Method and system for operation control of wind power converter in microgrid mode

Abstract

The application discloses a wind power converter operation control method and system in a micro-grid mode, which dynamically generates equivalent rotational inertia and equivalent damping coefficient adapting to the current power grid operation state through real-time sensing and nonlinear analysis of power grid frequency deviation and its change rate, so as to realize adaptive adjustment of VSG parameters, and make the VSG parameters show optimal performance under different disturbances. In this way, the contradiction between the traditional VSG control in suppressing frequency overshoot and accelerating frequency recovery can be effectively solved, and the operation control performance of the wind power converter in the micro-grid mode is significantly improved.

Description

Technical Field

[0001] This application relates to the field of intelligent control, and more specifically, to a method and system for the operation control of a wind power converter in a microgrid mode. Background Technology

[0002] With the increasingly widespread development and utilization of renewable energy globally, wind power, as a crucial component, plays an increasingly vital role in the power system. Especially in the construction and development of microgrids, the integration of wind power can effectively improve the reliability, flexibility, and energy self-sufficiency of regional power supply. However, the random and intermittent nature of wind power poses a significant challenge to the stable operation of microgrids. Therefore, developing an efficient and robust operation control method for wind power converters in microgrid mode to ensure power quality and stability under various operating conditions is of paramount importance.

[0003] In the control schemes for wind power integrated into microgrids, Virtual Synchronous Generator (VSG) control technology has become a research hotspot due to its ability to simulate the inertia and damping characteristics of traditional synchronous generators, providing inertia support and damping for the microgrid. However, existing VSG control methods generally suffer from a high-value technical problem: the nonlinear adaptive adjustment of virtual inertia and damping coefficients is imperfect. In most existing adaptive methods, the parameters of virtual inertia J and damping D are mostly adjusted linearly or piecewise, making it difficult to achieve an optimal balance between the conflicting objectives of suppressing frequency overshoot and accelerating frequency recovery, especially resulting in poor system performance under continuous disturbances. The fundamental reason is that traditional VSG designs use fixed J and D values ​​to simulate the second-order characteristics of synchronous generators. These fixed parameters cannot adapt to the real-time changes in load, wind power, and active and reactive power during microgrid operation, often leaving the system in a suboptimal state under different operating conditions. Existing adaptive methods cannot achieve optimal control across the entire time domain in complex dynamic processes, which remains a technical challenge that has not yet been fully resolved by the industry.

[0004] Therefore, an optimized operation and control method for wind power converters in microgrid mode is needed. Summary of the Invention

[0005] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a method and system for the operation and control of a wind power converter in a microgrid mode. By real-time sensing and nonlinear analysis of the grid frequency deviation and its rate of change, it dynamically generates equivalent rotational inertia and equivalent damping coefficients adapted to the current grid operating state, thereby achieving adaptive adjustment of VSG parameters and ensuring optimal performance under various disturbances. In this way, the contradiction between suppressing frequency overshoot and accelerating frequency recovery in traditional VSG control can be effectively resolved, significantly improving the operation and control performance of the wind power converter in a microgrid mode.

[0006] According to one aspect of this application, a method for operating and controlling a wind power converter in a microgrid mode is provided, comprising: Real-time system status sensing is performed based on three-phase voltage and three-phase current to obtain grid angular frequency, active power and reactive power; Calculate the frequency deviation and frequency change rate based on the power grid angular frequency and the rated angular frequency; Nonlinear inertia and damping calculations based on phase plane partitioning are performed on frequency deviation and frequency change rate to obtain equivalent rotational inertia and equivalent damping coefficient. The equivalent moment of inertia, equivalent damping coefficient, active power reference, and reactive power reference are input into the VSG model to obtain the virtual potential amplitude and virtual potential phase angle. Inner-loop control is performed based on virtual potential amplitude, virtual potential phase angle, and three-phase current to obtain the PWM drive signal for the three-phase bridge arm of the inverter.

[0007] According to another aspect of this application, a wind power converter operation control system in microgrid mode is provided, comprising: The system real-time status sensing module is used to perform real-time system status sensing based on three-phase voltage and three-phase current to obtain the grid angular frequency, active power and reactive power. The frequency deviation calculation module is used to calculate the frequency deviation and frequency change rate based on the grid angular frequency and the rated angular frequency. The nonlinear inertia and damping calculation module is used to perform nonlinear inertia and damping calculations based on phase plane partitioning for frequency deviation and frequency change rate to obtain equivalent rotational inertia and equivalent damping coefficient. The VSG control module is used to input the equivalent moment of inertia, equivalent damping coefficient, active power reference, and reactive power reference into the VSG model to obtain the virtual potential amplitude and virtual potential phase angle. The inner loop modulation and control module is used to perform inner loop control based on the virtual potential amplitude, virtual potential phase angle and three-phase current to obtain the PWM drive signal of the inverter's three-phase bridge arm.

[0008] Compared with existing technologies, this application provides a method and system for the operation and control of wind power converters in microgrid mode. By real-time sensing and nonlinear analysis of grid frequency deviation and its rate of change, it dynamically generates equivalent rotational inertia and equivalent damping coefficients adapted to the current grid operating state, thereby achieving adaptive adjustment of VSG parameters and ensuring optimal performance under various disturbances. This approach effectively resolves the contradiction between suppressing frequency overshoot and accelerating frequency recovery in traditional VSG control, significantly improving the operation and control performance of wind power converters in microgrid mode. Attached Figure Description

[0009] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0010] Figure 1 This is a flowchart of a wind power converter operation control method in microgrid mode according to an embodiment of this application; Figure 2 This is a schematic diagram of data flow in the operation control method of a wind power converter in microgrid mode according to an embodiment of this application; Figure 3 This is a block diagram of the operation control system of a wind power converter in microgrid mode according to an embodiment of this application. Detailed Implementation

[0011] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0012] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0013] While this application makes various references to certain modules of the systems according to embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The modules described are merely illustrative, and different aspects of the systems and methods may use different modules.

[0014] Flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0015] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0016] The technical solution of this application proposes a method for the operation and control of a wind power converter in a microgrid mode. Figure 1 This is a flowchart of a wind power converter operation control method in microgrid mode according to an embodiment of this application. Figure 2 This is a schematic diagram of data flow in the operation control method of a wind power converter in microgrid mode according to an embodiment of this application. Figure 1 and Figure 2 As shown, the wind power converter operation control method in microgrid mode according to an embodiment of this application includes the following steps: S1, performing real-time system state sensing based on three-phase voltage and three-phase current to obtain grid angular frequency, active power and reactive power; S2, calculating frequency deviation and frequency change rate based on grid angular frequency and rated angular frequency; S3, performing nonlinear inertia and damping calculation based on phase plane partitioning on frequency deviation and frequency change rate to obtain equivalent rotational inertia and equivalent damping coefficient; S4, inputting equivalent rotational inertia, equivalent damping coefficient, active power reference and reactive power reference into VSG model to obtain virtual potential amplitude and virtual potential phase angle; S5, performing inner loop control based on virtual potential amplitude, virtual potential phase angle and three-phase current to obtain PWM drive signals for the three-phase bridge arms of the inverter.

[0017] Specifically, S1 involves real-time system state sensing based on three-phase voltage and three-phase current to obtain the grid angular frequency, active power, and reactive power. It should be understood that the stable operation of the wind power grid-connected inverter is closely related to the overall power balance and power quality of the microgrid, and the grid angular frequency, active power, and reactive power are the most direct and crucial parameters reflecting the microgrid's operating state. Accurate and real-time state sensing provides accurate basic data for subsequent frequency deviation calculations, nonlinear inertia and damping adjustments, and VSG model control. It is a prerequisite for the effective implementation of the entire control strategy, ensuring that the controller can respond promptly and accurately to dynamic changes in the grid, thereby maintaining the stability and reliability of the microgrid.

[0018] Among them, three-phase voltage and three-phase current refer to the voltage and current vectors on the three phases that constitute the AC power system. They are the basic physical quantities of power grid energy transmission. The grid angular frequency is a physical quantity that describes the oscillation speed of AC grid voltage or current. Its stability directly affects the quality of system frequency. Active power represents the electrical energy actually transmitted and consumed in the circuit and is a direct manifestation of electrical energy conversion. Reactive power, on the other hand, represents the part of electrical energy exchanged rather than consumed. It plays an important role in maintaining grid voltage stability and energy transmission efficiency.

[0019] In practical implementation, firstly, the three-phase voltage is transformed and synchronized with the phase-locked loop (PLL) to obtain the grid angular frequency, grid voltage phase angle, voltage d-axis component, and voltage q-axis component. That is, the detected three-phase AC voltage signal is transformed into a synchronously rotating dq coordinate system through specific coordinate transformations (such as Clarke and Park transformations), thereby converting AC quantities into DC quantities, greatly facilitating the design and implementation of the controller. In this process, the PLL plays a crucial role. It can accurately track the phase and frequency of the grid voltage, ensuring that the synchronously rotating coordinate system is synchronized with the grid, thus accurately extracting the real-time angular frequency of the grid and the instantaneous phase angle of the grid voltage, and decomposing the voltage components on the d-axis and q-axis. Next, based on the grid voltage phase angle, the three-phase current is synchronously transformed to obtain the d-axis and q-axis components of the current. That is, the controller uses the grid voltage phase angle obtained from the phase-locked loop in the previous step to synchronously transform the three-phase AC current signal that is also detected in real time, and also transforms it to the dq coordinate system, thereby obtaining the components of the current on the d-axis and q-axis; Furthermore, based on the d-axis component of voltage, the q-axis component of voltage, the d-axis component of current, and the q-axis component of current, active power and reactive power are calculated. Specifically, active power and reactive power are calculated using the following formula:

[0020]

[0021] in, For the d-axis component of voltage, For the q-axis component of voltage, For the d-axis component of the current, This represents the q-axis component of the current. Through this series of sophisticated signal processing and calculations, the system can obtain the current microgrid's angular frequency, active power, and reactive power in real time and accurately.

[0022] Specifically, S2 calculates the frequency deviation and frequency change rate based on the grid angular frequency and the rated angular frequency. It should be understood that the stability of the grid frequency is a core indicator of the power system's operational quality. Any deviation from the grid frequency (frequency deviation) and the rate of frequency change (frequency change rate) directly reflect the immediate state and dynamic trend of the active power balance within the microgrid. Accurately obtaining these parameters enables the control system to promptly detect frequency disturbances, thus providing a basis for dynamically adjusting the inertia and damping characteristics of the converter. This ensures that the microgrid maintains frequency stability and rapid response capabilities under various operating conditions, especially during sudden load changes or rapid wind power fluctuations, avoiding excessive frequency drops or prolonged recovery times. This is crucial for maintaining the reliable and safe operation of the microgrid.

[0023] In practice, the first step is to calculate the difference between the grid angular frequency and the rated angular frequency to obtain the frequency deviation. That is, the real-time sensed grid angular frequency is directly subtracted from the preset rated angular frequency to obtain the instantaneous difference between the two. The frequency deviation is the amount by which the real-time grid angular frequency deviates from the rated angular frequency; it is a direct indicator of grid frequency stability. Furthermore, the frequency change rate is calculated by applying a low-pass filter to the grid angular frequency based on the filtering time constant. It should be understood that directly differentiating the grid angular frequency signal obtained from measurement stages such as phase-locked loops to calculate the frequency change rate would significantly amplify unavoidable high-frequency noise during data acquisition, resulting in a calculated frequency change rate signal filled with spikes and glitches. If this unstable and inaccurate signal is directly used for subsequent adaptive inertia and damping adjustment, it will cause oscillations or even instability in the control system, leading the controller to overreact to minor noise rather than responding to the true dynamic trend of the grid frequency. Therefore, in the technical solution of this application, a calculation method with a low-pass filter is used to accurately calculate the rate of change of the grid angular frequency, effectively filtering out high-frequency interference in the signal. This extracts a smooth, continuous frequency change rate that accurately reflects the macroscopic trend of the grid frequency change, providing a stable and reliable dynamic input for the entire control system and ensuring the accuracy and robustness of control decisions. In this process, firstly, a low-pass filter with a specific filtering time constant is applied to the grid angular frequency signal. Here, the filtering time constant is a parameter applied to the low-pass filter, which determines the filter's response speed and its ability to suppress high-frequency components. Appropriate selection of this constant is crucial for ensuring the accuracy of the frequency change rate calculation. Then, based on this, its first derivative is estimated to ensure that the calculated frequency change rate accurately reflects the actual trend of the grid frequency. The frequency change rate represents how quickly the frequency changes with time; it indicates the trend of frequency change and is an important parameter for evaluating the dynamic performance of the grid and predicting future frequency trends.

[0024] Specifically, in step S3, nonlinear inertia and damping calculations based on phase plane partitioning are performed on the frequency deviation and frequency change rate to obtain the equivalent rotational inertia and equivalent damping coefficient. It should be understood that traditional virtual synchronous generator (VSG) control schemes mostly employ fixed or simple linear adaptive inertia and damping coefficients. This strategy cannot achieve an ideal balance between the contradictory performance indicators of "suppressing frequency overshoot" and "accelerating frequency recovery." For example, in the initial stage of a disturbance, the system requires a large inertia to suppress the rapid frequency drop, while in the recovery stage, a smaller inertia is needed to accelerate the frequency return to its rated value. Existing technologies cannot make optimal adjustments based on the real-time dynamics of the microgrid's operating state. To overcome this technical bottleneck, the technical solution of this application introduces a nonlinear calculation method based on phase plane partitioning, enabling the virtual inertia and damping coefficient to undergo complex nonlinear adaptive adjustments according to the system state defined by the current frequency deviation and its rate of change. This achieves optimal dynamic response under various operating conditions, significantly improving the frequency stability and robustness of the microgrid.

[0025] In this context, phase plane partitioning refers to the implicit or explicit division of a two-dimensional state plane (i.e., the phase plane) with frequency deviation as the horizontal axis and frequency change rate as the vertical axis. The position and movement trend of the system operating point's trajectory on the phase plane represent the dynamic characteristics of the microgrid frequency. This application, by designing the aforementioned nonlinear adjustment term, essentially assigns different adjustment strategies to inertia and damping based on the different positions of the system state point in the phase plane, thereby achieving more refined and efficient control than simple linear or piecewise regulation. Specifically, the nonlinear adjustment term refers to the adjustment amount calculated from the system state variables through nonlinear operations (such as taking absolute values ​​or multiplying variables), and it constitutes the dynamic incremental part of the total equivalent inertia and damping.

[0026] In practical implementation, firstly, nonlinear adjustment terms are calculated based on frequency deviation and frequency change rate to obtain the rate-of-change response inertia term, trend prediction inertia term, and deviation response damping term. That is, by decomposing the overall adjustment task into three independent nonlinear adjustment terms that respond to the rate of frequency change, the trend of change, and the magnitude of deviation, respectively, the complex frequency dynamic process is finely deconstructed and responded to. The separate calculation of these three adjustment terms makes the control strategy design more modular and interpretable, allowing the system to selectively adjust the gain coefficients of each component to optimize the specific performance of the system at different disturbance stages (such as the initial disturbance stage and the recovery period). This provides a flexible and powerful dynamic foundation for the subsequent synthesis of the total equivalent inertia and damping. Specifically, the rate-of-change response inertia term is an inertia adjustment component designed to respond to the rate of frequency change; the trend prediction inertia term is a more advanced inertia adjustment component that predicts the dynamic trend of the system and intervenes in advance by analyzing the relationship between frequency position and velocity; and the deviation response damping term is a damping adjustment component specifically designed to suppress oscillations caused by frequency deviation. Specifically, the nonlinear adjustment term is calculated using the following formula:

[0027]

[0028]

[0029] in, For frequency deviation, The rate of change of frequency, , and This is the preset gain coefficient; Furthermore, the inertia term of the rate of change response, the inertia term of the trend prediction, and the damping term of the deviation response are synthesized by combining inertia and total damping to obtain the equivalent rotational inertia and equivalent damping coefficient. That is, the dynamically calculated adjustment components are combined with the inherent basic parameters of the system to form the effective control parameters ultimately applied to the virtual synchronous generator model. Here, it should be understood that dynamic adjustment terms alone may lead to excessively small control parameters when the system is stationary or the disturbance is extremely small, which is detrimental to system stability; while fixed basic parameters alone cannot cope with complex operating condition changes. Therefore, in the technical solution of this application, by linearly superimposing a preset basic inertia and basic damping that ensures the basic stability margin of the system with the three nonlinear adjustment terms calculated in the previous step that reflect real-time dynamics, an equivalent control parameter can be generated, enabling the wind power converter to obtain optimal and complete inertia and damping support under any operating state. Wherein, the equivalent moment of inertia is the final inertia value obtained after synthesis, which will actually act on the VSG oscillation equation within the current control cycle; the equivalent damping coefficient is the final damping coefficient value obtained after synthesis, which will actually act on the VSG oscillation equation within the current control cycle. Specifically, the inertia and total damping are synthesized using the following formula:

[0030]

[0031] in, and The preset basic inertia and basic damping constant, For the rate of change response inertia term, For trend prediction inertia term, This is the deviation response damping term.

[0032] Specifically, in step S4, the equivalent moment of inertia, equivalent damping coefficient, active power reference, and reactive power reference are input into the VSG model to obtain the virtual potential amplitude and virtual potential phase angle. By inputting the equivalent moment of inertia, equivalent damping coefficient, active power reference, and reactive power reference into the VSG model, the system can convert abstract power commands and dynamic characteristic parameters (inertia, damping) into two fundamental physical quantities—the amplitude and phase angle of the virtual potential—that can directly guide the inverter voltage output in real time. This essentially completes the conversion from power domain control objectives to voltage domain control commands, providing a direct and clear reference for the subsequent inner-loop controller to generate accurate PWM drive signals.

[0033] In practice, this step mainly includes two core parts of the VSG model: the active power-frequency control loop (used to generate the virtual potential phase angle) and the reactive power-voltage control loop (used to generate the virtual potential amplitude). First, the generation of the virtual potential phase angle is based on the swing equation simulating the rotor motion of a synchronous generator. This equation uses the active power difference as the driving force, combined with the equivalent moment of inertia and equivalent damping coefficient at the current moment, to calculate the angular frequency and phase angle of the virtual rotor in real time.

[0034] Secondly, the generation of the virtual potential amplitude is usually coupled with the control of reactive power. It compares the reference value of reactive power with the actual measured reactive power output value, and uses the difference between the two as input to adjust the amplitude of the virtual potential through a controller (usually a PI controller or a simple proportional droop circuit).

[0035] Specifically, S5 performs inner-loop control based on the virtual potential amplitude, virtual potential phase angle, and three-phase current to obtain the PWM drive signal for the three-phase bridge arm of the inverter. That is, the virtual potential amplitude and phase angle, representing the behavior of an ideal synchronous generator, calculated by the upper-level VSG model, are accurately, quickly, and stably reproduced at the actual AC output of the inverter. The inner-loop control (usually referring to a fast control loop for voltage and current) is crucial to achieving this goal. It is responsible for real-time tracking of the reference signal, suppressing the impact of grid disturbances and internal system parameter fluctuations on the output power quality, and providing necessary overcurrent protection. The final generated PWM drive signal is the underlying instruction that directly controls the switching action of power semiconductor devices (such as IGBTs) in the inverter, and is the final physical implementation of the entire control algorithm.

[0036] In practice, firstly, based on the virtual potential amplitude and phase angle, d-axis and q-axis reference voltages are generated. This step aims to convert the virtual potential in polar coordinate form output by the VSG model into rectangular coordinate components in a synchronously rotating dq coordinate system, serving as the direct tracking target for the inner-loop controller. Typically, the virtual potential vector can be aligned with either the d-axis or q-axis of this coordinate system to simplify control. These two DC reference voltages provide stable and definite setpoints for subsequent dual-loop control. Specifically, the d-axis reference voltage value is directly assigned the magnitude of the virtual potential; correspondingly, since the entire vector is on the d-axis, its component on the perpendicular q-axis is naturally zero, therefore the q-axis reference voltage value is set to zero. These two DC reference voltages provide stable and definite setpoints for subsequent dual-loop control. Next, the actual d-axis voltage, actual q-axis voltage, actual d-axis current, actual q-axis current, d-axis reference voltage, and q-axis reference voltage input voltage and current are controlled in a dual closed-loop manner to obtain the q-axis modulated voltage and q-axis modulated voltage. That is, a cascaded control structure is used, with the current loop as the inner loop and the voltage loop as the outer loop. Specifically, firstly, the voltage loop controller compares the actual output voltage on the dq-axis with the aforementioned reference voltage. The resulting voltage error is passed through a PI (proportional-integral) regulator to generate a reference value for the dq-axis current. Subsequently, the current loop controller compares the actual measured dq-axis current with the current reference value output by the voltage loop. The resulting current error is then passed through another set of PI regulators to finally output the modulated voltage on the dq-axis. This dual closed-loop structure enables precise and rapid decoupling control of the output voltage and current, and possesses inherent current-limiting capability, greatly improving the dynamic performance and robustness of the system. Furthermore, based on the grid synchronization phase angle, SVPWM modulation is applied to the q-axis modulation voltage and the q-axis modulation voltage to obtain the PWM drive signals for the three-phase bridge arms of the inverter. That is, the control quantity in the dq coordinate system is converted back to the three-phase PWM signal in the physical world. Specifically, firstly, using the grid synchronization phase angle obtained by the phase-locked loop, the dq-axis modulation voltage is subjected to an inverse Park transformation, converting it to a two-phase stationary α-β coordinate system; then, these two orthogonal voltage components are input to the Space Vector Pulse Width Modulation (SVPWM) module. The SVPWM algorithm calculates the precise on-time of the six power switches on the three bridge arms of the inverter within one switching cycle based on the sector and position of the voltage vector synthesized from these two voltage components; finally, this timing information is converted into a high-frequency PWM drive signal with a specific duty cycle, which is applied to the three-phase bridge arms of the inverter through the drive circuit, thereby accurately synthesizing the required output voltage.

[0037] In summary, the operation control method of the wind power converter in microgrid mode according to the embodiments of this application is explained. It dynamically generates equivalent rotational inertia and equivalent damping coefficients adapted to the current grid operating state by real-time sensing and nonlinear analysis of the grid frequency deviation and its rate of change, thereby achieving adaptive adjustment of VSG parameters and ensuring optimal performance under different disturbances. In this way, the contradiction between suppressing frequency overshoot and accelerating frequency recovery in traditional VSG control can be effectively resolved, significantly improving the operation control performance of the wind power converter in microgrid mode.

[0038] Furthermore, an operation control system for a wind power converter in microgrid mode is also provided.

[0039] Figure 3 This is a block diagram of the operation control system of a wind power converter in microgrid mode according to an embodiment of this application. Figure 3As shown, the wind power converter operation control system 300 in microgrid mode according to an embodiment of this application includes: a system real-time status sensing module 310, used to perform real-time system status sensing based on three-phase voltage and three-phase current to obtain grid angular frequency, active power and reactive power; a frequency deviation calculation module 320, used to calculate frequency deviation and frequency change rate based on grid angular frequency and rated angular frequency; a nonlinear inertia and damping calculation module 330, used to perform nonlinear inertia and damping calculation based on phase plane partitioning on frequency deviation and frequency change rate to obtain equivalent rotational inertia and equivalent damping coefficient; a VSG control module 340, used to input equivalent rotational inertia, equivalent damping coefficient, active power reference and reactive power reference into VSG model to obtain virtual potential amplitude and virtual potential phase angle; and an inner loop modulation and control module 350, used to perform inner loop control based on virtual potential amplitude, virtual potential phase angle and three-phase current to obtain PWM drive signals for the inverter three-phase bridge arms.

[0040] As described above, the wind power converter operation control system 300 in microgrid mode according to the embodiments of this application can be implemented in various wireless terminals, such as servers with operation control algorithms for wind power converters in microgrid mode. In one possible implementation, the wind power converter operation control system 300 in microgrid mode according to the embodiments of this application can be integrated into the wireless terminal as a software module and / or hardware module. For example, the wind power converter operation control system 300 in microgrid mode 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 wind power converter operation control system 300 in microgrid mode can also be one of the many hardware modules of the wireless terminal.

[0041] Alternatively, in another example, the wind power converter operation control system 300 in microgrid mode and the wireless terminal can also be separate devices, and the wind power converter operation control system 300 in microgrid mode can be connected to the wireless terminal via wired and / or wireless networks, and transmit interactive information in accordance with an agreed data format.

[0042] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for operating and controlling a wind power converter in a microgrid mode, characterized in that, include: Real-time system status sensing is performed based on three-phase voltage and three-phase current to obtain grid angular frequency, active power and reactive power; Calculate the frequency deviation and frequency change rate based on the power grid angular frequency and the rated angular frequency; Nonlinear inertia and damping calculations based on phase plane partitioning are performed on frequency deviation and frequency change rate to obtain equivalent rotational inertia and equivalent damping coefficient. The equivalent moment of inertia, equivalent damping coefficient, active power reference, and reactive power reference are input into the VSG model to obtain the virtual potential amplitude and virtual potential phase angle. Inner-loop control is performed based on virtual potential amplitude, virtual potential phase angle, and three-phase current to obtain the PWM drive signal for the three-phase bridge arm of the inverter.

2. The operation control method for a wind power converter in microgrid mode according to claim 1, characterized in that, Real-time system state sensing based on three-phase voltage and three-phase current is used to obtain the grid angular frequency, active power, and reactive power, including: Three-phase voltage is converted into voltage signals and synchronized with a phase-locked loop to obtain the grid angular frequency, grid voltage phase angle, voltage d-axis component, and voltage q-axis component; Synchronous transformation of three-phase current signals is performed based on the phase angle of grid voltage to obtain the d-axis and q-axis components of the current. Calculate active power and reactive power based on the d-axis component of voltage, the q-axis component of voltage, the d-axis component of current, and the q-axis component of current.

3. The operation control method for a wind power converter in microgrid mode according to claim 2, characterized in that, Based on the d-axis component of voltage, the q-axis component of voltage, the d-axis component of current, and the q-axis component of current, active power and reactive power are calculated, including: active power and reactive power are calculated using the following formula, where the formula is: in, For the d-axis component of voltage, For the q-axis component of voltage, For the d-axis component of the current, This represents the q-axis component of the current.

4. The operation control method for a wind power converter in microgrid mode according to claim 1, characterized in that, Based on the power grid angular frequency and the rated angular frequency, calculate the frequency deviation and the rate of frequency change, including: Calculate the difference between the grid angular frequency and the rated angular frequency to obtain the frequency deviation; The frequency change rate is calculated by performing a low-pass filter on the grid angular frequency based on the filter time constant.

5. The operation control method for a wind power converter in microgrid mode according to claim 1, characterized in that, Nonlinear inertia and damping calculations based on phase plane partitioning are performed on frequency deviation and frequency change rate to obtain equivalent rotational inertia and equivalent damping coefficient, including: Nonlinear adjustment terms are calculated based on frequency deviation and frequency change rate to obtain the rate of change response inertia term, trend prediction inertia term, and deviation response damping term; The inertia term of the rate of change response, the inertia term of the trend prediction, and the damping term of the deviation response are synthesized with total damping to obtain the equivalent rotational inertia and the equivalent damping coefficient.

6. The operation control method for a wind power converter in microgrid mode according to claim 5, characterized in that, The calculation of nonlinear adjustment terms based on frequency deviation and frequency change rate yields the rate-of-change response inertia term, trend prediction inertia term, and deviation response damping term, including: calculating the nonlinear adjustment term using the following formula, wherein the formula is: in, For frequency deviation, The rate of change of frequency, , and This is the preset gain coefficient.

7. The operation control method for a wind power converter in microgrid mode according to claim 5, characterized in that, The inertia term of the rate of change response, the inertia term of the trend prediction, and the damping term of the deviation response are combined with the total damping to obtain the equivalent rotational inertia and the equivalent damping coefficient. This includes combining the inertia and total damping using the following formula: in, and The preset basic inertia and basic damping constant, For the rate of change response inertia term, For trend prediction inertia term, This is the deviation response damping term.

8. The operation control method for a wind power converter in microgrid mode according to claim 1, characterized in that, Inner-loop control is performed based on virtual potential amplitude, virtual potential phase angle, and three-phase current to obtain the PWM drive signals for the three-phase bridge arms of the inverter, including: Based on the virtual potential amplitude and virtual potential phase angle, generate the d-axis reference voltage and the q-axis reference voltage; The d-axis actual voltage, q-axis actual voltage, d-axis actual current, q-axis actual current, d-axis reference voltage, and q-axis reference voltage input voltage and current are controlled by a dual closed loop to obtain the q-axis modulated voltage and q-axis modulated voltage. Based on the grid synchronization phase angle, SVPWM modulation is performed on the q-axis modulation voltage and the q-axis modulation voltage to obtain the PWM drive signal of the three-phase bridge arm of the inverter.

9. A wind power converter operation control system in microgrid mode, characterized in that, include: The system real-time status sensing module is used to perform real-time system status sensing based on three-phase voltage and three-phase current to obtain the grid angular frequency, active power and reactive power. The frequency deviation calculation module is used to calculate the frequency deviation and frequency change rate based on the grid angular frequency and the rated angular frequency. The nonlinear inertia and damping calculation module is used to perform nonlinear inertia and damping calculations based on phase plane partitioning for frequency deviation and frequency change rate to obtain equivalent rotational inertia and equivalent damping coefficient. The VSG control module is used to input the equivalent moment of inertia, equivalent damping coefficient, active power reference, and reactive power reference into the VSG model to obtain the virtual potential amplitude and virtual potential phase angle. The inner loop modulation and control module is used to perform inner loop control based on the virtual potential amplitude, virtual potential phase angle and three-phase current to obtain the PWM drive signal of the inverter's three-phase bridge arm.

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