CN116678079A - Variable air volume air conditioner working condition analysis evaluation and optimization algorithm - Google Patents
Variable air volume air conditioner working condition analysis evaluation and optimization algorithm Download PDFInfo
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- 238000005457 optimization Methods 0.000 title claims abstract description 79
- 238000004458 analytical method Methods 0.000 title claims abstract description 69
- 238000011156 evaluation Methods 0.000 title claims abstract description 11
- 238000012544 monitoring process Methods 0.000 claims abstract description 26
- 238000004378 air conditioning Methods 0.000 claims abstract description 24
- 238000001816 cooling Methods 0.000 claims abstract description 14
- 238000010438 heat treatment Methods 0.000 claims abstract description 7
- 230000000694 effects Effects 0.000 claims abstract description 6
- 238000013459 approach Methods 0.000 claims abstract description 5
- 230000003068 static effect Effects 0.000 claims description 14
- 238000005259 measurement Methods 0.000 claims description 11
- 230000002159 abnormal effect Effects 0.000 claims description 8
- 230000005856 abnormality Effects 0.000 claims description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 4
- 238000012986 modification Methods 0.000 claims description 3
- 230000004048 modification Effects 0.000 claims description 3
- 239000000463 material Substances 0.000 description 2
- 241001391944 Commicarpus scandens Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
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- 238000011160 research Methods 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
Classifications
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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
- F24F11/64—Electronic processing using pre-stored data
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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
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/52—Indication arrangements, e.g. displays
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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
- F24F11/70—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
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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
- F24F11/70—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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
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- 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 discloses an analysis, evaluation and optimization algorithm for the working condition of a variable air volume air conditioner, which relates to the technical field of air conditioner control and comprises the following steps: after the big data of the variable air volume air conditioning system air cabinet and the administered VAVBOX operation are collected according to a certain period, the big data are compared with a preset value periodically by using an average value algorithm; when the preset deviation occurs and a trend is formed in a certain time, sending out early warning and entering different analysis optimization models according to the trend condition; the system comprises a room cooling anomaly analysis optimization algorithm model, a room heating anomaly analysis optimization algorithm model, a room sudden-cold and sudden-hot analysis optimization algorithm model and an energy-saving analysis optimization algorithm model; according to the logic flow of the analysis optimization model, gradually correcting the corresponding operation parameters of the VAVBOX and the wind cabinet according to a certain amplitude so as to achieve the purpose of optimizing the operation effect of the air conditioning system, and continuously monitoring operation feedback data until the operation data approaches a preset normal range; and the system optimization is completed, and the stability and the comfort of the air conditioning system are improved.
Description
Technical Field
The invention relates to the technical field of air conditioner control, in particular to an analysis, evaluation and optimization algorithm for the working condition of a variable air volume air conditioner.
Background
The building automation system is one comprehensive management system for centralized monitoring, controlling and managing air conditioner, water supply and drainage, power supply and distribution and lighting equipment inside building. The system is based on a direct digital controller, a sensor and an actuator, and management equipment of the system is realized through computer software and a communication network, so that the equipment utilization rate is improved, and the equipment operation cost is reduced.
The existing building automation system only carries out automatic operation management aiming at the on-site single equipment of the variable air volume air conditioner, such as: setting the indoor temperature to 25 ℃, and increasing the air valve when the indoor temperature is detected to be higher than 25 ℃; or if the measured value of the air duct static pressure sensor is smaller than the set value, increasing the frequency of the fan; in the above situation, the whole air balance of the air conditioning system is not considered, so that the room supply temperature in the jurisdiction of the air conditioning system is easy to obviously swing, the air valve is opened to the maximum, but the indoor temperature still cannot reach the set value; or the fan frequency is opened to the maximum, but the air duct static pressure measurement value still cannot reach the set value.
The existing building automation system cannot judge whether the running condition of the existing air conditioning system is normal or not according to the existing data, cannot analyze reasons for occurrence of abnormal conditions, cannot optimize the abnormal running conditions, and provides an analysis, evaluation and optimization algorithm for the working condition of the variable air conditioner based on the defects.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides an analysis, evaluation and optimization algorithm for the working condition of the variable air volume air conditioner, expands the data points and logic volumes collected by a conventional building automation system, collects big data from the viewpoint of the overall operation balance of the variable air volume air conditioner system, and establishes an analysis model; and the operation is carried out by combining the data through the model, so that the operation condition of the variable air volume air conditioning system is comprehensively analyzed, and the operation condition of the system is more accurately judged, analyzed and optimized.
To achieve the above object, according to an embodiment of the first aspect of the present invention, a variable air volume air conditioner operating condition analysis, evaluation and optimization algorithm is provided, including:
SS1: after the big data of the variable air volume air conditioning system air cabinet and the administered VAVBOX operation are collected according to a certain period, the big data are compared with a preset value periodically by using an average value algorithm;
SS2: when the preset deviation occurs and a trend is formed in a certain time, sending out early warning and entering different analysis optimization models according to the trend condition; the analysis optimization model comprises a room cooling anomaly analysis optimization algorithm model, a room heating anomaly analysis optimization algorithm model, a room sudden-cold and sudden-hot analysis optimization algorithm model and an energy-saving analysis optimization algorithm model;
SS3: and according to the logic flow of the analysis optimization model, gradually correcting the corresponding operation parameters of the VAVBOX and the wind cabinet according to a certain amplitude so as to achieve the purpose of optimizing the operation effect of the air conditioning system, and continuously monitoring the operation feedback data until the operation data approaches to a preset normal range.
Further, the room cooling anomaly analysis optimization algorithm model comprises the following steps:
step 1: collecting and monitoring the actual temperature value T transmitted back by the indoor temperature sensor, when T is more than T set When the duration time t is more than n minutes, judging that the room is in a cooling abnormal state, and outputting alarm information;
step 2: collecting a room VAVBox air valve opening actual measurement value of 1;
step 3: when% 1 is less than maxOpen, modifying the set value V1 of the maximum air supply amount of VAVBox, and collecting and monitoring the actual temperature value T transmitted by the indoor temperature sensor at intervals of n minutes after the setting is finished, wherein when T=T set Then the optimization is completed and the alarm information is cancelled; when T > T set And repeating the step 2 when the duration t is more than n minutes;
step 4: when%1=maxpen, judging whether the air supply temperature set value of the floor air conditioner air cabinet is the lowest value; if yes, the static pressure set value P of the system is increased to increase the air supply quantity of the air conditioner air cabinet; if not, increasing the opening of the chilled water valve of the air conditioner air cabinet; after the adjustment is finished, the interval is n minutes, the measured value T of the temperature returned by the indoor temperature sensor is collected and monitored, and when T=T set Then the optimization is completed and the alarm information is cancelled; when T > T set And repeating the step 4 when the duration t is greater than n minutes.
Further, the room temperature rise abnormality analysis optimization algorithm model comprises the following steps:
step 1: collecting and monitoring the actual temperature value T transmitted back by the indoor temperature sensor, when T is less than T set When the duration time t is more than n minutes, judging that the room is in an abnormal temperature rise state, and outputting alarm information;
step 2: collecting all VAVBox air supply volume measured data in the system;
step 3: when the actual measurement value of the VAVBox air supply quantity of less than 50% in the system reaches the lower limit value, modifying the minimum air supply quantity set value P1 of the VAVBox; after the setting is completed, the interval of n minutes is reserved, the actual temperature value T transmitted by the indoor temperature sensor is collected and monitored, and when T=T set Then the optimization is completed and the alarm information is cancelled; when T > T set And repeating the step 2 when the duration t is more than n minutes;
step 4: when the condition that the actual measurement value of the VAVBox air supply quantity exceeds 50% in the system reaches the lower limit value exists, judging whether the running frequency of the floor air conditioner fan cabinet fan is the minimum value or not;
if so, increasing the air-conditioning air cabinet air supply temperature set value P3 to increase the air-conditioning air cabinet air supply temperature; if not, reducing the static pressure set value P2 of the system; after the adjustment is finished, the interval is n minutes, the measured value T of the temperature returned by the indoor temperature sensor is collected and monitored, and when T=T set Then the optimization is completed and the alarm information is cancelled; when T > T set And repeating the step 4 when the duration t is greater than n minutes.
Further, the room cooling and heating analysis optimization algorithm model comprises the following steps:
step 1: collecting and monitoring all the maximum valve position values of the VAVBox in the system;
step 2: when a certain VAVBox maximum valve value is more than 95%, the system is free from risk of sudden cold and sudden heat, and a monitoring state is maintained;
step 3: when a certain VAVBox maximum valve position value is more than 95%, judging whether the fan frequency of the air conditioner air cabinet reaches the maximum value, if so, reducing the air conditioner air cabinet air supply temperature set value P3; if not, increasing the system static pressure set value P3; after the adjustment is finished, the interval is n minutes, and all the maximum valve position values of the VAVBox in the system are collected and monitored; and (5) repeating the step 2.
Further, the energy-saving analysis optimization algorithm model comprises the following steps:
step 1: collecting and monitoring measured values of temperature sensors in all rooms in the system, e.g. all rooms t=t set Step 2 is entered, if not, step 1 is repeated;
step 2: collecting and monitoring all the maximum valve position values of the VAVBox in the system;
step 3: when the maximum valve position value is less than 70%, reducing the static pressure setting P1 of the system;
step 4: step 2 is repeated at intervals of n minutes.
Further, n can be set by itself, the modification range of the setting value V1 can be set by itself, and P can be set by itself with a larger range.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, all equipment big data in a closed loop of the variable air volume air conditioner are collected and are compared with a preset value periodically by using a mean value algorithm; when a preset deviation occurs and a trend is formed in a certain time, sending out early warning, entering different analysis optimization models according to trend conditions, gradually correcting corresponding operation parameters of the VAVBOX and the wind cabinet according to a certain amplitude according to a logic flow of the analysis optimization models so as to achieve the purpose of optimizing the operation effect of the air conditioning system, and continuously monitoring operation feedback data until the operation data approaches a preset normal range; the system can timely find the running problem of the variable air volume air conditioning system and give a warning according to the measured data, and meanwhile, the corresponding running parameters are modified according to the algorithm analysis flow, so that the system optimization is completed, and the stability and the comfort of the air conditioning system are improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the 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 schematic block diagram of an analysis, evaluation and optimization algorithm for the working condition of a variable air volume air conditioner.
FIG. 2 is a flow chart of a room cooling anomaly analysis optimization algorithm model in the present invention.
FIG. 3 is a flow chart of a room temperature anomaly analysis optimization algorithm model in the present invention.
FIG. 4 is a flow chart of a room cooling and heating analysis optimization algorithm model in the invention.
FIG. 5 is a flow chart of the energy saving analysis optimization algorithm model in the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the variable air volume air conditioner working condition analysis evaluation and optimization algorithm comprises:
SS1: after the big data of the variable air volume air conditioning system air cabinet and the administered VAVBOX operation are collected according to a certain period, the big data are compared with a preset value periodically by using an average value algorithm;
SS2: when the preset deviation occurs and a trend is formed in a certain time, sending out early warning and entering different analysis optimization models according to the trend condition; the analysis optimization model comprises a room cooling anomaly analysis optimization algorithm model, a room heating anomaly analysis optimization algorithm model, a room sudden-cold and sudden-hot analysis optimization algorithm model and an energy-saving analysis optimization algorithm model;
SS3: according to the logic flow of the analysis optimization model, gradually correcting the corresponding operation parameters of the VAVBOX and the wind cabinet according to a certain amplitude so as to achieve the purpose of optimizing the operation effect of the air conditioning system, and continuously monitoring operation feedback data until the operation data approaches a preset normal range;
as shown in fig. 2, the room cooling abnormality analysis optimization algorithm model includes the following steps:
step 1: collecting and monitoring the actual temperature value T transmitted back by the indoor temperature sensor, when T is more than T set When the duration time t is more than n minutes (n can be set by oneself), judging that the room is in a cooling abnormal state, and outputting alarm information;
step 2: collecting a room VAVBox air valve opening actual measurement value of 1;
step 3: when% 1 < maxOpen (maxOpen can be set by itself), modifying the maximum air supply quantity set value V1 of VAVBox
Amplitude can be set by itself, and after setting is completed, the interval n minutes (n can be set by itself) is set, and the actual temperature value T transmitted back by the indoor temperature sensor is collected and monitored, when T=T set Then the optimization is completed and the alarm information is cancelled; when T > T set And repeating the step 2 when the duration t is more than n minutes (n can be set by oneself);
step 4: when%1=maxpen, judging whether the air supply temperature set value of the floor air conditioner air cabinet is the lowest value; if so, the static pressure set value P of the system is increased (the P can be set by itself to be increased) so as to increase the air quantity of the air conditioner air cabinet; if not, increasing the opening of the chilled water valve of the air conditioner air cabinet; after the adjustment is finished, the interval is n minutes (n can be set by self), the measured value T of the temperature returned by the indoor temperature sensor is collected and monitored, and when T=T set Then the optimization is completed and the alarm information is cancelled; when T > T set And repeating the step 4 when the duration t is more than n minutes (n can be set by oneself);
as shown in fig. 3, the room temperature rising abnormality analysis optimization algorithm model includes the following steps:
step 1: collecting and monitoring the actual temperature value T transmitted back by the indoor temperature sensor, when T is less than T set When the duration time t is more than n minutes (n can be set by oneself), judging that the room is in an abnormal temperature rise state, and outputting alarm information;
step 2: collecting all VAVBox air supply volume measured data in the system;
step 3: when the actual measurement value of the VAVBox air supply quantity of less than 50 percent (capable of being set by self) in the system reaches the lower limit value, modifying the minimum air supply quantity set value P1 of the VAVBox, collecting and monitoring the actual measurement value T of the temperature returned by the indoor temperature sensor at intervals of n minutes (n can be set by self) after the setting is finished, and when T=T set Then the optimization is completed and the alarm information is cancelled; when T > T set And repeating the step 2 when the duration t is more than n minutes (n can be set by oneself);
step 4: when the actual measurement value of the VAVBox air supply quantity exceeding 50% (capable of being set by itself) in the system reaches the lower limit value, judging whether the running frequency of the fan of the floor air conditioner air cabinet is the minimum value, if so, increasing the air conditioner air cabinet air supply temperature set value P3 (P3 can be set by itself to a large extent) to improve the air conditioner air cabinet air supply temperature; if not, reducing the static pressure set value P2 of the system; after the adjustment is finished, the interval is n minutes (n can be set by self), the measured value T of the temperature returned by the indoor temperature sensor is collected and monitored, and when T=T set Then the optimization is completed and the alarm information is cancelled; when T > T set And repeating the step 4 when the duration t is more than n minutes (n can be set by oneself);
in this embodiment, when the valve opening of a certain VAV is maximum, this indicates that the zone temperature does not reach the set temperature. When the valve of the VAV exceeds 95%, the wind balance is very easy to break, the room has a risk of 'sudden cold and sudden heat', the comfort is low, and the system is unstable;
as shown in FIG. 4, the room cooling and heating analysis optimization algorithm model comprises the following steps:
step 1: collecting and monitoring all the maximum valve position values of the VAVBox in the system;
step 2: when a certain VAVBox maximum valve value is more than 95% (capable of being set by self), the system is free from sudden cold and sudden heat risks, and a monitoring state is maintained;
step 3: when a certain VAVBox maximum valve value is more than 95% (capable of being set by self), judging whether the fan frequency of the air conditioner air cabinet reaches the maximum value, if so, reducing the air conditioner air cabinet air supply temperature set value P3; if not, increasing the system static pressure set value P3;
after the adjustment is finished, the interval is n minutes (n can be set by oneself), and all the maximum valve position values of the VAVBox in the system are collected and monitored; repeating the step 2;
when the system runs stably and reaches a certain condition, carrying out energy-saving analysis on the system; as shown in fig. 5, the energy-saving analysis optimization algorithm model includes the following steps:
step 1: collecting and monitoring measured values of temperature sensors in all rooms in the system, e.g. all rooms t=t set Step 2 is entered, if not, step 1 is repeated;
step 2: collecting and monitoring all the maximum valve position values of the VAVBox in the system;
step 3: when the maximum valve position value is less than 70% (capable of being set by self), the static pressure set value of the system is reduced
P1 (the reduction amplitude can be set by itself);
step 4: repeating the step 2 at intervals of n minutes (n can be set by oneself);
the existing building automation technology can only perform basic automation setting for a certain device (such as a wind cabinet and a VAVBOX) of an air conditioning system, but the output effect of the actual device and the running condition of the system cannot be subjected to closed-loop analysis, judgment and optimization, and only can perform artificial single-point judgment through feedback data by operators through practical experience, so that different degrees of hysteresis and uncertainty of wind balance exist due to the difference of the operators;
according to the invention, by collecting big data of all equipment in the closed loop of the variable air conditioner and establishing an analysis model, analyzing, evaluating and optimizing the running condition of the variable air conditioner by utilizing an algorithm, the running problem of the variable air conditioner system can be found in time and a warning can be sent out by depending on actual measurement data, and meanwhile, corresponding running parameters are modified according to the analysis flow of the algorithm, so that the system optimization is completed, and the stability and the comfort of the air conditioner system are improved.
The analysis optimization algorithm model is based on field experience and research of technical engineers, and is proved in practice, so that the analysis optimization algorithm model can cover most of conditions in the operation of the variable air volume air conditioning system, and has wide coverage; the parameter optimization mode and the specific numerical value of the analysis model are based on the operation data of the variable air volume air conditioning project, so that the accuracy is high, and the stability and the comfort of the variable air volume air conditioning system can be greatly improved.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (6)
1. The variable air volume air conditioner working condition analysis, evaluation and optimization algorithm is characterized by comprising the following steps:
SS1: after the big data of the variable air volume air conditioning system air cabinet and the administered VAVBOX operation are collected according to a certain period, the big data are compared with a preset value periodically by using an average value algorithm;
SS2: when the preset deviation occurs and a trend is formed in a certain time, sending out early warning and entering different analysis optimization models according to the trend condition; the analysis optimization model comprises a room cooling anomaly analysis optimization algorithm model, a room heating anomaly analysis optimization algorithm model, a room sudden-cold and sudden-hot analysis optimization algorithm model and an energy-saving analysis optimization algorithm model;
SS3: and according to the logic flow of the analysis optimization model, gradually correcting the corresponding operation parameters of the VAVBOX and the wind cabinet according to a certain amplitude so as to achieve the purpose of optimizing the operation effect of the air conditioning system, and continuously monitoring the operation feedback data until the operation data approaches to a preset normal range.
2. The variable air volume air conditioner operating condition analysis, assessment and optimization algorithm according to claim 1, wherein the room cooling anomaly analysis and optimization algorithm model comprises the following steps:
step 1: collecting and monitoring the actual temperature value T transmitted back by the indoor temperature sensor, when T is more than T set When the duration time t is more than n minutes, judging that the room is in a cooling abnormal state, and outputting alarm information;
step 2: collecting a room VAVBox air valve opening actual measurement value of 1;
step 3: when the percentage 1 is less than maxOpen, modifying the maximum air supply quantity set value V1 of the VAVBox; after the setting is completed, the interval of n minutes is reserved, the actual temperature value T transmitted by the indoor temperature sensor is collected and monitored, and when T=T set Then the optimization is completed and the alarm information is cancelled; when T > T set And repeating the step 2 when the duration t is more than n minutes;
step 4: when%1=maxpen, judging whether the air supply temperature set value of the floor air conditioner air cabinet is the lowest value; if yes, the static pressure set value P of the system is increased to increase the air supply quantity of the air conditioner air cabinet; if not, increasing the opening of the chilled water valve of the air conditioner air cabinet; after the adjustment is finished, the interval is n minutes, the measured value T of the temperature returned by the indoor temperature sensor is collected and monitored, and when T=T set Then the optimization is completed and the alarm information is cancelled; when T > T set And repeating the step 4 when the duration t is greater than n minutes.
3. The variable air volume air conditioner operating condition analysis, assessment and optimization algorithm according to claim 1, wherein the room temperature rise abnormality analysis and optimization algorithm model comprises the following steps:
step 1: collecting and monitoring the actual temperature value T transmitted back by the indoor temperature sensor, when T is less than T set When the duration time t is more than n minutes, judging that the room is in an abnormal temperature rise state, and outputting alarm information;
step 2: collecting all VAVBox air supply volume measured data in the system;
step 3: when the actual measurement value of the VAVBox air supply quantity of less than 50% in the system reaches the lower limit value, modifying the minimum air supply quantity set value P1 of the VAVBox; after the setting is completed, the interval of n minutes is reserved, the actual temperature value T transmitted by the indoor temperature sensor is collected and monitored, and when T=T set Then the optimization is completed and the alarm information is cancelled; when T > T set And repeating the step 2 when the duration t is more than n minutes;
step 4: when the condition that the actual measurement value of the VAVBox air supply quantity exceeds 50% in the system reaches the lower limit value exists, judging whether the running frequency of the floor air conditioner fan cabinet fan is the minimum value or not;
if so, increasing the air-conditioning air cabinet air supply temperature set value P3 to increase the air-conditioning air cabinet air supply temperature; if not, reducing the static pressure set value P2 of the system; after the adjustment is finished, the interval is n minutes, the measured value T of the temperature returned by the indoor temperature sensor is collected and monitored, and when T=T set Then the optimization is completed and the alarm information is cancelled; when T > T set And repeating the step 4 when the duration t is greater than n minutes.
4. The variable air volume air conditioner operating condition analysis, assessment and optimization algorithm according to claim 1, wherein the room sudden-cold and sudden-hot analysis optimization algorithm model comprises the following steps:
step 1: collecting and monitoring all the maximum valve position values of the VAVBox in the system;
step 2: when a certain VAVBox maximum valve value is more than 95%, the system is free from risk of sudden cold and sudden heat, and a monitoring state is maintained;
step 3: when a certain VAVBox maximum valve position value is more than 95%, judging whether the fan frequency of the air conditioner air cabinet reaches the maximum value, if so, reducing the air conditioner air cabinet air supply temperature set value P3; if not, increasing the system static pressure set value P3; after the adjustment is finished, the interval is n minutes, and all the maximum valve position values of the VAVBox in the system are collected and monitored; and (5) repeating the step 2.
5. The variable air volume air conditioner operating condition analysis, assessment and optimization algorithm according to claim 1, wherein the energy saving analysis and optimization algorithm model comprises the following steps:
step 1: collecting and monitoring measured values of temperature sensors in all rooms in the system, e.g. all rooms t=t set Step 2 is entered, if not, step 1 is repeated;
step 2: collecting and monitoring all the maximum valve position values of the VAVBox in the system;
step 3: when the maximum valve position value is less than 70%, reducing the static pressure setting P1 of the system;
step 4: step 2 is repeated at intervals of n minutes.
6. The variable air volume air conditioner working condition analysis evaluation and optimization algorithm according to claim 2, wherein n can be set by itself, maxpen can be set by itself, the modification range of the set value V1 can be set by itself, and the increase range of P can be set by itself.
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| CN202310617230.1A CN116678079B (en) | 2023-05-29 | 2023-05-29 | Variable air volume air conditioner working condition analysis evaluation and optimization algorithm |
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