Adaptive adjustment method and system for grid connection point frequency of wind power converter

Abstract

This invention relates to the field of frequency regulation technology at the grid connection point of wind power converters, providing an adaptive regulation method and system for the frequency of wind power converters at the grid connection point. By dynamically capturing transient signals in the instantaneous values ​​of the three-phase voltage and current at the grid connection point (PCC) and quantifying their disturbance intensity and type characteristics, the virtual inertia and damping coefficient can be adaptively adjusted to generate an active power command that precisely matches the actual disturbances in the power grid. This refined regulation enables the wind power converter to provide highly differentiated frequency support for different disturbances, significantly enhancing the frequency stability of the power grid, while effectively suppressing unnecessary power fluctuations and energy losses, thus improving the economy and reliability of system operation.

Description

Technical Field

[0001] This invention relates to the field of frequency regulation technology at the grid connection point of wind power converters, and particularly to an adaptive regulation method and system for the frequency at the grid connection point of wind power converters. Background Technology

[0002] As the penetration rate of new energy sources such as wind power in the power grid increases, the randomness and volatility of their output pose a serious challenge to the stability of the power grid frequency. Wind turbines are connected to the grid through wind power converters, but they lack the rotational inertia of traditional synchronous generators and cannot provide effective inertial support for changes in the power grid frequency. Therefore, it is urgent to construct an active frequency support scheme for wind power converters to improve the stability of the power grid.

[0003] To address this issue, existing technologies typically employ virtual synchronous generator (VRG) technology. By simulating the inertia and damping characteristics of a synchronous generator, wind power converters can participate in grid frequency regulation. However, current adaptive control logic is relatively coarse, often applying the same regulation mode to frequency disturbances of all scales. This fails to effectively distinguish between small disturbances such as normal line switching and severe faults such as large generator disconnections, resulting in a nonlinear matching problem between disturbance intensity and response strategy. This one-size-fits-all approach prevents the controller from perceiving the true nature of the disturbance, forcing it to passively respond to its appearance and thus produce a response disproportionate to the severity of the disturbance. This can not only lead to unnecessary power fluctuations and unit losses but also fail to provide sufficient frequency support during severe disturbances. The lack of refined regulation has become a bottleneck restricting technological development. Summary of the Invention

[0004] The present invention aims to solve at least one of the problems existing in the prior art, and provides an adaptive adjustment method and system for the frequency of the grid connection point of a wind power converter.

[0005] One aspect of the present invention provides an adaptive adjustment method for the grid connection point frequency of a wind power converter, comprising: The instantaneous sampling sequences of the three-phase voltage and the three-phase current of the PCC are input into a digital signal processor to obtain transient voltage signals, transient current signals, the fundamental frequency of the power grid, and the rate of change of the fundamental frequency of the power grid. Disturbance intensity and type characteristics are quantized based on transient voltage and current signals to obtain disturbance intensity index and disturbance type entropy index; Based on the disturbance intensity index and the disturbance type entropy index, the basic virtual inertia and basic damping coefficient are adaptively adjusted to obtain the adaptive virtual inertia and adaptive damping coefficient. Based on adaptive virtual inertia and adaptive damping coefficient, active power command analysis based on adaptive parameters is performed on the grid fundamental frequency, grid rated frequency, grid fundamental frequency change rate and MPPT power to obtain the final active power command. The final active power command, reactive power command, PCC three-phase voltage and converter output three-phase current are used to generate PWM signals to obtain the PWM signals to drive the converter IGBT.

[0006] Another aspect of the present invention provides an adaptive adjustment system for the grid connection point frequency of a wind power converter, comprising: The digital signal processing module is used to input the instantaneous sampling sequence of the three-phase voltage and the instantaneous sampling sequence of the three-phase current of the PCC into the digital signal processor to obtain the transient voltage signal, the transient current signal, the grid fundamental frequency, and the rate of change of the grid fundamental frequency; The disturbance intensity and type feature quantization module is used to quantize the disturbance intensity and type features based on transient voltage signals and transient current signals to obtain disturbance intensity index and disturbance type entropy index; The adaptive adjustment module is used to adaptively adjust the basic virtual inertia and basic damping coefficient based on the disturbance intensity index and the disturbance type entropy index to obtain the adaptive virtual inertia and adaptive damping coefficient. The active power command analysis module is used to perform active power command analysis based on adaptive parameters on the grid fundamental frequency, grid rated frequency, grid fundamental frequency change rate and MPPT power to obtain the final active power command. The final active power command generation module is used to generate PWM signals from the final active power command, reactive power command, PCC three-phase voltage and converter output three-phase current to obtain the PWM signal to drive the converter IGBT.

[0007] Compared with existing technologies, the present invention provides an adaptive adjustment method and system for the grid connection point frequency of a wind power converter. By dynamically capturing transient signals in the instantaneous values ​​of the three-phase voltage and current at the grid connection point (PCC) and quantifying their disturbance intensity and type characteristics, it can adaptively adjust the virtual inertia and damping coefficient to generate an active power command that precisely matches the actual disturbances in the power grid. This refined adjustment enables the wind power converter to provide highly differentiated frequency support for different disturbances, significantly enhancing the frequency stability of the power grid, while effectively suppressing unnecessary power fluctuations and energy losses, thus improving the economy and reliability of system operation. Attached Figure Description

[0008] One or more embodiments are illustrated by way of example with the corresponding pictures in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0009] Figure 1 A flowchart of an adaptive adjustment method for the grid connection point frequency of a wind power converter according to an embodiment of the present invention; Figure 2 This is a data flow diagram illustrating the adaptive adjustment method of the grid connection point frequency of a wind power converter according to an embodiment of the present invention. Figure 3 This is a block diagram of an adaptive adjustment system for the grid connection point frequency of a wind power converter according to an embodiment of the present invention. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details are presented in the various embodiments of the present invention to facilitate a better understanding of the invention. However, the technical solutions claimed in the present invention can be implemented even without these technical details and with various variations and modifications based on the following embodiments. The division of the various embodiments below is for ease of description and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined with and referenced by each other without contradiction.

[0011] In the technical solution of this invention, an adaptive adjustment method for the grid connection point frequency of a wind power converter is proposed. Figure 1 This is a flowchart of an adaptive adjustment method for the grid connection point frequency of a wind power converter according to an embodiment of the present invention. Figure 2 This is a data flow diagram illustrating the adaptive adjustment method of the grid connection point frequency of a wind power converter according to an embodiment of the present invention. (In conjunction with...) Figure 1 and Figure 2According to an embodiment of the present invention, an adaptive adjustment method for the grid connection point frequency of a wind power converter includes the following steps: S1, inputting the sampling sequence of instantaneous PCC three-phase voltage and the sampling sequence of instantaneous PCC three-phase current into a digital signal processor to obtain transient voltage signal, transient current signal, grid fundamental frequency, and grid fundamental frequency change rate; S2, performing disturbance intensity and type feature quantization based on the transient voltage signal and transient current signal to obtain disturbance intensity index and disturbance type entropy index; S3, adaptively adjusting the basic virtual inertia and basic damping coefficient based on the disturbance intensity index and disturbance type entropy index to obtain adaptive virtual inertia and adaptive damping coefficient; S4, performing active power command analysis based on adaptive parameters on the grid fundamental frequency, grid rated frequency, grid fundamental frequency change rate, and MPPT power based on the adaptive virtual inertia and adaptive damping coefficient to obtain the final active power command; S5, generating PWM signals from the final active power command, reactive power command, PCC three-phase voltage, and converter output three-phase current to obtain the PWM signal driving the converter IGBT.

[0012] Specifically, in S1, the instantaneous sampling sequences of the three-phase voltage and current of the PCC are input into a digital signal processor to obtain transient voltage signals, transient current signals, the fundamental frequency of the power grid, and the rate of change of the fundamental frequency of the power grid. It should be understood that the wind power converter, as the interface for interaction with the power grid, must be able to constantly monitor the operating status of the power grid, especially its frequency dynamics and possible disturbances. On the one hand, by accurately acquiring the fundamental frequency of the power grid and its rate of change, it is possible to determine whether the power grid is in a normal state of frequency stability, or whether it is experiencing frequency deviation due to power imbalance or other problems. This is the direct basis for virtual inertia support and frequency regulation. On the other hand, by extracting transient voltage and current signals, various disturbances occurring in the power grid can be captured, such as short-circuit faults, lightning strikes, and large load switching. These disturbances exist in the form of high-frequency or non-fundamental components in the signal waveform. Only by separating these transient signals from the original sampled data can their intensity and type be quantified in subsequent steps, thereby enabling adaptive adjustment of control parameters. Therefore, step S1 is a key bridge connecting the physical state of the external power grid with the decision-making of the internal control algorithm, and a prerequisite for realizing intelligent and refined power grid support functions.

[0013] In this context, PCC refers to the electrical node connecting the wind power converter system to the public power grid. The voltage and current at this node are key measurements for assessing grid status and the interaction between the converter and the grid. Instantaneous value sampling sequence refers to a series of discrete digital values ​​generated by periodically sampling the continuously changing analog three-phase voltage and current signals at the PCC point using a high-frequency sampler. These sequences are the raw inputs for digital signal processors (DSPs). Transient voltage / current signals refer to the signal portion remaining after stripping the fundamental sinusoidal component from the original voltage and current signals. They primarily contain non-fundamental frequency components, harmonics, or DC offsets caused by grid disturbances and are direct indicators of grid disturbances. The grid fundamental frequency refers to the most important and fundamental frequency component in the AC power system and is a core indicator of grid stability. The grid fundamental frequency change rate refers to the rate of change of the grid fundamental frequency.

[0014] In specific implementation of S1, firstly, the instantaneous value sampling sequence of the PCC three-phase voltage is input into a synchronous reference frame phase-locked loop (SRF-PLL) to obtain the fundamental frequency and rate of change of the fundamental frequency of the power grid. Specifically, the synchronous reference frame phase-locked loop (SRF-PLL) is an algorithm widely used in power electronics for accurately tracking the phase and frequency of the power grid voltage. Its working principle is to first transform the voltage sampling sequence, which is in a stationary three-phase coordinate system (abc coordinate system), into a coordinate system that rotates synchronously with the power grid voltage (dq coordinate system) through coordinate transformations (Clarke transformation and Park transformation). In an ideal, balanced three-phase system, the fundamental voltage vector will exhibit a DC component in the synchronously rotating coordinate system (dq coordinate system). The SRF-PLL algorithm uses a feedback control loop to adjust the frequency of its internal oscillator, enabling the d-axis component to accurately track the amplitude of the voltage vector while controlling the q-axis component to zero. When the q-axis component stabilizes at zero, the control loop successfully locks the phase and frequency of the power grid voltage. At this point, the frequency value used to maintain synchronization inside the phase-locked loop is the precise fundamental frequency of the power grid, and the derivative or difference of this frequency value with respect to time is the rate of change of the fundamental frequency of the power grid.

[0015] Furthermore, the instantaneous sampling sequences of the PCC three-phase voltage and current are input into a digital high-pass filter to obtain transient voltage and current signals. It should be understood that the original voltage and current sampling sequences are a superposition of the fundamental steady-state component and the transient disturbance component. To obtain a pure transient signal, in this embodiment, a digital high-pass filter is used to filter out low-frequency components (mainly the fundamental frequency component of the power grid) while allowing high-frequency components to pass through. In a digital signal processor, this digital high-pass filter is implemented using difference equations. A digital filter is essentially an operational rule that specifies how the current output value is calculated from the current and past input values ​​and past output values. By carefully designing the filter coefficients, the desired high-pass filtering characteristics can be achieved. When the instantaneous sampling sequences of the PCC three-phase voltage and current flow through this digital high-pass filter, the corresponding output sequences are the transient voltage and current signals containing only disturbance information.

[0016] Specifically, S2 quantifies the disturbance intensity and type characteristics based on transient voltage and current signals to obtain disturbance intensity and disturbance type entropy indices. It should be understood that in complex power grid environments, disturbance events vary greatly; a minor load switching and a severe three-phase short-circuit fault pose drastically different threats to power grid frequency stability, requiring different support responses. In the technical solution of this invention, by calculating the disturbance intensity index, the energy or impact amplitude of the disturbance can be quantified, providing a basis for adjusting the overall gain of virtual inertia and damping coefficients. Furthermore, by calculating the disturbance type entropy index, the complexity and energy concentration of the disturbance signal can be identified, distinguishing between highly concentrated energy-driven impact faults and dispersed energy-driven oscillations or harmonics, thereby enabling more precise correction of control parameters. In this way, the original disturbance phenomenon can be transformed into structured information that the control system can understand and utilize.

[0017] The disturbance intensity index is a scalar value used to comprehensively assess the overall energy or amplitude of voltage and current disturbances in transient events. A higher value generally indicates a more severe disturbance and a greater impact on the power grid; it measures the "quantity" of the disturbance. The disturbance type entropy index uses the concept of information entropy to quantify the energy distribution characteristics of transient signals in the time-frequency domain. A lower entropy value indicates that the disturbance energy is highly concentrated at a certain point in time or within a certain frequency range, typically corresponding to a highly impactful deterministic event (such as a short-circuit fault). A higher entropy value indicates a more dispersed and chaotic energy distribution, typically corresponding to persistent oscillations, background noise, or complex harmonic interference. The disturbance type entropy index measures the "quality" of the disturbance.

[0018] In its specific implementation, S2 first involves time-frequency decomposition of the transient voltage and current signals to obtain the wavelet coefficient matrices of the voltage transient signal and the current transient signal. In a digital signal processor, this step is achieved by applying the Discrete Wavelet Transform (DWT) algorithm to the time-series data of the transient voltage and current signals. The wavelet transform algorithm maps a one-dimensional transient signal to a two-dimensional time-frequency plane. By inputting the time-series data of the transient voltage and current signals into the wavelet transform algorithm, the wavelet coefficient matrices of the voltage transient signal and the current transient signal output by the algorithm are obtained. These two matrices characterize the distribution of disturbance energy at different time and frequency scales in detail and are direct inputs for calculating the disturbance type entropy index.

[0019] Next, based on the transient voltage and current signals, the disturbance intensity index is calculated. It should be understood that although the original transient voltage and current waveforms contain complete disturbance information, their complex form and large data volume make them unsuitable for direct use in control decisions. Therefore, in the technical solution of this invention, a disturbance intensity index is calculated to address the problem of measuring the severity of disturbances. Specifically, a higher disturbance intensity index value will directly lead to a higher base control gain in the system, thereby mobilizing more resources (such as virtual inertia) to support the power grid. This ensures that the system's response behavior is proportional to the actual threat level of the external disturbance, avoiding energy waste and mechanical losses caused by overreacting to small disturbances, and also preventing the risk of system instability that may be caused by insufficient response to large disturbances. Specifically, the disturbance intensity index is calculated using the following formula: ; in, , , These are the transient voltage signals numbered as follows: The sampling points corresponding to Phase voltage transient value, Phase voltage transient value, Phase voltage transient value, , , These are the transient current signals numbered as follows: The sampling points corresponding to Phase current transient value, Phase current transient value, Phase current transient value, Indicates the total number of sampling points. This indicates the intensity of the disturbance. Indicates the sampling period.

[0020] Furthermore, based on the wavelet coefficient matrices of the voltage transient signal and the current transient signal, the disturbance type entropy index is calculated. It should be understood that disturbance events in the power grid vary in form. A highly concentrated instantaneous short-circuit fault and a relatively dispersed, long-lasting power oscillation, although they may have similar disturbance intensity indices, pose drastically different threats to system stability and require different optimal control strategies. The former requires the system to provide instantaneous power support at the fastest speed and with the greatest force, while the latter requires appropriate damping to suppress oscillations. Applying the same control indiscriminately may lead to insufficient or excessive response, or even exacerbate system instability. The disturbance type entropy index provides a quantitative basis for distinguishing disturbance types by quantifying the distribution of disturbance energy in the time-frequency domain. A low entropy value indicates highly concentrated energy, pointing to a deterministic impact event; a high entropy value indicates dispersed and chaotic energy, pointing to oscillations or noise-like events. This distinction enables subsequent control actions to be both targeted and efficient. Specifically, the disturbance type entropy index is calculated using the following formula: ; ; ; in, This indicates the entropy index representing the type of disturbance. The midscale of the wavelet coefficient matrix of the voltage transient signal ,time The value at that location, The midscale of the wavelet coefficient matrix of the current transient signal ,time The value at that location, Indicated in scale ,time The combined transient energy of voltage and current at the point. Indicated in scale ,time The energy probability density at that location.

[0021] Specifically, S3, based on the disturbance intensity index and the disturbance type entropy index, adaptively adjusts the basic virtual inertia and basic damping coefficient to obtain adaptive virtual inertia and adaptive damping coefficient. It should be understood that existing gain adjustment mechanisms, at their core, construct a static mapping relationship from external disturbance intensity to internal control gain, essentially representing an open control logic. Such mechanisms unilaterally determine the system's response intention solely based on the intensity of external grid disturbances, completely ignoring the close physical coupling and dynamic constraints between disturbance response requirements and the actual response capability of the wind power converter as the executor. Specifically, the wind turbine provides virtual inertia support by releasing or absorbing the kinetic energy of its rotating components, and its dispatchable active power range is strictly limited by current aerodynamic characteristics, mechanical speed limits, and the safe operating boundaries of the power converter—all finite and dynamically changing physical resources.

[0022] In certain common but critical operating conditions, such as when a wind turbine is already operating at high speeds near its stall point, with almost zero kinetic energy margin for further acceleration to absorb energy or deceleration to release energy, if a very strong disturbance occurs in the power grid, the open gain regulation mechanism will indiscriminately calculate an extremely high control gain, instructing the wind power converter to provide support that is physically unsustainable. This control command exceeding physical capabilities can lead to controller output saturation, system instability and oscillation, and may even trigger speed protection or overload protection, threatening equipment safety. Conversely, for frequent, low-intensity background noise or minor disturbances in the power grid, the smooth and monotonic response function of the open gain regulation mechanism may also trigger unnecessary control actions, frequently causing the wind turbine to deviate from its maximum power point (MPPT), resulting not only in unnecessary power generation losses but also increased mechanical fatigue in the drivetrain. Therefore, this single-input, single-output static mapping, lacking closed-loop constraints on the system's own physical state, has significant weaknesses in adaptability, stability, and operational economy when dealing with complex and ever-changing real-world power grid environments.

[0023] To address the aforementioned technical shortcomings, this invention proposes an optimized mechanism that improves the single, disturbance-intensity-driven demand-side assessment process into a dynamic fusion mechanism that integrates both demand and supply assessments. This dynamic fusion mechanism introduces a dynamic factor that quantifies the current available response capability of the wind turbine to modulate the base gain determined by the disturbance intensity in real time. This ensures that the final adaptive parameters not only match the severity of external disturbances but also remain within the system's own physical tolerance limits. This design enables the wind power converter to achieve a frequency response behavior that combines high efficiency, safety, and economy, realizing a transformation from a passive grid-friendly power source to an active, highly intelligent, and autonomous grid stability regulator.

[0024] In its specific implementation, S3 first determines the base inertia gain based on the disturbance intensity index. That is, to determine the baseline intention of the response, the intensity of the external disturbance is initially quantified, establishing a preliminary assessment of the disturbance intensity. Specifically, a nonlinear function with a response dead zone is used to evaluate the input disturbance intensity index. This process generates a basic inertia gain. This provides an initial target for subsequent fine-tuning. The effect is that the system will only initiate a response when the disturbance intensity actually exceeds the preset noise threshold, thereby effectively filtering out the interference of power grid background noise and avoiding unnecessary control actions.

[0025] Next, the available power margin modulation factor is calculated based on the MPPT power, maximum allowable output power, and minimum allowable output power to obtain the capability modulation factor. In other words, to introduce constraints on the system's own physical capabilities and address the core shortcomings of the original mechanism, a modulation factor that dynamically reflects the system's current true response capability is calculated in real time. Specifically, this is first based on the currently available maximum power, i.e., the MPPT power. Maximum allowable output power Minimum allowable output power Calculate the overall available power margin This margin directly quantifies the maximum power range that the wind power converter can adjust upwards or downwards at this moment, and is the most concrete and direct manifestation of the system's response capability; subsequently, through an exponential nonlinear function, the comprehensive available power margin is... Mapped to a capability modulation factor The purpose is to generate a capability permission signal between 0 and 1. When the system does not have the ability to adjust, the capability modulation factor is 0, which acts as a veto. When the system has sufficient adjustment capability, the capability modulation factor approaches 1, which acts as a complete permission.

[0026] In this process, the available power margin modulation factor is calculated using the following formula: First, calculate the available power margin, which can be expressed by the formula: ; in, To ensure comprehensive available power margin; This refers to the current maximum allowable output power, i.e., the maximum permissible output power. This is the current minimum allowable output power, i.e., the minimum permissible output power. The available power calculated by the current maximum power point tracking algorithm is the MPPT power. Next, the capability modulation factor is calculated, a process expressed by the formula: ; in, It is the capability modulation factor; This is a coefficient used to adjust the sensitivity of the modulation factor to changes in margin; This is the rated power of the wind power converter, used to normalize the margin; The base of the natural logarithm is given. This step creatively constructs a soft-gating function that determines the degree of approval for external response requests based on the system's tolerance.

[0027] Furthermore, based on the capacity modulation factor, the base inertia gain, and the disturbance type entropy index, the base virtual inertia and base damping coefficient are adaptively adjusted to obtain adaptive virtual inertia and adaptive damping coefficient. In other words, to generate the final feasible control parameters, the base gain representing demand and the capacity factor representing supply are intelligently fused. Specifically, firstly, the capacity modulation factor is used... To scale the expected additional gain This yields a practically permissible additional gain, from which the effective inertia gain is calculated. Subsequently, the effective inertia gain and the basic virtual inertia are compared. Multiply, and then add the perturbation type entropy index. The type of correction coefficient is determined The final output is adaptive virtual inertia. In this way, by organically combining information from three dimensions—disturbance intensity, inherent capability, and disturbance type—the final control parameters not only reflect the urgency of the response but also respect physical limitations and demonstrate the identification of the disturbance's nature.

[0028] In other words, adaptive virtual inertia and adaptive damping coefficient are obtained by adaptively adjusting the basic virtual inertia and basic damping coefficient based on the capability modulation factor, basic inertia gain, and disturbance type entropy index, including: The adaptive virtual inertia and adaptive damping coefficient are determined according to the following formulas: ; ; in, For adaptive virtual inertia, For adaptive damping coefficient, These are the preset basic virtual inertia and basic damping coefficient, respectively. For effective inertia gain and , Based on inertia gain and , This indicates the intensity of the disturbance. The set perturbation dead zone threshold, The slope coefficient is used to adjust the steepness of the gain curve. The preset maximum gain amplitude, For effective damping gain and , Based on the damping gain, Entropy index for disturbance type The type of correction factor is determined.

[0029] In summary, the optimal mechanism proposed in this invention transforms the original static, open-loop gain generation logic into a closed-loop adaptive mechanism that dynamically assesses and intelligently integrates demand and supply. This enables the wind power converter to achieve high efficiency, safety, and economy in its control behavior when providing grid connection frequency support. This optimal mechanism significantly improves the stability of the control system and the safety of equipment operation. Because each control gain generation is verified by its own physical capabilities, it fundamentally eliminates the risks of controller saturation, system oscillation, or triggering protection shutdowns that may result from forcibly executing commands exceeding physical limits, ensuring that control commands always remain within the safe range executable by the hardware. Simultaneously, this optimal mechanism also significantly optimizes the system's operational economy. By introducing a response dead zone and margin-based smooth adjustment, it effectively avoids over-response to grid background noise and harmless disturbances, maximizing the stable operating time of the wind turbine at its maximum power point. This provides high-quality frequency support while reducing unnecessary power generation losses and fatigue wear of mechanical components. Ultimately, this optimization mechanism ensures that every frequency response action of the wind power converter is an optimal decision made after comprehensively considering multi-dimensional information such as the severity of the disturbance, its own response capability, and the type of disturbance. It achieves rapid and strong response when capacity allows, stable and measured response when capacity is limited, and different response strategies when facing different types of disturbances. This transforms the wind turbine generator from a passive grid-friendly power source into an active grid stability regulator with high intelligence and autonomous security, laying a solid technical foundation for its reliable operation and efficient utilization in future high-proportion renewable energy power systems.

[0030] Specifically, S4, based on adaptive virtual inertia and adaptive damping coefficients, performs adaptive parameter-based active power command analysis on the grid fundamental frequency, grid rated frequency, grid fundamental frequency change rate, and MPPT power to obtain the final active power command. That is, by constructing the original static, open-loop gain generation logic into a closed-loop adaptive mechanism that dynamically assesses and intelligently integrates demand and supply, it fundamentally solves the shortcomings of traditional methods that generate control gains indiscriminately based solely on the intensity of external disturbances, potentially leading to controller output saturation, system instability, or even threats to equipment safety. Through this active power command analysis process, the wind power converter can achieve rapid and robust response when capacity allows, stable and measured response when capacity is limited, and differentiated response strategies when facing different types of disturbances. Ultimately, this transforms the wind turbine generator from a passive grid-friendly power source into an active, highly intelligent, and autonomous grid stability regulator, laying a solid technical foundation for its reliable operation and efficient utilization in future high-proportion renewable energy power systems.

[0031] In its specific implementation, S4 first involves calculating the additional power of a virtual synchronous generator based on the grid fundamental frequency, grid rated frequency, grid fundamental frequency change rate, and MPPT power, using adaptive virtual inertia and adaptive damping coefficients, to obtain the theoretically optimal power command. This step is the core implementation of the Virtual Synchronous Generator (VSG) control strategy. It utilizes adaptive virtual inertia and adaptive damping coefficients, combined with current grid frequency information and MPPT power, to calculate the theoretically optimal active power command. Specifically, the VSG control module in the wind power converter simulates the oscillation equation of a synchronous generator. When the grid frequency deviates or changes, it adjusts the active power output to provide inertia support and damping support. The calculation of the additional power typically follows this form: the inertial response is proportional to the frequency change rate, and the damping response is proportional to the frequency deviation.

[0032] Furthermore, the theoretically optimal power command is constrained to obtain the final active power command. It should be understood that the theoretically optimal power command is obtained without considering the actual physical limitations of the wind power converter. However, any power electronic converter has physical boundaries for maximum and minimum output power, as well as limitations to cope with rapid power change rates. Therefore, to ensure the safe and stable operation of the wind power converter and prevent control saturation, the theoretically optimal power command is further constrained. Specifically, a limiter (or clamper) is used to limit the theoretically optimal power command to a safe and achievable range based on the wind power converter's operating capabilities and protection strategies, thereby obtaining the final active power command.

[0033] In embodiments of the present invention, if the theoretical optimal power command is higher than the maximum permissible output power, the final active power command is set to the maximum permissible output power; if the theoretical optimal power command is lower than the minimum permissible output power, the final active power command is set to the minimum permissible output power; otherwise, the final active power command will be equal to the theoretical optimal power command. In this way, while ensuring grid support, the wind power converter itself operates within a safe and reliable physical range.

[0034] It's worth noting that in practical implementations, in addition to rigid power limiting, a limit on the rate of power change is also considered, i.e., the maximum change in power command per unit time. This helps smooth the command and avoids excessive stress on mechanical components and power electronic devices.

[0035] Specifically, S5 generates PWM signals from the final active power command, reactive power command, PCC three-phase voltage, and converter output three-phase current to obtain the PWM signals driving the converter's IGBTs. That is, the active and reactive power commands generated by the advanced control layer, along with the perception of the grid status, are transformed into switching signals that the internal semiconductor switching devices (IGBTs) of the power electronic converter can understand and execute. The core function of a wind power converter is to convert the irregular electrical energy generated by the wind turbine into AC electrical energy that meets the grid quality requirements, and in this process, to achieve precise control of the grid's active and reactive power. Pulse Width Modulation (PWM) technology is a key means to achieve this goal. Through precise control of the PWM signal, the wind power converter can synthesize an AC voltage with the required frequency, amplitude, and phase, thereby controlling its output current and achieving the following of active and reactive power commands. Therefore, PWM signal generation is an indispensable part of ensuring that the wind power converter, as an energy conversion and grid support device, can operate efficiently and reliably, and ultimately achieve the goal of frequency adaptive regulation.

[0036] In its specific implementation, S5 first uses the final active power command and reactive power command as the setpoints for the outer loop control. Simultaneously, to achieve precise control, the real-time measured PCC three-phase voltage and converter output three-phase current are converted to a convenient synchronous rotating coordinate system, namely the dq coordinate system. This conversion requires the grid synchronization angle information obtained from the phase-locked loop (PLL) in the preceding steps.

[0037] Specifically, in the dq coordinate system, the outer power control loop typically controls the d-axis current reference value, while reactive power commands are controlled by the q-axis current reference value. These two current reference values ​​are the targets of the inner current control loop.

[0038] For example, in a standard grid-connected inverter, active power is mainly controlled by the d-axis current, and reactive power is mainly controlled by the q-axis current. The specific control algorithm can generate the corresponding current reference value based on the power reference value and the feedback PCC voltage, through an appropriate proportional-integral (PI) regulator.

[0039] Simultaneously, the dq-axis current reference values ​​are compared with the components of the actual measured wind power converter output current in the dq coordinate system. The error signal is processed by a high-performance current regulator (usually a PI regulator, which may include feedforward decoupling terms) to generate a reference value for the wind power converter output voltage in the dq coordinate system. The generated voltage reference value is the basis for controlling the wind power converter output.

[0040] Next, after generating the voltage reference value in the dq coordinate system, it is transformed back to the three-phase stationary coordinate system, i.e., the abc coordinate system, to facilitate the input of the PWM modulator. This is achieved through inverse Park transform or inverse Clarke transform, and the transformed three-phase voltage signals constitute the modulation waveform of the PWM modulator.

[0041] Further, the modulated waveform of the PWM modulator is compared with a high-frequency carrier signal to generate digital switching signals. Commonly used PWM techniques include space vector pulse width modulation (SVM) and sinusoidal pulse width modulation (SPWM). Specifically, SVM decomposes the three-phase voltage reference vector in space and selects two adjacent basic voltage vectors and a zero vector to synthesize the desired voltage. Its calculation principle involves determining the action time and sequence of each basic vector to synthesize the desired voltage vector on average within each PWM cycle. This method can utilize the DC bus voltage more effectively and produce lower harmonic distortion. The processor calculates the on-time of the upper and lower switches of the three bridge arms and converts them into high and low level digital signals. SPWM compares the three-phase modulated waveforms with a high-frequency triangular carrier wave. When the modulated waveform is higher than the carrier wave, the corresponding IGBT is turned on; when the modulated waveform is lower than the carrier wave, the corresponding IGBT is turned off.

[0042] In summary, the adaptive frequency adjustment method for the grid connection point of a wind power converter according to embodiments of the present invention is explained. By dynamically capturing transient signals in the instantaneous values ​​of the three-phase voltage and current at the grid connection point (PCC) and quantifying their disturbance intensity and type characteristics, it can adaptively adjust the virtual inertia and damping coefficient to generate an active power command that precisely matches the actual disturbances in the power grid. This refined adjustment enables the wind power converter to provide highly differentiated frequency support for different disturbances, significantly enhancing grid frequency stability while effectively suppressing unnecessary power fluctuations and energy losses, thus improving the economy and reliability of system operation.

[0043] This invention also provides an adaptive adjustment system for the grid connection point frequency of a wind power converter.

[0044] Figure 3 This is a block diagram of an adaptive adjustment system for the grid connection point frequency of a wind power converter according to an embodiment of the present invention. Figure 3 As shown, the adaptive adjustment system 300 for the grid connection point frequency of a wind power converter according to an embodiment of the present invention includes: a digital signal processing module 310, used to input the sampling sequence of instantaneous PCC three-phase voltage and the sampling sequence of instantaneous PCC three-phase current into a digital signal processor to obtain transient voltage signal, transient current signal, grid fundamental frequency, and grid fundamental frequency change rate; a disturbance intensity and type feature quantization module 320, used to perform disturbance intensity and type feature quantization based on the transient voltage signal and transient current signal to obtain disturbance intensity index and disturbance type entropy index; and an adaptive adjustment module 330, used to adjust the disturbance intensity index and disturbance type entropy index based on the disturbance type entropy index. The system includes: an adaptive virtual inertia and an adaptive damping coefficient; an active power command analysis module 340, which performs adaptive parameter-based active power command analysis on the grid fundamental frequency, grid rated frequency, grid fundamental frequency change rate, and MPPT power based on the adaptive virtual inertia and adaptive damping coefficient to obtain the final active power command; and a final active power command generation module 350, which generates PWM signals from the final active power command, reactive power command, PCC three-phase voltage, and converter output three-phase current to obtain the PWM signal driving the converter IGBT.

[0045] The specific implementation method of the adaptive adjustment system 300 for the grid connection point frequency of the wind power converter provided in this embodiment of the invention can be found in the adaptive adjustment method for the grid connection point frequency of the wind power converter provided in this embodiment of the invention, and will not be repeated here.

[0046] The adaptive frequency adjustment system 300 for the grid connection point of a wind power converter according to embodiments of the present invention can be implemented in various wireless terminals, such as servers with adaptive frequency adjustment algorithms for the grid connection point of the wind power converter. In one possible implementation, the adaptive frequency adjustment system 300 for the grid connection point of the wind power converter according to embodiments of the present invention can be integrated into the wireless terminal as a software module and / or a hardware module. For example, the adaptive frequency adjustment system 300 for the grid connection point of the wind power converter can be a software module in the operating system of the wireless terminal, or it can be an application developed for the wireless terminal; of course, the adaptive frequency adjustment system 300 for the grid connection point of the wind power converter can also be one of many hardware modules of the wireless terminal.

[0047] Alternatively, in another example, the adaptive adjustment system 300 for the grid connection point frequency of the wind power converter and the wireless terminal can also be separate devices, and the adaptive adjustment system 300 for the grid connection point frequency of the wind power converter can be connected to the wireless terminal via wired and / or wireless networks, and transmit interactive information in accordance with an agreed data format.

[0048] Those skilled in the art will understand that the above embodiments are specific implementations of the present invention, and in practical applications, various changes can be made in form and detail without departing from the spirit and scope of the present invention.

Claims

1. An adaptive adjustment method for the grid connection point frequency of a wind power converter, characterized in that, include: The instantaneous sampling sequences of the three-phase voltage and the three-phase current of the PCC are input into a digital signal processor to obtain transient voltage signals, transient current signals, the fundamental frequency of the power grid, and the rate of change of the fundamental frequency of the power grid. Disturbance intensity and type characteristics are quantized based on transient voltage and current signals to obtain disturbance intensity index and disturbance type entropy index; Based on the disturbance intensity index and the disturbance type entropy index, the basic virtual inertia and basic damping coefficient are adaptively adjusted to obtain the adaptive virtual inertia and adaptive damping coefficient. Based on adaptive virtual inertia and adaptive damping coefficient, active power command analysis based on adaptive parameters is performed on the grid fundamental frequency, grid rated frequency, grid fundamental frequency change rate and MPPT power to obtain the final active power command. The final active power command, reactive power command, PCC three-phase voltage and converter output three-phase current are used to generate PWM signals to obtain the PWM signals to drive the converter IGBT.

2. The adaptive adjustment method for the grid connection point frequency of the wind power converter according to claim 1, characterized in that, The instantaneous sampling sequences of the three-phase voltage and current of the PCC are input into a digital signal processor to obtain transient voltage signals, transient current signals, the fundamental frequency of the power grid, and the rate of change of the fundamental frequency of the power grid, including: The sampling sequence of the instantaneous three-phase voltage of the PCC is input into the phase-locked loop of the synchronous reference coordinate system to obtain the fundamental frequency of the power grid and the rate of change of the fundamental frequency of the power grid; The instantaneous sampling sequences of the three-phase voltage and the three-phase current of the PCC are input into a digital high-pass filter to obtain transient voltage and transient current signals.

3. The adaptive adjustment method for the grid connection point frequency of the wind power converter according to claim 1, characterized in that, Disturbance intensity and type characteristics are quantized based on transient voltage and current signals to obtain disturbance intensity and disturbance type entropy indices, including: Time-frequency decomposition is performed on transient voltage and transient current signals to obtain the wavelet coefficient matrices of the voltage transient signal and the current transient signal; The disturbance intensity index is calculated based on transient voltage and transient current signals. The perturbation type entropy index is calculated based on the wavelet coefficient matrices of voltage transient signals and current transient signals.

4. The adaptive adjustment method for the grid connection point frequency of the wind power converter according to claim 3, characterized in that, Based on transient voltage and current signals, the disturbance intensity index is calculated, including: The disturbance intensity index is calculated using the following formula: ； in, , , These are the transient voltage signals numbered as follows: The sampling points corresponding to Phase voltage transient value, Phase voltage transient value, Phase voltage transient value, , , These are the transient current signals numbered as follows: The sampling points corresponding to Phase current transient value, Phase current transient value, Phase current transient value, Indicates the total number of sampling points. This indicates the intensity of the disturbance. Indicates the sampling period.

5. The adaptive adjustment method for the grid connection point frequency of the wind power converter according to claim 3, characterized in that, Based on the wavelet coefficient matrices of the voltage transient signal and the current transient signal, the perturbation type entropy index is calculated, including: the perturbation type entropy index is calculated using the following formula: ； ； ； in, This indicates the entropy index representing the type of disturbance. The midscale of the wavelet coefficient matrix of the voltage transient signal ,time The value at that location, The midscale of the wavelet coefficient matrix of the current transient signal ,time The value at that location, Indicated in scale ,time The combined transient energy of voltage and current at the point. Indicated in scale ,time The energy probability density at that location.

6. The adaptive adjustment method for the grid connection point frequency of the wind power converter according to claim 1, characterized in that, Based on the disturbance intensity index and the disturbance type entropy index, the basic virtual inertia and basic damping coefficient are adaptively adjusted to obtain adaptive virtual inertia and adaptive damping coefficient, including: The basic inertia gain is determined based on the disturbance intensity index; The available power margin modulation factor is calculated based on MPPT power, maximum allowable output power, and minimum allowable output power to obtain the capability modulation factor; The basic virtual inertia and basic damping coefficient are adaptively adjusted based on the capability modulation factor, basic inertia gain, and disturbance type entropy index to obtain adaptive virtual inertia and adaptive damping coefficient.

7. The adaptive adjustment method for the grid connection point frequency of the wind power converter according to claim 6, characterized in that, The available power margin modulation factor is calculated based on MPPT power, maximum permissible output power, and minimum permissible output power to obtain the capability modulation factor, including: The available power margin modulation factor is calculated using the following formula: ； ； in, To ensure comprehensive available power margin; Maximum permissible output power; Minimum allowable output power; MPPT power; It is the capability modulation factor; This is a coefficient used to adjust the sensitivity of the modulation factor to changes in margin; This refers to the rated power of the wind power converter. is the base of the natural logarithm.

8. The adaptive adjustment method for the grid connection point frequency of the wind power converter according to claim 7, characterized in that, The basic virtual inertia and basic damping coefficient are adaptively adjusted based on the capability modulation factor, basic inertia gain, and disturbance type entropy index to obtain adaptive virtual inertia and adaptive damping coefficient, including: The adaptive virtual inertia and adaptive damping coefficient are determined according to the following formulas: ； ； in, For adaptive virtual inertia, For adaptive damping coefficient, These are the preset basic virtual inertia and basic damping coefficient, respectively. For effective inertia gain and , Based on inertia gain and , This indicates the intensity of the disturbance. The set perturbation dead zone threshold, The slope coefficient is used to adjust the steepness of the gain curve. The preset maximum gain amplitude, For effective damping gain and , Based on the damping gain, Entropy index for disturbance type The type of correction factor is determined.

9. The adaptive adjustment method for the grid connection point frequency of the wind power converter according to claim 1, characterized in that, Based on adaptive virtual inertia and adaptive damping coefficient, an adaptive parameter-based active power command analysis is performed on the grid fundamental frequency, grid rated frequency, grid fundamental frequency change rate, and MPPT power to obtain the final active power command, including: Based on adaptive virtual inertia and adaptive damping coefficient, virtual synchronous generator additional power is calculated for grid fundamental frequency, grid rated frequency, grid fundamental frequency change rate and MPPT power to obtain theoretical optimal power command. The theoretically optimal power command is constrained to obtain the final active power command.

10. An adaptive adjustment system for the grid connection point frequency of a wind power converter, characterized in that, include: The digital signal processing module is used to input the instantaneous sampling sequence of the three-phase voltage and the instantaneous sampling sequence of the three-phase current of the PCC into the digital signal processor to obtain the transient voltage signal, the transient current signal, the grid fundamental frequency, and the rate of change of the grid fundamental frequency; The disturbance intensity and type feature quantization module is used to quantize the disturbance intensity and type features based on transient voltage signals and transient current signals to obtain disturbance intensity index and disturbance type entropy index; The adaptive adjustment module is used to adaptively adjust the basic virtual inertia and basic damping coefficient based on the disturbance intensity index and the disturbance type entropy index to obtain the adaptive virtual inertia and adaptive damping coefficient. The active power command analysis module is used to perform active power command analysis based on adaptive parameters on the grid fundamental frequency, grid rated frequency, grid fundamental frequency change rate and MPPT power to obtain the final active power command. The final active power command generation module is used to generate PWM signals from the final active power command, reactive power command, PCC three-phase voltage and converter output three-phase current to obtain the PWM signal to drive the converter IGBT.

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