CN114865888B - Power feedforward inductance parameter identification method and system for energy storage converter - Google Patents

Power feedforward inductance parameter identification method and system for energy storage converter Download PDF

Info

Publication number
CN114865888B
CN114865888B CN202210807930.2A CN202210807930A CN114865888B CN 114865888 B CN114865888 B CN 114865888B CN 202210807930 A CN202210807930 A CN 202210807930A CN 114865888 B CN114865888 B CN 114865888B
Authority
CN
China
Prior art keywords
parameter
energy storage
inductance
output current
storage converter
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.)
Active
Application number
CN202210807930.2A
Other languages
Chinese (zh)
Other versions
CN114865888A (en
Inventor
张杰明
陈显超
梁妍陟
王辉
李小燕
何启洪
钟榜
汤健东
秦熙
淡言亮
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhaoqing Power Supply Bureau of Guangdong Power Grid Co Ltd
Original Assignee
Zhaoqing Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Zhaoqing Power Supply Bureau of Guangdong Power Grid Co Ltd filed Critical Zhaoqing Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority to CN202210807930.2A priority Critical patent/CN114865888B/en
Publication of CN114865888A publication Critical patent/CN114865888A/en
Application granted granted Critical
Publication of CN114865888B publication Critical patent/CN114865888B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
    • H02M1/00—Details of apparatus for conversion
    • H02M1/0003—Details of control, feedback or regulation circuits
    • H02M1/0016—Control circuits providing compensation of output voltage deviations using feedforward of disturbance parameters
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
    • H02M3/00—Conversion of dc power input into dc power output
    • H02M3/22—Conversion of dc power input into dc power output with intermediate conversion into ac
    • H02M3/24—Conversion of dc power input into dc power output with intermediate conversion into ac by static converters
    • H02M3/28—Conversion of dc power input into dc power output with intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode to produce the intermediate ac
    • H02M3/325—Conversion of dc power input into dc power output with intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode to produce the intermediate ac using devices of a triode or a transistor type requiring continuous application of a control signal
    • H02M3/335—Conversion of dc power input into dc power output with intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode to produce the intermediate ac using devices of a triode or a transistor type requiring continuous application of a control signal using semiconductor devices only
    • H02M3/33507—Conversion of dc power input into dc power output with intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode to produce the intermediate ac using devices of a triode or a transistor type requiring continuous application of a control signal using semiconductor devices only with automatic control of the output voltage or current, e.g. flyback converters
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
    • H02M3/00—Conversion of dc power input into dc power output
    • H02M3/22—Conversion of dc power input into dc power output with intermediate conversion into ac
    • H02M3/24—Conversion of dc power input into dc power output with intermediate conversion into ac by static converters
    • H02M3/28—Conversion of dc power input into dc power output with intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode to produce the intermediate ac
    • H02M3/325—Conversion of dc power input into dc power output with intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode to produce the intermediate ac using devices of a triode or a transistor type requiring continuous application of a control signal
    • H02M3/335—Conversion of dc power input into dc power output with intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode to produce the intermediate ac using devices of a triode or a transistor type requiring continuous application of a control signal using semiconductor devices only
    • H02M3/33507—Conversion of dc power input into dc power output with intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode to produce the intermediate ac using devices of a triode or a transistor type requiring continuous application of a control signal using semiconductor devices only with automatic control of the output voltage or current, e.g. flyback converters
    • H02M3/33523—Conversion of dc power input into dc power output with intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode to produce the intermediate ac using devices of a triode or a transistor type requiring continuous application of a control signal using semiconductor devices only with automatic control of the output voltage or current, e.g. flyback converters with galvanic isolation between input and output of both the power stage and the feedback loop
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Dc-Dc Converters (AREA)

Abstract

The invention provides a power feedforward inductance parameter identification method and system for an isolated direct current energy storage converter, wherein the method provided by the invention comprises the steps of determining a first output current model under a corresponding modulation mode and a second output current model under a steady state condition based on power models of the isolated direct current energy storage converter under different modulation modes; then determining constraint conditions met by the isolation type direct current energy storage converter in the actual discrete sampling system and defining a first parameter related to a control period and a second parameter related to output current; and taking the proportionality coefficient of the first parameter and the second parameter as the series inductance parameter of the isolated DC energy storage converter. The method can realize real-time on-line identification of the series inductance parameters of the isolated DC energy storage converter to improve the accuracy of an advanced control algorithm, and can eliminate identification errors caused by factors such as switch dead zones, parasitic parameters, device voltage drop and the like and improve the accuracy of identification of the inductance parameters.

Description

Power feedforward inductance parameter identification method and system for energy storage converter
Technical Field
The invention belongs to the technical field of energy storage converters, and particularly relates to a power feedforward inductance parameter identification method and system for an isolated direct current energy storage converter.
Background
Due to the continuous progress of society, the demand of human beings for new energy is highly concerned among countries in the world. How to effectively develop, store and utilize energy is always a difficult problem which needs to be solved urgently. With the development of China in the aspects of energy consumption, energy conservation and emission reduction, strong driving force is injected for the rapid development of the smart grid. With the development of smart power grids, micro power grids are paid more and more attention as one of important achievements, and operation, maintenance, economic cost and stability of a power system are directly influenced by adjusting light storage charging and discharging of the micro power grid system under the condition of considering time-of-use electricity price.
At present, the battery energy storage technology is developed at a high speed, and a large number of battery energy storage systems are configured at a power plant and a user side for smoothing output fluctuation or reducing electricity consumption cost and the like. Accordingly, various advanced control algorithms are proposed: if students propose various micro power supply strategies of optimal operation, the annual cost expense of the micro power grid is minimized; scholars propose to optimize the micro-grid dispatching and improve the utilization efficiency of renewable energy sources; the scholars propose a V2G (vehicle to grid) scheme with multiple operation modes facing users, and the peak value of power utilization is reduced.
Although various advanced control algorithms have been proposed in succession for energy storage microgrid systems. However, in these advanced control methods, the isolated dc energy storage converter often needs accurate inductance parameters to realize precise control of the converter. The accuracy and control performance of these advanced control algorithms can be compromised if the inductance parameter is biased. Therefore, how to realize the online accurate identification of the series inductance parameters of the isolated DC energy storage converter is a key problem to be solved urgently.
Disclosure of Invention
In view of this, the present invention is directed to solve the problem that, in various advanced control methods for an energy storage microgrid system, the accuracy and the control performance of advanced control algorithms may be reduced due to the occurrence of offset in inductance parameters of an isolated dc energy storage converter.
In order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, the present invention provides a method for identifying a power feedforward inductance parameter of an energy storage converter, which is suitable for an isolated dc energy storage converter, and includes the following steps:
determining a first output current model under a corresponding modulation mode based on power models of the isolated DC energy storage converter under different modulation modes;
determining a second output current model under a steady-state condition according to the first output current model;
determining constraint conditions met by the isolated DC energy storage converter in the actual discrete sampling system based on the second output circuit model, wherein the constraint conditions are used for determining constraint relations among a control period, output current and series inductance parameters;
defining a first parameter and a second parameter of the output current respectively, wherein the first parameter and the second parameter are in a direct proportion relation, and the first parameter and the second parameter are related to the control period based on the constraint condition;
and taking the proportionality coefficient of the first parameter and the second parameter as the series inductance parameter of the isolated DC energy storage converter.
Further, if the modulation mode is single phase shift modulation, the second output current model specifically includes:
Figure 514859DEST_PATH_IMAGE001
in the formula (I), the compound is shown in the specification,
Figure 100561DEST_PATH_IMAGE002
which is indicative of the output current of the power converter,
Figure 327143DEST_PATH_IMAGE003
the battery side voltage of the isolated dc energy storage converter is shown,
Figure 631085DEST_PATH_IMAGE004
indicating a control period，
Figure 171788DEST_PATH_IMAGE005
Which represents the ratio of the transformation of the converter,
Figure 611997DEST_PATH_IMAGE006
the series inductance of the isolated DC energy storage converter is shown,
Figure 275059DEST_PATH_IMAGE007
the amount of phase shift is indicated.
Further, the expression of the constraint condition is specifically:
Figure 800719DEST_PATH_IMAGE008
in the formula (I), the compound is shown in the specification,
Figure 207429DEST_PATH_IMAGE009
a steady state value representing the amount of phase shift during the kth sampling period,
Figure 439827DEST_PATH_IMAGE010
and
Figure 273791DEST_PATH_IMAGE011
representing the steady state values of the output current and the battery voltage at the kth sampling period.
Further, a first parameter related to the control period and a second parameter related to the output current are respectively defined based on the constraint condition, specifically according to the following formula:
Figure 286747DEST_PATH_IMAGE012
in the formula (I), the compound is shown in the specification,
Figure 169252DEST_PATH_IMAGE013
is a function of the first parameter and is,
Figure 584053DEST_PATH_IMAGE014
is the second parameter.
Further, still include:
and (3) carrying out adaptive filtering on the series inductance parameters by utilizing an inductance parameter identification adaptive filtering algorithm based on a recursive least square algorithm, wherein the adaptive filtering is carried out according to the following formula:
Figure 588918DEST_PATH_IMAGE015
in the formula (I), the compound is shown in the specification,
Figure 89169DEST_PATH_IMAGE016
indicating the error for the k-th sampling period,
Figure 775366DEST_PATH_IMAGE017
the inductance parameter of the recursive least square algorithm represents the inductance calculated quantity of the kth sampling period under the inductance parameter identification adaptive filtering algorithm,
Figure 44673DEST_PATH_IMAGE018
inductance calculation amount and forgetting factor of k-1 sampling period under inductance parameter identification adaptive filtering algorithm representing recursive least square algorithm
Figure 220439DEST_PATH_IMAGE019
Used for adjusting the influence degree of the original data on the calculation result of the current period, as a forgetting factor
Figure 145670DEST_PATH_IMAGE019
When approaching 1, the representation calculation result depends more on the previous data, and when forgetting the factor
Figure 432295DEST_PATH_IMAGE019
When the value is close to 0, the calculation result is more dependent on the latest data;
Figure 821688DEST_PATH_IMAGE020
and
Figure 902776DEST_PATH_IMAGE021
respectively representing a first intermediate variable value and a second intermediate variable value for the kth sampling period,
Figure 315303DEST_PATH_IMAGE022
representing the first intermediate variable value for the (k-1) th sampling period.
In a second aspect, the present invention provides a power feedforward inductance parameter identification system for an energy storage converter, which is suitable for an isolated dc energy storage converter, and includes:
the first output current model determining unit is used for determining a first output current model in a corresponding modulation mode based on power models of the isolated DC energy storage converter in different modulation modes;
a second output current model determination unit for determining a second output current model under a steady-state condition according to the first output current model;
the constraint condition determining unit is used for determining a constraint condition which is met by the isolated DC energy storage converter in the actual discrete sampling system based on the second output circuit model, and the constraint condition is used for determining a constraint relation among a control period, output current and series inductance parameters;
a parameter defining unit for defining a first parameter regarding the control period and a second parameter regarding the output current, respectively, based on a constraint condition, the first parameter and the second parameter being in a direct proportional relationship;
and the inductance parameter identification unit is used for taking the proportionality coefficient of the first parameter and the second parameter as the series inductance parameter of the isolated DC energy storage converter.
Further, if the modulation mode is single phase shift modulation, the second output current model determined by the second output current model determining unit specifically includes:
Figure 405619DEST_PATH_IMAGE001
in the formula (I), the compound is shown in the specification,
Figure 649518DEST_PATH_IMAGE002
which is representative of the output current of the power supply,
Figure 104771DEST_PATH_IMAGE003
the battery side voltage of the isolated dc energy storage converter is shown,
Figure 801331DEST_PATH_IMAGE004
it is indicated that the control period is,
Figure 429759DEST_PATH_IMAGE005
which represents the ratio of the transformation of the converter,
Figure 793744DEST_PATH_IMAGE006
the series inductance of the isolated DC energy storage converter is shown,
Figure 419897DEST_PATH_IMAGE007
the amount of phase shift is indicated.
Further, the expression of the constraint condition determined by the constraint condition determining unit is specifically:
Figure 603754DEST_PATH_IMAGE008
in the formula (I), the compound is shown in the specification,
Figure 35872DEST_PATH_IMAGE009
a steady state value representing the amount of phase shift for the kth sampling period,
Figure 192047DEST_PATH_IMAGE010
and
Figure 51419DEST_PATH_IMAGE011
representing the steady state values of the output current and the battery voltage at the kth sampling period.
Further, the parameter definition unit defines a first parameter and a second parameter, specifically according to the following formula:
Figure 722571DEST_PATH_IMAGE012
in the formula (I), the compound is shown in the specification,
Figure 630485DEST_PATH_IMAGE013
is a first parameter of the plurality of parameters,
Figure 703483DEST_PATH_IMAGE014
is the second parameter.
Further, still include:
the adaptive filtering unit is used for carrying out adaptive filtering on the series inductance parameters by utilizing an inductance parameter identification adaptive filtering algorithm based on a recursive least square algorithm, and the adaptive filtering is carried out according to the following formula:
Figure 733756DEST_PATH_IMAGE015
in the formula (I), the compound is shown in the specification,
Figure 829888DEST_PATH_IMAGE016
indicating the error for the k-th sampling period,
Figure 603809DEST_PATH_IMAGE017
inductance parameters representing the recursive least square algorithm identify the inductance calculation amount of the kth sampling period under the adaptive filtering algorithm,
Figure 531313DEST_PATH_IMAGE018
inductance calculation amount and forgetting factor of k-1 sampling period under inductance parameter identification adaptive filtering algorithm representing recursive least square algorithm
Figure 732488DEST_PATH_IMAGE019
Used for adjusting the influence degree of the original data on the calculation result of the current period, as a forgetting factor
Figure 315916DEST_PATH_IMAGE019
When approaching 1, the calculation result is more dependent on the previous data, andwhen forgetting factor
Figure 893528DEST_PATH_IMAGE019
When the value is close to 0, the calculation result is more dependent on the latest data;
Figure 675539DEST_PATH_IMAGE020
and
Figure 47614DEST_PATH_IMAGE021
respectively representing a first intermediate variable value and a second intermediate variable value for the kth sampling period,
Figure 118338DEST_PATH_IMAGE022
representing the first intermediate variable value for the (k-1) th sampling period.
In summary, the present invention provides a method and a system for identifying a power feedforward inductance parameter of an isolated dc energy storage converter, wherein the method provided by the present invention includes determining a first output current model in a corresponding modulation mode based on power models of the isolated dc energy storage converter in different modulation modes; determining a second output current model under a steady-state condition according to the first output current model; determining constraint conditions met by the isolated DC energy storage converter in the actual discrete sampling system based on the second output circuit model; defining a first parameter and a second parameter of the output current respectively, wherein the first parameter and the second parameter are in a direct proportion relation, and the first parameter and the second parameter are related to the control period based on the constraint condition; and taking the proportionality coefficient of the first parameter and the second parameter as the series inductance parameter of the isolated DC energy storage converter. The method can realize real-time on-line identification of the series inductance parameters of the isolated DC energy storage converter so as to improve the accuracy of an advanced control algorithm, and can eliminate identification errors caused by factors such as switch dead zones, parasitic parameters, device voltage drop and the like and improve the accuracy of inductance parameter identification.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a schematic flowchart of a method for identifying a power feedforward inductance parameter for an isolated dc energy storage converter according to an embodiment of the present invention;
fig. 2 is a circuit structure diagram of an isolated dc energy storage converter according to an embodiment of the present invention;
fig. 3 is a control block diagram of a power feedforward inductance parameter identification method according to an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Due to the continuous progress of society, the demand of human beings for new energy is highly concerned among countries in the world. How to effectively develop, store and utilize energy is a difficult problem which needs to be solved urgently. With the development of China in the aspects of energy consumption, energy conservation and emission reduction, strong driving force is injected for the rapid development of the smart grid. With the development of smart power grids, micro power grids are paid more and more attention as one of important achievements, and operation, maintenance, economic cost and stability of a power system are directly influenced by adjusting light storage charging and discharging of the micro power grid system under the condition of considering time-of-use electricity price.
At present, the battery energy storage technology is developed at a high speed, and a large number of battery energy storage systems are configured at a power plant and a user side and used for smoothing output fluctuation or reducing electricity consumption cost and the like. Accordingly, various advanced control algorithms are proposed: aiming at the problem that the flexibility and the economy are cooperated with each other in the energy optimization scheduling method of the micro-grid, students propose various micro-power supply strategies for optimal operation, so that the annual cost of the micro-grid is minimum. Meanwhile, the load response of the demand side is considered, an internal search algorithm is improved, and the micro-grid is optimized. In addition, scholars propose that optimizing the micro-grid dispatching is one of important ways for improving the utilization efficiency of renewable energy sources, and propose a particle swarm optimization algorithm based on which a micro-grid system including a photo-electricity, a wind power, a diesel engine and a battery is optimized. Meanwhile, the degree of power consumption requirements is getting larger and larger, and in order to reduce the high peak value of the power consumption, a scholars puts forward a V2G (vehicle-to-grid) scheme of multiple operation modes facing users, so that more EVs participate in V2G operation to carry out coordination charging, peak clipping and valley filling to reduce the peak value of the grid. In addition, wind power generation is considered, the optimal running state of the energy storage SOC (state of charge) is taken as a target in the face of variable power change, PSO (particle swarm optimization) is selected to optimize the capacity of the energy storage system, and solution calculation is carried out. Further, in the face of the economic dispatching problem of the power grid, the PSO optimization algorithm is improved for solving. And optimizing the particle swarm algorithm to optimize the capacity configuration by considering self-balancing constraint. And carrying out optimization solution by utilizing a wolf algorithm aiming at the time-of-use electricity price, and realizing the economic optimum.
Although various advanced control algorithms have been proposed in succession for energy storage microgrid systems. However, in these advanced control methods, the isolated dc energy storage converter often needs accurate inductance parameters to realize precise control of the converter. The accuracy and control performance of these advanced control algorithms can be compromised if the inductance parameter is biased. Therefore, how to realize the online accurate identification of the series inductance parameters of the isolated direct current energy storage converter is a key problem to be solved urgently.
Based on the method, the invention provides a method and a system for identifying the parameters of the power feedforward inductance of the isolated DC energy storage converter.
Firstly, for isolated DC energy-storage converterThe circuit structure will be briefly explained. As shown in fig. 2, in the circuit structure of the isolated dc energy storage converter,
Figure 234062DEST_PATH_IMAGE023
the output power of the isolated DC energy storage converter is shown,
Figure 136159DEST_PATH_IMAGE003
and
Figure 616819DEST_PATH_IMAGE024
respectively representing the battery side voltage and the output voltage of the isolated DC energy storage converter,
Figure 237156DEST_PATH_IMAGE004
it is indicated that the control period is,
Figure 890991DEST_PATH_IMAGE005
which represents the transformation ratio of the transformer,
Figure 585278DEST_PATH_IMAGE006
the series inductance of the isolated DC energy storage converter is shown,
Figure 564735DEST_PATH_IMAGE025
indicating the amount of phase shift under single phase shift modulation.
The following describes an embodiment of a method for identifying a power feedforward inductance parameter for an isolated dc energy storage converter according to the present invention in detail.
Referring to fig. 1, the present embodiment provides a method for identifying a power feedforward inductance parameter of an isolated dc energy storage converter, including the following steps:
s100: and determining a first output current model under the corresponding modulation mode based on the power models of the isolated DC energy storage converter under different modulation modes.
For the isolated DC energy storage converter, the modulation modes comprise single phase shift modulation, double phase shift modulation and triple phase shift modulation. The converter has a different number of controllable degrees of freedom in different modulation modes. Under the condition of steady-state working, the isolated direct-current energy storage converter realizes the charging and discharging of the energy storage battery module by controlling the charging and discharging current of the energy storage battery. Taking single phase shift modulation as an example, the power model of the isolated dc storage converter can be expressed as,
Figure 406789DEST_PATH_IMAGE026
（1）
wherein the content of the first and second substances,
Figure 864315DEST_PATH_IMAGE023
the output power of the isolated DC energy storage converter is shown,
Figure 678688DEST_PATH_IMAGE003
and
Figure 563467DEST_PATH_IMAGE024
respectively representing the battery side voltage and the output voltage of the isolated DC energy storage converter,
Figure 916255DEST_PATH_IMAGE004
it is indicated that the control period is,
Figure 177472DEST_PATH_IMAGE005
which represents the transformation ratio of the transformer,
Figure 846350DEST_PATH_IMAGE006
the series inductance of the isolated DC energy storage converter is shown,
Figure 902031DEST_PATH_IMAGE025
indicating the amount of phase shift under single phase shift modulation.
On the basis, the output current model of the isolated DC energy storage converter under the condition of single phase shift modulation can be expressed as,
Figure 718677DEST_PATH_IMAGE027
（2）
wherein the content of the first and second substances,
Figure 455689DEST_PATH_IMAGE028
and the output current of the isolated direct current energy storage converter under the condition of single phase shift modulation is shown.
From the above formula, under the steady-state operation condition, the load current of the isolated energy storage dc converter is independent of the output voltage, that is, the optimal phase shift amount of the converter can be further derived according to the load current of the converter. In the conventional output voltage closed-loop control method, the dynamic response speed of the converter is relatively slow because the phase shift amount 1 of the converter is mainly adjusted according to the error of the output voltage. When the load suddenly changes, the change of the output voltage is relatively small, so the phase shift amount of the converter cannot be quickly adjusted. However, when the load of the converter suddenly changes, the output current of the converter can suddenly change. The optimum amount of phase shift of the converter can thus be derived from the output current, i.e.,
Figure 41391DEST_PATH_IMAGE029
（3）
wherein the content of the first and second substances,
Figure 533553DEST_PATH_IMAGE030
the output current reference of the isolated DC energy storage converter under the condition of single phase shift modulation is shown to meet the requirement,
Figure 837495DEST_PATH_IMAGE031
（4）
s200: a second output current model under steady state conditions is determined from the first output current model.
According to the output current model of the isolated DC energy storage converter under the condition of single phase shift modulation, under the steady state condition, the output current model can be further expressed as,
Figure 112618DEST_PATH_IMAGE032
（5）
s300: and determining constraint conditions met by the isolated DC energy storage converter in the actual discrete sampling system based on the second output circuit model, wherein the constraint conditions are used for determining constraint relations among the control period, the output current and the series inductance parameters.
Based on the second output circuit model of step S200, further, the output current model can be expressed as,
Figure 818406DEST_PATH_IMAGE033
（6）
considering that in an actual discrete sampling system, the output current of the isolated energy storage dc transformer, the battery side voltage and the phase shift amount should satisfy a constraint condition, that is,
Figure 215890DEST_PATH_IMAGE034
（7）
wherein, the first and the second end of the pipe are connected with each other,
Figure 944811DEST_PATH_IMAGE009
a steady state value representing the amount of phase shift for the kth sampling period,
Figure 85943DEST_PATH_IMAGE010
and
Figure 646237DEST_PATH_IMAGE011
representing the steady state values of the output current and the battery voltage at the kth sampling period.
S400: a first parameter and a second parameter of the output current with respect to the control period are respectively defined based on the constraint condition, and the first parameter and the second parameter are in a direct proportional relationship.
In order to facilitate the realization of the on-line parameter identification of the series inductance, the series inductance is respectively defined
Figure 417884DEST_PATH_IMAGE013
And
Figure 430839DEST_PATH_IMAGE014
satisfy the following requirements
Figure 375661DEST_PATH_IMAGE013
And
Figure 790462DEST_PATH_IMAGE014
proportional relation is formed, and the proportionality coefficient is the series inductance coefficient,
Figure 795327DEST_PATH_IMAGE035
（8）
s500: and taking the proportionality coefficient of the first parameter and the second parameter as the series inductance parameter of the isolated DC energy storage converter.
In addition, sampling noise always exists in the practical implementation of the inductance parameter identification algorithm, so that the sampling noise also exists in the calculation of the inductance parameter. Therefore, in order to realize mutual decoupling of the dynamic response of the on-line identification of the inductance parameters and the dynamic response of the control loop, an inductance parameter identification adaptive filtering algorithm based on a recursion least square algorithm is provided so as to realize real-time identification of the series inductance parameters. Specifically, the inductance parameter identification adaptive filtering algorithm for realizing the recursive least square algorithm comprises the following steps,
Figure 233262DEST_PATH_IMAGE036
（9）
in the formula (I), the compound is shown in the specification,
Figure 716196DEST_PATH_IMAGE016
indicating the error for the k-th sampling period,
Figure 251082DEST_PATH_IMAGE017
the inductance parameter of the recursive least square algorithm represents the inductance calculated quantity of the kth sampling period under the inductance parameter identification adaptive filtering algorithm,
Figure 364532DEST_PATH_IMAGE018
inductance calculation amount and forgetting factor of k-1 sampling period under inductance parameter identification adaptive filtering algorithm representing recursive least square algorithm
Figure 352079DEST_PATH_IMAGE019
Used for adjusting the influence degree of the original data on the calculation result of the current period, as a forgetting factor
Figure 638704DEST_PATH_IMAGE019
When approaching 1, the representation calculation result depends more on the previous data, and when forgetting the factor
Figure 762518DEST_PATH_IMAGE019
When the value is close to 0, the calculation result is more dependent on the latest data;
Figure 46869DEST_PATH_IMAGE020
and
Figure 521713DEST_PATH_IMAGE021
respectively representing a first intermediate variable value and a second intermediate variable value for the kth sampling period,
Figure 612028DEST_PATH_IMAGE022
representing the first intermediate variable value for the (k-1) th sampling period.
Based on the steps, the control method for the isolated type direct current energy storage converter comprises the steps of firstly detecting input voltage, output voltage and output current of the isolated type energy storage direct current transformer; then, a proportional-integral controller of an output voltage closed loop is adopted to realize real-time control; simultaneously, combining the output voltage, the input voltage and the output current obtained by sampling, and identifying the inductance parameter of the converter in real time by adopting an inductance parameter identification adaptive filtering algorithm based on a recursive least square algorithm; and finally, based on the obtained identification inductance parameter, combining the deduced current model of the isolated energy storage direct current transformer to complete the power feedforward control of the converter so as to improve the dynamic response speed of the energy storage direct current converter.
FIG. 3 is a control block diagram of the proposed inductance parameter identification method, in which the output voltage v of the converter is measured o And a reference voltage v ref Comparing, sending the voltage error to a proportional integral controller Gc(s), calculating a phase shift value phi for realizing the voltage control of the converter, and then sampling the output current of the converter to pass through a current feedforward controller in a formula (3) to output a feedforward phase shift value phi m The obtained final phase shift phi is superposed, the actual output current of the converter can be obtained through the phase shift phi-output voltage transfer function of the converter, and then the equivalent output impedance model Z is converted out (s) the actual output voltage is obtained.
The embodiment provides a power feedforward inductance parameter identification method for an isolated direct current energy storage converter, which takes a power feedforward control algorithm as an example, and firstly analyzes a power model of the isolated direct current energy storage converter under the traditional single-phase shift modulation, and further deduces a corresponding current model. On the basis, a feedforward phase shift amount calculation method of the converter under a power feedforward control algorithm is deduced; meanwhile, an identification model of the series inductor is deduced on the basis of a current model of the direct-current energy storage converter; in order to improve identification accuracy and suppress sampling noise, an inductance parameter identification adaptive filtering algorithm based on a recursive least square algorithm is provided so as to realize real-time identification of series inductance parameters.
The method for identifying the power feedforward inductance parameter of the isolated DC energy storage converter can realize real-time online identification of the series inductance parameter of the isolated DC energy storage converter so as to improve the accuracy of an advanced control algorithm, and meanwhile, the method can eliminate identification errors caused by factors such as switch dead zones, parasitic parameters, device voltage drop and the like, and improve the accuracy of inductance parameter identification.
The above is a detailed description of an embodiment of the present invention of a power feedforward inductance parameter identification method for an isolated dc energy storage converter, and the following is a detailed description of an embodiment of the present invention of a power feedforward inductance parameter identification system for an isolated dc energy storage converter.
The embodiment provides a power feedforward inductance parameter identification system for an energy storage converter, which is suitable for an isolated direct current energy storage converter and comprises the following components:
and the first output current model determining unit is used for determining a first output current model in a corresponding modulation mode based on the power models of the isolated DC energy storage converter in different modulation modes.
And the second output current model determining unit is used for determining a second output current model under the steady-state condition according to the first output current model.
It should be noted that, if the modulation mode is single phase shift modulation, the second output current model determined by the second output current model determining unit specifically includes:
Figure 793611DEST_PATH_IMAGE001
in the formula (I), the compound is shown in the specification,
Figure 45601DEST_PATH_IMAGE002
which is indicative of the output current of the power converter,
Figure 7741DEST_PATH_IMAGE003
the battery side voltage of the isolated DC energy storage converter is shown,
Figure 636168DEST_PATH_IMAGE004
it is indicated that the control period is,
Figure 937836DEST_PATH_IMAGE005
which represents the ratio of the transformation of the converter,
Figure 360727DEST_PATH_IMAGE006
the series inductance of the isolated DC energy storage converter is shown,
Figure 810163DEST_PATH_IMAGE007
the amount of phase shift is indicated.
And the constraint condition determining unit is used for determining a constraint condition which is met by the isolated DC energy storage converter in the actual discrete sampling system based on the second output circuit model, and the constraint condition is used for determining a constraint relation among the control period, the output current and the series inductance parameter.
It should be noted that the expression of the constraint condition determined by the constraint condition determining unit is specifically:
Figure 242282DEST_PATH_IMAGE008
in the formula (I), the compound is shown in the specification,
Figure 132877DEST_PATH_IMAGE009
a steady state value representing the amount of phase shift during the kth sampling period,
Figure 992249DEST_PATH_IMAGE010
and
Figure 663402DEST_PATH_IMAGE011
representing the steady state values of the output current and the battery voltage at the kth sampling period.
And the parameter definition unit is used for respectively defining a first parameter and a second parameter of the output current relative to the control period based on the constraint condition, and the first parameter and the second parameter are in a direct proportional relation.
It should be noted that, the parameter defining unit defines the first parameter and the second parameter, and specifically performs according to the following formula:
Figure 899211DEST_PATH_IMAGE012
in the formula (I), the compound is shown in the specification,
Figure 909892DEST_PATH_IMAGE013
is a first parameter of the plurality of parameters,
Figure 940165DEST_PATH_IMAGE014
is the second parameter.
And the inductance parameter identification unit is used for taking the proportionality coefficient of the first parameter and the second parameter as the series inductance parameter of the isolated direct current energy storage converter.
In addition, the system provided in this embodiment further includes an adaptive filtering unit, configured to perform adaptive filtering on the series inductance parameter by using an inductance parameter identification adaptive filtering algorithm based on a recursive least square algorithm, where the adaptive filtering is performed according to the following equation:
Figure 98614DEST_PATH_IMAGE015
in the formula (I), the compound is shown in the specification,
Figure 872535DEST_PATH_IMAGE016
indicating the error for the k-th sampling period,
Figure 737723DEST_PATH_IMAGE017
inductance parameters representing the recursive least square algorithm identify the inductance calculation amount of the kth sampling period under the adaptive filtering algorithm,
Figure 938897DEST_PATH_IMAGE018
inductance calculation amount and forgetting factor of k-1 sampling period under inductance parameter identification adaptive filtering algorithm representing recursive least square algorithm
Figure 584642DEST_PATH_IMAGE019
Used for adjusting the influence degree of the original data on the calculation result of the current period, as a forgetting factor
Figure 834358DEST_PATH_IMAGE019
When approaching 1, the representation calculation result depends more on the previous data, and when the forgetting factor is
Figure 881948DEST_PATH_IMAGE019
When the value is close to 0, the calculation result is more dependent on the latest data;
Figure 254024DEST_PATH_IMAGE020
and
Figure 387065DEST_PATH_IMAGE021
respectively representing a first intermediate variable value and a second intermediate variable value for the kth sampling period,
Figure 440471DEST_PATH_IMAGE022
representing the first intermediate variable value for the (k-1) th sampling period.
It should be noted that, the inductance parameter identification system provided in this embodiment is used to implement the inductance parameter identification method provided in the foregoing embodiment, and the specific configuration of each unit is subject to complete implementation of the method, which is not described herein again.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A method for identifying power feedforward inductance parameters of an energy storage converter is suitable for an isolated direct current energy storage converter and is characterized by comprising the following steps:
determining a first output current model under a corresponding modulation mode based on power models of the isolated DC energy storage converter under different modulation modes;
determining a second output current model under a steady-state condition according to the first output current model;
determining constraint conditions met by the isolated DC energy storage converter in the actual discrete sampling system based on the second output current model, wherein the constraint conditions are used for determining constraint relations among a control period, output current and series inductance parameters;
defining a first parameter and a second parameter of the output current respectively with respect to the control period based on the constraint condition, the first parameter and the second parameter being in a direct proportional relationship;
and taking the proportionality coefficient of the first parameter and the second parameter as the series inductance parameter of the isolated direct current energy storage converter.
2. A method as claimed in claim 1, wherein if the modulation scheme is single phase shift modulation, the second output current model is specifically:
Figure 534425DEST_PATH_IMAGE001
in the formula (I), the compound is shown in the specification,
Figure 79675DEST_PATH_IMAGE002
which is representative of the output current of the power supply,
Figure 178344DEST_PATH_IMAGE003
the battery side voltage of the isolated DC energy storage converter is shown,
Figure 291793DEST_PATH_IMAGE004
it is indicated that the control period is,
Figure 341658DEST_PATH_IMAGE005
which represents the ratio of the transformation of the converter,
Figure 565966DEST_PATH_IMAGE006
the series inductance of the isolated DC energy storage converter is shown,
Figure 253561DEST_PATH_IMAGE007
the amount of phase shift is indicated.
3. A method for identifying parameters of a power feed forward inductance for an energy storage converter as claimed in claim 2, wherein the constraint is expressed by the following expression:
Figure 662546DEST_PATH_IMAGE008
in the formula (I), the compound is shown in the specification,
Figure 75073DEST_PATH_IMAGE009
a steady state value representing the amount of phase shift during the kth sampling period,
Figure 729170DEST_PATH_IMAGE010
and
Figure 910753DEST_PATH_IMAGE011
representing the steady state values of the output current and the battery voltage at the kth sampling period.
4. A power feed forward inductance parameter identification method for an energy storage converter according to claim 3, characterized in that the first parameter related to the control period and the second parameter related to the output current are respectively defined based on the constraint conditions, and specifically according to the following formula:
Figure 225059DEST_PATH_IMAGE012
in the formula (I), the compound is shown in the specification,
Figure 750981DEST_PATH_IMAGE013
in order to be able to determine the first parameter,
Figure 317091DEST_PATH_IMAGE014
is the second parameter.
5. A power feed forward inductance parameter identification method for a power storage converter as claimed in claim 4, further comprising:
and carrying out adaptive filtering on the series inductance parameters by using an inductance parameter identification adaptive filtering algorithm based on a recursive least square algorithm, wherein the adaptive filtering is carried out according to the following formula:
Figure 743394DEST_PATH_IMAGE015
in the formula (I), the compound is shown in the specification,
Figure 103968DEST_PATH_IMAGE016
indicating the error for the k-th sampling period,
Figure 117185DEST_PATH_IMAGE017
the inductance parameter of the recursive least square algorithm represents the inductance calculated quantity of the kth sampling period under the inductance parameter identification adaptive filtering algorithm,
Figure 611621DEST_PATH_IMAGE018
inductance calculation amount and forgetting factor of k-1 sampling period under inductance parameter identification adaptive filtering algorithm representing recursive least square algorithm
Figure 502216DEST_PATH_IMAGE019
Used for adjusting the influence degree of the original data on the calculation result of the current period, as a forgetting factor
Figure 925370DEST_PATH_IMAGE019
When approaching 1, the representation calculation result depends more on the previous data, and when the forgetting factor is
Figure 534205DEST_PATH_IMAGE019
When the value is close to 0, the calculation result is more dependent on the latest data;
Figure 832332DEST_PATH_IMAGE020
and
Figure 469112DEST_PATH_IMAGE021
respectively representing a first intermediate variable value and a second intermediate variable value for the kth sampling period,
Figure 437068DEST_PATH_IMAGE022
representing the first intermediate variable value for the (k-1) th sampling period.
6. A power feedforward inductance parameter identification system for an energy storage converter, suitable for an isolated DC energy storage converter, comprising:
the first output current model determining unit is used for determining a first output current model in a corresponding modulation mode based on power models of the isolated direct-current energy storage converter in different modulation modes;
a second output current model determination unit for determining a second output current model under a steady-state condition according to the first output current model;
the constraint condition determining unit is used for determining a constraint condition which is met by the isolated direct-current energy storage converter in the actual discrete sampling system based on the second output current model, and the constraint condition is used for determining a constraint relation among a control period, output current and series inductance parameters;
a parameter defining unit for defining a first parameter regarding a control period and a second parameter regarding an output current, respectively, based on the constraint condition, the first parameter and the second parameter being in a direct proportional relationship;
and the inductance parameter identification unit is used for taking the proportionality coefficient of the first parameter and the second parameter as the series inductance parameter of the isolated DC energy storage converter.
7. A power feedforward inductance parameter identification system for an energy storage converter according to claim 6, wherein if the modulation mode is single phase shift modulation, the second output current model determined by the second output current model determining unit is specifically:
Figure 657833DEST_PATH_IMAGE001
in the formula (I), the compound is shown in the specification,
Figure 369437DEST_PATH_IMAGE002
which is indicative of the output current of the power converter,
Figure 860724DEST_PATH_IMAGE003
the battery side voltage of the isolated DC energy storage converter is shown,
Figure 999581DEST_PATH_IMAGE004
it is indicated that the control period is,
Figure 707643DEST_PATH_IMAGE005
which represents the ratio of the transformation of the converter,
Figure 957359DEST_PATH_IMAGE006
the series inductance of the isolated DC energy storage converter is shown,
Figure 545294DEST_PATH_IMAGE007
the amount of phase shift is indicated.
8. A power feedforward inductance parameter identification system for an energy storage converter according to claim 7, wherein the constraint condition determined by the constraint condition determining unit is specifically expressed as:
Figure 855052DEST_PATH_IMAGE008
in the formula (I), the compound is shown in the specification,
Figure 50410DEST_PATH_IMAGE009
a steady state value representing the amount of phase shift during the kth sampling period,
Figure 729915DEST_PATH_IMAGE010
and
Figure 694329DEST_PATH_IMAGE011
representing the steady state values of the output current and the battery voltage at the kth sampling period.
9. A power feed forward inductance parameter identification system for a power storage converter as claimed in claim 8 wherein said parameter definition unit defines a first parameter and a second parameter in accordance with the following equation:
Figure 174989DEST_PATH_IMAGE012
in the formula (I), the compound is shown in the specification,
Figure 93529DEST_PATH_IMAGE013
is a function of the first parameter and is,
Figure 950626DEST_PATH_IMAGE014
is the second parameter.
10. A power feed forward inductance parameter identification system for a power storage converter as claimed in claim 9 further comprising:
the adaptive filtering unit is used for carrying out adaptive filtering on the series inductance parameters by utilizing an inductance parameter identification adaptive filtering algorithm based on a recursive least square algorithm, and the adaptive filtering is carried out according to the following formula:
Figure 769547DEST_PATH_IMAGE015
in the formula (I), the compound is shown in the specification,
Figure 421108DEST_PATH_IMAGE016
represents the kth sampleThe error in the period of time of the cycle,
Figure 92523DEST_PATH_IMAGE017
inductance parameters representing the recursive least square algorithm identify the inductance calculation amount of the kth sampling period under the adaptive filtering algorithm,
Figure 487732DEST_PATH_IMAGE018
inductance calculation amount and forgetting factor of k-1 sampling period under inductance parameter identification adaptive filtering algorithm representing recursive least square algorithm
Figure 161159DEST_PATH_IMAGE019
Used for adjusting the influence degree of the original data on the calculation result of the current period, as a forgetting factor
Figure 875299DEST_PATH_IMAGE019
When approaching 1, the representation calculation result depends more on the previous data, and when forgetting the factor
Figure 142333DEST_PATH_IMAGE019
When the value is close to 0, the calculation result is more dependent on the latest data;
Figure 465867DEST_PATH_IMAGE020
and
Figure 869166DEST_PATH_IMAGE021
respectively representing a first intermediate variable value and a second intermediate variable value for the kth sampling period,
Figure 754208DEST_PATH_IMAGE022
representing the first intermediate variable value for the (k-1) th sampling period.
CN202210807930.2A 2022-07-11 2022-07-11 Power feedforward inductance parameter identification method and system for energy storage converter Active CN114865888B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210807930.2A CN114865888B (en) 2022-07-11 2022-07-11 Power feedforward inductance parameter identification method and system for energy storage converter

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210807930.2A CN114865888B (en) 2022-07-11 2022-07-11 Power feedforward inductance parameter identification method and system for energy storage converter

Publications (2)

Publication Number Publication Date
CN114865888A CN114865888A (en) 2022-08-05
CN114865888B true CN114865888B (en) 2022-10-04

Family

ID=82626776

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210807930.2A Active CN114865888B (en) 2022-07-11 2022-07-11 Power feedforward inductance parameter identification method and system for energy storage converter

Country Status (1)

Country Link
CN (1) CN114865888B (en)

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102006028103A1 (en) * 2006-06-19 2007-12-20 Siemens Ag Line-side converter with uninterrupted switching between pulsed voltage-controlled operation and fundamental frequency unregulated operation, as well as method for uninterrupted switching of such a converter
WO2016032981A1 (en) * 2014-08-25 2016-03-03 NuVolta Technologies Wireless power transfer system and method
CN105048821B (en) * 2015-08-25 2017-07-11 西南交通大学 Improve the load-current feedforward control method that full-bridge isolates DC DC converter output voltage dynamic responses
CN106199236A (en) * 2016-06-21 2016-12-07 广东电网有限责任公司肇庆供电局 A kind of transmission line parameter method of estimation, system and power system
CN107994812B (en) * 2017-12-26 2020-02-04 奥克斯空调股份有限公司 Inductance parameter online identification method and device
CN108288935B (en) * 2018-02-11 2019-10-01 安徽江淮汽车集团股份有限公司 A kind of permanent magnet synchronous motor inductance parameters acquisition methods and system
CN110456165B (en) * 2019-07-04 2021-09-10 哈尔滨工程大学 Direct current converter inductance and capacitance parameter identification circuit
CN112865340B (en) * 2021-04-26 2021-07-23 中国人民解放军海军工程大学 Mutual inductance parameter identification method and device of wireless charging system

Also Published As

Publication number Publication date
CN114865888A (en) 2022-08-05

Similar Documents

Publication Publication Date Title
CN111738497B (en) Virtual power plant double-layer optimal scheduling method considering demand side response
CN111817570B (en) DAB converter wide-range regulation and control method based on multi-mode model prediction
CN111244988B (en) Electric automobile considering distributed power supply and energy storage optimization scheduling method
CN109760523A (en) Composite power source energy management method based on BP neural network speed prediction
CN110752629B (en) Energy optimization management method for AC/DC hybrid household micro-grid
CN106953535A (en) A kind of model-free Poewr control method of PFC AC/DC converters
CN107069807A (en) Containing uncertain budget adjust without balance nodes microgrid Robust Scheduling method
Akshya et al. Grid integration for electric vehicle and photovoltaic panel for a smart home
CN111064214A (en) Power distribution network optimal scheduling method based on electric vehicle two-stage rolling strategy
CN112350369B (en) Energy efficiency evaluation method for optical storage and charging integrated power station
Sun et al. Real-time optimal EMS of adaptive charge sustenance for fuel cell hybrid buses based on driving characteristics recognition
CN114865888B (en) Power feedforward inductance parameter identification method and system for energy storage converter
CN109066823B (en) Alternating current-direct current hybrid micro-grid two-layer optimization method suitable for three-port power electronic transformer
Ma et al. A novel energy management strategy based on minimum internal loss for a Fuel Cell UAV
CN113595404A (en) Back-to-back converter control method of new energy traction power supply system
CN115549091B (en) Multi-target control method and system for energy storage system
CN114094623B (en) Alternating-current/direct-current hybrid power distribution network distributed optimization scheduling method based on alternating-current direction multiplier method
Zhang et al. A Novel Energy Management Strategy Based on Efficiency Optimization for Fuel Cell Electric Vehicle
CN117078030B (en) Fuel cell bus energy management method based on vehicle speed prediction
Wentao et al. Economic Analysis of Energy Storage System Installation by Industrial Users
Sun et al. Adaptive Energy Management Strategy of Fuel Cell Electric Vehicle
CN117856196A (en) Hybrid energy storage system droop control energy management method based on fractional order sliding mode
CN113199949B (en) Multi-port energy conversion device for charging electric automobile and control method thereof
Guo et al. PI model predictive control algorithm for bidirectional active full bridge DC-DC converter
Lu et al. Research on Distributed Control of Energy Storage Based on Big Data Algorithm

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
GR01 Patent grant
GR01 Patent grant