A photovoltaic grid-connected system resonance suppression method, electronic equipment and computer readable storage medium

Abstract

The application discloses a kind of photovoltaic grid-connected system resonance suppression method, electronic equipment and computer readable storage medium, solve the existing photovoltaic grid-connected system using fixed parameter resonance suppression strategy, leading to the technical problem of poor adaptability to grid impedance fluctuation, poor real-time performance;The application dynamically and accurately identifies the frequency, amplitude of system resonance by high-precision signal parameter estimation algorithm, and calculates key parameters such as harmonic distortion rate, realizes the rapid identification of time-varying resonance.A band-pass filter with self-adaptive adjustment of parameters is designed, which can optimize the center frequency and damping ratio according to the actual resonance characteristics, maximize the retention of resonance feedback components while effectively blocking the fundamental component, and avoid power loss.The composition criterion of total harmonic distortion rate and preset distortion rate threshold realizes the dynamic closed-loop cooperation of resonance identification and suppression strategy, effectively improves the suppression ability and robustness of photovoltaic grid-connected system to resonance.

Description

Technical Field

[0001] This invention relates to power electronics technology and new energy power generation optimization methods, specifically to a method for suppressing resonance in a photovoltaic grid-connected system, electronic equipment, and computer-readable storage medium. Background Technology

[0002] Currently, the global energy transition is accelerating, and photovoltaic power generation is being widely adopted due to its advantages such as being green, low-carbon, and resource-rich. In photovoltaic grid-connected systems, inverters, as typical power electronic devices, bring low inertia and weak damping characteristics to the power grid due to their large-scale integration, easily causing power quality problems such as wideband resonance, which seriously threatens the safe and stable operation of the power system.

[0003] Currently, numerous research achievements have been made in strategies to suppress converter resonance in photovoltaic grid-connected systems, mainly focusing on source-side converter control, including control algorithm optimization, passive filtering, active damping, and impedance reshaping. Passive damping methods are simple but introduce additional losses, and their suppression performance is affected by parameters. Active damping methods are gradually becoming mainstream, but their performance is affected by algorithm parameter settings, and they have poor adaptability to grid impedance fluctuations and poor real-time performance. In particular, filters with fixed parameters struggle to cope with the challenge of time-varying resonant frequencies during photovoltaic system operation.

[0004] Therefore, how to quickly and accurately identify the resonant frequency and dynamically adjust the suppression strategy parameters accordingly to adapt to broadband resonance suppression under different operating conditions has become a technical problem that urgently needs to be solved. Summary of the Invention

[0005] The purpose of this invention is to solve the technical problems of poor adaptability and poor real-time performance of existing photovoltaic grid-connected systems that use fixed-parameter resonance suppression strategies. The invention provides a resonance suppression method, electronic device, and computer-readable storage medium for photovoltaic grid-connected systems.

[0006] To achieve the above objectives, the technical solution provided by this invention is as follows:

[0007] A method for suppressing resonance in a photovoltaic grid-connected system, wherein the photovoltaic grid-connected system has built-in voltage and current control loops, is characterized by the following steps:

[0008] Step 1: Collect voltage and current signals at the common coupling point of the photovoltaic grid-connected system and perform data denoising processing to obtain harmonic signals;

[0009] Step 2: Use a high-precision signal parameter estimation algorithm to dynamically identify harmonic signals, obtain the frequency and amplitude of each resonant component in the harmonic signal, and calculate the distortion rate of each harmonic and the total harmonic distortion rate based on the amplitude.

[0010] Step 3: Set a preset distortion rate threshold. Determine whether the total harmonic distortion rate exceeds the preset distortion rate threshold. If it exceeds the preset distortion rate threshold, resonance occurs, and proceed to step 4. Otherwise, repeat steps 1-3.

[0011] Step 4: Input the frequency of each resonant component and the distortion rate of each harmonic to the bandpass filter, and adaptively calculate and adjust the center frequency and damping ratio of the bandpass filter;

[0012] Step 5: Connect the bandpass filter adjusted in Step 4 to the common coupling point of the photovoltaic grid-connected system. Collect voltage and current signals, and after passing them through a phase-locked loop and abc-dq transformation, output a negative feedback signal. Input the negative feedback signal into the voltage and current control loops through the voltage loop and current loop respectively to cancel the resonant components in the harmonic signal and achieve resonance suppression.

[0013] Furthermore, in step 2, the high-precision signal parameter estimation algorithm is a rotation-invariant signal parameter estimation algorithm.

[0014] Furthermore, in step 2, the calculation formulas for calculating the harmonic distortion rate of each harmonic and the total harmonic distortion rate based on the amplitude are as follows:

[0015] ;

[0016] ;

[0017] In the formula, Represents the distortion rate of the h-th harmonic. α represents the total harmonic distortion rate, α1 represents the fundamental amplitude, and α p This represents the amplitude of the p-th harmonic, where p is the number of harmonics.

[0018] Furthermore, in step 3, the distortion rate threshold is 5%.

[0019] Furthermore, in step 4, the adaptive calculation of the center frequency of the bandpass filter is specifically as follows:

[0020] Using the squares of the harmonic distortion rates corresponding to each resonant component as weights, the weighted average of the frequencies of all resonant components is calculated and used as the center frequency of the bandpass filter; the expression is:

[0021] ;

[0022] In the formula, f c The center frequency of the bandpass filter is represented by ψ, and the number of clusters that identify the resonant components is represented by f. h Let h be the frequency of the h-th resonant component.

[0023] Furthermore, in step 4, the damping ratio of the bandpass filter is adaptively calculated as follows:

[0024] The damping ratio of the bandpass filter is calculated by inverse kinematics based on the fundamental frequency, the target gain value to be achieved at the fundamental frequency, and the center frequency of the bandpass filter. The expression is as follows:

[0025] ;

[0026] In the formula, ζ represents the damping ratio of the bandpass filter, and f c f represents the center frequency of the bandpass filter. s G represents the fundamental frequency, and |G| represents the target gain value that needs to be achieved at the fundamental frequency.

[0027] Furthermore, in step 4, the bandpass filter is a second-order bandpass filter, and the transfer function of the second-order bandpass filter is... as follows:

[0028] ;

[0029] ;

[0030] In the formula, s represents the Laplace complex variable, and ζ represents the damping ratio of the bandpass filter. f represents the center angular frequency of the bandpass filter. c This indicates the center frequency of the bandpass filter.

[0031] Furthermore, in step 5, the negative feedback signal is one or both of the capacitor voltage harmonic feedback quantity and the grid-side inductor current harmonic feedback quantity.

[0032] Another object of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when running the computer program, implements the steps of the above-described photovoltaic grid-connected system resonance suppression method.

[0033] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the steps of the above-described method for suppressing resonance in a photovoltaic grid-connected system.

[0034] The beneficial effects of this invention are as follows:

[0035] (1) By using a high-precision signal parameter estimation algorithm, the frequency and amplitude of the system resonance are dynamically and accurately identified, and key parameters such as harmonic distortion rate are calculated, thus realizing the rapid identification of time-varying resonance.

[0036] (2) Based on the identification results, a bandpass filter with adaptively adjustable parameters was designed. It can optimize the center frequency and damping ratio according to the actual resonance characteristics, effectively blocking the fundamental component while preserving the resonance feedback component to the maximum extent, thus avoiding power loss.

[0037] (3) By comparing the total harmonic distortion rate with the preset distortion rate threshold to form a criterion, the dynamic closed-loop coordination of resonance identification and suppression strategies is realized, which effectively improves the suppression capability and robustness of the photovoltaic grid-connected system for broadband resonance under different operating scenarios. Attached Figure Description

[0038] Figure 1 This is a flowchart of a photovoltaic grid-connected system resonance suppression method according to the present invention;

[0039] Figure 2 This is a schematic diagram of the main topology of an LCL-type photovoltaic grid-connected system in an embodiment of a photovoltaic grid-connected system resonance suppression method according to the present invention;

[0040] Figure 3 This is a two-dimensional / three-dimensional distribution diagram of frequency-time-distortion rate based on ESPRIT in an embodiment of a photovoltaic grid-connected system resonance suppression method of the present invention;

[0041] Among them, (a) is a two-dimensional distribution map of time-distortion rate; (b) is a two-dimensional distribution map of frequency-distortion rate; and (c) is a three-dimensional distribution map of frequency-time-distortion rate.

[0042] Figure 4 This is a topology diagram of an LCL-type photovoltaic grid-connected system with an SVG device connected in parallel at its grid connection point PCC, as described in an embodiment of a photovoltaic grid-connected system resonance suppression method of the present invention.

[0043] Figure 5 This is a THD curve before the suppression strategy is implemented in an embodiment of the resonance suppression method for a photovoltaic grid-connected system according to the present invention;

[0044] Figure 6 This is a spectrum diagram of the output inductor current feedback, capacitor voltage feedback, and resonant component in an embodiment of a photovoltaic grid-connected system resonance suppression method of the present invention;

[0045] Among them, (a) is the spectrum of the inductor current feedback-resonance component; (b) is the spectrum of the capacitor voltage feedback-resonance component.

[0046] Figure 7 This is a three-dimensional frequency-time-distortion rate distribution diagram based on the ESPRIT algorithm in an embodiment of a photovoltaic grid-connected system resonance suppression method of the present invention;

[0047] Figure 8 The THD curve is shown in an embodiment of the resonance suppression method for a photovoltaic grid-connected system according to the present invention after the suppression strategy is implemented. Detailed Implementation

[0048] To make the objectives, advantages, and features of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Those skilled in the art should understand that these embodiments are merely used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0049] like Figure 1 As shown, this embodiment provides a method for suppressing resonance in a photovoltaic grid-connected system. The photovoltaic grid-connected system has a built-in voltage and current control loop, and the suppression method includes the following steps:

[0050] Step 1: Collect voltage and current signals at the common coupling point of the photovoltaic grid-connected system and perform data denoising processing to obtain harmonic signals;

[0051] Step 2: Dynamically identify harmonic signals using a rotation-invariant signal parameter estimation algorithm, obtain the frequency and amplitude of each resonant component in the harmonic signal, and calculate the distortion rate of each harmonic and the total harmonic distortion rate based on the amplitude. The calculation formula is as follows:

[0052]

[0053]

[0054] In the formula, Represents the distortion rate of the h-th harmonic. α represents the total harmonic distortion rate, α1 represents the fundamental amplitude, and α p This represents the amplitude of the p-th harmonic, where p is the number of harmonics.

[0055] In this embodiment, when identifying harmonic signals, in addition to using the rotation-invariant signal parameter estimation algorithm, decomposition techniques can also be used, such as filter extraction, wavelet transform, Hilbert transform, Hilbert-Huang transform, Fourier transform, fast Fourier transform, empirical mode decomposition, variational mode decomposition, rotation-invariant techniques, and other harmonic analysis techniques, as well as corresponding improved algorithms and combined algorithms. This invention takes the rotation-invariant signal parameter estimation algorithm (ESPRIT algorithm) as an example. If the corresponding algorithm is replaced, it is also within the scope of protection of this invention.

[0056] Step 3: Set the preset distortion rate threshold to 5%. Determine whether the total harmonic distortion rate exceeds the preset distortion rate threshold. If it exceeds the preset distortion rate threshold, resonance occurs, and proceed to step 4. Otherwise, repeat steps 1-3.

[0057] Step 4: Input the frequencies of each resonant component and the harmonic distortion rates of each harmonic into a bandpass filter. The bandpass filter is a second-order bandpass filter, and its transfer function is... as follows:

[0058] ;

[0059] ;

[0060] In the formula, s represents the Laplace complex variable, and ζ represents the damping ratio of the bandpass filter. f represents the center angular frequency of the bandpass filter. c This indicates the center frequency of the bandpass filter.

[0061] Using the squares of the harmonic distortion rates corresponding to each resonant component as weights, the weighted average of the frequencies of all resonant components is calculated and used as the center frequency of the bandpass filter; the formula is:

[0062] ;

[0063] In the formula, f c The center frequency of the bandpass filter is represented by ψ, and the number of clusters that identify the resonant components is represented by f. h Let h be the frequency of the h-th resonant component.

[0064] The bandpass filter is adjusted based on its center frequency.

[0065] The damping ratio of the bandpass filter is calculated by inverse kinematics based on the fundamental frequency, the target gain value to be achieved at the fundamental frequency, and the center frequency of the bandpass filter; the calculation formula is as follows:

[0066] ;

[0067] In the formula, ζ represents the damping ratio of the bandpass filter, and f c f represents the center frequency of the bandpass filter. s G represents the fundamental frequency, and |G| represents the target gain value that needs to be achieved at the fundamental frequency.

[0068] The bandpass filter is adjusted based on its damping ratio.

[0069] The above-mentioned bandpass filter can be a second-order bandpass filter to achieve adaptive resonance suppression. If a higher-order bandpass filter is used, different types of bandpass filters, bandstop filters, and notch filters are all within the scope of this invention.

[0070] Step 5: Connect the bandpass filter adjusted in Step 4 to the common coupling point of the photovoltaic grid-connected system. Collect voltage and current signals, and after passing them through a phase-locked loop and abc-dq transformation, output a negative feedback signal. Input the negative feedback signal into the voltage and current control loops through the voltage loop and current loop respectively to cancel the resonant components in the harmonic signal and achieve resonance suppression.

[0071] Among them, the negative feedback signal is one or both of the capacitor voltage harmonic feedback quantity and the grid-side inductor current harmonic feedback quantity.

[0072] This embodiment also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor runs the computer program, it implements the steps of the above-described photovoltaic grid-connected system resonance suppression method.

[0073] Furthermore, a computer-readable storage medium is disclosed, on which a computer program is stored, which, when executed by a processor, implements the steps of a photovoltaic grid-connected system resonance suppression method.

[0074] The photovoltaic grid-connected system constructed in this embodiment is an LCL-type photovoltaic grid-connected system. The main topology diagram of the LCL-type photovoltaic grid-connected system is as follows. Figure 2 As shown, the system mainly includes photovoltaic panels, a BOOST boost circuit, a converter, an LCL filter, and then connects to an infinite bus. The converter control primarily uses grid voltage orientation, obtaining the grid voltage phase via a phase-locked loop (PLL), while the inductor current i in the LCL filter... La i Lb i Lc With capacitor voltage u a u b u c The feedback quantity is transformed into dq components using a dq coordinate system and then fed into their respective adaptive bandpass filters. The bandpass filters are then adaptively adjusted to have a center frequency f. c With the damping ratio ζ, the amplitude-frequency response changes, allowing most harmonic components to pass through the filter and block the power frequency. Then, voltage feedforward enters the current loop, and inductor feedback enters the voltage command point. At this point, the photovoltaic inverter adjusts its output, and resonance is effectively suppressed. This LCL-type photovoltaic grid-connected system is characterized by the LCL filter at the inverter output, and its oscillation mechanism is mainly due to the mismatch between the filter's resonant characteristics and the system's dynamics. From a control perspective, if the inverter's inner current loop control bandwidth is not adapted to the inherent resonant frequency of the LCL filter, the inner loop's ability to suppress high-frequency disturbances will be insufficient: when the bandwidth covers the filter's resonant frequency, it will amplify the resonant components, inducing kHz-level mid-to-high frequency oscillations; if the bandwidth is too narrow, although it can avoid the resonant peak, it will reduce the system's dynamic response speed, forming low-damped oscillations in the subsynchronous frequency band. Simultaneously, if the active damping coefficient of the LCL filter is too small, it cannot dissipate resonant energy, leading to a continuous accumulation of oscillation amplitude; if the damping coefficient is too large, it will increase system losses and may create dynamic conflicts with the inner current loop control, triggering new wideband oscillation modes.

[0075] I. Verification of the rotationally invariant signal parameter estimation algorithm (ESPRIT algorithm) for dynamic identification of harmonic signals:

[0076] A time-varying harmonic signal was constructed using the MATLAB platform. The voltage or current signal to be analyzed was designed as a superposition of the fundamental wave and multiple time-varying harmonics, and its time-domain expression is as follows:

[0077] ;

[0078] For the time-varying harmonic signal to be analyzed, A fund f is the fundamental amplitude. fund For the fundamental frequency, A h Let f be the time-varying amplitude of the h-th harmonic, where t is the time variable and f is the amplitude of the h-th harmonic. h Let be the frequency of the h-th harmonic, n(t) be the noise level, and H be the highest harmonic order. Let h be the amplitude of the h-th time-varying harmonic. This represents the initial phase of the h-th harmonic.

[0079] The experimental procedure is as follows: The voltage and current signals of the LCL-type photovoltaic grid-connected system are sampled at a frequency of 10kHz, with a signal duration of 4 seconds. The fundamental frequency component is 50Hz / 220V, and a harmonic switching point is set at 2 seconds (1500Hz / 11V for t<2s, and 994Hz / 22V for t≥2s). The ESPRIT algorithm (rotationally invariant signal parameter estimation algorithm) is used to identify the total harmonic distortion (THD) of the signal. A 100-point analysis window (corresponding to a 10ms time resolution) is set, the frequency analysis range is 0-2000Hz, the frequency resolution is 1Hz, and the harmonic distortion threshold is 7%. The algorithm's ability to identify THD is tested under test scenarios with theoretical harmonic distortion rates of 5% and 10%. The dynamic tracking characteristics of the algorithm are verified by setting a sudden harmonic switching point in the time domain. The three-dimensional distribution of frequency-time-distortion rate based on the ESPRIT algorithm is shown below. Figure 3 As shown.

[0080] Depend on Figure 3 It can be seen that the ESPRIT algorithm can clearly capture the switching process of harmonics from 1500Hz to 1000Hz at 2 seconds, and the frequency estimation error is less than 0.1Hz. The distortion rate measurement results are in high agreement with the theoretical values ​​(5% and 10%), and the error is controlled within 0.5%.

[0081] Total harmonic distortion rate changes over time as follows Figure 5 As shown in the figure. The results show that when t < 2s, the THD stabilizes at 5%, which is the 1500Hz harmonic; when t ≥ 2s, the THD jumps to 10%, which is the 1000Hz harmonic. The THD variation curve matches the set analog signal parameters, and tracking can be completed in only 10ms at the switching time, demonstrating excellent THD tracking speed.

[0082] Compared with the traditional FFT method (power system harmonic analysis algorithm), the rotationally invariant signal parameter estimation algorithm (ESPRIT algorithm) in this embodiment significantly improves frequency resolution and amplitude measurement accuracy while maintaining high time resolution, and the single calculation time is still within a reasonable range, meeting the real-time requirements of engineering. Simulation results show that this method has excellent analytical capabilities and robustness for non-stationary signals.

[0083] II. Verification of the effectiveness of the adaptive bandpass resonant suppression method proposed in this embodiment on an LCL-type photovoltaic grid-connected system:

[0084] based on Figure 4 The LCL-type photovoltaic grid-connected system constructed in China, after connecting an SVG device in parallel at its grid connection point PCC, has the following system topology diagram: Figure 4 As shown in the diagram. The suppression strategy process starts from 0.3s: First, the strategy performs harmonic limit detection. Based on ESPRIT, it identifies when the system's THD exceeds the 5% threshold, indicating resonance. At this point, the algorithm inputs the resonant component frequency and corresponding IHD obtained from the resonance identification into an adaptive bandpass filter, and the filter's damping ratio is adaptively adjusted. The THD curve identified by the ESPRIT algorithm during this process is shown in the diagram. Figure 5 .

[0085] The adjusted bandpass filter is connected to the common coupling point of the photovoltaic grid-connected system. The acquired voltage and current signals are sequentially processed through a phase-locked loop and an abc-dq transform. The resulting spectrum of the resonant components of the output inductor current feedback and capacitor voltage feedback is shown below. Figure 6 (IHD < 1% is considered noise negligible). The frequency spectrum data of inductor current and capacitor voltage feedback are shown in Table 1. The frequency components that enter the passband (-3dB) are marked in bold.

[0086] Table 1

[0087]

[0088] The resonance suppression strategy was immediately implemented in the LCL-type photovoltaic grid-connected system. The three-dimensional distribution map of the frequency-time-distortion rate of the resonant components and the overall THD curve obtained by the ESPRIT algorithm were as follows: Figure 7 and Figure 8 .

[0089] Depend on Figure 7 and Figure 8Analysis shows that when the suppression strategy is applied to the newly built photovoltaic grid-connected system with reactive power compensation device at 0.3s, all resonant components are significantly suppressed and disappear, and the overall THD rapidly decreases from 15.14% to about 1%. Secondly, in the harmonic cluster dominated by 1950Hz before 0.3s, more than half of the components have IHD exceeding 10%. This dominant harmonic cluster, as the key frequency inducing resonance, is significantly suppressed, and the resonant components completely disappear after 0.3s. Simultaneously, the real-time IHD data of all resonant components throughout the entire process can be identified using ESPRIT. The system remains stable after suppression, verifying the effectiveness of the proposed resonance suppression strategy and the feasibility of strategy application judgment.

[0090] Through the above methods, this invention proposes a novel resonance suppression strategy based on a parameter-adaptive bandpass filter and the ESPRIT algorithm for identifying and suppressing broadband resonance risks in photovoltaic grid-connected systems. Theoretical analysis and simulation verification lead to the following conclusions:

[0091] (1) The ESPRIT algorithm can dynamically identify key parameters such as frequency, amplitude, single harmonic distortion rate and total harmonic distortion rate of each component in real time. This method is characterized by its insensitivity to noise and fast calculation speed, which meets the needs of real-time monitoring of power systems.

[0092] (2) A dynamic criterion for total harmonic distortion (THD) is proposed for wideband resonance discrimination, which effectively identifies resonance phenomena and improves the judgment ability of suppression strategy input. In the design of adaptive bandpass filter, the center frequency and damping ratio of wideband resonance design are adaptively adjusted to achieve precise locking of the dominant resonance component of the system, maximize the inclusion of the dominant resonance component in the feedback and avoid the loss of power frequency quantity, and improve the suppression strategy's ability to suppress harmonics.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the present invention.

[0094] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for suppressing resonance in a photovoltaic grid-connected system, wherein the photovoltaic grid-connected system has a built-in voltage and current control loop, characterized in that, The suppression method includes the following steps: Step 1: Collect voltage and current signals at the common coupling point of the photovoltaic grid-connected system and perform data denoising processing to obtain harmonic signals; Step 2: Use a high-precision signal parameter estimation algorithm to dynamically identify harmonic signals, obtain the frequency and amplitude of each resonant component in the harmonic signal, and calculate the distortion rate of each harmonic and the total harmonic distortion rate based on the amplitude. Step 3: Set a preset distortion rate threshold. Determine whether the total harmonic distortion rate exceeds the preset distortion rate threshold. If it exceeds the preset distortion rate threshold, resonance occurs, and proceed to Step 4; otherwise, return to Step 1. Step 4: Input the frequency of each resonant component and the distortion rate of each harmonic to the bandpass filter, adaptively calculate and adjust the center frequency and damping ratio of the bandpass filter; Step 5: Connect the bandpass filter adjusted in Step 4 to the common coupling point of the photovoltaic grid-connected system. Collect the filtered voltage and current signals, and after passing them through a phase-locked loop and abc-dq transformation, output a negative feedback signal. Input the negative feedback signal into the voltage and current control loop through the voltage loop and / or current loop to cancel the resonant components in the harmonic signal and achieve resonance suppression.

2. The method for suppressing resonance in a photovoltaic grid-connected system according to claim 1, characterized in that: In step 2, the high-precision signal parameter estimation algorithm is a rotation-invariant signal parameter estimation algorithm.

3. The method for suppressing resonance in a photovoltaic grid-connected system according to claim 1, characterized in that, In step 2, the formulas for calculating the harmonic distortion rate of each harmonic and the total harmonic distortion rate based on the amplitude are as follows: ； ； In the formula, Represents the distortion rate of the h-th harmonic. α represents the total harmonic distortion rate, α1 represents the fundamental amplitude, and α p This represents the amplitude of the p-th harmonic, where p is the number of harmonics.

4. The method for suppressing resonance in a photovoltaic grid-connected system according to claim 1, characterized in that: In step 3, the distortion rate threshold is 5%.

5. The method for suppressing resonance in a photovoltaic grid-connected system according to claim 3, characterized in that, In step 4, the adaptive calculation of the center frequency of the bandpass filter is specifically as follows: Using the squares of the harmonic distortion rates corresponding to each resonant component as weights, the weighted average of the frequencies of all resonant components is calculated and used as the center frequency of the bandpass filter. The expression is as follows: ； In the formula, f c The center frequency of the bandpass filter is represented by ψ, and the number of clusters that identify the resonant components is represented by f. h Let h be the frequency of the h-th resonant component.

6. The method for suppressing resonance in a photovoltaic grid-connected system according to claim 1, characterized in that, In step 4, the damping ratio of the bandpass filter is adaptively calculated as follows: The damping ratio of the bandpass filter is calculated by inverse kinematics based on the fundamental frequency, the target gain value to be achieved at the fundamental frequency, and the center frequency of the bandpass filter; the expression is: ； In the formula, ζ represents the damping ratio of the bandpass filter, and f c f represents the center frequency of the bandpass filter. s G represents the fundamental frequency, and |G| represents the target gain value that needs to be achieved at the fundamental frequency.

7. The method for suppressing resonance in a photovoltaic grid-connected system according to claim 1, characterized in that, In step 4, the bandpass filter is a second-order bandpass filter, and the transfer function of the second-order bandpass filter is... as follows: ； ； In the formula, s represents the Laplace complex variable, and ζ represents the damping ratio of the bandpass filter. f represents the center angular frequency of the bandpass filter. c This indicates the center frequency of the bandpass filter.

8. The method for suppressing resonance in a photovoltaic grid-connected system according to claim 1, characterized in that: In step 5, the negative feedback signal is one or both of the capacitor voltage harmonic feedback quantity and the grid-side inductor current harmonic feedback quantity.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor runs the computer program, it implements the steps of a photovoltaic grid-connected system resonance suppression method as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of a photovoltaic grid-connected system resonance suppression method as described in any one of claims 1-8.

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