Network type electrolytic water hydrogen production unit topology control method adaptive to new energy direct connection

Abstract

The application discloses a network-constructing type electrolytic water hydrogen production unit topology control method suitable for new energy direct connection, and relates to the field of hydrogen production unit topology control. The application constructs a network-constructing type control system for a new energy direct connection scene, forms a unified closed-loop control structure in terms of power modeling, damping adjustment, electricity-hydrogen cooperation and impedance reconstruction, and enables the rectifier to have stronger dynamic support capability under weak power grid conditions. The application can timely enhance system damping in the oscillation energy growth stage and reduce low-frequency and subsynchronous oscillation amplitude. The application realizes dynamic cooperation of the electrolytic cell and the rectifier, weakens the influence of negative resistance characteristics on system stability, and improves the equivalent output impedance of the rectifier under voltage disturbance, so as to improve the equivalent short-circuit capacity and damping ratio of the system.

Description

Technical Field

[0001] This invention relates to the field of hydrogen production unit topology control, specifically a topology control method for grid-type water electrolysis hydrogen production units adapted to direct connection with new energy sources. Background Technology

[0002] In new energy direct-connection water electrolysis hydrogen production systems, rectifiers typically employ a grid-based control structure based on phase-locked loops (PLLs). This involves acquiring voltage and current signals at the point of common coupling (PCC), calculating active and reactive power in a synchronous rotating coordinate system, and adjusting the rectifier output power using inner current loop and outer power loop control strategies. Some improved solutions utilize virtual synchronous generators (VSGs) or droop control methods, introducing virtual inertia and damping parameters to simulate the oscillation equations of a synchronous generator, thereby enhancing the system's adaptability to frequency disturbances. Furthermore, in weak grid scenarios, existing technologies also improve system stability by adding fixed virtual impedances or additional damping elements to suppress voltage fluctuations and low-frequency oscillations.

[0003] However, in direct-connection hydrogen production scenarios with large-scale integration of new energy sources and low grid short-circuit capacity, the aforementioned control methods still have significant shortcomings. First, PLL-based grid control is prone to coupling with grid impedance under weak grid conditions, amplifying low-frequency oscillation energy and inducing subsynchronous oscillations in the 10–40 Hz range. Second, in existing virtual synchronous machine control, inertia and damping parameters are mostly fixed values ​​or linearly adjusted based on frequency deviation, making it difficult to accurately reflect the growth trend of oscillation energy and failing to enhance damping in the early stages of oscillation. Third, electrolyzers are typically equivalent to constant power or slowly varying loads, and their dynamic characteristics are not incorporated into the grid control framework, leading to power coupling mismatch between the rectifier and the electrolyzer, easily resulting in negative resistance effects. Furthermore, existing virtual impedances mostly adopt fixed integer order structures with singular impedance frequency characteristics, failing to achieve targeted damping enhancement in the subsynchronous frequency band. Therefore, under the combined effects of rapid fluctuations in new energy power and weak grid impedance, the system still suffers from oscillation amplification and insufficient stability margin. Summary of the Invention

[0004] This invention proposes a topology control method for grid-connected water electrolysis hydrogen production units adapted to direct connection with new energy sources. It aims to solve the technical problems of subsynchronous oscillation caused by impedance coupling between rectifier and grid under conditions of large-scale direct connection of new energy sources and low grid short-circuit capacity, continuous accumulation of oscillation energy due to lag in virtual synchronous machine damping adjustment, negative resistance effect caused by the non-participation of electrolyzer load dynamics in grid control, and the difficulty of providing effective damping support in the low-frequency oscillation range due to the single frequency characteristics of traditional virtual impedance.

[0005] The topology control method for grid-connected water electrolysis hydrogen production units adapted to direct connection with new energy sources includes the following steps:

[0006] S1. Acquire voltage and current signals from the rectifier output side, calculate electromagnetic power based on the acquired signal values, set an initial virtual mechanical power reference value, construct a virtual synchronous machine differential equation containing virtual moment of inertia and fractional-order damping based on the active power balance principle, use the virtual mechanical power reference value as input, calculate the virtual angular frequency and internal potential reference value according to the virtual synchronous machine differential equation, and generate a pulse signal for controlling the rectifier;

[0007] Specifically, in existing direct-connect hydrogen production systems from new energy sources, rectifiers typically employ grid-following control, tracking the grid voltage phase via a phase-locked loop (PLL) and using an inner current loop to control active and reactive power output. However, under weak grid conditions, the PLL is prone to coupling with grid impedance, leading to low-frequency oscillations or even subsynchronous oscillations. To address this issue, existing technologies propose a Virtual Synchronous Machine (VSG) control method, which equates power electronic devices to a synchronous rotating system, endowing them with inertia and damping characteristics, thereby enhancing grid support capabilities. This step, based on this, calculates electromagnetic power by collecting voltage and current signals from the rectifier output side, and performs a basic modeling process based on the principle of instantaneous active power balance. Furthermore, it sets a virtual mechanical power reference value, enabling the rectifier to have a power mapping relationship between mechanical input and electromagnetic output at the control level. A virtual synchronous machine differential equation is constructed based on the synchronous generator oscillation equation. However, unlike traditional integer-order damping structures, this scheme introduces a fractional-order damping element into the differential equation, giving the damping characteristics memory and frequency-dependent features, thus making it more adaptable to the operating environment of wide power fluctuation spectrum and significant low-frequency oscillations in new energy sources. By solving the differential equation, the virtual angular frequency and internal potential reference values ​​are obtained, and then the pulse control signal of the rectifier is generated, realizing the transformation from the grid-following type to the grid-forming type control mode.

[0008] S2. Based on the virtual angular frequency, calculate the deviation value from the preset rated angular frequency of the power grid, and construct an energy function to reflect the oscillation state of the system based on the deviation value; obtain the energy change rate by calculating the fractional derivative of the energy function, and correct the coefficient of the fractional damping element in the differential equation in real time based on the energy change rate;

[0009] Specifically, in existing VSG control technology, the damping coefficient is usually a fixed value or a simple adaptive adjustment, and its adjustment is mostly based on frequency deviation or power deviation, which makes it difficult to accurately reflect the true trend of system oscillation energy change. Therefore, under weak grid conditions, damping adjustment is often lagging or insufficient.

[0010] This step uses the virtual angular frequency obtained in step S1 as a basis to calculate its deviation from the rated angular frequency of the power grid. This deviation is then mapped to an equivalent oscillation energy function to characterize the dynamic energy state of the system. Compared to traditional methods based on frequency amplitude, this scheme further calculates the fractional derivative of the energy function to obtain the energy change rate. The fractional derivative has historical memory characteristics and can reflect the cumulative effect of oscillation trends. When the energy change rate is positive, it indicates that the system's oscillation energy is increasing. At this time, the fractional damping coefficient is increased in real time to suppress oscillation propagation from an energy perspective. This method differs from traditional frequency domain filtering or fixed damping control and belongs to an adaptive damping adjustment mechanism based on energy growth criteria.

[0011] S3. Collect the actual operating power of the electrolytic cell, calculate the dynamic change of the electrolytic cell power, perform fractional derivative processing on the dynamic change to obtain the additional power command, and superimpose the additional power command with the virtual mechanical power reference value;

[0012] Specifically, in existing new energy hydrogen production systems, electrolyzers are typically considered controlled loads, with their power variations primarily determined by upper-level dispatch and not participating in grid dynamic stability control. However, electrolyzers are essentially electro-hydrogen energy conversion devices, and their power variations possess a certain inertia and buffering characteristics. If utilized properly, they can serve as an active resource for system damping regulation. This step involves collecting the actual operating power of the electrolyzer and calculating its dynamic changes, treating the electrolyzer power changes as an electro-hydrogen coupled dynamic signal, and performing fractional-order differential processing on this change. Fractional-order differential processing enhances the response capability to slow changing trends and avoids over-regulation caused by transient spikes. After fractional-order processing, an additional power command is obtained and superimposed with a virtual mechanical power reference value, thereby changing the input terms of the virtual synchronous machine differential equation. This method realizes the transformation of the electrolyzer from a passive load to a cooperative power unit participating in grid control, improving the overall system damping and dynamic stability.

[0013] S4. Acquire the voltage at the point of common coupling and calculate the amplitude fluctuation of the voltage at that point. Construct a nonlinear fractional virtual impedance model based on the amplitude fluctuation. Output the virtual impedance voltage drop through the nonlinear fractional virtual impedance model. Compensate the virtual impedance voltage drop to the internal potential reference value and update the pulse signal according to the compensated voltage reference value to suppress subsynchronous oscillation.

[0014] Specifically, under weak grid conditions, the voltage at the point of common coupling (PCC) is prone to fluctuations. Traditional rectifier control often only regulates voltage through reactive power, but its equivalent output impedance remains low, making it difficult to effectively improve the system's short-circuit capacity. While existing virtual impedance control methods exist, they mostly employ fixed inductor or resistor models with integer order structures, failing to adjust for oscillation characteristics across different frequency bands. This step collects the PCC voltage and calculates its amplitude fluctuation, using this fluctuation as a characterization of system disturbance intensity. Based on this indicator, a nonlinear fractional-order virtual impedance model is constructed. The nonlinearity causes the impedance value to change with the voltage disturbance amplitude, and the fractional-order structure allows the impedance to exhibit adjustable phase and amplitude characteristics in the frequency domain. The virtual impedance voltage drop output from this model is compensated to the internal potential reference value, thereby changing the rectifier's output equivalent impedance and achieving active impedance reconstruction. By updating the pulse signal, the rectifier exhibits higher damping characteristics in the subsynchronous frequency band, achieving the goal of suppressing subsynchronous oscillations.

[0015] The beneficial effects of the invention are:

[0016] This invention constructs a grid-based control system for direct-connection scenarios of new energy sources, forming a unified closed-loop control structure at the levels of power modeling, damping adjustment, electric-hydrogen synergy, and impedance reconstruction. This enables the rectifier to have stronger dynamic support capabilities under weak grid conditions. The invention can promptly enhance system damping during the oscillation energy growth phase, reducing the amplitude of low-frequency and subsynchronous oscillations; achieve dynamic synergy between the electrolyzer and the rectifier, weakening the impact of negative resistance characteristics on system stability; and increase the equivalent output impedance of the rectifier under voltage disturbance conditions, thereby improving the system's equivalent short-circuit capacity and damping ratio. Through these mechanisms, the operational stability and disturbance resistance of direct-connection hydrogen production systems for new energy sources in weak grid environments can be significantly improved. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the topology control method for a grid-type water electrolysis hydrogen production unit adapted to direct connection with new energy sources, as proposed in Embodiment 1 of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the following description.

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention; that is, the described embodiments are only a part of the embodiments of the invention, and not all of them. The components of the embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention. It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0021] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0022] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0023] Example 1

[0024] Among them, such as Figure 1 A topology control method for grid-connected water electrolysis hydrogen production units adapted to direct connection with new energy sources includes the following steps:

[0025] S1. Acquire voltage and current signals from the rectifier output side, calculate electromagnetic power based on the acquired signal values, set an initial virtual mechanical power reference value, construct a virtual synchronous machine differential equation containing virtual moment of inertia and fractional-order damping based on the active power balance principle, use the virtual mechanical power reference value as input, calculate the virtual angular frequency and internal potential reference value according to the virtual synchronous machine differential equation, and generate a pulse signal for controlling the rectifier;

[0026] S2. Based on the virtual angular frequency, calculate the deviation value from the preset rated angular frequency of the power grid, and construct an energy function to reflect the oscillation state of the system based on the deviation value; obtain the energy change rate by calculating the fractional derivative of the energy function, and correct the coefficient of the fractional damping element in the differential equation in real time based on the energy change rate;

[0027] S3. Collect the actual operating power of the electrolytic cell, calculate the dynamic change of the electrolytic cell power, perform fractional derivative processing on the dynamic change to obtain the additional power command, and superimpose the additional power command with the virtual mechanical power reference value to dynamically change the input terms of the differential equation in step S1.

[0028] S4. Acquire the voltage at the point of common coupling and calculate the amplitude fluctuation of the voltage at that point. Construct a nonlinear fractional virtual impedance model based on the amplitude fluctuation. Output the virtual impedance voltage drop through the nonlinear fractional virtual impedance model. Compensate the virtual impedance voltage drop to the internal potential reference value and update the pulse signal according to the compensated voltage reference value to suppress subsynchronous oscillation.

[0029] It should be noted that the process of constructing and solving the nonlinear fractional virtual synchronous machine model essentially simulates the control characteristics of the power electronic converter as the operating characteristics of a synchronous generator. By acquiring the voltage and current data at the rectifier output port in real time, the instantaneous electromagnetic power is calculated using instantaneous power theory. This electromagnetic power physically represents the load torque on the grid side. The system sets an initial virtual mechanical power reference value, which simulates the input torque of the prime mover (exemplarily, a steam turbine). Based on energy conservation and the rotor motion equation, a second-order differential equation describing the unit's frequency dynamic characteristics is constructed. On this basis, a fractional-order calculus operator is introduced to replace the conventional integer-order damping term, thereby giving the system flexible adjustment capability in a wide frequency range. The virtual mechanical power reference value and the calculated electromagnetic power are substituted into the differential equation for real-time numerical solution. The output is a virtual angular frequency and a virtual internal potential reference value that can maintain the active power balance of the system. These two key variables are converted into space vector pulse width modulation (SVPWM) signals to drive the switching of the rectifier power switches, so that the unit exhibits grid-type voltage source characteristics with inertia and damping.

[0030] Furthermore, the adaptive damping adjustment mechanism based on the fractional-order energy change rate aims to dynamically stabilize frequency fluctuations by monitoring the flow of oscillating energy in the system. Specifically, the system compares the virtual angular frequency calculated in real time in step S1 with the standard rated angular frequency of the power grid to obtain the frequency deviation. This deviation is used to construct the Lyapunov energy function, the magnitude of which directly reflects the severity of the current system frequency oscillation. By taking the fractional derivative of this energy function with respect to time, the energy change rate, which characterizes the accumulation or dissipation trend of oscillating energy, is obtained. When a positive energy change rate is detected, indicating that oscillating energy is accumulating, the control algorithm uses this rate of change as a gain signal to increase the fractional-order damping coefficient in the differential equation of step S1 in real time. This dynamic adjustment changes the dissipation characteristics of the virtual synchronous machine model, providing a high-intensity damping torque in the early stages of oscillation, thereby quickly suppressing frequency overshoot. After the system stabilizes, the basic damping is automatically restored.

[0031] Furthermore, an electro-hydrogen synergistic fractional-order power injection model was constructed, achieving deep coupling between the physical characteristics of the electrolyzer and the electrical control system. Specifically, this step utilizes the electrolyzer's characteristic as a high-power controllable load, treating it as a chemical energy storage flywheel of the system. The DC power consumed by the electrolyzer is collected in real time by sensors, and its dynamic change, i.e., the first or fractional derivative of the power, is calculated. This rate of change is processed by a fractional-order differential circuit and converted into an additional power compensation command. This command physically represents the electrolyzer's inertial response requirement to changes in grid frequency. This additional command is directly superimposed on the virtual mechanical power reference value described in step S1. By adjusting the electrolyzer's power setpoint, virtual kinetic energy is actively injected into or absorbed into the virtual synchronous machine equation, ensuring that the differential equation in step S1 incorporates the dynamic support capability of the source side (electrolyzer) during solution, utilizing the electrolyzer's wide power adjustment range to share the power impact on the grid.

[0032] Furthermore, the nonlinear fractional impedance reconstruction under weak grid conditions actively corrects the output voltage to offset the instability caused by the high impedance of the weak grid. Specifically, for the common weak grid characteristics in direct-connection scenarios of new energy sources, the voltage waveform of the point of common coupling (PCC) is monitored in real time. When a voltage fluctuation is detected, a nonlinear fractional virtual impedance algorithm is activated. This algorithm calculates a virtual impedance value (containing virtual resistance and virtual inductance components) using a nonlinear function based on the amplitude of the voltage fluctuation. This impedance value increases nonlinearly with the increase of the voltage fluctuation amplitude. The virtual voltage drop is calculated using this virtual impedance and the output current, and this voltage drop is subtracted from or compensated for from the internal potential reference value calculated in step S1. The corrected voltage reference value includes dynamic adaptation to the grid impedance characteristics, enabling the pulse signal output by the rectifier to generate a voltage component opposite to the grid disturbance, effectively improving the phase margin of the system under weak connection conditions, thereby suppressing the occurrence of subsynchronous oscillations (SSRs).

[0033] Furthermore, in step S1, the specific process of constructing and solving the differential equations of the virtual synchronous machine includes the following sub-steps:

[0034] S101. Based on Newton's second law and the synchronous motor rotor motion equation, establish the second-order dynamic equation;

[0035] S102. Introducing the Caputo fractional derivative operator Construct a nonlinear fractional form of the damping power term. This yields the final nonlinear fractional dynamic equation.

[0036] Specifically, the above implementation process aims to establish the core mathematical model of the control system, enabling it to simulate the physical characteristics of a synchronous generator. Step S101 uses classical mechanics principles to map the dynamic changes in the grid frequency to the rotational motion of a virtual rotor, establishing the physical relationship between torque imbalance and speed change. Step S102 introduces fractional calculus theory on this basis, replacing the constant damping coefficient in the traditional model with a fractional operator with memory characteristics, further enabling the model to cover a wider frequency range when describing the energy dissipation process of the system, thereby constructing a nonlinear fractional dynamic system that can adapt to complex grid environments.

[0037] Furthermore, in step S101, the second-order dynamic equation is specifically expressed as:

[0038] ;

[0039] Among them, the The preset virtual moment of inertia, the For virtual angular frequency, the The virtual mechanical power reference value, the For electromagnetic power, the The damping power term is to be determined.

[0040] It should be noted that the core motion equations of the virtual synchronous machine control algorithm described above physically characterize the power balance state of the system. The left side of the equation represents the inertial response of the virtual rotor, i.e., the product of moment of inertia and angular acceleration, reflecting the system's ability to resist frequency changes. The right side of the equation describes the torque (power) balance relationship acting on the virtual rotor, i.e., the virtual mechanical power on the input side acts as the driving source, the electromagnetic power on the output side acts as the load resistance, and the damping power term is used to simulate the frictional losses of the windings and bearings, playing a role in suppressing speed oscillations. The control system calculates the frequency command for the next moment based on the power difference by solving this equation in real time.

[0041] Furthermore, in step S102, the nonlinear fractional dynamic equation is specifically expressed as follows:

[0042] ;

[0043] Among them, the The preset rated angular frequency, the The fractional-order damping coefficient is corrected in real time. The order is fractional; the output is obtained by solving the equation through discretization. .

[0044] It should be noted that the above equation provides a specific definition for the damping term, introducing the Caputo fractional derivative operator. Unlike traditional linear damping, this term... It is related not only to the current frequency deviation, but also to the historical trend of frequency deviation, in which the fractional order determines the distribution of memory weights; this design enables the system to exhibit nonlocal dynamic response characteristics when facing sudden disturbances, and achieves better transient stability and anti-interference capability than integer order control by adjusting the magnitude and phase of the damping torque in real time.

[0045] Furthermore, step S2 specifically includes the following sub-steps:

[0046] S201. Obtain the output virtual angular frequency and calculate the frequency deviation. ;

[0047] S202. Based on the frequency deviation, construct the system oscillation energy function. :

[0048] ;

[0049] S203. Calculate the energy function. The fractional time derivative yields the fractional energy change rate. :

[0050] ;

[0051] S204. Based on the fractional-order energy change rate For fractional damping coefficients Update.

[0052] Specifically, the above implementation process establishes a stability enhancement mechanism based on an energy perspective. Steps S201 and S202, starting from Lyapunov stability theory, convert the frequency deviation into a virtual potential energy function of the system, quantifying the severity of the current system oscillation. Step S203 uses fractional calculus to calculate the rate of change of this energy function, thereby predicting whether the oscillation energy is in the accumulation (deterioration) or dissipation (recovery) stage. Step S204, based on this prediction result, dynamically adjusts the control parameters to ensure that a stronger suppression effect is applied only when the oscillation energy increases, while maintaining low damping to accelerate the response speed when the system recovers.

[0053] Furthermore, in step S204, the update process for the fractional-order damping coefficient is specifically expressed as follows:

[0054] ;

[0055] Among them, the Based on the basic damping value, the The energy feedback gain coefficient is used; the updated fractional-order damping coefficient is fed back to step S1 to participate in the equation solution at the next time step. It should be noted that a unidirectional gain adjustment logic is defined to implement nonlinear variable damping control; the base damping value ensures the basic stability of the system in steady state. The function is used for the directional filter to ensure that the damping coefficient is superimposed on the base value only when the rate of energy change is positive (i.e., oscillations intensify); if energy is dissipated (Φ<0), the additional damping is zero. Through this mechanism, the extended settling time caused by overdamping during system recovery is avoided, achieving an adaptive control effect with on-demand allocation.

[0056] Furthermore, step S3 specifically includes the following sub-steps:

[0057] S301. Monitor the DC power consumed by the electrolytic cell in real time, and obtain the dynamic change of power by calculating the derivative of DC power with respect to time;

[0058] S302. The dynamic change in power is processed through a fractional derivative element to generate inertial support power, and the inertial support power is used as a compensation term to update the virtual mechanical power reference value.

[0059] Specifically, the above implementation process utilizes the rapid response characteristics of the electrolyzer as a controllable load to support the grid frequency. Step S301 extracts the high-frequency component of the electrolyzer's operating power through real-time differential calculation. This component reflects the transient characteristics of the load. Step S302 converts the transient characteristics into virtual mechanical energy increments after fractional-order processing and feeds them back to the front end of the virtual synchronous machine model. The above process is essentially at the control algorithm level, coupling the DC-side energy change of the electrolyzer into the virtual inertia of the AC side, enabling the electrolyzer to actively adjust power absorption during grid frequency fluctuations, playing an inertial support role similar to flywheel energy storage.

[0060] Furthermore, in step S302, the update process of the virtual mechanical power is specifically represented as follows:

[0061] ;

[0062] Among them, the The reference power set for operators, the For coupling gain, the It is a fractional order.

[0063] It should be noted that the above update process is used for the mathematical decoupling and reorganization of chemical processes and electrical controls. Among them,

[0064] This represents the steady-state reference power set for the hydrogen production process. (The second term...) A fractional-order differential operator is used to extract specific frequency band components of the electrolytic cell power fluctuations and convert them into additional mechanical power input for a virtual generator. This is achieved by adjusting the order of the differential operator. and gain By precisely controlling the bandwidth and response intensity of the electrolyzer's participation in grid regulation, the grid can be stabilized to the maximum extent possible without affecting the main hydrogen production process.

[0065] Furthermore, step S4 specifically includes the following sub-steps:

[0066] S401. Calculate the absolute value of the deviation between the actual value and the rated value of the voltage at the point of common coupling, i.e., the voltage amplitude fluctuation.

[0067] S402. Construct the fractional virtual reactance function on the output side of the rectifier:

[0068] ;

[0069] Among them, the As a reference virtual reactance, the The adjustment coefficient is the one mentioned above. It is a non-linear exponent;

[0070] S403. Calculate the virtual voltage drop using the virtual reactance function and correct the generated internal potential reference value.

[0071] Specifically, the above implementation process aims to solve the subsynchronous oscillation problem caused by excessive line impedance in weak power grid environments. Steps S401 and S402 construct a virtual impedance model that adapts to voltage fluctuations, simulating a nonlinear virtual reactor connected in series at the rectifier output. Step S403 calculates the voltage drop across this virtual reactor and performs reverse compensation on the controller's voltage command, changing the equivalent output impedance characteristics of the rectifier port. This active reconstruction mechanism can reshape the phase margin of the system at the resonant frequency, effectively blocking the amplification loop of oscillation energy. Furthermore, it should be noted that the correction function in step S403 describes the nonlinear gain characteristics of the virtual impedance as the power grid state changes. Provides the minimum damping support required by the system, the latter using a power function. ( >1) Nonlinear effects are introduced. For example, when the grid voltage fluctuation is small, the increase in impedance is negligible and does not affect the normal operating efficiency; once the voltage fluctuation amplitude increases, the virtual reactance value increases exponentially, thereby ensuring that the system can quickly exhibit high impedance characteristics when a large disturbance occurs, limiting the impact of fault current and enhancing the ability to support the grid voltage.

[0072] Furthermore, in step S403, the correction process for the generated internal potential reference value is specifically expressed as follows:

[0073] ;

[0074] Among them, the The rectifier output current is collected. The corrected voltage reference command is sent to the pulse width modulation module to drive the rectifier. It should be noted that the formula performs impedance compensation in a virtual rotating coordinate system based on Kirchhoff's voltage law. This represents the inductive voltage drop vector across the virtual reactance, where the imaginary unit is... This represents a 90-degree phase rotation. By subtracting this virtual voltage drop from the original reference voltage, the controller effectively adjusts the magnitude and phase of the output voltage vector. This adjustment is electrically equivalent to increasing the electrical distance between the rectifier and the grid, changing the system's output admittance, and thus improving the system's grid-connected stability margin under weak grid conditions.

[0075] Example 2

[0076] Furthermore, this embodiment provides a topology and control system for a grid-connected water electrolysis hydrogen production unit adapted to direct connection with new energy sources. This system is implemented based on a heterogeneous hardware platform using digital signal processors and field-programmable gate arrays. Specifically, it includes:

[0077] The signal sensing and model building module is used to collect voltage and current signals on the output side of the rectifier in real time through high-precision sensors, calculate electromagnetic power reflecting the grid load, and construct virtual synchronous machine differential equations containing virtual rotational inertia and fractional-order damping elements based on the principle of synchronous generator.

[0078] The adaptive damping adjustment module is responsible for monitoring the oscillation state of the system. It receives the virtual angular frequency calculated by the virtual synchronizer in real time, compares it with the rated frequency of the power grid to obtain the frequency deviation, and constructs an oscillation energy function that reflects the energy flow direction of the system. By calculating the fractional derivative of this energy function with respect to time, it determines whether the oscillation energy is accumulating or dissipating. Once the accumulation of oscillation energy is detected, the fractional damping coefficient in the virtual synchronizer model is immediately increased to quickly suppress frequency fluctuations.

[0079] The electro-hydrogen co-coupling module is used to connect the physical system of the electrolyzer and the electrical control system. It monitors the actual DC power consumed by the electrolyzer in real time, extracts its dynamic power change, performs fractional derivative processing, and converts it into a virtual mechanical power increment, which is then superimposed on the input of the virtual synchronous machine model. This allows the electrolyzer to provide inertial support to the power grid by utilizing its wide power regulation characteristics.

[0080] The weak network impedance reconstruction module is used to monitor the voltage amplitude fluctuation of the point of common coupling in real time. Based on the fluctuation magnitude, it generates a virtual reactance value using a nonlinear function and compensates the voltage drop generated by the virtual reactance into the internal potential reference value generated by the virtual synchronous machine. The rectifier is driven by the pulse width modulation module to achieve active suppression of subsynchronous oscillation.

[0081] Example 3

[0082] This embodiment demonstrates the specific application of this method in an off-grid offshore wind power hydrogen production system.

[0083] The scenario is set as a large-scale water electrolysis hydrogen production station located on an island, directly powered by unstable offshore wind turbines. Due to the lack of a large power grid and long transmission lines, the system operates in a typical weak grid environment. During normal operation, the system maintains stable bus voltage through grid-based control. When a sudden gust of wind on the sea surface causes drastic fluctuations in the wind turbine output power, leading to a voltage drop at the point of common coupling accompanied by subsynchronous oscillations, the system's control logic responds rapidly: First, the electro-hydrogen co-processing module detects the power change on the electrolyzer side and uses a fractional-order differential algorithm to couple the DC-side energy change of the electrolyzer into a virtual inertia on the AC side, instantly absorbing or releasing energy like a flywheel to mitigate frequency surges. Simultaneously, the adaptive damping module detects the accumulation of oscillating energy within the system and immediately dynamically increases the fractional-order damping coefficient in the control model. Utilizing the wide-frequency attenuation characteristics unique to fractional-order operators, it rapidly consumes energy in the first few cycles of oscillation to prevent oscillation divergence. Next, in response to the voltage drop, the impedance reconstruction module automatically calculates the required virtual impedance compensation, virtually increasing the rectifier's output impedance at the control level. Through this series of coordinated actions, the hydrogen production unit successfully suppressed the subsynchronous oscillation of the power grid caused by sudden changes in wind speed without shutting down the unit, and quickly restored the voltage at the point of common coupling to a safe range, ensuring the continuity and safety of offshore wind power hydrogen production operations.

[0084] Example 4

[0085] A simulation model of a 2MW grid-type water electrolysis hydrogen production system was built using the Matlab / Simulink platform to simulate the short-circuit ratio. In a weak grid operating environment, the system DC bus voltage is set to 800V, the grid connection point rated voltage is 10kV, and the grid rated frequency is... That is, the rated angular frequency The control parameters are initialized according to the method described above, wherein the virtual moment of inertia is... Basic fractional damping order Foundation damping coefficient Fractional order of electrolytic cell power response Coupling gain coefficient Impedance reconstruction nonlinear index Impedance adjustment coefficient Simulation conditions are set at A frequency step disturbance occurred in the power grid, with the frequency momentarily dropping by 0.5Hz and accompanied by 18Hz synchronous oscillations. The dynamic change in the power of the electrolytic cell was detected. The calculation generates a virtual inertial power compensation of 150kW, causing the actual power of the electrolyzer to rapidly decrease from 2.0MW to 1.85MW within 50ms to release energy to support the grid. The system detects that the deviation between the virtual angular frequency and the grid frequency has widened to [a certain value]. According to the energy function The fractional-order energy change rate was calculated. Upon reaching a peak value of 5.8, the controller immediately adjusts according to the adaptive law. Instantly increase the fractional damping coefficient from 20 to Meanwhile, in response to the 8% voltage drop at the grid connection point caused by frequency fluctuations, Step S4 is based on The virtual reactance increment was calculated to be approximately and through The internal potential reference value is corrected, increasing the output current phase margin by 12 degrees. Comparative test data shows that after adopting the nonlinear fractional-order control scheme of this invention, the maximum system frequency deviation is limited to within 0.48Hz, which is 43.5% lower than the 0.85Hz deviation of the traditional integer-order VSG control scheme. The oscillation convergence time is shortened from 4.2s to 1.1s, and the amplitude that persists under traditional control is reduced. The subsynchronous oscillations completely decayed to zero, verifying that the topology and control method have significant damping enhancement and stability improvement effects under large disturbance conditions in weak power grids.

[0086] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the present invention.

Claims

1. A topology control method for a grid-connected water electrolysis hydrogen production unit adapted to direct connection with new energy sources, characterized in that, Includes the following steps: S1. Acquire voltage and current signals from the rectifier output side, calculate electromagnetic power based on the acquired signal values, set an initial virtual mechanical power reference value, construct a virtual synchronous machine differential equation containing virtual moment of inertia and fractional-order damping based on the active power balance principle, use the virtual mechanical power reference value as input, calculate the virtual angular frequency and internal potential reference value according to the virtual synchronous machine differential equation, and generate a pulse signal for controlling the rectifier; S2. Based on the virtual angular frequency, calculate the deviation value from the preset rated angular frequency of the power grid, and construct an energy function to reflect the oscillation state of the system based on the deviation value; obtain the energy change rate by calculating the fractional derivative of the energy function, and correct the coefficient of the fractional damping element in the differential equation in real time based on the energy change rate; S3. Collect the actual operating power of the electrolytic cell, calculate the dynamic change of the electrolytic cell power, perform fractional derivative processing on the dynamic change to obtain the additional power command, and superimpose the additional power command with the virtual mechanical power reference value; S4. Acquire the voltage at the point of common coupling and calculate the amplitude fluctuation of the voltage at that point. Construct a nonlinear fractional virtual impedance model based on the amplitude fluctuation. Output the virtual impedance voltage drop through the nonlinear fractional virtual impedance model. Compensate the virtual impedance voltage drop to the internal potential reference value and update the pulse signal according to the compensated voltage reference value to suppress subsynchronous oscillation.

2. The topology control method for a grid-type water electrolysis hydrogen production unit adapted to direct connection with new energy sources as described in claim 1, characterized in that, In step S1, the specific process of constructing and solving the differential equations of the virtual synchronous machine includes the following sub-steps: S101. Based on Newton's second law and the synchronous motor rotor motion equation, establish the second-order dynamic equation; S102. Introducing the Caputo fractional derivative operator Construct a nonlinear fractional form of the damping power term. This yields the final nonlinear fractional dynamic equation.

3. The topology control method for a grid-type water electrolysis hydrogen production unit adapted to direct connection with new energy sources as described in claim 2, characterized in that, In step S101, the second-order dynamic equation is specifically expressed as follows: ； Among them, the The preset virtual moment of inertia, the For virtual angular frequency, the The virtual mechanical power reference value, the For electromagnetic power, the The damping power term is to be determined.

4. The topology control method for a grid-type water electrolysis hydrogen production unit adapted to direct connection with new energy sources as described in claim 3, characterized in that, In step S102, the nonlinear fractional dynamic equation is specifically expressed as follows: ； Among them, the The preset rated angular frequency, the The fractional-order damping coefficient is corrected in real time. The order is fractional; the output is obtained by solving the equation through discretization. .

5. The topology control method for a grid-type water electrolysis hydrogen production unit adapted to direct connection with new energy sources as described in claim 1, characterized in that, Step S2 specifically includes the following sub-steps: S201. Obtain the output virtual angular frequency and calculate the frequency deviation. ; S202. Based on the frequency deviation, construct the system oscillation energy function. : ； S203. Calculate the energy function. The fractional time derivative yields the fractional energy change rate. : ； S204. Based on the fractional-order energy change rate For fractional damping coefficients Update; Among them, the This represents the Caputo fractional derivative operator, the... This represents the preset virtual moment of inertia; Indicating frequency deviation, the Represents the virtual angular frequency, the This is the preset rated angular frequency.

6. The topology control method for a grid-type water electrolysis hydrogen production unit adapted to direct connection with new energy sources as described in claim 5, characterized in that, In step S204, the update process for the fractional-order damping coefficient is specifically expressed as follows: ； Among them, the Based on the damping value, the The energy feedback gain coefficient is used; the updated fractional-order damping coefficient is fed back to step S1 to participate in the equation solution at the next time step.

7. The topology control method for a grid-type water electrolysis hydrogen production unit adapted to direct connection with new energy sources as described in claim 1, characterized in that, Step S3 specifically includes the following sub-steps: S301. Monitor the DC power consumed by the electrolytic cell in real time, and obtain the dynamic change of power by calculating the derivative of DC power with respect to time; S302. The dynamic change in power is processed through a fractional derivative element to generate inertial support power, and the inertial support power is used as a compensation term to update the virtual mechanical power reference value.

8. The topology control method for a grid-type water electrolysis hydrogen production unit adapted to direct connection with new energy sources as described in claim 7, characterized in that, In step S302, the update process of the virtual mechanical power is specifically represented as follows: ； Among them, the The reference power set for operators, the For coupling gain, the It is a fractional order.

9. The topology control method for a grid-type water electrolysis hydrogen production unit adapted to direct connection with new energy sources as described in claim 1, characterized in that, Step S4 specifically includes the following sub-steps: S401. Calculate the absolute value of the deviation between the actual value and the rated value of the voltage at the point of common coupling, i.e., the voltage amplitude fluctuation. S402. Construct the fractional virtual reactance function on the output side of the rectifier: ； Among them, the As a reference virtual reactance, the The adjustment coefficient is the one mentioned above. It is a non-linear exponent; S403. Calculate the virtual voltage drop using the virtual reactance function and correct the generated internal potential reference value.

10. The topology control method for a grid-type water electrolysis hydrogen production unit adapted to direct connection with new energy sources as described in claim 9, characterized in that, In step S403, the correction process for the generated internal potential reference value is specifically expressed as follows: ； Among them, the The rectifier output current is collected. The corrected voltage reference command is sent to the pulse width modulation module to drive the rectifier.

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