Photovoltaic optimization regulation and control method and equipment based on MPPT (Maximum Power Point Tracking) technology

Abstract

The invention discloses a photovoltaic optimization regulation and control method and device based on an MPPT technology, and particularly relates to the technical field of photovoltaic power generation control, and the method specifically comprises the following steps: S1, full-link operation state synchronous collection, S2, DC bus ripple feature extraction, S3, multi-source disturbance quantitative evaluation, S4, cooperative control parameter decision making, and S5, grid-connected signal synthesis modulation. A multi-source disturbance quantitative evaluation system is adopted, the irradiance change gradient, the frequency ratio and the switching noise contribution ratio are subjected to multi-dimensional fusion, a dynamic weight distribution mechanism is established, and parallel optimization of MPPT mode selection, disturbance step length adjustment and PWM phase shift angle is achieved through a cooperative control parameter decision architecture. A response delay bottleneck existing in a traditional serial decision mode is broken through, and a ripple compensation component is dynamically injected while stable output of fundamental current is maintained based on a grid-connected signal modulation strategy of vector synthesis.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of photovoltaic power generation control technology, in particular to a photovoltaic optimization control method and device based on MPPT technology. BACKGROUND

[0002] The field of photovoltaic power generation control technology includes photovoltaic system energy conversion efficiency optimization and grid-connected stability control technology system. The core content of this field involves photovoltaic array maximum power point tracking technology, DC-AC power conversion device control technology, and grid synchronization coordination technology, focusing on solving the problem of photovoltaic system output power fluctuation caused by changes in light intensity, temperature fluctuations, and changes in load characteristics, and realizing power quality optimization by real-time adjustment of switching frequency and duty cycle of power electronic devices.

[0003] Among them, the photovoltaic optimization control method based on MPPT technology refers to sampling the output voltage and current parameters of the photovoltaic array, using a dynamic comparison algorithm to determine the position of the maximum power point, and automatically adjusting the working state of the DC-DC converter according to the change of environmental parameters. This method specifically involves photovoltaic component output characteristic curve scanning technology, power differential calculation technology, and pulse width modulation signal generation technology, and continuously corrects the working parameters of the power converter by establishing a dynamic mathematical model of the photovoltaic array and using gradient optimization.

[0004] The existing photovoltaic control technology relies on time-sharing sampling mechanism to collect node parameters, resulting in inconsistent data timestamps, making it difficult to accurately capture the coupling relationship between light mutation and grid fluctuation. The traditional ripple analysis method only focuses on the characteristics of a single frequency band, and cannot effectively distinguish the composite ripple components caused by MPPT refresh, switching noise and environmental disturbance, resulting in insufficient compensation strategy. The existing MPPT control algorithm uses a fixed step optimization mode, which is prone to power oscillation in rapid irradiance change scenarios, resulting in a loss of about 3%-5% of the annual average power generation. The grid-connected modulation link mostly uses independent control of the fundamental current, lacking an active suppression mechanism for the DC bus ripple energy, resulting in high harmonic content of the grid-connected current, especially in low irradiance conditions, with a total harmonic distortion rate exceeding 5%. In addition, there is a lack of collaborative optimization mechanism between the control modules of the existing system, and there is a time sequence conflict between MPPT adjustment and harmonic compensation, which aggravates power fluctuation and equipment loss. SUMMARY

[0005] The main purpose of the present application is to provide a photovoltaic optimization control method and device based on MPPT technology, which can effectively solve the problem of traditional grid-connected modulation that only controls the fundamental current and ignores the DC bus ripple energy.

[0006] To achieve the above purpose, the technical scheme adopted by the present application is:

[0007] A photovoltaic optimization control method and device based on MPPT technology, comprising the following steps: S1, full-link operation state synchronous acquisition: collecting the voltage and current of the photovoltaic array and the DC bus capacitor and the solar irradiance, and establishing full-link synchronous state data after assigning a unified time stamp;

[0008] S2, DC bus ripple feature extraction: calling the DC bus voltage in the full-link synchronous state data, parallelly feeding into low, medium and high frequency processing channels, calculating the instantaneous amplitude and phase angle of the ripple in each channel, and generating a DC bus ripple feature matrix;

[0009] S3, multi-source disturbance quantitative evaluation: calling the instantaneous value of the solar irradiance in the full-link synchronous state data, calculating the maximum irradiance change gradient, calling the frequency ratio and switch noise contribution ratio calculated from the DC bus ripple feature matrix, integrating the three results, and establishing a multi-source disturbance quantitative index;

[0010] S4, collaborative control parameter decision: according to the multi-source disturbance quantitative index, parallelly deciding the MPPT mode selection, disturbance step adjustment and PWM phase shift angle calculation, and collaboratively optimizing the control instruction set of the three decisions;

[0011] S5, grid-connected signal synthesis modulation: calling the collaborative optimization control instruction set to determine the fundamental current, generating a compensation current according to the DC bus ripple feature matrix, and inputting the two into a PWM module for modulation after vector summation, and outputting a grid-connected modulation signal.

[0012] Preferably, the full-link synchronous state data specifically includes but is not limited to photovoltaic array voltage, photovoltaic array current, DC bus capacitor voltage, DC bus capacitor current, and solar irradiance data.

[0013] Preferably, the DC bus ripple feature matrix specifically refers to low-frequency ripple amplitude and phase characteristics, medium-frequency ripple amplitude and phase characteristics, and high-frequency ripple amplitude and phase characteristics, and the multi-source disturbance quantitative index includes the maximum irradiance change gradient, the frequency ratio, and the switch noise contribution ratio.

[0014] Preferably, the collaborative optimization control instruction set specifically includes MPPT mode selection parameters, disturbance step adjustment parameters, and PWM phase shift angle parameters, and the grid-connected modulation signal includes a fundamental current component, a ripple compensation component, and a PWM modulation waveform.

[0015] Preferably, in S2, the following sub-steps are specifically included:

[0016] S2a: calling the DC bus voltage signal in the full-link synchronous state data, processing the low-frequency channel with a Butterworth low-pass filter with a cutoff frequency of 1 kHz, calculating the sliding window RMS amplitude A low , and obtaining the phase Generate low-frequency ripple characteristics;

[0017] S2b: Based on the DC bus voltage signal, use 1kHz-5kHz band-pass filter to process the medium-frequency channel, calculate the sliding window RMS amplitude A mid , use Hilbert transform method to obtain the phase Generate medium-frequency ripple characteristics;

[0018] S2c: Call the DC bus voltage signal, use 5kHz-10kHz band-pass filter to process the high-frequency channel, calculate the sliding window RMS amplitude A high , use Hilbert transform method to obtain the phase Align the low-frequency ripple characteristics, medium-frequency ripple characteristics and high-frequency characteristics according to the time stamp to generate the DC bus ripple characteristic matrix.

[0019] Preferably, in the S3, the following sub-steps are specifically included:

[0020] S3a: Call the solar irradiance sequence in the full-link synchronous state data, perform difference operation on 20000 sampling points in a continuous 1 second time window, calculate the gradient of each sampling point, take the absolute value and select the maximum value, and generate the maximum irradiance change gradient;

[0021] S3b: Based on the DC bus ripple characteristic matrix, perform FFT transform after applying Hanning window to the DC bus voltage signal, search for the highest point of amplitude-frequency characteristic in the range of 0.1Hz-10kHz, call the MPPT refresh frequency, calculate the frequency ratio, and generate the frequency ratio;

[0022] S3c: Call the inverter switching frequency, extract the amplitude at the switching frequency and the double-frequency amplitude in the FFT amplitude-frequency characteristic, obtain the low-frequency band ripple amplitude, medium-frequency band ripple amplitude and high-frequency band ripple amplitude from the DC bus ripple characteristic matrix, calculate the switching noise contribution ratio, integrate the maximum irradiance change gradient, the frequency ratio and the switching noise contribution ratio, and generate the multi-source disturbance quantization index.

[0023] Preferably, in the S4, the following sub-steps are specifically included:

[0024] S4a: Call the maximum irradiance change gradient in the multi-source disturbance quantization index, compare it with the preset threshold value, and when it is greater than the preset threshold value, trigger the golden section mode, set the ±5% interval of the current voltage, calculate V1 and V2, and generate the golden section test voltage point;

[0025] S4b: Based on the frequency ratio in the multi-source disturbance quantization index and the judgment threshold value 0.3 of the frequency ratio R f , when it is greater than the judgment threshold value, call the basic disturbance step, calculate 1.5 times the disturbance step, and generate the updated disturbance step;

[0026] S4c: calling the switch noise contribution ratio in the multi-source disturbance quantization index, and the switch noise contribution ratio R sw compared with the determination threshold 0.4, when not greater than the threshold, the phase shift compensation is not triggered, the golden section test voltage point, the disturbance step length and the default phase shift angle θ=0° are combined to generate the cooperative optimization control instruction set.

[0027] The application further provides a regulation device, characterized in that: comprising a processor and a memory, the memory is used for storing a program, and the processor executes the program stored in the memory, so that the regulation device implements the photovoltaic optimization regulation method based on the MPPT technology according to any one of claims 1-7.

[0028] Compared with the prior art, the application has the following beneficial effects:

[0029] The application realizes the space-time alignment of the electrical parameters and irradiance data of key nodes such as photovoltaic arrays and DC bus capacitors through the full-link state synchronous acquisition technology, effectively eliminates the data phase difference problem caused by the traditional time-sharing sampling. Based on the multi-frequency band decomposition method of the DC bus ripple feature matrix, the instantaneous amplitude and phase characteristics of different frequency band ripple components can be accurately identified, providing high-resolution data support for subsequent disturbance source positioning. A multi-source disturbance quantization evaluation system is used to multi-dimensionally integrate the irradiance change gradient, frequency ratio and switch noise contribution ratio, establish a dynamic weight distribution mechanism, and significantly improve the response accuracy of the system to the composite disturbance. Through the cooperative control parameter decision architecture, the parallel optimization of the MPPT mode selection, disturbance step length adjustment and PWM phase shift angle is realized, breaking through the response delay bottleneck existing in the traditional serial decision mode. Based on the grid-connected signal modulation strategy of vector synthesis, while maintaining the stable output of the fundamental current, the ripple compensation component is dynamically injected, so that the total harmonic distortion rate of the grid-connected current is reduced by about 40%, and the overall efficiency of the system is improved to 98.2%. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 It is a whole flowchart of the application. DETAILED DESCRIPTION

[0031] In order to make the technical means, creative features, purposes and effects realized by the application easy to understand, the application is further described below in combination with specific embodiments.

[0032] As shown in Figure 1 The application provides a photovoltaic optimization regulation method based on the MPPT technology, comprising the following steps:

[0033] S1, Synchronization acquisition of system full-link operation state: In the 50 μs control cycle, the output voltage and current of the photovoltaic array, the instantaneous value of solar irradiance, the instantaneous value of the DC bus capacitor voltage, and the grid-connected output current of the inverter are collected, and a unified timestamp is assigned to all collected items to establish full-link synchronous state data;

[0034] In the 50 μs control cycle, the output voltage and current of the photovoltaic array are collected, the voltage signal V pv and the current signal I pv are obtained using a Hall sensor, the sampling frequency is set to 20 kHz, the G value (instantaneous value of solar irradiance) output by the irradiance sensor is synchronously collected, and the RS485 communication protocol is used to transmit to the main control unit. The DC bus capacitor voltage V dc is measured by a differential probe, the inverter grid-connected current I grid is obtained by a current transformer, and all sensor signals are synchronously sampled by an AD7606 analog-digital conversion chip. The timestamp alignment accuracy is controlled within ±100 ns. For example, when the photovoltaic array output voltage suddenly increases by 2V, V pv , the sampling value is collected 3 times redundantly in the 50 μs cycle, and the median is taken as the effective value. The irradiance data G is subjected to 5 times sliding average filtering in the same cycle to eliminate instantaneous cloud cover interference. The DC bus voltage V dc is collected with RC low-pass filtering, and the cutoff frequency is set to 1 kHz to suppress switching noise. The grid-connected current I grid is subjected to 50 Hz power frequency extraction using an FIR digital filter. After timestamp alignment, the data of each channel generates a structured array containing timestamp field, V pv , I pv , G, V dc , I grid , etc. For example, when the timestamp is marked as T0, V pv = 320.5V, I pv = 8.2A, G = 850W / m 2 , V dc = 600.3V, I grid = 10.1A, the data packet is transmitted to the data processing unit through the CAN bus to complete the construction of full-link synchronous state data.

[0035] S2, Frequency band decomposition of DC bus voltage disturbance source: Call (V dc ) in full-link synchronous state data, and send it to 0.1 Hz to 1 kHz, 1 kHz to 5 kHz, and 5 kHz to 10 kHz three digital signal processing channels. Calculate the instantaneous amplitude and instantaneous phase angle of the ripple of each channel to generate a DC bus ripple feature matrix.

[0036] Call Vdc The signal is divided into frequency bands by using a FIR digital filter set. A Butterworth low-pass filter with a cutoff frequency of 1 kHz is configured for a low-frequency channel, a band-pass filter of 1 kHz-5 kHz is configured for a medium-frequency channel, and a band-pass filter of 5 kHz-10 kHz is configured for a high-frequency channel. The order of each filter is set to 8. The sliding window RMS algorithm is used to calculate the amplitude A low of the 0.1 Hz-1 kHz signal in the low-frequency channel. mid The same RMS window is used to calculate the amplitude A high of the 1 kHz-5 kHz signal in the medium-frequency channel. band The amplitude A filtered of the 5 kHz-10 kHz signal is calculated in the high-frequency channel.

[0037] The sliding window RMS algorithm is used to calculate the ripple instantaneous amplitude, and the formula is as follows:

[0038]

[0039] wherein A band represents the dynamic amplitude of the frequency band (unit: V), V filtered [i] represents the i-th filtered voltage sampling value (unit: V), N represents the sliding window width (dimensionless, value 10), represents the average voltage in the window (unit: V), and a represents the DC component compensation coefficient (dimensionless, value 0.15), represents the cumulative operation of the N sampling points in the window, and |·| represents the absolute value operation.

[0040] S3, quantitative evaluation of dynamic environment and system disturbance: the instantaneous value of solar irradiance in the full-link synchronous state data is called, the difference operation result of the instantaneous value of solar irradiance in a 1-second time window is monitored, the maximum irradiance change gradient is extracted, the DC bus ripple feature matrix is called, the DC bus voltage fluctuation main frequency is extracted and the ratio of the MPPT refresh frequency is calculated, and the ratio of the sum of the inverter current switching frequency and its double frequency to the total ripple energy of the three frequency bands is analyzed, to obtain a switching noise contribution ratio. The maximum irradiance change gradient, the frequency ratio, and the switching noise contribution ratio are integrated to establish a multi-source disturbance quantitative index.

[0041] Call the full-link synchronous state data, extract the instantaneous value G of solar irradiance in the continuous 1s time window ending at the current time from the data, which contains 20000 sampling points based on the control period of 50us, and perform the difference operation on every two consecutive sampling points G [i] With G [i-1] Perform the difference operation to obtain the instantaneous irradiance change gradient at this time Take the absolute value of all 19999 gradient values calculated in the 1s time window and compare them to filter out the maximum value as the maximum irradiance change gradient

[0042] Then call the DC bus ripple feature matrix and synchronously call the DC bus voltage V dc The original data, apply the Hanning window function to the data sequence to smooth the data at both ends, and then generate the amplitude-frequency characteristic diagram of the voltage through fast Fourier transform, in the frequency range of 0.1Hz to 10KHz, retrieve the frequency point corresponding to the highest amplitude value excluding the DC component, which is determined as the main frequency f of the DC bus voltage fluctuation dom , read the currently effective MPPT refresh frequency f from the system control parameter register mppt , divide f dom by f mppt to obtain the frequency ratio R f At the same time, get the currently used switching frequency f sw from the inverter control unit dc , and accurately find the ripple amplitudes corresponding to the two frequency points f sw and its double frequency in the previously generated V fsw amplitude-frequency characteristic diagram, and the amplitudes obtained are A 2fsw and A low mid high , calculate the switching noise related energy term, calculate the total ripple energy term, divide the switching noise related energy term by the total ripple energy term to obtain the switching noise contribution ratio R sw Integrate the maximum irradiance change gradient, frequency ratio, and switching noise contribution ratio to establish a multi-source disturbance quantification index.

[0043] The above analysis of the sum of the ripple energy at the current switching frequency and its double frequency of the inverter and the ratio of the total ripple energy in three frequency bands uses the formula:

[0044]

[0045] Where, R sw ​​representing the switching noise contribution ratio (dimensionless), representing the ripple amplitude of the frequency band corresponding to the switching frequency (unit: V), representing the ripple amplitude of the frequency band corresponding to the double frequency (unit: V), representing the ripple amplitude of the low frequency, medium frequency and high frequency bands (unit: V), and β represents the amplitude difference compensation coefficient (dimensionless, value 0.25), representing the cumulative operation on the switching frequency and the double frequency, representing the cumulative operation on the three frequency bands, and |·| represents the absolute value operation.

[0046] S4, MPPT and harmonic compensation cooperative control parameter decision: according to the multi-source disturbance quantization index, when the maximum irradiance change gradient exceeds 200W / m 2 ·s, select the golden section search mode, when the frequency ratio is greater than 0.3, set the MPPT basic disturbance step multiplied by 1.5, when the switching noise contribution ratio is greater than 0.4, calculate a phase shift angle applied to the PWM carrier signal generator, and obtain the cooperative optimization control instruction set;

[0047] According to the multi-source disturbance quantization index, read the current maximum irradiance change gradient from the system register and the maximum irradiance change gradient preset threshold 200W / m 2 ·s comparison, when greater than 200W / m 2 ·s, trigger the golden section search mode, set the MPPT search interval to the current working voltage V mppt ±5% range, the golden section ratio coefficient is 0.618, calculate the first test point V1 and the second test point V2, read the frequency ratio R f and the frequency ratio R f determination threshold 0.3, when greater than 0.3, retrieve the basic disturbance step ΔV base from the parameter library, execute ΔV new = ΔV base ×1.5, update the MPPT disturbance step ΔV new , and then obtain the switching noise contribution ratio R sw and the switching noise contribution ratio R sw determination threshold 0.4, when greater than 0.4, calculate the phase shift angle θ, which is applied to the PWM carrier signal generator, and the carrier phases of the three-phase bridge arms are respectively offset by 0°, 42° and 84°, finally combine the golden section test voltage point, the updated disturbance step and the phase shift angle into the control instruction set;

[0048] The above calculation of phase shift angle θ uses the formula:

[0049]

[0050] wherein θ represents the phase shift angle (unit: °), R sw represents the switching noise contribution ratio (dimensionless), ΔV new represents the updated MPPT perturbation step (unit: V), ΔV base represents the basic perturbation step (unit: V), γ represents the step compensation coefficient (dimensionless, value 0.5), |·| represents the absolute value operation.

[0051] S5, optimization and output of the composition of the grid-connected modulation signal: according to the MPPT mode and step in the collaborative optimization control instruction set, the maximum power tracking is performed, the fundamental current instruction is determined, the harmonic compensation current instruction is generated according to the DC bus ripple characteristic matrix, the vector sum of the two is input into the PWM modulation module corrected by the phase shift angle in the instruction set, and the final grid-connected modulation signal is generated;

[0052] According to the collaborative optimization control instruction set, the golden section test voltage points V1 and V2 are extracted from the instruction set, when performing MPPT tracking, the two test voltages are applied to the photovoltaic array respectively, the corresponding power P1 and P2 are measured, the values of P1 and P2 are compared, when P 2大于 P1, the new search interval is adjusted to V1 to the upper limit of the original interval, the golden section point V 1new is recalculated, V 2new , the power difference is continuously iterated until it is less than 5W, the maximum power point voltage V mppt is determined, the fundamental current is calculated according to the voltage value, and the low-frequency phase , the medium-frequency phase , and the high-frequency phase of the current time are extracted from the DC bus ripple characteristic matrix. delay , the low-frequency compensation current, the medium-frequency compensation current amplitude A compmid , and the high-frequency compensation current amplitude A comphigh are calculated, and the harmonic compensation current is synthesized.

[0053] The fundamental current and the harmonic compensation current are vector superimposed to obtain the total current, the total current is input into the PWM modulation module, the module carrier signal has been adjusted in three-phase phase according to the phase shift angle θ=42° in the instruction set, three-phase modulation waves with U-phase carrier phase 0°, V-phase 42° and W-phase 84° are generated, and the duty cycle variable PWM signal is output after comparison, which drives the inverter power device to generate the final grid-connected modulation signal.

[0054] The final grid-connected modulation signal adjusts the duty cycle and phase of the PWM wave to ensure that the output current is synchronized with the grid voltage, and contains the fundamental component to realize energy transmission, and the harmonic compensation component to suppress the harmonic pollution caused by the DC bus ripple.

[0055] The application also provides a regulation device, characterized by comprising a processor and a memory, the memory is used for storing a program, and the processor executes the program stored in the memory, and when the program stored in the memory is executed, the regulation device realizes the photovoltaic optimization regulation method based on the MPPT technology according to any one of claims 1-7.

[0056] The above shows and describes the basic principles and main features of the application and the advantages of the application. Those skilled in the art should understand that the application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only to illustrate the principles of the application, and various changes and improvements can be made to the application without departing from the spirit and scope of the application, and these changes and improvements all fall within the scope of the application claimed. The scope of protection of the application is defined by the appended claims and their equivalents.

Claims

1. A photovoltaic optimization control method based on MPPT technology, characterized in that, Includes the following steps: S1. Synchronous acquisition of full-link operation status: Collect the voltage, current and solar irradiance of the photovoltaic array and DC bus capacitor, and establish full-link synchronous status data after assigning a unified timestamp; S2. DC bus ripple feature extraction: Call the DC bus voltage in the full-link synchronization status data and send it in parallel to the low, medium and high frequency processing channels. Calculate the instantaneous amplitude and phase angle of the ripple in each channel to generate the DC bus ripple feature matrix. S3. Quantitative assessment of multi-source disturbances: Call the instantaneous value of solar irradiance in the full-link synchronization status data, calculate the maximum irradiance change gradient, call the DC bus ripple characteristic matrix to calculate the frequency ratio and the switching noise contribution ratio, integrate the three results, and establish a quantitative index for multi-source disturbances. S4. Cooperative control parameter decision-making: Based on the multi-source disturbance quantification index, the MPPT mode selection, disturbance step size adjustment and PWM phase shift angle calculation are made in parallel, and the three decision results are coordinated to optimize the control instruction set. S5. Grid-connected signal synthesis and modulation: The fundamental current is determined by calling the collaborative optimization control instruction set, and the compensation current is generated based on the DC bus ripple characteristic matrix. The two are vector-summed and then input into the PWM module for modulation, and the grid-connected modulation signal is output.

2. The photovoltaic optimization and control method based on MPPT technology according to claim 1, characterized in that: The full-link synchronization status data specifically includes, but is not limited to, photovoltaic array voltage, photovoltaic array current, DC bus capacitor voltage, DC bus capacitor current, and solar irradiance data.

3. The photovoltaic optimization and control method based on MPPT technology according to claim 1, characterized in that: The DC bus ripple characteristic matrix specifically refers to the low-frequency ripple amplitude and phase characteristics, the mid-frequency ripple amplitude and phase characteristics, and the high-frequency ripple amplitude and phase characteristics. The multi-source disturbance quantification index includes the maximum irradiance change gradient, frequency ratio, and switching noise contribution ratio.

4. The photovoltaic optimization and control method based on MPPT technology according to claim 1, characterized in that: The collaborative optimization control instruction set specifically includes MPPT mode selection parameters, disturbance step size adjustment parameters, and PWM phase shift angle parameters. The grid-connected modulation signal includes fundamental current component, ripple compensation component, and PWM modulation waveform.

5. The photovoltaic optimization and control method based on MPPT technology according to claim 1, characterized in that: S2 specifically includes the following sub-steps: S2a: Call the DC bus voltage signal from the full-link synchronization status data, process the low-frequency channel using a Butterworth low-pass filter with a cutoff frequency of 1kHz, and calculate the sliding window RMS amplitude A. low Phase was obtained using the Hilbert transform method. Generate low-frequency ripple characteristics; S2 b: Based on the DC bus voltage signal, a 1kHz-5kHz bandpass filter is used to process the intermediate frequency channel, and the sliding window RMS amplitude A is calculated. mid Phase was obtained using the Hilbert transform method. Generate mid-frequency ripple characteristics; S2c: Calls the DC bus voltage signal, processes the high-frequency channel using a 5kHz-10kHz bandpass filter, and calculates the sliding window RMS amplitude A. high Phase was obtained using the Hilbert transform method. Align the low-frequency ripple features, mid-frequency ripple features, and high-frequency features according to timestamps to generate a DC bus ripple feature matrix.

6. The photovoltaic optimization and control method based on MPPT technology according to claim 1, characterized in that: S3 specifically includes the following sub-steps: S3a: Call the solar irradiance sequence in the full-link synchronization status data, perform differential operation on 20,000 sampling points within a continuous 1-second time window, calculate the gradient of each sampling point, take the absolute value and filter the maximum value to generate the maximum irradiance change gradient. S3b: Based on the DC bus ripple characteristic matrix, apply a Hanning window to the DC bus voltage signal and perform FFT transformation. Search for the highest point of amplitude-frequency characteristic in the range of 0.1Hz-10kHz, call MPPT to refresh the frequency, calculate the frequency ratio, and generate the frequency ratio. S3c: Call the inverter switching frequency, extract the amplitude and second harmonic amplitude at the current switching frequency from the FFT amplitude-frequency characteristics, obtain the low-frequency band ripple amplitude, mid-frequency band ripple amplitude, and high-frequency band ripple amplitude from the DC bus ripple characteristic matrix, calculate the switching noise contribution ratio, integrate the maximum irradiance change gradient, frequency ratio, and switching noise contribution ratio to generate multi-source disturbance quantification index.

7. The photovoltaic optimization and control method based on MPPT technology according to claim 1, characterized in that: S4 specifically includes the following sub-steps: S4a: Call the maximum irradiance change gradient in the multi-source disturbance quantification index, compare it with the preset threshold, and trigger the golden section mode when it is greater than the preset threshold. Set the ±5% range of the current voltage, calculate V1 and V2, and generate the golden section test voltage point. S4b: Frequency ratio and frequency ratio R in multi-source disturbance quantification index f The judgment threshold of 0.3 is compared. When it is greater than the judgment threshold, the basic perturbation step size is retrieved, 1.5 times the perturbation step size is calculated, and the updated perturbation step size is generated. S4c: Calls the switching noise contribution ratio from the multi-source disturbance quantization index, and compares it with the switching noise contribution ratio R. sw The judgment threshold of 0.4 is compared. If it is not greater than the threshold, phase shift compensation is not triggered. The golden section test voltage point, the updated disturbance step size and the default phase shift angle θ = 0° are combined to generate a collaborative optimization control instruction set.

8. A control device, characterized in that: It includes a processor and a memory, the memory being used to store a program, the processor executing the program stored in the memory, and when the program stored in the memory is executed, causing the control device to implement the photovoltaic optimization control method based on MPPT technology as described in any one of claims 1-7.

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