, The application relates to a wind turbine blade power generation system based on flexible perovskite solar cells. S1. At least two groups of flexible perovskite solar cells are attached to the surface of a wind turbine blade, one group is defined as a reference cell, and the other groups are defined as slave cells, all the cells are rigidly fixed to the blade and the relative spatial positions are kept unchanged; S2. When the system is initialized, the open-circuit voltage of the master cell is disconnected from the load, the initial working voltage is calculated based on a preset coefficient, and the rotation period of the blade is synchronously measured; S3. The master cell establishes an MPPT working point model in the rotation period by mapping, and the MPTT working point model reflects the functional relationship between the maximum power point voltage and the rotation angle of the blade; S4. The fixed spatial phase difference between each slave cell and the reference cell is calculated by comparing the power output curve of the reference cell with the power output curves of the slave cells through a cross-correlation analysis algorithm, and the current optimal working point of the slave cell is derived in real time based on the phase difference and the MPPT working point model of the reference cell. S5, periodically trigger calibration mechanism, make reference cell piece disconnected load re-measurement at the best phase point of light, compare the newly measured maximum voltage with the original reference value in the model, scale the whole model curve, correct all control parameters from the cell piece at the same time, adapt to the decay characteristics of perovskite cells and environmental light changes, and realize all cell pieces working at their respective maximum power points.

2. The MPPT control method for a wind-solar integrated generator based on a flexible perovskite battery according to claim 1, characterized in that, Further comprising: S6. Fault and anomaly automatic detection and regulation: Single-point preliminary screening: the single cell is measured multiple times, and the limit value in the measured value is removed by adopting the 3sigma principle to obtain a preliminary effective value; Lateral comparison: a neighbor group is defined for the current cell to be calibrated, and the power change rate of the current cell is calculated, the specific formula is: The average power change rate of the neighbor group is calculated, the specific formula is: The difference between the two change rates is calculated, the specific formula is: If exceeds a preset threshold , it is determined that the battery sheet measurement is affected by local interference and is rejected; if the neighbor group change rates are all abnormal but the current battery sheet is consistent with the neighbor group change rate, it is determined as a systematic environmental change; Comprehensive decision and alarm: when a cell is continuously determined as abnormal data in the periodic calibration for three times through lateral comparison, a precise alarm is triggered, and the position information of the faulty cell is indicated. 3.The MPPT control method of a wind-solar integrated generator based on flexible perovskite battery according to claim 1, characterized in that, The reference cell establishes the MPPT working point model in step S3 in one of the following modes or a combination thereof: Independent sensor mode: a part of the reference cell is independently connected to the operation processing module, the rotation period T and the open-circuit voltage Voc of the blade are measured in real time through a sensor, and the MPPT working point of the reference cell is controlled in combination with the corresponding list established through advance testing; Adopting periodic mapping mode: when the blade speed is low and the output power changes slowly, every interval of the preset rotation period, the reference cell adopts the improved perturbance and observation method to map Curve; Real-time dynamic disturbance mode: when the rotation speed of the blade is extremely high and the output power fluctuation frequency exceeds the response capability of the MPTT circuit, the reference cell establishes a simplified MPPT model based on the constant voltage method of average illumination, or runs a small-step disturbance observation method and shares the results in real time with the slave cells.

4. The MPPT control method for a wind-solar integrated generator based on a flexible perovskite battery according to claim 3, characterized in that, When the period mapping mode without independent sensor is adopted, the starting operation of the reference cell comprises the following steps: The module starts collecting voltage parameters, at this time, the output is closed to collect the open-circuit voltage, the output working voltage of the reference cell is controlled to be the MPPT maximum power point voltage according to the test list, after a complete rotation period, the highest point voltage in the period and the voltage value of the whole period are obtained, which are used as the basic data for establishing the MPPT working point model.

5. The method of claim 1, wherein the method is a method of MPPT control of a wind-solar hybrid generator based on a flexible perovskite cell, and the method comprises: In step S4, the optimal working point of the slave cell is derived based on the phase difference and the MPPT working point model, which comprises the following steps: The operation processing module monitors the current time and the corresponding optimal working voltage of the reference cell in real time, calculates the power cycle stage of the slave cell at the time according to the fixed spatial phase difference, and directly outputs the preset optimal control point for the slave cell by querying the change rule of the MPPT voltage of the reference cell in a cycle.

6. The method of claim 1, wherein the method is a method of MPPT control of a wind-solar hybrid generator based on a flexible perovskite cell, and The triggering conditions and functions of the mode comprise: Start-up and initialization mode: suitable for system power-on or wake-up at any speed, determine the initial operating point by collecting open-circuit voltage of reference cell, and measure the blade rotation period; Normal operation mode: according to the blade speed, select any or combination of the model establishment mode to realize reference cell MPPT tracking and synchronous execution derived from the cell operating point; Periodic recalibration mode: triggered every 30 minutes or when significant step changes in ambient light are detected, update the MPPT operating point model of the reference cell, and synchronize the control reference of all slave cells.

7. The method according to claim 1, wherein, When the blade speed changes, the system only needs to measure the current speed and adjust the time axis, without recalculating the fixed spatial phase difference between each slave cell and the reference cell, maintaining the effectiveness of the phase relationship and the MPPT operating point model; Wherein, when the speed is very low, the reference cell measures the MPPT operating point model in a nearly static way, adapting to the characteristics of large output power difference between cells; when the speed is very high, the reference cell establishes a simplified MPPT model based on the average output power. 8.The MPPT control method of a wind-solar integrated generator based on flexible perovskite battery according to claim 1, characterized in that, The optimal phase point of light in step S5 is the strongest point of sunlight in the output power cycle of the reference cell, and the calibration process also includes: Compare the newly measured open-circuit maximum voltage with the standard light intensity voltage list, calibrate the parameters of the current control algorithm, and ensure that the MPPT operating point model matches the actual light environment. 9.The MPPT control method of a wind-solar hybrid generator based on flexible perovskite battery according to claim 1, characterized in that, When establishing the MPPT operating point model in step S3, the characteristics of perovskite cells include: Based on the correlation that the maximum power point voltage Vmpp of perovskite cells increases slightly with the increase of light intensity G, add a related compensation parameter of Vmpp and G in the model; For the long-term decay characteristics and short-term hysteresis effect of perovskite cells, periodically offset the reference value of the MPPT operating point model in the recalibration stage to ensure the long-term effectiveness of the model.

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