New energy inertia support adaptive control method based on cloud model

Abstract

The invention discloses a new energy inertia support self-adaptive control method based on a cloud model, and belongs to the technical field of cloud model self-adaptive control. Based on a traditional synchronous generator second-order classic model, a new energy unit control system is used for simulating inertia response characteristics of a traditional synchronous generator set. The maximum advantage of the method is that parameters can be adaptively changed, and the degree of freedom of new energy inertia support is increased; a power grid side frequency variable quantity and a change rate are used as input, a cloud inference rule of virtual inertia is designed according to a relationship between the virtual inertia and the power grid side frequency variable quantity and the change rate, and cooperative self-adaption of parameters is realized through a cloud model, so that new energy can adaptively adjust the virtual inertia when a system is subjected to external disturbance, and the system is more stable in performance. And stable operation of the system is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of cloud model adaptive control, and specifically is a new energy inertia support adaptive control method based on cloud model. Background Art

[0002] At present, in view of the prominent problems of inertia reduction and frequency safety in the new power system environment, virtual inertia control of new energy inverters is introduced, so that new energy can simulate the inertia response similar to that of traditional synchronous generators, thereby providing certain support for the grid frequency. Most of the existing methods for adaptive inertia control of new energy converters focus on the design of virtual inertia parameters introduced into the active power-frequency loop of the converter, and these control methods mostly adopt a single parameter adaptive control strategy, especially for the virtual inertia adaptive control strategy based on fuzzy theory, it is difficult to fully utilize the advantage of freely adjustable parameters in virtual inertia control technology, and thus cannot effectively suppress the problem of too fast frequency change and too large offset on the grid side. Summary of the invention

[0003] In view of the above problems, the present invention provides a new energy inertia support adaptive control method based on a cloud model. Based on the second-order classical model of the traditional synchronous generator, the new energy unit control system is used to simulate the inertial response characteristics of the traditional synchronous generator set. Compared with the traditional inertia support control method, the greatest advantage of the invention is that the parameters can be adaptively changed, which increases the freedom of the new energy inertia support. The frequency change and change rate on the grid side are used as input, and the cloud inference rules of the virtual inertia are designed according to the relationship between the virtual inertia and the frequency change and change rate on the grid side. The cloud model is used to realize the coordinated adaptation of the parameters, so that the new energy can adaptively adjust the virtual inertia when the system is subjected to external disturbances, thereby ensuring the stable operation of the system.

[0004] The technical solution of the present invention is as follows: a cloud model-based adaptive control method for new energy inertia support, which introduces the rotor motion mechanism in the synchronous generator into the outer loop control of the new energy converter, and establishes a cloud reasoning model for the new energy converter parameters according to the physical mechanism of the rotor motion, the dynamic characteristics and the limitations of the converter's own conditions. According to the correlation between the system frequency change quantization rate and the virtual inertia within an oscillation cycle, the frequency change amount and the frequency change rate are used as input variables, and the corresponding new energy virtual inertia change amount is output through input cloudification, cloud reasoning and output inverse cloudification operations, thereby realizing the adaptive change of the new energy inertia support parameters.

[0005] Furthermore, according to the physical mechanism of rotor motion, dynamic characteristics and the limitations of the converter's own conditions, a control model based on cloud model theory is established for the parameters of the new energy converter, specifically:

[0006] S101 determines whether the system is in a stable operating state. If so, the change in the output virtual inertia is the initial setting value of the virtual inertia. Otherwise, execute step S102;

[0007] S102 establishes a control model based on cloud model theory, and obtains the difference between the instantaneous value and the rated value of the virtual rotor angular frequency and the coupling relationship between the virtual rotor angular frequency change rate and the virtual inertia according to the power angle oscillation curve of the new energy converter;

[0008] S103 uses the coupling relationship obtained in step S102 to establish a cloud inference rule for virtual inertia, and outputs a change in virtual inertia according to inverse cloudification, so that the virtual inertia of the new energy can change adaptively;

[0009] S104 applies step S103 to the active power-frequency control loop in the new energy converter control strategy to obtain a frequency support effect that is analogous to the actual synchronous generator rotational inertia effect; and outputs a virtual inertia value.

[0010] Further, step S102 is specifically as follows: obtaining the power angle oscillation curve of the new energy converter under the simulated disturbance, respectively obtaining the difference between the instantaneous value of the virtual rotor angular frequency and the rated value and the rate of change of the virtual rotor angular frequency, and establishing a virtual inertia adaptive parameter control strategy based on the cloud model based on the difference between the instantaneous value of the rotor angular frequency and the rated value and the rate of change of the virtual rotor angular frequency. In the power angle oscillation curve of the new energy converter under the simulated disturbance, when within a specific time interval, the rate of change dω / dt of the virtual rotor angular velocity first increases suddenly and then gradually decreases, and the virtual rotor angular velocity of the new energy converter is greater than the rated angular velocity of the power grid and continues to increase, then within this interval, the new energy virtual inertia needs to be increased to suppress dω / dt and the difference Δω between the instantaneous angular frequency and the rated angular frequency. In this interval, dω / dt>0, Δω> 0; when the rate of change of the virtual rotor angular velocity of the new energy converter dω / dt is less than 0, the virtual rotor angular velocity will gradually decrease from the maximum value, but the virtual rotor angular velocity is still greater than the grid angular velocity, then the new energy virtual inertia needs to be reduced during this time period; when the rate of change of the virtual rotor angular velocity of the new energy converter dω / dt is less than 0, and the change trend of dω / dt is first a sudden decrease and then gradually decreases, the virtual rotor angular velocity gradually decreases to the minimum value, at this time the virtual rotor angular velocity is less than the grid angular velocity, then the new energy virtual inertia needs to be reduced during this time period; when the rate of change of the virtual rotor angular velocity of the new energy converter dω / dt>0, Δω>0, and dω / dt gradually increases, the virtual rotor angular velocity is less than the grid angular velocity, then the new energy virtual inertia needs to be increased during this time period.

[0011] Furthermore, through the cloudification of input, cloud reasoning and output inverse cloudification operations, the corresponding new energy virtual inertia change is output, specifically:

[0012] S201 formulates corresponding logical reasoning rules according to various qualitative concepts for different input conditions of converter virtual inertia control;

[0013] S202 collects the actual angular frequency ω output by the new energy converter, calculates the difference Δω between the instantaneous angular frequency and the rated angular frequency and its corresponding time derivative dω / dt, and normalizes Δω and dω / dt by quantization factors K1 and K2 respectively to obtain the input variables E and Ec of the cloud model controller; performs cloud processing on E and Ec to make them the input variables E* and Ec* of cloud reasoning;

[0014] S203 performs cloud reasoning on the input quantities E* and Ec* according to the cloud reasoning rules designed in step S103 and obtains the cloudified output U* in the corresponding domain;

[0015] S204 performs an inverse cloudification transformation on U* to obtain the precise value U of the cloudification output, and quantizes the output U controlled by the cloud model through a proportional factor K3 to obtain a virtual inertia increment ΔJ.

[0016] Further, in step S202, the cloudification process is based on the expected E x 、Entropy E n and super entropy H e Based on the three digital features, statistical analysis is performed on the sample data of input variables E and Ec.

[0017] Further, in step S204, the reverse cloudification process is performed based on the expected E x 、Entropy E n and super entropy H e Based on the three digital features, the sample data of U* is statistically analyzed.

[0018] Furthermore, input variables E and E c Statistical analysis of the sample data of and U* is performed according to the formula

[0019]

[0020] Compute its three numerical features.

[0021] The beneficial effects of the present invention are:

[0022] 1) The present invention is based on the cloud model theory and can adjust the virtual inertia parameters online in quasi-real time according to the frequency deviation of the new energy system, thereby achieving the purpose of adaptive regulation of virtual inertia; 2) The cloud model control method comprehensively considers the uncertainty brought by fuzziness and randomness. There is no need to establish a complex converter mathematical model. The basic control behavior law only needs to be described by three digital characteristics: expectation, entropy and super entropy. The new energy virtual inertia control method based on cloud theory is flexible and adaptable. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a block diagram of the adaptive control of new energy inertia support based on the cloud model of the present invention.

[0024] Figure 2 This is the active power-frequency control block diagram of the new energy converter of the present invention.

[0025] Figure 3 It is a schematic diagram of the control process of the virtual inertia cloud model of the new energy converter of the present invention. DETAILED DESCRIPTION

[0026] It should be noted that in the description of the present invention, the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", "clockwise", "counterclockwise", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction.

[0027] In the present invention, unless otherwise clearly specified and limited, the terms "set", "install", "connect", "connect" and the like should be understood in a broad sense. For example, "fixed" can be a fixed connection, a detachable connection, or an integral connection; the connection can be a mechanical connection or an electrical connection; the connection can be a direct connection or an indirect connection through an intermediate medium, and can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0028] A cloud model-based adaptive control method for inertia support of new energy sources, which introduces the rotor motion mechanism in a synchronous generator into the outer loop control of a new energy converter, establishes a cloud inference model for the parameters of the new energy converter according to the physical mechanism and dynamic characteristics of the rotor motion and the limitations of the converter's own conditions, takes the frequency change and the frequency change rate as input variables according to the correlation between the system frequency change quantization rate and the virtual inertia within an oscillation cycle, and outputs the corresponding new energy virtual inertia change through input cloudification, cloud inference and output inverse cloudification operations, thereby realizing adaptive changes in the inertia support parameters of new energy sources.

[0029] Figure 1 is the overall control block diagram of the new energy inertia support adaptive control method based on the cloud model of the present invention, consisting of Figure 1 As shown in the figure, the new energy inertia support is mainly achieved through the virtual synchronous control of the converter. Figure 1 The virtual mechanical power (Pm) and actual output power (Pe) of the virtual synchronous generator (VSG) are used to calculate the electrical angle (δvsg ), δ vsg After the inner loop control, a three-phase modulated wave current is generated, and finally the switching state of the inverter switch tube is controlled by the space vector pulse width modulation (SVPWM) generator to achieve precise control of the VSG system. The virtual inertia in the VSG body algorithm controls the change of its value through the control rules based on the cloud model theory. The method of the present invention introduces the rotor equation of the synchronous generator into the outer loop control strategy of the virtual synchronous generator (VSG) converter to adjust the output frequency of the converter. In virtual synchronous control, the converter inner loop control strategy is based on the active-frequency control loop, such as Figure 2 . Figure 2 The power control structure block diagram of the grid-connected converter using virtual synchronous generator technology is shown. This technology enables the converter power supply to have the off-grid characteristics of the synchronous generator set, such as inertial frequency modulation and reactive voltage regulation, but the inertia parameters in the traditional method are fixed and cannot be adjusted in real time to cope with system disturbances. The present invention introduces cloud control to adaptively adjust the virtual inertia to achieve adaptive adjustment of the virtual inertia control parameters.

[0030] According to the relationship between the change amount and change rate of the virtual rotor angular frequency and the virtual inertia, the frequency change rate and change amount are used as input variables respectively, and ΔJ is output through input cloudification, cloud inference and output inverse cloudification operations, thereby realizing the adaptive change of virtual inertia parameters.

[0031] The virtual inertia control process based on the cloud model is as follows Figure 3 The specific design steps of the cloud model controller are as follows:

[0032] a) Design cloud inference rules. Cloud inference rules are the core of the cloud model. When designing a new energy inertia support adaptive control method based on the cloud model, it is necessary to first design qualitative cloud inference rules based on the relationship between the virtual inertia and the virtual rotor angular frequency change rate and frequency deviation.

[0033] The three independent domains of virtual rotor angular frequency, angular frequency change rate and virtual inertia increment are divided into five qualitative concepts, which are NB (negative large), NS (negative small), ZE (zero), PS (positive small) and PB (positive large) from small to large according to mathematical expectation, which corresponds to the actual situation.

[0034] According to the virtual inertia control rules, the cloud inference rules of the cloud model controller of VSG can be designed as shown in Table 1.

[0035] Table 1 Virtual inertia cloud model control rules

[0036]

[0037]

[0038] b) Collect the actual angular frequency ω output by the new energy converter, calculate the difference Δω between the instantaneous angular frequency and the rated angular frequency and its corresponding time derivative dω / dt, normalize Δω and dω / dt by quantization factors K1 and K2 respectively, and obtain the input variables E and Ec of the cloud model controller; perform cloud processing on E and Ec to make them the input quantities E* and Ec* of cloud reasoning.

[0039] The cloudification process is based on the expectation of the cloud model (E x ), Entropy (E n ) and super entropy (H e ) three digital features. The sample data of input variables E and Ec are statistically analyzed, and their three digital features are calculated according to formula (1). In this embodiment, the expected value (E x ), Entropy (E n ) and super entropy (H e ) as shown in Table 2.

[0040] Table 2 Expectations of E and Ec (E x ), Entropy (E n ) and super entropy (H e )

[0041]

[0042] c) According to the cloud inference rules designed in step 4.1, perform cloud inference on the input quantities E* and Ec* and obtain the cloudified output U* in the corresponding domain.

[0043] d) Perform inverse cloudification transformation on U* to obtain the precise value U of the cloudification output, and quantize the output U controlled by the cloud model through the proportional factor K3 to obtain the virtual inertia increment ΔJ.

[0044] The reverse cloudification process is also based on the expectation of the cloud model (E x ), Entropy (E n ) and super entropy (H e ) are used as the basis. The sample data of U* is statistically analyzed and its three digital features are calculated using formula (1).

[0045] Table 3 Expected (E x ), Entropy (E n ) and super entropy (H e )

[0046]

[0047]

[0048] The above is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any modification, equivalent replacement and improvement made by any person skilled in the art within the technical scope disclosed by the present invention and within the spirit and principle of the present invention shall be covered within the protection scope of the present invention. At the same time, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0049] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

Claims

1. A new energy inertia support adaptive control method based on cloud model, characterized in that: The rotor motion mechanism in the synchronous generator is introduced into the outer loop control of the new energy converter. According to the physical mechanism of the rotor motion, the dynamic characteristics and the limitations of the converter's own conditions, a cloud inference model is established for the parameters of the new energy converter. According to the correlation between the quantization rate of system frequency change and the virtual inertia within an oscillation cycle, the frequency change amount and frequency change rate are used as input variables. Through input cloudification, cloud inference and output inverse cloudification operations, the corresponding new energy virtual inertia change amount is output, thereby realizing the adaptive change of the new energy inertia support parameters.

2. The cloud model-based new energy inertia support adaptive control method according to claim 1 is characterized in that: According to the physical mechanism of rotor motion, dynamic characteristics and the limitations of the converter's own conditions, a control model based on cloud model theory is established for the parameters of the new energy converter, specifically: S101 determines whether the system is in a stable operating state. If so, the change in the output virtual inertia is the initial setting value of the virtual inertia. Otherwise, execute step S102; S102 establishes a control model based on cloud model theory, and obtains the difference between the instantaneous value and the rated value of the virtual rotor angular frequency and the coupling relationship between the virtual rotor angular frequency change rate and the virtual inertia according to the power angle oscillation curve of the new energy converter; S103 uses the coupling relationship obtained in step S102 to establish a cloud inference rule for virtual inertia, and outputs a change in virtual inertia according to inverse cloudification, so that the virtual inertia of the new energy can change adaptively; S104 applies step S103 to the active power-frequency control loop in the new energy converter control strategy to obtain a frequency support effect that is analogous to the actual synchronous generator rotational inertia effect; and outputs a virtual inertia value.

3. The cloud model-based new energy inertia support adaptive control method according to claim 2 is characterized in that: Step S102 is specifically as follows: obtaining the power angle oscillation curve of the new energy converter under the simulated disturbance, respectively obtaining the difference between the instantaneous value of the virtual rotor angular frequency and the rated value and the rate of change of the virtual rotor angular frequency, and establishing a virtual inertia adaptive parameter control strategy based on the cloud model based on the difference between the instantaneous value of the rotor angular frequency and the rated value and the rate of change of the virtual rotor angular frequency. In the power angle oscillation curve of the new energy converter under the simulated disturbance, when within a specific time interval, the rate of change dω / dt of the virtual rotor angular velocity first increases suddenly and then gradually decreases, and the virtual rotor angular velocity of the new energy converter is greater than the rated angular velocity of the power grid and continues to increase, then within this interval, the new energy virtual inertia needs to be increased to suppress dω / dt and the difference Δω between the instantaneous angular frequency and the rated angular frequency from being too large, and within this interval, dω / dt>0, Δω>0; when When the rate of change of the virtual rotor angular velocity of the new energy converter dω / dt is less than 0, the virtual rotor angular velocity will gradually decrease from the maximum value, but the virtual rotor angular velocity is still greater than the grid angular velocity. In this case, the virtual inertia of the new energy needs to be reduced during this time period; when the rate of change of the virtual rotor angular velocity of the new energy converter dω / dt is less than 0, and the change trend of dω / dt is first a sudden decrease and then gradually decreases, the virtual rotor angular velocity gradually decreases toward the minimum value. At this time, the virtual rotor angular velocity is less than the grid angular velocity. In this case, the virtual inertia of the new energy needs to be reduced during this time period; when the rate of change of the virtual rotor angular velocity of the new energy converter dω / dt is greater than 0, Δω>0, and dω / dt gradually increases, the virtual rotor angular velocity is less than the grid angular velocity. In this case, the virtual inertia of the new energy needs to be increased during this time period.

4. The cloud model-based new energy inertia support adaptive control method according to claim 2 is characterized in that: Through the cloudification of input, cloud reasoning and reverse cloudification of output, the corresponding new energy virtual inertia change is output, specifically: S201 formulates corresponding logical reasoning rules according to various qualitative concepts for different input conditions of converter virtual inertia control; S202 collects the actual angular frequency ω output by the new energy converter, calculates the difference Δω between the instantaneous angular frequency and the rated angular frequency and its corresponding time derivative dω / dt, and normalizes Δω and dω / dt by quantization factors K1 and K2 respectively to obtain the input variables E and Ec of the cloud model controller; performs cloud processing on E and Ec to make them the input variables E* and Ec* of cloud reasoning; S203 performs cloud reasoning on the input quantities E* and Ec* according to the cloud reasoning rules designed in step S103 and obtains the cloudified output U* in the corresponding domain; S204 performs an inverse cloudification transformation on U* to obtain the precise value U of the cloudification output, and quantizes the output U controlled by the cloud model through a proportional factor K3 to obtain a virtual inertia increment ΔJ.

5. The cloud model-based new energy inertia support adaptive control method according to claim 4 is characterized in that: In step S202, the cloudification process is based on the expected E x 、Entropy E n and super entropy H e Based on the three digital features, statistical analysis is performed on the sample data of input variables E and Ec.

6. The cloud model-based new energy inertia support adaptive control method according to claim 4 is characterized in that: In step S204, the reverse cloudification process is performed based on the expected E x 、Entropy E n and super entropy H e Based on the three digital features, the sample data of U* is statistically analyzed.

7. A cloud model-based new energy inertia support adaptive control method according to claim 5 or 6, characterized in that: Input variables E and E c Statistical analysis of the sample data of and U* is performed according to the formula Compute its three numerical features.

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