Virtual inertia adjustment method, device and terminal equipment based on fuzzy control

Abstract

The present application is applicable to the field of virtual synchronous generator technology and provides a virtual inertia adjustment method, apparatus, and terminal device based on fuzzy control. The method includes: constructing a virtual synchronous generator unit, determining a first mathematical model of virtual inertia in the virtual synchronous generator unit, wherein the virtual synchronous generator unit includes an energy storage unit and a converter station; constructing a second mathematical model of virtual inertia based on the first mathematical model of virtual inertia and the safe operation coefficient of the virtual synchronous generator unit, wherein the safe operation coefficient uses the charge state preset value of the energy storage unit and the safety preset value of the converter station as fuzzy control input signals and is determined based on preset fuzzy control rules; adjusting the safe operation coefficient to obtain the virtual inertia of the virtual synchronous generator unit. The present application can prioritize the safe operation of the virtual synchronous generator unit itself while adjusting the virtual inertia of the virtual synchronous generator unit.

Description

Technical Field

[0001] The present application belongs to the technical field of virtual synchronous motors, and in particular relates to a virtual inertia control method, apparatus, and terminal device. Background Art

[0002] In recent years, renewable distributed energy, mainly photovoltaic and wind power, has been widely used in power systems. However, when distributed energy is integrated into the power grid based on the power electronics interface, it does not have rotational inertia. As the penetration rate increases, the dynamic response and stability of the power grid system gradually deteriorate.

[0003] Since its proposal in 2007, Virtual Synchronous Generator (VSG) technology has been continuously improved. Distributed power sources using VSG technology can not only participate in the system's active and reactive frequency regulation, but also provide damping and inertia characteristics. This helps maintain the stability of the power grid when disturbances or failures occur, thereby achieving "friendly" access for distributed power sources.

[0004] However, existing research has not fully considered the fact that the inertial support capacity of the VSG unit will also be affected by its own safety performance in the adjustment strategy of the VGS virtual inertia. For example, when the state of charge (SOC) of the energy storage unit in the VSG unit is poor or the safety capacity of the converter station is insufficient, the safe operation of the VSG unit is not prioritized to maintain the stable operation of the system. Summary of the Invention

[0005] To overcome the problems existing in the related art, the embodiments of the present application provide a virtual inertia adjustment method, apparatus and terminal device based on fuzzy control, which can adjust the virtual inertia of the VGS unit while ensuring the safe operation of the VSG unit itself.

[0006] This application is achieved through the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides a virtual inertia adjustment method based on fuzzy control, comprising:

[0008] A virtual synchronous generator unit is constructed, and a first mathematical model of virtual inertia in the virtual synchronous generator unit is determined. The virtual synchronous generator unit includes an energy storage unit and a converter station. A second mathematical model of virtual inertia is constructed based on the first mathematical model of virtual inertia and a safe operation coefficient of the virtual synchronous generator unit. The safe operation coefficient uses a preset state of charge value of the energy storage unit and a preset safety value of the converter station as fuzzy control input signals and is determined based on a preset fuzzy control rule. The safe operation coefficient is adjusted to obtain the virtual inertia of the virtual synchronous generator unit.

[0009] In a possible implementation of the first aspect, the state of charge preset values ​​are preset values ​​in a first preset set, where the first preset set is A1∈[0, 1]; the safety preset values ​​are preset values ​​in a second preset set, where the second preset set is A2∈[0, 1].

[0010] In a possible implementation of the first aspect, the expression of the security preset value is: Where, P s Indicates the safety preset value for safe operation of the converter station, P N Indicates the rated capacity of the converter station, P o Indicates the actual power output value of the converter station, P set Represents the safety margin threshold preset by the converter station, where when the ratio P s When it is greater than 1, the value is also 1.

[0011] In one possible implementation of the first aspect, based on the magnitude of each state of charge preset value in the first preset set, the first preset set is divided into multiple first fuzzy subsets, including negative large NB, negative small NS, positive small PS, and positive large PB. Based on the magnitude of each safety preset value in the second preset set, the second preset set is divided into multiple second fuzzy subsets, including negative large NB, negative small NS, positive small PS, and positive large PB. The multiple first fuzzy subsets and the multiple second fuzzy subsets serve as fuzzy control input signals.

[0012] In a possible implementation of the first aspect, the safe operation coefficient is divided into multiple third fuzzy subsets, and the multiple third fuzzy subsets include negative large NB, negative small NS, moderate NO, positive small PS, and positive large PB; a first fuzzy subset among the multiple first fuzzy subsets and a second fuzzy subset among the multiple second fuzzy subsets are used as fuzzy control input signals, and based on preset fuzzy control rules, a third fuzzy subset among the multiple third fuzzy subsets is determined.

[0013] In a possible implementation of the first aspect, an expression of the first mathematical model of virtual inertia is:

[0014]

[0015] Where H represents the virtual inertia of the virtual synchronous generator unit, f represents the frequency of the power grid system; k1 and k2 represent the adjustment coefficients of the virtual inertia; H0 represents a preset constant, and M f Indicates the threshold value when virtual inertia switches;

[0016] The expression of the second mathematical model of virtual inertia is:

[0017]

[0018] Where H F represents the virtual inertia of the virtual synchronous generator unit based on fuzzy control, α represents the safe operation coefficient, and the meanings of the other symbols are the same as those in the first mathematical model of virtual inertia.

[0019] In a possible implementation manner of the first aspect, the preset fuzzy control rule is determined based on the influence of the state of charge preset value and the safety preset value on the safe operation of the virtual synchronous generator unit.

[0020] In a second aspect, an embodiment of the present application provides a virtual inertia adjustment device based on fuzzy control, comprising:

[0021] The first mathematical model construction module is configured to construct a virtual synchronous generator unit and determine a first mathematical model of virtual inertia in the virtual synchronous generator unit, which includes an energy storage unit and a converter station. The second mathematical model construction module is configured to construct a second mathematical model of virtual inertia based on the first mathematical model of virtual inertia and a safe operation coefficient of the virtual synchronous generator unit. The safe operation coefficient is determined based on preset fuzzy control rules, using a preset state of charge value for safe operation of the energy storage unit and a preset safety value for safe operation of the converter station as fuzzy control input signals. An output module is configured to adjust the safe operation coefficient to obtain the virtual inertia of the virtual synchronous generator unit.

[0022] In a third aspect, an embodiment of the present application provides a terminal device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements the virtual inertia adjustment method based on fuzzy control as described in any one of the first aspects.

[0023] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the virtual inertia adjustment method based on fuzzy control as described in any one of the first aspects is implemented.

[0024] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when executed on a terminal device, enables the terminal device to execute the virtual inertia adjustment method based on fuzzy control as described in any one of the above-mentioned first aspects.

[0025] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0026] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0027] In the embodiment of the present application, a VSG unit is first constructed to obtain a first mathematical model of virtual inertia. The first mathematical model of virtual inertia is then combined with a fuzzy control algorithm to obtain a second mathematical model of virtual inertia with a safe operation coefficient of the VSG unit. Finally, the virtual inertia of the VSG unit is obtained by adjusting the safe operation coefficient. Because the obtained virtual inertia is based on the charge state of the energy storage unit in the VSG unit and the safety preset value of the converter station, the present application can prioritize the safe operation of the VSG unit itself while adjusting the virtual inertia of the VSG unit.

[0028] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0030] Figure 1 is a schematic block diagram of a VGS unit provided in one embodiment of the present application;

[0031] Figure 2 1 is a flow chart of a virtual inertia adjustment method based on fuzzy control provided in one embodiment of the present application;

[0032] Figure 3 This is a block diagram of the VSG unit control strategy provided by an embodiment of the present application;

[0033] Figure 4 This is a power-frequency equation control block diagram of the VSG unit control strategy provided by an embodiment of the present application;

[0034] Figure 5 This is a control block diagram of the excitation equation of the VSG unit control strategy provided by an embodiment of the present application;

[0035] Figure 6 is a membership function diagram corresponding to each fuzzy subset in the fuzzy algorithm provided in an embodiment of the present application;

[0036] FIG7( a ) is a three-dimensional surface diagram of input and output signals of a battery in a discharge state provided by an embodiment of the present application;

[0037] FIG7( b ) is a three-dimensional surface diagram of input and output signals of a battery in a charging state provided by an embodiment of the present application;

[0038] FIG8( a ) is a block diagram of a simulation test system platform design according to an embodiment of the present application;

[0039] FIG8( b ) is a block diagram of a simulation test system platform device provided in an embodiment of the present application;

[0040] FIG9( a ) shows the change of virtual inertia under different adjustment strategies when the VSG unit is in a safe operation state according to an embodiment of the present application;

[0041] FIG9( b ) shows the changes in the active output of the VSG unit under different adjustment strategies when the VSG unit is in a safe operating state according to an embodiment of the present application;

[0042] FIG9( c ) shows the system frequency changes under different adjustment strategies when the VSG unit is in a safe operation state according to an embodiment of the present application;

[0043] FIG10( a ) shows the change of virtual inertia under different adjustment strategies when the SOC is low according to an embodiment of the present application;

[0044] FIG10( b ) shows the change in active power output under different regulation strategies when the SOC is low, according to an embodiment of the present application;

[0045] FIG10( c ) shows the SOC changes under different adjustment strategies when the SOC is low according to an embodiment of the present application;

[0046] FIG11( a ) shows the change of virtual inertia under different adjustment strategies when the safe operation condition of the converter station is poor, according to an embodiment of the present application;

[0047] FIG11( b ) shows the changes in active power output under different regulation strategies when the safe operation condition of the converter station is poor, according to an embodiment of the present application;

[0048] FIG11( c ) shows the system frequency changes under different regulation strategies when the safe operation condition of the converter station is poor, according to an embodiment of the present application;

[0049] Figure 12 Schematic diagram of the structure of a virtual inertia adjustment device based on fuzzy control provided in an embodiment of the present application;

[0050] Figure 13 It is a structural diagram of the terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0052] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0053] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0054] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0055] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0056] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0057] Virtual Synchronous Generator (VSG) technology simulates the ontological model of a synchronous generator, making the distributed renewable energy connected to the grid through the power electronics interface similar to the synchronous generator in terms of operation mode and voltage and frequency regulation characteristics. This allows it to autonomously participate in the voltage and frequency regulation of the grid while also having inertia and damping characteristics, thereby enhancing the stability of system operation. In addition, inverters using the VSG control strategy have the advantages of flexibility and controllability compared to traditional synchronous generators, which is of great significance for promoting the interconnection and absorption of distributed renewable energy.

[0058] Existing research has mostly focused on real-time adjustment of the VSG unit's virtual inertia to achieve inertial support from the inverter and to improve the quality of system frequency adjustment. However, the priority of the VSG unit's own safe operation has not been fully considered in the adjustment strategy of the VSG unit's virtual inertia.

[0059] Based on the above problems, the virtual inertia adjustment method based on fuzzy control in the embodiment of the present application can prioritize the safe operation of the VSG unit itself while adjusting the virtual inertia of the VSG unit.

[0060] For example, the embodiments of the present application can be applied to Figure 1 In the exemplary scenario shown, for ease of explanation, only the parts related to the embodiments of the present application are shown.

[0061] The following combination Figure 1 The visual interaction method of this application is described in detail.

[0062] In some embodiments, the VSG unit mainly includes three parts, namely, renewable energy generation unit, energy storage unit and converter station. It should be noted that Figure 1 The VSG unit only shows a renewable energy power generation unit, an energy storage unit and a converter station, which does not mean that the method of the present application is only applicable to the VSG unit shown in the figure, and the present application does not further limit the number of each part in the VSG unit.

[0063] The renewable energy power generation unit can be a photovoltaic power generation unit that uses solar energy to generate electricity, a power generation unit that uses wind energy to generate electricity, or other types of power generation units. This application does not further limit the type of renewable energy power generation unit and only uses the photovoltaic power generation unit as an example for explanation.

[0064] The energy storage unit is a minimum energy storage system consisting of a battery pack, a battery management system and a power conversion system connected thereto. In this application, a battery is used as an example for explanation.

[0065] A converter station is a site built to convert AC into DC or DC into AC, and to meet the power system's requirements for safety, stability and power quality.

[0066] exist Figure 1 In the diagram, renewable energy generation units and batteries are connected in parallel, then in series with a converter station. In this diagram, electricity is stored and released in the batteries based on grid load and the power generation units' output. Finally, the converter station adjusts the frequency and voltage before it enters the grid system.

[0067] Figure 2 This is a flow chart of a virtual inertia adjustment method based on fuzzy control provided by an embodiment of the present application, with reference to Figure 2 , the method is described in detail as follows:

[0068] In step 101 , a VSG unit is constructed and a first mathematical model of virtual inertia in the VSG unit is determined.

[0069] In some embodiments, Figure 3 This is a block diagram of the VSG unit control strategy provided by an embodiment of the present application, in which an equivalent DC power supply u dc Instead of the photovoltaic power generation unit and the battery composed of the light storage unit, C dc Represents the DC side capacitance, L, R, C represent the line parameters of the transmission line between the inverter power supply and the power grid, and the circuit composed of T1-T6 simulates the inverter power supply of the converter station. Figure 3 It can be seen that the VSG unit strategy collects the active and reactive output power of the inverter power supply, and then connects it with the power dispatching instructions of the power grid system through the power-frequency equation and excitation equation, so that the photovoltaic storage unit can meet the dispatching instructions while also having the power input characteristics of the synchronous generator.

[0070] For example, Figure 4 This is a power frequency equation control block diagram of the VSG unit control strategy provided in one embodiment of the present application. Figure 4 In the figure, ω represents the output angular frequency of the inverter power supply, ω g represents the angular frequency on the grid side, H represents the virtual inertia in the VSG unit, K d represents the damping coefficient in the VSG unit, P ref Represents the active dispatch command value of the inverter power supply, P o It indicates the actual output value of the active power of the inverter power supply. Figure 4 The expression of the power-frequency equation in the VSG unit control strategy can be obtained:

[0071]

[0072] Where, Indicates the phase size of the inverter power supply.

[0073] For example, Figure 5 This is a control block diagram of the excitation equation of the VSG unit control strategy provided in one embodiment of the present application. Figure 5 In, Q ref Indicates the reactive power dispatch instruction of the inverter power supply, Q indicates the actual output value of the reactive power of the inverter power supply, E ref Indicates the reference value of the inverter power supply voltage, E m It represents the actual value of the inverter power supply voltage, K q Indicates the droop coefficient; K v Indicates the voltage regulation coefficient. Figure 5 The expression of the excitation equation in the VSG unit control strategy can be obtained:

[0074] E=E0+(Q ref -Q)K q +(E ref -E m )K v

[0075] It should be pointed out that the excitation equation in VSG control uses droop control to establish the connection between reactive power and voltage.

[0076] In some embodiments, according to the control strategy of the VSG unit, a first mathematical model of virtual inertia can be determined, and its expression is:

[0077]

[0078] Where H represents the virtual inertia of the VSG unit, f represents the frequency of the power grid system, k1 and k2 represent the adjustment coefficients of the virtual inertia, H0 represents a preset constant, which is set according to actual conditions and experience, and M f Indicates the switching threshold of the virtual inertia.

[0079] When VSG units are used for control at converter stations, the virtual inertia of the power grid system can dynamically change with changes in system frequency, providing accurate inertial support for the system. A large virtual inertia increases the steady-state recovery time and overshoot of the VSG unit during frequency regulation. Therefore, comprehensive considerations should be taken when setting the values ​​of the k1 and k2 virtual inertias. While ensuring that the VSG unit has a high ability to support system frequency, the upper limit of the virtual inertia value must be reasonably controlled to avoid affecting the system's steady-state recovery process when the virtual inertia value is large.

[0080] In step 102 , a second virtual inertia mathematical model is constructed based on the first virtual inertia mathematical model and the safe operation coefficient of the VSG unit.

[0081] VSG unit control flexibly adjusts virtual inertia based on system frequency, enabling the PV-storage unit to provide greater inertial support to the grid system after load disturbances. At the same time, when providing inertial output to the system, the PV-storage unit's primary consideration should be maintaining its own safe operation. Only by maintaining stable operation of the PV-storage unit can system frequency quality be improved.

[0082] The photovoltaic storage unit is an important component of the VSG unit, and the battery is an important component of the photovoltaic storage unit. When the state of charge (SOC) of the battery is too high or too low, the battery is at risk of overcharging or over-discharging, which seriously affects the battery's own safe operation and service life. Therefore, this application uses the battery's SOC preset value as an important indicator for evaluating the safe operation of the VSG unit.

[0083] At the same time, the converter station is a key hub connecting the photovoltaic storage unit to the power grid system. However, the rated capacity of the converter station is limited. When the power flowing through the converter station is too large, there is a risk of overcurrent shutdown. Therefore, this application uses the safe preset value of the converter station's safe operation as another important indicator for evaluating the safe operation of the VSG unit.

[0084] In some embodiments, in order to better maintain the safe operation of the VSG unit itself, the present application introduces a fuzzy control algorithm, uses the output signal of the fuzzy control as a safe operation coefficient, and constructs a second virtual inertia mathematical model based on the first virtual inertia mathematical model, which is expressed as:

[0085]

[0086] Where H F represents the virtual inertia of the virtual synchronous generator unit based on fuzzy control, α represents the safe operation coefficient of the VSG unit, and the meanings of the other symbols are the same as those in the first mathematical model of virtual inertia and are not repeated here.

[0087] It should be noted that the value of α is determined by the introduced fuzzy control algorithm according to the battery SOC and the safety preset value of the converter station.

[0088] Optionally, the battery SOC preset values ​​are preset values ​​in a first preset set, where the first preset set is A1∈[0,1]. That is, when SOC=0, the battery is fully discharged, and when SOC=1, the battery is fully charged. In other words, the value of α is related to the current SOC of the battery in the VSG unit.

[0089] Optionally, the safety preset values ​​of the converter station are preset values ​​in a second preset set, where the second preset set is A2∈[0,1]. The safety preset values ​​of the converter station are related to the rated capacity of the converter station, the actual output power of the converter station, and the safety margin value of the converter station itself. Therefore, the safety preset values ​​of the converter station can be determined based on the actual situation of the VSG unit.

[0090] For example, the expression of the safety preset value of the converter station can be:

[0091]

[0092] Where, P s Indicates the safety preset value for safe operation of the converter station, P N Indicates the rated capacity of the converter station, P o Indicates the actual power output value of the converter station, P set It represents the safety margin threshold preset by the converter station. When the ratio Ps is greater than 1, the value is also 1.

[0093] Combining the above expressions, we can see that when ΔP is greater than P set When ΔP is small, it means that the converter is safe and there is no risk of overcurrent shutdown. set For comparison, the safe preset value P s The smaller the value, the output power P of the converter station. o The closer to the rated capacity P N , the risk of overcurrent shutdown is greater and the safety performance is poor.

[0094] The following is an introduction to the fuzzy control algorithm used in this application:

[0095] In step A1, the battery SOC preset value and the converter station safety preset value are used as input signals for fuzzy control.

[0096] In some embodiments, since the SOC preset values ​​are the various preset values ​​in the first preset set and the safety preset values ​​are the various preset values ​​in the second preset set, in order to make the fuzzy control algorithm accurately applicable to this application, this application further divides the first preset set and the second preset set according to the size of each preset value.

[0097] Exemplarily, the first preset set is divided into a plurality of first fuzzy subsets, and the plurality of first fuzzy subsets may include negative large NB, negative small NS, positive small PS and positive large PB.

[0098] Exemplarily, the second preset set is divided into a plurality of second fuzzy subsets, and the plurality of second fuzzy subsets may include negative large NB, negative small NS, positive small PS and positive large PB.

[0099] It should be noted that the number and type of division of the first / second fuzzy subsets can be adjusted according to specific circumstances, and this application does not impose any further restrictions.

[0100] Therefore, in this application, the four first fuzzy subsets and the four second fuzzy subsets are used as fuzzy control input signals.

[0101] Step A2: Use the safe operation coefficient of the VSG unit as the output signal of the fuzzy control.

[0102] In some embodiments, in order to make the fuzzy algorithm accurately applicable to the present application, the present application divides the safe operation coefficient into multiple fuzzy subsets of the fuzzy control output signals.

[0103] Exemplarily, the safe operation coefficient is divided into a plurality of third fuzzy subsets, and the plurality of third fuzzy subsets may include negative large NB, negative small NS, moderate NO, positive small PS, and positive large PB.

[0104] Similarly, the number and type of division of the third fuzzy subsets can be adjusted accordingly according to specific circumstances, and this application does not make further restrictions.

[0105] Step A3: Preset fuzzy control rules and obtain a fuzzy control rule table.

[0106] In some embodiments, by rationally selecting membership functions for different fuzzy subsets based on the changing patterns of safety operating parameters, the fuzzy algorithm can be used to more effectively achieve flexible adjustment of virtual inertia. Therefore, the preset fuzzy control rules are determined based on the impact of the state of charge preset value and the safety preset value on the safe operation of the VSG unit.

[0107] For example, this application will select a more sensitive triangular membership function in the area where the fuzzy control input signal and the fuzzy control output signal are more frequent, and select a more gentle membership function in the edge area of ​​the fuzzy control input signal and the fuzzy control output signal. Figure 6 shown.

[0108] Analyze based on actual conditions: When the battery SOC is too low or too high, there is a risk of over-discharge or overcharge. The purpose of introducing the fuzzy algorithm is to prevent the battery SOC from changing in the direction of being too large or too small. Therefore, when the battery is working in the discharge state, the smaller the SOC is, the smaller the safety state adjustment coefficient α should be. By reducing the virtual inertia, the output of the battery is reduced to maintain its own SOC. When the battery is working in the charging state, when the SOC is large, the value of the adjustment coefficient α should be reduced to reduce the risk of overcharging when the battery provides inertial output. At the same time, the safety preset value P of the converter station s The safe operation status of the converter station is reflected, Ps When it is small, the virtual inertia should be reduced to reduce the change in active power output and reduce the risk of overcurrent shutdown of the converter station.

[0109] Based on the above analysis, this application provides a fuzzy control rule table for the battery working in the charging state, as shown in Table 1:

[0110] Table 1 Fuzzy rule table under battery discharge status

[0111]

[0112] At the same time, this application provides a fuzzy control rule table for the battery working in the discharge state, as shown in Table 2:

[0113] Table 2 Fuzzy rules table for battery charging status

[0114]

[0115]

[0116] Furthermore, the present application, in conjunction with the fuzzy rule table for battery charging and discharging, also provides a three-dimensional surface diagram of the input and output signals under the battery charging / discharging state in fuzzy control, as shown in Figure 7. As can be seen from Figure 7(a), the value of the safety operation coefficient α gradually decreases as the SOC preset value decreases, i.e., the battery is over-discharged, and the safety preset value decreases, i.e., the actual output power of the converter station approaches the rated capacity. At the same time, as can be seen from Figure 7(b), the value of the safety operation coefficient α also gradually decreases as the SOC preset value increases, i.e., the battery is over-charged, and the safety preset value decreases, i.e., the actual output power of the converter station approaches the rated capacity.

[0117] Furthermore, if the value of the safety factor α gradually decreases, it indicates that the safe operating conditions of the batteries and / or converter stations in the VSG unit are declining. In order to ensure the safe operation of the VSG unit itself, it is necessary to reduce the inertial support of the VSG unit to the power grid system, that is, to reduce the virtual inertia of the VSG unit.

[0118] In step 103, the safety operation factor is adjusted to obtain the virtual inertia of the VSG unit.

[0119] In some embodiments, it can be seen from the second mathematical model of virtual inertia constructed in step 102 that as the safe operating conditions of the battery and / or converter station decrease and the safe operating coefficient α decreases, the virtual inertia of the VSG unit also decreases.

[0120] Therefore, the virtual inertia adjustment method based on fuzzy control provided in this application can reasonably control the inertial support capability of the VSG unit to the system according to the safe operation status of the VSG unit itself, thereby providing a guarantee for the stable operation of the power grid system.

[0121] In order to verify the feasibility and beneficial effects of this application solution, a real-time simulation system platform was built for verification. The verification process is as follows:

[0122] This study uses the RT-LAB real-time simulation experimental system to experimentally verify the control strategy of this application in order to further enhance the engineering application value of this application. The experimental system is mainly composed of RT-LAB testing machine, host computer, DSP controller, oscilloscope and other experimental devices. The DSP controller deploys a control strategy based on virtual inertia adjustment of fuzzy control. The PWM modulation signal obtained by the DSP controller is connected to the corresponding port in RT-LAB through the optoelectronic isolation module. The final experimental waveform can be observed on the oscilloscope. The experimental platform is shown in Figure 8. In order to more clearly compare the experimental results of collaborative control with those of traditional control, the experimental data in the oscilloscope are exported, and the obtained experimental waveforms are plotted in a unified coordinate system using drawing software.

[0123] The energy storage unit provides the inertial output required when a power disturbance occurs in the system. The fuzzy controller monitors the system's operating status and flexibly adjusts the virtual inertia to maintain safe operation of the energy storage device and converter. The values ​​of key system parameters are shown in Table 3.

[0124] Table 3 Key parameter values ​​in the system

[0125]

[0126] The following describes the experimental results of virtual inertia adjustment based on fuzzy control.

[0127] 1. Experimental results of virtual inertia adjustment under safe working conditions

[0128] When the VSG unit is operating safely, the virtual inertia dynamically changes with the rate of change of the system frequency after a power disturbance occurs in the power system, providing significant inertial support to the system and effectively improving the frequency quality of the system after a power disturbance. In this experiment, the initial battery SOC value was set to 70%, the converter station safety capacity was set to 10 kW, and the system was operating normally at the initial moment. After 5 seconds, a 2.5 kW active load was added to the system. The changes in relevant system parameters are shown in Figure 9. As shown in Figure 9(a), when the VSG unit is operating safely, the virtual inertia can dynamically change with the system frequency, providing significant inertial support to the system after a power disturbance occurs. Figure 9(b) clearly shows that when the virtual inertia value is adjusted to a larger value, it provides more inertial output to the system after a disturbance occurs. Figure 9(c) shows the changes in system frequency under the two control modes. The system frequency offset amplitude is reduced, and the speed of recovery to the steady-state value is also improved. This allows the VSG unit to provide more inertial support to the system.

[0129] 2. Inertia adjustment test results under insufficient SOC

[0130] The above experimental results show that when the safety constraint adjustment strategy is adopted, the VSG unit can provide greater inertial support for the system under safe operating conditions. When the battery SOC is insufficient, the solution of this application should be able to adaptively change the virtual inertia. By reducing the virtual inertia, the state of charge can be maintained safe. Next, the optimization effect of the safety constraint adjustment strategy under the condition of poor battery safety operation is verified. In this experiment, the initial SOC of the battery is set to 20%. At first, the system operates normally, and after 5s, a 2.5kW active load disturbance is added to the system. In order to further demonstrate the optimization effect of the control strategy provided by this application, the experimental results of the battery under the same working conditions using FVSG control are given in Figure 10. The changes in relevant parameters in the system under different controls are shown in Figure 10.

[0131] Figure 10(a) shows the changes in virtual inertia under different virtual inertia adjustment strategies. When the FVSG control strategy is used, the system inertia is dynamically adjusted only in accordance with the system frequency. When a load disturbance occurs in the system, the virtual inertia maintains a large value. Figure 10(b) also shows that the inertia output of the VSG unit remains large at this time. A large inertia output accelerates the discharge of the battery, which may bring the risk of over-discharge. Further observation of Figures 10(a) and (b) shows that when the safety constraint adjustment strategy is used, the virtual inertia value decreases when insufficient SOC is detected, and the inertia output provided after the load disturbance occurs is reduced. Figure 10(c) shows that after introducing a safety adjustment factor to adaptively adjust the virtual inertia, the battery SOC changes more slowly due to the reduced virtual inertia output of the VSG unit, reducing the risk of over-discharge and better maintaining the battery's service life.

[0132] 3. Experimental results when the converter station's safe operation condition is poor

[0133] The rated capacity of the converter station in the VSG unit is limited. By introducing a safety operating factor α, the virtual inertia can be adaptively adjusted when the converter station's safe operating conditions deteriorate, avoiding overcurrent shutdowns. To experimentally verify this optimization effect, the rated capacity of the converter station was set to 4 kW. The system initially operated normally, and a 5 kW active load was introduced into the system after 5 seconds. Figure 11 also shows the experimental results of virtual inertia adjustment using the FVSG strategy under the same operating conditions. The changes in relevant parameters in the VSG unit during system operation are shown in Figure 11. As shown in Figure 11(a), the virtual inertia under FVSG control changes dynamically with the system frequency change rate. When a large load is switched on in the system, the virtual inertia value also increases. Figure 11(b) shows that the VSG unit maintains a large virtual inertia under FVSG control, resulting in a relatively large active output and causing the converter station to shut down. When the safe operation coefficient α is introduced to adaptively adjust the virtual inertia, the system's virtual inertia decreases as the safe operation of the converter station deteriorates. This reduced virtual inertia reduces the power output variation of the VSG unit, maintaining the safe operation of the converter station. Figure 11(c) clearly shows that due to the converter station's deactivation under FVSG control, the system frequency quality after the load disturbance is poor. The control strategy provided in this application achieves safe operation of the converter station and better maintains the system frequency quality.

[0134] Therefore, this application proposes a virtual inertia flexible adjustment control strategy that takes into account the safe operation constraints of the VSG unit. By introducing a safe operation coefficient to flexibly adjust the virtual inertia of the VSG unit, the VSG unit provides good inertial support capabilities for the system while maintaining its own safe operation.

[0135] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0136] Corresponding to the virtual inertia adjustment method based on fuzzy control described in the above embodiment, Figure 12 A structural block diagram of a virtual inertia adjustment device based on fuzzy control provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0137] See also Figure 12 The virtual inertia adjustment device based on fuzzy control in the embodiment of the present application may include: a first mathematical model construction module 201, a second mathematical model construction module 202 and an output module 203.

[0138] The first mathematical model building module 201 is used to build a virtual synchronous generator unit and determine a first mathematical model of virtual inertia in the virtual synchronous generator unit, wherein the virtual synchronous generator unit includes an energy storage unit and a converter station.

[0139] Optionally, the expression of the constructed first mathematical model of virtual inertia is:

[0140]

[0141] Where H represents the virtual inertia of the virtual synchronous generator unit, f represents the frequency of the power grid system, k1 and k2 represent the adjustment coefficients of the virtual inertia, H0 represents a preset constant, and M f Indicates the switching threshold of the virtual inertia.

[0142] The second mathematical model construction module 202 is configured to construct a second virtual inertia mathematical model based on the first virtual inertia mathematical model and the safe operation coefficient of the virtual synchronous generator unit. The safe operation coefficient uses a preset state of charge value for safe operation of the energy storage unit and a preset safety value for safe operation of the converter station as fuzzy control input signals and is determined based on preset fuzzy control rules.

[0143] Optionally, the expression of the constructed second mathematical model of virtual inertia is:

[0144]

[0145] Where H F represents the virtual inertia of the virtual synchronous generator unit based on fuzzy control, α represents the safe operation coefficient, and the meanings of the other symbols are the same as those in the first mathematical model of virtual inertia.

[0146] Optionally, the state of charge preset values ​​are preset values ​​in a first preset set, which is A1∈[0, 1]. The safety preset values ​​are preset values ​​in a second preset set, which is A2∈[0, 1].

[0147] The expression of the safety preset value is:

[0148]

[0149] Where, P s Indicates the safety preset value for safe operation of the converter station, P N Indicates the rated capacity of the converter station, P o Indicates the actual power output value of the converter station, P set Represents the safety margin threshold preset by the converter station, where when the ratio P s When it is greater than 1, the value is also 1.

[0150] Optionally, the preset fuzzy control rule is determined based on the impact of the state of charge preset value and the safety preset value on the safe operation of the virtual synchronous generator unit.

[0151] The second mathematical model construction module 202 is further configured to divide the first preset set into a plurality of first fuzzy subsets based on the magnitude of each state of charge preset value in the first preset set, the plurality of first fuzzy subsets comprising negative large NB, negative small NS, positive small PS, and positive large PB. Based on the magnitude of each safety preset value in the second preset set, the second preset set is divided into a plurality of second fuzzy subsets comprising negative large NB, negative small NS, positive small PS, and positive large PB. The plurality of first fuzzy subsets and the plurality of second fuzzy subsets serve as fuzzy control input signals.

[0152] The second mathematical model construction module 202 is also used to divide the safe operation coefficient into multiple third fuzzy subsets, and the multiple third fuzzy subsets include negative large NB, negative small NS, moderate NO, positive small PS, and positive large PB; a first fuzzy subset among the multiple first fuzzy subsets and a second fuzzy subset among the multiple second fuzzy subsets are used as fuzzy control input signals, and based on preset fuzzy control rules, a third fuzzy subset among the multiple third fuzzy subsets is determined.

[0153] The output module 203 is used to adjust the safe operation coefficient and obtain the virtual inertia of the virtual synchronous generator unit.

[0154] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0155] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0156] The present application also provides a terminal device. Figure 13 The terminal device 300 may include: at least one processor 310, a memory 320, and a computer program 321 stored in the memory 320 and executable on the at least one processor 310. When the processor 310 executes the computer program, the steps in any of the above-mentioned method embodiments are implemented, for example Figure 2 Steps 101 to 103 in the embodiment shown. Alternatively, when the processor 310 executes the computer program 321, the functions of the modules / units in the above-mentioned device embodiments are realized, for example Figure 12 Functions of modules 201 to 203 are shown.

[0157] Exemplarily, the computer program 321 may be divided into one or more modules / units, one or more of which are stored in the memory 320 and executed by the processor 310 to complete the present application. The one or more modules / units may be a series of computer program segments capable of completing specific functions, and the program segments are used to describe the execution process of the computer program 321 in the terminal device 300.

[0158] Those skilled in the art will understand that Figure 13 These are merely examples of terminal devices and do not constitute a limitation on the terminal devices. The terminal devices may include more or fewer components than shown in the figure, or a combination of certain components, or different components, such as input and output devices, network access devices, buses, etc.

[0159] The processor 310 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0160] The memory 320 can be an internal storage unit of the terminal device or an external storage device of the terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. The memory 320 is used to store the computer program 321 and other programs and data required by the terminal device. The memory 320 can also be used to temporarily store data that has been output or is about to be output.

[0161] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be classified into address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0162] The virtual inertia adjustment method based on fuzzy control provided in the embodiments of the present application can be applied to terminal devices such as computers, wearable devices, vehicle-mounted devices, tablet computers, laptop computers, netbooks, personal digital assistants (PDAs), augmented reality (AR) / virtual reality (VR) devices, and mobile phones. The embodiments of the present application do not impose any restrictions on the specific type of terminal device.

[0163] An embodiment of the present application further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the steps in each embodiment of the virtual inertia adjustment method based on fuzzy control can be implemented.

[0164] An embodiment of the present application provides a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal can implement the steps in each embodiment of the above-mentioned virtual inertia adjustment method based on fuzzy control when executing the computer program product.

[0165] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process of the above-mentioned method embodiment by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can at least include: any entity or device capable of carrying computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, mobile hard drive, magnetic disk, or optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0166] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0167] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0168] In the embodiments provided in this application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0169] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0170] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A virtual inertia adjustment method based on fuzzy control, characterized in that: include: Constructing a virtual synchronous generator unit and determining a first mathematical model of virtual inertia in the virtual synchronous generator unit, wherein the virtual synchronous generator unit includes an energy storage unit and a converter station; Constructing a second virtual inertia mathematical model based on the first virtual inertia mathematical model and the safe operation coefficient of the virtual synchronous generator unit, wherein the safe operation coefficient uses a preset state of charge value of the energy storage unit and a preset safety value of the converter station as fuzzy control input signals and is determined based on a preset fuzzy control rule; Adjusting the safety operation factor to obtain a virtual inertia of the virtual synchronous generator unit; The first mathematical model of virtual inertia is constructed according to the frequency of the power grid system, the adjustment coefficient of the virtual inertia, a preset constant and a switching threshold of the virtual inertia; The expression of the first mathematical model of virtual inertia is: Where H represents the virtual inertia of the virtual synchronous generator unit, f represents the frequency of the power grid system, k1 and k2 represent the adjustment coefficients of the virtual inertia, H0 represents a preset constant, and M f Indicates the switching threshold of virtual inertia; The second mathematical model of virtual inertia is constructed according to the safe operation coefficient, the frequency of the power grid system, the adjustment coefficient of the virtual inertia, the preset constant and the switching threshold of the virtual inertia; The expression of the second mathematical model of virtual inertia is: Where H F represents the virtual inertia of the virtual synchronous generator unit based on fuzzy control, α represents the safe operation coefficient, and the meanings of the other symbols are the same as those in the first mathematical model of virtual inertia; The state of charge preset values ​​are preset values ​​in a first preset set, where the first preset set is A1∈[0,1]; The security preset values ​​are preset values ​​in a second preset set, where the second preset set is A2∈[0,1]; The preset fuzzy control rule is determined based on the influence of the state of charge preset value and the safety preset value on the safe operation of the virtual synchronous generator unit.

2. The method according to claim 1, wherein The expression of the safety preset value is: Where, P s Indicates the safety preset value for safe operation of the converter station, P N Indicates the rated capacity of the converter station, P o Indicates the actual power output value of the converter station, P set Represents the safety margin threshold preset by the converter station, where when the ratio P s When it is greater than 1, the value is also 1.

3. The method according to claim 1, wherein Dividing the first preset set into a plurality of first fuzzy subsets according to the magnitude of each state of charge preset value in the first preset set, wherein the plurality of first fuzzy subsets include negative large NB, negative small NS, positive small PS, and positive large PB; According to the size of each safety preset value in the second preset set, the second preset set is divided into a plurality of second fuzzy subsets, wherein the plurality of second fuzzy subsets include negative large NB, negative small NS, positive small PS, and positive large PB; The plurality of first fuzzy subsets and the plurality of second fuzzy subsets serve as the fuzzy control input signals.

4. The method according to claim 3, wherein The safety operation coefficient is divided into a plurality of third fuzzy subsets, wherein the plurality of third fuzzy subsets include negative large NB, negative small NS, moderate NO, positive small PS, and positive large PB; A first fuzzy subset among the plurality of first fuzzy subsets and a second fuzzy subset among the plurality of second fuzzy subsets are used as fuzzy control input signals, and a third fuzzy subset among the plurality of third fuzzy subsets is determined based on a preset fuzzy control rule.

5. A virtual inertia adjustment device based on fuzzy control, characterized in that: include: A first mathematical model building module is used to build a virtual synchronous generator unit and determine a first mathematical model of virtual inertia in the virtual synchronous generator unit, wherein the virtual synchronous generator unit includes an energy storage unit and a converter station; The first mathematical model of virtual inertia is constructed according to the frequency of the power grid system, the adjustment coefficient of the virtual inertia, a preset constant and a switching threshold of the virtual inertia; The expression of the first mathematical model of virtual inertia is: Where H represents the virtual inertia of the virtual synchronous generator unit, f represents the frequency of the power grid system, k1 and k2 represent the adjustment coefficients of the virtual inertia, H0 represents a preset constant, and M f Indicates the switching threshold of virtual inertia; a second mathematical model construction module, configured to construct a second virtual inertia mathematical model based on the first virtual inertia mathematical model and a safe operation coefficient of the virtual synchronous generator unit, wherein the safe operation coefficient uses a preset state of charge value for safe operation of the energy storage unit and a preset safety value for safe operation of the converter station as fuzzy control input signals and is determined based on a preset fuzzy control rule; The second mathematical model of virtual inertia is constructed according to the safe operation coefficient, the frequency of the power grid system, the adjustment coefficient of the virtual inertia, the preset constant and the switching threshold of the virtual inertia; The expression of the second mathematical model of virtual inertia is: Where H F represents the virtual inertia of the virtual synchronous generator unit based on fuzzy control, α represents the safe operation coefficient, and the meanings of the other symbols are the same as those in the first mathematical model of virtual inertia; The state of charge preset values ​​are preset values ​​in a first preset set, where the first preset set is A1∈[0,1]; The security preset values ​​are preset values ​​in a second preset set, where the second preset set is A2∈[0,1]; The preset fuzzy control rule is determined based on the influence of the state of charge preset value and the safety preset value on the safe operation of the virtual synchronous generator unit; The output module is used to adjust the safe operation coefficient and obtain the virtual inertia of the virtual synchronous generator unit.

6. A terminal device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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