CN110943528B - Uninterrupted power source learning type load current estimation system - Google Patents

Uninterrupted power source learning type load current estimation system Download PDF

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CN110943528B
CN110943528B CN201911186849.1A CN201911186849A CN110943528B CN 110943528 B CN110943528 B CN 110943528B CN 201911186849 A CN201911186849 A CN 201911186849A CN 110943528 B CN110943528 B CN 110943528B
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李江伟
王成成
鲍海波
李绍坚
王清成
李宇烨
黄翰民
张兰
钟志东
刘文韬
杨鹏
王愚
石瑞才
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Nanning Power Supply Bureau of Guangxi Power Grid Co Ltd
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    • 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
    • H02J9/00—Circuit arrangements for emergency or stand-by power supply, e.g. for emergency lighting
    • H02J9/04—Circuit arrangements for emergency or stand-by power supply, e.g. for emergency lighting in which the distribution system is disconnected from the normal source and connected to a standby source
    • H02J9/06—Circuit arrangements for emergency or stand-by power supply, e.g. for emergency lighting in which the distribution system is disconnected from the normal source and connected to a standby source with automatic change-over, e.g. UPS systems
    • H02J9/062—Circuit arrangements for emergency or stand-by power supply, e.g. for emergency lighting in which the distribution system is disconnected from the normal source and connected to a standby source with automatic change-over, e.g. UPS systems for AC powered loads
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    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/40—Testing power supplies
    • G01R31/42—AC power supplies
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Abstract

The invention discloses an uninterruptible power supply learning type load current estimation system, wherein the system comprises: the system comprises a main inverter circuit, a filter inductor, a fixed three-phase current sensing group, a filter capacitor, an inverter controller, a three-phase rectifier and a storage battery; the input end of the main inverter circuit is connected with the three-phase rectifier through a storage battery, and the output end of the main inverter circuit is sequentially connected with the filter inductor and the fixed three-phase current sensing group in series; one end of the filter capacitor is arranged on a three-phase line at the output end of the fixed three-phase current sensing group, and the other end of the filter capacitor is grounded; and one end of the inverter controller is respectively connected with the fixed three-phase current sensing group and the filter capacitor, and the other end of the inverter controller is connected with the main inverter circuit. In the implementation of the invention, the number of current sensors in the UPS is reduced, and the accuracy of the parameters of the inverter model can be improved, so that the accuracy of the output current estimation is ensured.

Description

Uninterrupted power source learning type load current estimation system
Technical Field
The invention relates to the technical field of uninterruptible power supplies, in particular to an uninterruptible power supply learning type load current estimation system.
Background
An Uninterruptible Power Supply (UPS) is widely used in the field of Power protection and Supply; a conventional single three-phase UPS generally requires two three-phase current sensor groups, one for measuring the current (i.e., the inductor current) of the main inverter circuit, and the other for measuring the output current of the UPS. Due to the filter capacitance, the two sets of capacitances are not equal but differ by the transient current on the filter capacitance. It can be seen that a single three-phase UPS requires at least six current sensors.
At present, some output current estimation algorithms have been proposed to reduce the number of current sensors used, thereby reducing the cost, and these algorithms are often based on the observer theory and are relatively complex to implement; on the other hand, in any current estimation algorithm, the estimation accuracy depends heavily on inverter parameters, namely filter capacitor inductance parameters, and the actual values of the inverter parameters are often different from the nominal values, and the parameters also change along with the aging of the inverter; the performance of UPS models and control algorithms can be affected by inaccuracies in the model parameters.
In summary, the above can be summarized in two problems: first, conventional UPSs require more current sensors, which need to be reduced to reduce costs; secondly, the proposed output current estimation algorithm is complex and depends on accurate inverter parameters, and a proper method is needed to ensure the accuracy of the model parameters.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides an uninterruptible power supply learning type load current estimation system which can reduce the number of current sensors in a UPS and improve the accuracy of inverter model parameters so as to ensure the accuracy of output current estimation.
In order to solve the above technical problem, an embodiment of the present invention provides an uninterruptible power supply learning type load current estimation system, where the system includes: the system comprises a main inverter circuit, a filter inductor, a fixed three-phase current sensing group, a filter capacitor, an inverter controller, a three-phase rectifier and a storage battery; wherein the content of the first and second substances,
the input end of the main inverter circuit is connected with the three-phase rectifier through a storage battery, and the output end of the main inverter circuit is sequentially connected with the filter inductor and the fixed three-phase current sensing group in series; one end of the filter capacitor is arranged on a three-phase line at the output end of the fixed three-phase current sensing group, and the other end of the filter capacitor is grounded; and one end of the inverter controller is respectively connected with the fixed three-phase current sensing group and the filter capacitor, and the other end of the inverter controller is connected with the main inverter circuit.
Optionally, the system further includes: a detachable three-phase current sensor group, wherein,
the detachable three-phase current sensor group is temporarily arranged on a three-phase line behind the filter capacitor at the initial stage and the maintenance period of the uninterruptible power supply and is connected with the inverter controller.
Optionally, when the detachable three-phase current sensor group is arranged on the system, the inverter controller on the system identifies parameters of the filter inductor and the filter capacitor based on a machine learning algorithm, and the parameters are as follows:
the ABC three-phase system is described in d-q axis as follows:
Figure GDA0003252553880000021
wherein x ═ iid,iiq,voq,voq]T，u＝[iod,ioq,vid,viq]TAnd A and B are inverter parameter matrixes formed by filter inductors, filter capacitors and electric frequency constants.
Optionally, after separating out the parameters in the inverter parameter matrix, sorting the parameters into a parameter vector form, and rewriting the formula (1) into the following form:
Figure GDA0003252553880000022
wherein z represents the parameter vector of the inverter controller, and in the process of identifying the parameter vector, the error function can be identified according to the parameter by the formula (2):
Figure GDA0003252553880000023
wherein M (x, u) represents a matrix of operating state data; in the operation of the uninterruptible power supply, an error function unit as shown in formula (3) can be obtained at each moment, the error function units in a period of time are combined to obtain a loss function in a database form, and the database is subjected to machine learning, namely according to a nonlinear optimization theory, when the loss function obtains a minimum value, parameter identification obtains an optimal value, namely:
Figure GDA0003252553880000024
wherein, the formula (4) is a standard quadratic programming algorithm, wherein D is expressed as
Figure GDA0003252553880000025
The estimation range can be designed to be near the nominal value of the parameter;
Figure GDA0003252553880000031
is the transpose of the error function;
Figure GDA0003252553880000032
is the remainder without the run data; and finding the optimal value of the parameter of the inverter controller by adopting a quadratic programming algorithm.
Optionally, the system further includes: UPS output current estimation;
wherein the UPS output current estimation is connected with the inversion controller and used for inputting the estimated current i to the inversion controllerod,ioq。
Optionally, the inverter controller needs to estimate the load current i output by the systemoA,ioB,ioCThe estimation algorithm is as follows:
the ABC three-phase system is described in d-q axis as follows:
Figure GDA0003252553880000033
the loss function is established directly, as follows:
Figure GDA0003252553880000034
wherein, x ═ iid,iiq,vod,voq]T，u＝[iod,ioq,vid,viq]TA and B are inverter parameter matrixes formed by filter inductors, filter capacitors and electric frequency constants; i.e. iidAnd iiqD-q axis current, v, representing inverter filter inductanceidAnd viqRepresenting the inverter output d-q axis voltage; i.e. iodAnd ioqD-q axis output current, v, representing filter capacitanceodAnd voqRepresenting the d-q axis output voltage of the filter capacitor.
Optionally, the inverter parameter matrices a and B are as follows:
Figure GDA0003252553880000035
where L represents the filter inductance, C represents the filter capacitance, and ω represents the electrical frequency constant.
In the embodiment of the invention, the learning type load current estimation system of the uninterruptible power supply is provided, the learning type load current estimation of the uninterruptible power supply in the system is simple to calculate and easy to realize, the number of current sensors in the UPS can be reduced, and meanwhile, the accuracy of the model parameters of the inverter can be improved, so that the accuracy of the estimation of the output current is ensured.
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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, 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 the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of an uninterruptible power supply learning-type load current estimation system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an exemplary learning-based load current estimation system for an uninterruptible power supply according to another embodiment of the invention;
fig. 3 is a schematic structural diagram of an uninterruptible power supply learning type load current estimation system according to another embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments 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.
Examples
Referring to fig. 1, fig. 1 is a schematic structural diagram of an uninterruptible power supply learning type load current estimation system according to an embodiment of the present invention.
As shown in fig. 1, an uninterruptible power supply learning type load current estimation system includes: the system comprises a main inverter circuit 1, a filter inductor 2, a fixed three-phase current sensing group 3, a filter capacitor 4, an inverter controller 6, a three-phase rectifier 7 and a storage battery 8; wherein the content of the first and second substances,
the input end of the main inverter circuit 1 is connected with a three-phase rectifier 7 through a storage battery 8, and the output end of the main inverter circuit is sequentially connected with a filter inductor 2 and a fixed three-phase current sensing group 3 in series; one end of the filter capacitor 4 is arranged on a three-phase line at the output end of the fixed three-phase current sensing group 3, and the other end of the filter capacitor is grounded; one end of the inverter controller 6 is respectively connected with the fixed three-phase current sensing group 3 and the filter capacitor 4, and the other end of the inverter controller is connected with the main inverter circuit 1.
The detachable three-phase current sensor group 5 is temporarily stored in the system, and when the detachable three-phase current sensor group is stored in the system, the detachable three-phase current sensor group is arranged on a three-phase line behind the filter capacitor 4.
Specifically, the other end of the three-phase rectifier 7 is connected to a three-phase external power supply.
Referring to fig. 2, fig. 2 is a schematic diagram illustrating a structural configuration of an uninterruptible power supply learning type load current estimation system according to another embodiment of the present invention.
As shown in fig. 2, this scheme is used in long-term operation of a UPS, with UPS output current estimation in fig. 2, but without a removable three-phase current sensor group 5, where an inverter controller 6 is provided for the inverter output current i at each sampling periodiAiiB iiCAnd UPS output voltage voA voB voCSampling as input to the inverter controller 6; in this mode, the inverter controller 6 needs to estimate the output current iod,ioqAs input to the inverter control, the inverter controller needs to estimate the load current i of the system outputoA,ioB,ioCThe estimation algorithm is as follows:
the ABC three-phase system is described in d-q axis as follows:
Figure GDA0003252553880000051
the loss function is established directly, as follows:
Figure GDA0003252553880000052
wherein, x ═ iid,iiq,vod,voq]T，u＝[iod,ioq,vid,viq]TA and B are inverter parameter matrixes formed by filter inductors, filter capacitors and electric frequency constants; establishing a loss function in the formula (5) at each moment in the operation of the inverter, and obtaining the output current i at the moment when the loss function G obtains the minimum value according to the nonlinear optimization theoryod，ioqI can be obtained by inverse transformationoA,ioB,ioC；iidAnd iiqD-q axis current, v, representing inverter filter inductanceidAnd viqRepresenting the inverter output d-q axis voltage; i.e. iodAnd ioqD-q axis output current, v, representing filter capacitanceodAnd voqRepresenting the d-q axis output voltage of the filter capacitor.
Referring to fig. 3, fig. 3 is a schematic diagram illustrating a structural configuration of an uninterruptible power supply learning type load current estimation system according to another embodiment of the present invention.
As shown in fig. 3, this solution is only used in the initial period of operation and maintenance of the UPS, i.e. the system further comprises: the detachable three-phase current sensor group is characterized in that the detachable three-phase current sensor group 5 is temporarily arranged on a three-phase line behind the filter capacitor 4 in the initial stage and the maintenance period of the uninterruptible power supply and is connected with the inverter controller 6.
The UPS output side in the figure is provided with a detachable three-phase current sensor group 5, wherein an inversion controller 6 outputs current i to an inverter in each sampling periodiA iiB iiCUPS output current ioA ioB ioCAnd UPS output voltage voA voB voCSampling is carried out and is used as the input of the inverter controller 6, and in the mode, the inverter control has enough input quantity and the output current does not need to be estimated any more; the parameter identification algorithm is used for identifying inverter parameters, is stored in the controller memory and is used for the operation of the disassembled three-phase current sensor group 5 after removal, and the algorithm is specifically as follows:
the ABC three-phase system is described in d-q axis as follows:
Figure GDA0003252553880000061
wherein x ═ iid，iiq，vod，voq]T，u＝[iod，ioq，vid，viq]TA and B are inverter parameter matrixes formed by filter inductors, filter capacitors and electric frequency constants; i.e. iidAnd iiqD-q axis current, v, representing inverter filter inductanceidAnd viqRepresenting the inverter output d-q axis voltage; i.e. iodAnd ioqD-q axis output current, v, representing filter capacitanceodAnd voqRepresenting the d-q axis output voltage of the filter capacitor.
After separating out the parameters in the inverter parameter matrix, arranging the parameters into a parameter vector form, and rewriting the formula (1) into the following form:
Figure GDA0003252553880000062
wherein z represents the parameter vector of the inverter controller, and in the process of identifying the parameter vector, the error function can be identified according to the parameter by the formula (2):
Figure GDA0003252553880000063
wherein M (x, u) represents a matrix of operating state data,
Figure GDA0003252553880000064
an estimate denoted z; in the operation of the uninterrupted power supply, an error function unit as shown in the formula (3) can be obtained at each moment, the error function units in a period of time are combined to obtain a loss function in the form of a database, and the database is subjected to machine learning, namely, according to a nonlinear optimization principleIn theory, when the loss function obtains the minimum value, the parameter identification obtains the optimal value, namely:
Figure GDA0003252553880000065
wherein, the formula (4) is a standard quadratic programming algorithm, and D is expressed as
Figure GDA0003252553880000066
The estimation range can be designed to be near the nominal value of the parameter;
Figure GDA0003252553880000067
is the transpose of the error function;
Figure GDA0003252553880000068
is the remainder without the run data; and finding the optimal value of the parameter of the inverter controller by adopting a quadratic programming algorithm.
Specifically, the inverter parameter matrices a and B are as follows:
Figure GDA0003252553880000071
where L represents the filter inductance, C represents the filter capacitance, and ω represents the electrical frequency constant.
In the embodiment of the invention, the learning type load current estimation system of the uninterruptible power supply is provided, the learning type load current estimation of the uninterruptible power supply in the system is simple to calculate and easy to realize, the number of current sensors in the UPS can be reduced, and meanwhile, the accuracy of the model parameters of the inverter can be improved, so that the accuracy of the estimation of the output current is ensured.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
In addition, the above detailed description is provided for the learning-type load current estimation system of the uninterruptible power supply according to the embodiment of the present invention, and a specific example should be used herein to explain the principle and the implementation manner of the present invention, and the description of the above embodiment is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (5)

1. An uninterruptible power supply learning type load current estimation system, the system comprising: the system comprises a main inverter circuit, a filter inductor, a fixed three-phase current sensing group, a filter capacitor, an inverter controller, a three-phase rectifier and a storage battery; wherein the content of the first and second substances,
the input end of the main inverter circuit is connected with the three-phase rectifier through a storage battery, and the output end of the main inverter circuit is sequentially connected with the filter inductor and the fixed three-phase current sensing group in series; one end of the filter capacitor is arranged on a three-phase line at the output end of the fixed three-phase current sensing group, and the other end of the filter capacitor is grounded; one end of the inverter controller is respectively connected with the fixed three-phase current sensing group and the filter capacitor, and the other end of the inverter controller is connected with the main inverter circuit;
the system further comprises: a detachable three-phase current sensor group, wherein,
the detachable three-phase current sensor group is temporarily arranged on a three-phase line behind the filter capacitor at the initial stage and the maintenance period of the uninterruptible power supply and is connected with the inverter controller;
when the detachable three-phase current sensor group is arranged on the system, the inverter controller on the system identifies parameters of the filter inductor and the filter capacitor based on a machine learning algorithm, and the parameters are as follows:
the ABC three-phase system is described in d-q axis as follows:
Figure FDA0003252553870000011
wherein x ═ iid,iiq,vod,voq]T，u＝[iod,ioq,vid,viq]TA and B are inverter parameter matrixes formed by filter inductors, filter capacitors and electric frequency constants; i.e. iidAnd iiqD-q axis current, v, representing inverter filter inductanceidAnd viqRepresenting the inverter output d-q axis voltage; i.e. iodAnd ioqD-q axis output current, v, representing filter capacitanceodAnd voqRepresenting the d-q axis output voltage of the filter capacitor.
2. The system according to claim 1, wherein the parameters in the inverter parameter matrix are separated and arranged into a parameter vector form, and the formula (1) is rewritten as follows:
Figure FDA0003252553870000012
wherein z represents the parameter vector of the inverter controller, and in the process of identifying the parameter vector, the error function can be identified according to the parameter by the formula (2):
Figure FDA0003252553870000013
wherein M (x, u) represents a matrix of operating state data,
Figure FDA0003252553870000021
an estimate denoted z; in the operation of the uninterrupted power supply, an error function unit as shown in formula (3) can be obtained at each moment, the error function units in a period of time are combined to obtain a loss function in the form of a database, and the loss function is subjected to the loss function in the form of a databaseThe database is subjected to machine learning, namely according to a nonlinear optimization theory, when the loss function obtains a minimum value, the parameter identification obtains an optimal value, namely:
Figure FDA0003252553870000022
wherein, the formula (4) is a standard quadratic programming algorithm, wherein D is expressed as
Figure FDA0003252553870000023
The estimation range can be designed to be near the nominal value of the parameter;
Figure FDA0003252553870000024
is the transpose of the error function;
Figure FDA0003252553870000025
is the remainder without the run data; and finding the optimal value of the parameter of the inverter controller by adopting a quadratic programming algorithm.
3. The uninterruptible power supply learning load current estimation system of claim 1, further comprising: UPS output current estimation;
wherein the UPS output current estimation is connected with the inversion controller and used for inputting the estimated current i to the inversion controllerod,ioq。
4. The UPS learning-based load current estimation system of claim 3, wherein the inverter controller needs to estimate the load current i output by the systemoA,ioB,ioCThe estimation algorithm is as follows:
the ABC three-phase system is described in d-q axis as follows:
Figure FDA0003252553870000026
the loss function is established directly, as follows:
Figure FDA0003252553870000027
wherein, x ═ iid,iiq,vod,voq]T，u＝[iod,ioq,vid,viq]TA and B are inverter parameter matrixes formed by filter inductors, filter capacitors and electric frequency constants; i.e. iidAnd iiqD-q axis current, v, representing inverter filter inductanceidAnd viqRepresenting the inverter output d-q axis voltage; i.e. iodAnd ioqD-q axis output current, v, representing filter capacitanceodAnd voqRepresenting the d-q axis output voltage of the filter capacitor.
5. The uninterruptible power supply learning load current estimation system of claim 1 or 4, wherein the inverter parameter matrices A and B are as follows:
Figure FDA0003252553870000031
where L represents the filter inductance, C represents the filter capacitance, and ω represents the electrical frequency constant.
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