CN113296412B - Parameter adjusting method and device for cascade sliding window filter - Google Patents
Parameter adjusting method and device for cascade sliding window filter Download PDFInfo
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Abstract
The invention discloses a parameter adjusting method and device for a cascade sliding window filter, and relates to the technical field of process control of thermal power units. The method comprises the following steps: converting actual controlled process response data of actual step input into controlled process response data of unit step input; acquiring Z-N rule parameters of the controlled process response data of unit step input according to a Z-N rule; setting the gain of the cascade sliding window filter and the time constant of the fast sliding window filter; calculating the error between the controlled process response data of the unit step input and the process response data of the cascade sliding window filter at the unit step input; and adjusting the time constant of the slow sliding window filter to minimize the square integral of the error, thereby obtaining the cascade sliding window filter. The method can set the parameters of the novel basic controller and provide parameter guarantee for the operation of the secondary superheated steam temperature control system.
Description
Technical Field
The invention relates to the technical field of process control of thermal power generating units, in particular to a parameter adjusting method and device of a cascade sliding window filter.
Background
In the field of process control of thermal power generating units, in order to improve the process control performance of the existing thermal power generating units, a novel basic controller (NFC) is proposed in the prior art. Among them, NFC is a cascade structure of a High performance proportional-integral controller (HPPI) and a High performance advanced observer (HPLO), and it can make a breakthrough in a constant observation mechanism and make a significant progress in an advanced observation mechanism.
However, setting NFC parameters is always an important problem, and the problem of setting NFC parameters is not well solved in the prior art.
Disclosure of Invention
The invention aims to provide a parameter adjusting method and device of a cascade sliding window filter, and aims to solve the problem that the NFC parameter setting is not well solved in the prior art.
In order to achieve the above object, an embodiment of the present invention provides a method for adjusting parameters of a cascaded sliding window filter, where the cascaded sliding window filter includes a fast sliding window filter and a slow sliding window filter, the slow sliding window filter is used for a high-performance proportional-integral controller parameter setting, and the fast sliding window filter is used for a high-performance advanced observer parameter setting, so as to better solve a problem of parameter setting of a novel basic controller, and the method includes:
converting actual controlled process response data of actual step input into controlled process response data of unit step input; wherein the actual controlled process response data comprises secondary superheat air temperature process data;
acquiring Z-N rule parameters of the controlled process response data input by the unit step according to a Z-N rule;
setting the gain of the cascade sliding window filter according to the gain of the Z-N rule parameter;
setting a time constant of the fast sliding window filter according to a time constant of the Z-N rule parameter;
acquiring process response data of the cascade sliding window filter in unit step input;
calculating the error between the controlled process response data of the unit step input and the process response data of the cascade sliding window filter at the unit step input;
and adjusting the time constant of the slow sliding window filter to minimize the square integral of the error so as to obtain the optimal parameter of the cascade sliding window filter.
Preferably, the transfer function of the cascaded sliding window filter is expressed as:
CSWF(s)＝KCSWFSSWF(s)FSWF(s),
wherein CSWF(s) is a transfer function of the cascaded sliding window filter, KCSWFFor the gain of the cascaded sliding window filter, SSWF(s) is the transfer function of the slow sliding window filter, TSSWFFor the time constant of the slow sliding window filter FSWF(s) is the transfer function of the fast sliding window filter TFSWFIs the time constant of the fast sliding window filter.
Preferably, the step of converting the actual controlled process response data of the actual step input into the controlled process response data of the unit step input uses the following expression:
wherein PVCP(t) is the controlled unit step inputProcess response data, PVACP(t) is the actual controlled process response data for the actual step input, and SAI is the actual step input.
Preferably, the expression of the Z-N law parameter of the unit step input controlled process response data is:
wherein Z-N(s) is a transfer function of the said Z-N law, KZ-NFor the gain of said Z-N law parameter, τZ-NPure lag time, T, for said Z-N law parameterZ-NIs the time constant of the Z-N law parameter.
Preferably, the expression for setting the gain of the cascaded sliding window filter according to the gain of the Z-N law parameter is as follows:
KCSWF＝KZ-N
wherein, KCSWFIs the gain, K, of the cascaded sliding window filterZ-NIs the gain of the Z-N law parameter.
Preferably, the expression for setting the time constant of the fast sliding window filter according to the time constant of the Z-N law parameter is:
wherein, TFSWFIs the time constant, T, of the fast sliding window filterZ-NIs the time constant of the Z-N law parameter.
Preferably, the expression of the square integral of the error is:
where ESI is the square integral of the error, T is the current time, TCLTo calculate the length of time for the ESI, ST is the steady state time,PVCP(t) controlled process response data for said unit step input, PVCSWFAnd (t) is the process response data of the cascade sliding window filter at the unit step input.
An embodiment of the present invention further provides a parameter adjusting apparatus for a cascaded sliding window filter, where the cascaded sliding window filter includes a fast sliding window filter and a slow sliding window filter, and the apparatus includes:
the controlled process analysis module is used for converting actual controlled process response data of actual step input into controlled process response data of unit step input; wherein the actual controlled process response data comprises secondary superheat air temperature process data;
the setting module is used for acquiring Z-N rule parameters of the controlled process response data input by the unit step according to a Z-N rule;
the gain acquisition module is used for setting the gain of the cascade sliding window filter according to the gain of the Z-N rule parameter;
the time constant acquisition module is used for setting the time constant of the fast sliding window filter according to the time constant of the Z-N rule parameter;
the process response analysis module is used for acquiring process response data of the cascade sliding window filter at unit step input;
the error calculation module is used for calculating the error between the controlled process response data of the unit step input and the process response data of the cascade sliding window filter at the unit step input;
and the time constant adjusting module is used for adjusting the time constant of the slow sliding window filter to ensure that the square integral of the error is minimum and obtain the optimal parameter of the cascade sliding window filter.
Preferably, the transfer function of the cascaded sliding window filter is expressed as:
CSWF(s)＝KCSWFSSWF(s)FSWF(s),
wherein CSWF(s) is a transfer function of the cascaded sliding window filter, KCSWFFor the gain of the cascaded sliding window filter, SSWF(s) is the transfer function of the slow sliding window filter, TSSWFFor the time constant of the slow sliding window filter FSWF(s) is the transfer function of the fast sliding window filter TFSWFIs the time constant of the fast sliding window filter.
Preferably, the step of converting the actual controlled process response data of the actual step input into the controlled process response data of the unit step input uses the following expression:
wherein PVCP(t) controlled process response data for said unit step input, PVACP(t) is the actual controlled process response data for the actual step input, and SAI is the actual step input.
Preferably, the expression of the Z-N law parameter of the unit step input controlled process response data is:
wherein Z-N(s) is a transfer function of the said Z-N law, KZ-NFor the gain of said Z-N law parameter, τZ-NPure lag time, T, for said Z-N law parameterZ-NIs the time constant of the Z-N law parameter.
Preferably, the expression for setting the gain of the cascaded sliding window filter according to the gain of the Z-N law parameter is as follows:
KCSWF＝KZ-N
wherein, KCSWFIs the gain, K, of the cascaded sliding window filterZ-NIs the gain of the Z-N law parameter.
Preferably, the expression for setting the time constant of the fast sliding window filter according to the time constant of the Z-N law parameter is:
wherein, TFSWFIs the time constant, T, of the fast sliding window filterZ-NIs the time constant of the Z-N law parameter.
Preferably, the expression of the square integral of the error is:
where ESI is the square integral of the error, T is the current time, TCLTo calculate the length of ESI time, ST is the steady-state time, PVCP(t) controlled process response data for said unit step input, PVCSWFAnd (t) is the process response data of the cascade sliding window filter at the unit step input.
The embodiment of the invention also provides computer terminal equipment which comprises one or more processors and a memory. A memory coupled to the processor for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors may implement the parameter adjustment method of the cascaded sliding window filter according to any of the embodiments.
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the parameter adjustment method for a cascaded sliding window filter according to any one of the above embodiments.
Compared with the prior art, the invention has the following beneficial effects:
in the parameter adjustment method of the cascade sliding window filter provided by the invention, the cascade sliding window filter comprises a fast sliding window filter and a slow sliding window filter, and the method comprises the following steps: converting actual controlled process response data of actual step input into controlled process response data of unit step input; acquiring Z-N rule parameters of the controlled process response data input by the unit step according to a Z-N rule; setting the gain of the cascade sliding window filter according to the gain of the Z-N rule parameter; setting a time constant of the fast sliding window filter according to a time constant of the Z-N rule parameter; acquiring process response data of the cascade sliding window filter in unit step input; calculating the error between the controlled process response data of the unit step input and the process response data of the cascade sliding window filter at the unit step input; and adjusting the time constant of the slow sliding window filter to minimize the square integral of the error so as to obtain the optimal parameter of the cascade sliding window filter.
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In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments 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 it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart illustrating a parameter adjustment method for a cascaded sliding window filter according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a parameter adjusting apparatus of a cascaded sliding window filter according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a two-stage superheat steam temperature control system constructed using a novel basic controller, according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of controlled process response data for a unit step input and a Z-N law parameter process provided by one embodiment of the present invention;
FIG. 5 is a schematic diagram of a process for providing controlled process response data per unit step input and process response data for a cascaded sliding window filter according to another embodiment of the present invention;
fig. 6 is a schematic diagram of a simulation experiment result of a parameter adjustment method for a cascaded sliding window filter according to another embodiment of the present invention;
fig. 7 is a schematic control characteristic diagram of a secondary superheated steam temperature control system in which a cascade sliding window filter according to an embodiment of the present invention is applied to a 1000MW thermal power generating unit;
fig. 8 is a schematic structural diagram of a computer terminal device according to an embodiment of the present 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.
It should be understood that the step numbers used herein are for convenience of description only and are not intended as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
The following are terms and abbreviations for embodiments of the present invention:
a New Foundation Controller (NFC); a High performance proportional-integral controller (HPPI); a High Efficiency Integrator (HEI); a High Performance Lead Observer (HPLO); a Cascade sliding window filter (cscaf sliding window filter, CSWF); a Slow Sliding Window Filter (SSWF); fast sliding window filter middle (FSWF). In the present application, unless otherwise specified, the gain is represented by dimensionless units, the order is represented by dimensionless units, the time and time constant is represented by s (seconds), and all the transfer functions are laplace transfer functions.
Referring to fig. 1, fig. 1 is a flowchart illustrating a parameter adjustment method for a cascaded sliding window filter according to an embodiment of the present invention. The cascade sliding window filter comprises a fast sliding window filter and a slow sliding window filter, and the parameter adjusting method of the cascade sliding window filter provided by the embodiment comprises the following steps:
s110, converting actual controlled process response data of actual step input into controlled process response data of unit step input; wherein the actual controlled process response data comprises secondary superheat air temperature process data;
s120, acquiring Z-N rule parameters of the unit step input controlled process response data according to a Z-N rule;
s130, setting the gain of the cascade sliding window filter according to the gain of the Z-N rule parameter;
s140, setting the time constant of the fast sliding window filter according to the time constant of the Z-N rule parameter;
s150, acquiring the response data of the cascade sliding window filter in the unit step input process;
s160, calculating the error between the controlled process response data of the unit step input and the process response data of the cascade sliding window filter at the unit step input;
and S170, adjusting the time constant of the slow sliding window filter to minimize the square integral of the error, so as to obtain the optimal parameter of the cascade sliding window filter.
In the embodiment of the present invention, the expression of the transfer function of the cascaded sliding window filter is:
CSWF(s)＝KCSWFSSWF(s)FSWF(s),
wherein CSWF(s) is a transfer function of the cascaded sliding window filter, KCSWFFor the gain of the cascaded sliding window filter, SSWF(s) is the transfer function of the slow sliding window filter, TSSWFFor the time constant of the slow sliding window filter FSWF(s) is the transfer function of the fast sliding window filter TFSWFIs the time constant of the fast sliding window filter.
In the embodiment of the present invention, the actual controlled process response data of the actual step input is converted into the controlled process response data of the unit step input, and the used expression is as follows:
wherein PVCP(t) controlled process response data for said unit step input, PVACP(t) is the actual controlled process response data for the actual step input, and SAI is the actual step input.
In the embodiment of the present invention, the expression of the Z-N rule parameter of the unit step input controlled process response data is:
wherein Z-N(s) is a transfer function of the said Z-N law, KZ-NFor the gain of said Z-N law parameter, τZ-NPure lag time, T, for said Z-N law parameterZ-NIs the time constant of the Z-N law parameter.
In the embodiment of the present invention, the expression for setting the gain of the cascaded sliding window filter according to the gain of the Z-N law parameter is as follows:
KCSWF＝KZ-N
wherein, KCSWFIs the gain, K, of the cascaded sliding window filterZ-NIs the gain of the Z-N law parameter.
In the embodiment of the present invention, the expression for setting the time constant of the fast sliding window filter according to the time constant of the Z-N law parameter is as follows:
wherein, TFSWFIs the time constant, T, of the fast sliding window filterZ-NIs the time constant of the Z-N law parameter.
In the embodiment of the present invention, the expression of the square integral of the error is:
where ESI is the square integral of the error, T is the current time, TCLTo calculate the length of ESI time, ST is the steady-state time, PVCP(t) controlled process response data for said unit step input, PVCSWFAnd (t) is the process response data of the cascade sliding window filter at the unit step input.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a parameter adjusting apparatus of a cascaded sliding window filter according to an embodiment of the present invention. The cascaded sliding window filter comprises a fast sliding window filter and a slow sliding window filter. The parameter adjusting device of the cascade sliding window filter provided by the embodiment comprises:
a controlled process analysis module 210, configured to convert actual controlled process response data of an actual step input into controlled process response data of a unit step input; wherein the actual controlled process response data comprises secondary superheat air temperature process data;
the setting module 220 is used for acquiring a Z-N rule parameter of the controlled process response data input by the unit step according to the Z-N rule;
a gain obtaining module 230, configured to set a gain of the cascaded sliding window filter according to a gain of the Z-N rule parameter;
a time constant obtaining module 240, configured to set a time constant of the fast sliding window filter according to the time constant of the Z-N rule parameter;
a process response analysis module 250, configured to obtain process response data of the cascade sliding window filter at unit step input;
an error calculation module 260 for calculating an error between the controlled process response data for the unit step input and the process response data for the cascaded sliding window filter at the unit step input;
and a time constant adjusting module 270, configured to adjust a time constant of the slow sliding window filter, so that a square integral of the error is minimum, and an optimal parameter of the cascaded sliding window filter is obtained.
In the embodiment of the present invention, the expression of the transfer function of the cascaded sliding window filter is:
CSWF(s)＝KCSWFSSWF(s)FSWF(s),
wherein CSWF(s) is a transfer function of the cascaded sliding window filter, KCSWFFor the gain of the cascaded sliding window filter, SSWF(s) is the transfer function of the slow sliding window filter, TSSWFFor the time constant of the slow sliding window filter FSWF(s) is the transfer function of the fast sliding window filter TFSWFIs the time constant of the fast sliding window filter.
In the embodiment of the present invention, the actual controlled process response data of the actual step input is converted into the controlled process response data of the unit step input, and the used expression is as follows:
wherein PVCP(t) controlled process response data for said unit step input, PVACP(t) is the actual controlled process response data for the actual step input, and SAI is the actual step input.
In the embodiment of the present invention, the expression of the Z-N rule parameter of the unit step input controlled process response data is:
wherein Z-N(s) is a transfer function of the said Z-N law, KZ-NFor the gain of said Z-N law parameter, τZ-NPure lag time, T, for said Z-N law parameterZ-NIs the time constant of the Z-N law parameter.
In the embodiment of the present invention, the expression for setting the gain of the cascaded sliding window filter according to the gain of the Z-N law parameter is as follows:
KCSWF＝KZ-N
wherein, KCSWFIs the stringGain, K, of a stage sliding window filterZ-NIs the gain of the Z-N law parameter.
In the embodiment of the present invention, the expression for setting the time constant of the fast sliding window filter according to the time constant of the Z-N law parameter is as follows:
wherein, TFSWFIs the time constant, T, of the fast sliding window filterZ-NIs the time constant of the Z-N law parameter.
In the embodiment of the present invention, the expression of the square integral of the error is:
where ESI is the square integral of the error, T is the current time, TCLTo calculate the length of ESI time, ST is the steady-state time, PVCP(t) controlled process response data for said unit step input, PVCSWFAnd (t) is the process response data of the cascade sliding window filter at the unit step input.
Using a cascaded sliding window filter for a novel basic controller parameter setting;
in one embodiment, the transfer function of the new base controller is:
NFC(s)＝KHPPI[1+HEI(s)]HPLO(s),
wherein NFC(s) is the transfer function of the novel base controller, KHPPIFor the gain of a high performance proportional-integral controller, HEI(s) is the transfer function of a high efficiency integrator, THEIFor the time constant of the high efficiency integrator, HPLO(s) is the transfer function of the high performance lead observer, THPLOIs the time constant of the high performance lead observer.
Using the parameters of the cascade sliding window filter for parameter setting of the novel basic controller;
the formula of parameter setting is as follows:
wherein, THPLOIs the time constant, T, of the high performance lead observerHEIIs the time constant, T, of the high-efficiency integratorFSWFIs the time constant, T, of the fast sliding window filterSSWFIs the time constant, K, of the slow sliding window filterHPPIIs the gain, K, of the high performance proportional-integral controllerCSWFIs the gain of the cascaded sliding window filter.
In a specific embodiment, a novel basic controller is adopted to perform Control characteristic simulation on a secondary superheated steam temperature Control system of a 600MW thermal power generating unit, wherein an approximate transfer function of a Controlled Process (CP) is obtained as
Where CP(s) is the approximate transfer function of the process being controlled.
A two-stage superheated steam temperature control system is constructed by using a novel basic controller, and is shown in figure 3.
In fig. 3, the process setting is specifically a secondary superheated steam temperature setting of the secondary superheated steam temperature control system, and the process output is specifically a secondary superheated steam temperature process output. The transfer functions of a Second Order Filter (SOF) and an External Disturbance Coupling Model (EDCM) are set as follows:
SOF(s) is the transfer function of the two filters, EDCM(s) is the transfer function of the external disturbance coupling model, and CP(s) is the transfer function of the controlled process.
Specifically, the response data of the controlled process at the unit step input and the process of the Z-N rule parameter are obtained, which is shown in fig. 4.
In FIG. 4, PVCP(t) controlled process response data for said unit step input, PVZ-N(t) is the Z-N law parameter process.
According to fig. 4, the transfer function of the Z-N law parameter is obtained as:
wherein Z-N(s) is a transfer function of the Z-N law parameter.
Obtaining a transfer function of the fast sliding window filter as
Wherein FSWF(s) is a transfer function of the fast sliding window filter.
Setting the time length T for calculating the square integral ESI of the error according to the expression of the square integral of the error, wherein the steady-state time ST is 950sCL2000s, time constant T of the slow sliding window filterSSWFWhen the minimum value ESImin of the square integral of the error is 1.037, which is 529s, the expression of the cascaded sliding window filter is shown in the specification：
Wherein CSWF(s) is a transfer function of the cascaded sliding window filter.
Specifically, the response data of the cascade sliding window filter in the unit step input process is obtained, and is shown in fig. 5.
In FIG. 5, PVCP(t) controlled process response data for said unit step input, PVCSWFAnd (t) is the process response data of the cascade sliding window filter at the unit step input. As can be seen from fig. 5, the cascaded sliding window filter can better express the controlled process.
Using the parameters of the cascade sliding window filter to adjust the parameters of the novel basic controller to obtain the parameters of the novel basic controller
THPLO＝233s,THEI＝529,KHPPI＝1
Wherein, THPLOIs the time constant, T, of the high performance lead observerHEIIs the time constant of the high-efficiency integrator, KHPPIIs the gain of the high performance proportional-integral controller.
The results of the simulation experiments, given a process of 1 and an external disturbance of-1, are shown in FIG. 6.
In FIG. 6, PVNFC(t) Process output, CO, for NFC controlled CPNFCAnd (t) is the control output of the NFC control CP. As can be seen from fig. 6, using the parameters of the cascaded sliding window filter for tuning the parameters of the novel basic controller can obtain better control performance.
In a specific embodiment, the cascade sliding window filter provided by the invention is applied to optimization of a secondary superheated steam temperature control system of a 1000MW thermal power generating unit, and parameters of a novel basic controller are given by specifically adopting the cascade sliding window filter, so that the obtained control characteristics are shown in fig. 7. In the trend range given, the unit load varies from 410MW to 820MW to 560MW, with a maximum deviation of the secondary superheated steam temperature of 6.7 ℃/-6.2 ℃ given relative to the secondary superheated steam temperature. Therefore, the cascade sliding window filter provided by the invention is used for providing parameters of a novel basic controller, and better control characteristics can be obtained.
Referring to fig. 8, an embodiment of the invention provides a computer terminal device, which includes one or more processors and a memory. The memory is coupled to the processor and configured to store one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the method for adjusting parameters of a cascaded sliding window filter as in any one of the above embodiments.
The processor is used for controlling the overall operation of the computer terminal equipment so as to complete all or part of the steps of the cascade sliding window filter method. The memory is used to store various types of data to support operation at the computer terminal device, and these data may include, for example, instructions for any application or method operating on the computer terminal device, as well as application-related data. The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
In an exemplary embodiment, the computer terminal Device may be implemented by one or more Application Specific 1 integrated circuits (AS 1C), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, for performing the above-mentioned serial sliding window filter method and achieving technical effects consistent with the above-mentioned methods.
In another exemplary embodiment, a computer readable storage medium comprising program instructions for implementing the steps of the cascade sliding window filter method in any one of the above embodiments when executed by a processor is also provided. For example, the computer readable storage medium may be the above-mentioned memory including program instructions executable by a processor of a computer terminal device to perform the above-mentioned cascaded sliding window filter method and achieve the technical effects consistent with the above-mentioned method.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (16)
1. A parameter adjustment method of a cascaded sliding window filter, wherein the cascaded sliding window filter comprises a fast sliding window filter and a slow sliding window filter, the parameter adjustment method comprises:
converting actual controlled process response data of actual step input into controlled process response data of unit step input; wherein the actual controlled process response data comprises secondary superheat air temperature process data;
acquiring Z-N rule parameters of the controlled process response data input by the unit step according to a Z-N rule;
setting the gain of the cascade sliding window filter according to the gain of the Z-N rule parameter;
setting a time constant of the fast sliding window filter according to a time constant of the Z-N rule parameter;
acquiring process response data of the cascade sliding window filter in unit step input;
calculating the error between the controlled process response data of the unit step input and the process response data of the cascade sliding window filter at the unit step input;
and adjusting the time constant of the slow sliding window filter to minimize the square integral of the error so as to obtain the optimal parameter of the cascade sliding window filter.
2. The method according to claim 1, wherein the transfer function of the cascaded sliding window filter is expressed as:
CSWF(s)＝KCSWFMSSWF(s)FSWF(s),
wherein CSWF(s) is a transfer function of the cascaded sliding window filter, KCSWFMFor the gain of the cascaded sliding window filter, SSWF(s) is the transfer function of the slow sliding window filter, TSSWFFor the time constant of the slow sliding window filter FSWF(s) is the transfer function of the fast sliding window filter TFSWFIs the time constant of the fast sliding window filter.
3. The method of claim 1, wherein the step of converting the actual controlled process response data at the actual step input to the controlled process response data at the unit step input is performed by the following expression:
wherein PVCP(t) is the unit step inputControlled process response data, PVACP(t) is the actual controlled process response data for the actual step input, and SAI is the actual step input.
4. The method of claim 1, wherein the Z-N law parameter of the controlled process response data of the unit step input is expressed as:
wherein Z-N(s) is a transfer function of the said Z-N law, KZ-NFor the gain of said Z-N law parameter, τZ-NPure lag time, T, for said Z-N law parameterZ-NIs the time constant of the Z-N law parameter.
5. The method according to claim 1, wherein the expression for setting the gain of the cascaded sliding window filter according to the gain of the Z-N rule parameter is:
KCSWF＝KZ-N
wherein, KCSWFIs the gain, K, of the cascaded sliding window filterZ-NIs the gain of the Z-N law parameter.
6. The method according to claim 1, wherein the expression for setting the time constant of the fast sliding window filter according to the time constant of the Z-N law parameter is as follows:
wherein, TFSWFIs the time constant, T, of the fast sliding window filterZ-NIs the time constant of the Z-N law parameter.
7. The method of claim 1, wherein the square integral of the error is expressed as:
where ESI is the square integral of the error, T is the current time, TCLTo calculate the length of ESI time, ST is the steady-state time, PVCP(t) controlled process response data for said unit step input, PVCSWFAnd (t) is the process response data of the cascade sliding window filter at the unit step input.
8. A parameter adjusting apparatus of a cascaded sliding window filter, the cascaded sliding window filter including a fast sliding window filter and a slow sliding window filter, comprising:
the controlled process analysis module is used for converting actual controlled process response data of actual step input into controlled process response data of unit step input; wherein the actual controlled process response data comprises secondary superheat air temperature process data;
the setting module is used for acquiring Z-N rule parameters of the controlled process response data input by the unit step according to a Z-N rule;
the gain acquisition module is used for setting the gain of the cascade sliding window filter according to the gain of the Z-N rule parameter;
the time constant acquisition module is used for setting the time constant of the fast sliding window filter according to the time constant of the Z-N rule parameter;
the process response analysis module is used for acquiring process response data of the cascade sliding window filter at unit step input;
the error calculation module is used for calculating the error between the controlled process response data of the unit step input and the process response data of the cascade sliding window filter at the unit step input;
and the time constant adjusting module is used for adjusting the time constant of the slow sliding window filter to ensure that the square integral of the error is minimum and obtain the optimal parameter of the cascade sliding window filter.
9. The apparatus of claim 8, wherein the transfer function of the cascaded sliding window filter is expressed as:
CSWF(s)＝KCSWFSSWF(s)FSWF(s),
wherein CSWF(s) is a transfer function of the cascaded sliding window filter, KCSWFFor the gain of the cascaded sliding window filter, SSWF(s) is the transfer function of the slow sliding window filter, TSSWFFor the time constant of the slow sliding window filter FSWF(s) is the transfer function of the fast sliding window filter TFSWFIs the time constant of the fast sliding window filter.
10. The apparatus of claim 8, wherein the actual controlled process response data at the actual step input is converted to controlled process response data at the unit step input using the expression:
wherein PVCP(t) is the unit step inputControl process response data, PVACP(t) is the actual controlled process response data for the actual step input, and SAI is the actual step input.
11. The apparatus of claim 8, wherein the Z-N law parameter of the controlled process response data of the unit step input is expressed as:
wherein Z-N(s) is a transfer function of the said Z-N law, KZ-NFor the gain of said Z-N law parameter, τZ-NPure lag time, T, for said Z-N law parameterZ-NIs the time constant of the Z-N law parameter.
12. The apparatus according to claim 8, wherein the expression for setting the gain of the cascaded sliding window filter according to the gain of the Z-N rule parameter is:
KCSWF＝KZ-N
wherein, KCSWFIs the gain, K, of the cascaded sliding window filterZ-NIs the gain of the Z-N law parameter.
13. The apparatus according to claim 8, wherein the expression for setting the time constant of the fast sliding window filter according to the time constant of the Z-N law parameter is:
wherein, TFSWFIs the time constant, T, of the fast sliding window filterZ-NIs the time constant of the Z-N law parameter.
14. The apparatus of claim 8, wherein the square integral of the error is expressed as:
where ESI is the square integral of the error, T is the current time, TCLTo calculate the length of ESI time, ST is the steady-state time, PVCP(t) controlled process response data for said unit step input, PVCSWFAnd (t) is the process response data of the cascade sliding window filter at the unit step input.
15. A computer terminal device, comprising:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method for parameter adjustment of a cascaded sliding window filter according to any one of claims 1 to 7.
16. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements a method of parameter adjustment of a cascaded sliding window filter according to any one of claims 1 to 7.
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