Harmonic suppression method and system for optical storage alternating current coupling system, and electronic equipment

Abstract

The invention provides an optical storage alternating current coupling system harmonic suppression method and system, electronic equipment and a storage medium, abnormal equipment is eliminated by acquiring the working states of harmonic suppression execution units such as a photovoltaic inverter and an energy storage converter, and instantaneous voltage and current signals of a common connection point and a key node are synchronously acquired by a monitoring point sensor; windowing interpolation FFT analysis is carried out on the collected signals to obtain harmonic data, a recursive least square method is combined to identify a system impedance model online and generate an impedance amplitude-frequency characteristic curve, the resonance risk is judged by comparing the impedance amplitude-frequency characteristic curve with a preset safety threshold value, and then the mode is switched to a resonance suppression mode or a harmonic compensation mode; and finally, an instruction is issued to an execution unit to cooperatively output damping current or harmonic compensation current, meanwhile, the effect is evaluated in real time, and parameters are readjusted to repeat the core process if the result does not reach the standard or meets new disturbance. The problems of compensation resource waste and no system-level regulation and control in the prior art are solved, and the coordination of harmonic suppression and resonance prevention and control is realized.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of power systems and their automation technology, and in particular to a harmonic suppression method, system, electronic device and storage medium for an optical-storage AC coupling system. Background Technology

[0002] As the global energy structure shifts towards cleaner and distributed energy, the scale of distributed energy sources such as photovoltaics and energy storage, as well as power electronic loads, on the AC side continues to expand. These devices rely on power electronic devices such as inverters and converters to achieve power conversion and grid connection. However, the high-frequency switching characteristics of power electronic devices inevitably generate a large number of harmonic components. These harmonics not only cause voltage waveform distortion and increased effective current values, but may also interfere with the normal operation of precision power equipment and even shorten the service life of critical equipment such as transformers and cables in the power grid. This poses a serious challenge to the power quality on the AC side and has become one of the core bottlenecks restricting the large-scale consumption of distributed energy.

[0003] Current mainstream harmonic suppression technologies have significant limitations and are difficult to adapt to the operational requirements of complex photovoltaic-storage AC coupling systems. While passive filters are low-cost, their filtering effect depends entirely on the system impedance characteristics, making them prone to parallel resonance with the grid impedance, which amplifies specific harmonics. Furthermore, they can only suppress harmonics at preset frequencies and cannot address scenarios where harmonic frequencies in the system change dynamically. Independent active filters, while capable of broadband harmonic compensation, require separate installation space as additional hardware, significantly increasing system construction costs and complexity. Moreover, their compensation capacity is fixed, and they are prone to failure due to insufficient capacity when the system is expanded or there is a sudden increase in harmonic current.

[0004] Furthermore, while some photovoltaic inverters and power conversion systems (PCS) integrate basic filtering functions and can optimize their own output harmonics through local control algorithms, they lack a system-level collaborative perspective. These devices can only passively suppress harmonics generated by themselves and cannot address harmonics introduced by other devices or loads, let alone identify and suppress network resonance risks at the system level. As the scale of photovoltaic and energy storage systems expands and operating conditions become more complex, the contradiction between the local filtering capability of individual devices and the overall power quality requirements of the system becomes increasingly prominent, urgently requiring a harmonic suppression solution that balances economy, flexibility, and systemic considerations. Summary of the Invention

[0005] This invention provides a harmonic suppression method, system, electronic device, and storage medium for an optical-storage AC coupling system, to solve the technical problems in the prior art, such as the lack of cloud collaboration mechanisms and network adaptability design leading to the easy retention and loss of key data, the waste of resources due to the lack of integration with vehicle operation plans, and the single dimension of video value judgment easily causing important data to be covered.

[0006] In a first aspect, embodiments of the present invention provide a harmonic suppression method for an optical-storage AC coupling system, comprising: S1. Obtain the operating status of each harmonic suppression execution unit in the photovoltaic-storage AC coupling system, eliminate harmonic suppression execution units with abnormal operating status, and collect voltage and current signals of the common connection point (PCC) and preset key nodes; the harmonic suppression execution unit includes a photovoltaic inverter and an energy storage converter; the preset key nodes include the AC output terminal of the photovoltaic inverter and the AC output terminal of the energy storage converter. S2. Perform windowed interpolation FFT analysis on the acquired voltage and current signals to calculate harmonic data, including total harmonic distortion (THD), amplitude, and phase of each harmonic. Based on the harmonic data obtained from the FFT analysis during normal operation of the photovoltaic-storage AC coupling system, use the recursive least squares (RLS) method to identify the system impedance model online and generate an impedance amplitude-frequency characteristic curve. Compare the impedance magnitude at each frequency point in the impedance amplitude-frequency characteristic curve with a preset safety threshold. If the impedance magnitude at any frequency point exceeds the corresponding preset safety threshold, it is determined that the photovoltaic-storage AC coupling system has a resonance risk and enters the resonance suppression mode. If the impedance magnitude at all frequency points does not exceed the preset safety threshold, it is determined that the photovoltaic-storage AC coupling system has no resonance risk and enters the harmonic compensation mode. S3. If in resonance suppression mode, calculate the required virtual impedance for the identified resonance frequency and convert the virtual impedance into the corresponding current compensation command; if in harmonic compensation mode, generate the optimal compensation task allocation scheme through a multi-objective optimization algorithm based on the total harmonic current analysis results and the real-time status information of each harmonic suppression execution unit. S4. Send the current compensation command or the optimal compensation task allocation scheme to the corresponding harmonic suppression execution unit, and have each harmonic suppression execution unit output the damping current or harmonic compensation current in coordination.

[0007] S5. Real-time evaluation of harmonic suppression effect. If the harmonic content drops to the preset allowable range, maintain the current strategy. If the target is not met or a new disturbance occurs in the photovoltaic-storage AC coupling system, trigger the optimization mechanism, readjust the decision parameters, and repeat S2-S4.

[0008] Secondly, an embodiment of the present invention provides a harmonic suppression system for an optical-storage AC coupling system, comprising: The data acquisition module acquires the operating status of each harmonic suppression execution unit in the photovoltaic-storage AC coupling system, eliminates harmonic suppression execution units with abnormal operating status, and acquires voltage and current signals of the common connection point (PCC) and preset key nodes. The harmonic suppression execution units include photovoltaic inverters and energy storage converters. The preset key nodes include the AC output terminals of the photovoltaic inverters and the AC output terminals of the energy storage converters. The analysis and mode determination module performs windowed interpolation FFT analysis on the acquired voltage and current signals to calculate harmonic data, including total harmonic distortion (THD) and the amplitude and phase of each harmonic. Based on the harmonic data obtained from the FFT analysis of the photovoltaic-storage AC coupling system during normal operation, the system impedance model is identified online using the recursive least squares (RLS) method to generate an impedance amplitude-frequency characteristic curve. The impedance magnitude at each frequency point in the impedance amplitude-frequency characteristic curve is compared with a preset safety threshold. If the impedance magnitude at any frequency point exceeds the corresponding preset safety threshold, the photovoltaic-storage AC coupling system is determined to have a resonance risk and enters the resonance suppression mode. If the impedance magnitude at all frequency points does not exceed the preset safety threshold, the photovoltaic-storage AC coupling system is determined to have no resonance risk and enters the harmonic compensation mode. If the instruction generation module is in resonance suppression mode, it calculates the required virtual impedance for the identified resonant frequency and converts the virtual impedance into the corresponding current compensation instruction; if it is in harmonic compensation mode, it generates the optimal compensation task allocation scheme through a multi-objective optimization algorithm based on the total harmonic current analysis results and the real-time status information of each harmonic suppression execution unit. The harmonic suppression execution module sends current compensation commands or optimal compensation task allocation schemes to the corresponding harmonic suppression execution units, which then work together to output damping current or harmonic compensation current.

[0009] Thirdly, embodiments of the present invention provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the harmonic suppression method for an optical-storage AC coupling system as described in the first aspect of the present invention.

[0010] Fourthly, embodiments of the present invention provide a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the harmonic suppression method for an optical-storage AC coupling system as described in the first aspect of the present invention.

[0011] This invention provides a method, system, electronic device, and storage medium for harmonic suppression in a photovoltaic-storage AC coupling system. The method involves acquiring the operating status of harmonic suppression execution units such as photovoltaic inverters and energy storage converters to eliminate abnormal devices. Then, sensors at monitoring points synchronously collect instantaneous voltage and current signals from the point of common coupling (PCC) and key nodes. Windowed interpolation FFT analysis is performed on the collected signals to obtain harmonic data. The system impedance model is then identified online using the recursive least squares (RLS) method, generating an impedance amplitude-frequency characteristic curve. The resonance risk is determined by comparing this curve with a preset safety threshold. The system then switches to either resonance suppression mode (calculating a virtual negative resistance and converting it into a damping current command) or harmonic compensation mode (generating a compensation task allocation scheme using a multi-objective optimization algorithm based on the real-time status of each execution unit). Finally, commands are sent to the execution units to collaboratively output damping current or harmonic compensation current, while simultaneously evaluating the effect in real time. If the target is not met or a new disturbance is encountered, the parameters are readjusted, and the core process is repeated. To address the issues of resource waste and lack of system-level control in existing technologies, this approach achieves synergy between harmonic suppression and resonance control through precise selection of effective equipment, synchronous signal analysis, and dynamic mode switching. This ensures the power quality of the photovoltaic-storage system (total harmonic distortion rate reduced to the national standard range) while avoiding equipment overload and battery loss, thereby improving system stability and economy. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a harmonic suppression method for an optical-storage AC coupling system according to an embodiment of the present invention; Figure 2 This is a block diagram of a harmonic suppression system for an optical-storage AC coupling system according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the physical structure according to an embodiment of the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] This invention provides a harmonic suppression method for an optical-storage AC coupling system, such as... Figure 1 As shown, it includes: S1. Obtain the operating status of each harmonic suppression execution unit in the photovoltaic-storage AC coupling system, eliminate harmonic suppression execution units with abnormal operating status, and collect voltage and current signals of the common connection point (PCC) and preset key nodes; the harmonic suppression execution unit includes a photovoltaic inverter and an energy storage converter; the preset key nodes include the AC output terminal of the photovoltaic inverter and the AC output terminal of the energy storage converter. S2. Perform windowed interpolation FFT analysis on the acquired voltage and current signals to calculate harmonic data, including total harmonic distortion (THD), amplitude, and phase of each harmonic. Based on the harmonic data obtained from the FFT analysis during normal operation of the photovoltaic-storage AC coupling system, use the recursive least squares (RLS) method to identify the system impedance model online and generate an impedance amplitude-frequency characteristic curve. Compare the impedance magnitude at each frequency point in the impedance amplitude-frequency characteristic curve with a preset safety threshold. If the impedance magnitude at any frequency point exceeds the corresponding preset safety threshold, it is determined that the photovoltaic-storage AC coupling system has a resonance risk and enters the resonance suppression mode. If the impedance magnitude at all frequency points does not exceed the preset safety threshold, it is determined that the photovoltaic-storage AC coupling system has no resonance risk and enters the harmonic compensation mode. S3. If in resonance suppression mode, calculate the required virtual impedance for the identified resonance frequency and convert the virtual impedance into the corresponding current compensation command; if in harmonic compensation mode, generate the optimal compensation task allocation scheme through a multi-objective optimization algorithm based on the total harmonic current analysis results and the real-time status information of each harmonic suppression execution unit. S4. Send the current compensation command or the optimal compensation task allocation scheme to the corresponding harmonic suppression execution unit, and have each harmonic suppression execution unit output the damping current or harmonic compensation current in coordination.

[0016] S5. Real-time evaluation of harmonic suppression effect. If the harmonic content drops to the preset allowable range, maintain the current strategy. If the target is not met or a new disturbance occurs in the photovoltaic-storage AC coupling system, trigger the optimization mechanism, readjust the decision parameters, and repeat S2-S4.

[0017] Specifically, in this embodiment, in step S1, the harmonic suppression execution unit refers to the core equipment in the photovoltaic-storage AC coupling system that has the ability to perform resonance suppression or harmonic compensation. In this embodiment, it includes a photovoltaic inverter (which converts photovoltaic DC power into AC power and integrates it into the system, and can use the remaining capacity to output compensation current) and an energy storage converter (which has both resonance suppression damping current output and harmonic compensation current output functions), and is the direct execution body of the central harmonic control unit's instructions.

[0018] Preset key nodes refer to the locations in the system where key electrical signals need to be monitored. In this embodiment, these are the AC output terminals of the photovoltaic inverter and the AC output terminals of the energy storage converter. The electrical signals at these nodes can directly reflect the operating status of the equipment and the harmonic contribution.

[0019] The point of common coupling (PCC) refers to the connection point between the photovoltaic and energy storage system and the power grid. Its electrical signals (voltage and current) are the core basis for assessing the overall power quality of the system and judging the risk of harmonics and resonance.

[0020] The central harmonic control unit (CHCU) acquires the operating status of the execution unit and eliminates abnormal devices, thus avoiding the subsequent issuance of instructions to faulty, standby, or other devices without execution capabilities, reducing ineffective control. Simultaneously, it collects the electrical signals of the PCC and key nodes, providing high-quality raw data for subsequent frequency domain analysis and impedance modeling in S2, ensuring the accuracy of subsequent decisions from the source and avoiding control deviations caused by missing or invalid data.

[0021] Furthermore, in step S2, the total harmonic distortion (THD) is an indicator reflecting the degree of distortion of electrical signal waveforms. It includes the total harmonic voltage distortion (THDU) and the total harmonic current distortion (THDI), which are calculated from the root mean square values ​​of the fundamental frequency and each harmonic, respectively. It is the core quantitative indicator for assessing the degree of harmonic pollution of the system.

[0022] Recursive Least Square (RLS): An online parameter identification algorithm that iteratively updates the parameters to be estimated (system resistance, reactance) and can quickly track parameter changes. It is suitable for constructing system impedance models in dynamic operation scenarios of photovoltaic and energy storage systems.

[0023] Impedance amplitude-frequency response curve: This curve reflects the change of the impedance magnitude of the system at different frequencies. It can intuitively present the system impedance characteristics at each frequency point and is a key basis for judging the risk of resonance (the frequency point of resonance risk corresponds to the local maximum of the impedance magnitude).

[0024] Preset safety threshold: Z is calculated based on the IEEE Std 519 standard, combined with the maximum permissible harmonic voltage limit and the maximum possible harmonic current value at each frequency point of the system. thre (f)=V hmax (f) / I hmax(f) is used to determine whether the impedance magnitude exceeds the safe range, and thus to determine the risk of resonance.

[0025] Windowed interpolation FFT analysis accurately acquires harmonic data, providing a quantitative basis for impedance modeling and mode determination; the RLS algorithm identifies the system impedance model online and generates amplitude-frequency characteristic curves, enabling real-time monitoring of the system's dynamic impedance characteristics; based on the comparison and determination mode between impedance magnitude and safety threshold, it can accurately distinguish between resonance risks and harmonic exceedance scenarios, avoiding blind control; the resonance suppression mode specifically addresses resonance hazards, and the harmonic compensation mode focuses on harmonic mitigation, improving the accuracy and targeting of system control.

[0026] Furthermore, in step S3, the virtual impedance is the equivalent impedance designed to suppress resonance. In this embodiment, a pure negative resistance structure is adopted. The resistance value is determined by calculating the ratio of the system impedance magnitude to the damping coefficient at the resonance frequency point, which can increase the damping of the system near the resonance frequency and offset the resonance energy. The current compensation command, i.e., the resonance suppression mode, converts the virtual impedance into the instantaneous value of the damping current according to Ohm's law, which is used to control the execution unit to output the corresponding current to suppress resonance. The total harmonic current analysis result is the FFT analysis data based on S2. The extracted and reconstructed total harmonic current information of the system (such as the total harmonic current amplitude and the proportion of each harmonic current) is the basis for determining the total harmonic compensation requirement. The multi-objective optimization algorithm takes into account the optimal compensation effect (total harmonic current reduced to within the limit), the minimum equipment loss (avoiding execution unit overload), and the optimal economy (prioritizing the use of remaining photovoltaic capacity). By analyzing the real-time status information of the execution unit, the compensation task of each device is allocated. The real-time status information of the execution unit includes the AC output active power and remaining available capacity of the photovoltaic inverter (reflecting its compensation capability), the operating mode of the energy storage converter, and the state of charge (SOC) of the battery (constraining its execution range), which are the core inputs for the optimization algorithm to formulate a scheme.

[0027] In resonance suppression mode, the current compensation command of virtual impedance conversion can accurately target the resonance frequency, effectively offset the resonance risk, and avoid the impact of resonance on system equipment. In harmonic compensation mode, the scheme generated by the multi-objective optimization algorithm can make full use of the capabilities of each execution unit, prioritize the allocation of tasks to low-loss and high-economical equipment (such as photovoltaic inverters), and avoid overload of energy storage converters or overcharging and discharging of batteries. While ensuring the harmonic compensation effect, it reduces equipment losses and improves system economy.

[0028] Furthermore, in S4, coordinated output refers to multiple harmonic suppression execution units operating synchronously according to instructions. The photovoltaic inverter, energy storage converter (and the backup dedicated APF) jointly output damping current (resonance suppression mode) or harmonic compensation current (harmonic compensation mode) according to the allocated task, so as to avoid excessive load on a single device and ensure stable execution effect.

[0029] Damping current: The current output by the execution unit in resonance suppression mode. Its amplitude and phase can cancel the energy generated by system resonance, suppress the abnormal rise of impedance magnitude, and eliminate the risk of resonance.

[0030] Harmonic compensation current: The current output by the execution unit in harmonic compensation mode has an amplitude and phase opposite to the system harmonic current, which can cancel the original harmonic current of the system and reduce the total harmonic distortion rate.

[0031] The instructions are accurately sent to the corresponding execution units to ensure that each device understands its own workload; through coordinated output, the system avoids overload shutdown caused by a single device bearing too much workload, thus ensuring the stability of the execution process; and finally, through the output of damping current or harmonic compensation current, the system achieves the goal of eliminating resonance risk and reducing harmonic content to the national standard allowable range, thereby significantly improving the power quality and operational stability of the photovoltaic-storage AC coupling system.

[0032] Based on the above embodiments, as a preferred implementation, in step S2, windowed interpolation FFT analysis is performed on the acquired voltage and current signals, including: To acquire discrete voltage and current signal sequences at a sampling frequency that satisfies the Nyquist sampling theorem, and the sampling rate must be able to capture at least the 50th harmonic; A windowed signal is obtained by applying a window function with excellent sidelobe attenuation characteristics to a discrete signal sequence. Perform an FFT transform on the windowed signal to obtain the discrete spectrum; The bi-spectral interpolation method is used to find the spectral line with the largest amplitude near the target harmonic and its adjacent spectral lines, and to calculate the frequency offset, the true harmonic frequency, and the corrected harmonic amplitude and phase.

[0033] Specifically, windowed interpolation FFT analysis is performed on the acquired voltage and current signals, including: A) Data acquisition and preprocessing: The high-speed synchronous acquisition unit operates at a fixed sampling frequency f s To ensure analysis accuracy, the sampling rate of a voltage or current signal x(n) is typically required to satisfy the Nyquist sampling theorem and capture at least the 50th harmonic. The acquired finite-length sequence is a windowed truncation of the actual signal.

[0034] B) Apply a window function w(n) to the discrete signal sequence x(n) to reduce the spectral leakage effect caused by non-integer period truncation. In this embodiment of the invention, a window function with excellent sidelobe attenuation characteristics is preferably used. The windowed signal is: x w (n)=x(n) w(n) Explanation: This indicates that the discrete signal x(n) is multiplied by the window function w(n) at sampling point n to obtain the windowed signal x. w (n). Where n = 0, 1, 2, ..., N 1, where N is the number of sampling points.

[0035] C) Perform a Fast Fourier Transform (FFT) on the windowed signal x / w(n) to transform it from the time domain to the frequency domain, obtaining its discrete spectrum X(k). This is achieved through the Discrete Fourier Transform. X(k) = x w (n)e -j(2π / N)nk This is the windowing signal x w The discrete Fourier transform formula for (n) is used to calculate its complex spectrum value X(k) at frequency point k, where k = 0, 1, 2, ..., N. 1, j is the imaginary unit, and e is a mathematical constant.

[0036] D) Spectral line interpolation calculation: This invention uses a dual-line interpolation method (interpolating the spectral line with the largest amplitude and its adjacent spectral line) to correct the measured values. The specific steps are as follows: 1) Find the spectral line with the largest amplitude near the target harmonic in the spectrum X(k), and let its index be k. m 2) Let the adjacent spectral line with a larger amplitude be k. m+1 or k m 3) Define the amplitude ratio function, which has different forms for different window functions.

[0037] Interpolation estimation formula for frequency offset (g) g≈(λβ-μ) / 1+β This formula is used to estimate the true harmonic frequency relative to the index k of the most recent FFT spectral line. m offset g β=∣Y(k m+1 )∣ / ∣Y(k m | is the ratio of the amplitudes of two adjacent spectral lines. λ and μ are specific interpolation coefficients that depend on the chosen window function (e.g., for a 4-term 3rd-order Blackman-Harris window, λ≈2.566, μ≈1.433).

[0038] E) Formula for calculating the true harmonic frequency f h =(k m +g) (f s / N) This formula is used to calculate the precise harmonic frequency f.h km is the FFT spectral index with the largest amplitude.

[0039] g is the frequency offset calculated through interpolation.

[0040] f s It is the sampling frequency.

[0041] N is the number of FFT points.

[0042] Δf=f s / N is the frequency resolution.

[0043] F) Interpolation correction formula for harmonic amplitude This formula is used to correct the estimated value A of the harmonic amplitude based on the interpolation result. h This makes it closer to the true value.

[0044] A h =(2∣Y(k m )∣ πg) / (N sin(πg) |W(g)|) |Y(km)| is the FFT at k m The amplitude of the spectral line.

[0045] |W(δ)| is the value of the main lobe amplitude function of the spectrum of the applied window function at the offset g (this is a pre-calculated coefficient related to the window function).

[0046] A) Interpolation correction formula for harmonic phase α=∠Y(k m )-πg ∠Y(k m ) is FFT at k m Phase angle (in radians) of the spectral line. πg is the phase offset used for correction.

[0047] Based on the above embodiments, as a preferred implementation, in S2, the total harmonic distortion rate includes the voltage total harmonic distortion rate THDU and the current total harmonic distortion rate THDI; THD U ={√( U h 2 )} / U1 THD I ={√( I h 2 )} / I1 Where U1 is the root mean square value of the fundamental voltage, U1 = A(U1) / √2, A(U1) represents the amplitude of the fundamental voltage in the photoelectric-storage AC coupling system, U h U is the root mean square value of the h-th harmonic voltage. h =A(U h ) / √2,A(U h Ih represents the amplitude of the h-th harmonic voltage in the photovoltaic-storage AC coupling system; I1 is the root mean square value of the fundamental current, I1 = A(I1) / √2, where A(I1) represents the amplitude of the fundamental current in the photovoltaic-storage AC coupling system. h I is the root mean square value of the h-th harmonic current. h =A(I h ) / √2, A(I h ) represents the amplitude of the h-th harmonic current in the photovoltaic-storage AC coupling system.

[0048] Specifically, harmonic amplitude calculation: For the h-th harmonic (h=2,3,4,...,H) max H max ≥50), its amplitude A h The output is directly derived from the interpolation FFT algorithm. Similarly, the initial phase angle α of the h-th harmonic is also directly output by the algorithm. Total harmonic distortion (THD) is a macroscopic indicator that measures the degree of waveform distortion.

[0049] THD is calculated based on the amplitude of the fundamental component (first harmonic), and the result is a percentage value that intuitively reflects the overall severity of harmonic pollution. THD values ​​are monitored in real time and compared with preset national standards (such as IEEE Std 519) limits to determine whether to activate or adjust compensation strategies.

[0050] Based on the above embodiments, as a preferred implementation, in step S2, the recursive least squares (RLS) method is used to identify the system impedance model online, including: For a specific frequency corresponding to a harmonic in a photovoltaic-storage AC coupling system, a voltage-current linear relationship model is constructed. The voltage frequency domain data measured at a specific time at a specific frequency is used as the output result of the voltage-current linear relationship model, and the real and imaginary parts of the current frequency domain data at the same time at a specific frequency are used as the input variables of the voltage-current linear relationship model. Noise and model error terms are included to form a complete linear regression relationship. Set initial values ​​for the parameters, setting the initial estimates of the system resistance and reactance parameters to be identified to 0; set the initial value of the covariance matrix so that the system impedance model is an identity matrix containing large positive numbers. For each new sampling time, update the parameters according to the following steps: Based on the current frequency domain data collected at the current moment, organize the input variable group containing the real part and imaginary part of the current; Calculate the prior error: Subtract the voltage frequency domain data predicted based on the parameter estimate of the previous moment from the voltage frequency domain data actually measured at the current moment to obtain the deviation between the current measured value and the predicted value; Calculate the gain matrix: Combine the covariance matrix of the previous time step, the current set of input variables, and a forgetting factor between 0 and 1 to determine the contribution weight of the current measurement data to the parameter update. Update parameter estimates: Use the parameter estimates from the previous time step, add the product of the gain matrix and the prior error, and obtain the corrected system resistance and reactance parameter estimates for the current time step. Update the covariance matrix: Combine the forgetting factor, gain matrix, and input variable set to adjust the covariance matrix of the previous time step to obtain a new matrix that reflects the uncertainty of parameter estimation at the current time step; Calculate system impedance: After the iterative process converges, extract the final estimated values ​​of system resistance and reactance, take the resistance as the real part and the reactance as the imaginary part, and combine them to form the complex impedance of the system at that frequency; calculate the magnitude of the impedance based on the values ​​of resistance and reactance, repeat the above steps to cover all the frequency points of interest, and finally generate the impedance amplitude-frequency response curve.

[0051] Specifically, in-depth system impedance analysis includes: A) In-depth system impedance analysis is a crucial technical prerequisite for the Central Harmonic Control Unit (CHCU) to achieve resonance early warning and active suppression. Its purpose is to obtain the system-side impedance frequency response curve Z, viewed from the point of common coupling, online and accurately. sys (f) thus identifying potential resonance points, providing accurate data for subsequent "impedance reshaping" strategies.

[0052] B) Based on Ohm's law, by injecting a known disturbance current ΔI at point PCC and measuring the resulting voltage response ΔV, the system impedance at that frequency can be calculated. Z sys (f) = ΔV(f) / ΔI(f) ΔV(f): The complex frequency domain representation of the voltage response measured at the same frequency f (obtained by FFT analysis).

[0053] ΔI(f): The complex frequency domain representation of the current response measured at the same frequency f.

[0054] C) Synchronous Acquisition: While injecting the disturbance current ΔI_{pcc}(t) into the PCS, the voltage response ΔV_{pcc}(t) at the PCC point is simultaneously measured through a high-speed synchronous acquisition unit. Synchronization is guaranteed by the IEEE 1588 PTP protocol, which is the cornerstone of accurate impedance phase calculation.

[0055] D) Spectrum Analysis: Windowed interpolation FFT analysis is performed on the acquired time-domain current and voltage signals ΔI_{pcc}(t) and ΔV_{pcc}(t) respectively to accurately extract the disturbance frequency f1. Current amplitude |ΔIf1| Voltage amplitude |ΔVf1| Current phase θ I (f1) Voltage phase θ V (f1) E) Complex impedance calculation: Based on the FFT analysis results, calculate the complex system impedance at frequency f1: Z sys (f1)=ΔV(f1) / ΔI(f1)=∣ΔVf1∣ e jθv / ∣ΔIf1∣ e jθI =∣Z sys | e jθz Impedance magnitude (amplitude) calculation formula |Z sys (f)∣=∣ΔV(f)∣ / ∣ΔI(f)∣ Impedance angle (phase) calculation formula: θz=θv-θ I By changing the frequency f1 of the injected signal and repeating the above steps, a series of impedance magnitudes and phases corresponding to different frequency points can be obtained. Connecting these points forms the system impedance-frequency characteristic curve Z as viewed from the PCC point. sys (f).

[0056] Based on the above embodiments, as a preferred implementation, step S3, calculating the required virtual impedance for the identified resonant frequency, includes: Based on the impedance amplitude-frequency characteristic curve, extract the system impedance magnitude corresponding to the resonant risk frequency point; determine the damping coefficient, and divide the system impedance magnitude at the resonant frequency point by the selected damping coefficient to obtain the specific resistance value of the virtual negative resistance. The instantaneous voltage signal of the point of common coupling (PCC) is acquired in real time. Based on the logical relationship of Ohm's law, the value of the acquired instantaneous voltage signal of PCC is divided by the resistance value of the virtual negative resistor, and a negative sign is added to the result to obtain the instantaneous value of the damping current output by the harmonic suppression execution unit.

[0057] Specifically, in this embodiment, in addition to using the active perturbation method for impedance measurement, an online system impedance identification scheme based on the recursive least squares method is also provided. This method, as a supplementary means without or with weak excitation, can continuously and silently update the system impedance model using harmonic data during normal system operation. It is particularly suitable for tracking the slow time-varying characteristics of system impedance, such as changes caused by load switching, transformer tap adjustment, or network topology changes.

[0058] The essence of this method is to treat the impedance value of the system at a specific frequency as a parameter to be identified. By establishing a linear relationship between the system's input (current) and output (voltage), the impedance identification problem is transformed into a parameter estimation problem. The recursive least squares method, through recursive calculation, can quickly update existing estimates with minimal computation after obtaining new data, making it very suitable for real-time operation of embedded systems.

[0059] A) Mathematical Modeling 1) For each specific frequency component of interest (e.g., the h-th harmonic), its frequency domain relationship can be expressed as: ΔV(h) = Z sys (h) ΔI(h)+η(h).

[0060] η(h) represents noise and model error. 2) To apply RLS (Recursive Least Squares), we transform it into a linear regression model. At time k, for frequency h, we have: y(k) = φ T (k) θ+η(k) 3) Observed value: y(k) = ΔV(h,k) That is, the voltage frequency domain component measured at frequency h at time k.

[0061] 4) Regression vector: φ T (k) represents the real and imaginary parts of the current frequency domain component measured at frequency h at time k, which decomposes a complex equation into two real equations.

[0062] 5) Parameter to be identified: θ=[R sys (h),X sys (h)] T That is, the system's resistance R and reactance X at that frequency, which are the real and imaginary parts that we need to solve for.

[0063] B) RLS Algorithm Steps 1) Initialization Set the initial value of the parameter: usually set it to ê(0) = 0. Set the initial value of the covariance matrix as: P(0) = δI, where δ is a very large positive number (e.g., 10). 3 Up to 10 6 I is the identity matrix, which means that the initial parameters have a large degree of uncertainty.

[0064] 2) For each new sampling time k=1,2,3,... a) Obtain new data: ΔI(h,k) is obtained through FFT analysis, and a regression vector φ(k) is constructed.

[0065] b) Calculate the prior error: e(k)=y(k)-φ T (k)ê(k-1) This error is the difference between the current observation and the predicted value based on the parameter estimate from the previous time step.

[0066] c) Calculate the gain matrix K(k)=P(k-1)φ(k) / λφ T (k)P(k-1)φ(k) Forgetting factor λ: This is an extremely important parameter, and its value is between 0 and λ≦1.

[0067] λ=1: represents infinite memory; λ<1 represents exponential forgetting; older data has lower weight. This allows the algorithm to track changes in time-varying system parameters.

[0068] The gain matrix K(k) determines the weight of the observation's contribution to the parameter update.

[0069] d) Update parameter estimates ê(k)=ê(k-1)+K(k)e(k) This is the core of RLS, which corrects the parameter estimates based on prior error and gain matrix.

[0070] e) Update the covariance matrix P(k) = 1 / λ[IK(k)φ T [(k)]P(k-1) The covariance matrix P(k) reflects the uncertainty of parameter estimation. C) Impedance Calculation and Model Establishment After convergence using the RLS algorithm, we obtain the parameter vector:

[0071] The complex impedance of the system at frequency h is:

[0072] Its magnitude and phase are:

[0073]

[0074] This is an estimated resistance value. is the estimated reactance value, and j is the imaginary unit.

[0075] By performing the above RLS identification process in parallel on multiple frequencies h of interest, the broadband impedance model Zsys(h) of the system can be constructed online.

[0076] Furthermore, in this embodiment, the impedance curve is compared with a safety threshold to determine the resonance risk, including: A) In this embodiment, the ultimate goal of conducting in-depth analysis of the system impedance characteristics is to scientifically and quantitatively determine whether the system has a risk of resonance. This determination process is automatically completed by the Central Harmonic Control Unit (CHCU), and its core is to compare the measured impedance amplitude-frequency characteristic curve with a pre-calculated safety threshold.

[0077] B) Definition and Calculation of Safety Thresholds Safety threshold Z thre It is not a fixed value, but a scientifically calculated result based on the maximum acceptable harmonic voltage of the system. The calculation is based on the limits on harmonic voltage distortion rate specified in international electrical standards (such as IEEE STd 519).

[0078] Theoretical basis: The harm of resonance lies in amplifying a certain harmonic voltage. According to Ohm's law, the h-th harmonic voltage V... h The harmonic voltage I h and system impedance Z sys (h) Joint decision: V h =I h |Z sys (h)∣ To ensure V h Not exceeding the standard limit V hmax, Then the system impedance must satisfy: |Z sys (h)∣≤V hmax / I h Threshold calculation: Considering the most stringent case (i.e., harmonic current I) h Reaching its maximum possible value I hmax We define the safe impedance threshold Z at frequency f. thre(f) is: Z thre (f)=V hmax (f) / I hmax (f) C) The Logic and Process of Risk Assessment The central harmonic control unit executes the following automated logic process to determine the risk of resonance. Input: The completed system impedance amplitude-frequency response curve |Z sys (f)∣.

[0079] Finding extreme points: The algorithm searches for local maxima (peak points) of all impedance amplitudes across the entire frequency sweep. Let the frequency of the found peak point be f. peak Its corresponding impedance magnitude is |Z sys (f peak )∣.

[0080] Judgment and Decision: For all the peak points found, |Z sys (f peak )∣≤Z thre If the condition is met, the system is determined to have no resonance risk, and the central harmonic control can maintain the current state or enter harmonic compensation mode. Conversely, if any peak point is found to not meet this condition, the system is determined to have resonance risk, and the risk point frequency f is adjusted accordingly. risk This parameter is passed as a key parameter to the virtual impedance calculator to generate precise damping current commands.

[0081] Furthermore, the virtual impedance calculation includes: A) In this embodiment, virtual impedance calculation is the core decision-making step under the "resonance suppression mode". Its purpose is to accurately calculate the virtual impedance value Z that needs to be simulated by the energy storage converter (PCS) or photovoltaic inverter based on the resonance risk points identified by impedance depth analysis. virtual (s), thereby actively changing the equivalent impedance characteristics of the system through the control algorithm to eliminate the resonance peak.

[0082] B) The goal is not to directly compensate for harmonic currents, but to make the grid-connected converter a controllable impedance element, connected in parallel with the original system impedance, so that the total equivalent impedance Z seen from the PCC point is... total (s) The amplitude decreases significantly at dangerous frequencies.

[0083] C) The equivalent total system impedance Z as seen from the point of common coupling (PCC) total (s) represents the original system impedance Z sys (s) and the introduced virtual impedance Z virtual Parallel results of (s) Z total (s)=Z sys(s)‖Z virtual (s)=Z sys (s) Z virtual (s) / Z sys (s)+Z virtual (s) D) Calculation steps and formulas for virtual impedance This invention preferably employs a simple and stable structure, such as a virtual resistor or a resistor-inductor (RL) series connection, to achieve impedance reshaping. The calculation process is as follows: Step 1: Determine the virtual impedance topology Choose the virtual impedance as a pure negative resistance, i.e.: Z virtual (s)=-R virt Among them, R virt >0. This negative impedance will be connected in parallel with the original system impedance, significantly increasing the damping near the resonant point, thereby suppressing the resonant peak. Step 2: Obtain the virtual impedance based on engineering simplification of the damping coefficient. In practical engineering, to ensure stability and simplify calculations, a relatively conservative approach is often adopted: directly set R... virt It is equal to a fraction of the impedance magnitude of the system at the resonant point.

[0084] R virt =∣Z sys (f risk )∣ / K damp |Z sys (f risk | is the impedance magnitude at the resonant point, K damp The damping coefficient (usually taken as 3≤K) damp (≤10), the physical meaning of this formula is clear: the higher the system resonant impedance, the greater the required damping strength; the greater the damping coefficient, the stronger the suppression effect. This method avoids complex calculations and is more practical.

[0085] Step 3: Convert into current control command Calculated R virt This needs to be converted into a current injection command for the converter (PCS). According to Ohm's law, the simulated virtual impedance requires an output current i. damp (t) satisfies: I damp (s)=V pcc (s) / Z virtual (s)=-V pcc (s) / R virt Transforming to the time domain, we obtain the final proportional control law: i damp(t)=-v pcc (s) / R virt The central harmonic control unit will use this proportionality factor -1 / R virt The instruction is sent to the PCS execution unit. The PCS generates a damping current proportional to the voltage at the PCC point by superimposing this instruction into its own current control loop, thereby accurately realizing the characteristics of a virtual negative resistance.

[0086] Furthermore, in this embodiment of the invention, the total harmonic current is a key observation, which refers to the vector sum of all harmonic currents flowing through the point of common coupling (PCC) or the target bus. Its analysis and acquisition follow the steps outlined below, with the core process shown in the figure: Step 1: At the system's point of common coupling (PCC), use a high-precision current sensor (such as a Rogowski coil) to synchronously acquire the instantaneous values ​​of the three-phase current i. a (t), i b (t), i c (t), the sampling frequency must satisfy the Nyquist sampling theorem, and is usually at least 2.5 times the highest harmonic of interest (e.g., the 50th harmonic, i.e., 2500Hz).

[0087] Step 2: Reference Coordinate System Transformation and Decomposition 1) Clarke transformation: transforms the current in a three-phase stationary coordinate system (abc) to a two-phase stationary coordinate system (α-β).

[0088] =

[0089] 2) Park Transformation: Using a phase-locked loop (PLL) with a rotation angle θ=ωt synchronized with the fundamental positive-sequence component of the grid voltage, the stationary coordinate system (α-β) is transformed into a rotating coordinate system (dq).

[0090] Physical meaning: In this synchronous rotating coordinate system, the fundamental positive sequence component is represented by DC, while all harmonic components (including negative and zero sequence) are represented by AC.

[0091] Step 3: Harmonic Extraction 1) For the transformed DC quantity i d and i q Using a low-pass filter (LPF) or a moving average filter, the I signal representing the fundamental frequency component can be extracted. dfound and I qfound 2) Subtracting the fundamental component from the original signal yields the AC component containing all harmonics: i dh(t)=i d (t)-I dfound i qh (t)=i q (t)-I qfound Step 4: Inverse transformation to generate total harmonic current command By performing inverse Park and inverse Clarke transforms on the harmonic AC components, the three-phase total harmonic current reference command i in the time domain is reconstructed. ha (t), i hb (t), i hc (t)

[0092]

[0093] At this point, ihabc(t) is the target total harmonic current that needs to be compensated.

[0094] Furthermore, the central controller periodically obtains the following real-time status information from each collaborative execution unit via a high-speed communication network for multi-objective optimization decision-making: 1) Photovoltaic inverter AC output active power P: The active power currently actually transmitted to the power grid. AC output reactive power Q: The reactive power that is currently actually transmitted to or absorbed by the power grid.

[0095] Rated apparent power S: The maximum permissible apparent power specified on the inverter nameplate.

[0096] Current output apparent power S out Total capacity of the current running point.

[0097] Remaining available capacity S remain The maximum permissible current output margin that can be used to perform harmonic compensation tasks is a key decision parameter.

[0098] DC bus voltage V dc Used to assess the health status of equipment and limit overload capacity.

[0099] 2) Energy storage converter (PCS) Current operating mode: charging, discharging, standby.

[0100] Active power setpoint P: The active power that the current dispatch command requires to be absorbed or emitted.

[0101] Current output apparent power S out : The total apparent power currently being output.

[0102] Rated apparent power S: PCS nameplate capacity.

[0103] Remaining available capacity S remain The calculation method is the same as that for photovoltaics, and it represents the upper limit of its compensation capacity.

[0104] Battery State of Charge (SOC) (%): The current remaining battery capacity. The central controller needs to decide whether to allow it to participate in compensation based on the SOC level (for example, when the SOC is extremely low or extremely high, its compensation task is limited to ensure battery life).

[0105] Running status flags: such as whether operation is allowed, whether there are alarms or faults, etc.

[0106] 3) Dedicated Active Power Filter (APF) Rated compensation capacity I apf : The maximum effective value of the compensation current that the APF device can output.

[0107] The current output compensation current I out : The effective value of the compensation current being output by the APF at the current moment.

[0108] Remaining available compensation capacity I remain APF can also provide current compensation capability.

[0109] Harmonic compensation mode settings: such as the number of compensations, priority, etc.

[0110] Furthermore, the Central Control Unit (CHCU) acts like a "brain," sensing the "problem" as the total harmonic current and the "available resources" as the real-time status information of each unit. Based on these two types of information, the CHCU can execute subsequent multi-objective optimization algorithms, intelligently, efficiently, and safely allocating compensation tasks to the most suitable execution unit, thereby achieving system-level collaborative governance.

[0111] Furthermore, the optimization mechanism of this invention is a multi-level, closed-loop adaptive intelligent system. Its core lies in the fact that the Central Harmonic Control Unit (CHCU) not only makes initial decisions but also continuously and automatically optimizes its decision parameters based on the execution results, thereby ensuring that the system performance remains optimal amidst dynamic changes.

[0112] A) Optimization Mechanism 1: Multi-objective Optimization Task Allocation This is the core decision algorithm of CHCU. When it is necessary to assign harmonic compensation tasks, it needs to solve a multi-objective optimization problem.

[0113] 1) Optimization variable: Compensation current command I allocated to each available unit (PV inverter, PCS, APF) comp .

[0114] 2) Optimization Objective Optimization of compensation effect: Minimize the total harmonic distortion (THD) at the PCC point after compensation.

[0115] Minimize equipment losses: Prioritize the use of units with low loss costs and high efficiency, and avoid overloading individual units.

[0116] System economic optimization: Prioritize the use of remaining photovoltaic capacity, followed by energy storage capacity, and lastly APF capacity.

[0117] 4) Constraints Capacity constraint: The compensation current allocated to each unit cannot exceed its remaining capacity I. remain .

[0118] Operating status constraints: The SOC of the energy storage PCS cannot exceed the limit.

[0119] Total constraint: The sum of the compensation current vectors allocated to all units should equal the total harmonic current reference value I. href .

[0120] Solution method: The CHCU (Central Harmonic Control Unit) uses a linear weighted method or a heuristic algorithm (such as Particle Swarm Optimization, PSO) to solve the above multi-objective problem. It combines multiple objective functions into a single overall objective function through weighting coefficients, and then finds the optimal solution under certain constraints.

[0121] min{ } Compensation effect weight Economic weight K loss Loss cost / weight B) Optimization Mechanism Two: Closed-Loop Adaptive Decision Parameter Optimization This is the key to the "intelligence" of this invention. When the multi-objective optimization decision is executed and the effect does not meet expectations, the system will not simply repeat the original instructions, but will adjust the parameters of the optimization algorithm itself.

[0122] 1) Effect Evaluation: The CHCU continuously monitors the power quality at the PCC point, calculates the THD and harmonic content after compensation, and compares it with the expected target (e.g., THD < 3%) to check whether THD is affected. after ≤THD target 2) If the desired effect is not achieved (e.g., THD remains above 5%), CHCU will initiate a parameter optimization procedure. It alters the decision bias by adjusting key parameters in the multi-objective optimization algorithm. Optimize weight coefficients ω1, ω2... Scenario: If THD remains high, it indicates that the compensation is ineffective.

[0123] Action: Increase ω1 (compensation effect weight), and decrease ω2 (economic weight) in the next decision. This means the system will no longer be "stingy," preferring to let the APF work harder or allow photovoltaic / energy storage to overshoot slightly in order to prioritize the governance effect.

[0124] Optimize virtual impedance parameters: Scenario: In resonance suppression mode, the amplitude at the resonance point does not decrease significantly.

[0125] Action: Adjust the damping coefficient K damp For example, K damp Decreasing from 5 to 3 means that, according to formula R virt =∣Z sys | / K damp The calculated virtual negative resistance value Rvirt will increase, thus requiring the PCS to inject a stronger damping current to more forcefully suppress resonance.

[0126] Optimize RLS algorithm parameters: Scenario: The system impedance changes rapidly, and the RLS algorithm lags behind in tracking.

[0127] Action: Reduce the forgetting factor λ (e.g., from 0.99 to 0.95) to make the algorithm "forget" old data faster and be more sensitive to new data, thereby improving the tracking speed of time-varying systems.

[0128] 2) Iteration and Convergence CHCU will recalculate the multi-objective optimization based on the new parameters and issue new instructions. This process will be repeated until the compensation effect meets the target. The system will record the optimal parameter combination to cope with similar operating conditions in the future, thus demonstrating a certain "learning" ability.

[0129] Based on the above embodiments, as a preferred implementation, in S3, the real-time status information of each harmonic suppression execution unit includes the AC output active power, reactive power, rated apparent power, remaining available capacity and DC bus voltage of the photovoltaic inverter; the operating mode, active power setpoint, remaining available capacity, battery state of charge and operating status flag of the energy storage converter; and the rated compensation capacity, current output compensation current and remaining available compensation capacity of the dedicated active power filter.

[0130] Secondly, an embodiment of the present invention provides a harmonic suppression system for an optical-storage AC coupling system, based on the harmonic suppression method for an optical-storage AC coupling system in the above-described format example, such as... Figure 2 As shown, it includes: The data acquisition module 210 acquires the working status of each harmonic suppression execution unit in the photovoltaic-storage AC coupling system, eliminates harmonic suppression execution units with abnormal working status, and acquires voltage and current signals of the common connection point (PCC) and preset key nodes. The harmonic suppression execution unit includes a photovoltaic inverter and an energy storage converter. The preset key nodes include the AC output terminal of the photovoltaic inverter and the AC output terminal of the energy storage converter. The analysis and mode determination module 220 performs windowed interpolation FFT analysis on the acquired voltage and current signals to calculate harmonic data, including total harmonic distortion (THD) and the amplitude and phase of each harmonic. Based on the harmonic data obtained from the FFT analysis during normal operation of the photovoltaic-storage AC coupling system, the system impedance model is identified online using the recursive least squares (RLS) method to generate an impedance amplitude-frequency characteristic curve. The impedance magnitude at each frequency point in the impedance amplitude-frequency characteristic curve is compared with a preset safety threshold. If the impedance magnitude at any frequency point exceeds the corresponding preset safety threshold, the photovoltaic-storage AC coupling system is determined to have a resonance risk and enters the resonance suppression mode. If the impedance magnitude at all frequency points does not exceed the preset safety threshold, the photovoltaic-storage AC coupling system is determined to have no resonance risk and enters the harmonic compensation mode. If the instruction generation module 230 is in resonance suppression mode, it calculates the required virtual impedance for the identified resonance frequency and converts the virtual impedance into the corresponding current compensation instruction; if it is in harmonic compensation mode, it generates the optimal compensation task allocation scheme through a multi-objective optimization algorithm based on the total harmonic current analysis results and the real-time status information of each harmonic suppression execution unit. The harmonic suppression execution module 240 sends the current compensation command or the optimal compensation task allocation scheme to the corresponding harmonic suppression execution unit, and each harmonic suppression execution unit outputs the damping current or harmonic compensation current in coordination.

[0131] Based on the same concept, this invention also provides a schematic diagram of a physical structure, such as... Figure 3 As shown, the server may include a processor 310, a communications interface 320, a memory 330, and a communication bus 340. The processor 310, communications interface 320, and memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions stored in the memory 330 to execute the steps of the harmonic suppression method for the optical-storage AC coupling system as described in the above embodiments.

[0132] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0133] Based on the same concept, embodiments of the present invention also provide a non-transitory computer-readable storage medium storing a computer program containing at least one piece of code that can be executed by a master control device to control the master control device to implement the steps of the harmonic suppression method for the optical-storage AC coupling system as described in the above embodiments.

[0134] Based on the same technical concept, this application also provides a computer program, which, when executed by a main control device, is used to implement the above-described method embodiments.

[0135] The program may be stored, in whole or in part, on a storage medium packaged with the processor, or in part or in whole on a memory not packaged with the processor.

[0136] Based on the same technical concept, this application also provides a processor for implementing the above-described method embodiments. The processor can be a chip.

[0137] The various embodiments of the present invention can be combined arbitrarily to achieve different technical effects.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not 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 of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A harmonic suppression method for an optical-storage AC coupling system, characterized in that, include: S1. Obtain the operating status of each harmonic suppression execution unit in the photovoltaic-storage AC coupling system, eliminate harmonic suppression execution units with abnormal operating status, and collect voltage and current signals of the common connection point (PCC) and preset key nodes; the harmonic suppression execution unit includes a photovoltaic inverter and an energy storage converter; the preset key nodes include the AC output terminal of the photovoltaic inverter and the AC output terminal of the energy storage converter. S2. Perform windowed interpolation FFT analysis on the acquired voltage and current signals to calculate harmonic data, including total harmonic distortion (THD), amplitude, and phase of each harmonic. Based on the harmonic data obtained from the FFT analysis during normal operation of the photovoltaic-storage AC coupling system, use the recursive least squares (RLS) method to identify the system impedance model online and generate an impedance amplitude-frequency characteristic curve. Compare the impedance magnitude at each frequency point in the impedance amplitude-frequency characteristic curve with a preset safety threshold. If the impedance magnitude at any frequency point exceeds the corresponding preset safety threshold, it is determined that the photovoltaic-storage AC coupling system has a resonance risk and enters the resonance suppression mode. If the impedance magnitude at all frequency points does not exceed the preset safety threshold, it is determined that the photovoltaic-storage AC coupling system has no resonance risk and enters the harmonic compensation mode. S3. If in resonance suppression mode, calculate the required virtual impedance for the identified resonance frequency and convert the virtual impedance into the corresponding current compensation command; if in harmonic compensation mode, generate the optimal compensation task allocation scheme through a multi-objective optimization algorithm based on the total harmonic current analysis results and the real-time status information of each harmonic suppression execution unit. S4. Send the current compensation command or the optimal compensation task allocation scheme to the corresponding harmonic suppression execution unit, and have each harmonic suppression execution unit output the damping current or harmonic compensation current in coordination.

2. The harmonic suppression method for an optical-storage AC coupling system according to claim 1, characterized in that, Also includes: S5. Real-time evaluation of harmonic suppression effect; if the harmonic content drops to the preset allowable range, maintain the current strategy. If the target is not met or a new disturbance occurs in the photovoltaic-storage AC coupling system, the optimization mechanism is triggered, the decision parameters are readjusted, and S2-S4 are repeated.

3. The harmonic suppression method for an optical-storage AC coupling system according to claim 1, characterized in that, In step S2, windowed interpolation FFT analysis is performed on the acquired voltage and current signals, including: To acquire discrete voltage and current signal sequences at a sampling frequency that satisfies the Nyquist sampling theorem, and the sampling rate must be able to capture at least the 50th harmonic; A windowed signal is obtained by applying a window function with excellent sidelobe attenuation characteristics to a discrete signal sequence. Perform an FFT transform on the windowed signal to obtain the discrete spectrum; The bi-spectral interpolation method is used to find the spectral line with the largest amplitude near the target harmonic and its adjacent spectral lines, and to calculate the frequency offset, the true harmonic frequency, and the corrected harmonic amplitude and phase.

4. The harmonic suppression method for an optical-storage AC coupling system according to claim 1, characterized in that, In S2, the total harmonic distortion rate includes the voltage total harmonic distortion rate THDU and the current total harmonic distortion rate THDI. THD U ={√( U h 2 )} / U1 THD I ={√( I h 2 )} / I1 Where U1 is the root mean square value of the fundamental voltage, U1 = A(U1) / √2, A(U1) represents the amplitude of the fundamental voltage in the photoelectric-storage AC coupling system, U h U is the root mean square value of the h-th harmonic voltage. h =A(U h ) / √2,A(U h Ih represents the amplitude of the h-th harmonic voltage in the photovoltaic-storage AC coupling system; I1 is the root mean square value of the fundamental current, I1 = A(I1) / √2, where A(I1) represents the amplitude of the fundamental current in the photovoltaic-storage AC coupling system. h I is the root mean square value of the h-th harmonic current. h =A(I h ) / √2, A(I h ) represents the amplitude of the h-th harmonic current in the photovoltaic-storage AC coupling system.

5. The harmonic suppression method for an optical-storage AC coupling system according to claim 1, characterized in that, In S2, the recursive least squares (RLS) method is used to identify the system impedance model online, including: For a specific frequency corresponding to a harmonic in a photovoltaic-storage AC coupling system, a voltage-current linear relationship model is constructed. The voltage frequency domain data measured at a specific time at a specific frequency is used as the output result of the voltage-current linear relationship model, and the real and imaginary parts of the current frequency domain data at the same time at a specific frequency are used as the input variables of the voltage-current linear relationship model. Noise and model error terms are included to form a complete linear regression relationship. Set initial values ​​for the parameters, setting the initial estimates of the system resistance and reactance parameters to be identified to 0; set the initial value of the covariance matrix so that the system impedance model is an identity matrix containing large positive numbers. For each new sampling time, update the parameters according to the following steps: Based on the current frequency domain data collected at the current moment, organize the input variable group containing the real part and imaginary part of the current; Calculate the prior error: Subtract the voltage frequency domain data predicted based on the parameter estimate of the previous moment from the voltage frequency domain data actually measured at the current moment to obtain the deviation between the current measured value and the predicted value; Calculate the gain matrix: Combine the covariance matrix of the previous time step, the current set of input variables, and a forgetting factor between 0 and 1 to determine the contribution weight of the current measurement data to the parameter update. Update parameter estimates: Use the parameter estimates from the previous time step, add the product of the gain matrix and the prior error, and obtain the corrected system resistance and reactance parameter estimates for the current time step. Update the covariance matrix: Combine the forgetting factor, gain matrix, and input variable set to adjust the covariance matrix of the previous time step to obtain a new matrix that reflects the uncertainty of parameter estimation at the current time step; Calculate system impedance: After the iterative process converges, extract the final estimated values ​​of system resistance and reactance, take the resistance as the real part and the reactance as the imaginary part, and combine them to form the complex impedance of the system at that frequency; calculate the magnitude of the impedance based on the values ​​of resistance and reactance, repeat the above steps to cover all the frequency points of interest, and finally generate the impedance amplitude-frequency response curve.

6. The harmonic suppression method for an optical-storage AC coupling system according to claim 5, characterized in that, In step S3, the required virtual impedance is calculated for the identified resonant frequency, including: Based on the impedance amplitude-frequency characteristic curve, extract the system impedance magnitude corresponding to the resonant risk frequency point; determine the damping coefficient, and divide the system impedance magnitude at the resonant frequency point by the selected damping coefficient to obtain the specific resistance value of the virtual negative resistance. The instantaneous voltage signal of the point of common coupling (PCC) is acquired in real time. Based on the logical relationship of Ohm's law, the value of the acquired instantaneous voltage signal of PCC is divided by the resistance value of the virtual negative resistor, and a negative sign is added to the result to obtain the instantaneous value of the damping current output by the harmonic suppression execution unit.

7. The harmonic suppression method for an optical-storage AC coupling system according to claim 1, characterized in that, In S3, the real-time status information of each harmonic suppression execution unit includes the AC output active power, reactive power, rated apparent power, remaining available capacity and DC bus voltage of the photovoltaic inverter; and the operating mode, active power setpoint, remaining available capacity, battery state of charge and operating status flag of the energy storage converter. The rated compensation capacity, current output compensation current, and remaining available compensation capacity of the dedicated active power filter.

8. A harmonic suppression system for an optical-storage AC coupling system, characterized in that, include: The data acquisition module acquires the operating status of each harmonic suppression execution unit in the photovoltaic-storage AC coupling system, eliminates harmonic suppression execution units with abnormal operating status, and acquires voltage and current signals of the common connection point (PCC) and preset key nodes. The harmonic suppression execution units include photovoltaic inverters and energy storage converters. The preset key nodes include the AC output terminals of the photovoltaic inverters and the AC output terminals of the energy storage converters. The analysis and mode determination module performs windowed interpolation FFT analysis on the acquired voltage and current signals to calculate harmonic data, including total harmonic distortion (THD) and the amplitude and phase of each harmonic. Based on the harmonic data obtained from the FFT analysis of the photovoltaic-storage AC coupling system during normal operation, the system impedance model is identified online using the recursive least squares (RLS) method to generate an impedance amplitude-frequency characteristic curve. The impedance magnitude at each frequency point in the impedance amplitude-frequency characteristic curve is compared with a preset safety threshold. If the impedance magnitude at any frequency point exceeds the corresponding preset safety threshold, the photovoltaic-storage AC coupling system is determined to have a resonance risk and enters the resonance suppression mode. If the impedance magnitude at all frequency points does not exceed the preset safety threshold, the photovoltaic-storage AC coupling system is determined to have no resonance risk and enters the harmonic compensation mode. If the instruction generation module is in resonance suppression mode, it calculates the required virtual impedance for the identified resonant frequency and converts the virtual impedance into the corresponding current compensation instruction; if it is in harmonic compensation mode, it generates the optimal compensation task allocation scheme through a multi-objective optimization algorithm based on the total harmonic current analysis results and the real-time status information of each harmonic suppression execution unit. The harmonic suppression execution module sends current compensation commands or optimal compensation task allocation schemes to the corresponding harmonic suppression execution units, which then work together to output damping current or harmonic compensation current.

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 executes the program, it implements the steps of the harmonic suppression method for the optical-storage AC coupling system as described in any one of claims 1 to 7.

10. A non-transitory 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 the harmonic suppression method for the optical-storage AC coupling system as described in any one of claims 1 to 7.

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