CN113139291B - Method and device for obtaining optimal sliding window filtering model of controlled process - Google Patents
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Abstract
The invention discloses a method and a device for obtaining an optimal sliding window filtering model in a controlled process, wherein the method comprises the following steps: acquiring steady-state time and steady-state gain of controlled process response data input by unit step of a controlled process, and setting the gain of a sliding window filter according to the steady-state gain; acquiring unit step input process response data of the sliding window filter; and in the steady-state time, judging the error between the unit step input process response data of the sliding window filter and the unit step input controlled process response data of the controlled process, and when the error is minimum, taking the current sliding window filtering model as the optimal sliding window filtering model of the controlled process. By obtaining the optimal sliding window filter model, the accuracy of expressing the controlled process model by adopting 1 sliding window filter is improved.
Description
Technical Field
The invention relates to the technical field of process control of thermal power generating units, in particular to a method, a device and terminal equipment for obtaining an optimal sliding window filtering model of a controlled process.
Background
From the perspective of thermal power unit process control, obtaining a controlled process model is of great significance. From a theoretical point of view, obtaining an accurate model of the process being controlled may not be a simple problem. However, from an engineering point of view, the controlled process model needs to be simple.
If SWF is used as a standard model of a feedback system, the process is simply considered a SWF, a "simple modeling" of the process. The SWF is an english abbreviation of a Sliding window filter (Sliding window filter), and 1 SWF is adopted to express a controlled process model, which is relatively simple.
However, in terms of the problem of obtaining the controlled process model, the method is simple and not enough, and certain accuracy is required, and no technology is provided at present for improving the accuracy of 1 sliding window filter.
Disclosure of Invention
The invention aims to provide a method and a device for obtaining an optimal sliding window filtering model of a controlled process, so as to solve the problem that the process control of a thermal power generating unit is not accurate enough by adopting a sliding window filter.
In order to achieve the above object, the present invention provides a method for obtaining an optimal sliding window filtering model in a controlled process, including:
acquiring the steady-state time and the steady-state gain of the controlled process response data input by unit step of the controlled process, and setting the gain of the sliding window filter according to the steady-state gain;
acquiring unit step input process response data of the sliding window filter;
and in the steady-state time, judging the error between the unit step input process response data of the sliding window filter and the unit step input controlled process response data of the controlled process, and when the error is minimum, taking the current sliding window filtering model as the optimal sliding window filtering model of the controlled process.
Preferably, before the obtaining the steady-state time and the steady-state gain of the controlled process response data of the unit step input of the controlled process, the method further includes obtaining actual controlled process response data of the controlled process at the actual step input, and converting the actual controlled process response data of the actual step input into controlled process response data of the unit step input.
Preferably, the actual controlled process response data of the actual step input is converted into controlled process response data of unit step input, and the calculation formula is as follows:
wherein PV CP (t) controlled process response data for said unit step input, PV ACP (t) is the actual controlled process response data for the actual step input, and ASI is the actual step input.
Preferably, the steady-state time and the steady-state gain of the response data of the controlled process, which is input in unit step of the controlled process, are obtained by the following calculation formula:
SG＝SV
wherein SV is PV CP (t) steady state value in dimensionless units, ST being the steady state time, PV CP (t) time to 0.99SV, SG being said steady state gain in dimensionless units, in number SG ═ SV.
Preferably, the transfer function of the sliding window filter is:
wherein s is Laplace operator, K SWF Is the gain, K, of the sliding window filter SWF SG is the steady state gain, T SWF Is the time constant of the sliding window filter.
Preferably, the determining an error between the process response data of the unit step input of the sliding window filter and the controlled process response data of the unit step input of the controlled process includes calculating a square integral of the error, and a calculation formula is as follows:
wherein ESI is the square integral of the error, t is the current time, ST is the steady state time, PV CP (t) controlled process response data for said unit step input, PV SWF (t) is process response data for the sliding window filter at a unit step input.
Preferably, the calculation formula of the optimal sliding window filtering model is as follows:
OSWFM(s) is an optimal sliding window filtering model of the controlled process, s is a Laplace operator, and SG is a steady gain of the controlled process.
The invention also provides a device for obtaining the optimal sliding window filtering model of the controlled process, which is applied to the method for obtaining the optimal sliding window filtering model of the controlled process and comprises the following steps:
the gain setting module is used for acquiring the steady-state time and the steady-state gain of the controlled process response data input by unit step of the controlled process and setting the gain of the sliding window filter according to the steady-state gain;
the process response data acquisition module is used for acquiring the process response data of unit step input of the sliding window filter;
and the optimal sliding window filtering model obtaining module is used for judging the error between the unit step input process response data of the sliding window filter and the controlled process response data of the unit step input of the controlled process within the steady-state time, and when the error is minimum, taking the current sliding window filtering model as the optimal sliding window filtering model of the controlled process.
Preferably, the device further comprises an actual controlled process response data acquiring module, which acquires actual controlled process response data of the controlled process at the actual step input, and converts the actual controlled process response data of the actual step input into controlled process response data of the unit step input.
The invention also provides a computer terminal device comprising 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 implement the method for obtaining the optimal sliding window filtering model of the controlled process according to any of the embodiments.
The 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 method for obtaining the optimal sliding window filtering model of the controlled process as described above.
According to the method and the device for obtaining the optimal sliding window filtering model in the controlled process, the optimal sliding window filtering model is obtained and used for parameter setting of the high-performance proportional-integral controller, and better control characteristics can be obtained. The effect of long-term application of the high-performance proportional-integral controller shows that the high-performance proportional-integral controller can effectively suppress disturbance, and the output of the high-performance proportional-integral controller is stable.
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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 of a method for obtaining an optimal sliding window filtering model of a controlled process according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a thermal power generating unit heating control system provided by the invention;
FIG. 3 is a diagram illustrating the results of the process response of the controlled process and the optimal sliding window filtering model of the controlled process at a unit step input according to the present invention;
FIG. 4 is a diagram illustrating the results of a controlled process given as a unit step according to the present invention;
FIG. 5 is a schematic diagram illustrating the operation of a thermal power generating unit heating control system provided by the present invention;
FIG. 6 is a graphical illustration of controlled process response data per unit step input for a controlled process provided by the present invention;
fig. 7 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 any and all possible combinations of one or more of the associated listed items and includes such combinations.
Referring to fig. 1, an embodiment of the present invention provides a method for obtaining an optimal sliding window filtering model of a controlled process, including:
s10, acquiring the steady-state time and the steady-state gain of the unit step input controlled process response data of the controlled process, and setting the gain of the sliding window filter according to the steady-state gain;
s20, acquiring unit step input process response data of the sliding window filter;
and S30, in the steady state time, judging the error between the unit step input process response data of the sliding window filter and the unit step input controlled process response data of the controlled process, and when the error is minimum, taking the current sliding window filtering model as the optimal sliding window filtering model of the controlled process.
In this embodiment, referring to fig. 2, in the thermal power unit heat supply control system, the transfer function of the controlled process is:
obtaining a steady state gain SG of the controlled process response data of the unit step input of the controlled process as 1.2, and a steady state time ST of the controlled process response data of the unit step input of the controlled process as 760s, wherein the steady state time is PV CP (t) time to reach said steady state value of 0.99. Wherein the Steady state value SV represents the Steady state gain (Steady gain) of the controlled process in dimensionless units.
Adjusting the time constant T of the sliding window filter at intervals of 1s during 760s SWF Wherein the time constant T is set in the sliding window filter SWF And obtaining the minimum ESI of the ESI, min is 1.7744, and the transfer function of the optimal sliding window filter model of the controlled process is obtained as follows:
referring to FIG. 3, the experimental results of the process response of the controlled process and the optimal sliding window filtering model of the controlled process at unit step input, PV, are obtained CP (t) controlled process response at unit step input, PV, for said controlled process CP:OSWFM And (t) is the process response of the optimal sliding window filtering model of the controlled process at unit step input.
The formula for calculating the transfer function of the high-performance proportional-integral controller is as follows:
HPPI(s)＝K HPPI [1+HEI(s)],
wherein HPPI(s) is the high performance proportional-integral controller transfer function. K HPPI Is the proportional gain of the high performance proportional-integral controller in dimensionless units. Hei(s) is the high efficiency integrator transfer function. Aswf(s) is (16 th order) approximated sliding window filter transfer function. T is HEI Is the time constant of the high efficiency integrator in s.
Using the optimal sliding window filter model for parameter tuning of the high-performance proportional-integral controller to
T HEI ＝T SWF ＝410s
Wherein, K HPPI Is the proportional gain of the high performance proportional-integral controller in dimensionless units. K SWF And obtaining the gain of the sliding window filter in the optimal sliding window filtering model in a dimensionless unit. T is HEI Is the time constant of the high efficiency integrator in s. T is SWF And the time constant of the sliding window filter in the optimal sliding window filtering model is in the unit of s.
The controlled process was given as unit step and the experimental results obtained are shown in fig. 4. In an actual thermal power generating unit heat supply control system, the temperature of heat supply steam is set to be increased by 10 ℃, the obtained process control characteristic is shown in fig. 5, and the optimal sliding window filtering model is used for parameter setting of the high-performance proportional-integral controller, so that better control characteristic can be obtained. The effect of long-term application of the high-performance proportional-integral controller shows that the high-performance proportional-integral controller can effectively suppress disturbance, and the output of the high-performance proportional-integral controller is stable.
In a certain embodiment, before the obtaining of the steady-state time and the steady-state gain of the unit step input controlled process response data of the controlled process, the method further includes obtaining actual controlled process response data of the controlled process at an actual step input, and converting the actual controlled process response data of the actual step input into the unit step input controlled process response data.
In the embodiment, actual controlled process response data of a controlled process at an actual step input is obtained, wherein the actual step input is specifically the actual (open-loop) step input of the controlled process of the thermal power unit heat supply control system; and converting the actual controlled process response data of the actual step input into controlled process response data of unit step input.
In a certain embodiment, 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 is as follows:
wherein PV CP (t) controlled process response data for said unit step input, PV ACP (t) is the actual controlled process response data for the actual step input, and ASI is the actual step input.
In this embodiment, the calculation formula for converting the actual controlled process response data of the actual step input into the controlled process response data of the unit step input is as follows:
SI＝1,
wherein, SI is the unit step input, and the unit is dimensionless. PV (photovoltaic) CP And (t) the unit step input controlled process response data, and the unit is dimensionless. PV (photovoltaic) ACP (t) Actual controlled process response data, ASI Actual step input (PV) ACP The units of (t) and ASI are determined by the physical quantities of the actual controlled process.
PV ACP (t) with actual physical quantities, inconvenient control, and the need for PV in general ACP (t) performing per unit treatment, and subjecting the PV ACP (t) conversion to unit step outputIncoming controlled process response data PV CP (t), i.e. independent of the actual physical quantity.
In a certain embodiment, the calculation formula for obtaining the steady-state time and the steady-state gain of the unit step input controlled process response data of the controlled process is as follows:
SG＝SV
wherein SV is PV CP (t) steady state value in dimensionless units, ST being the steady state time, PV CP (t) time to 0.99SV, SG being said steady state gain in dimensionless units, in number SG ═ SV.
In the present embodiment, the process response data PV of the unit step input is acquired CP (t) Steady time (Steady time) and Steady value (Steady value), referring to FIG. 6, the Steady time refers to PV CP (t) time to reach said steady state value of 0.99, as follows:
SG＝SV
wherein SV represents the Steady state gain (Steady gain) of the controlled process in dimensionless units.
In one embodiment, the transfer function of the sliding window filter is:
wherein s is Laplace operator, K SWF Is the gain, K, of the sliding window filter SWF SG being said steady state gain, T SWF Is the time constant of the sliding window filter.
In this embodiment, the equation for calculating the transfer function of the sliding window filter is:
K SWF ＝SG
wherein SWF(s) is the sliding window filter transfer function. K SWF The unit is dimensionless for the sliding window filter gain. T is SWF Is the sliding window filter time constant in units of s. SG is the Steady state gain (Steady gain) in dimensionless units.
In one embodiment, the determining an error between the unit step input process response data of the sliding window filter and the unit step input controlled process response data of the controlled process includes calculating a square integral of the error, and a calculation formula is as follows:
wherein ESI is the square integral of the error, t is the current time, ST is the steady state time, PV CP (t) controlled process response data for said unit step input, PV SWF (t) is process response data for the sliding window filter at a unit step input.
In this embodiment, a calculation formula of an error between a process response of the sliding window filter at the unit step input and a process response of the controlled process at the unit step input is:
ESI is
Where ESI is the square integral of the error in dimensionless units. ST is the steady state time in units of s. PV (photovoltaic) CP And (t) is the response data of the controlled process at the unit step input of the controlled process, and the unit is dimensionless. PV SWF (t) is that the sliding window filter is inThe process response data of the bit step input is in dimensionless units.
In a certain embodiment, the calculation formula of the optimal sliding window filtering model is as follows:
OSWFM(s) is the optimal sliding window filtering model of the controlled process, s is a Laplace operator, and SG is the steady gain of the controlled process.
In this embodiment, the calculation formula of the optimal sliding window filtering model in the controlled process is as follows:
ESI＝ESI:min
OSWFM(s) is the transfer function of the Optimal sliding window filter model (Optimal SWF model) of the controlled process. SG is the steady state gain of the controlled process in dimensionless units. And ESI: min is the minimum value of the square integral of the error ESI, and the unit is dimensionless.
Acquiring the controlled process response data PV of the unit step input CP (t) steady state value SV, obtaining controlled process response data PV of said unit step input CP (t) steady state value SV, see FIG. 6.
The invention also provides a device for obtaining the optimal sliding window filtering model of the controlled process, which is applied to the method for obtaining the optimal sliding window filtering model of the controlled process and comprises the following steps:
the gain setting module is used for acquiring the steady-state time and the steady-state gain of the response data of the controlled process input by unit step of the controlled process and setting the gain of the sliding window filter according to the steady-state gain;
the process response data acquisition module is used for acquiring the process response data of unit step input of the sliding window filter;
and the optimal sliding window filtering model obtaining module is used for judging the error between the unit step input process response data of the sliding window filter and the controlled process response data of the unit step input of the controlled process within the steady-state time, and when the error is minimum, taking the current sliding window filtering model as the optimal sliding window filtering model of the controlled process.
In one embodiment, the system further comprises an actual controlled process response data acquisition module, which acquires actual controlled process response data of the controlled process at an actual step input, and converts the actual controlled process response data of the actual step input into controlled process response data of a unit step input.
For specific definition of the device for obtaining the optimal sliding window filtering model of the controlled process, reference may be made to the above definition, which is not described herein again. All modules in the device for obtaining the optimal sliding window filtering model of the controlled process can be completely or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
Referring to fig. 7, 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 obtaining an optimal sliding window filtering model for a controlled process as described in any 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 method for obtaining the optimal sliding window filtering model of the controlled process. The memory is used to store various types of data to support the operation at the computer terminal device, which 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, and is configured to perform the above method for obtaining the optimal sliding window filter model of the controlled process, and achieve technical effects consistent with the above method.
In another exemplary embodiment, a computer readable storage medium is further provided, which includes program instructions, which when executed by a processor, implement the steps of the method for obtaining an optimal sliding window filtering model of a controlled process in any of the above embodiments. For example, the computer readable storage medium may be the above-mentioned memory including program instructions, which are executable by the processor of the computer terminal device to perform the above-mentioned method for obtaining the optimal sliding window filtering model of the controlled process, and achieve the technical effects consistent with the above-mentioned method.
According to the method and the device for obtaining the optimal sliding window filtering model in the controlled process, the optimal sliding window filtering model is obtained and used for parameter setting of the high-performance proportional-integral controller, and better control characteristics can be obtained. The effect of long-term application of the high-performance proportional-integral controller shows that the high-performance proportional-integral controller can effectively suppress disturbance, and the output of the high-performance proportional-integral controller is stable.
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 (4)
1. A method for obtaining an optimal sliding window filtering model of a controlled process is characterized by comprising the following steps:
acquiring actual controlled process response data of a controlled process at actual step input, and converting the actual controlled process response data of the actual step input into controlled process response data of unit step input, wherein the calculation formula is as follows:
wherein PV CP (t) controlled process response data for said unit step input, PV ACP (t) actual controlled process response data for the actual step input, ASI actual step input;
acquiring the steady-state time and the steady-state gain of the response data of the controlled process input by unit step of the controlled process, wherein the calculation formula is as follows:
wherein SV is PV CP (t) steady state value in dimensionless units, ST being the steady state time, PV CP (t) time to 0.99SV, SG being said steady state gain in dimensionless units, in number SG ═ SV;
setting a gain of a sliding window filter according to the steady-state gain; the transfer function of the sliding window filter is:
wherein s is Laplace operator, K SWF Is the gain, K, of the sliding window filter SWF SG is the steady state gain, T SWF Is the time constant of the sliding window filter;
acquiring unit step input process response data of the sliding window filter;
within the steady state time, determining an error between the unit step input process response data of the sliding window filter and the unit step input controlled process response data of the controlled process, including calculating a square integral of the error, the calculation formula being as follows:
wherein ESI is the square integral of the error, t is the current time, ST is the steady state time, PV CP (t) controlled process response data for said unit step input, PV SWF (t) process response data for the sliding window filter at a unit step input;
and when the error is minimum, taking the current sliding window filtering model as the optimal sliding window filtering model of the controlled process.
2. The method for obtaining the optimal sliding window filtering model of the controlled process according to claim 1, wherein the optimal sliding window filtering model is calculated as follows:
OSWFM(s) is the optimal sliding window filtering model of the controlled process, s is a Laplace operator, and SG is the steady gain of the controlled process.
3. An apparatus for obtaining an optimal sliding window filtering model of a controlled process, comprising:
the actual controlled process response data acquisition module is used for acquiring actual controlled process response data of a controlled process at actual step input and converting the actual controlled process response data of the actual step input into controlled process response data of unit step input, and the calculation formula is as follows:
wherein PV CP (t) controlled process response data for said unit step input, PV ACP (t) actual controlled process response data for the actual step input, ASI actual step input;
the gain setting module is used for acquiring the steady-state time and the steady-state gain of the response data of the controlled process input by unit step of the controlled process, and the calculation formula is as follows:
wherein SV is PV CP (t) steady state value in dimensionless units, ST being the steady state time, PV CP (t) time to 0.99SV, SG being said steady state gain in dimensionless units, in number SG ═ SV;
setting a gain of a sliding window filter according to the steady-state gain; the transfer function of the sliding window filter is:
wherein s is Laplace operator, K SWF Is the gain, K, of the sliding window filter SWF SG is the steady state gain, T SWF Is the time constant of the sliding window filter;
the process response data acquisition module is used for acquiring the process response data of unit step input of the sliding window filter;
an optimal sliding window filtering model obtaining module, configured to determine, within the steady-state time, an error between unit step input process response data of the sliding window filter and unit step input controlled process response data of the controlled process, where the error includes calculating a square integral of the error, and a calculation formula is as follows:
wherein ESI is the square integral of the error, t is the current time, ST is the steady state time, PV CP (t) controlled process response data for said unit step input, PV SWF (t) process response data for the sliding window filter at a unit step input;
and when the error is minimum, taking the current sliding window filtering model as the optimal sliding window filtering model of the controlled process.
4. 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 of obtaining a controlled process optimal sliding window filtering model according to any one of claims 1 to 2.
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