CN104566797A - Central air conditioner cooling tower fan frequency control method - Google Patents
Central air conditioner cooling tower fan frequency control method Download PDFInfo
- Publication number
- CN104566797A CN104566797A CN201410799503.XA CN201410799503A CN104566797A CN 104566797 A CN104566797 A CN 104566797A CN 201410799503 A CN201410799503 A CN 201410799503A CN 104566797 A CN104566797 A CN 104566797A
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- Prior art keywords
- particle
- cooling tower
- value
- blower fan
- frequency
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Classifications
-
- 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
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
-
- 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
- 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
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- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Atmospheric Sciences (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The invention discloses a central air conditioner cooling tower fan frequency control method. The method comprises the following steps: S1, determining the size and maximum iteration times of a particle swarm and an initial maximum speed and an initial farthest position of a particle; S2, establishing an initial particle swarm; S3, calculating an adaptability value Fid of each particle; S4, comparing the adaptability value of each particle with an individual extreme value, and replacing the individual extreme value if the adaptability value is better than the individual extreme value; S5, comparing the adaptability value of each particle with a global extreme value, and replacing the global extreme value if the adaptability value is better than the global extreme value; S6, updating the speed and position of the particle; S7, judging whether an ending condition is satisfied or not, and entering the step S8 if the ending condition is satisfied, and entering the step S9 if the ending condition is not satisfied; S8, outputting the global extreme value of the optimum position of the particle; S9, returning to the step S3. According to the frequency control method, the power loss of the central air conditioner cooling tower fan can be reduced, and the system operating efficiency can be improved.
Description
Technical field
The present invention relates to air-condition cooling tower fan operation optimisation technique field, in particular to a kind of air-condition cooling tower blower fan control method for frequency.
Background technology
Central air-conditioning is indispensable energy consumption operational system in modern architecture.How in central air conditioner system, to accomplish energy-saving and emission-reduction, be a focus of current air conditioner industry research, and superior control method has a significant impact for the energy-conservation of air-conditioning.
Central air-conditioning be non-linear a, large time delay, time the system that becomes, mainly adopt pid control algorithm, for improving the stability of system and reaching adaptive ability.Although traditional pid control algorithm is simple, reliability is high, because its pid parameter is adjusted difficulty, at control accuracy, the aspect of performance such as stability and reliability of system, be difficult to the multiple requirement meeting air-conditioning system.
Summary of the invention
A kind of air-condition cooling tower blower fan control method for frequency is provided in the embodiment of the present invention, the power attenuation of air-condition cooling tower blower fan can be reduced, improve running efficiency of system, reach energy-saving effect to greatest extent.
For solving the problems of the technologies described above, the embodiment of the present invention provides a kind of air-condition cooling tower blower fan control method for frequency, comprising:
Step S1: obtain the running frequency of N number of detection period through all cooling towers of same cooling house steward respectively, according to the running frequency of all cooling towers of each detection period, determine the initial frequency value of each detection period, determine the initial maximum speed Vmax of the size N of particle populations, maximum iteration time Tm, particle
justwith initial highest distance position Xmax
just, Vmax
just=α Xmax
just, what wherein α obedience [0.1,1.0] was interval is uniformly distributed random value;
Step S2: determine N number of particle, sets up primary population, the corresponding particle of each detection period, determine that by the blower fan of cooling tower original frequency of logical calculated be particle position Xi, step-length is particle rapidity Vi, wherein, step-length Vi is the real number of stochastic generation, obeys [-Vmax
just, Vmax
just] interval being uniformly distributed at random, i=1,2 ..., N;
Step S3: the fitness value Fid calculating each particle in particle populations;
Step S4: compared by the fitness value Fid of each particle and individual extreme value Pid, if the fitness value Fid of particle is better than individual extreme value Pid, then replaces individual extreme value Pid with this fitness value Fid;
Step S5: compared by the fitness value Fid of each particle and global extremum Pgd, if the fitness value Fid of particle is better than global extremum Pgd, then replaces global extremum Pgd with this fitness value Fid;
Step S6: the more speed of new particle and position;
Step S7: judge whether to meet termination condition, if met, then enters step S8; If do not met, then enter step S9;
Step S8: the global extremum Pgd exporting particle optimal location, as the running frequency of blower fan, terminate the adjustment of blower fan frequency;
Step S9: be back to step S3.
As preferably, the size N of particle populations is 20, maximum iteration time Tm is 200.
As preferably, the cooling house steward return water temperature of cooling tower and cooling house steward return water temperature setting value to be compared by programme-control and do lifting frequency and calculate and obtain afterwards by the fitness value Fid of particle.
As preferably, particle rapidity Vi is obtained by following formula:
v
id(t+1)＝wv
id(t)+c
1r
1(p
id-x
id(t))+c
2r
2(p
gd-x
id(t))，
Wherein Vid represents the speed of i-th particle when d ties up, and Xid represents the position of i-th particle when d ties up and i=1 ..., N; T represents t generation and t=2 ..., Tm; W is the inertia weight factor and w=0.8; C1 and c2 is positive accelerated factor and c1+c2=2; R1 and r2 is equally distributed random number between 0 to 1; P
idrepresent individual extreme value, Pgd represents global extremum.
As preferably, particle position Xi is obtained by following formula:
x
id(t+1)＝x
id(t)+v
id(t+1)。
As preferably, global extremum Pgd is obtained by following formula:
f(P
g(t))＝max{f(P
1t))，f(P
2(t))，……f(P
m(t))}。
As preferably, more the speed of new particle and position are undertaken by following formula:
As t=1, Pi=Xi.
As preferably, termination condition is for reaching maximum iteration time or error reaches setting range.
Apply technical scheme of the present invention, wild goose group algorithm is incorporated in air-condition cooling tower air-blower control, by the optimized algorithm in wild goose group algorithm, blower fan of cooling tower frequency is regulated, can parallel processing be realized, strong robustness, the global optimum of problem can be found with greater probability, computational efficiency is high, the power attenuation of air-condition cooling tower blower fan can be reduced, improve running efficiency of system, reach energy-saving effect to greatest extent.
Accompanying drawing explanation
Fig. 1 is the air-condition cooling tower blower fan control method for frequency flow chart of the embodiment of the present invention;
Fig. 2 is the optimal particle iterative process schematic diagram of the embodiment of the present invention.
Detailed description of the invention
Below in conjunction with the drawings and specific embodiments, the present invention is described in further detail, but not as a limitation of the invention.
Wild goose group algorithm, i.e. particle swarm optimization algorithm gains enlightenment from nature birds look for food this biotic population behavioral trait, and for optimization problem.In particle swarm optimization algorithm, the potential solution of each optimization problem can tie up a point (being called " particle ") not having quality not have volume on search volume by the idealized D of being abstracted into, each particle has an adaptive value of trying to achieve according to optimised function, a speed is also had to determine their headings and distance, this speed carrys out dynamic conditioning according to the flying experience of itself and the flying experience of companion, and then particles are just searched in solution space according to current individuality optimum and global optimum's particle.Particle cluster algorithm is that a group particle is done random process, then finally finds optimal solution by iteration.When iteration each time, particle upgrades oneself according to two extreme values (is individual extreme value, and another is global extremum), finally obtains the optimal solution of required problem.
Shown in Fig. 1 and Fig. 2, according to embodiments of the invention, air-condition cooling tower blower fan control method for frequency comprises:
Step S1: initialize population, obtains the running frequency of N number of detection period through all cooling towers of same cooling house steward respectively, according to the running frequency of all cooling towers of each detection period, determines the initial frequency value of each detection period.Using blower fan of cooling tower as particle, determine the initial maximum speed Vmax of the size N of particle populations, maximum iteration time Tm, particle
justwith initial highest distance position Xmax
just.
Determine the maximal rate Vmax of particle, be the possibility leaving search volume in order to limit particle, this maximal rate Vmax determines particle displacement maximum in each iteration, Vmax is too high, particle may leap optimal frequency value, and Vmax is too little, and particle is easily absorbed in local optimum.In the present embodiment, Vmax is set
just=α Xmax
just, wherein, α obey [0.1,1.0] interval be uniformly distributed random value, thus the Vmax determined is remained in maximum displacement Xmax, and can not be too small and be absorbed in local optimum due to Vmax value.
Particle populations number N can between value 10 to 50, and particle populations number N is taken as 20 in the present embodiment, and particle maximum iteration time Tm is 200, thus ensures that last determined optimal particle can be positioned at.
Step S2: determine N number of particle, sets up primary population, the corresponding particle of each detection period, determine that by the blower fan of cooling tower original frequency of logical calculated be particle position Xi, step-length is particle rapidity Vi, wherein, step-length Vi is the real number of stochastic generation, obeys [-Vmax
just, Vmax
just] interval being uniformly distributed at random, i=1,2 ..., 20; Each particle has oneself position Xi and speed Vi.
Particle rapidity Vi is obtained by following formula:
v
id(t+1)＝wv
id(t)+c
1r
1(p
id-x
id(t))+c
2r
2(p
gd-x
id(t))，
Wherein Vid represents the speed of i-th particle when d ties up, and Xid represents i-th position of particle when d ties up, and i=1 ..., 20; T represents t generation and t=2 ..., 200; W is the inertia weight factor, value w=0.8 in the present embodiment; C1 and c2 is positive accelerated factor, and in the present embodiment, accelerated factor value meets c1+c2=2; R1 and r2 is equally distributed random number between 0 to 1; Pid represents individual extreme value, and Pgd represents global extremum.
Particle position Xi is obtained by following formula:
x
id(t+1)＝x
id(t)+v
id(t+1)。
Global extremum Pgd is obtained by following formula:
f(P
g(t))＝max{f(P
1t))，f(P
2(t))，……f(P
m(t))}。
Step S3: the fitness value Fid calculating each particle in particle populations, be also blower fan ongoing frequency.Fitness value Fid is compared by cooling house steward's return water temperature and cooling house steward return water temperature setting value and is done after lifting frequency calculates and draws, this part is dealt with by program, and Fid is exactly current fitness value (blower fan ongoing frequency).Wherein cool house steward's return water temperature to be obtained by reading temperature sensor, cooling house steward return water temperature setting value obtains by reading wet bulb temperature calcuation.Wherein the initial value of individual extreme value Pid is initial fitness value Fid.
Step S4: compared by the fitness value Fid of each particle and individual extreme value Pid, if the fitness value Fid of particle is better than individual extreme value Pid, then replaces individual extreme value Pid with this fitness value Fid, otherwise, retain former individual extreme value Pid.Obtain the individual extreme value Pid of each particle, and be stored in data base.
Step S5: compared by the fitness value Fid of each particle and global extremum Pgd, if the fitness value Fid of particle is better than global extremum Pgd, then replaces global extremum Pgd with this fitness value Fid, otherwise, retain former global extremum Pgd.Obtain the global extremum Pgd of each particle, and be stored in data base.
Step S6: the more speed of new particle and position.More the speed of new particle and the mode of position are carried out especially by following formula:
Circular treatment is done according to the basic procedure of particle cluster algorithm and iterations, iteration all can produce a new position vector Xi (t+l) by this formula and once judge each time, this solution vector is evaluated, and compare with the adaptive value Pi (t+l) of personal best particle, if current solution vector Xi (t+l) is more excellent, then preserve it for personal best particle; If not, then personal best particle remains unchanged, and is Pi (t+l).Individual optimal value at the end of circulation is the optimal frequency of this blower fan of cooling tower.As t=1, Pi=Xi.
Step S7: judge whether to meet termination condition, if met, then enters step S8; If do not met, then enter step S9.The termination condition judged is as reaching maximum iteration time or error reaches setting range.In the present embodiment, when number of iterations reaches maximum iteration time 200, or error amount is less than or equal to the allowable error of setting, then can stop circulation, and the global optimum particle position Pg exported in data base, this particle position is the optimal frequency of blower fan of cooling tower.
Wherein step S8 is the global extremum Pgd exporting particle optimal location, as the running frequency of blower fan, terminate the adjustment of blower fan frequency, now blower fan of cooling tower runs at most optimal frequency, CS central air conditioner cooling tower blower fan control system can be made to be optimized, reduce the power attenuation of air-condition cooling tower blower fan, improve running efficiency of system, reach energy-saving effect to greatest extent.
Step S9, for being back to step S3, proceeds iterative cycles, its objective is and ensures to seek in solution preocess can have enough iterationses, to ensure that air-condition cooling tower blower fan control system can seek the optimal solution met the demands.
As seen from Figure 2, in the process of iteration, the fitness value of particle reduces gradually, illustrates that particle cluster algorithm has the function of approaching optimal solution gradually.
Certainly, be more than the preferred embodiment of the present invention.It should be pointed out that for those skilled in the art, under the prerequisite not departing from its general principles, can also make some improvements and modifications, these improvements and modifications are also considered as protection scope of the present invention.
Claims (8)
1. an air-condition cooling tower blower fan control method for frequency, is characterized in that, comprising:
Step S1: obtain the running frequency of N number of detection period through all cooling towers of same cooling house steward respectively, according to the running frequency of all cooling towers of each detection period, determine the initial frequency value of each detection period, determine the size N of particle populations, maximum iteration time T
m, particle initial maximum speed V
at the beginning of maxwith initial highest distance position X
at the beginning of max, V
at the beginning of max=α X
at the beginning of max, what wherein α obedience [0.1,1.0] was interval is uniformly distributed random value;
Step S2: determine N number of particle, sets up primary population, and the corresponding particle of each detection period, determine that by the blower fan of cooling tower original frequency of logical calculated be particle position Xi, step-length is particle rapidity V
i, wherein, step-length V
ifor the real number of stochastic generation, obey [-V
at the beginning of max, V
at the beginning of max] interval being uniformly distributed at random, i=1,2 ..., N;
Step S3: the fitness value F calculating each particle in particle populations
id;
Step S4: by the fitness value F of each particle
idwith individual extreme value P
idcompare, if the fitness value F of particle
idbe better than individual extreme value P
id, then with this fitness value F
idreplace individual extreme value P
id;
Step S5: by the fitness value F of each particle
idwith global extremum P
gdcompare, if the fitness value F of particle
idbe better than global extremum P
gd, then with this fitness value F
idreplace global extremum P
gd;
Step S6: the more speed of new particle and position;
Step S7: judge whether to meet termination condition, if met, then enters step S8; If do not met, then enter step S9;
Step S8: the global extremum P exporting particle optimal location
gd, as the running frequency of blower fan, terminate the adjustment of blower fan frequency;
Step S9: be back to step S3.
2. air-condition cooling tower blower fan control method for frequency according to claim 1, is characterized in that, the size N of described particle populations is 20, maximum iteration time T
mbe 200.
3. air-condition cooling tower blower fan control method for frequency according to claim 1, is characterized in that, the fitness value F of particle
idby programme-control the cooling house steward return water temperature of cooling tower and cooling house steward return water temperature setting value compared and do lifting frequency and calculates and obtain afterwards.
4. air-condition cooling tower blower fan control method for frequency according to claim 1, is characterized in that, described particle rapidity V
iobtained by following formula:
v
id(t+1)＝wv
id(t)+c
1r
1(p
id-x
id(t))+c
2r
2(p
gd-x
id(t))，
Wherein V
idrepresent the speed of i-th particle when d ties up, X
idrepresent the position of i-th particle when d ties up and i=1 ..., N; T represents t generation and t=2 ..., T
m; W is the inertia weight factor and w=0.8; C1 and c2 is positive accelerated factor and c1+c2=2; R1 and r2 is equally distributed random number between 0 to 1; P
idrepresent individual extreme value, P
gdrepresent global extremum.
5. air-condition cooling tower blower fan control method for frequency according to claim 4, is characterized in that, described particle position X
iobtained by following formula:
x
id(t+1)＝x
id(t)+v
id(t+1)。
6. air-condition cooling tower blower fan control method for frequency according to claim 5, is characterized in that, described global extremum P
gdobtained by following formula:
f(P
g(t))＝max{f(P
1(t))，f(P
2(t))，......f(P
m(t))}。
7. air-condition cooling tower blower fan control method for frequency according to claim 6, is characterized in that, more the speed of new particle and position are undertaken by following formula:
As t=1, P
i=X
i.
8. air-condition cooling tower blower fan control method for frequency according to any one of claim 1 to 7, is characterized in that, described termination condition is for reaching maximum iteration time or error reaches setting range.
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| CN201410799503.XA CN104566797A (en) | 2014-12-18 | 2014-12-18 | Central air conditioner cooling tower fan frequency control method |
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|---|---|---|---|
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106123218A (en) * | 2016-06-24 | 2016-11-16 | 珠海格力电器股份有限公司 | A kind of method for determining operation parameters for air-conditioning, device and air-conditioning |
| CN109882995A (en) * | 2019-01-16 | 2019-06-14 | 珠海格力电器股份有限公司 | A kind of method of equipment and its Energy Saving Control |
| CN109916090A (en) * | 2018-11-29 | 2019-06-21 | 青岛经济技术开发区海尔热水器有限公司 | Heat-pump water heater control method and Teat pump boiler |
-
2014
- 2014-12-18 CN CN201410799503.XA patent/CN104566797A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106123218A (en) * | 2016-06-24 | 2016-11-16 | 珠海格力电器股份有限公司 | A kind of method for determining operation parameters for air-conditioning, device and air-conditioning |
| CN106123218B (en) * | 2016-06-24 | 2020-01-24 | 珠海格力电器股份有限公司 | Operation parameter determination method and device for air conditioner and air conditioner |
| CN109916090A (en) * | 2018-11-29 | 2019-06-21 | 青岛经济技术开发区海尔热水器有限公司 | Heat-pump water heater control method and Teat pump boiler |
| CN109882995A (en) * | 2019-01-16 | 2019-06-14 | 珠海格力电器股份有限公司 | A kind of method of equipment and its Energy Saving Control |
| CN109882995B (en) * | 2019-01-16 | 2020-03-27 | 珠海格力电器股份有限公司 | Equipment and energy-saving control method thereof |
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Application publication date: 20150429 |