Network construction type energy storage PCS voltage control method and system based on virtual impedance dynamic adjustment

Abstract

The invention relates to the technical field of network construction type energy storage systems, and discloses a network construction type energy storage PCS voltage control method and system based on virtual impedance dynamic adjustment. According to the method, the power grid side electrical parameters and the load side electrical parameters are analyzed, so that the dynamic stability risk and the harmonic pollution degree of the power grid are quantified into two coefficients respectively, the current working condition of the power grid is sensed, and a core decision basis is provided for dynamic adjustment of subsequent virtual impedance; according to the method, a differential fusion strategy is adopted according to a coupling state, so that the inherent defect that a fixed impedance parameter cannot give consideration to dynamic stability and maintenance of electric energy quality under a complex working condition in a traditional method is overcome; the problem that in a traditional method, the fixed virtual impedance cannot be adaptively adjusted under the operation conditions of multiple working conditions is solved, and accurate control over the output voltage of the PCS is achieved under the complex working conditions of various loads and harmonic disturbance.

Description

Technical Field

[0001] This invention relates to the field of grid-type energy storage system technology, and in particular to a voltage control method and system for grid-type energy storage PCS based on dynamic adjustment of virtual impedance. Background Technology

[0002] Grid-based power storage circuit breakers (PCS), as key devices connecting energy storage batteries to the grid / load, typically have the following architecture: the energy storage battery is connected to the DC side of the PCS via a DC bus, and the AC side of the PCS is connected to the point of common coupling (PCC) through components such as filter inductors and transformers, thus connecting to the grid or local load. Grid-based PCS is increasingly widely used in scenarios such as microgrids in remote areas, the end points of large-scale new energy bases, high-end manufacturing industrial parks, and large-scale energy storage power stations. In these scenarios, the energy storage PCS not only needs to perform basic power conversion but also needs to maintain the stability of the PCC voltage and frequency, and cope with problems such as instantaneous voltage drops and flicker in the grid caused by impulsive loads such as the start-up and shutdown of large motors and the operation of electric arc furnaces. Therefore, it is necessary to control the output voltage of the energy storage PCS to ensure the normal operation of associated equipment.

[0003] Traditional voltage control methods for energy storage PCS generally employ fixed-parameter virtual impedance control technology. This technology introduces a preset, constant impedance element into the control loop to simulate the internal impedance characteristics of a synchronous generator. The voltage drop generated by the output current across the virtual impedance is then used to correct the voltage command, thereby achieving voltage control. However, this fixed virtual impedance-based control method suffers from adaptability issues because its impedance parameters cannot be dynamically adjusted according to operating conditions. Specifically, under light load conditions, the fixed virtual impedance value is relatively too large, resulting in a voltage drop exceeding the required value, causing the output voltage to deviate from the reference value and reducing voltage control accuracy. Under heavy load or when encountering impulsive loads, the fixed impedance cannot provide sufficient damping as the current increases, leading to slow system dynamic response, prolonged voltage recovery process, and even system oscillation and instability, resulting in poor voltage control performance. Summary of the Invention

[0004] The main objective of this invention is to provide a voltage control method and system for grid-type energy storage PCS based on dynamic adjustment of virtual impedance, aiming to solve the technical problems in the prior art.

[0005] This invention proposes a voltage control method for grid-type energy storage PCS based on dynamic adjustment of virtual impedance, comprising: Obtain the grid-side electrical parameters and load-side electrical parameters of the grid-type energy storage PCS access point, and obtain the converter operating parameters and preset reference impedance of the grid-type energy storage PCS; Data analysis is performed on the electrical parameters of the power grid side and the electrical parameters of the load side to obtain the stability risk coefficient and harmonic disturbance index; The coupling state is determined based on the stability risk coefficient and the harmonic disturbance index to obtain the real-time coupling state. Based on the real-time coupling state, the stability risk coefficient, the harmonic disturbance index and the preset reference impedance are adaptively fused to obtain the damping adjustment component and the harmonic suppression component. A virtual impedance function is constructed based on the damping adjustment component, the harmonic suppression component, and the converter operating parameters. The virtual impedance function includes a fundamental domain virtual impedance function, a harmonic domain virtual impedance function, and a dynamic phase compensator function. The real-time current sequence is obtained based on the converter operating parameters, and the real-time convolution operation is performed on the real-time current sequence based on the virtual impedance function to obtain the feedforward compensation voltage. The power switching devices are uniformly modulated based on the feedforward compensation voltage to regulate the output voltage of the PCS.

[0006] This application also provides a grid-type energy storage PCS voltage control system based on virtual impedance dynamic adjustment, including multiple modules, which are used to implement the steps of the above-mentioned grid-type energy storage PCS voltage control method based on virtual impedance dynamic adjustment.

[0007] Preferably, the module includes multiple units, which are used to implement the steps of the above-described voltage control method for grid-type energy storage PCS based on dynamic adjustment of virtual impedance.

[0008] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described grid-type energy storage PCS voltage control method based on virtual impedance dynamic adjustment.

[0009] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described voltage control method for grid-type energy storage PCS based on dynamic adjustment of virtual impedance.

[0010] The beneficial effects of this invention are as follows: By analyzing the electrical parameters on the grid side and the electrical parameters on the load side, this invention quantifies the dynamic stability risk and harmonic pollution level of the power grid into stability risk coefficient and harmonic disturbance index, respectively. Based on these two coefficients, it perceives whether the current operating condition of the power grid is biased towards light load, heavy load, or severe harmonics, thus providing a core decision basis for the subsequent dynamic adjustment of virtual impedance. This invention introduces a coupling state determination mechanism and adopts a differentiated fusion strategy based on the coupling state, which helps to solve the inherent defects of traditional control schemes where fixed impedance parameters cannot simultaneously take into account dynamic stability and maintain power quality under complex operating conditions. This invention, through the method of discretely constructing functions and then coordinating their effects, helps to solve the problems of traditional methods where fixed virtual impedance cannot adaptively adjust under various operating conditions, resulting in deterioration of voltage accuracy under light load, slow dynamic response under heavy load, and inability to suppress harmonic circulating current when multiple machines are connected in parallel. This invention is beneficial for achieving precise control of PCS output voltage under complex operating conditions with diverse load changes and harmonic disturbances. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of a method flow according to an embodiment of the present invention.

[0012] Figure 2 This is a schematic diagram of the system structure according to an embodiment of the present invention.

[0013] Figure 3 This is a schematic diagram of the internal structure of a computer device according to an embodiment of this application.

[0014] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0015] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0016] like Figure 1 As shown, this application provides a voltage control method for grid-type energy storage PCS based on dynamic adjustment of virtual impedance, including: S1. Obtain the grid-side electrical parameters and load-side electrical parameters of the grid-type energy storage PCS access point, and obtain the converter operating parameters and preset reference impedance of the grid-type energy storage PCS. S2. Perform data analysis on the electrical parameters of the power grid side and the electrical parameters of the load side to obtain the stability risk coefficient and harmonic disturbance index; S3. Based on the stability risk coefficient and the harmonic disturbance index, the coupling state is determined to obtain the real-time coupling state. Based on the real-time coupling state, the stability risk coefficient, the harmonic disturbance index and the preset reference impedance are adaptively fused to obtain the damping adjustment component and the harmonic suppression component. S4. Construct a virtual impedance function based on the damping adjustment component, the harmonic suppression component, and the converter operating parameters, wherein the virtual impedance function includes a fundamental domain virtual impedance function, a harmonic domain virtual impedance function, and a dynamic phase compensator function; S5. Obtain the real-time current sequence based on the converter operating parameters, and perform real-time convolution operation on the real-time current sequence based on the virtual impedance function to obtain the feedforward compensation voltage. S6. Based on the feedforward compensation voltage, the power switching devices are uniformly modulated to perform PCS output voltage regulation.

[0017] As described in steps S1-S6 above, this invention synchronously collects grid-side and load-side electrical parameters at the access point of the grid-type energy storage PCS for real-time sensing of grid operating conditions and load disturbances. Grid-side electrical parameters refer to the electrical parameters characterizing the grid operating state at the common connection point between the AC connection terminal of the energy storage PCS and the upstream grid system, including grid voltage and grid current. Load-side electrical parameters refer to the electrical parameters characterizing load characteristics and disturbances at the common connection point between the AC connection terminal of the energy storage PCS and the downstream local load system, including load current and load voltage. Simultaneously, the converter operating parameters of the energy storage PCS are acquired to reflect the real-time operating status of the energy storage PCS. The virtual impedance is an equivalent impedance simulated in the PCS output characteristics through a control algorithm; its core function is to provide damping to suppress system oscillations. Shaping output impedance characteristics improves power distribution, enhances system stability, and improves disturbance rejection. However, in traditional solutions, this impedance parameter is a fixed value, and the design is only optimized for specific operating conditions such as rated load. This leads to the system being unable to maintain optimal performance under various operating conditions, including large fluctuations in load level (such as changes from light load to heavy load), diverse changes in load characteristics (such as transitions from linear load to nonlinear load), and parallel operation of multiple machines. Therefore, this invention analyzes the electrical parameters on the grid side and the electrical parameters on the load side, thereby quantifying the dynamic stability risk and harmonic pollution level of the power grid into a stability risk coefficient and a harmonic disturbance index, respectively. Based on these two coefficients, the control system can perceive whether the current operating condition of the power grid is biased towards light load, heavy load, or severe harmonics, thus providing a core decision basis for the subsequent dynamic adjustment of virtual impedance.

[0018] This invention introduces a coupling state determination mechanism to determine the strength of the correlation between grid stability risk and harmonic disturbance based on the stability risk coefficient and harmonic disturbance index. It then employs a differentiated fusion strategy based on the coupling state: in a weakly coupled state, a cross-suppression strategy is used to actively suppress unnecessary damping enhancement when harmonics are significant, addressing voltage drops caused by excessive fixed virtual impedance under light load conditions; while in a strongly coupled state, a master-slave collaborative strategy is activated, allocating control resources with the highest priority of system energy balance margin to ensure sufficient damping is provided to maintain transient stability under heavy loads or impulsive loads, and then collaboratively suppressing accompanying harmonics. This achieves intelligent decoupling and collaborative dynamic adjustment of the damping component and harmonic suppression component in the virtual impedance, thereby solving the inherent defect of traditional control schemes where fixed impedance cannot simultaneously ensure dynamic stability and maintain power quality under complex conditions, and obtaining the damping adjustment component and harmonic suppression component.

[0019] By using the damping adjustment component, the real-time system frequency, and the harmonic distortion rate of the output current as dynamic inputs, and based on the preset reference virtual resistance and reference virtual inductance, an adaptive coefficient fusion algorithm is used to construct a fundamental domain virtual impedance function whose parameters can be adjusted in real time. By using the harmonic suppression component, the switching frequency parameter, and the target harmonic order as control inputs, and based on the reference virtual impedance value, a bandpass filter bank and adaptive gain are used to construct a harmonic domain virtual impedance function for a specific harmonic. At the same time, by using the stability risk coefficient as an adjustment parameter, and based on the system stability margin requirements, a lead-lag compensator structure and an asymmetric parameter adjustment strategy are used to construct a dynamic phase compensator function. Based on the virtual impedance function in the fundamental domain, the virtual impedance function in the harmonic domain, and the dynamic phase compensator function, a real-time convolution operation is performed on the real-time current sequence to generate a feedforward compensation voltage. The feedforward compensation voltage refers to the instantaneous voltage compensation value used to dynamically correct the reference modulation wave. This voltage directly reflects the voltage drop generated by the dynamically adjusted virtual impedance. This invention solves the problem that a single impedance function cannot simultaneously achieve optimal impedance characteristics at the fundamental and harmonic frequencies by constructing functions separately and then working in synergy. It is beneficial to solve the problems of traditional methods where the fixed virtual impedance cannot be adaptively adjusted under a wide range of operating conditions, resulting in deterioration of voltage accuracy under light load, slow dynamic response under heavy load, and inability to suppress harmonic circulating current when multiple machines are connected in parallel.

[0020] This invention introduces an adaptive limiting mechanism based on stability risk and an SVPWM vector correction algorithm that integrates the feedforward compensation voltage into the modulation wave to uniformly modulate the power switching device. This achieves a high-fidelity conversion from control command to voltage control, ensuring that the effect of dynamically adjusted virtual impedance control is accurately and instantly converted into the actual output voltage of the PCS. This is beneficial for achieving precise control of the PCS output voltage under complex operating conditions with diverse load changes and harmonic disturbances.

[0021] In one embodiment, step S2, which involves performing data analysis on the grid-side electrical parameters and the load-side electrical parameters to obtain the stability risk coefficient and harmonic disturbance index, includes: S21. Extract the grid-side voltage and current sequence and the load-side voltage and current sequence from the grid-side electrical parameters and the load-side electrical parameters respectively, and perform two-phase rotating coordinate transformation on the grid-side voltage and current sequence and the load-side voltage and current sequence respectively to obtain multiple grid-side voltage components, grid-side current components, load-side voltage components and load-side current components; S22. Obtain the power change feature set on the grid side based on the grid side voltage component and the grid side current component, and obtain the power change feature set on the load side based on the load side voltage component and the load side current component; S23. Construct a complex sequence of grid-side voltage based on multiple grid-side voltage components, and perform numerical differentiation and curvature calculation on the complex sequence of grid-side voltage to obtain the instantaneous curvature value; S24. Apply voltage disturbance to the output port of PCS and simultaneously acquire composite voltage signal and composite current signal, and obtain the impedance dynamic change rate based on the composite voltage signal and composite current signal; S25. Input the power change feature set on the grid side, the power change feature set on the load side, the instantaneous curvature value, and the dynamic change rate of impedance into the stability classifier to perform broadband oscillation instability risk assessment and obtain the stability margin probability. S26. Obtain the system baseline parameters, and obtain the stability risk coefficient based on the system baseline parameters and the stability margin probability; S27. Based on the electrical parameters of the load side, perform adaptive harmonic analysis to obtain the effective value of harmonic current and the moving average value of phase fluctuation corresponding to multiple harmonics of different orders, and obtain the harmonic disturbance index sequence according to the effective value of harmonic current, the moving average value of phase fluctuation and the system reference parameters.

[0022] As described in steps S21-S27 above, this invention extracts grid-side voltage and current sequences from grid-side electrical parameters, including grid-side voltage sequences and grid-side current sequences. To convert time-varying AC quantities into steady-state DC quantities for precise control and analysis, Clarke transform and Park transform are performed on each three-phase voltage and three-phase current in the grid-side voltage and current sequences, respectively. Through this synchronous coordinate transformation, the three-phase voltage and three-phase current are converted into direct-axis and quadrature-axis components in a two-phase rotating coordinate system, resulting in grid-side voltage components and grid-side current components. The grid-side voltage components include grid-side direct-axis voltage components and grid-side quadrature-axis voltage components, and the grid-side current components include grid-side direct-axis current components and grid-side quadrature-axis current components. Based on the above data, the instantaneous active power and instantaneous reactive power at each sampling point are calculated. A first-in-first-out buffer of length N is set up to store the latest N instantaneous active power sequence values ​​and instantaneous reactive power sequence values. The first-order backward difference and second-order backward difference of the instantaneous active power and instantaneous reactive power at the latest sampling time are calculated respectively to obtain the rate of change of active power, the rate of change of reactive power, the acceleration of change of active power, and the acceleration of change of reactive power. These together constitute the power change feature set on the grid side. Similarly, the power change feature set on the load side is obtained. Thus, the full-dimensional quantitative capture of system power from static amplitude to dynamic change trend and even change inertia is realized, which solves the problem of poor response effect caused by traditional methods that only focus on instantaneous power values.

[0023] Based on the direct-axis and quadrature-axis voltage components of the grid side, a time-varying complex number is constructed on the complex plane, resulting in a time-varying complex voltage sequence of the grid side. The grid side complex voltage sequence is then numerically differentiated to calculate its first and second numerical derivatives with respect to time, yielding the voltage vector rate of change and voltage vector acceleration. These are then substituted into the curvature calculation formula in differential geometry to calculate and output the instantaneous curvature value. As a sensitive indicator, this value can keenly detect early signs of system strength changes and power angle instability, providing a new dimension for proactive early warning of voltage stability that is not available in traditional amplitude / phase analysis.

[0024] In the internal control loop of a grid-type energy storage PCS, a preset small sinusoidal voltage disturbance signal is superimposed on its output voltage modulation wave signal. The composite voltage signal and composite current signal of the PCS output port after this disturbance injection are simultaneously acquired. Through a bandpass filter with a center frequency of the grid rated frequency plus the small fixed frequency offset, the voltage disturbance component and current disturbance component generated only by this disturbance and with the same frequency as the injected signal are extracted from the acquired composite voltage signal and composite current signal, respectively. Based on this, the impedance estimate of the PCS output at the specific test frequency is calculated, the impedance estimate stored in the previous calculation cycle is obtained, and the impedance dynamic change rate is calculated. This invention provides a key input reflecting the real operating environment of the grid for the adaptive adjustment of virtual impedance by real-time and dynamic sensing of the grid background impedance, a key system parameter.

[0025] In obtaining a lightweight stability classifier based on transfer learning, the power change feature set of the grid side, the power change feature set of the load side, the instantaneous curvature value, and the impedance dynamic change rate are input into the lightweight stability classifier. Based on the mapping relationship between the instability features and the system state learned by its offline training, the classifier performs a broadband oscillation instability risk assessment on the currently input features and outputs a stability margin probability between 0 and 1. This value represents the probability that the system will maintain stable operation in the next few hundred milliseconds based on the current system response characteristics. The closer the value is to 0, the higher the instability risk.

[0026] Based on the impedance estimate obtained from the preceding steps, the current equivalent impedance of the power grid is obtained through online identification using the recursive least squares method. Simultaneously, system reference parameters are acquired, including system rated power, power grid reference impedance, and system rated current, and are determined using the formula… "Calculate the stability risk coefficient, where K..." risk Let S represent the stability risk coefficient, α represent the stability margin probability, α represent the adjustable coefficient of the weighting effect of changes in adjustment power, and Δ represent the stability risk coefficient. 2 P represents the acceleration due to the change in active power. rated Z represents the system's rated power. grid Z represents the current equivalent impedance of the power grid. base The term "(1-S)" in the formula represents the grid reference impedance, which is a predictive risk factor that transforms the probability prediction of stability over the next few hundred milliseconds into a negative risk value. The term "impact risk factor" quantifies the impact of instantaneous power surges caused by load switching or faults on the power grid at the current moment, using a normalized value of the power change acceleration. The term "" represents the structural risk factor, which characterizes the degree of deviation of the current grid equivalent impedance from the benchmark value. An increase in grid impedance will directly amplify the actual harm of the first two types of risks. The three are coupled through a multiplicative structure, and the resulting stability risk coefficient is an indicator that comprehensively characterizes the strength of the trend of the grid evolving from its current operating state to the instability boundary.

[0027] The load-side instantaneous current sequence is formed by extracting multiple instantaneous load-side current values ​​from a first-in-first-out buffer of length N from the load-side electrical parameters. Based on this, the current effective value at the current moment is calculated, and the ratio of the current effective value to the system rated current is calculated to obtain the real-time load rate. The quality factor of the wavelet transform is dynamically determined through a preset piecewise function. The quality factor and the load-side instantaneous current sequence are input into the wavelet transform processor, and adaptive variable Q-factor wavelet decomposition is performed to obtain a set of time-domain component coefficients containing different frequency subbands. The time-domain component signals corresponding to the 5th, 7th, 11th, and 13th characteristic harmonics are extracted from this set of time-domain component coefficients. The phase fluctuation moving average is obtained by calculating the analytical signal. At the same time, the root mean square value of the time-domain component signal of each harmonic is calculated within the time window, and the effective value of the harmonic current is output. "Calculate the harmonic disturbance index, where D represents the harmonic disturbance index, h represents the harmonic order, and w..." h I represents the weighting coefficient of the h-th harmonic. h(rms) I represents the effective value of the harmonic current of the h-th harmonic. rated The system rated current is represented by k, the phase fluctuation sensitivity coefficient is represented by δ. h This represents the moving average of the phase fluctuation of the h-th harmonic, where "" in the formula The term "(1+k*δ" is used to normalize the effective value of each harmonic current to a per-unit value relative to the system's rated capacity, thereby eliminating the influence of system size. h The ")" item introduces harmonic phase fluctuations and adjusts their weights using a phase fluctuation sensitivity coefficient. This design ensures that even when the harmonic amplitude is small but the phase fluctuates drastically, the contribution of that harmonic will still be significantly amplified, thereby correcting the phase fluctuations. This is achieved through a preset "w" parameter. h "Highlight the destructive impact of key harmonic orders (such as the 5th and 7th) that are prone to causing grid resonance, and sum the correction values ​​of all specified harmonic orders to generate a harmonic disturbance index that reflects both harmonic intensity and quantifies phase stability. This value is used to accurately characterize the severity of harmonic pollution in the grid environment at the access point of the grid-type energy storage PCS."

[0028] In one embodiment, step S3, which involves determining the coupling state based on the stability risk coefficient and the harmonic disturbance index to obtain a real-time coupling state, and then adaptively fusing the stability risk coefficient, the harmonic disturbance index, and the preset reference impedance based on the real-time coupling state to obtain a damping adjustment component and a harmonic suppression component, includes: S31. Within a preset sliding time window, continuously collect and record the stability risk coefficient and the harmonic disturbance index to form a stability risk sequence and a harmonic disturbance index sequence. S32. Based on the stability risk sequence and the harmonic disturbance index sequence, perform modal correlation analysis to obtain the dynamic coupling factor; S33. Obtain the control coefficient set and decision state threshold, and obtain the coupling factor threshold and harmonic disturbance threshold based on the decision state threshold; S34. Compare the coupling factor threshold with the dynamic coupling factor to obtain the real-time coupling state, wherein the real-time coupling state includes a weak coupling state and a strong coupling state; If the weak coupling state is determined, the damping adjustment component is obtained according to the control coefficient set, the preset reference impedance and the harmonic disturbance index, and the harmonic suppression component is obtained according to the control coefficient set, the preset reference impedance and the stability risk coefficient. If the system is determined to be in a strongly coupled state, the system energy balance margin is obtained based on the converter operating parameters, and the damping adjustment component and harmonic suppression component are obtained based on the system energy balance margin, stability risk coefficient, harmonic disturbance index and preset reference impedance.

[0029] As described in steps S31-S34 above, this invention stores the stability risk coefficient and harmonic disturbance index calculated within a continuous sampling period in chronological order within a preset sliding time window. Based on two equal-length stability risk sequences and harmonic disturbance index sequences, the covariance of the two is calculated to obtain the modal correlation degree. This value is used to reveal the consistency between system stability risk and harmonic disturbance. Its absolute value is not only affected by the consistency of the trends of the two, but also by the absolute level of the fluctuation amplitude of the two indicators themselves. In order to eliminate the influence of the indicators' own dimensions and fluctuation amplitude, and thus extract the pure trend correlation characteristics, this invention further calculates the standard deviation of the stability risk sequence and the harmonic disturbance index sequence respectively, calculates the product of the two standard deviations, and further calculates the ratio of the modal correlation degree to the product to obtain the dynamic coupling factor. This value effectively removes the influence of their respective fluctuation amplitudes and quantifies the inherent statistical dependence and cooperative change direction of system stability risk and harmonic disturbance in the changing trend within the above-mentioned time window.

[0030] The system acquires a set of control coefficients and decision state thresholds. The control coefficient set refers to coefficients designed using control theory methods such as pole placement and frequency domain analysis, based on the mathematical model of the PCS and its connected power grid, to transform the system's state assessment quantities into specific damping and harmonic suppression command values. These coefficients include damping gain coefficients, damping suppression coefficients, harmonic suppression gain coefficients, and harmonic suppression constraint coefficients. Based on the decision state thresholds, coupling factor thresholds and harmonic disturbance thresholds are obtained. The absolute value of the dynamic coupling factor is compared with the coupling factor threshold to determine the real-time coupling state between system stability risk and harmonic disturbance: if the absolute value of the dynamic coupling factor is less than the coupling factor threshold, the system stability risk and harmonic disturbance are considered weakly coupled, indicating that they are independent or have minimal influence on each other during the dynamic process, and their disturbance root cause and dynamic response mechanism exhibit significant decoupling characteristics; if the absolute value of the dynamic coupling factor is not less than the coupling factor threshold, the system stability risk and harmonic disturbance are considered strongly coupled, indicating that they originate from the same disturbance source or have a close interaction mechanism, and their dynamic process exhibits high synergy and covariance.

[0031] Based on the coupling state, a corresponding data fusion method is adopted. When the coupling state is determined to be weak, the product of the preset reference impedance, damping gain coefficient, and stability risk coefficient is calculated to obtain the dominant damping value. This value is used to characterize the reference damping requirement determined under the current stability risk level, according to the formula "Z=Z base *k s *max(0, DD thd )” calculate the damping suppression value, where Z represents the damping suppression value, Z base Indicates the preset reference impedance, k s D represents the damping suppression coefficient, and D represents the harmonic disturbance index. thdThis involves representing the harmonic disturbance threshold, calculating the difference between the dominant damping value and the damping suppression value to obtain the damping adjustment component. This operation aims to automatically suppress unnecessary damping enhancement when grid harmonic disturbances are significant but stability risks are low, thereby avoiding voltage drops under light load conditions. Simultaneously, the product of the preset reference impedance, harmonic suppression gain coefficient, and harmonic disturbance index is calculated to obtain the dominant harmonic suppression value. This value corresponds to the reference harmonic suppression requirement determined by the current harmonic disturbance level. The product of the preset reference impedance, harmonic suppression constraint coefficient, and stability risk coefficient is also calculated to obtain the harmonic suppression constraint value. This value characterizes an upper limit adjustment amount set for the harmonic suppression command when there is a high stability risk. The difference between the dominant harmonic suppression value and the harmonic suppression constraint value is calculated to obtain the harmonic suppression component. When a strong coupling state is determined, the preset reference impedance and harmonic suppression constraint coefficient are obtained. The harmonic gain coefficient, based on the stability risk coefficient and harmonic disturbance index and their corresponding weighting coefficients, is used to calculate the comprehensive disturbance intensity through weighted fusion. Based on the converter operating parameters, the difference between the instantaneous output power of the PCS and its maximum available active / reactive power capacity is calculated in real time to obtain the system energy balance margin characterizing the system's transient stability reserve. The ratio of the comprehensive disturbance intensity to the system energy balance margin is calculated to obtain the normalized disturbance intensity. The product of the preset reference impedance, the normalized disturbance intensity, and the reference gain is calculated to obtain the damping adjustment component. This allows for the dynamic and proportional provision of the most needed damping impedance value based on the real-time disturbance and system state, within the preset maximum damping capacity. The product of the damping adjustment component, the harmonic disturbance index, the dynamic coupling factor, and the harmonic gain coefficient is calculated to obtain the harmonic suppression component.

[0032] It should be noted that this invention addresses the fundamental deficiency of insufficient control adaptability caused by the inability of fixed virtual impedance to distinguish the root causes of disturbances by intelligently identifying and collaboratively integrating the problems of power grid stability and harmonic disturbances that are treated in isolation in traditional control at the decision-making level.

[0033] In one embodiment, step S4, which constructs the virtual impedance function based on the damping adjustment component, the harmonic suppression component, and the converter operating parameters, includes: S41. Obtain the reference impedance parameters, wherein the reference impedance parameters include the reference virtual resistance, the reference virtual inductance, and the reference time constant; S42. Obtain the fundamental adaptive coefficient and the inertia enhancement coefficient based on the damping adjustment component and the converter operating parameters, and adjust the reference virtual resistance and the reference virtual inductance based on the fundamental adaptive coefficient and the inertia enhancement coefficient to obtain the adaptive virtual resistance and the adaptive virtual inductance. S43. Construct a fundamental domain virtual impedance function based on the adaptive virtual resistance and the adaptive virtual inductance; S44. Obtain the harmonic adaptive coefficients corresponding to each harmonic based on the converter operating parameters and the harmonic suppression components. S45. Obtain the bandpass filter function, and construct the harmonic domain virtual impedance function based on the harmonic adaptive coefficient, the reference virtual resistance, the reference virtual inductance and the bandpass filter function; S46. Based on the stability risk coefficient, the reference time constant is dynamically adjusted to obtain the compensator lead time constant and the compensator lag time constant, and a dynamic phase compensator function is constructed based on the compensator lead time constant and the compensator lag time constant.

[0034] As described in steps S41-S46 above, this invention obtains the grid fundamental synchronous frequency, output current sequence, output voltage sequence, and current switching frequency through converter operating parameters. Simultaneously, it performs Fast Fourier Analysis and calculations on the output current sequence to obtain the total harmonic distortion (THD) rate of the output current. Furthermore, it obtains the rated switching frequency and the grid standard power frequency rating. The ratio of the rated switching frequency to the current switching frequency is calculated to obtain the switching frequency compensation factor. The THD rate of the output current is then incremented by 1, and its natural logarithm is calculated. Finally, 1 is added to the calculation result to obtain the harmonic damping enhancement coefficient. This process aims to map the harmonic distortion rate to a smoothly increasing damping enhancement coefficient. The fundamental frequency adaptive coefficient is obtained by multiplying the damping adjustment component, the switching frequency compensation factor, and the harmonic damping enhancement coefficient. The adaptive virtual resistance is obtained by multiplying the fundamental frequency adaptive coefficient with the preset reference virtual resistance. The absolute value of the deviation between the grid fundamental synchronous frequency and the grid standard power frequency rated value is calculated, and the ratio of this absolute value to the grid standard power frequency rated value is calculated to obtain the relative frequency deviation ratio. "1" is added to this value to obtain the inertia enhancement coefficient. The inertia adaptive coefficient is obtained by multiplying the damping adjustment component with the inertia enhancement coefficient. The adaptive virtual inductance is obtained by multiplying the inertia adaptive coefficient with the preset reference virtual inductance.

[0035] Constructing the virtual impedance function in the fundamental domain: , among which, U f R represents the compensation voltage value at the f-th sampling time. a I represents the adaptive virtual resistance. f L represents the output current value at the f-th sampling time. a I represents the adaptive virtual inductor. f-1Let f represent the output current value at the (f-1)th sampling time, and t represent the sampling period. This invention constructs a virtual impedance function in the fundamental domain, thereby creating a dynamically adaptive output impedance for the network-type PCS at the fundamental frequency. This impedance can simultaneously and independently provide adjustable damping and inertial support, thus becoming a key stabilization mechanism to cope with power oscillations and frequency fluctuations. The function uses the classic circuit model of a resistor and inductor in series as its mathematical core. The resistance term is used to simulate energy dissipation characteristics, suppressing system oscillations by generating a voltage drop proportional to the instantaneous current value. The inductance term uses its differential characteristics to simulate inertia, providing buffer support for power fluctuations and frequency disturbances by generating an induced electromotive force that resists current changes.

[0036] Through the formula " "Calculate the harmonic adaptive coefficients, where α..." h K represents the harmonic adaptive coefficient of the h-th harmonic. x f represents the harmonic suppression component. e f represents the rated switching frequency. d T represents the current switching frequency. i The formula represents the total harmonic distortion (THD) of the output current, where h represents the order of the harmonics. The formula introduces the term "f". e / f d "This factor, acting as a dynamic compensation factor, aims to compensate for changes in control loop characteristics caused by variations in switching frequency, ensuring that harmonic suppression maintains consistent stability and effectiveness across different operating modes. (T)" i *h) 2 "This design achieves dual precision enhancement: it can sense the overall harmonic pollution level of the power grid and automatically raise the suppression benchmark of all harmonic channels when the total harmonic distortion rate increases; secondly, it focuses on enhancing higher harmonics, because higher harmonics are more difficult to filter out in the system and are more harmful. Through this non-linear relationship, it can achieve a sensitive response to higher harmonics and severe harmonic pollution."

[0037] A virtual impedance function in the harmonic domain is constructed. The output current sequence is filtered in real time using a bandpass filter whose center frequency is precisely tuned to a specific harmonic frequency. This yields the harmonic current values ​​corresponding to different harmonics, and thus, virtual impedance functions for different harmonics are constructed: Z h =(R base +h*ω0*L base )*α h *I h Among them, Z h R represents the harmonic voltage compensation amount for the h-th harmonic. base The reference virtual resistance is represented by h, the harmonic order is represented by ω0, and the rated angular frequency is represented by L. baseα represents the reference virtual inductance. h I represents the adaptive coefficient of the h-th harmonic. h This invention represents the harmonic current value of the h-th harmonic. By constructing a virtual impedance function in the harmonic domain, it establishes a harmonic suppression mechanism with frequency selectivity and intensity adaptability over a wide frequency range. This achieves precise control of specific harmonics and equal distribution of harmonic power in multi-machine parallel operation. The function uses "h*ω0*L" to represent the harmonic current value of the h-th harmonic. base "Obtain the inductive reactance value at the h-th harmonic frequency, and further combine the resistance with the inductive reactance at the corresponding harmonic frequency to provide basic impedance characteristics for harmonic suppression, forming a static reference for impedance construction. On this basis, a harmonic adaptive coefficient is introduced to dynamically enhance the reference virtual resistance, so that the impedance strength can be nonlinearly adjusted according to the total harmonic distortion rate of the power grid and the harmonic order, realizing the key suppression of severe harmonic pollution and high-order harmonics, completing the transition from static parameters to dynamic adaptive parameters. By multiplying the harmonic current value with the dynamically enhanced harmonic impedance, a harmonic voltage compensation amount is generated to offset the harmonic voltage of that order."

[0038] Using the formula respectively "and" "Calculate the compensator's lead time constant and lag time constant, where τ1 represents the compensator's lead time constant, τ..." base K represents the reference time constant. risk Let τ2 represent the stability risk coefficient, and τ3 represent the compensator lag time constant. An asymmetric variation mechanism is employed, causing the ratio of the two time constants to increase sharply with rising grid stability risk. This significantly enhances the phase lead effect of the phase compensator, proactively increasing the phase margin when the system faces instability risk, achieving intelligent suppression and adaptive stability control of wideband oscillations. Based on the output voltage sequence, the voltage value is calculated using first-order backward differential calculation over two consecutive sampling periods to obtain the differential value. The ratio of the differential value to the sampling period is calculated to obtain the voltage change rate. The product of the voltage change rate and the compensator lag time constant is then calculated to obtain the lead compensation voltage. Similarly, the product of the voltage change rate and the compensator lag time constant is calculated to obtain the lag compensation voltage. Based on this, a dynamic phase compensator function is constructed: Among them, G comp U represents the stable compensation voltage. lead U represents the lead compensation voltage. layThe hysteresis compensation voltage is represented by the aforementioned differential element to achieve forward-looking phase correction: when a decrease in stability margin is detected, the compensator generates a stronger phase lead in the critical frequency band, effectively offsetting the potential phase hysteresis in the system, thereby suppressing the oscillation trend. Furthermore, this invention separates the module that implements specific impedance functions from the module responsible for global stability. This modular architecture ensures the accurate implementation of impedance control functions and provides global stability margin protection for the system, thereby effectively suppressing the risk of broadband oscillation while improving control performance.

[0039] In one embodiment, step S5, which involves performing real-time convolution on the real-time current sequence based on the virtual impedance function to obtain the feedforward compensation voltage, includes: S51. Perform real-time convolution operation on the real-time current sequence based on the fundamental domain virtual impedance function to obtain the fundamental voltage compensation component, and perform real-time convolution operation on the real-time current sequence based on the harmonic domain virtual impedance function to obtain the harmonic voltage compensation component. S52. Perform real-time convolution operation on the real-time current sequence based on the dynamic phase compensator function to obtain the phase compensation component, and adjust the phase compensation component according to the stability risk coefficient to obtain the phase adjustment amount; S53. Perform vector fusion of the fundamental voltage compensation component and the harmonic voltage compensation component to obtain an initial compensation voltage vector, and obtain the initial composite amplitude and initial composite phase based on the initial compensation voltage vector; S54. Obtain the modulation phase based on the phase adjustment amount and the initial synthesized phase, and adjust the initial synthesized amplitude based on the stability risk coefficient and the harmonic disturbance index to obtain the modulation amplitude; S55. Obtain the feedforward compensation voltage based on the modulation phase and the modulation amplitude through inverse coordinate transformation.

[0040] As described in steps S51-S55 above, this invention obtains the real-time current sequence output by the PCS within a specific sliding time window based on the converter operating parameters. The real-time current sequence is then fed into three independent, pre-built digital convolution engines. These three digital convolution engines correspond to the fundamental domain virtual impedance function, the harmonic domain virtual impedance function, and the dynamic phase compensator function, respectively, initiating parallel convolution calculations. Within each control sampling period, the above-mentioned parallel convolution operation is executed synchronously through the three dedicated digital convolution engines. During the calculation, each engine processes the real-time current sequence within the sliding time window point by point, comprehensively calculating the current samples from the current and past times. The fundamental impedance engine calculates the fundamental voltage compensation component required to provide adaptive damping and inertial support in real time for the cumulative contribution of voltage compensation at the current moment. The harmonic impedance engine calculates the harmonic voltage compensation component used to accurately suppress harmonics and improve the averaging effect. The phase compensation engine executes its phase correction algorithm in parallel to generate the phase compensation component used to improve the stability margin of the system. The entire parallel processing architecture relies on the pipeline technology of FPGA to ensure that two voltage compensation quantities and phase adjustment quantities can be output synchronously at the end of each sampling period, so as to overcome the inherent defects of traditional single impedance models that cannot simultaneously take into account fundamental performance and harmonic suppression over a wide frequency range.

[0041] The fundamental voltage compensation component and the harmonic voltage compensation component are vector synthesized to obtain the initial compensation voltage vector, which is described by the initial synthesized amplitude and the initial synthesized phase. Adaptive phase modulation is performed based on stability risk: a preset stability risk threshold is obtained, and based on… "Calculate the phase weights, where ω represents the phase weights, and K..." risk K represents the stability risk coefficient. t The stability risk threshold is represented by the product of the phase compensation component and the phase weight, which is used to obtain the phase adjustment amount. This phase adjustment amount is then applied to the initial synthesized phase to obtain the modulation phase. Through this design, when the stability risk coefficient is less than the stability risk threshold, i.e. when the power grid is operating stably, the phase compensation component output by the phase compensation engine will be suppressed proportionally, thereby avoiding voltage distortion or oscillation that may be caused by excessive phase compensation under stable operating conditions. Only when the stability risk increases significantly will full phase compensation be activated to provide maximum phase margin support and strongly suppress potential oscillation instability.

[0042] According to "φ=max[0.2, min(1.5, K risk The global intensity modulation coefficient is calculated as φ = φ *ω1 + D * ω2), where φ represents the global intensity modulation coefficient and K is the global intensity modulation coefficient. riskLet ω1 represent the stability risk coefficient, D represent the harmonic disturbance index, and ω2 represent the power quality weight coefficient. This design allows the power grid to provide up to 150% enhanced compensation when the stability risk coefficient is high, such as during a fault or impact. However, under steady-state light load conditions, when both the stability risk coefficient and harmonic disturbance index are low, the compensation automatically decreases to 20% to avoid over-adjustment, thus achieving a balance between accuracy and robustness. The modulation amplitude is obtained by multiplying the global intensity modulation coefficient and the initial synthesized amplitude. The final feedforward voltage vector defined by the modulation amplitude and modulation phase is then converted into a feedforward compensation voltage through inverse coordinate transformation. This invention employs a global intensity adaptive modulation strategy to dynamically adjust the global gain of the entire virtual impedance compensation loop. This enables the control system to actively enhance the virtual impedance strength under adverse conditions such as faults to improve stability and support capabilities, while automatically weakening its impact under steady-state light load conditions to ensure voltage control accuracy.

[0043] In one embodiment, step S6, which uniformly modulates the power switching devices based on the feedforward compensation voltage to perform regulation of the PCS output voltage, includes: S61. Obtain the reference modulation wave voltage, and vector-superimpose the feedforward compensation voltage and the reference modulation wave voltage to generate a composite modulation wave voltage; S62. Based on the stability risk coefficient, the composite modulated wave voltage is adaptively limited to obtain the final modulated wave voltage. S63. Input the final modulated wave voltage to the space vector pulse width modulation module, and correct the basic action time of the final modulated wave voltage through the feedforward compensation voltage to obtain the basic action correction time. S64. Obtain the zero vector action time, and generate a PWM drive signal based on the basic action correction time and the zero vector action time; S65. The PWM drive signal is sent to the drive circuit of the power switching device, and the PCS output voltage is adjusted according to the switching state of the power switching device controlled by the PWM drive signal.

[0044] As described in steps S61-S65 above, this invention obtains the reference modulated wave voltage generated by the PCS internal control loop (including voltage controller and current controller) in the current cycle and obtained through coordinate inverse transformation. The feedforward compensation voltage and the reference modulated wave voltage are vector-superimposed in the stationary αβ coordinate system to generate a composite modulated wave voltage. Adaptive limiting processing based on stability risk is applied to the composite modulated wave voltage: the stability risk coefficient is compared with the stability risk threshold. When the stability risk coefficient is greater than the stability risk threshold, the power grid is determined to be in a high stability risk state. At this time, the dynamic limiting mode is activated, and the limiting is achieved through the formula "U d=U n *[1-k safe *(K risk -K t )]” calculate the dynamic limit value, where U d U represents the dynamic limit value. n Indicates the rated amplitude limit, k safe K represents the safety factor. risk K represents the stability risk coefficient. t The "(K" in the formula represents the stability risk threshold. risk -K t The ")" item characterizes the degree of exceeding stability risk limits; a larger value indicates that the system deviates further from a stable operating state. The safety factor, as a preset adjustment gain, converts the degree of risk exceeding limits into a corresponding derating ratio. The dynamic derating value is obtained by subtracting a derating amount proportional to the risk level from the rated derating value. The instantaneous amplitude of the composite modulated wave voltage is calculated in real time and compared with the dynamic derating value. If the instantaneous amplitude does not exceed the dynamic derating value, the composite modulated wave voltage is directly used as the final modulated wave voltage; if the instantaneous amplitude exceeds the dynamic derating value, the amplitude of the composite modulated wave voltage is adjusted. The voltage is compressed and its amplitude is limited to the dynamic limiting range while strictly maintaining its original phase information to obtain the final modulated wave voltage. This design ensures that the system automatically reduces the voltage output limiting when facing a high risk of instability, avoids overmodulation by limiting the maximum modulation ratio, increases the system stability margin, and prioritizes global operational stability. When the stability risk coefficient recovers to below the threshold, the system automatically switches back to the fixed limiting mode to ensure the output voltage performance under normal operating conditions, thereby achieving an adaptive balance between the output voltage capability and stability requirements of the system during transient processes.

[0045] The final modulated wave voltage is input to the space vector pulse width modulation module. This module determines the sector based on the amplitude and phase of the final modulated wave voltage, calculates the basic action time and zero vector action time of the basic voltage vector, and further incorporates the phase information of the feedforward compensation voltage for correction. The specific correction formula is as follows: ,in, T represents the basic action correction time. i k represents the basic duration of action. c θ represents the compensation coefficient. ff θ represents the phase of the feedforward compensation voltage. sectorThis indicates the center phase of the current sector of the final modulated wave voltage. Based on the fundamental action correction time and the zero vector action time, a corresponding PWM drive signal is generated. According to the preset seven-segment SVPWM switching sequence, the fundamental action correction time corresponding to two non-zero fundamental voltage vectors and the zero vector action time corresponding to zero output voltage are allocated to the time axis of a control cycle in a symmetrical pattern of "zero vector - fundamental voltage vector - fundamental voltage vector - zero vector," forming the conduction time sequence of each bridge arm. The conduction time of each bridge arm is converted into the corresponding duty cycle, and the ratio of the bridge arm conduction time to the PWM control cycle is calculated. Furthermore, the product of this ratio and the PWM timer cycle value is calculated to obtain the PWM comparator register value. This value is loaded into the comparator register of the PWM generator and compared in real time with the triangular carrier signal generated by the timer. When the timer count value is less than the comparison value, a high level is output. Otherwise, a low level is output, thereby generating six original PWM signals. Based on the switching characteristics of the power switching devices, an adaptive dead time is inserted into each pair of complementary upper and lower bridge arm drive signals to ensure a protection interval during the switching state transition where the upper and lower bridge arms are simultaneously turned off, outputting six safe PWM drive signals with dead time. This invention ensures that the phase compensation intention generated by the virtual impedance algorithm can be accurately reflected in the final stage of space vector synthesis through the above operations, thereby overcoming the technical defect of reduced virtual impedance control effect caused by phase distortion in the modulation stage in traditional control. The PWM drive signal is sent to the drive circuit of the power switching devices, and the drive circuit precisely controls the turn-on and turn-off sequence of each power switching device to generate the PCS bridge arm output voltage corresponding to the final modulated wave voltage, thereby realizing dynamic, adaptive and high-precision control of the output voltage of the grid-type energy storage PCS.

[0046] This application also provides a grid-type energy storage PCS voltage control system based on virtual impedance dynamic adjustment, including: The data acquisition module is used to acquire the grid-side electrical parameters and load-side electrical parameters of the grid-type energy storage PCS access point, as well as the converter operating parameters and preset reference impedance of the grid-type energy storage PCS. The status assessment module is used to perform data analysis on the electrical parameters of the power grid side and the electrical parameters of the load side to obtain the stability risk coefficient and harmonic disturbance index. The decision fusion module is used to determine the coupling state based on the stability risk coefficient and the harmonic disturbance index to obtain the real-time coupling state, and to perform adaptive data fusion on the stability risk coefficient, harmonic disturbance index and preset reference impedance based on the real-time coupling state to obtain the damping adjustment component and harmonic suppression component. The impedance reconstruction module is used to construct a virtual impedance function based on the damping adjustment component, the harmonic suppression component, and the converter operating parameters. The virtual impedance function includes a fundamental domain virtual impedance function, a harmonic domain virtual impedance function, and a dynamic phase compensator function. The data acquisition module is used to acquire a real-time current sequence based on the converter operating parameters, and to perform a real-time convolution operation on the real-time current sequence based on the virtual impedance function to obtain the feedforward compensation voltage. A voltage modulation module is used to uniformly modulate the power switching devices based on the feedforward compensation voltage to perform PCS output voltage regulation.

[0047] In one embodiment, the state assessment module includes: The data processing unit is used to extract the grid-side voltage and current sequence and the load-side voltage and current sequence from the grid-side electrical parameters and the load-side electrical parameters, respectively, and to perform two-phase rotating coordinate transformation on the grid-side voltage and current sequence and the load-side voltage and current sequence to obtain multiple grid-side voltage components, grid-side current components, load-side voltage components and load-side current components; The feature extraction unit is used to obtain a power change feature set on the grid side based on the grid-side voltage component and the grid-side current component, and to obtain a power change feature set on the load side based on the load-side voltage component and the load-side current component; The curvature calculation unit is used to construct a complex sequence of grid-side voltage based on multiple grid-side voltage components, and to perform numerical differentiation and curvature calculation on the complex sequence of grid-side voltage to obtain the instantaneous curvature value; Impedance identification unit is used to apply voltage disturbance to the output port of PCS and simultaneously acquire composite voltage signal and composite current signal, and obtain the dynamic rate of change of impedance based on the composite voltage signal and composite current signal; The risk assessment unit is used to input the power change feature set on the grid side, the power change feature set on the load side, the instantaneous curvature value, and the dynamic change rate of impedance into the stability classifier to conduct a wideband oscillation instability risk assessment and obtain the stability margin probability. The risk quantification unit is used to obtain system baseline parameters and obtain stability risk coefficients based on the system baseline parameters and the stability margin probability. The disturbance quantization unit is used to perform adaptive harmonic analysis based on the electrical parameters of the load side, obtain the effective value of the harmonic current, and obtain the harmonic disturbance index according to the effective value of the harmonic current and the system reference parameters.

[0048] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described grid-type energy storage PCS voltage control method based on virtual impedance dynamic adjustment.

[0049] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described voltage control method for grid-type energy storage PCS based on dynamic adjustment of virtual impedance.

[0050] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0051] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0052] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A voltage control method for a grid-type energy storage PCS based on dynamic adjustment of virtual impedance, characterized in that, include: Obtain the grid-side electrical parameters and load-side electrical parameters of the grid-type energy storage PCS access point, and obtain the converter operating parameters and preset reference impedance of the grid-type energy storage PCS; Data analysis is performed on the electrical parameters of the power grid side and the electrical parameters of the load side to obtain the stability risk coefficient and harmonic disturbance index; The coupling state is determined based on the stability risk coefficient and the harmonic disturbance index to obtain the real-time coupling state. Based on the real-time coupling state, the stability risk coefficient, the harmonic disturbance index and the preset reference impedance are adaptively fused to obtain the damping adjustment component and the harmonic suppression component. A virtual impedance function is constructed based on the damping adjustment component, the harmonic suppression component, and the converter operating parameters. The virtual impedance function includes a fundamental domain virtual impedance function, a harmonic domain virtual impedance function, and a dynamic phase compensator function. The real-time current sequence is obtained based on the converter operating parameters, and the real-time convolution operation is performed on the real-time current sequence based on the virtual impedance function to obtain the feedforward compensation voltage. The power switching devices are uniformly modulated based on the feedforward compensation voltage to regulate the output voltage of the PCS.

2. The voltage control method for grid-type energy storage PCS based on dynamic adjustment of virtual impedance according to claim 1, characterized in that, The step of performing data analysis on the electrical parameters of the power grid side and the electrical parameters of the load side to obtain the stability risk coefficient and harmonic disturbance index includes: The grid-side voltage and current sequences and the load-side voltage and current sequences are extracted from the grid-side electrical parameters and the load-side electrical parameters, respectively. Then, two-phase rotating coordinate transformations are performed on the grid-side voltage and current sequences and the load-side voltage and current sequences to obtain multiple grid-side voltage components, grid-side current components, load-side voltage components, and load-side current components. A power change feature set on the grid side is obtained based on the grid-side voltage component and the grid-side current component, and a power change feature set on the load side is obtained based on the load-side voltage component and the load-side current component. A complex sequence of grid-side voltages is constructed based on multiple grid-side voltage components, and numerical differentiation and curvature calculation are performed on the complex sequence of grid-side voltages to obtain the instantaneous curvature value; A voltage disturbance is applied to the output port of the PCS and a composite voltage signal and a composite current signal are acquired simultaneously. The dynamic rate of change of impedance is obtained based on the composite voltage signal and the composite current signal. The power change feature set on the grid side, the power change feature set on the load side, the instantaneous curvature value, and the dynamic rate of change of impedance are input together into the stability classifier to assess the risk of broadband oscillation instability and obtain the stability margin probability. Obtain the system baseline parameters, and obtain the stability risk coefficient based on the system baseline parameters and the stability margin probability; Adaptive harmonic analysis is performed based on the electrical parameters of the load side to obtain the effective value of the harmonic current, and the harmonic disturbance index is obtained based on the effective value of the harmonic current and the system reference parameters.

3. The voltage control method for grid-type energy storage PCS based on dynamic adjustment of virtual impedance according to claim 1, characterized in that, The step of determining the coupling state based on the stability risk coefficient and the harmonic disturbance index to obtain the real-time coupling state, and then adaptively fusing the stability risk coefficient, the harmonic disturbance index, and the preset reference impedance based on the real-time coupling state to obtain the damping adjustment component and the harmonic suppression component, includes: Within a preset sliding time window, the stability risk coefficient and the harmonic disturbance index are continuously collected and recorded to form a stability risk sequence and a harmonic disturbance index sequence. Modal correlation analysis is performed based on the stability risk sequence and the harmonic disturbance index sequence to obtain the dynamic coupling factor; Obtain the control coefficient set and decision state threshold, and obtain the coupling factor threshold and harmonic disturbance threshold based on the decision state threshold; The coupling factor threshold is compared with the dynamic coupling factor to obtain the real-time coupling state, wherein the real-time coupling state includes a weak coupling state and a strong coupling state; If the weak coupling state is determined, the damping adjustment component is obtained according to the control coefficient set, the preset reference impedance and the harmonic disturbance index, and the harmonic suppression component is obtained according to the control coefficient set, the preset reference impedance and the stability risk coefficient. If the system is determined to be in a strongly coupled state, the system energy balance margin is obtained based on the converter operating parameters, and the damping adjustment component and harmonic suppression component are obtained based on the system energy balance margin, stability risk coefficient, harmonic disturbance index and preset reference impedance.

4. The voltage control method for grid-type energy storage PCS based on dynamic adjustment of virtual impedance according to claim 1, characterized in that, The step of constructing a virtual impedance function based on the damping adjustment component, the harmonic suppression component, and the converter operating parameters includes: Obtain reference impedance parameters, wherein the reference impedance parameters include reference virtual resistance, reference virtual inductance, and reference time constant; The fundamental adaptive coefficient and the inertia enhancement coefficient are obtained based on the damping adjustment component and the converter operating parameters. The reference virtual resistance and the reference virtual inductance are adjusted based on the fundamental adaptive coefficient and the inertia enhancement coefficient to obtain the adaptive virtual resistance and the adaptive virtual inductance. A fundamental domain virtual impedance function is constructed based on the adaptive virtual resistance and the adaptive virtual inductance. Based on the converter operating parameters and the harmonic suppression component, the harmonic adaptive coefficients corresponding to each harmonic are obtained; Obtain the bandpass filter function, and construct the harmonic domain virtual impedance function based on the harmonic adaptive coefficient, the reference virtual resistance, the reference virtual inductance, and the bandpass filter function; The reference time constant is dynamically adjusted based on the stability risk coefficient to obtain the compensator lead time constant and compensator lag time constant, and a dynamic phase compensator function is constructed based on the compensator lead time constant and compensator lag time constant.

5. The voltage control method for grid-type energy storage PCS based on dynamic adjustment of virtual impedance according to claim 1, characterized in that, The step of performing real-time convolution operation on the real-time current sequence based on the virtual impedance function to obtain the feedforward compensation voltage includes: The real-time current sequence is convolved with the fundamental domain virtual impedance function to obtain the fundamental voltage compensation component, and the real-time current sequence is convolved with the harmonic domain virtual impedance function to obtain the harmonic voltage compensation component. The real-time current sequence is convolved based on the dynamic phase compensator function to obtain a phase compensation component, and the phase compensation component is adjusted according to the stability risk coefficient to obtain the phase adjustment amount. The fundamental voltage compensation component and the harmonic voltage compensation component are vector-fused to obtain an initial compensation voltage vector, and the initial composite amplitude and initial composite phase are obtained based on the initial compensation voltage vector. The modulation phase is obtained based on the phase adjustment amount and the initial synthesized phase, and the initial synthesized amplitude is adjusted based on the stability risk coefficient and the harmonic disturbance index to obtain the modulation amplitude; The feedforward compensation voltage is obtained by inverse coordinate transformation based on the modulation phase and the modulation amplitude.

6. The voltage control method for grid-type energy storage PCS based on dynamic adjustment of virtual impedance according to claim 1, characterized in that, The step of uniformly modulating the power switching devices based on the feedforward compensation voltage to perform PCS output voltage regulation includes: Obtain the reference modulation wave voltage, and then vectorally superimpose the feedforward compensation voltage and the reference modulation wave voltage to generate a composite modulation wave voltage; Based on the stability risk coefficient, the composite modulated wave voltage is adaptively limited to obtain the final modulated wave voltage. The final modulated wave voltage is input to the space vector pulse width modulation module, and the basic action time of the final modulated wave voltage is corrected by the feedforward compensation voltage to obtain the basic action correction time. Obtain the zero vector action time, and generate a PWM drive signal based on the basic action correction time and the zero vector action time; The PWM drive signal is sent to the drive circuit of the power switching device, and the switching state of the power switching device is controlled according to the PWM drive signal to adjust the output voltage of the PCS.

7. A voltage control system for a grid-type energy storage PCS based on dynamic adjustment of virtual impedance, characterized in that, It includes multiple modules for implementing the steps of the method according to any one of claims 1 to 6.

8. The grid-type energy storage PCS voltage control system based on virtual impedance dynamic adjustment according to claim 7, characterized in that, It includes multiple units, which are used to implement the steps of the method according to any one of claims 1 to 6.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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