A harmonic suppression method based on virtual impedance cooperative control

Abstract

The present invention discloses a harmonic suppression method based on virtual impedance collaborative control, which relates to the field of power grid harmonic control technology and is used to solve the technical problems of strong time-varying harmonics, high energy consumption, and parameter fixation in high-penetration photovoltaic scenarios. The method comprises: obtaining the original electrical signal, impedance parameters, and filtering parameters on the grid side; performing real-time phase correction based on the original electrical signal to obtain multi-band harmonic frequencies; constructing a virtual impedance based on the multi-band harmonic frequencies, and generating an impedance characteristic matrix based on the impedance parameters and filtering parameters; using the impedance characteristic matrix to perform parameter optimization of the virtual impedance based on deep reinforcement learning to obtain the optimal virtual impedance parameters; and changing the equivalent output impedance of the inverter through the virtual impedance under the optimal virtual impedance parameters to achieve harmonic suppression.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid harmonic control, and in particular to a harmonic suppression method based on virtual impedance collaborative control, a harmonic suppression device based on virtual impedance collaborative control, an electronic device, and a storage medium. Background Art

[0002] Harmonics are sinusoidal components with frequencies that are integer multiples of the fundamental frequency that appear in power supplies or signals due to nonlinear loads or switching operations in circuits. In switching power supplies (such as DC / AC converters (Direct Current to Alternating Current Converters) and inverters), the high-frequency switching of switching elements (such as MOSFETs (Metal-Oxide-Semiconductor Field-Effect Transistors) and IGBTs (Insulated-Gate Bipolar Transistors)) can introduce harmonics into the output waveform. Harmonics can also be generated by nonlinear current or voltage variations in nonlinear loads (such as rectifiers and inverters). In power systems, harmonics can be caused by the nonlinear characteristics of transformers, cables, or other equipment.

[0003] Harmonics are a crucial parameter for grid power quality. Stable grid operation requires that harmonics be kept within a certain level. The presence of harmonics can negatively impact circuit performance, efficiency, and system stability. Therefore, to minimize the impact of harmonics, harmonic suppression is a crucial aspect of circuit design.

[0004] Currently, common harmonic suppression methods include passive filtering and active filtering. Passive filtering involves adding passive components such as inductors and capacitors to the circuit to form LC filters or other types of filtering networks, filtering out harmonics of specific frequencies. While this method is easy to implement, its parameters are fixed, filtering effectiveness is limited, and it requires design for specific frequencies, resulting in poor adaptability. This makes it unsuitable for the time-varying harmonics found in high-penetration photovoltaic scenarios. Active filtering, on the other hand, uses devices such as active power filters (APFs) to detect harmonics in real time and inject an equal amount of reverse harmonics to offset them. While this method offers better filtering effectiveness and can handle harmonics of multiple frequencies, it is costly, requires complex control algorithms (such as APFs), and consumes a lot of energy to mitigate harmonics.

[0005] With the integration of high-penetration distributed photovoltaic systems, the switching of power electronic devices will generate more harmonics. Therefore, there is an urgent need to develop a more effective harmonic suppression method to address the three major challenges of high-penetration photovoltaic scenarios: strong time-varying harmonics, high energy consumption, and fixed parameters. Summary of the Invention

[0006] The present invention provides a harmonic suppression method based on virtual impedance cooperative control, a harmonic suppression device based on virtual impedance cooperative control, an electronic device and a storage medium, which are used to solve or partially solve the technical problems of strong time-varying harmonics, high energy consumption and parameter solidification in high-penetration photovoltaic scenarios.

[0007] The present invention provides a harmonic suppression method based on virtual impedance cooperative control, comprising:

[0008] Obtain the original electrical signal, impedance parameters on the grid side and filtering parameters on the inverter side;

[0009] Performing real-time phase correction on the original electrical signal to obtain multi-band harmonic frequencies;

[0010] Constructing a virtual impedance based on the multi-band harmonic frequencies, and generating an impedance characteristic matrix based on the impedance parameters and the filtering parameters;

[0011] Using the impedance characteristic matrix to perform parameter optimization on the virtual impedance based on deep reinforcement learning to obtain optimal virtual impedance parameters;

[0012] The equivalent output impedance of the inverter is changed by the virtual impedance under the optimal virtual impedance parameters to achieve harmonic suppression.

[0013] Optionally, performing real-time phase correction according to the original electrical signal to obtain multi-band harmonic frequencies includes:

[0014] performing signal decomposition on the original electrical signal to obtain a non-steady-state harmonic component;

[0015] Extracting the instantaneous frequency according to the non-steady-state harmonic component, and performing real-time phase error correction on the instantaneous phase of the non-steady-state harmonic component based on Kalman filtering to obtain a phase correction error;

[0016] Optimizing the instantaneous frequency according to the phase correction error to obtain an optimized instantaneous frequency;

[0017] According to the optimized instantaneous frequency, multi-band harmonic frequencies are obtained through integral calculation.

[0018] Optionally, constructing a virtual impedance based on the multi-band harmonic frequencies includes:

[0019] The gain coefficient and the damping ratio are used as virtual impedance parameters to be optimized, and a frequency-band adaptive virtual impedance is constructed according to the multi-band harmonic frequencies to achieve frequency-band suppression of harmonic resonance.

[0020] Optionally, the impedance parameters include line resistance and line inductance; the filtering parameters include filtering inductance, filtering capacitance and damping resistance; the impedance characteristic matrix includes a system matrix and an input matrix; and generating the impedance characteristic matrix based on the impedance parameters and the filtering parameters includes:

[0021] Based on Kirchhoff's law, a differential equation system is constructed according to the line resistance, the line inductance, the filter inductance, the filter capacitor and the damping resistor;

[0022] The differential equations are converted into a standard state equation form to obtain a system matrix and an input matrix.

[0023] Optionally, the using the impedance characteristic matrix to perform parameter optimization on the virtual impedance based on deep reinforcement learning to obtain optimal virtual impedance parameters includes:

[0024] Reconstructing grid current components by combining the system matrix and the input matrix, and performing harmonic separation on the grid current components to obtain a total grid current;

[0025] constructing an output matrix related to the voltage at the common coupling point according to the line resistance, and solving the voltage at the common coupling point based on the output matrix;

[0026] constructing a state space based on the multi-band harmonic frequencies, the total grid current, and the common coupling point voltage, while taking into account the harmonic distortion level;

[0027] generating an action space based on the state space and using a virtual impedance parameter as an update parameter;

[0028] constructing a reward function according to the virtual impedance, the multi-band harmonic frequencies, and the harmonic distortion level;

[0029] Constructing an action-value function according to the reward function, and constructing a policy network having a double-Q network structure according to the action-value function;

[0030] Iteratively updating the policy network within the action space and minimizing the loss function during the iterative updating process;

[0031] When the harmonic distortion level is less than the preset fluctuation threshold and the reward function reaches a steady state, the iteration is stopped and the virtual impedance parameter obtained after the last iteration is output as the optimal virtual impedance parameter.

[0032] Optionally, generating an action space based on the state space and using a virtual impedance parameter as an update parameter includes:

[0033] generating candidate parameter adjustment amounts of virtual impedance parameters to be optimized according to the state space;

[0034] An action space for parameter adjustment is constructed based on the candidate parameter adjustment amount, and a parameter update strategy for the action space is set; the parameter update strategy is to use the sum of the pre-update virtual impedance parameter and the candidate parameter adjustment amount as the post-update virtual impedance parameter.

[0035] Optionally, the method further includes:

[0036] Calculate the optimized harmonic power using the optimal virtual impedance parameter as a constraint condition;

[0037] An energy recovery efficiency is calculated according to the optimized harmonic power, and a reward function of deep reinforcement learning is adjusted and optimized based on the energy recovery efficiency.

[0038] The present invention also provides a harmonic suppression device based on virtual impedance coordinated control, comprising:

[0039] A data acquisition unit, used to obtain the original electrical signal and impedance parameters on the grid side and the filtering parameters on the inverter side;

[0040] A phase correction unit, configured to perform real-time phase correction on the original electrical signal to obtain multi-band harmonic frequencies;

[0041] a virtual impedance and characteristic matrix construction unit, configured to construct a virtual impedance based on the multi-band harmonic frequencies, and generate an impedance characteristic matrix based on the impedance parameters and the filtering parameters;

[0042] a parameter optimization unit, configured to perform parameter optimization on the virtual impedance based on deep reinforcement learning using the impedance characteristic matrix to obtain optimal virtual impedance parameters;

[0043] The harmonic suppression unit is used to change the equivalent output impedance of the inverter through the virtual impedance under the optimal virtual impedance parameters to achieve harmonic suppression.

[0044] The present invention further provides an electronic device, comprising a processor and a memory:

[0045] The memory is used to store program code and transmit the program code to the processor;

[0046] The processor is configured to execute the harmonic suppression method based on virtual impedance cooperative control as described in any one of the above items according to the instructions in the program code.

[0047] The present invention also provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the harmonic suppression method based on virtual impedance cooperative control as described in any one of the above items.

[0048] It can be seen from the above technical solutions that the present invention has the following advantages:

[0049] A harmonic suppression method based on multimodal virtual impedance coordinated control and deep reinforcement learning is presented. The first step is to obtain the original electrical signal, impedance parameters, and filter parameters on the grid side, which serve as the basic data for subsequent harmonic suppression calculations. The second step is to perform real-time phase correction on the original electrical signal to obtain multi-band harmonic frequencies. This allows for digital noise suppression and real-time error correction based on the real-time phase correction, achieving dynamic harmonic decomposition and adaptive tracking of the original electrical signal. The third step is to construct a virtual impedance based on the multi-band harmonic frequencies, and generate an impedance characteristic matrix based on the impedance parameters and filter parameters. This approach constructs a frequency-band adaptive virtual impedance network and combines it with adaptive sub-band virtual impedance for coordinated control. Independent adjustment of each frequency band avoids the resonance risk of traditional LCL filters and achieves sub-band harmonic suppression. The fourth step is to use the impedance characteristic matrix to perform deep reinforcement learning-based parameter optimization of the virtual impedance to obtain the optimal virtual impedance parameters. By optimizing the virtual impedance parameters through an intelligent algorithm, a parameter combination with a wider range of harmonic distortion suppression and better harmonic suppression can be obtained. The fifth step is to change the equivalent output impedance of the inverter through the virtual impedance under the optimal virtual impedance parameters to achieve harmonic suppression. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 A flowchart of a harmonic suppression method based on virtual impedance cooperative control;

[0052] Figure 2 It is a schematic diagram of the overall process of a harmonic suppression method based on virtual impedance cooperative control;

[0053] Figure 3 This is a structural block diagram of a harmonic suppression device based on virtual impedance cooperative control. DETAILED DESCRIPTION

[0054] Embodiments of the present invention provide a harmonic suppression method based on virtual impedance collaborative control, a harmonic suppression device based on virtual impedance collaborative control, an electronic device and a storage medium, which are used to solve or partially solve the technical problems of strong time-varying harmonics, high energy consumption and parameter solidification in high-penetration photovoltaic scenarios.

[0055] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0056] For example, harmonics are a key parameter in power quality for power grids. Stable grid operation requires keeping harmonics within a certain range. The presence of harmonics can negatively impact circuit performance, efficiency, and system stability. Therefore, to minimize the impact of harmonics, harmonic suppression is a crucial aspect of circuit design.

[0057] Currently, common harmonic suppression methods include passive filtering and active filtering. Passive filtering involves adding passive components such as inductors and capacitors to the circuit to form LC filters or other types of filtering networks, filtering out harmonics of specific frequencies. While this method is easy to implement, its parameters are fixed, filtering effectiveness is limited, and it requires design for specific frequencies, resulting in poor adaptability. This makes it unsuitable for the time-varying harmonics found in high-penetration photovoltaic scenarios. Active filtering, on the other hand, uses devices such as active power filters (APFs) to detect harmonics in real time and inject an equal amount of reverse harmonics to offset them. While this method offers better filtering effectiveness and can handle harmonics of multiple frequencies, it is costly, requires complex control algorithms (such as APFs), and consumes a lot of energy to mitigate harmonics.

[0058] With the integration of high-penetration distributed photovoltaic systems, the switching of power electronic devices will generate more harmonics. Therefore, there is an urgent need to develop a more effective harmonic suppression method to address the three major challenges of high-penetration photovoltaic scenarios: strong time-varying harmonics, high energy consumption, and fixed parameters.

[0059] Therefore, one of the core inventive points of the embodiments of the present invention is to provide a harmonic suppression method based on multi-modal virtual impedance collaborative control and deep reinforcement learning. The first step is to obtain the original electrical signal, impedance parameters, and filter parameters on the grid side as basic data for subsequent harmonic suppression calculations. The second step is to perform real-time phase correction based on the original electrical signal to obtain multi-band harmonic frequencies, thereby performing digital noise suppression based on real-time phase correction, correcting errors in real time, and achieving dynamic harmonic decomposition and adaptive tracking of the original electrical signal. The third step is to construct a virtual impedance based on the multi-band harmonic frequencies, and generate an impedance characteristic matrix based on the impedance parameters and filter parameters. By constructing a frequency-band adaptive virtual impedance network and combining it with adaptive sub-band virtual impedance for collaborative control, the resonance risk of traditional LCL filters can be avoided by independently adjusting the sub-bands, achieving sub-band harmonic resonance suppression. The fourth step is to use the impedance characteristic matrix to perform deep reinforcement learning-based parameter optimization of the virtual impedance to obtain the optimal virtual impedance parameters. Thus, by optimizing the virtual impedance parameters through intelligent algorithms, a parameter combination with a wider range of harmonic distortion suppression and better harmonic suppression effect can be obtained. In the fifth step, the equivalent output impedance of the inverter is changed using the virtual impedance under the optimal virtual impedance parameters to achieve harmonic suppression. Furthermore, the optimal virtual impedance parameters can be used as constraints to calculate the optimized harmonic power. The energy recovery efficiency is calculated based on the optimized harmonic power, and the reward function of deep reinforcement learning is adjusted based on the energy recovery efficiency to achieve bidirectional harmonic energy recycling and reduce the power consumption required for harmonic suppression.

[0060] Reference Figure 1 , shows a flowchart of a harmonic suppression method based on virtual impedance cooperative control provided by an embodiment of the present invention, which may specifically include the following steps:

[0061] Step 101, obtaining the original electrical signal, impedance parameters on the grid side and filtering parameters on the inverter side;

[0062] The original electrical signal represents the original current or voltage signal (including harmonic components). Impedance parameters primarily include line resistance and line inductance. Filter parameters primarily include filter inductance, filter capacitance, and damping resistance.

[0063] Step 102: performing real-time phase correction on the original electrical signal to obtain multi-band harmonic frequencies;

[0064] This step primarily implements dynamic harmonic decomposition and adaptive tracking of the original electrical signal. Its working principle is to use an improved Hilbert-Huang transform (HHT) to decompose the non-stationary harmonic components and combine it with Kalman filtering for real-time phase calibration.

[0065] In some embodiments, the process of performing real-time phase correction based on the original electrical signal to obtain multi-band harmonic frequencies may include the following sub-steps S01 to S04:

[0066] Step S01: Decomposing the original electrical signal to obtain a non-steady-state harmonic component;

[0067] The signal decomposition formula is as follows:

[0068]

[0069] in, Represents the original current or voltage signal (including harmonic components); represents the intrinsic mode function (IMF) after the k-th order empirical mode decomposition, that is, the local symmetric oscillation component generated by HHT decomposition; It is the residual term after HHT decomposition, which is used to reflect the signal trend component.

[0070] Step S02: extracting the instantaneous frequency according to the non-steady-state harmonic component, and performing real-time phase error correction on the instantaneous phase of the non-steady-state harmonic component based on Kalman filtering to obtain a phase correction error;

[0071] Specifically, the instantaneous frequency can be extracted by the following formula:

[0072]

[0073] in, represents the Hilbert transform result of the k-th order IMF (constructing the analytical signal); represents the raw instantaneous frequency of the k-th order IMF (uncalibrated, containing noise); P represents the Cauchy Principal Value; and V represents the measurement noise covariance matrix.

[0074] Compared with traditional FFT (Fast Fourier Transform), HHT can process non-stationary signals and improve decomposition accuracy by more than 30%.

[0075] The phase error can then be calibrated based on Kalman filtering.

[0076] The state variable of the Kalman filter is the phase error , the observed variables are The instantaneous phase The state equation and observation equation are designed as follows:

[0077]

[0078] in, is process noise, which is Gaussian white noise used to describe phase drift); is the measurement noise, i.e., Gaussian white noise from the HHT decomposition error; represents the phase error of the k-th order IMF (caused by noise and non-stationarity); represents the observed phase (phase measurement value including sensor error); Represents the original instantaneous phase, which is given by Direct calculation.

[0079] Through the Kalman gain matrix Correct phase error in real time to obtain phase correction error for subsequent instantaneous frequency optimization The modified form is as follows:

[0080]

[0081] in, Represents the Kalman gain matrix, which is used to dynamically balance the prediction and observation confidence.

[0082] Step S03: Optimizing the instantaneous frequency according to the phase correction error to obtain an optimized instantaneous frequency;

[0083] Instantaneous frequency after optimization and correction for:

[0084]

[0085] Therefore, the phase error of the instantaneous frequency is eliminated through Kalman filtering correction.

[0086] By executing steps S02 to S03, the present invention embeds Kalman filtering into the instantaneous frequency calculation of HHT. Compared to currently used noise suppression methods that rely on external filters or digital filters, this method can correct errors in real time and offers the advantages of dynamic adaptability, high precision, and low latency. Furthermore, this method is implemented digitally, without increasing hardware consumption.

[0087] Step S04: obtaining multi-band harmonic frequencies through integral calculation according to the optimized instantaneous frequency.

[0088] The output multi-band harmonic frequencies are as follows:

[0089]

[0090] Where T is the signal period, which is determined by the harmonic order h and the fundamental frequency (50Hz). Indicates the hth harmonic frequency, indicating that The mean value within the time window.

[0091] Through the equation of state By performing noise suppression and phase calibration, it can output a steady-state frequency that is a strict integer multiple of the fundamental frequency. ( ( ), corresponding to the harmonic frequencies of multiple frequency bands or sub-bands. For example, in the 50Hz fundamental wave scenario, the third harmonic .

[0092] Multi-band or frequency-segmented banding means dividing the harmonics in the power grid into multiple independent intervals based on frequency (e.g., 3rd harmonic 150Hz, 5th harmonic 250Hz, etc.). Virtual impedance parameters are designed separately for each interval.

[0093] Step 103: constructing a virtual impedance based on the multi-band harmonic frequencies, and generating an impedance characteristic matrix based on the impedance parameters and the filtering parameters;

[0094] This step mainly realizes the coordinated control of multi-modal virtual impedance. By building a frequency-adaptive virtual impedance network, it can achieve frequency-band suppression of harmonic resonance.

[0095] In some embodiments, a virtual impedance is constructed based on multi-band harmonic frequencies. Specifically, the gain coefficient and damping ratio are used as virtual impedance parameters to be optimized, and a frequency-adaptive virtual impedance is constructed according to the multi-band harmonic frequencies to achieve frequency-band suppression of harmonic resonance.

[0096] The constructed virtual impedance is as follows:

[0097]

[0098] in, Represents the multimodal virtual impedance, that is, the equivalent impedance of frequency band adaptive adjustment; is the virtual impedance gain coefficient corresponding to the hth harmonic, which is used to control the impedance amplitude; is the virtual impedance damping ratio corresponding to the hth harmonic, which is used to suppress the resonance peak; represents the h-th harmonic angular frequency; s represents the Laplace variable (complex frequency domain operator).

[0099] Furthermore, the impedance characteristic matrix may include a system matrix and an input matrix. Generating the impedance characteristic matrix based on the impedance parameters and filter parameters may include: constructing a differential equation system based on Kirchhoff's law, line resistance, line inductance, filter inductance, filter capacitance, and damping resistance; and converting the differential equation system into a standard state equation form to obtain the system matrix and input matrix.

[0100] The state space equation is as follows:

[0101]

[0102] Where the coefficient matrix A and input matrix B are dynamically generated from the grid impedance and the parameters of the photovoltaic inverter LCL filter (inductor-capacitor-inductor filter). C and D are output matrices (C maps state to output, and D passes directly from input to output); x represents the state variable vector, which contains the inductor current , capacitor voltage Isodynamic quantities; u represents the control input vector, corresponding to the inverter PWM modulation (Pulse Width Modulation) signal or current command.

[0103] Combined with the virtual impedance of the adaptive frequency band for collaborative control, the resonance risk of the traditional LCL filter can be avoided by adjusting the frequency bands independently.

[0104] (voltage at the common coupling point, the interaction voltage between the grid and the inverter) is the virtual impedance The result of the interaction with the grid impedance. It is calculated by the output of the state space equation Decision, namely:

[0105]

[0106] In subsequent processing, the virtual impedance The harmonic current can be suppressed by changing the equivalent output impedance of the inverter. The distortion at the.

[0107] The impedance characteristic matrices A and B generated in this step serve as dynamic model parameters for the grid impedance and the PV inverter LCL filter, and are directly passed to the deep reinforcement learning in the following step to define the grid state space.

[0108] The real-time measurement value is used as the state variable of deep reinforcement learning It directly affects the reward function R of reinforcement learning and is used to evaluate the harmonic suppression effect.

[0109] To enable those skilled in the art to better understand the multimodal virtual impedance coordinated control in the technical solution of the present invention, the following describes the generation process of the correlation matrices A, B, C, and D in the above state-space equations, combined with the physical modeling of the grid impedance and the LCL filter parameters.

[0110] From the perspective of grid topology, the grid side impedance parameters include line resistance ,inductance For example, typical values: , .

[0111] The filter parameters of the inverter LCL filter include filter inductance ,capacitance , damping resistor For example, typical values: ).

[0112] The state variable is defined as .in, Indicates the grid side inductor current (corresponding to ); Indicates the filter capacitor voltage (corresponding to ); Indicates the inductor current on the inverter side (corresponding to ).

[0113] Matrix A (system matrix) is used to describe the dynamic characteristics of grid resonance and energy exchange.

[0114] Based on Kirchhoff's voltage / current law, the following differential equations are established:

[0115]

[0116] It can be organized into the standard state equation form , we can get:

[0117]

[0118] Matrix B (input matrix) is used to represent the inverter output voltage Driving effect on the state.

[0119] In differential equations middle, As the control input, we can get:

[0120]

[0121] The matrix C (output matrix) defines the observed output (corresponding to the common coupling point voltage ).

[0122] The output equation of matrix C is , which can be transformed into: .

[0123] The matrix D (through matrix) is used for direct transmission from input to output. Since there is no direct feedthrough scenario in the technical solution of the present invention, it can be simplified to .

[0124] For example, suppose the parameters of a photovoltaic power station are , , , , .

[0125] By calculation, we can get:

[0126]

[0127] Furthermore, the virtual impedance The influence of can also be embedded in the matrix A through a feedback loop to achieve dynamic expansion. Specifically, by modifying the equation: Will The transfer function is converted into state space form and the matrix dimension is expanded (equivalent to adding a virtual impedance state variable).

[0128] Step 104: Optimize the virtual impedance parameters based on deep reinforcement learning using the impedance characteristic matrix to obtain optimal virtual impedance parameters.

[0129] This step mainly uses the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to dynamically optimize the virtual impedance parameters to achieve parameter optimization based on deep reinforcement learning.

[0130] In the specific implementation, the impedance characteristic matrix is ​​used to perform parameter optimization of the virtual impedance based on deep reinforcement learning to obtain the optimal virtual impedance parameters. The execution process mainly includes the following sub-steps S11 to S18:

[0131] Step S11: Reconstruct the grid current components by combining the system matrix and the input matrix, and perform harmonic separation on the grid current components to obtain the total grid current;

[0132] Total grid current It can be obtained by the aid of computational model, i.e., the inversion of state space equation. Specifically, firstly, the state space model of step 103 is combined with , reconstructing the grid current components that cannot be directly measured through observers (such as Luenberger observer) :

[0133]

[0134] in, is the estimated value of the state variable.

[0135] Then the grid current components are subjected to harmonic separation. Perform Fast S Transform (FST) to separate the fundamental wave and harmonic currents . fundamental wave and harmonic currents The vector sum of the total grid current is :

[0136]

[0137] In another feasible embodiment, the total grid current may be obtained by deploying sensors to directly measure the source.

[0138] Specifically, a high-precision Hall effect current sensor is installed at the point of common coupling (PCC) to collect the grid-side current waveform in real time. , sampling frequency ≥ 10kHz (meeting the 50th harmonic measurement requirements). Then, the analog signal is converted into a discrete sequence through an anti-aliasing filter and ADC (Analog-to-Digital Converter). , to eliminate high-frequency noise interference.

[0139] When the grid environment is stable (e.g., no strong electromagnetic interference, well-calibrated sensors), direct measurement (high accuracy and strong real-time performance) is preferred. However, when sensors are faulty or interfered with (e.g., signal distortion caused by lightning strikes), high-frequency harmonics exceed the sensor bandwidth (e.g., components > 2kHz), or when predicting future conditions (e.g., predicting current changes before a sudden change in photovoltaic output), calculation models must be used to calculate the total grid current RMS.

[0140] For measurement or calculation errors, the allowable error for direct measurement is ±0.5% (e.g., for a sensor with a range of 200A, the error is ≤1A). The allowable error for model calculation is ±1.2%, which is mainly caused by fluctuations in power grid parameters.

[0141] Furthermore, when a large error (>1.2%) occurs, different levels of response measures can be set based on the specific error situation.

[0142] For example, when the error is between 1.5% and 3%, a level 1 (primary) response is set. At this time, you can: switch to a redundant sensor or backup model; trigger online calibration (such as injecting white noise to calibrate the sensor frequency response); and limit the inverter output to a safe threshold (such as 80% of rated power).

[0143] When the error is between 3% and 5%, a secondary (intermediate) response is set. At this time, you can: enable historical optimal parameter interpolation compensation; switch to "passive filtering" mode (in this case, safety is prioritized over efficiency); and upload the fault code to the operation and maintenance platform.

[0144] When the error is greater than 5%, a level 3 (advanced or emergency) response is set. At this time, the following can be performed: hard off-grid protection (to prevent equipment damage); manual inspection work orders are initiated (to locate sensor or grid fault points); and the model parameter library is updated (based on iterative training of fault data).

[0145] Therefore, by setting corresponding response measures for different error calculation situations, even if large errors occur during the calculation process, timely response can be made to avoid accidents.

[0146] Step S12: constructing an output matrix for the voltage at the point of common coupling according to the line resistance, and solving the voltage at the point of common coupling based on the output matrix;

[0147] Step S13: constructing a state space based on the multi-band harmonic frequencies, the total grid current, and the common coupling point voltage, while taking into account the harmonic distortion level;

[0148] The constructed state space is as follows:

[0149]

[0150] in, Reflects the current harmonic distortion level; multi-band harmonic frequency Characterize harmonic frequency band, common coupling point voltage and total grid current Characterize the operating status of the power grid. Represents the state vector (including real-time operation indicators of the power grid).

[0151] Step S14: generating an action space based on the state space and using the virtual impedance parameter as an update parameter;

[0152] Specifically, candidate parameter adjustment amounts of the virtual impedance parameter to be optimized are first generated according to the state space. Then, an action space for parameter adjustment is constructed based on the candidate parameter adjustment amounts, and a parameter update strategy for the action space is set. The parameter update strategy is to use the sum of the pre-update virtual impedance parameter and the candidate parameter adjustment amount as the updated virtual impedance parameter.

[0153] The first step is to generate the action space. Output: According to Generate candidate parameter adjustments and ,Right now:

[0154]

[0155] in, Represents the action vector (virtual impedance parameter adjustment amount); represents the increment of the hth harmonic virtual impedance gain (generated by the TD3 strategy network); Represents the h-th harmonic damping ratio increment (generated by the strategy network).

[0156] Followed by parameter update:

[0157]

[0158] The superscript new represents the updated parameters, and the superscript old represents the parameters before the update. The limit is ±10%. Action space constraints The limit is 0.1~0.9.

[0159] Step S15: constructing a reward function based on the virtual impedance, the multi-band harmonic frequencies, and the harmonic distortion levels;

[0160] The constructed reward function is as follows:

[0161]

[0162] Where R represents the immediate reward function, which is used to comprehensively evaluate harmonic suppression and energy consumption; 、 is the weight coefficient, which is used to dynamically balance THD suppression and energy consumption; the optimization goal is to maximize , that is, simultaneously reducing and reducing virtual impedance adjustment energy consumption; Represents the frequency domain characteristics of virtual impedance (i.e. exist The value at .

[0163] Step S16: constructing an action-value function based on the reward function, and constructing a policy network with a double-Q network structure based on the action-value function;

[0164] Action-value function Used to evaluate the long-term benefits of a state-action pair, the calculation formula is as follows:

[0165]

[0166] The policy network is updated as follows:

[0167]

[0168] in, Represents a discount factor that weighs the importance of current and future rewards; Represents the policy network parameters (Actor network weight matrix); Represents the Q network parameters (Critic network weight matrix).

[0169] There are other model parameters in the policy network, such as D, which represents the experience replay buffer, used to store historical state-action-reward sequences, with a capacity of 10 6 N represents the number of batch training samples, that is, the amount of data for a single gradient update, and the default is 256 per batch; t represents the time step, which is used to discretize the control cycle, and the control cycle is 1ms (synchronized with PWM).

[0170] Step S17: Iteratively update the policy network within the action space and minimize the loss function during the iterative update process;

[0171] The iterative update logic of the policy network is:

[0172] Calculate the target value through the dual Q network structure of the TD3 algorithm , and minimize the loss function .

[0173] Compared with the traditional trial-and-error method, the response speed can be improved by 50% through iterative updates of the strategy network, and the parameters can adapt to changes in the power grid topology.

[0174] Step S18: When the harmonic distortion level is less than the preset fluctuation threshold and the reward function reaches a steady state, the iteration is stopped, and the virtual impedance parameter obtained after the last iteration is output as the optimal virtual impedance parameter.

[0175] When 10 consecutive iterations When the fluctuation is less than 0.1% and the reward function R reaches a steady state, the current optimal parameters are output 、 .

[0176] Step 105 : changing the equivalent output impedance of the inverter by using the virtual impedance under the optimal virtual impedance parameters to achieve harmonic suppression.

[0177] When the optimal virtual impedance parameters are obtained through parameter optimization, the optimal virtual impedance parameters can be 、 The virtual impedance under 、 Injecting virtual impedance ) Change the equivalent output impedance of the inverter to achieve harmonic suppression.

[0178] Furthermore, harmonic energy recovery can be achieved through a three-level bidirectional DC / AC converter. Specifically, the optimal virtual impedance parameters are used as constraints to calculate the optimized harmonic power. The energy recovery efficiency is calculated based on the optimized harmonic power, and the reward function of deep reinforcement learning is adjusted based on the energy recovery efficiency to achieve bidirectional harmonic energy recycling.

[0179] Among them, optimizing harmonic power Calculated by the following formula:

[0180]

[0181] Constraint effect: When When optimized to high frequency band, Increase, so that compressed into a safe range.

[0182] The energy conversion efficiency is calculated by the following formula :

[0183]

[0184] in, is the DC power. is the switching loss; is the core loss; is the effective value of AC current; is the switching frequency.

[0185] Output After the value is set, the feedback parameter Feedback is given to the reward function R to modify the weight coefficient. The adaptive adjustment rule for weight coefficient modification is: , .

[0186] Therefore, through the two-way recycling of harmonic energy, 15% to 20% of the harmonic energy can be recovered, reducing the power consumption of the governance system itself.

[0187] For example, the traditional APF algorithm is compared with the technical solution provided by the embodiment of the present invention. The key indicator comparison results are shown in Table 1 below:

[0188] Table 1: Comparison results of key indicators between the traditional solution and the solution of the present invention

[0189]

[0190] As can be seen from Table 1, the technical solution of the present invention achieves faster response time, a wider range of harmonic distortion suppression, and better adaptability compared to traditional processing methods. Furthermore, due to the bidirectional recycling of harmonic energy, power consumption is reduced.

[0191] In an embodiment of the present invention, a harmonic suppression method based on multimodal virtual impedance collaborative control and deep reinforcement learning is provided. The first step is to obtain the original electrical signal, impedance parameters, and filter parameters on the grid side, which serve as basic data for subsequent harmonic suppression calculations. The second step is to perform real-time phase correction on the original electrical signal to obtain multi-band harmonic frequencies. Digital noise suppression and error correction are then performed based on the real-time phase correction, achieving dynamic harmonic decomposition and adaptive tracking of the original electrical signal. The third step is to construct a virtual impedance based on the multi-band harmonic frequencies, and generate an impedance characteristic matrix based on the impedance parameters and filter parameters. By constructing a frequency-band adaptive virtual impedance network and combining it with adaptive sub-band virtual impedance for collaborative control, the resonance risk of traditional LCL filters can be avoided by independently adjusting each frequency band, achieving sub-band harmonic resonance suppression. The fourth step is to use the impedance characteristic matrix to perform deep reinforcement learning-based parameter optimization of the virtual impedance to obtain the optimal virtual impedance parameters. By optimizing the virtual impedance parameters through an intelligent algorithm, a parameter combination with a wider range of harmonic distortion suppression and better harmonic suppression can be obtained. The fifth step is to change the equivalent output impedance of the inverter through the virtual impedance under the optimal virtual impedance parameters to achieve harmonic suppression. Furthermore, the optimal virtual impedance parameters can be used as constraints to calculate the optimized harmonic power, calculate the energy recovery efficiency based on the optimized harmonic power, and adjust the reward function of deep reinforcement learning based on the energy recovery efficiency to achieve bidirectional harmonic energy recycling and reduce the power consumption required for harmonic suppression. The technical solution of the present invention effectively solves the three major problems of strong time-varying harmonics, high energy consumption for governance, and parameter solidification in high-penetration photovoltaic scenarios through the coordination of four stages: signal decomposition-impedance control-intelligent optimization-energy circulation.

[0192] For better explanation, refer to Figure 2 , showing a schematic diagram of the overall process of a harmonic suppression method based on virtual impedance cooperative control provided by an embodiment of the present invention. It should be noted that this embodiment only briefly describes the general process of harmonic suppression based on virtual impedance cooperative control. The specific implementation process of each step can be understood by referring to the relevant content in the aforementioned embodiments. It will not be described here in detail. It is understood that the present invention is not limited to this.

[0193] Step 201: Obtaining the original electrical signal, impedance parameters on the grid side, and filtering parameters on the inverter side;

[0194] Step 202: Perform real-time phase correction on the original electrical signal by combining HHT signal decomposition and Kalman filtering to obtain multi-band harmonic frequencies;

[0195] Step 203: Using the gain coefficient and the damping ratio as virtual impedance parameters to be optimized, constructing a frequency-band adaptive virtual impedance according to the multi-band harmonic frequencies, and generating an impedance characteristic matrix according to the impedance parameters and the filter parameters based on Kirchhoff's law;

[0196] Step 204: Optimizing the virtual impedance parameters based on deep reinforcement learning using the impedance characteristic matrix to obtain optimal virtual impedance parameters, and changing the equivalent output impedance of the inverter using the virtual impedance under the optimal virtual impedance parameters to achieve harmonic suppression;

[0197] Step 205: Using the optimal virtual impedance parameter as a constraint, calculate the optimized harmonic power, calculate the energy recovery efficiency based on the optimized harmonic power, and adjust the reward function of the optimized deep reinforcement learning based on the energy recovery efficiency.

[0198] Reference Figure 3 , shows a structural block diagram of a harmonic suppression device based on virtual impedance coordinated control provided by an embodiment of the present invention, which may specifically include:

[0199] The data acquisition unit 301 is used to obtain the original electrical signal and impedance parameters on the grid side and the filter parameters on the inverter side;

[0200] A phase correction unit 302 is configured to perform real-time phase correction on the original electrical signal to obtain multi-band harmonic frequencies;

[0201] A virtual impedance and characteristic matrix construction unit 303 is configured to construct a virtual impedance based on the multi-band harmonic frequencies, and generate an impedance characteristic matrix based on the impedance parameters and the filtering parameters;

[0202] A parameter optimization unit 304 is configured to perform parameter optimization on the virtual impedance based on deep reinforcement learning using the impedance characteristic matrix to obtain optimal virtual impedance parameters;

[0203] The harmonic suppression unit 305 is configured to change the equivalent output impedance of the inverter by using the virtual impedance under the optimal virtual impedance parameters to achieve harmonic suppression.

[0204] In an optional embodiment, the phase correction unit 302 includes:

[0205] A signal decomposition unit, configured to decompose the original electrical signal to obtain a non-steady-state harmonic component;

[0206] A real-time phase error correction unit, configured to extract an instantaneous frequency according to the non-steady-state harmonic component, and perform real-time phase error correction on the instantaneous phase of the non-steady-state harmonic component based on Kalman filtering to obtain a phase correction error;

[0207] an instantaneous frequency optimization unit, configured to optimize the instantaneous frequency according to the phase correction error to obtain an optimized instantaneous frequency;

[0208] The multi-band harmonic frequency calculation unit is used to obtain the multi-band harmonic frequency by integral calculation according to the optimized instantaneous frequency.

[0209] In an optional embodiment, the virtual impedance and characteristic matrix construction unit 303 includes:

[0210] The virtual impedance construction unit is used to use the gain coefficient and the damping ratio as virtual impedance parameters to be optimized, and to construct a frequency-band adaptive virtual impedance according to the multi-band harmonic frequencies to achieve frequency-band suppression of harmonic resonance.

[0211] In an optional embodiment, the impedance parameters include line resistance and line inductance; the filtering parameters include filter inductance, filter capacitance, and damping resistance; the impedance characteristic matrix includes a system matrix and an input matrix; the virtual impedance and characteristic matrix construction unit 303 includes an impedance characteristic matrix generation unit, and the impedance characteristic matrix generation unit is specifically configured to:

[0212] Based on Kirchhoff's law, a differential equation system is constructed according to the line resistance, the line inductance, the filter inductance, the filter capacitor and the damping resistor;

[0213] The differential equations are converted into a standard state equation form to obtain a system matrix and an input matrix.

[0214] In an optional embodiment, the parameter optimization unit 304 includes:

[0215] a grid current processing unit, configured to reconstruct grid current components by combining the system matrix and the input matrix, and perform harmonic separation on the grid current components to obtain a total grid current;

[0216] a common coupling point voltage solving unit, configured to construct an output matrix related to the common coupling point voltage according to the line resistance, and solve the common coupling point voltage based on the output matrix;

[0217] a state space construction unit, configured to construct a state space based on the multi-band harmonic frequencies, the total grid current, and the common coupling point voltage, while taking into account the harmonic distortion level;

[0218] an action space construction unit, configured to generate an action space based on the state space and using a virtual impedance parameter as an update parameter;

[0219] a reward function construction unit, configured to construct a reward function according to the virtual impedance, the multi-band harmonic frequencies, and the harmonic distortion level;

[0220] a policy network construction unit, configured to construct an action-value function according to the reward function, and to construct a policy network having a dual-Q network structure according to the action-value function;

[0221] an iterative updating unit, configured to iteratively update the policy network within the action space and minimize a loss function during the iterative updating process;

[0222] The optimal virtual impedance parameter output unit is used to stop the iteration when the harmonic distortion level is less than a preset fluctuation threshold and the reward function reaches a steady state, and output the virtual impedance parameter obtained after the last iteration as the optimal virtual impedance parameter.

[0223] In an optional embodiment, the action space construction unit includes:

[0224] a candidate parameter adjustment amount generating unit, configured to generate a candidate parameter adjustment amount of a virtual impedance parameter to be optimized according to the state space;

[0225] An action space construction subunit is used to construct an action space for parameter adjustment based on the candidate parameter adjustment amount and set a parameter update strategy for the action space; the parameter update strategy is to use the sum of the virtual impedance parameter before update and the candidate parameter adjustment amount as the virtual impedance parameter after update.

[0226] In an optional embodiment, the device further includes:

[0227] An optimized harmonic power calculation unit, configured to calculate the optimized harmonic power using the optimal virtual impedance parameter as a constraint condition;

[0228] A reward function adjustment and optimization unit is used to calculate the energy recovery efficiency according to the optimized harmonic power, and adjust and optimize the reward function of deep reinforcement learning based on the energy recovery efficiency.

[0229] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the aforementioned method embodiment.

[0230] An embodiment of the present invention further provides an electronic device, the device including a processor and a memory:

[0231] The memory is used to store program codes and transmit the program codes to the processor;

[0232] The processor is configured to execute the harmonic suppression method based on virtual impedance cooperative control according to any embodiment of the present invention according to the instructions in the program code.

[0233] An embodiment of the present invention further provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the harmonic suppression method based on virtual impedance cooperative control according to any embodiment of the present invention.

[0234] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0235] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0236] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0237] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0238] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0239] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A harmonic suppression method based on virtual impedance cooperative control, characterized in that: include: Obtain the original electrical signal, impedance parameters on the grid side and filtering parameters on the inverter side; Performing real-time phase correction on the original electrical signal to obtain multi-band harmonic frequencies; Constructing a virtual impedance based on the multi-band harmonic frequencies, and generating an impedance characteristic matrix based on the impedance parameters and the filtering parameters; Using the impedance characteristic matrix to perform parameter optimization on the virtual impedance based on deep reinforcement learning to obtain optimal virtual impedance parameters; Changing the equivalent output impedance of the inverter by using the virtual impedance under the optimal virtual impedance parameters to achieve harmonic suppression; The performing real-time phase correction according to the original electrical signal to obtain multi-band harmonic frequencies includes: performing signal decomposition on the original electrical signal to obtain a non-steady-state harmonic component; Extracting the instantaneous frequency according to the non-steady-state harmonic component, and performing real-time phase error correction on the instantaneous phase of the non-steady-state harmonic component based on Kalman filtering to obtain a phase correction error; Optimizing the instantaneous frequency according to the phase correction error to obtain an optimized instantaneous frequency; According to the optimized instantaneous frequency, multi-band harmonic frequencies are obtained by integral calculation; The constructing of a virtual impedance based on the multi-band harmonic frequencies includes: The gain coefficient and the damping ratio are used as virtual impedance parameters to be optimized, and a frequency-band adaptive virtual impedance is constructed according to the multi-band harmonic frequencies to achieve frequency-band suppression of harmonic resonance.

2. The harmonic suppression method based on virtual impedance cooperative control according to claim 1, characterized in that: The impedance parameters include line resistance and line inductance; the filtering parameters include filter inductance, filter capacitance and damping resistance; The impedance characteristic matrix includes a system matrix and an input matrix; and generating the impedance characteristic matrix based on the impedance parameters and the filter parameters includes: Based on Kirchhoff's law, a differential equation system is constructed according to the line resistance, the line inductance, the filter inductance, the filter capacitor and the damping resistor; The differential equations are converted into a standard state equation form to obtain a system matrix and an input matrix.

3. The harmonic suppression method based on virtual impedance cooperative control according to claim 2, characterized in that: The adopting the impedance characteristic matrix to perform parameter optimization on the virtual impedance based on deep reinforcement learning to obtain optimal virtual impedance parameters includes: Reconstructing grid current components by combining the system matrix and the input matrix, and performing harmonic separation on the grid current components to obtain a total grid current; constructing an output matrix related to the voltage at the common coupling point according to the line resistance, and solving the voltage at the common coupling point based on the output matrix; constructing a state space based on the multi-band harmonic frequencies, the total grid current, and the common coupling point voltage, while taking into account the harmonic distortion level; generating an action space based on the state space and using a virtual impedance parameter as an update parameter; constructing a reward function according to the virtual impedance, the multi-band harmonic frequencies, and the harmonic distortion level; Constructing an action-value function according to the reward function, and constructing a policy network having a double-Q network structure according to the action-value function; Iteratively updating the policy network within the action space and minimizing the loss function during the iterative updating process; When the harmonic distortion level is less than the preset fluctuation threshold and the reward function reaches a steady state, the iteration is stopped and the virtual impedance parameter obtained after the last iteration is output as the optimal virtual impedance parameter.

4. The harmonic suppression method based on virtual impedance cooperative control according to claim 3 is characterized in that: The step of generating an action space based on the state space and using a virtual impedance parameter as an update parameter includes: generating candidate parameter adjustment amounts of virtual impedance parameters to be optimized according to the state space; An action space for parameter adjustment is constructed based on the candidate parameter adjustment amount, and a parameter update strategy for the action space is set; the parameter update strategy is to use the sum of the pre-update virtual impedance parameter and the candidate parameter adjustment amount as the post-update virtual impedance parameter.

5. The harmonic suppression method based on virtual impedance cooperative control according to any one of claims 1 to 4, characterized in that: Also includes: Calculate the optimized harmonic power using the optimal virtual impedance parameter as a constraint condition; An energy recovery efficiency is calculated according to the optimized harmonic power, and a reward function of deep reinforcement learning is adjusted and optimized based on the energy recovery efficiency.

6. A harmonic suppression device based on virtual impedance cooperative control, characterized in that: include: A data acquisition unit, used to obtain the original electrical signal and impedance parameters on the grid side and the filtering parameters on the inverter side; A phase correction unit, configured to perform real-time phase correction on the original electrical signal to obtain multi-band harmonic frequencies; a virtual impedance and characteristic matrix construction unit, configured to construct a virtual impedance based on the multi-band harmonic frequencies, and generate an impedance characteristic matrix based on the impedance parameters and the filtering parameters; a parameter optimization unit, configured to perform parameter optimization on the virtual impedance based on deep reinforcement learning using the impedance characteristic matrix to obtain optimal virtual impedance parameters; a harmonic suppression unit, configured to change the equivalent output impedance of the inverter by using the virtual impedance under the optimal virtual impedance parameters to achieve harmonic suppression; The phase correction unit comprises: A signal decomposition unit, configured to decompose the original electrical signal to obtain a non-steady-state harmonic component; A real-time phase error correction unit, configured to extract an instantaneous frequency according to the non-steady-state harmonic component, and perform real-time phase error correction on the instantaneous phase of the non-steady-state harmonic component based on Kalman filtering to obtain a phase correction error; an instantaneous frequency optimization unit, configured to optimize the instantaneous frequency according to the phase correction error to obtain an optimized instantaneous frequency; a multi-band harmonic frequency calculation unit, configured to obtain the multi-band harmonic frequency by integral calculation according to the optimized instantaneous frequency; The virtual impedance and characteristic matrix construction unit includes: The virtual impedance construction unit is used to use the gain coefficient and the damping ratio as virtual impedance parameters to be optimized, and to construct a frequency-band adaptive virtual impedance according to the multi-band harmonic frequencies to achieve frequency-band suppression of harmonic resonance.

7. An electronic device, characterized in that: The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the harmonic suppression method based on virtual impedance cooperative control according to any one of claims 1 to 5 according to the instructions in the program code.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store program code, and the program code is used to execute the harmonic suppression method based on virtual impedance cooperative control according to any one of claims 1 to 5.

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