CN104534627B - Central air conditioning cooling water system comprehensive energy efficiency control method - Google Patents
Central air conditioning cooling water system comprehensive energy efficiency control method Download PDFInfo
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- 239000000498 cooling water Substances 0.000 title claims abstract description 106
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000004378 air conditioning Methods 0.000 title claims abstract description 24
- 238000001816 cooling Methods 0.000 claims abstract description 50
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 34
- 238000005265 energy consumption Methods 0.000 claims abstract description 21
- 230000002068 genetic effect Effects 0.000 claims abstract description 13
- 238000005057 refrigeration Methods 0.000 claims description 24
- 238000005457 optimization Methods 0.000 claims description 7
- 238000011156 evaluation Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 230000017525 heat dissipation Effects 0.000 claims description 5
- 230000033228 biological regulation Effects 0.000 claims description 4
- 230000035772 mutation Effects 0.000 claims description 4
- 238000009790 rate-determining step (RDS) Methods 0.000 claims description 4
- 230000006978 adaptation Effects 0.000 claims description 3
- 241000208340 Araliaceae Species 0.000 claims 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 claims 1
- 235000003140 Panax quinquefolius Nutrition 0.000 claims 1
- 235000008434 ginseng Nutrition 0.000 claims 1
- 230000001537 neural effect Effects 0.000 claims 1
- 230000007935 neutral effect Effects 0.000 abstract description 5
- 230000007613 environmental effect Effects 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
- 229910052739 hydrogen Inorganic materials 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 238000004321 preservation Methods 0.000 description 1
- 230000001568 sexual 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
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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
- F24F2110/20—Humidity
-
- 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
- F24F11/56—Remote control
- F24F11/59—Remote control for presetting
-
- 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
- F24F11/85—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 variable-flow pumps
-
- 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
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Abstract
The invention discloses a kind of central air conditioning cooling water system comprehensive energy efficiency control method, which passes through the performance parameter data that cooling water system is run under various circumstances, the method of application curves fitting or neutral net is processed to data, sets up related parameter and energy consumption model；The power consumption values that the different operational factors of selection when varying environment is with system loading are calculated by these models；The system operational parameters for being found under designated environment and system load conditions by genetic algorithm again and making cooling water system energy consumption minimum when meeting system requirements；The control that cooling water system is carried out finally by this operational factor can consume minimum.The present invention considers the impact of environmental factorss and cooling load of the air-conditioning system to cooling water system energy consumption, coordinates the running frequency of control blower fan of cooling tower frequency and cooling pump, on the premise of handpiece Water Chilling Units Effec-tive Function is ensured, makes cooling water system comprehensive energy consumption minimum.
Description
Technical field
The invention belongs to the energy and field of energy-saving technology, and in particular to a kind of comprehensive energy efficiency of central air conditioning cooling water system
Control method.
Background technology
Cooling water system is the important component part of central air conditioner system, and its energy consumption accounts for central air conditioner system energy consumption
15%～20%, the Energy Saving Control of cooling water system has very important significance to whole energy.With handpiece Water Chilling Units technology
Development, the flow of current cooling water system can change in the range of 30%～130%, when air conditioner load and outdoor environment
During change, the operational factor of regulation cooling pump and cooling tower becomes the major way of the Energy Saving Control of cooling water system, current work
In journey, the most frequently used control method has constant difference vari- able flow control, determines the control of cooling water return water temperature, constant flow control etc..But
Above-mentioned control method does not consider comprehensive energy efficiency, system loading and the environmental factorss of cooling water system to cooling water system
The impact of energy consumption.
Content of the invention
The purpose of the present invention be under the conditions of different outdoor dry-bulb temperature, humidity and cooling load of the air-conditioning system, and ensure cold
On the premise of condenser temperature is system requirements temperature, cooling water system is made to realize lowest energy consumption control.
The technical solution adopted in the present invention is as follows：A kind of central air conditioning cooling water system comprehensive energy efficiency control method, bag
Include and set up cooling water system parameter and energy consumption model, the running status model for setting up cooling water system, efficiency optimizing and most Canon
The control of effect point；
The efficiency optimizing includes drawing optimal efficiency operating point, the i.e. general power of cooling water system using genetic algorithm
PtollFor minimum when cooling water return water temperature ToutValue；
The rate-determining steps of the optimal efficiency point are as follows：
First, read current dry-bulb temperature T (t), humidity H (t), cooling load of the air-conditioning system Q in real timec(t) data；
Second, judge whether to need to find optimal efficiency operating point, if it is desired, then using the data read in the first step
Described efficiency optimizing is carried out, optimal efficiency operating point A (M are found outw1, Ma1), and load is respectively max (Q in such circumstancescj-
Δ Q, Qcmin), min (Qcj+ Δ Q, Qcmax) optimal efficiency point B (Mw2, Ma2) and C (Mw3, Ma3), wherein：QcjFor calculating the moment
Refrigeration duty, Δ Q is the load deviation point of a very little；
3rd, according to the linear relationship that B and C points calculate air quantity and refrigeration duty：
In formula, Qcmax-- system maximum refrigeration duty, Qcmin-- system minimum refrigeration duty, Ma(t) -- the real-time air quantity matter of cooling tower
Amount flow, min () -- taking minimum value function, max () -- take max function；
4th, according to the real-time cold Q of systemcT () calculates real-time airflow value M by the 3rd clotha(t), then according to real-time wind
Value MaT () calculates blower fan frequency ffan, then blower fan is controlled by this value；
5th, according to condenser temperature setting value TkRegulation is controlled to cooling water pump with the difference of actual value.
Further, the cooling water system parameter and energy consumption model set up is included with curve matching or neutral net side
Method establishes the relation of cooling water system heat exchange amount and cooling load of the air-conditioning system respectively, sets up condenser parameter model, sets up cooling tower and dissipate
Thermal model, set up cooling water pump and blower fan of cooling tower model.
Further, the running status model for setting up cooling water system includes measuring outdoor dry-bulb temperature T, humidity H,
Central air conditioner system refrigeration duty Qc, the electrical power P that handpiece Water Chilling Units compressor is consumed, condenser actual temperature Tkfk, and set cold
Condenser temperature Tk, and the flow mass M of cooling water is drawn using the cooling water system parameter and the relation in energy consumption modelw, cold
But tower air quantity flow mass MaGeneral power P with cooling water systemtoll.
Further, described according to condenser temperature setting value TkRegulation is controlled to cooling water pump with the difference of actual value
The step of as follows：
First, system is carried out PID arithmetic or is inquired about adopting Fuzzy Control with the difference of actual value to condenser temperature setting value Tk
Method processed sets up fuzzy reasoning table or other control methods draw cooling water flow value Mwc；
Secondly, the Mw1 for calculating in the control second step by cooling water flow value Mwc with the optimal efficiency point is added
Arrive Mw4；
Then, amplitude limiting processing then to Mw4 is carried out, i.e., according to cooling water biggest quality flow Mmax and the minimum of default
The process of flow mass M min, is exported with Mmin if Mw4 is less than or equal to Mmin, if Mw4 more than Mmin and is less than or equal to
Mmax is then exported with the value of Mw4, is exported with Mmax, obtain output valve Mw5 after amplitude limiting processing if Mw4 is more than Mmax；
Finally, Mw5 is converted into cooling water pump by relation of the system according to cooling water mass flow with cooling pump running frequency
Running frequency fpump, then the frequency converter frequency of cooling pump is set by this frequency values, and then is realized to condenser temperature
Control.
Further, the utilization genetic algorithm show that the target of optimal efficiency operating point is to find PtollMinima；
Its object function is：
Ptoll=f8(Mw,Ma)
Meet condition：Toutmax≥Tout≥Toutmin, Mwmax≥Mw≥Mwmin, Mamax≥Ma≥Mamin, Q=f1(Qc), Mw=
f2(Tout,Tk, Q), Ma=f3(T,H,Tout,Mw, Q), Ptoll=f6(Mw)+f7(Ma)；
In formula, Toutmax-- the maximum of cooling water return water temperature；Toutmin-- the minima of cooling water return water temperature；
Mwmax-- the maximum of cooling water mass flow；Mwmin-- the minima of cooling water mass flow；Mamax-- air volume cooling tower quality
The maximum of flow；Mamin-- the minima of air volume cooling tower mass flow.
The T for meeting condition is found using the genetic algorithmoutValue, further according to ToutValue calculates corresponding MwAnd MaValue, that is, look for
Arrive optimal efficiency point (Mw, Ma), its step is as follows：
(1) population is initialized
Determine population scale N, crossover probability Pc, mutation probability Pm, selection opertor be roulette and and terminate evolutionary criterion,
In [Toutmin,Toutmax] in the range of randomly generate N number of ToutFor initial population X (t), evolutionary generation t is set to 0.
(2) individual evaluation
Calculate each individual corresponding M in population X (t)wAnd Ma,
(2.1) work as MwOr MaMore than during its maximum, then this individual adaptation degree is 0；
(2.2) work as MwOr MaIts minima is taken during less than its minima, then calculates its target function value Ptoll, work as MwOr Ma?
In the range of when directly calculate its target function value, then such individual fitness is：
In formula, the corresponding M of H--wAnd MaQuantity less than the individuality of its maximum；The natural number of i--1 to H；S(i)--
The fitness of i-th individuality；Ptoll(i) i-th individual corresponding total power value；
(3) swarm optimization
(3.1) selection opertor is used to select the individual individualities of M/2 (M >=N) from population X (t) as parent；
(3.2) parent to choosing.With PcProbability executes intersection, forms M middle individuality；
(3.3) to individual in the middle of M, one by one with PmProbability enters row variation, forms M candidate individual；
(3.4) in population X (t) (N-M) individual individual and (3.3) are selected to draw by individual fitness using selection opertor
In M candidate individual as population X (t+1) of future generation；
(4) stop evolutional operation
When evolutionary generation continues several generations variable quantity more than H or increments of change is less than certain numerical value, termination algorithm；Output kind
In group x (t), the maximum individuality of fitness is otherwise put t=t+1, goes to step " (2) individual evaluation " as optimum solution.
In scheme, described judge whether that the condition for needing to find optimal efficiency operating point is：
The current refrigeration duty of a, system is more than Δ Q or less than-Δ Q with the difference of the last refrigeration duty for calculating point；
Absolute value between b, outdoor current humidity and the last humidity for calculating point exceedes Δ H；
Absolute value between c, outdoor current dry-bulb temperature and the last dry-bulb temperature for calculating point exceedes Δ T；
It is more than a certain numerical value when d, system start-up or from last optimizing interval；
When any of the above condition is met, then execution step " (3) swarm optimization " and " (4) stop evolutional operation ", i.e.,
Need to find optimum, otherwise skip (3) (4) step.
Beneficial effects of the present invention consider the impact of environmental factorss and cooling load of the air-conditioning system to cooling water system energy consumption, association
Regulation and control blower fan of cooling tower frequency processed and the running frequency of cooling pump, on the premise of handpiece Water Chilling Units Effec-tive Function is ensured, make cooling
Water system comprehensive energy consumption is minimum.
Description of the drawings
Fig. 1 is the system running state illustraton of model of the present invention.
Fig. 2 is the system control block figure of the present invention.
Fig. 3 is the system control process figure of the present invention.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.
The performance parameter data that the present invention is run under various circumstances by cooling water system, application curves fitting or nerve
The method of network is processed to data, sets up related parameter and energy consumption model；Calculated in different rings by these models
The power consumption values of border and selection different operational factors during system loading；Designated environment and system loading bar are found by genetic algorithm again
The system operational parameters for making cooling water system energy consumption minimum under part and when meeting system requirements；Carry out finally by this operational factor
The control of cooling water system can consume minimum.
1st, cooling water system parameter and energy consumption model are set up.
1.1 cooling water system heat exchange amounts and the relation of cooling load of the air-conditioning system.
If the refrigeration duty of central air conditioner system is Qc, handpiece Water Chilling Units are Q with cooling water system heat exchange amount.According to preservation of energy
The heat dissipation capacity of law cooling water should be equal to the electrical power P sum that central air-conditioning refrigeration duty is consumed with handpiece Water Chilling Units compressor, so
There is following relation：
Q=f1(Qc, P) and=Qc+P (1)
In formula, Q-- cooling water heat exchange amounts；
Qc-- central air conditioner system refrigeration duty；
The electrical power that P-- handpiece Water Chilling Units compressor is consumed.
The foundation of 1.2 condenser parameter models.
According to condenser thermal transfer principle：
(3) (4) simultaneous=》
In formula, the heat transfer coefficient of K-- condensers and cooling water；
A-- condensers and the heat exchange area of cooling water；
Tout-- for cooling water return water temperature；
Tin-- for cooling water supply temperature；
Tk-- for condenser temperature；
Specific heat capacities of the C-- for water；
Mw-- for cooling water mass flow；
Δ T-- is cooling water supply backwater temperature difference.
T is understood by formula (5)out、TkAnd MwThere is certain functional relationship, be met where like the method by testing
Specified TkDuring value, different ToutWith MwThe data of combination, and corresponding between them is found out using curve matching or neutral net
Relation such as formula (6)：
Mw=f2(Tout,Tk,Q) (6)
1.3 cooling tower heat dissipation models are set up.
Heat dissipation capacity Q of cooling tower and the dry-bulb temperature (T) of outdoor is understood by the operation principle of cooling tower, and humidity (H) is cold
But mass flow (the M of waterw), the air mass flow (M of cooling towera), the return water temperature (T of cooling waterout) relevant, by the side that tests
Method obtains the mass flow of different cooling waters under the conditions of different dry-bulb temperatures (T), humidity (H) and heat dissipation capacity Q of cooling tower
(Mw), air volume cooling tower mass flow (Ma) and cooling water return water temperature (Tout) data splitting, many by carrying out to data
Variable data is fitted or sets up the methods such as neutral net to find the functional relationship between these variables, as shown in formula (7)：
Ma=f3(T,H,Tout,Mw,Q) (7)
Dry-bulb temperature in formula, outside T-- rooms；
Humidity outside H-- rooms.
1.4 cooling water pumps and blower fan of cooling tower model are set up.
According to water pump and the operation principle of blower fan, it is known that water pump frequency (fpump) and cooling water mass flow (Mw) be directly proportional,
Pump power (Ppump) be then directly proportional to 3 powers of frequency, blower fan of cooling tower is same, is not line between this tittle in engineering
Sexual intercourse, herein for being also the relation data that obtains between these parameters by experiment for the sake of accurate, then passes through curve matching
Or neutral net finds out the relation between them, is set to：
fpunp=f4(Mw) (8)
Ppump=f5(Mw) (9)
ffan=f6(Ma) (10)
Pfan=f7(Ma) (11)
=》Ptoll=f8(Mw,Ma) (12)
In formula, fpump-- for cooling pump running frequency；
Ppump-- cooling pump power；
ffan-- blower fan frequency；
Pfan--- power of fan；
Ptoll-- the general power of cooling water system.
All set up to each parameter model of cooling water system here and energy consumption model.
2nd, the running status model of cooling water system is set up.
Outdoor dry-bulb temperature (T), humidity (H) are determined by environment, are measured using corresponding Temperature Humidity Sensor；Central hollow
Adjusting system refrigeration duty QcDetermined by system end load, be calculated using cold meter or chilled water parameter；Handpiece Water Chilling Units are compressed
The electrical power P of machine consumption can be obtained using power meter measures or can be by being obtained with the communication of handpiece Water Chilling Units own controller；
Condenser design temperature TkIt is set by the user or have central air-conditioning turn-key system to give；Condenser actual temperature TkfkBy with cold
The communication of water dispenser group own controller is obtained.Other specification and cooling can be obtained by the model of above-mentioned foundation according to above-mentioned parameter
Water system energy consumption, comprises the following steps that：
(1) by QcQ is obtained by formula (1) with P；
(2) according to Q, TKAnd select a ToutM can be obtained by (6)w；
(3) again by T, H, Tout, Mw, Q obtains M by formula (7)a；
(4) according to the M for obtainingwAnd Ma, by can be cooled the general power (P of water system with formula (12)toll)；
Can should calculate in aforementioned manners in any outdoor environment (i.e. different T and H-number) and different system refrigeration duty
QcAnd under conditions of ensureing that condenser temperature is setting value, select difference ToutCorresponding Mw、MaAnd PtollNumerical value, that is, cool down
Running status point (the M of water systemw, Ma) and this running status under performance number Ptoll.
Different Ts are selected under prescribed conditionsoutThe corresponding different P of valuetollValue, PtollValue is minimum to be system energy efficiency most
Good, make PtollIt is worth for minimum ToutCorresponding (the M of valuew, Ma) it is optimal efficiency operating point of the cooling water system in this condition.
3rd, efficiency optimizing algorithm.
Genetic algorithm be a kind of based on during biological evolution, excellent deposit the bad principle that dies on the basis of the optimization that grows up
Search technique.The searching under prescribed conditions for solving to propose in upper chapters and sections used here as this algorithm makes PtollFor minimum
ToutThe problem of value, the implementation method of genetic algorithm are varied, are selected according to practical situation, a kind of simple realization side
Method：
(1) coding mode selection real coding；
(2) genetic operator selects arithmetic crossover operator and uniform mutation operator；
(3) selection of fitness function.
The purpose of system is to realize that the efficiency of cooling water system is optimal, so object function is：
Ptoll=f8(Mw,Ma)
Meet condition：
1)Toutmax≥Tout≥Toutmin
2)Mwmax≥Mw≥Mwmin
3)Mamax≥Ma≥Mamin
4) Q=f1(Qc)
5)Mw=f2(Tout,Tk,Q)
6)Ma=f3(T,H,Tout,Mw,Q)
7)Ptoll=f6(Mw)+f7(Ma)
Target：Find PtollMinima；
In formula, Toutmax-- the maximum of cooling water return water temperature；
Toutmin-- the minima of cooling water return water temperature；
Mwmax-- the maximum of cooling water mass flow；
Mwmin-- the minima of cooling water mass flow；
Mamax-- the maximum of air volume cooling tower mass flow；
Mamin-- the minima of air volume cooling tower mass flow.
The basic step of genetic algorithm：
(1) population is initialized
Determine population scale N, crossover probability Pc, mutation probability Pm, selection opertor be roulette and and terminate evolutionary criterion,
In [Toutmin,Toutmax] in the range of randomly generate N number of ToutFor initial population X (t), evolutionary generation t is set to 0.
(2) individual evaluation
Calculate each individual corresponding M in population X (t)wAnd Ma.
1) work as MwOr MaMore than which for during maximum, then this individual adaptation degree is 0；
2) work as MwOr MaIts minima is taken during less than its minima, then calculates its target function value Ptoll, work as MwOr MaIn model
Its target function value is directly calculated when enclosing interior, then such individual fitness is：
The corresponding M of H-- in formulawAnd MaQuantity less than the individuality of its maximum；
The natural number of i--1 to H；
The fitness of S (i) -- i-th individuality；
Ptoll(i) i-th individual corresponding total power value.
(3) swarm optimization
1) use selection opertor from population x (t), select M/2 (M >=N) to individuality as parent；
2) parent to choosing.With PcProbability executes intersection, forms M middle individuality；
3) to individual in the middle of M, one by one with PmProbability enters row variation, forms M candidate individual；
4) using selection opertor in population X (t) by individual fitness select (N-M) individual and 3) in draw
M candidate individual is used as population X (t+1) of future generation；.
(4) stop evolutional operation
When following either condition is met, termination algorithm exports the individuality of fitness maximum in population x (t) as optimal
Solution, otherwise puts t=t+1, goes to step " (2) individual evaluation "；
1) evolutionary generation is more than H (depending on concrete system, according to required precision, H-number can pass through default and configuration)；
2) fitness variation tendency is observed, when increments of change continues several generations variable quantity less than certain numerical value (depending on concrete system
Depending on, according to required precision, the numerical value can pass through default and configuration) when, termination algorithm.
The T for meeting condition is found by above-mentioned genetic algorithmoutValue, further according to this ToutValue calculates corresponding MwAnd MaValue, i.e.,
Have found optimal efficiency point (M under the conditions of thisw, Ma).
4th, according to the control of optimal efficiency point.
As outdoor environment and cooling load of the air-conditioning system are always in change, and the interference in the external world is also uncertain, in order to protect
The demand for control of card system, the above-mentioned optimal efficiency point of integrated application here carry out real-time control to system.
Comprise the following steps that：
(1) real-time current dry-bulb temperature T (t) that reads, humidity H (t), cooling load of the air-conditioning system Qc(t) data；
(2) as long as then execution step " (3) swarm optimization " and " (4) stop evolutional operation " are i.e. when meeting following either condition
Need to find optimum, otherwise skip (3) (4) step.
1) difference of the current refrigeration duty of system and the last refrigeration duty for calculating point more than Δ Q or less than-Δ Q (depending on concrete system
Depending on system, according to required precision, Δ Q-value can pass through default and configuration)；
2) absolute value between outdoor current humidity and the last humidity for calculating point exceed Δ H (depending on concrete system,
According to required precision, Δ H-number can pass through default and configuration)；
3) absolute value between outdoor current dry-bulb temperature and the last dry-bulb temperature for calculating point exceedes Δ T ' (depending on concrete
Depending on system, according to required precision, Δ T ' values can pass through default and configuration)；
4) (depending on concrete system, wanted according to precision more than a certain numerical value during system start-up or from last optimizing interval
Ask, the numerical value can pass through default and configuration).
(3) parameter read according to (1), finds optimal efficiency operating point A (Mw1, Ma1), and load point in such circumstances
Wei not max (Qcj- Δ Q, Qcmin), min (Qcj+ Δ Q, Qcmax), optimal efficiency point B (Mw2, Ma2) and C (Mw3, Ma3), its
In：QcjFor calculating the refrigeration duty at moment, Δ Q is the load deviation point of a very little, and value is fixed according to practical situation.
(4) linear relationship of air quantity and refrigeration duty is calculated according to B and C points：
In formula, Qcmax-- system maximum refrigeration duty；
Qcmin-- system minimum refrigeration duty；
Ma(t) -- the real-time air quantity mass flow of cooling tower；
Min () -- take minimum value function；
Max () -- take maximum；
(5) according to the real-time cold Q of systemcT () calculates real-time airflow value M by formula (14)a(t), then according to real-time air quantity
Value MaT () calculates blower fan frequency ffan, then blower fan is controlled by this value；
(6) regulation is controlled with actual difference according to condenser temperature setting value to cooling water pump, its step is as follows：
First, system is carried out PID arithmetic or is inquired about adopting Fuzzy Control with the difference of actual value to condenser temperature setting value Tk
Method processed sets up fuzzy reasoning table or other control methods draw cooling water flow value Mwc；
Secondly, cooling water flow value Mwc is added with the Mw1 calculated in the rate-determining steps two of the optimal efficiency point
Arrive Mw4；
Then, amplitude limiting processing then to Mw4 is carried out, i.e., according to cooling water biggest quality flow Mmax and the minimum of default
The process of flow mass M min, is exported with Mmin if Mw4 is less than or equal to Mmin, if Mw4 more than Mmin and is less than or equal to
Mmax is then exported with the value of Mw4, is exported with Mmax, obtain output valve Mw5 after amplitude limiting processing if Mw4 is more than Mmax；
Finally, Mw5 is converted into cooling water pump by relation of the system according to cooling water mass flow with cooling pump running frequency
Running frequency fpump, then the frequency converter frequency of cooling pump is set by this frequency values, and then is realized to condenser temperature
Control.
Cooling water system should be made when central control system is started shooting in the present invention with full-power mode (i.e. blower fan of cooling tower and cold
But design parameter operation pressed by pump) operation a period of time, Optimal Control pattern is entered after system boot is stable.
Part that the present invention does not relate to is same as the prior art or can be realized using prior art.
Claims (7)
1. a kind of central air conditioning cooling water system comprehensive energy efficiency control method, it is characterised in that：Including setting up cooling water system ginseng
Number and energy consumption model, the running status model for setting up cooling water system, efficiency optimizing and the control of optimal efficiency point；
The efficiency optimizing includes drawing optimal efficiency operating point, i.e. general power P of cooling water system using genetic algorithmtollFor
Cooling water return water temperature T when minimumoutValue；
The rate-determining steps of the optimal efficiency point are as follows：
First, read current dry-bulb temperature T (t), humidity H (t), cooling load of the air-conditioning system Q in real timec(t) data；
Second, judge whether to need to find optimal efficiency operating point, if it is desired, then carry out using the data read in the first step
Described efficiency optimizing, finds out optimal efficiency operating point A (Mw1, Ma1), and load is respectively max (Q in such circumstancescj- Δ Q,
Qcmin), min (Qcj+ Δ Q, Qcmax) optimal efficiency point B (Mw2, Ma2) and C (Mw3, Ma3), wherein：QcjFor calculating the cold of moment
Load, Δ Q are the load deviation of a very little；Mw1, Ma1It is the cooling water mass flow at optimal efficiency operating point A and cooling
The air mass flow of tower；Mw2, Ma2It is the air mass flow of cooling water mass flow at optimal efficiency point B and cooling tower；Mw3, Ma3It is
The air mass flow of cooling water mass flow and cooling tower at optimal efficiency point C；
3rd, according to the linear relationship that B and C points calculate air quantity and refrigeration duty：
In formula, Qcmax-- system maximum refrigeration duty, Qcmin-- system minimum refrigeration duty, Ma(t) -- the real-time air quantity quality stream of cooling tower
Amount, min () -- taking minimum value function, max () -- take max function；
4th, according to the real-time cold Q of systemcT () calculates real-time airflow value M by the 3rd stepa(t), then according to real-time airflow value
MaT () calculates blower fan frequency ffan, then blower fan is controlled by this value；
5th, according to condenser temperature setting value TkRegulation is controlled to cooling water pump with the difference of actual value.
2. central air conditioning cooling water system comprehensive energy efficiency control method according to claim 1, it is characterised in that：Described build
Vertical cooling water system parameter and energy consumption model include that establishing cooling water system respectively with curve matching or neural net method changes
Heat and the relation of cooling load of the air-conditioning system, condenser parameter model is set up, cooling tower heat dissipation model is set up, is set up cooling water pump and cold
But tower blower fan model.
3. central air conditioning cooling water system comprehensive energy efficiency control method according to claim 2, it is characterised in that：Described build
The running status model of vertical cooling water system includes measuring outdoor dry-bulb temperature T, humidity H, central air conditioner system refrigeration duty Qc, cold
The electrical power P that water machine set compressor is consumed, condenser actual temperature Tkfk, and set condenser temperature Tk, and using described cold
But the relation in water system parameter and energy consumption model draws the flow mass M of cooling waterw, air volume cooling tower flow mass MaWith cold
But general power P of water systemtoll.
4. central air conditioning cooling water system comprehensive energy efficiency control method according to claim 1, it is characterised in that：Described
According to condenser temperature setting value TkThe step of regulation is controlled to cooling water pump with the difference of actual value is as follows：
First, difference of the system to condenser temperature setting value Tk with actual value carries out PID arithmetic or inquiry using fuzzy control side
Method sets up fuzzy reasoning table or other control methods draw cooling water flow value Mwc；
Secondly, cooling water flow value Mwc is added with the Mw1 calculated in the rate-determining steps two of the optimal efficiency point and is obtained
Mw4；
Then, amplitude limiting processing then to Mw4 is carried out, i.e., according to the cooling water biggest quality flow Mmax and minimum mass of default
Flow Mmin process, is exported with Mmin if Mw4 is less than or equal to Mmin, if Mw4 is more than Mmin and is less than or equal to Mmax
Exported with the value of Mw4, exported with Mmax if Mw4 is more than Mmax, after amplitude limiting processing, obtain output valve Mw5；
Finally, Mw5 is converted into system the fortune of cooling water pump according to the relation of cooling water mass flow and cooling pump running frequency
Line frequency fpump, then the frequency converter frequency of cooling pump is set by this frequency values, and then realize the control to condenser temperature
System.
5. central air conditioning cooling water system comprehensive energy efficiency control method according to claim 1, it is characterised in that：The profit
Show that the target of optimal efficiency operating point is to find P with genetic algorithmtollMinima；Its object function is：
Ptoll=f8(Mw,Ma)
Meet condition：Toutmax≥Tout≥Toutmin, Mwmax≥Mw≥Mwmin, Mamax≥Ma≥Mamin, Q=f1(Qc), Mw=f2
(Tout,Tk, Q), Ma=f3(T,H,Tout,Mw, Q), Ptoll=f6(Mw)+f7(Ma)；
In formula, Toutmax-- the maximum of cooling water return water temperature；Toutmin-- the minima of cooling water return water temperature；Mwmax-- cold
But the maximum of water quality flow；Mwmin-- the minima of cooling water mass flow；Mamax-- air volume cooling tower mass flow is most
Big value；Mamin-- the minima of air volume cooling tower mass flow.
6. central air conditioning cooling water system comprehensive energy efficiency control method according to claim 5, it is characterised in that：Using institute
State genetic algorithm and find the T for meeting conditionoutValue, further according to ToutValue calculates corresponding MwAnd MaValue, that is, find optimal efficiency point
(Mw, Ma), its step is as follows：
(1) population is initialized
Determine population scale N, crossover probability Pc, mutation probability Pm, selection opertor be roulette and and terminate evolutionary criterion,
[Toutmin,Toutmax] in the range of randomly generate N number of ToutFor initial population X (t), evolutionary generation t is set to 0；
(2) individual evaluation
Calculate each individual corresponding M in population X (t)wAnd Ma,
(2.1) work as MwOr MaMore than during its maximum, then this individual adaptation degree is 0；
(2.2) work as MwOr MaIts minima is taken during less than its minima, then calculates its target function value Ptoll, work as MwOr MaIn scope
Its target function value is directly calculated when interior, then such individual fitness is：
In formula, the corresponding M of H--wAnd MaQuantity less than the individuality of its maximum；The natural number of i--1 to H；S (i) -- i-th
Individual fitness；Ptoll(i) i-th individual corresponding total power value；
(3) swarm optimization
(3.1) use selection opertor from population X (t), select M/2 (M >=N) to individuality as parent；
(3.2) parent to choosing, with PcProbability executes intersection, forms M middle individuality；
(3.3) to individual in the middle of M, one by one with PmProbability enters row variation, forms M candidate individual；
(3.4) in population X (t) selecting (N-M) individuality and (3.3) to draw by individual fitness using selection opertor
M candidate individual is used as population X (t+1) of future generation；
(4) stop evolutional operation
When evolutionary generation continues several generations variable quantity more than H or increments of change is less than certain numerical value, termination algorithm；Output population x
T in (), the maximum individuality of fitness is otherwise put t=t+1, goes to step " (2) individual evaluation " as optimum solution.
7. central air conditioning cooling water system comprehensive energy efficiency control method according to claim 6, it is characterised in that：Described sentence
Disconnected the condition for finding optimal efficiency operating point whether is needed to be：
The current refrigeration duty of a, system is more than Δ Q or less than-Δ Q with the difference of the last refrigeration duty for calculating point；
Absolute value between b, outdoor current humidity and the last humidity for calculating point exceedes Δ H；
Absolute value between c, outdoor current dry-bulb temperature and the last dry-bulb temperature for calculating point exceedes Δ T；
It is more than a certain numerical value when d, system start-up or from last optimizing interval；
When any of the above condition is met, then execution step " (3) swarm optimization " and " (4) stop evolutional operation ", that is, need
Optimum is found, (3) (4) step is otherwise skipped.
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| CN110805997A (en) * | 2019-11-14 | 2020-02-18 | 中金新源（天津）科技有限公司 | Energy-saving control method for central air-conditioning system |
| CN112413762B (en) * | 2020-11-27 | 2021-11-05 | 上海碳索能源服务股份有限公司 | Parameter optimization method and system for cooling water system of refrigerating room |
| CN113028596B (en) * | 2021-03-18 | 2021-09-28 | 清华大学 | Energy consumption evaluation and diagnosis method and system for air conditioning system |
| CN114279042B (en) * | 2021-12-27 | 2024-01-26 | 苏州科技大学 | Central air conditioner control method based on multi-agent deep reinforcement learning |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20090021471A (en) * | 2007-08-27 | 2009-03-04 | 삼성전자주식회사 | Controlling method for compound air conditioning system |
| CN101413709A (en) * | 2008-11-26 | 2009-04-22 | 湖南工程学院 | Cooling water flow control method for optimizing total energy consumption of refrigerating machine and cooling water pump |
| CN102721156A (en) * | 2012-06-30 | 2012-10-10 | 李钢 | Central air-conditioning self-optimization intelligent fuzzy control device and control method thereof |
| CN103868190A (en) * | 2012-12-12 | 2014-06-18 | 同方泰德国际科技(北京)有限公司 | Energy-saving control method suitable for central air-conditioning cooling water system |
| CN203719032U (en) * | 2014-01-24 | 2014-07-16 | 深圳达实智能股份有限公司 | Central air-conditioning cooling tower fan control system based on energy efficiency optimization |
-
2015
- 2015-01-14 CN CN201510019435.5A patent/CN104534627B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20090021471A (en) * | 2007-08-27 | 2009-03-04 | 삼성전자주식회사 | Controlling method for compound air conditioning system |
| CN101413709A (en) * | 2008-11-26 | 2009-04-22 | 湖南工程学院 | Cooling water flow control method for optimizing total energy consumption of refrigerating machine and cooling water pump |
| CN102721156A (en) * | 2012-06-30 | 2012-10-10 | 李钢 | Central air-conditioning self-optimization intelligent fuzzy control device and control method thereof |
| CN103868190A (en) * | 2012-12-12 | 2014-06-18 | 同方泰德国际科技(北京)有限公司 | Energy-saving control method suitable for central air-conditioning cooling water system |
| CN203719032U (en) * | 2014-01-24 | 2014-07-16 | 深圳达实智能股份有限公司 | Central air-conditioning cooling tower fan control system based on energy efficiency optimization |
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