CN117267911A - Air conditioner control method and device and air conditioner - Google Patents

Air conditioner control method and device and air conditioner Download PDF

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Publication number
CN117267911A
CN117267911A CN202210667453.4A CN202210667453A CN117267911A CN 117267911 A CN117267911 A CN 117267911A CN 202210667453 A CN202210667453 A CN 202210667453A CN 117267911 A CN117267911 A CN 117267911A
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CN
China
Prior art keywords
air conditioner
frequency
control
period
operation period
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Pending
Application number
CN202210667453.4A
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Chinese (zh)
Inventor
董明珠
夏光辉
梁博
王现林
梁之琦
陶梦春
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Gree Electric Appliances Inc of Zhuhai
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Gree Electric Appliances Inc of Zhuhai
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Priority to CN202210667453.4A priority Critical patent/CN117267911A/en
Publication of CN117267911A publication Critical patent/CN117267911A/en
Pending legal-status Critical Current

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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F24F11/65—Electronic processing for selecting an operating mode
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F24F11/46—Improving electric energy efficiency or saving
    • F24F11/47—Responding to energy costs
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/50—Control or safety arrangements characterised by user interfaces or communication
    • F24F11/61—Control or safety arrangements characterised by user interfaces or communication using timers
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F24F11/64—Electronic processing using pre-stored data
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/70—Control systems characterised by their outputs; Constructional details thereof
    • F24F11/72—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure
    • F24F11/74—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity
    • F24F11/77—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity by controlling the speed of ventilators
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/70—Control systems characterised by their outputs; Constructional details thereof
    • F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
    • F24F11/83—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling the supply of heat-exchange fluids to heat-exchangers
    • F24F11/84—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling the supply of heat-exchange fluids to heat-exchangers using valves
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/70—Control systems characterised by their outputs; Constructional details thereof
    • F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
    • F24F11/86—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling compressors within refrigeration or heat pump circuits
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/70—Control systems characterised by their outputs; Constructional details thereof
    • F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
    • F24F11/87—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling absorption or discharge of heat in outdoor units
    • F24F11/871—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling absorption or discharge of heat in outdoor units by controlling outdoor fans
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/10—Temperature
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B30/00—Energy efficient heating, ventilation or air conditioning [HVAC]
    • Y02B30/70—Efficient control or regulation technologies, e.g. for control of refrigerant flow, motor or heating

Abstract

The invention provides a control method and device of an air conditioner and the air conditioner, wherein the method comprises the following steps: inputting control parameters of the current operation period of the air conditioner into a neural network forward prediction model, a target optimizing algorithm and a reverse prediction model to obtain control parameters of the next operation period of the air conditioner meeting operation requirements; determining whether the rotating speed of the inner fan in the control parameter of the predicted next operation period meets a preset condition, and executing a frequency protection point cooling capacity compensation mode by the air conditioner if the frequency of the compressor in the air conditioner control parameter of the next operation period is in a frequency protection point set when the rotating speed of the inner fan meets the preset condition; if the compressor frequency in the control parameter of the next operation cycle of the air conditioner is not within the frequency guard point set, the air conditioner performs the cold compensation anti-fluctuation control. According to the scheme of the invention, the problems of temperature control stability of the room by the frequency protection point and frequent jump of control parameters can be reduced, and effective control of system reliability and room comfort can be realized.

Description

Air conditioner control method and device and air conditioner
Technical Field
The present invention relates to the field of automatic control, and more particularly, to a control method and apparatus for an air conditioner, and an air conditioner.
Background
With the improvement of living standard, people have higher and higher requirements on the quality of living environment. Air conditioning has become a necessity in people's life as an important device for indoor temperature and humidity adjustment. The traditional air conditioner control mode is difficult to adapt to multi-scene application, and meanwhile, the cooling capacity prediction and compensation cannot be accurately performed due to the frequency control point in the running process of the existing air conditioner, so that the temperature stability of a room can be affected to a certain extent.
The air conditioner in the prior art realizes the high-energy-efficiency control of the air conditioner near the frequency control point through the neural network algorithm, but the influence of the control algorithm on the room temperature fluctuation is not considered, and the room comfort cannot be considered.
Therefore, there is a need for a method that can accurately predict and compensate for the cooling capacity of an air conditioner without affecting the stability of the room temperature.
The above information disclosed in the background section is only for a further understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The invention relates to a control method and device of an air conditioner and the air conditioner. The scheme of the invention can solve the problem of frequent change of the output control parameters of the air conditioner neural network control algorithm and can solve the problem of accurate cooling capacity matching in the control process of the frequency protection points.
A first aspect of the invention provides S1: inputting control parameters of the current operation period of the air conditioner into a neural network forward prediction model, a target optimizing algorithm and a reverse prediction model to obtain control parameters of the next operation period of the air conditioner meeting operation requirements; s2: determining whether the rotating speed of the inner fan in the control parameter of the predicted next operation period meets a preset condition or not; s3, when a preset condition is met, if the frequency of a compressor in the control parameter of the air conditioner in the next operation period is in the frequency protection point set, the air conditioner executes a frequency protection point cooling capacity compensation mode; when the preset condition is satisfied, if the frequency of the compressor in the control parameter of the next operation cycle of the air conditioner is not in the frequency protection point set, the air conditioner performs the cold compensation anti-fluctuation control.
According to one embodiment of the invention, in said step S1: inputting control parameters of a current operation period of an air conditioner into a neural network forward prediction model to obtain parameters related to the operation capability of the air conditioner, using a target optimizing algorithm to obtain optimal parameters related to the operation capability of the air conditioner, and using a reverse prediction model to obtain control parameters of a next operation period which are related to the optimal parameters related to the operation capability of the air conditioner and are met by the next period, wherein the control parameters of the next operation period are optimal control parameters.
According to one embodiment of the invention, the control parameters include at least: air conditioner compressor frequency, inner fan rotating speed, outer fan rotating speed and expansion valve opening.
According to one embodiment of the present invention, the parameters related to the operation capability of the air conditioner include at least the operation capability of the air conditioner, the power of the dry operation and the operation energy efficiency.
According to one embodiment of the present invention, the preset condition is: the rotation speed NI of the fan in the air conditioner in the next operation period n ∈{NI Setting up ±ΔNI }, where NI Setting up And setting the rotating speed of the inner fan corresponding to the wind gear for a user, wherein delta NI is the allowable change threshold value of the rotating speed of the fan.
According to one embodiment of the invention, the method further comprises: s4: and when the rotation speed of the inner fan in the predicted control parameters of the next period does not meet the preset condition, continuously executing the step S1 until the control parameters of the next operation period meeting the operation requirement are obtained.
According to one embodiment of the present invention, the frequency protection point cooling capacity compensation mode of step S3 includes: s21: selecting a frequency point F of a first unprotected frequency around the compressor frequency of the nth run period within a first preset time n1 Operating the air conditioner, detecting fluctuation of indoor environment temperature, and calculating a first capacity loss amount of the air conditioner when the fluctuation is larger than a preset value; s22: selecting a frequency point F of a second unprotected frequency around the compressor frequency of the nth run period n2 Operating the air conditioner and calculating a second capacity compensation amount of operation of the air conditioner in real time, wherein F n1 >F n >F n2 Or F n1 <F n <F n2 The method comprises the steps of carrying out a first treatment on the surface of the S23, the air conditioner operates according to control parameters corresponding to frequency points corresponding to a second unprotected frequency, when the sum of the first capacity loss amount of the air conditioner operation and the second capacity compensation amount of the air conditioner operation is zero, the air conditioner exits the cold compensation control of the frequency protected point of the nth operation period and enters the cold compensation control of the (n+1) th operation period; the n-1 operation period is the current operation period of the air conditioner, and the n period is the next operation period of the air conditioner.
According to one embodiment of the invention, wherein the compressor frequency F is at the nth run period n Compressor frequency F for current run period n-1 When the air conditioner is in the frequency reducing stage; then F n1 <F n <F n2 The method comprises the steps of carrying out a first treatment on the surface of the Compressor frequency F at the nth run period n >Compressor frequency F of n-1 th cycle n-1 When the air conditioner is in the frequency raising stage; then F n1 >F n >F n2 。
According to one embodiment of the invention, the first capacity loss amount and the second capacity compensation amount are calculated by a neural network forward prediction model, a target optimization algorithm, and a backward prediction model.
According to an embodiment of the present invention, in the step S4, the cold compensation anti-surge control includes: s31: calculating whether the difference value of the control parameters of the air conditioner in the nth period and the (n-1) th period meets the threshold requirement; s32: if the control parameter is smaller than the preset threshold value, the control parameter of the nth cycle is not executed, and the control parameter of the (n-1) th cycle is still executed; s33: executing the control parameter of the nth cycle if the difference between the control parameters before and after the preset time does not meet the threshold requirement; the n-1 operation period is the current operation period of the air conditioner, and the n period is the next operation period of the air conditioner.
According to one embodiment of the present invention, the threshold requirement of the step S31 is:
ΔF＝|F n -F n-1 |＜θ1
and Δni= |ni n -NI n-1 |＜θ2
And Δno= |no n -NO n-1 |＜θ3
And Δb= |b n -B n-1 |＜θ4
Wherein, theta 1, theta 2, theta 3 and theta 4 are respectively the operating frequency of the compressor, the rotating speed of the inner fan, the rotating speed of the outer fan and the threshold value of the opening change of the expansion valve; f (F) n ，F n-1 The variable quantity of the running frequency of the delta F air conditioner compressor is respectively the running frequency of the compressor in the next running period of the air conditioner and the current running period of the air conditioner; NI (NI) n ，NI n-1 The rotation speed of the inner fan is respectively the next operation period of the air conditioner and the current operation period of the air conditioner, and delta NI is the change amount of the rotation speed of the inner fan; NO (NO) n -NO n-1 The rotation speed of the external fan in the next operation period of the air conditioner and the current operation period of the air conditioner, wherein delta NO is the variation of the rotation speed of the external fan; b (B) n ，B n-1 The expansion valve opening of the next operation period of the air conditioner and the current operation period of the air conditioner are respectively, and delta B is the variation of the expansion valve opening.
A second aspect of the present invention provides a control apparatus of an air conditioner, including a memory and a processor; the memory is used for storing a computer program; the processor is used for realizing the control method of the air conditioner when executing the computer program.
A third aspect of the present invention provides an air conditioner using the control method of the air conditioner described above, or a control apparatus including the air conditioner described above.
The invention is based on the air-conditioning neural network energy efficiency optimizing control and the cold compensation control principle, adopts the protection point cold compensation control at the frequency protection point, adopts the anti-fluctuation control at the non-protection point, reduces the problem of frequent jump of the protection point to the room temperature control stability and the control parameter, and realizes the effective control of the intelligent algorithm to the system reliability and the room comfort.
Drawings
In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a control method of an air conditioner according to an exemplary embodiment of the present invention.
Fig. 2 is a diagram of an air conditioning system neural network forward prediction SP-DNN model according to an exemplary embodiment of the present invention.
Fig. 3 is a diagram of a reverse predictive CP-DNN model of an air conditioning system neural network according to an exemplary embodiment of the present invention.
Fig. 4 is a flowchart of an anti-surge control algorithm for an air conditioning system according to an exemplary embodiment of the present invention.
Fig. 5 is a schematic diagram of a frequency guard point cooling capacity compensation control according to an exemplary embodiment of the present invention.
Fig. 6 is a schematic diagram of a frequency guard point down-conversion cooling capacity compensation mode according to an exemplary embodiment of the present invention.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
As used herein, the terms "first," "second," and the like may be used to describe elements in exemplary embodiments of the present invention. These terms are only used to distinguish one element from another element, and the inherent feature or sequence of the corresponding element, etc. is not limited by the terms. Unless defined otherwise, all terms (including technical or scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Those skilled in the art will understand that the devices and methods of the present invention described herein and illustrated in the accompanying drawings are non-limiting exemplary embodiments and that the scope of the present invention is defined solely by the claims. The features illustrated or described in connection with one exemplary embodiment may be combined with the features of other embodiments. Such modifications and variations are intended to be included within the scope of the present invention.
Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the drawings, detailed descriptions of related known functions or configurations are omitted so as not to unnecessarily obscure the technical gist of the present invention. In addition, throughout the description, the same reference numerals denote the same circuits, modules or units, and repetitive descriptions of the same circuits, modules or units are omitted for brevity.
Furthermore, it should be understood that one or more of the following methods or aspects thereof may be performed by at least one control unit or controller. The terms "control unit," "controller," "control module," or "master control module" may refer to a hardware device that includes a memory and a processor. The memory or computer-readable storage medium is configured to store program instructions, and the processor is specifically configured to execute the program instructions to perform one or more processes that will be described further below. Moreover, it should be appreciated that the following methods may be performed by including a processor in combination with one or more other components, as will be appreciated by those of ordinary skill in the art.
The invention relates to an air conditioning system cold compensation fluctuation prevention control algorithm, which can reduce the influence of control parameter changes on comfort and system reliability in the running process of an air conditioner. In addition, the invention provides cooling capacity compensation control of the frequency protection point, and the cooling capacity compensation control principle and the capacity prediction algorithm are used for realizing the temperature control stability of a room running near the protection point of the system. In addition, the control method of the invention adds the anti-fluctuation control of the system operation parameters, solves the problem of output parameter output jump of the neural network, and further enhances the operation reliability of the system.
In the actual running process of the air conditioner, the system has larger resonance noise or the pipelines have stress exceeding fracture due to the vibration action of the system pipelines and the compressor, so that the selective shielding of partial vibration frequency points of the compressor can be considered in the existing air conditioner control process, and the problems are avoided.
Fig. 1 is a flowchart of a control method of an air conditioner according to an exemplary embodiment of the present invention. As shown in fig. 1:
in step S1, inputting control parameters of a current operation period of an air conditioner into a neural network forward prediction model, a target optimizing algorithm and a reverse prediction model to obtain control parameters of a next operation period of the air conditioner meeting operation requirements;
in step S2, it is determined whether the inner fan rotational speed satisfies a preset condition among the control parameters of the predicted next operation cycle,
in step S3, when the preset condition is satisfied, if the compressor frequency in the air conditioner control parameter is within the frequency protection point set in the next operation period, the air conditioner executes the frequency protection point cooling capacity compensation mode; when the preset condition is satisfied, if the frequency of the compressor in the control parameter of the next operation cycle of the air conditioner is not in the frequency protection point set, the air conditioner performs the cold compensation anti-fluctuation control.
In step S4, when the predicted rotational speed of the inner fan in the control parameter of the next cycle does not meet the preset condition, step S1 is continuously executed until the control parameter of the next operation cycle meeting the operation requirement is obtained.
Fig. 2 is a diagram of an air conditioning system neural network forward prediction SP-DNN model according to an exemplary embodiment of the present invention. Fig. 3 is a diagram of a reverse predictive CP-DNN model of an air conditioning system neural network according to an exemplary embodiment of the present invention.
The control method is based on the air conditioner energy efficiency optimizing algorithm principle, and the capacity Q corresponding to the current control parameter is firstly forward predicted through the air conditioner neural network n-1 Energy efficiency COP n-1 System operation capability set { q under similar control condition 0 ，…，q m Set { cop }/energy efficiency 0 ，…，cop m FIG. 2 schematically illustrates an air conditioning system neural network forward prediction SP-DNN model graph; secondly, quickly finding out the parameter (the lowest power) of the lowest highest energy efficiency state meeting the requirement of the target capacity in the output result of the forward prediction model by using a target optimizing algorithm such as a simulated annealing algorithm/a Newton mountain-down method/a genetic algorithm; in addition, the control parameters corresponding to the optimal capacity and the energy efficiency value are found through the reverse prediction model and sent to the controller for execution, so that the air conditioner can be operated at the optimal energy efficiency point at any time, the energy-saving operation of the air conditioner is realized, and a neural network reverse prediction CP-DNN model diagram of the reverse prediction model is shown in an exemplary way in FIG. 3.
According to one or more embodiments of the present invention, the forward prediction and the backward prediction are DNN neural network models, and other neural network models capable of implementing specific functions of the present invention may be used.
In accordance with one or more embodiments of the present invention, an air conditioning system neural network forward prediction model is used to estimate current PID control/fuzzy control, etc. traditional classical algorithm input control parameters (e.g., compressor frequency F n-1 Inner fan rotation speed NI n-1 Outer fan rotational speed NO n-1 Expansion valve opening degree B n-1 ) And the parameters such as the running capacity/power/energy efficiency of the air conditioner corresponding To the current environmental parameters (inner ring temperature Ti, indoor humidity Hi, outer ring temperature To, outdoor humidity Ho, air deflector position N and the like), and the model structure is shown in figure 2.
According to one or more embodiments of the present invention, the neural network inverse prediction model of the air conditioning system finds the optimal operation of the air conditioner according to the optimizing modelThe capacity/power/energy efficiency and other parameters and the current environmental parameters (inner ring temperature Ti, indoor humidity Hi, outer ring temperature To, outdoor humidity Ho, air deflector position N, etc.) are used To reversely estimate the current input control parameters (compressor frequency F n Inner fan rotation speed NI n Outer fan rotational speed NO n Expansion valve opening degree B n ) The model structure is shown in fig. 3.
According to one or more embodiments of the present invention, the control parameters of the air conditioner further include a control angle of an air deflector of the air conditioner.
Fig. 4 is a flowchart of an anti-surge control algorithm for an air conditioning system according to an exemplary embodiment of the present invention.
As shown in fig. 4, the air conditioner controller sets the current (n-1 th period) system operation control parameter (compressor frequency F n-1 Inner fan rotation speed NI n-1 Outer fan rotational speed NO n-1 Expansion valve opening degree B n-1 ) And the control parameters of the air conditioner also comprise the regulation and control angle of an air deflector of the air conditioner.
Then, the control parameters (compressor frequency F) with equivalent capacity and optimal energy efficiency in the next period (n period) are quickly found out through a forward prediction model, an optimizing algorithm and a reverse optimizing algorithm in an air conditioner energy efficiency optimizing algorithm module n Inner fan rotation speed NI n Outer fan rotational speed NO n Expansion valve opening degree B n )。
Judging whether the rotating speed of the fan in the current running period (namely the n-1 running period) meets the NI or not n-1 ∈{NI Setting up Setting a condition of plus or minus delta NI, wherein the condition of plus or minus delta NI is that a user sets the rotating speed of the inner fan corresponding to the wind gear, delta NI is a threshold value of the allowable change of the rotating speed of the fan, and the value of delta NI is generally 0-100 rpm, wherein, E represents the condition of belonging to the wind gear; { } represents a set. The purpose of this limitation is to prevent the change of the air volume or noise of the blower from being significant, thereby causing discomfort in the sense of body and sound of the user.
If it isThe control parameters which simultaneously meet the rotation speed range of the inner fan and have the best energy efficiency are searched in the optimizing algorithmUntil a control parameter meeting the requirement is found; />The representation does not belong.
If NI ε { NI Setting up A + -DeltaNI } condition, then the following procedure is performed;
judging whether the frequency in the current control parameter is a frequency protection point or not again, if so, executing the cooling capacity compensation control of the frequency protection point; if not, the cold compensation anti-surge control is performed. The frequency protection point is a preset value, and the frequency value to be protected is verified through experiments in the air conditioner design stage and is stored in the controller.
According to one or more embodiments of the present invention, the execution flow of the frequency guard point cooling capacity compensation mode includes:
s21: selecting a frequency point F of a first unprotected frequency around the compressor frequency of the nth run period within a first preset time n1 Operating the air conditioner, detecting fluctuation of indoor environment temperature, and calculating a first capacity loss delta Qa of the air conditioner when the fluctuation is larger than a preset value;
s22: selecting a frequency point F of a second unprotected frequency around the compressor frequency of the nth run period n2 Operating the air conditioner and calculating a second capacity compensation amount Δqb of the operation of the air conditioner in real time, wherein F n1 >F n >F n2 Or F n1 <F n <F n2 The method comprises the steps of carrying out a first treatment on the surface of the Wherein the compressor frequency F at the nth run period n Compressor frequency F for current run period n-1 When the air conditioner is in the frequency reducing stage; then F n1 <F n <F n2 The method comprises the steps of carrying out a first treatment on the surface of the Compressor frequency F at the nth run period n >Compressor frequency F of n-1 th cycle n-1 When the air conditioner is in the frequency raising stage; then F n1 >F n >F n2 ；
S23, the air conditioner operates according to control parameters corresponding to frequency points corresponding to a second unprotected frequency, when the sum of the first capacity loss amount of the air conditioner operation and the second capacity compensation amount of the air conditioner operation is zero, the air conditioner exits the cold compensation control of the frequency protected point of the nth operation period and enters the cold compensation control of the (n+1) th operation period;
the n-1 operation period is the current operation period of the air conditioner, and the n period is the next operation period of the air conditioner.
The principle of frequency protected point cooling compensation control is shown in fig. 5, according to one or more embodiments of the present invention.
Fig. 5 is a schematic diagram of a frequency guard point up-conversion cooling capacity compensation mode according to an exemplary embodiment of the present invention. Fig. 6 is a schematic diagram of a frequency guard point down-conversion cooling capacity compensation mode according to an exemplary embodiment of the present invention.
As shown in fig. 5, in the down-conversion process (F n-1 ＞F n ) In the method, when the optimal control parameters predicted by the reverse neural network are the compressor frequency F n Is a frequency protection point (corresponding capability is Q n Dotted line), t to t+Δt 1 The compressor frequency needs to be continuously reduced to a non-frequency protection point F in a time period fl (corresponding capability is Q in the figure) f Solid line).
As shown in fig. 5, at t+Δt 1 ～t+Δt 2 The compressor frequency needs to be increased to a non-frequency protection point F in a time period fl ' corresponding energy line is Q in the figure f Solid line).
Then, for t to t+Δt respectively 1 Time period and t+Δt 1 ～t+Δt 2 Actual running ability of time period (Q f Solid line) and predictive power (Q) n Dashed line) difference value, and the delta Qa and the delta Qb are equivalent, so that the actual cold output and the predicted cold output of one period of the up-conversion cold compensation mode are balanced, and the stability of the running period of the system is ensured.
Fig. 6 is a schematic diagram of a frequency guard point cooling capacity compensation mode according to an exemplary embodiment of the present invention, and the cooling capacity compensation mode is similar to that of fig. 5, and is not described in detail herein.
According to one or more embodiments of the invention, when the optimal control parameters (compressor frequency F n Inner fan rotation speed NI n Outer fan rotational speed NO n Expansion valve opening degree B n ) F in F) n And (3) entering the frequency protection point cooling capacity compensation control for the frequency protection point, wherein the frequency protection point is a preset value, and the frequency value required to be protected is verified through experiments in the air conditioner design stage and is stored in the controller.
I. If F n ＜F n-1 If the system is in the down-conversion stage, entering an up-conversion cold compensation mode, wherein the specific flow is as follows:
(1) The system frequency is then from F n Downward optimizing nearest non-frequency guard point F fl Running, starting timing t, and simultaneously calculating Q by using a forward neural network model n ＝f(F n ，NI n ，NO n ，B n )。
(2) Searching for the satisfied frequency F by using the optimizing model fl ，NI∈{NI Setting up + -DeltaNI, highest energy efficiency COP and capacity approaching Q n Capability Q of (2) a Combining, reverse prediction Q a ＝f(F fl ，NI n ′，NO n ′，B n ′)。
(3) The running process detecting fluctuations in the indoor ambient temperature, e.g. |DeltaT Inner ring The temperature is higher than 0.5 ℃, and the time t+delta t is counted 1 Calculating the capacity loss amount Δq a ＝∫(Q n -Q f )dt。Q f Representing the operating capacity corresponding to the current actual operating frequency.
At this time F n Immediate upward optimizing of nearest non-frequency guard point F fh Running, searching for the satisfying frequency F by using the optimizing model fh ，NI∈{NI Setting up + -DeltaNI, highest energy efficiency COP and capacity approaching Q n Capability Q of (2) b Combining, reverse prediction Q b ＝f(F fh ，NI n ″，NO n ″，B n ") and calculate the capacity compensation amount Δq b ＝∫(Q n -Q f )dt。
The system operating according to the control parameter (F fh ，NI n ″，NO n ″，B n ") up to DeltaQ a +ΔQ b And if the temperature is=0, exiting the frequency protection point cooling capacity compensation control of the period, and entering the cooling capacity compensation control of the next period. Up to F n Exit frequency protection for unprotected pointsAnd (5) controlling the point cooling capacity compensation. I.e. if the next cycle predicts the optimum operating frequency F n If the frequency is still the frequency protection point, the periodic cooling capacity compensation control is continued; up to the next period of optimum operating frequency F n And as a non-frequency protection point, the periodic cooling capacity compensation control is not continued.
II, if F n ≥F n-1 If the system is in the frequency-up stage, entering a frequency-down cooling capacity compensation mode, wherein the specific flow is as follows:
(1) The system frequency is then from F n Searching for the nearest non-frequency guard point F fl ' running, starting timing t, and simultaneously calculating Q by using forward neural network model n ＝f(F n ，NI n ，NO n ，B n )。
(2) Searching for the satisfied frequency F by using the optimizing model fl ′，NI′∈{NI Setting up + -DeltaNI, highest energy efficiency COP and capacity approaching Q n Capacity loss quantity Q of (2) a ' Combined, reverse predictive Q a ′＝f(F fl ’，NI n ′，NO n ′，B n ′)。
(3) The running process detecting fluctuations in the indoor ambient temperature, e.g. |DeltaT Inner ring The temperature is higher than 0.5 ℃, and the time t+delta t is counted 1 Calculating the capacity loss quantity Q a ′＝∫(Q n -Q f )dt。
At this time F n Immediately find the nearest non-frequency guard point F fh ' running, likewise searching for a satisfied frequency F using an optimization model fh ′，NI∈{NI Setting up + -DeltaNI, highest energy efficiency COP and capacity approaching Q n Capability Q of (2) b ' Combined, reverse predictive Q b ′＝f(F fh ″，NI n ″，NO n ″，B n ") and calculate the capacity compensation amount Δq b ′＝∫(Q n -Q f )dt。
The system operating according to the control parameter (F fh ″，NI n ″，NO n ″，B n ") up to DeltaQ a ′+ΔQ b ' =0, the control of the frequency protection point cold compensation in the present period is exited, and the process is advancedAnd (5) entering the next period of cold compensation control. Up to F n And (5) exiting the frequency protection point cooling capacity compensation control for the non-protection point.
According to one or more embodiments of the present invention, the principle of the cold compensation anti-surge control in the above step S4 is:
optimal control parameters (e.g., compressor frequency F) output via inverse predictive model n Inner fan rotation speed NI n Outer fan rotational speed NO n Expansion valve opening degree B n ) If the requirements of an internal machine wind shield and a non-frequency protection point are met, the cold compensation anti-fluctuation control is directly carried out, and the specific flow is as follows:
s31: calculating that the difference between the control parameters of the nth operation period and the (n-1) th operation period is smaller than a certain threshold (for example, taking 1 minute as one period):
ΔF＝|F n -F n-1 |＜θ1
and Δni= |ni n -NI n-1 |＜θ2
And Δno= |no n -NO n-1 |＜θ3
And Δb= |b n -B n-1 |＜θ4；
Wherein, theta 1, theta 2, theta 3 and theta 4 are respectively frequency, inner fan rotating speed, outer fan rotating speed and opening change threshold; f (F) n ，F n-1 The variable quantity of the running frequency of the delta F air conditioner compressor is respectively the running frequency of the compressor in the next running period of the air conditioner and the current running period of the air conditioner; NI (NI) n ，NI n-1 The rotation speed of the inner fan is respectively the next operation period of the air conditioner and the current operation period of the air conditioner, and delta NI is the change amount of the rotation speed of the inner fan; NO (NO) n -NO n-1 The rotation speed of the external fan in the next operation period of the air conditioner and the current operation period of the air conditioner, wherein delta NO is the variation of the rotation speed of the external fan; b (B) n ，B n-1 The expansion valve opening of the next operation period of the air conditioner and the current operation period of the air conditioner are respectively, and delta B is the variation of the expansion valve opening;
s32 if the threshold requirement is satisfied, the calculated control parameter is not allowed to be executed, and the control parameter before calculation is still executed (F n-1 ，NI n-1 ，NO n-1 ，B n-1 )。
S33: if the difference between the control parameters before and after the calculation of the 1min running period does not meet the threshold requirement, indicating that the load change is large, the system is required to make necessary control adjustment, the control parameters (F n ，NI n ，NO n ，B n )。
In addition, when the control parameter is smaller than the threshold value control is executed, the calculation is performed simultaneously
ΔQ c ＝∫(Q n -Q n-1 )dt1；
Real-time calculation while performing control parameters greater than threshold control
ΔQ d ＝∫(Q n ′-Q n-1 ′)dt2；
When DeltaQ c ＝ΔQ d And when the control compensation period is exited, the next cold compensation period is entered.
Wherein DeltaQ c Is the actual operating parameter (F) in the t1 time period n-1 ，NI n-1 ，NO n-1 ，B n-1 ) And the inverse neural network predicts optimal control parameters (F n ，NI n ，NO n ，B n ) Making a corresponding air conditioning capacity difference integral quantity; ΔQ d Is the actual operating parameter (F) in the t2 time period n-1 ′，NI n-1 ′，NO n-1 ′，B n-1 ' predicting optimal control parameters (F) for the inverse neural network n ′，NI n ′，NO n ′，B n ') is used as the integrated value of the corresponding air conditioning capacity difference.
In accordance with one or more embodiments of the present invention, entering the next cooling capacity compensation cycle here is also cooling capacity compensation control. In fig. 5 or 6, the optimal control frequency predicted by the inverse neural network is a frequency protection point (or the predicted control parameter affects the system stability), and the actual operating frequency (or parameter) is between t and t+Δt 1 Within time, deviate from the optimal predicted control frequency (or parameter run) and at t+Δt 1 ～t+Δt 2 Adjusting the control parameters to make the cooling capacity delta Q in the time period b Compensating the cold quantity change delta Q caused by parameter deviation at the time of t-t+delta t1 a . Where it isThe difference between the start of the next cooling capacity compensation cycle and the previous one is that the control purpose is different, the former one (Δq a And DeltaQ b ) To avoid the frequency guard point, the latter (Δq c And DeltaQ d ) To avoid control parameters that affect system stability.
The control flow circulation can further reduce the influence of the frequency protection point on indoor temperature fluctuation and prevent the influence of frequent change of the predicted value of the neural network algorithm on system control parameters, and further improve the control reliability and the room comfort of the air conditioner while realizing the efficient operation of the air conditioner.
According to one or more embodiments of the present invention, there is also provided a control device of an air conditioner, including a memory and a processor; the memory is used for storing a computer program; the processor is used for realizing the control method of the air conditioner when executing the computer program.
According to one or more embodiments of the present invention, there is also provided an air conditioner using the control method of the air conditioner described above, or a control apparatus of the air conditioner described above.
According to one or more embodiments of the invention, control logic in the present invention may implement processes as in the above systems of the invention using encoded instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium (e.g., hard drive, flash memory, read-only memory, optical disk, digital versatile disk, cache, random access memory, and/or any other storage device or storage disk) where information is stored for any period of time (e.g., for extended periods of time, permanent, transient instances, temporary caches, and/or information caches). As used herein, the term "non-transitory computer-readable medium" is expressly defined to include any type of computer-readable storage device and/or storage disk and to exclude propagating signals and to exclude transmission media.
Logic in the system of the present invention may be implemented using control circuitry, (control logic, a master control system, or a control module) that may include one or more processors or may include a non-transitory computer readable medium therein, in accordance with one or more embodiments of the present invention. In particular, the master control system or control module may comprise a microcontroller MCU. Processors used to implement the processing of logic in the system of the present invention may be, for example, but are not limited to, one or more single-core or multi-core processors. The processor(s) may include any combination of general-purpose processors and special-purpose processors (e.g., graphics processors, application processors, etc.). The processor may be coupled to and/or may include a memory/storage device and may be configured to execute instructions stored in the memory/storage device to implement various applications and/or operating systems running on the controller of the present invention.
The figures and detailed description of the invention referred to above as examples of the invention are intended to illustrate the invention, but not to limit the meaning or scope of the invention described in the claims. Accordingly, modifications may be readily made by one skilled in the art from the foregoing description. In addition, one skilled in the art may delete some of the constituent elements described herein without deteriorating the performance, or may add other constituent elements to improve the performance. Furthermore, one skilled in the art may vary the order of the steps of the methods described herein depending on the environment of the process or equipment. Thus, the scope of the invention should be determined not by the embodiments described above, but by the claims and their equivalents.
While the invention has been described in connection with what is presently considered to be practical, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims (13)

1. A control method of an air conditioner includes
S1: inputting control parameters of the current operation period of the air conditioner into a neural network forward prediction model, a target optimizing algorithm and a reverse prediction model to obtain control parameters of the next operation period of the air conditioner meeting operation requirements;
s2: determining whether the rotation speed of the inner fan in the control parameters of the predicted next operation period meets the preset condition,
s3, when a preset condition is met, if the frequency of a compressor in the control parameter of the air conditioner in the next operation period is in the frequency protection point set, the air conditioner executes a frequency protection point cooling capacity compensation mode; when the preset condition is satisfied, if the frequency of the compressor in the control parameter of the next operation cycle of the air conditioner is not in the frequency protection point set, the air conditioner performs the cold compensation anti-fluctuation control.
2. The method according to claim 1, wherein in said step S1: inputting control parameters of a current operation period of an air conditioner into a neural network forward prediction model to obtain parameters related to the operation capability of the air conditioner, using a target optimizing algorithm to obtain optimal parameters related to the operation capability of the air conditioner, and using a reverse prediction model to obtain control parameters of a next operation period which are related to the optimal parameters related to the operation capability of the air conditioner and are met by the next period, wherein the control parameters of the next operation period are optimal control parameters.
3. The method of claim 1, wherein the control parameters include at least: air conditioner compressor frequency, inner fan rotating speed, outer fan rotating speed and expansion valve opening.
4. The method of claim 2, wherein the parameters related to the operation capability of the air conditioner include at least the operation capability of the air conditioner, the power of the dry operation, and the operation energy efficiency.
5. The method of claim 1, wherein the preset condition is: the rotation speed NI of the fan in the air conditioner in the next operation period n ∈{NI Setting up + - ΔNI }, where NI Setting up And setting the rotating speed of the inner fan corresponding to the wind gear for a user, wherein delta NI is the allowable change threshold value of the rotating speed of the fan.
6. The method of claim 1, wherein the method further comprises:
s4: and when the rotation speed of the inner fan in the predicted control parameters of the next period does not meet the preset condition, continuously executing the step S1 until the control parameters of the next operation period meeting the operation requirement are obtained.
7. The method of claim 1, wherein the frequency guard point cooling capacity compensation mode of step S3 includes:
s21: selecting a frequency point F of a first unprotected frequency around the compressor frequency of the nth run period within a first preset time n1 Operating the air conditioner, detecting fluctuation of indoor environment temperature, and calculating a first capacity loss amount of the air conditioner when the fluctuation is larger than a preset value;
s22: selecting a frequency point F of a second unprotected frequency around the compressor frequency of the nth run period n2 Operating the air conditioner and calculating a second capacity compensation amount of operation of the air conditioner in real time, wherein F n1 >F n >F n2 Or F n1 <F n <F n2 ；
S23, the air conditioner operates according to control parameters corresponding to frequency points corresponding to a second unprotected frequency, when the sum of the first capacity loss amount of the air conditioner operation and the second capacity compensation amount of the air conditioner operation is zero, the air conditioner exits the cold compensation control of the frequency protected point of the nth operation period and enters the cold compensation control of the (n+1) th operation period;
the n-1 operation period is the current operation period of the air conditioner, and the n period is the next operation period of the air conditioner.
8. The method of claim 7, wherein the compressor frequency F at the nth run period n Compressor frequency F for current run period n-1 When the air conditioner is in the frequency reducing stage; then F n1 <F n <F n2 ；
Compressor frequency F at the nth run period n >Compressor frequency F of n-1 th cycle n-1 When the air conditioner is inA frequency raising stage; then F n1 >F n >F n2 。
9. The method of claim 7, wherein the first and second capacity loss amounts are calculated by a neural network forward prediction model, a target optimization algorithm, and a reverse prediction model.
10. The method according to claim 1, wherein in the step S4, the cold compensation anti-surge control includes:
s31: calculating whether the difference value of the control parameters of the air conditioner in the nth period and the (n-1) th period meets the threshold requirement;
s32: if the threshold requirement is met, the control parameter of the nth cycle is not executed, and the control parameter of the (n-1) th cycle is still executed;
s33: executing the control parameter of the nth cycle if the difference between the control parameters before and after the preset time does not meet the threshold requirement;
the n-1 operation period is the current operation period of the air conditioner, and the n period is the next operation period of the air conditioner.
11. The method of claim 9, wherein the threshold requirement of step S31 is:
ΔF＝|F n -F n-1 |＜θ1
and Δni= |ni n -NI n-1 |＜θ2
And Δno= |no n -NO n-1 |＜θ3
And Δb= |b n -B n-1 |＜θ4
Wherein, theta 1, theta 2, theta 3 and theta 4 are respectively the operating frequency of the compressor, the rotating speed of the inner fan, the rotating speed of the outer fan and the threshold value of the opening change of the expansion valve; f (F) n ，F n-1 The variable quantity of the running frequency of the delta F air conditioner compressor is respectively the running frequency of the compressor in the next running period of the air conditioner and the current running period of the air conditioner; NI (NI) n ，NI n-1 Respectively the next operation period of the air conditioner and the current operation period of the air conditionerThe rotating speed of the inner fan, delta NI is the rotating speed variation of the inner fan; NO (NO) n -NO n-1 The rotation speed of the external fan in the next operation period of the air conditioner and the current operation period of the air conditioner, wherein delta NO is the variation of the rotation speed of the external fan; b (B) n ，B n-1 The expansion valve opening of the next operation period of the air conditioner and the current operation period of the air conditioner are respectively, and delta B is the variation of the expansion valve opening.
12. A control device of an air conditioner comprises a memory and a processor; the memory is used for storing a computer program; the processor is configured to implement the control method of an air conditioner according to any one of claims 1 to 11 when executing the computer program.
13. An air conditioner using the control method of an air conditioner according to any one of claims 1 to 11, or comprising the control device of an air conditioner according to claim 12.
CN202210667453.4A 2022-06-13 2022-06-13 Air conditioner control method and device and air conditioner Pending CN117267911A (en)

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CN202210667453.4A CN117267911A (en) 2022-06-13 2022-06-13 Air conditioner control method and device and air conditioner

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