CN117767419A - Self-adaptive parameter adjusting method and device for virtual synchronous machine - Google Patents
Self-adaptive parameter adjusting method and device for virtual synchronous machine Download PDFInfo
- Publication number
- CN117767419A CN117767419A CN202311817288.7A CN202311817288A CN117767419A CN 117767419 A CN117767419 A CN 117767419A CN 202311817288 A CN202311817288 A CN 202311817288A CN 117767419 A CN117767419 A CN 117767419A
- Authority
- CN
- China
- Prior art keywords
- angular frequency
- virtual
- synchronous machine
- coefficient
- damping coefficient
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000001360 synchronised effect Effects 0.000 title claims abstract description 135
- 238000000034 method Methods 0.000 title claims abstract description 55
- 238000013016 damping Methods 0.000 claims abstract description 164
- 230000008859 change Effects 0.000 claims abstract description 89
- 230000003044 adaptive effect Effects 0.000 claims abstract description 52
- 230000006870 function Effects 0.000 claims description 41
- 239000013598 vector Substances 0.000 claims description 34
- 238000012549 training Methods 0.000 claims description 24
- 238000011156 evaluation Methods 0.000 claims description 21
- 238000005070 sampling Methods 0.000 claims description 10
- 238000012795 verification Methods 0.000 claims description 10
- 230000010355 oscillation Effects 0.000 claims description 8
- 230000009977 dual effect Effects 0.000 claims description 4
- 238000013507 mapping Methods 0.000 claims description 3
- 238000004590 computer program Methods 0.000 description 15
- 238000004364 calculation method Methods 0.000 description 8
- 238000004891 communication Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 6
- 238000012545 processing Methods 0.000 description 6
- 238000011217 control strategy Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 238000012360 testing method Methods 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 230000001419 dependent effect Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Landscapes
- Control Of Ac Motors In General (AREA)
Abstract
The embodiment of the invention discloses a method and a device for adaptively adjusting parameters of a virtual synchronous machine, wherein the method comprises the following steps: acquiring operation related parameters of a virtual synchronous machine in a target operation state, and determining the angular frequency deviation amount and the angular frequency change rate of the virtual synchronous machine based on the operation related parameters, wherein the operation related parameters at least comprise an operation scene, an operation working condition and a control target; determining adaptive coefficients corresponding to virtual inertia and damping coefficients of the virtual synchronous machine based on the angular frequency deviation amount and the angular frequency change rate, respectively, and determining maximum values, minimum values and steady-state values of the virtual inertia and the damping coefficients, respectively; and determining the virtual inertia and the damping coefficient based on the self-adaptive coefficient, the maximum value, the minimum value and the steady state value corresponding to the virtual inertia and the damping coefficient, thereby realizing self-adaptive adjustment of the virtual inertia and the damping coefficient of the virtual synchronous machine.
Description
Technical Field
The invention relates to the technical field of distributed photovoltaics, in particular to a method and a device for adaptively adjusting parameters of a virtual synchronous machine.
Background
In virtual synchronous generator (virtual synchronous generator, VSG) control techniques, virtual inertia and damping coefficients are critical control parameters. The damping coefficient may improve the steady state characteristics of the system, while the virtual inertia may optimize the dynamic characteristics of the system. Because photovoltaic distribution networks lack inertial support, oscillations are easily generated when power fluctuations occur or are disturbed.
In the conventional VSG control strategy, since the virtual inertia and damping coefficient are fixed values, an optimal state of the rapidity and stability of the system cannot be simultaneously realized. If the virtual inertia is selected to be too large, the response time increases although the amplitude of the system frequency fluctuation can be reduced, and the energy storage capacity of the system needs to be increased when the virtual inertia is too large. In contrast, where the virtual inertia is selected to be too small, the rapidity of the system may be greatly improved, but may cause system breakdown after suffering from large disturbances. If the damping coefficient is improperly selected, the power overshoot and the adjustment time of the system are affected, even the droop characteristics of the VSG are affected, and then the virtual speed regulation system of the system is affected.
Disclosure of Invention
In view of the above, the present invention provides a method and apparatus for adaptively adjusting parameters of a virtual synchronous machine, so as to achieve adaptive adjustment of virtual inertia and damping coefficients of a virtual synchronous generator.
According to an aspect of the present invention, there is provided a method for adaptively adjusting parameters of a virtual synchronous machine, the method comprising:
acquiring operation related parameters of a virtual synchronous machine in a target operation state, and determining the angular frequency deviation amount and the angular frequency change rate of the virtual synchronous machine based on the operation related parameters, wherein the operation related parameters at least comprise an operation scene, an operation working condition and a control target;
determining adaptive coefficients corresponding to virtual inertia and damping coefficients of the virtual synchronous machine based on the angular frequency deviation amount and the angular frequency change rate, respectively, and determining maximum values, minimum values and steady-state values of the virtual inertia and the damping coefficients, respectively;
the virtual inertia and the damping coefficient are determined based on the adaptive coefficient, the maximum value, the minimum value, and the steady state value corresponding to the virtual inertia and the damping coefficient.
According to another aspect of the present invention, there is provided a parameter adaptive adjustment apparatus for a virtual synchronous machine, the apparatus comprising:
the system comprises an operation parameter acquisition module, a control module and a control module, wherein the operation parameter acquisition module is used for acquiring operation related parameters of a virtual synchronous machine in a target operation state, and determining the angular frequency deviation amount and the angular frequency change rate of the virtual synchronous machine based on the operation related parameters, wherein the operation related parameters at least comprise an operation scene, an operation working condition and a control target;
The key parameter determining module is used for respectively determining self-adaptive coefficients corresponding to virtual inertia and damping coefficients of the virtual synchronous machine based on the angular frequency deviation amount and the angular frequency change rate, and respectively determining maximum values, minimum values and steady-state values of the virtual inertia and the damping coefficients;
and the adjustment parameter determining module is used for determining the virtual inertia and the damping coefficient based on the adaptive coefficient, the maximum value, the minimum value and the steady-state value corresponding to the virtual inertia and the damping coefficient.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method for adaptively adjusting parameters of a virtual synchronous machine according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the method for adaptively adjusting parameters of a virtual synchronous machine according to any embodiment of the present invention when the computer instructions are executed.
According to the technical scheme, the angular frequency deviation amount and the angular frequency change rate of the virtual synchronous machine are determined based on the operation association parameters by acquiring the operation association parameters under the target operation state of the virtual synchronous machine, and the angular frequency deviation amount and the angular frequency change rate of the virtual synchronous machine can be effectively and accurately acquired by combining the target operation state of the virtual synchronous machine because the operation association parameters at least comprise an operation scene, an operation working condition and a control target; by determining the adaptive coefficients corresponding to the virtual inertia and the damping coefficient of the virtual synchronous machine based on the angular frequency deviation amount and the angular frequency change rate, respectively, and determining the maximum value, the minimum value, and the steady-state value of the virtual inertia and the damping coefficient, respectively, it is possible to accurately determine the dependent calculation parameters associated with the virtual inertia and the damping coefficient; the virtual inertia and the damping coefficient are determined based on the self-adaptive coefficient, the maximum value, the minimum value and the steady state value corresponding to the virtual inertia and the damping coefficient, so that the technical problem that the fixed virtual inertia and the damping coefficient cannot realize the optimal state of the rapidity and the stability of the system at the same time is solved, the self-adaptive adjustment of the virtual inertia and the damping coefficient of the virtual synchronous machine is realized, the optimal selection of the virtual inertia and the damping coefficient of the virtual synchronous machine is realized, and the self-adaptive adjustment method has important significance in the aspect of propelling a novel electric power system.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for adaptively adjusting parameters of a virtual synchronous machine according to a first embodiment of the present invention;
fig. 2 is a flow chart of a method for adaptively adjusting parameters of a virtual synchronous machine according to a second embodiment of the present invention;
fig. 3 is a flowchart of a method for adaptively adjusting parameters of a virtual synchronous machine according to a second embodiment of the present invention;
fig. 4 is a schematic diagram showing the comparison of the effect of parameter adjustment based on the conventional method and the adaptive adjustment method for parameters of the virtual synchronous machine according to the embodiment of the present invention;
Fig. 5 is a schematic structural diagram of a parameter adaptive adjustment device for a virtual synchronous machine according to a third embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device implementing a method for adaptively adjusting parameters of a virtual synchronous machine according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
It will be appreciated that prior to using the technical solutions disclosed in the embodiments of the present disclosure, the user should be informed and authorized of the type, usage range, usage scenario, etc. of the personal information related to the present disclosure in an appropriate manner according to the relevant legal regulations.
It will be appreciated that the data (including but not limited to the data itself, the acquisition or use of the data) involved in the present technical solution should comply with the corresponding legal regulations and the requirements of the relevant regulations.
Example 1
Fig. 1 is a flowchart of a method for adaptively adjusting parameters of a virtual synchronous machine according to an embodiment of the present invention, where the method may be performed by a device for adaptively adjusting parameters of a virtual synchronous machine, and the device for adaptively adjusting parameters of a virtual synchronous machine may be implemented in hardware and/or software, and optionally, may be implemented by an electronic device, where the electronic device may be a mobile terminal, a PC side, a server, or the like. As shown in fig. 1, the specific embodiments may include:
S110, acquiring operation related parameters of the virtual synchronous machine in a target operation state, and determining the angular frequency deviation amount and the angular frequency change rate of the virtual synchronous machine based on the operation related parameters.
The target running state can be understood as a state when the virtual inertia and damping coefficient virtual synchronous machine are to be predicted. The target operating state may be an instantaneous state or a staged operating state of the virtual synchronous machine. The operation-related parameter may be understood as a parameter related to an operation state of the virtual synchronous machine. Illustratively, the operation-related parameters include at least one or more of an operation scene, an operation condition, and a control target.
It will be appreciated that the virtual synchronous machine may also vary in the amount of angular frequency deviation and the rate of angular frequency change of operation under different operation related parameters. Therefore, in the embodiment of the invention, the angular frequency deviation amount and the angular frequency change rate of the virtual synchronous machine in the target running state can be determined based on the running related parameters.
S120, respectively determining adaptive coefficients corresponding to virtual inertia and damping coefficients of the virtual synchronous machine based on the angular frequency deviation amount and the angular frequency change rate, and respectively determining maximum values, minimum values and steady-state values of the virtual inertia and the damping coefficients.
In the embodiment of the present invention, the adaptive coefficients corresponding to the virtual inertia and the damping coefficient of the virtual synchronous machine are determined based on the angular frequency deviation amount and the angular frequency change rate, and specifically may be determined based on the angular frequency deviation amount, the angular frequency change rate, and a pre-established algorithm model associated with the angular frequency deviation amount and the angular frequency change rate. Illustratively, the algorithm model may be a support vector regression model or the like.
Alternatively, steady state values of the virtual inertia and the damping coefficient may be determined based on an internal potential of the virtual synchronous machine, an output voltage, a total impedance from the inverter to the grid, and an oscillation angular frequency, respectively.
For example, the steady state values of the virtual inertia and the damping coefficient may be determined based on the following formula with reference to a natural oscillation angular frequency range:
wherein J is 0 E is the internal potential of the virtual synchronous machine; u is the output voltage of the virtual synchronous machine, X 0 Omega for total impedance from inverter to grid n Is the oscillation angular frequency.
Illustratively, the mathematical expression of the steady state value of the damping coefficient may be specifically:
Wherein D is 0 E is the internal potential of the virtual synchronous machine and is the steady state value of the damping coefficient; u is the output voltage of the virtual synchronous machine; x is X 0 Omega for total impedance from inverter to grid n Is the oscillation angular frequency.
Optionally, a minimum value of the virtual inertia is determined based on a maximum power of the virtual synchronous machine, the angular frequency deviation amount, and the angular frequency variation amount.
Further, a maximum value of the virtual inertia is determined based on steady state values of the virtual inertia and the damping coefficient, maximum and minimum powers of the virtual synchronous machine, and maximum and minimum angular frequency amounts of the virtual synchronous machine.
Illustratively, the mathematical expression of the maximum value and the minimum value of the virtual inertia may be specifically:
wherein: j (J) max Represents the maximum value of the virtual inertia, J min Representing the minimum value of the virtual inertia, Δω (k+1) Represents the angular frequency deviation amount, deltaomega, at the (k+1) th time (k) Represents the angular frequency deviation amount at the kth time, D 0 J is the steady state value of the damping coefficient 0 P is the steady state value of the virtual inertia max Maximum power of virtual synchronous machine, P min And K is a preset regulating coefficient for the minimum power of the virtual synchronous machine.
Optionally, the maximum value of the damping coefficient is determined based on the damping coefficient, a steady state value of the virtual inertia, a grid side angular frequency, an output voltage of the virtual synchronous machine, an internal potential, and a total impedance from the inverter to the grid.
Optionally, the minimum value of the damping coefficient is determined based on the output voltage of the virtual synchronous machine, an internal potential, a total impedance from the inverter to the grid, the grid side angular frequency, and an open loop function cut-off frequency of the active power loop.
Illustratively, the mathematical expression of the maximum and minimum values of the damping coefficient is:
wherein D is max Represents the maximum value of the damping coefficient, J 0 For the steady state value of the virtual inertia, ζ represents the damping coefficient, ω 0 Representing the grid side angular frequency corresponding to the virtual synchronous machine, wherein E is the internal potential of the virtual synchronous machine; u represents the output voltage of the virtual synchronous machine, X 0 Representing the total impedance from the inverter to the grid, f cmax And the open loop function cut-off frequency of the active power loop corresponding to the virtual synchronous machine is represented.
S130, determining the virtual inertia and the damping coefficient based on the adaptive coefficient, the maximum value, the minimum value and the steady state value corresponding to the virtual inertia and the damping coefficient.
In order to improve the convenience and the calculation efficiency of calculation, specifically, the angular frequency deviation amount and the angular frequency change rate may be discretized first to obtain an inertia change rule function corresponding to the virtual inertia and a damping change rule function corresponding to the damping coefficient, respectively; and substituting the self-adaptive coefficient, the maximum value, the minimum value and the steady state value corresponding to the virtual inertia into the inertia change rule function, calculating to obtain the virtual inertia, and substituting the self-adaptive coefficient, the maximum value, the minimum value and the steady state value corresponding to the damping coefficient into the damping change rule function, and calculating to obtain the damping coefficient.
Illustratively, discretizing the angular frequency deviation amount and the angular frequency change rate, and the obtained inertia change rule function corresponding to the virtual inertia and the obtained damping change rule function corresponding to the damping coefficient may be respectively expressed based on the following formulas:
wherein J represents the virtual inertia, D represents the damping coefficient, G 1 Representing an adaptation coefficient corresponding to the virtual inertia, G 2 Representing the adaptive coefficient corresponding to the damping coefficient, J max Represents the maximum value of the virtual inertia, J min Representing the minimum value of the virtual inertia, D max Represents the maximum value of the damping coefficient, D min Represents the minimum value of the damping coefficient, J 0 Representing steady state values of the virtual inertia, D 0 Represents the steady state value of the damping coefficient, Δω (k+1) Represents the angular frequency deviation amount, deltaomega, at the (k+1) th time (k) Represents the angular frequency deviation amount at the kth time, T represents the minimum sampling period,indicating the rate of change of angular frequency.
According to the technical scheme, the angular frequency deviation amount and the angular frequency change rate of the virtual synchronous machine are determined based on the operation association parameters by acquiring the operation association parameters under the target operation state of the virtual synchronous machine, and the angular frequency deviation amount and the angular frequency change rate of the virtual synchronous machine can be effectively and accurately acquired by combining the target operation state of the virtual synchronous machine because the operation association parameters at least comprise an operation scene, an operation working condition and a control target; by determining the adaptive coefficients corresponding to the virtual inertia and the damping coefficient of the virtual synchronous machine based on the angular frequency deviation amount and the angular frequency change rate, respectively, and determining the maximum value, the minimum value, and the steady-state value of the virtual inertia and the damping coefficient, respectively, it is possible to accurately determine the dependent calculation parameters associated with the virtual inertia and the damping coefficient; the virtual inertia and the damping coefficient are determined based on the self-adaptive coefficient, the maximum value, the minimum value and the steady state value corresponding to the virtual inertia and the damping coefficient, so that the technical problem that the fixed virtual inertia and the damping coefficient cannot realize the optimal state of the rapidity and the stability of the system at the same time is solved, the self-adaptive adjustment of the virtual inertia and the damping coefficient of the virtual synchronous machine is realized, the optimal selection of the virtual inertia and the damping coefficient of the virtual synchronous machine is realized, and the self-adaptive adjustment method has important significance in the aspect of propelling a novel electric power system.
Example two
Fig. 2 is a flowchart of a method for adaptively adjusting parameters of a virtual synchronous machine according to a second embodiment of the present invention, where the method for determining adaptive coefficients corresponding to virtual inertia and damping coefficients of the virtual synchronous machine is refined based on the above embodiments. Reference is made to the description of this example for a specific implementation. The technical features that are the same as or similar to those of the foregoing embodiments are not described herein. As shown in fig. 2, the specific embodiments may include:
s210, acquiring operation related parameters of the virtual synchronous machine in a target operation state, and determining the angular frequency deviation amount and the angular frequency change rate of the virtual synchronous machine based on the operation related parameters.
The operation-related parameters include at least an operation scene, an operation condition, and a control target.
S220, inputting the angular frequency deviation and the angular frequency change rate into a coefficient prediction model with pre-selected training, and obtaining self-adaptive coefficients corresponding to the virtual inertia and damping coefficients of the virtual synchronous machine based on the output result of the coefficient prediction model.
The coefficient prediction model is obtained by training a pre-established support vector regression model based on angular frequency deviation and angular frequency change rate under a sample running state. Similarly, the sample operational state may be determined based on the operational association parameters. Specifically, angular frequency deviation amount and angular frequency change rate of target operation data of the virtual synchronous machine under different operation scenes, different operation working conditions and different control targets are obtained, virtual inertia and damping coefficient which are expected to be output by the support vector regression model and correspond to the angular frequency deviation amount and the angular frequency change rate are further used as input variables in the support vector regression model, virtual inertia and damping coefficient which are expected to be output by the support vector regression model and correspond to the angular frequency deviation amount and the angular frequency change rate are further used as label data, and a training sample set of the support vector regression model is constructed. And training a pre-established vector regression model function based on the training sample set to obtain a coefficient prediction model. During the training process, the support vector regression model may be optimized based on the dual function and constraints of the support vector regression model. Specifically, a penalty factor and a kernel function parameter of a support vector regression model can be obtained by training a training sample set, then a prediction result and an error analysis can be obtained by testing the sample set, and the model parameter and the error analysis are combined to obtain a coefficient prediction model to be used.
Illustratively, the functional expression of the support vector regression model is:
wherein G is 1 Representing an adaptive coefficient corresponding to the virtual inertia, G 2 Representing an adaptive coefficient, Δω, corresponding to the damping coefficient (k+1) Represents the angular frequency deviation amount, deltaomega, at the (k+1) th time (k) Represents the angular frequency deviation at the kth time, T represents the minimum sampling period, Δω and Δω (k+1) -Δω (k) Each of which represents an amount of angular frequency deviation,andall represent angular frequency rate of change, delta represents the weight parameter of the support vector regression model, b represents the bias parameter in the support vector regression model, +.>Representing a training sample set.
Further, the dual function and constraint condition of the support vector regression model may be respectively:
wherein c represents a penalty factor, i represents the ith training sample, ζ i And (3) withAll represent the corresponding relaxation factor of the ith training sample,/->Representing a mapping function corresponding to the angular frequency deviation amount and the angular frequency change rate, G 1 Representing the adaptive coefficients corresponding to the virtual inertia, b representing the bias parameters in the support vector regression model, G 2 And (3) representing the self-adaptive coefficient corresponding to the damping coefficient, wherein delta represents the weight parameter of the support vector regression model, T represents the minimum sampling period, and epsilon is a insensitive loss function.
Further, by solving the above equation, it can be obtained:
wherein: θ j ,Are Lagrangian multipliers, nsv are the number of support vectors, h (x j X) is a kernel function, and the kernel function used in the embodiment of the invention is:
h(x j ,x)＝exp(-g‖x-x j ‖ 2 )
where g is a kernel function parameter.
In the vector regression model, the penalty factor C controls complexity and precision, and the kernel function parameter g affects the effect range and is a key parameter for determining the model precision. The training set is used for optimizing parameters of the model c and g and building a vector regression model by combining the optimal parameters c and g. The test set is used to evaluate the accuracy of the vector regression model to predict the virtual synchrony parameters J, D. And combining the optimal parameters c and g of the vector regression model obtained through training with the result obtained through testing to obtain an optimized vector regression model. The specific implementation of the test and training can be completed through simulation software.
S230, respectively determining the maximum value, the minimum value and the steady-state value of the virtual inertia and the damping coefficient.
S240, determining the virtual inertia and the damping coefficient based on the adaptive coefficient, the maximum value, the minimum value and the steady state value corresponding to the virtual inertia and the damping coefficient.
According to the technical scheme, the virtual inertia and the damping coefficient under different scenes, different working conditions and different control targets can be predicted through the support vector regression model, the angular frequency deviation and the angular frequency change rate are input into the coefficient prediction model which is subjected to pre-selection training, the adaptive coefficient corresponding to the virtual inertia and the damping coefficient of the virtual synchronous machine can be obtained based on the output result of the coefficient prediction model, the operation is simple and convenient, and the adaptive coefficient corresponding to the virtual inertia and the damping coefficient of the virtual synchronous machine can be automatically and accurately determined.
Example III
Fig. 3 is a flowchart of a method for adaptively adjusting parameters of a virtual synchronous machine according to a third embodiment of the present invention. The present embodiment adds verification operations on virtual synchronous machine parameters on the basis of the above embodiments. Reference is made to the description of this example for a specific implementation. The technical features that are the same as or similar to those of the foregoing embodiments are not described herein. As shown in fig. 3, the method of this embodiment may specifically include:
s310, acquiring operation related parameters of the virtual synchronous machine in a target operation state, and determining the angular frequency deviation amount and the angular frequency change rate of the virtual synchronous machine based on the operation related parameters.
S320, respectively determining adaptive coefficients corresponding to virtual inertia and damping coefficients of the virtual synchronous machine based on the angular frequency deviation amount and the angular frequency change rate, and respectively determining maximum values, minimum values and steady-state values of the virtual inertia and the damping coefficients.
S330, determining the virtual inertia and the damping coefficient based on the adaptive coefficient, the maximum value, the minimum value and the steady state value corresponding to the virtual inertia and the damping coefficient.
S340, determining performance evaluation indexes corresponding to the virtual inertia and the damping coefficient based on the frequency change rate, the maximum frequency deviation and the steady-state frequency deviation of the virtual synchronous machine, and generating a verification result based on the performance evaluation indexes.
Optionally, adding the frequency change rate, the maximum frequency deviation and the steady-state frequency deviation of the virtual synchronous machine to obtain a performance evaluation index corresponding to the virtual inertia and the damping coefficient; or, the frequency change rate, the maximum frequency deviation and the steady-state frequency deviation of the virtual synchronous machine may be weighted and added to obtain the performance evaluation index corresponding to the virtual inertia and the damping coefficient.
The calculation manner of the performance evaluation index may be specifically expressed as:
wherein L represents a performance evaluation index corresponding to the virtual inertia and the damping coefficient, Δω (k+1) Represents the angular frequency deviation amount, deltaomega, at the (k+1) th time (k) Represents the angular frequency deviation amount at the kth time, T represents the minimum sampling period,represents the angular frequency change rate, alpha represents the weight coefficient corresponding to the angular frequency change rate, and delta omega max Represents the maximum frequency deviation, beta represents the weight corresponding to the maximum frequency deviationWeight coefficient, Δω p The steady-state frequency deviation is represented, and γ represents a weight coefficient corresponding to the steady-state frequency deviation.
In the embodiment of the invention, the verification result can be determined based on the performance evaluation index and a preset index threshold range. For example, when the performance evaluation index is within a preset index threshold, it may be determined that the adjustment results indicating the virtual inertia and the damping coefficient are expected, that is, the adaptive coefficients corresponding to the virtual inertia and the damping coefficient are reasonable, by verification. When the performance evaluation index is not within the preset index threshold range, it is determined that the verification is not passed, and at this time, the adaptive coefficients corresponding to the virtual inertia and the damping coefficient may be redetermined.
In order to verify the rationality and superiority of the vector regression-based virtual synchronous machine parameter self-adaptive adjustment method, a simulation model is built by MATLAB/Simulink for verification, and the traditional VSG control strategy and the self-adaptive control strategy change condition of the virtual inertia and damping coefficient of the virtual synchronous machine provided by the embodiment of the invention are compared and analyzed. And during simulation, VSG grid-connected operation is selected, the active power reference value is 10kW during starting, the active power reference value is suddenly increased to 15kW during 0.7s, the active power reference value is reduced to 10kW during 0.9s, the change conditions under two control strategies are obtained through simulation, and the result of the virtual inertia and damping coefficient self-adaptive change comparison graph of the virtual synchronous machine is obtained, as shown in fig. 4. As can be seen from fig. 4, the manner of fixing the virtual inertia and the damping coefficient in the conventional VSG control and the adaptive control strategy in the embodiment of the present invention have respective frequency overshoot of 0.21% and 0.06%, and the maximum frequency offset is-0.0831 and-0.0362 Hz, so that the method for adaptively adjusting the parameters of the virtual synchronous machine according to the embodiment of the present invention not only significantly reduces the overshoot, but also effectively controls the maximum frequency offset within the range of 0.05Hz, thereby meeting the power quality requirement. In addition, good response speed is maintained, the oscillation amplitude and the attenuation rate of the frequency in the process of sudden increase of the active load are improved, and the adjustment capability of the virtual synchronous machine parameters to the active power and the frequency of the system is improved.
According to the technical scheme, the performance evaluation indexes corresponding to the virtual inertia and the damping coefficient are determined based on the frequency change rate, the maximum frequency deviation and the steady-state frequency deviation of the virtual synchronous machine, and the verification result is generated based on the performance evaluation indexes, so that the self-adaptively adjusted virtual inertia and damping coefficient can be effectively evaluated and generated to the verification result by combining multiple dimensions, and the adjustment effects of the virtual inertia and the damping coefficient can be intuitively known.
Example IV
Fig. 5 is a schematic structural diagram of a parameter adaptive adjustment device for a virtual synchronous machine according to a third embodiment of the present invention. As shown in fig. 5, the apparatus includes: an operating parameter acquisition module 510, a key parameter determination module 520, and an adjustment parameter determination module 530. The operation parameter obtaining module 510 is configured to obtain an operation related parameter in a target operation state of a virtual synchronous machine, and determine an angular frequency deviation amount and an angular frequency change rate of the virtual synchronous machine based on the operation related parameter, where the operation related parameter at least includes an operation scene, an operation working condition, and a control target; a key parameter determining module 520, configured to determine adaptive coefficients corresponding to a virtual inertia and a damping coefficient of the virtual synchronous machine based on the angular frequency deviation amount and the angular frequency change rate, and determine a maximum value, a minimum value, and a steady state value of the virtual inertia and the damping coefficient, respectively; an adjustment parameter determination module 530 is configured to determine the virtual inertia and the damping coefficient based on the adaptive coefficient, the maximum value, the minimum value, and the steady state value corresponding to the virtual inertia and the damping coefficient.
According to the technical scheme, the angular frequency deviation amount and the angular frequency change rate of the virtual synchronous machine are determined based on the operation association parameters by acquiring the operation association parameters under the target operation state of the virtual synchronous machine, and the angular frequency deviation amount and the angular frequency change rate of the virtual synchronous machine can be effectively and accurately acquired by combining the target operation state of the virtual synchronous machine because the operation association parameters at least comprise an operation scene, an operation working condition and a control target; by determining the adaptive coefficients corresponding to the virtual inertia and the damping coefficient of the virtual synchronous machine based on the angular frequency deviation amount and the angular frequency change rate, respectively, and determining the maximum value, the minimum value, and the steady-state value of the virtual inertia and the damping coefficient, respectively, it is possible to accurately determine the dependent calculation parameters associated with the virtual inertia and the damping coefficient; the virtual inertia and the damping coefficient are determined based on the self-adaptive coefficient, the maximum value, the minimum value and the steady state value corresponding to the virtual inertia and the damping coefficient, so that the technical problem that the fixed virtual inertia and the damping coefficient cannot realize the optimal state of the rapidity and the stability of the system at the same time is solved, the self-adaptive adjustment of the virtual inertia and the damping coefficient of the virtual synchronous machine is realized, the optimal selection of the virtual inertia and the damping coefficient of the virtual synchronous machine is realized, and the self-adaptive adjustment method has important significance in the aspect of propelling a novel electric power system.
On the basis of the embodiment of the present invention, optionally, the key parameter determining module 520 includes an adaptive coefficient prediction unit, where the adaptive coefficient prediction unit is specifically configured to: inputting the angular frequency deviation amount and the angular frequency change rate into a coefficient prediction model which is subjected to pre-selection training, and obtaining self-adaptive coefficients corresponding to virtual inertia and damping coefficients of the virtual synchronous machine based on an output result of the coefficient prediction model, wherein the coefficient prediction model is obtained by training a pre-established support vector regression model based on the angular frequency deviation amount and the angular frequency change rate under a sample running state.
On the basis of the embodiment of the invention, optionally, the functional expression of the support vector regression model is:
wherein G is 1 Representing an adaptive coefficient corresponding to the virtual inertia, G 2 Representing an adaptive coefficient, Δω, corresponding to the damping coefficient (k+1) Represents the (k+1) thAngular frequency deviation at time, Δω (k) Represents the angular frequency deviation at the kth time, T represents the minimum sampling period, Δω and Δω (k+1) -Δω (k) Each of which represents an amount of angular frequency deviation,andall represent angular frequency rate of change, delta represents the weight parameter of the support vector regression model, b represents the bias parameter in the support vector regression model, +. >Representing a training sample set.
On the basis of the embodiment of the invention, optionally, the dual functions and constraint conditions of the support vector regression model are respectively as follows:
/>
wherein c is penalty factor, ζ i And (3) withAll are relaxation factors corresponding to the ith training sample,>for the mapping function, ε is the insensitive loss function.
On the basis of the embodiment of the present invention, optionally, the key parameter determining module 520 includes a parameter key value determining unit, where the parameter key value determining unit specifically includes: the damping coefficient maximum value determination subunit and the damping coefficient minimum value determination subunit.
The steady state value determining subunit is used for respectively determining steady state values of the virtual inertia and the damping coefficient based on internal potential, output voltage, total impedance from the inverter to the power grid and oscillation angular frequency of the virtual synchronous machine; the virtual inertia minimum value determining subunit is used for determining the minimum value of the virtual inertia based on the maximum power of the virtual synchronous machine, the angular frequency deviation amount and the angular frequency variation amount; the virtual inertia maximum value determining subunit is configured to determine a maximum value of the virtual inertia based on steady state values of the virtual inertia and the damping coefficient, maximum power and minimum power of the virtual synchronous machine, and maximum angular frequency and minimum angular frequency of the virtual synchronous machine; the damping coefficient maximum value determining subunit is configured to determine a maximum value of the damping coefficient based on the damping coefficient, a steady state value of the virtual inertia, a grid side angular frequency, an output voltage of the virtual synchronous machine, an internal potential, and a total impedance from the inverter to the grid; the damping coefficient minimum value determining subunit is configured to determine a minimum value of the damping coefficient based on an output voltage, an internal potential, a total impedance from the inverter to the power grid, the power grid side angular frequency, and an open-loop function cut-off frequency of the active power loop of the virtual synchronous machine.
On the basis of the embodiment of the present invention, optionally, the adjustment parameter determining module 530 includes a change rule function constructing unit, a virtual inertia calculating unit, and a damping coefficient calculating unit. The change rule function construction unit is used for discretizing the angular frequency deviation amount and the angular frequency change rate to respectively obtain an inertia change rule function corresponding to the virtual inertia and a damping change rule function corresponding to the damping coefficient; the virtual inertia calculation unit is configured to substitute the adaptive coefficient, the maximum value, the minimum value, and the steady state value corresponding to the virtual inertia into the inertia change rule function, and calculate to obtain the virtual inertia; the damping coefficient calculation unit is configured to calculate the damping coefficient by substituting the adaptive coefficient, the maximum value, the minimum value, and the steady state value corresponding to the damping coefficient into the damping change rule function.
On the basis of the embodiment of the present invention, optionally, the change rule function construction unit is configured to:
wherein J represents the virtual inertia, D represents the damping coefficient, G 1 Representing an adaptive coefficient corresponding to the virtual inertia, G 2 Representing the adaptive coefficient corresponding to the damping coefficient, J max Represents the maximum value of the virtual inertia, J min Representing the minimum value of the virtual inertia, D max Represents the maximum value of the damping coefficient, D min Represents the minimum value of the damping coefficient, J 0 Representing steady state values of the virtual inertia, D 0 Represents the steady state value of the damping coefficient, Δω (k+1) Represents the angular frequency deviation amount, deltaomega, at the (k+1) th time (k) Represents the angular frequency deviation amount at the kth time, T represents the minimum sampling period,indicating the rate of change of angular frequency.
On the basis of the embodiment of the invention, optionally, the parameter self-adaptive adjusting device of the virtual synchronous machine further comprises: and a parameter performance evaluation module. The parameter performance evaluation module is used for determining performance evaluation indexes corresponding to the virtual inertia and the damping coefficient based on the frequency change rate, the maximum frequency deviation and the steady-state frequency deviation of the virtual synchronous machine, and generating a verification result based on the performance evaluation indexes.
On the basis of the embodiment of the invention, optionally, the parameter performance evaluation module is specifically configured to:
wherein L represents a performance evaluation index corresponding to the virtual inertia and the damping coefficient, Δω (k+1) Represents the angular frequency deviation amount, deltaomega, at the (k+1) th time (k) Represents the angular frequency deviation amount at the kth time, T represents the minimum sampling period,represents the angular frequency change rate, alpha represents the weight coefficient corresponding to the angular frequency change rate, and delta omega max Represents the maximum frequency deviation, beta represents the weight coefficient corresponding to the maximum frequency deviation, delta omega p The steady-state frequency deviation is represented, and γ represents a weight coefficient corresponding to the steady-state frequency deviation.
The self-adaptive adjusting device for the parameters of the virtual synchronous machine provided by the embodiment of the invention can execute the self-adaptive adjusting method for the parameters of the virtual synchronous machine provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the self-adaptive adjusting method for the parameters of the virtual synchronous machine.
Example five
Fig. 6 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 6, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the virtual synchronous machine parameter adaptive adjustment method.
In some embodiments, the virtual synchrony machine parameter adaptive adjustment method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the virtual synchrony machine parameter adaptive adjustment method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the virtual synchronous machine parameter adaptive adjustment method in any other suitable way (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. The self-adaptive parameter adjusting method for the virtual synchronous machine is characterized by comprising the following steps of:
acquiring operation related parameters of a virtual synchronous machine in a target operation state, and determining the angular frequency deviation amount and the angular frequency change rate of the virtual synchronous machine based on the operation related parameters, wherein the operation related parameters at least comprise an operation scene, an operation working condition and a control target;
determining adaptive coefficients corresponding to virtual inertia and damping coefficients of the virtual synchronous machine based on the angular frequency deviation amount and the angular frequency change rate, respectively, and determining maximum values, minimum values and steady-state values of the virtual inertia and the damping coefficients, respectively;
The virtual inertia and the damping coefficient are determined based on the adaptive coefficient, the maximum value, the minimum value, and the steady state value corresponding to the virtual inertia and the damping coefficient.
2. The method of claim 1, wherein the determining adaptive coefficients corresponding to virtual inertia and damping coefficients of the virtual synchronous machine based on the angular frequency deviation amount and the angular frequency change rate, respectively, comprises:
inputting the angular frequency deviation amount and the angular frequency change rate into a coefficient prediction model which is subjected to pre-selection training, and obtaining self-adaptive coefficients corresponding to virtual inertia and damping coefficients of the virtual synchronous machine based on an output result of the coefficient prediction model, wherein the coefficient prediction model is obtained by training a pre-established support vector regression model based on the angular frequency deviation amount and the angular frequency change rate under a sample running state.
3. The method of claim 2, wherein the functional expression of the support vector regression model is:
wherein G is 1 Representing an adaptive coefficient corresponding to the virtual inertia, G 2 Representing an adaptive coefficient, Δω, corresponding to the damping coefficient (k+1) Represents the angular frequency deviation amount, deltaomega, at the (k+1) th time (k) Represents the angular frequency deviation at the kth time, T represents the minimum sampling period, Δω and Δω (k+1) -Δω (k) Each of which represents an amount of angular frequency deviation,andall represent angular frequency rate of change, delta represents the weight parameter of the support vector regression model, b represents the bias parameter in the support vector regression model, +.>Representing a training sample set.
4. A method according to claim 3, wherein the dual functions and constraints of the support vector regression model are respectively:
wherein c is a penalty factor, i represents the ith training sample, ζ i And (3) withAll are relaxation factors corresponding to the ith training sample,>for the mapping function, ε is the insensitive loss function.
5. The method of claim 1, wherein the determining the maximum, minimum, and steady state values of the virtual inertia and the damping coefficient, respectively, comprises:
determining steady-state values of the virtual inertia and the damping coefficient based on an internal potential, an output voltage, a total impedance from an inverter to a power grid, and an oscillation angular frequency of the virtual synchronous machine, respectively;
determining a minimum value of the virtual inertia based on the maximum power of the virtual synchronous machine, the angular frequency deviation amount and the angular frequency variation amount;
Determining a maximum value of the virtual inertia based on steady state values of the virtual inertia and the damping coefficient, maximum and minimum powers of the virtual synchronous machine, and maximum and minimum angular frequency amounts of the virtual synchronous machine;
determining a maximum value of the damping coefficient based on the damping coefficient, a steady state value of the virtual inertia, a grid side angular frequency, an output voltage of the virtual synchronous machine, an internal potential, and a total impedance from an inverter to a grid;
the minimum value of the damping coefficient is determined based on the output voltage of the virtual synchronous machine, an internal potential, a total impedance from the inverter to the grid, the grid side angular frequency, and an open loop function cut-off frequency of the active power loop.
6. The method of claim 1, wherein the determining the virtual inertia and the damping coefficient based on the adaptive coefficient, the maximum value, the minimum value, the steady state value corresponding to the virtual inertia and the damping coefficient comprises:
discretizing the angular frequency deviation amount and the angular frequency change rate to respectively obtain an inertia change rule function corresponding to the virtual inertia and a damping change rule function corresponding to the damping coefficient;
Substituting the self-adaptive coefficient, the maximum value, the minimum value and the steady state value corresponding to the virtual inertia into the inertia change rule function, and calculating to obtain the virtual inertia;
substituting the self-adaptive coefficient, the maximum value, the minimum value and the steady state value which correspond to the damping coefficient into the damping change rule function, and calculating to obtain the damping coefficient.
7. The method of claim 6, wherein discretizing the angular frequency deviation amount and the angular frequency change rate to obtain an inertia change law function corresponding to the virtual inertia and a damping change law function corresponding to the damping coefficient, respectively, comprises:
wherein J represents the virtual inertia, D represents the damping coefficient, G 1 Representing an adaptive coefficient corresponding to the virtual inertia, G 2 Representing the adaptive coefficient corresponding to the damping coefficient, J max Represents the maximum value of the virtual inertia, J min Representing the minimum value of the virtual inertia, D max Represents the maximum value of the damping coefficient, D min Represents the minimum value of the damping coefficient, J 0 Representing steady state values of the virtual inertia, D 0 Represents the steady state value of the damping coefficient, Δω (k+1) Represents the angular frequency deviation amount, deltaomega, at the (k+1) th time (k) Represents the angular frequency deviation amount at the kth time, T represents the minimum sampling period,indicating the rate of change of angular frequency.
8. The method as recited in claim 1, further comprising:
and determining performance evaluation indexes corresponding to the virtual inertia and the damping coefficient based on the frequency change rate, the maximum frequency deviation and the steady-state frequency deviation of the virtual synchronous machine, and generating a verification result based on the performance evaluation indexes.
9. The method of claim 8, wherein the determining a performance evaluation index corresponding to the virtual inertia and the damping coefficient based on the frequency change rate, the maximum frequency deviation, and the steady-state frequency deviation of the virtual synchronous machine comprises:
wherein L represents a performance evaluation index corresponding to the virtual inertia and the damping coefficient, Δω (k+1) Represents the angular frequency deviation amount, deltaomega, at the (k+1) th time (k) Represents the angular frequency deviation amount at the kth time, T represents the minimum sampling period,represents the angular frequency change rate, alpha represents the weight coefficient corresponding to the angular frequency change rate, and delta omega max Represents the maximum frequency deviation, beta represents the weight coefficient corresponding to the maximum frequency deviation, delta omega p The steady-state frequency deviation is represented, and γ represents a weight coefficient corresponding to the steady-state frequency deviation.
10. A virtual synchronous machine parameter adaptive adjustment device, comprising:
the system comprises an operation parameter acquisition module, a control module and a control module, wherein the operation parameter acquisition module is used for acquiring operation related parameters of a virtual synchronous machine in a target operation state, and determining the angular frequency deviation amount and the angular frequency change rate of the virtual synchronous machine based on the operation related parameters, wherein the operation related parameters at least comprise an operation scene, an operation working condition and a control target;
the key parameter determining module is used for respectively determining self-adaptive coefficients corresponding to virtual inertia and damping coefficients of the virtual synchronous machine based on the angular frequency deviation amount and the angular frequency change rate, and respectively determining maximum values, minimum values and steady-state values of the virtual inertia and the damping coefficients;
and the adjustment parameter determining module is used for determining the virtual inertia and the damping coefficient based on the adaptive coefficient, the maximum value, the minimum value and the steady-state value corresponding to the virtual inertia and the damping coefficient.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311817288.7A CN117767419A (en) | 2023-12-26 | 2023-12-26 | Self-adaptive parameter adjusting method and device for virtual synchronous machine |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311817288.7A CN117767419A (en) | 2023-12-26 | 2023-12-26 | Self-adaptive parameter adjusting method and device for virtual synchronous machine |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117767419A true CN117767419A (en) | 2024-03-26 |
Family
ID=90319889
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311817288.7A Pending CN117767419A (en) | 2023-12-26 | 2023-12-26 | Self-adaptive parameter adjusting method and device for virtual synchronous machine |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117767419A (en) |
-
2023
- 2023-12-26 CN CN202311817288.7A patent/CN117767419A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9964572B2 (en) | Wide-area measurement system based control of grid-scale storage for power system stability enhancement | |
| Thangaiah et al. | Delay‐dependent stability analysis of power system considering communication delays | |
| CN102130454A (en) | Dynamic stability control method and system for computer aided design based power system | |
| CN110994589B (en) | Online evaluation method and system for frequency modulation capability of power electronic access power system | |
| CN116931438A (en) | Method, device, equipment and medium for determining parameters of speed regulator of water turbine | |
| CN115833115B (en) | Distributed resource edge control method and device of multi-time scale distribution model | |
| CN117767419A (en) | Self-adaptive parameter adjusting method and device for virtual synchronous machine | |
| CN114611805A (en) | Net load prediction method and device, electronic equipment and storage medium | |
| CN117117904B (en) | Frequency modulation contribution evaluation method and device and electronic equipment | |
| Safari | Multi-objective robust PSSs tuning in large scale power system using particle swarm optimisation | |
| CN116247734B (en) | Distributed consistency power control method for edge-side weak communication environment | |
| CN113111533A (en) | Method and device for determining heating temperature | |
| CN117477644A (en) | Control method and device of virtual synchronous machine, electronic equipment and medium | |
| LU102141B1 (en) | Method and system for determining parameter of wide area damping controller of electric power system | |
| CN115561999B (en) | Parameter optimization method, device, equipment and medium of PI (proportional-integral) controller | |
| Saka et al. | Neural network based heuristic fractional order adaptive PID‐controller for eliminating communication time delay in multi‐area LFC | |
| Arumugam et al. | Damping of inter-area oscillations in multimachine systems | |
| CN116914737A (en) | Photovoltaic bearing capacity evaluation method, device, equipment and storage medium for power distribution network | |
| CN116976107A (en) | Model updating method, device, equipment and medium based on wind-fire bundling system | |
| CN117791579A (en) | Distributed photovoltaic cluster division optimization method, electronic equipment and storage medium | |
| CN115912439A (en) | Method, device and equipment for determining modulation signal of current transformer and storage medium | |
| CN115441444A (en) | Distribution network state judgment method and control method for distribution network area | |
| CN115498649A (en) | Method, device, equipment and storage medium for joint regulation of power distribution network voltage | |
| CN115375136A (en) | Frequency adjusting method and device, electronic equipment and storage medium | |
| CN116905604A (en) | Multi-mode control method, device, equipment and medium for hybrid engineering machinery |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |