CN113285481A - Grid-connected converter inductance parameter online estimation method, prediction control method and system - Google Patents
Grid-connected converter inductance parameter online estimation method, prediction control method and system Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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- H02M7/00—Conversion of ac power input into dc power output; Conversion of dc power input into ac power output
- H02M7/42—Conversion of dc power input into ac power output without possibility of reversal
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- H02M7/00—Conversion of ac power input into dc power output; Conversion of dc power input into ac power output
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Abstract
The invention belongs to the field of new energy and energy storage grid-connected systems and power electronics, and particularly relates to a grid-connected converter inductance parameter online estimation method, a prediction control method and a prediction control system. The method comprises the steps of obtaining operation parameters and actual reactive power of the grid-connected converter, and calculating a grid-connected converter power grid side reactive power estimated value by using a virtual flux linkage method; obtaining an estimation error of reactive power according to the estimated value of the reactive power at the power grid side and the actual reactive power; obtaining a calculated inductance parameter deviation according to the direct proportion relation between the estimation error of the reactive power and the inductance parameter deviation; and accumulating the known inductance nameplate value parameters and the inductance parameter deviation to obtain the estimated inductance parameters.
Description
Technical Field
The invention belongs to the field of new energy and energy storage grid-connected systems and power electronics, and particularly relates to a grid-connected converter inductance parameter online estimation method, a prediction control method and a prediction control system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The vigorous development of renewable energy sources (such as wind, solar, wave, etc.) is the fundamental means to solve energy and environmental problems. The energy storage system (such as hydrogen production, super capacitor energy storage, flywheel energy storage and the like) can effectively realize the demand side management of the power system and reduce the problem of power system stability caused by the intermittent renewable energy. The grid-connected converter is one of the core components of a renewable energy source and an energy storage system, and the working characteristics of the grid-connected converter directly influence the overall performance of the system. The renewable energy source and the energy storage system have the characteristics of multi-time scale, nonlinearity, multi-target strong coupling and the like on the physical structure, and the more severe requirements are provided for the control strategy of the grid-connected converter. Two types of traditional control strategies of the grid-connected converter, namely linear vector directional control and nonlinear direct control, have the following principle defects: (1) belongs to the control of 'correction after error'; (2) the method belongs to an optimization method with limited control targets, and is difficult to give consideration to multiple control targets of new energy and an energy storage system; (3) there is no flexibility to incorporate multiple non-linear constraints (e.g., switching frequency, heat dissipation requirements, etc.). The model predictive control is used as a novel third-generation control strategy, and has the advantages of good dynamic performance, simple design, flexible structure, capability of simultaneously containing a plurality of control targets of the system, nonlinear constraint and the like. Therefore, model prediction control becomes a more promising control method for a new energy and energy storage grid-connected converter system.
Conventional model predictive control utilizes existing system models to predict the state derivative trajectory of the system and select the optimal switching action. As a model-based control method, its control performance is highly dependent on accurate system models and parameters. System parameters such as filter inductance are easy to change along with factors such as system running state. A mismatch between the controller parameters and the system parameters will result in a prediction error for the state variables. Therefore, the controller cannot select the optimal switching state, which degrades the system control performance (e.g., steady state offset and large ripple of the controlled variable). Therefore, improving the parameter robustness of model prediction control is important for improving the control performance of the grid-connected system.
Aiming at the problem, academic and industrial fields provide prediction control methods for improving parameter robustness, and the prediction control methods are divided into three types, namely model-free prediction control, state variable averaging and parameter online estimation. The model-free predictive control does not need a model and parameters of a system, and realizes the prediction of the state variable according to the derivation tracks of the current actual value and the predicted value. The state variable averaging method averages the measured value and the predicted value of the state variable, and the state variable is used as a new state variable to participate in prediction. The method has a 'sliding filtering' effect on prediction errors and sampling noise, thereby improving robustness. The existing parameter estimation method obtains accurate inductance parameters through comparing errors of current measurement values and predicted values and through a series of calculation and processing. And then, the accurate inductance parameter is used for predicting the state variable, so that the parameter robustness of predictive control can be effectively improved.
The inventor finds that the following problems exist in the existing three types of robust prediction control methods respectively.
Firstly, accurate state variable derivation tracks can be constructed only by accurate current measurement required by model-free predictive control. The small measurement noise/bias can greatly affect the accuracy of the prediction and thus the system control performance.
Secondly, the state variable averaging method only improves robustness by modifying the state variables, and still uses wrong system parameters for prediction. The method has limited robustness improvement, and the control performance can not meet the requirement when the parameter change is large.
And thirdly, calculating inductance parameters according to the prediction error by using the conventional parameter online estimation method. However, the prediction error caused by the inaccuracy of the inductance parameter is rather small. Measurement errors due to current sensor noise and drift will contribute significantly to prediction errors. This will result in a large deviation of the estimated inductance parameter, affecting the performance of the predictive control.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a grid-connected converter inductance parameter online estimation method, a prediction control method and a system. Then, the actual inductance value is calculated by using the estimation error of the reactive power and is used for state variable prediction, so that the inductance value can be estimated more accurately and rapidly, and the influence of the current measurement error is small.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a grid-connected converter inductance parameter online estimation method.
A grid-connected converter inductance parameter online estimation method comprises the following steps:
acquiring operation parameters and actual reactive power of the grid-connected converter, and calculating a grid-connected converter power grid side reactive power estimated value by using a virtual flux linkage method;
obtaining an estimation error of reactive power according to the estimated value of the reactive power at the power grid side and the actual reactive power;
obtaining a calculated inductance parameter deviation according to the direct proportion relation between the estimation error of the reactive power and the inductance parameter deviation;
and accumulating the known inductance nameplate value parameters and the inductance parameter deviation to obtain the estimated inductance parameters.
The second aspect of the invention provides a grid-connected converter prediction control method.
A grid-connected converter prediction control method comprises the following steps:
acquiring power grid side current and voltage at historical time;
delaying and compensating the current of the power grid side at the current moment based on a delay compensation model;
according to the power grid side current value after the time delay compensation, traversing eight preset voltage vectors of the grid-connected converter, and predicting all power grid side current values which may appear at the next moment based on a prediction model;
screening out an optimal voltage vector corresponding to the minimum system cost function according to a system cost function established by the control target current and all possible power grid side current values predicted at the next moment, and outputting an optimal switching state corresponding to the optimal voltage vector to the grid-connected converter so as to control the on-off of a switching tube of the grid-connected converter;
the delay compensation model and the prediction model are constructed by the grid-connected converter inductance parameters estimated by the grid-connected converter inductance parameter online estimation method.
The third aspect of the invention provides an online estimation system for inductance parameters of a grid-connected converter.
An online estimation system for inductance parameters of a grid-connected converter comprises:
the reactive power calculation module is used for acquiring the operation parameters and the actual reactive power of the grid-connected converter and calculating the estimated value of the grid-connected converter reactive power on the power grid side by using a virtual flux linkage method;
the reactive power error estimation module is used for obtaining an estimation error of the reactive power according to the power grid side reactive power estimation value and the actual reactive power;
the inductance parameter deviation calculation module is used for obtaining calculated inductance parameter deviation according to the direct proportion relation between the estimation error of the reactive power and the inductance parameter deviation;
and the inductance estimation parameter module is used for accumulating the known inductance nameplate value parameters and the inductance parameter deviation to obtain the estimated inductance parameters.
The fourth aspect of the invention provides a grid-connected converter predictive control system.
A grid-connected converter predictive control system, comprising:
the current and voltage acquisition module is used for acquiring the current and voltage of the power grid side at historical time;
the time delay compensation module is used for time delay compensation of the current power grid side current based on the time delay compensation model;
the current value prediction module is used for traversing eight preset voltage vectors of the grid-connected converter according to the power grid side current value after the time delay compensation, and predicting all possible power grid side current values at the next moment based on a prediction model;
the voltage vector screening module is used for screening out an optimal voltage vector corresponding to the minimum system cost function according to a system cost function established by the control target current and all possible power grid side current values at the next predicted moment, and outputting an optimal switching state corresponding to the optimal voltage vector to the grid-connected converter so as to control the on-off of a switching tube of the grid-connected converter;
the delay compensation model and the prediction model are constructed by the grid-connected converter inductance parameters estimated by the grid-connected converter inductance parameter online estimation method.
A fifth aspect of the invention provides a computer-readable storage medium.
A computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in the grid-connected converter inductance parameter online estimation method as described above.
A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the grid-connected converter predictive control method as described above.
A sixth aspect of the invention provides a computer apparatus.
A computer device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps in the online estimation method of the grid-connected converter inductance parameter.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the grid-connected converter predictive control method as described above when executing the program.
Compared with the prior art, the invention has the beneficial effects that:
(1) the method firstly estimates the reactive power transmitted to the power grid by the converter by using a virtual flux linkage method, and compares the reactive power with the reactive power obtained by actual measurement and calculation. Then, the actual inductance value is calculated by utilizing the estimation error of the reactive power and is used for state variable prediction, the method can quickly and accurately estimate inductance parameters, reduces the dependence of prediction control on system parameters, and improves the robustness; the general scheme for the on-line estimation of the inductance parameters is suitable for various types of grid-connected converter topologies (two-level, three-level, modular multi-level converters and the like), and can be popularized to the scenes of four-quadrant motor driving and the like.
(2) The method adopts a model prediction control method, wherein a delay compensation model and a prediction model are both constructed by grid-connected converter inductance parameters estimated by a grid-connected converter inductance parameter online estimation method, an optimal voltage vector corresponding to the minimum system cost function is screened out according to a system cost function established by control target current and all predicted possible grid side current values at the next moment, and an optimal switching state corresponding to the optimal voltage vector is output to a grid-connected converter to control the on-off of a switching tube of the grid-connected converter, so that on one hand, multiple control targets and various nonlinear constraint conditions of a new energy system can be flexibly processed; on the other hand, the physical limit of the converter can be fully exerted, and the dynamic response is fast.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a circuit topology diagram of a new energy and energy storage grid-connected converter system in an embodiment of the invention;
fig. 2 is a block diagram of an inductance parameter online estimation method based on virtual flux linkage according to an embodiment of the present invention;
fig. 3 is a block diagram of robust predictive control based on inductance online estimation according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
Fig. 1 is a circuit topology diagram of a new energy four-quadrant grid-connected system power converter of the present embodiment. The front stage of the circuit is a new energy power generation system, such as a solar panel of a photovoltaic power generation system, a generator and a front stage rectifier of a wind power and wave energy power generation system, and the like. The output electric energy of the preceding-stage new energy power generation system is in a direct current form, and is converted into alternating current through the grid-connected converter system to be incorporated into a power grid. In addition, the converter can realize bidirectional energy flow, namely four-quadrant operation. The specific topology of the grid-connected converter is not limited, and the grid-connected converter comprises a three-phase/single-phase two-level converter, a three-level converter, a modular multi-level converter and the like.
The following takes a two-level grid-connected converter as an example:
the three-phase two-level topology comprises a three-phase inductor La＝Lb＝LcL, parasitic resistance Ra＝Rb＝RcR; three-phase full-bridge power switch Sa，S′a，Sb，S′b，Sc，S′c(ii) a DC bus capacitor Cdc. By converting current from one to anotherThe direction of the device flowing into the power grid is defined as positive direction and is marked as ia，ib，icThe grid voltage is ea，eb，ecThe voltage on the DC side is Vdc. The output voltage of the converter with respect to the reference point is va，vb，vcTaking a two-level converter as an example, the two-level converter can be calculated by the dc bus voltage and the converter switch state:
here, the
Is a three-phase switch state vector. For example, S a1 denotes a switch SaIs turned on, switch S'aTurning off; sa0 denotes a switch SaOff, switch S'aAnd conducting.
According to fig. 1, a kirchhoff voltage equation on the ac side of the converter can be established:
for subsequent analysis, all variables were transformed to the α β coordinate system by Clarke:
here, the
In grid-connected converter systems, virtual flux linkages are typically used to achieve both active and reactive power estimation for grid-less voltage sensors. The implementation steps of the virtual flux linkage are briefly described below.
By integrating the equal signs of equation (3) simultaneously in the left and right directions:
here we equate the grid side of the grid-connected inverter to an ac machine.
Is the integral of the grid voltage, i.e. the flux linkage of the motor. Network voltage
Viewed as a flux linkage
The induced back emf. L and R are respectively considered as the winding inductance and the internal resistance of the ac machine. In a real-time controller, in equation (4)
And
both terms can be calculated from the following equations:
here TsIs the sampling period of the controller. According to the formulas (4) to (6), the virtual flux linkage of the power grid voltage can be calculated in real time
According to the virtual flux linkage calculation value, the grid voltage estimation value can be written as:
where ω is the grid voltage angular frequency. According to the instantaneous power theory (8), the estimated values of the active power and the reactive power of the converter power grid side are shown as a formula (9).
According to the formula (4), inaccuracy of the filter inductance parameter L of the grid-connected converter affects the estimation result of the virtual flux linkage, so that errors exist in power estimation. Because the internal resistance R of the filter inductor is relatively small, the estimation error caused by the parameter deviation of R can be ignored. The relationship between the inductance parameter deviation and the power estimation error is quantitatively analyzed, and the inductance parameter deviation and the accurate inductance parameter are solved by utilizing the relationship.
Inductance parameter bias versus power estimation error:
defining the actual inductance value of the grid-connected converter filter as L and the nameplate value parameter (namely the parameter used by the controller prediction model) as L0. The difference Δ L between the nameplate value parameter and the actual inductance parameter, i.e., L ═ L0+ Δ L. According to equation (4), when there is a deviation in the inductance parameter, the estimated virtual flux linkage can be expressed as:
and substituting the virtual flux linkage containing the estimation error into an equation (8) to obtain the estimated values of the active power and the reactive power containing the error:
here ImThe grid side current amplitude. As can be seen from the equations (11) and (12), the deviation of the inductance parameter does not affect the estimation of the active power, and a large estimation error exists in the reactive power
Omega and I in steady statemConstant and known. Therefore, the estimation error of the reactive power is proportional to the deviation Δ L of the inductance parameter.
And analyzing to obtain the relation between the inductance parameter deviation and the reactive power estimation error. According to the relation, the inductance parameter can be estimated on line. The inductance parameter estimation method is shown in fig. 2, and the online estimation process of the inductance parameter of the grid-connected converter comprises the following steps:
acquiring operation parameters and actual reactive power of the grid-connected converter, and calculating a grid-connected converter power grid side reactive power estimated value by using a virtual flux linkage method;
obtaining an estimation error of reactive power according to the estimated value of the reactive power at the power grid side and the actual reactive power;
obtaining a calculated inductance parameter deviation according to the direct proportion relation between the estimation error of the reactive power and the inductance parameter deviation;
and accumulating the known inductance nameplate value parameters and the inductance parameter deviation to obtain the estimated inductance parameters.
The ratio of the estimation error of the reactive power to the inductance parameter deviation is a constant, and the constant is a product value of the square of the amplitude of the current on the power grid side and the angular frequency of the voltage of the power grid.
As shown in FIG. 2, the converter output voltage is calculated according to the formula (1)
Grid side current sampled by sensor
And voltage
And terminal voltage
Is converted into an alpha beta coordinate system by Clarke to obtain
And
according to the formulas (4) - (6), (9), and the inductance nameplate value parameter L0Calculating the estimated value of the reactive power at the power grid side
According to the instantaneous power theory (8), the actual reactive power Q is calculated. Calculating the estimation error of reactive power, and calculating the deviation Delta L of inductance parameter according to the formula (12), i.e. calculating the estimation error of reactive power
According to the formula
Calculating an estimated inductance parameter
As shown in fig. 2, to eliminate steady state errors in the inductance estimate,
an alternative is a proportional integral PI controller.
Analysis of the proposed inductance estimation method: the reactive power estimation error caused by the inductance parameter change is
This error is very sensitive to the inductance deviation Δ LThis means that a very small Δ L can lead to considerable estimation errors. Therefore, the inductance deviation value is calculated more quickly and accurately by utilizing the estimation error, and the influence of the current sampling error is small.
Example two
The inductance estimation and the prediction control are combined, and a prediction control method with a grid-connected converter is introduced, and the method specifically comprises the following steps:
acquiring power grid side current and voltage at historical time;
delaying and compensating the current of the power grid side at the current moment based on a delay compensation model;
according to the power grid side current value after the time delay compensation, traversing eight preset voltage vectors of the grid-connected converter, and predicting all power grid side current values which may appear at the next moment based on a prediction model;
screening out an optimal voltage vector corresponding to the minimum system cost function according to a system cost function established by the control target current and all possible power grid side current values predicted at the next moment, and outputting an optimal switching state corresponding to the optimal voltage vector to the grid-connected converter so as to control the on-off of a switching tube of the grid-connected converter;
the delay compensation model and the prediction model are constructed by the grid-connected converter inductance parameter estimated online by the grid-connected converter inductance parameter online estimation method according to the embodiment I.
The control block diagram is shown in FIG. 3:
grid side current sampled by sensor
And voltage
Transforming to alpha beta coordinate system to obtain
And
because the digital controller has a delay of a control period, the prediction control needs to compensate the delay, and the delay compensation formula is as follows:
wherein,
for the power grid side current value at the time k +1 after the delay compensation,
for the inductive parameter to be estimated on-line,
grid side voltage at time k, R is parasitic resistance, TsIn order to control the period of the cycle,
is the grid side current at time k.
According to the current value after time delay compensation
All possible situations of the current value at the moment k +2 are predicted by traversing 8 voltage vectors of the current transformer. The prediction equation is as follows:
wherein,
the grid-side current value at time k +2,
the current value of the power grid side at the k +1 moment after the time delay compensation is carried out;
j is belonged to {1, 2.. 8} is a voltage vector which can be output by the converter and is written in advance,
is the grid side voltage at time k + 1,
for the on-line estimation of the inductance parameter, R is the parasitic resistance, TsIs a control cycle.
In the above formula
Can be composed of
Is obtained by forward pushing. Inductance parameter in formulas (13) and (14)
All are obtained by online estimation of the inductance. When the inductance nameplate value parameter is inaccurate or the inductance parameter changes, the inductance estimation method can quickly estimate the actual inductance value and is used for two steps of delay compensation and model prediction of prediction control. Therefore, the prediction control performance is not influenced by inaccurate inductance parameters, and the system robustness is improved.
Establishing a system cost function J according to a control target (namely current):
in the formula
And
obtained in step three
And
the reference value for the output current of the grid-connected converter may be generated by a preceding stage controller (such as a power controller, a dc voltage controller, etc.) of the grid-connected converter. Converting 8 voltage vectors in the third step into 8 current predicted values (
And
) The formula (15) is substituted, and one voltage vector capable of minimizing J, that is, an optimal voltage vector, is found.
The optimal switching state S corresponding to the optimal voltage vectora，Sb，ScThe output is sent to the converter to control the on-off of the switching tube of the converter.
EXAMPLE III
The embodiment provides an online estimation system for inductance parameters of a grid-connected converter, which comprises:
the reactive power calculation module is used for acquiring the operation parameters and the actual reactive power of the grid-connected converter and calculating the estimated value of the grid-connected converter reactive power on the power grid side by using a virtual flux linkage method;
the reactive power error estimation module is used for obtaining an estimation error of the reactive power according to the power grid side reactive power estimation value and the actual reactive power;
the inductance parameter deviation calculation module is used for obtaining calculated inductance parameter deviation according to the direct proportion relation between the estimation error of the reactive power and the inductance parameter deviation;
and the inductance estimation parameter module is used for accumulating the known inductance nameplate value parameters and the inductance parameter deviation to obtain the estimated inductance parameters.
It should be noted that, each module in the present embodiment corresponds to each step in the first embodiment one to one, and the specific implementation process is the same, which is not described herein again.
Example four
The embodiment provides a grid-connected converter predictive control system, which specifically comprises the following modules:
the current and voltage acquisition module is used for acquiring the current and voltage of the power grid side at historical time;
the time delay compensation module is used for time delay compensation of the current power grid side current based on the time delay compensation model;
the current value prediction module is used for traversing eight preset voltage vectors of the grid-connected converter according to the power grid side current value after the time delay compensation, and predicting all possible power grid side current values at the next moment based on a prediction model;
the voltage vector screening module is used for screening out an optimal voltage vector corresponding to the minimum system cost function according to a system cost function established by the control target current and all possible power grid side current values at the next predicted moment, and outputting an optimal switching state corresponding to the optimal voltage vector to the grid-connected converter so as to control the on-off of a switching tube of the grid-connected converter;
the delay compensation model and the prediction model are constructed by the inductance parameters of the grid-connected converter estimated by the online estimation method of the inductance parameters of the grid-connected converter according to the embodiment.
It should be noted that, each module in the present embodiment corresponds to each step in the second embodiment one to one, and the specific implementation process is the same, which is not described here again.
EXAMPLE five
The embodiment provides a computer readable storage medium, on which a computer program is stored, and the program is executed by a processor to implement the steps in the online estimation method of the grid-connected converter inductance parameter as described in the first embodiment.
EXAMPLE six
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in the grid-connected converter predictive control method according to the second embodiment described above.
EXAMPLE seven
The embodiment provides a computer device, which includes a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the steps in the online estimation method of the grid-connected converter inductance parameter according to the first embodiment.
Example eight
The embodiment provides a computer device, which includes a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the steps in the grid-connected converter prediction control method according to the second embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The grid-connected converter inductance parameter online estimation method is characterized by comprising the following steps:
acquiring operation parameters and actual reactive power of the grid-connected converter, and calculating a grid-connected converter power grid side reactive power estimated value by using a virtual flux linkage method;
obtaining an estimation error of reactive power according to the estimated value of the reactive power at the power grid side and the actual reactive power;
obtaining a calculated inductance parameter deviation according to the direct proportion relation between the estimation error of the reactive power and the inductance parameter deviation;
and accumulating the known inductance nameplate value parameters and the inductance parameter deviation to obtain the estimated inductance parameters.
2. The grid-connected converter inductance parameter online estimation method according to claim 1, wherein the ratio of the estimation error of the reactive power to the inductance parameter deviation is a constant, and the constant is the product of the square of the grid-side current amplitude and the grid voltage angular frequency.
3. A grid-connected converter prediction control method is characterized by comprising the following steps:
acquiring power grid side current and voltage at historical time;
delaying and compensating the current of the power grid side at the current moment based on a delay compensation model;
according to the power grid side current value after the time delay compensation, traversing eight preset voltage vectors of the grid-connected converter, and predicting all power grid side current values which may appear at the next moment based on a prediction model;
screening out an optimal voltage vector corresponding to the minimum system cost function according to a system cost function established by the control target current and all possible power grid side current values predicted at the next moment, and outputting an optimal switching state corresponding to the optimal voltage vector to the grid-connected converter so as to control the on-off of a switching tube of the grid-connected converter;
the method comprises the steps of obtaining a time delay compensation model and a prediction model, wherein the time delay compensation model and the prediction model are constructed by the grid-connected converter inductance parameters estimated by the grid-connected converter inductance parameter online estimation method according to any one of claims 1-2.
4. The grid-connected converter predictive control method according to claim 3, characterized in that the delay compensation model is:
wherein,
for the power grid side current value at the time k +1 after the delay compensation,
for the inductive parameter to be estimated on-line,
grid side voltage at time k, R is parasitic resistance, TsIn order to control the period of the cycle,
is the grid side current at time k.
5. The grid-connected converter predictive control method according to claim 3, characterized in that the predictive model is:
wherein,
the grid-side current value at time k +2,
the current value of the power grid side at the k +1 moment after the time delay compensation is carried out;
for a pre-written voltage vector that the converter can output,
is the grid side voltage at time k +1,
for the on-line estimation of the inductance parameter, R is the parasitic resistance, TsIs a control cycle.
6. The grid-connected converter predictive control method according to claim 3, characterized in that the system cost function is:
wherein J is a system cost function,
and
is the power grid side current value under the alpha beta coordinate system at the moment of k +2,
and
and outputting the reference value of the current for the grid-connected converter.
7. The grid-connected converter inductance parameter online estimation system is characterized by comprising:
the reactive power calculation module is used for acquiring the operation parameters and the actual reactive power of the grid-connected converter and calculating the estimated value of the grid-connected converter reactive power on the power grid side by using a virtual flux linkage method;
the reactive power error estimation module is used for obtaining an estimation error of the reactive power according to the power grid side reactive power estimation value and the actual reactive power;
the inductance parameter deviation calculation module is used for obtaining calculated inductance parameter deviation according to the direct proportion relation between the estimation error of the reactive power and the inductance parameter deviation;
and the inductance estimation parameter module is used for accumulating the known inductance nameplate value parameters and the inductance parameter deviation to obtain the estimated inductance parameters.
8. A grid-connected converter predictive control system is characterized by comprising:
the current and voltage acquisition module is used for acquiring the current and voltage of the power grid side at historical time;
the time delay compensation module is used for time delay compensation of the current power grid side current based on the time delay compensation model;
the current value prediction module is used for traversing eight preset voltage vectors of the grid-connected converter according to the power grid side current value after the time delay compensation, and predicting all possible power grid side current values at the next moment based on a prediction model;
the voltage vector screening module is used for screening out an optimal voltage vector corresponding to the minimum system cost function according to a system cost function established by the control target current and all possible power grid side current values at the next predicted moment, and outputting an optimal switching state corresponding to the optimal voltage vector to the grid-connected converter so as to control the on-off of a switching tube of the grid-connected converter;
the method comprises the steps of obtaining a time delay compensation model and a prediction model, wherein the time delay compensation model and the prediction model are constructed by the grid-connected converter inductance parameters estimated by the grid-connected converter inductance parameter online estimation method according to any one of claims 1-2.
9. A computer-readable storage medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the steps in the grid-connected converter inductance parameter online estimation method according to any one of claims 1-2;
or
The program is executed by a processor to implement the steps of the grid-connected converter predictive control method according to any one of claims 3 to 6.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the grid-connected converter inductance parameter online estimation method according to any one of claims 1-2;
or
The processor, when executing the program, implements the steps in the grid-connected converter predictive control method according to any one of claims 3 to 6.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114142589A (en) * | 2021-11-30 | 2022-03-04 | 山东美凯新能源科技有限公司 | Control method, device and equipment of optical storage converter and storage medium |
| CN115001016A (en) * | 2022-06-09 | 2022-09-02 | 山东大学 | Converter grid-connected optimization control method and system based on model-free prediction |
| CN115754484A (en) * | 2022-11-07 | 2023-03-07 | 上能电气股份有限公司 | Online monitoring method and device for inductance of optical storage system |
| CN116633126A (en) * | 2023-07-24 | 2023-08-22 | 成都希望森兰智能制造有限公司 | Power factor control method for aging system of frequency converter without network voltage sensor |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102307004A (en) * | 2011-08-22 | 2012-01-04 | 中国矿业大学 | L-capacitance-L (LCL)-filtering-based controlled rectifier parameter identification method |
| CN103208815A (en) * | 2013-04-02 | 2013-07-17 | 清华大学 | d-q axis parameter identification method for grid-connected inverter of photovoltaic power generation system |
| CN106130381A (en) * | 2016-08-23 | 2016-11-16 | 东南大学 | The control method of power feedforward prediction Direct Power based on Virtual shipyard orientation |
-
2021
- 2021-05-25 CN CN202110571165.4A patent/CN113285481B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102307004A (en) * | 2011-08-22 | 2012-01-04 | 中国矿业大学 | L-capacitance-L (LCL)-filtering-based controlled rectifier parameter identification method |
| CN103208815A (en) * | 2013-04-02 | 2013-07-17 | 清华大学 | d-q axis parameter identification method for grid-connected inverter of photovoltaic power generation system |
| CN106130381A (en) * | 2016-08-23 | 2016-11-16 | 东南大学 | The control method of power feedforward prediction Direct Power based on Virtual shipyard orientation |
Non-Patent Citations (4)
| Title |
|---|
| PATRYCJUSZ ANTONIEWICZ 等: "Virtual-Flux-Based Predictive Direct Power Control of AC/DC Converters With Online Inductance Estimation", 《IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS》 * |
| ZHANG RUIDONG 等: "On-line Monitoring of Filter Inductance Suitable for Voltage Sensor-less Direct Power Control", 《2020 IEEE/IAS INDUSTRIAL AND COMMERCIAL POWER SYSTEM ASIA (I&CPS ASIA)》 * |
| 向紫欣: "光伏并网逆变器的直接功率控制研究", 《中国优秀博硕士学位论文全文数据库(硕士)工程科技Ⅱ辑》 * |
| 李晖 等: "一种新型三相电压型PWM整流器无差拍预测直接功率控制", 《电网技术》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114142589A (en) * | 2021-11-30 | 2022-03-04 | 山东美凯新能源科技有限公司 | Control method, device and equipment of optical storage converter and storage medium |
| CN115001016A (en) * | 2022-06-09 | 2022-09-02 | 山东大学 | Converter grid-connected optimization control method and system based on model-free prediction |
| CN115754484A (en) * | 2022-11-07 | 2023-03-07 | 上能电气股份有限公司 | Online monitoring method and device for inductance of optical storage system |
| CN115754484B (en) * | 2022-11-07 | 2023-11-10 | 上能电气股份有限公司 | On-line monitoring method and device for inductance of optical storage system |
| CN116633126A (en) * | 2023-07-24 | 2023-08-22 | 成都希望森兰智能制造有限公司 | Power factor control method for aging system of frequency converter without network voltage sensor |
| CN116633126B (en) * | 2023-07-24 | 2023-10-17 | 成都希望森兰智能制造有限公司 | Power factor control method for aging system of frequency converter without network voltage sensor |
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