CN201335488Y - Intelligently optimized control device for central air conditioner - Google Patents
Intelligently optimized control device for central air conditioner Download PDFInfo
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- CN201335488Y CN201335488Y CNU2008202140779U CN200820214077U CN201335488Y CN 201335488 Y CN201335488 Y CN 201335488Y CN U2008202140779 U CNU2008202140779 U CN U2008202140779U CN 200820214077 U CN200820214077 U CN 200820214077U CN 201335488 Y CN201335488 Y CN 201335488Y
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Abstract
The utility model is applicable to the technical field of the control of central air conditioners,and provides an intelligently optimized control device for a central air conditioner. The intelligently optimized control device comprises a sensor, an intelligent controller and a system device control unit, wherein the sensor obtains operational parameter information of each system, and is connected with the intelligent controller; the intelligent controller analyzes and predicts users' cold use demands in a dynamic load manner according to the operational parameter information obtained by the sensor; and the intelligent controller is also connected with system device control unit and can control the system device control unit according to the obtained load predicting results. The intelligently optimized control device for a central air conditioner enables the energy-saving potential of the air conditioner to be developed to the maximum so as to obtain maximal economic benefit.
Description
Technical field
The utility model belongs to central air-conditioning control technology field, relates in particular to a kind of central air-conditioning intelligent optimization control device.
Background technology
Ratio along with the whole energy consumptions of building energy consumption account improves constantly in recent years, the whole society is increasing to the concern of building energy conservation, and central air-conditioning has occupied the major part of building energy consumption, and therefore, the energy consumption that how to reduce central air-conditioning is one of technology of central air-conditioning primary study.
In the prior art central air-conditioning energy technology, comprise that pump variable frequency technology, chilled water system fuzzy control technology etc. have been widely used in all kinds of central air-conditioning technical innovation projects, and obtain more considerable energy-saving effect.
But, the subject matter that these technology exist is only to focus on the energy-conservation of certain specific installation of central air-conditioning or local mini system, its overall situation is not considered, therefore energy-saving effect is limited, sometimes also certain device energy conservation can appear, increase and influence other equipment power consumption, whole negative benefit result finally may occur.
The utility model content
The purpose of the utility model embodiment is to provide a kind of central air-conditioning intelligent optimization control device, be intended to solve in the prior art owing to only focus on the energy-conservation of certain specific installation of central air-conditioning or local mini system, its overall situation is not considered, so the limited problem of energy-saving effect.
The utility model embodiment is achieved in that a kind of central air-conditioning intelligent optimization control device, and described device comprises sensor, intelligent controller and system equipment control module, wherein,
Described sensor obtains the trip information of each system, and described sensor is connected with described intelligent controller, and the trip information that described intelligent controller obtains according to described sensor carries out the dynamic load analyses and prediction to the user with cold demand,
Described intelligent controller also is connected with described system equipment control module, and described intelligent controller is controlled described system equipment control module according to the load prediction results that draws.
The utility model embodiment is by the trip information of each system of pick-up transducers, by intelligent controller the user is carried out the dynamic analysis prediction with cold demand, and predict the outcome, equipment operational factor etc. utilized intelligence database analysis, propose to optimize operating scheme and carry out the control system equipment control module, make the central air-conditioning energy potentiality obtain maximum performance, obtain maximum economic benefit.
Description of drawings
Fig. 1 is the structure chart of the central air-conditioning intelligent optimization control device that provides of the utility model embodiment;
Fig. 2 is that the comprehensive COP of actual measurement that the utility model embodiment provides changes and equipment power consumption schematic diagram;
Fig. 3 is the workflow diagram of the central air-conditioning intelligent optimization control device that provides of the utility model.
The specific embodiment
In order to make the purpose of this utility model, technical scheme and advantage clearer,, the utility model is further elaborated below in conjunction with drawings and Examples.Should be appreciated that specific embodiment described herein only in order to explanation the utility model, and be not used in qualification the utility model.
The utility model embodiment is by the trip information of each system of pick-up transducers, by intelligent controller the user is carried out the dynamic analysis prediction with cold demand, and predict the outcome, equipment operational factor etc. utilized intelligence database analysis, propose to optimize operating scheme and also carry out the control system equipment control module.
See also Fig. 1, the central air-conditioning intelligent optimization control device that the utility model embodiment provides comprises sensor, intelligent controller and system equipment control module.
Wherein, described system equipment control module comprises: cooling water pump switch board, chilled water pump switch board and cooling tower switch board.
Described sensor obtains the trip information of each system, and sensor is mainly gathered outdoor environment humiture, indoor temperature and humidity, chilled-water flow, freezingly advanced return water temperature, return water temperature, least favorable terminal temperature difference cold water pressure reduction, freezing host setting leaving water temperature etc. are advanced in cooling.
Described sensor is connected with described intelligent controller by communication interface, after obtaining above data, is transferred to intelligent controller through communication interface.Wherein, each sensor acquisition parameter information is aggregated into its inner data module, and the latter gets in touch by communication interface and intelligent controller.
The trip information that described intelligent controller obtains according to described sensor carries out the dynamic load analyses and prediction to the user with cold demand.
Preferably, multiple parameter and service data that described intelligent controller obtains according to described sensor, when the user is carried out the dynamic analysis prediction with cold demand, use following formula:
Q(t)＝cpGtΔTt
Q(t+dt)＝Q(t)+[Q(t)-Q(t-dt)]，
Wherein, Q (t) is a t air conditioner load constantly;
Cp is the water specific heat at constant pressure;
Gt is a t chilled-water flow constantly;
Δ Tt is the t freezing water temperature difference of sending back to constantly;
The load prediction value is: Q (t+dt)=Q (t)+[Q (t)-Q (t-dt)]
In the formula: Q (t+dt)---next sense cycle air conditioner load predicted value
Q (t-dt)---last sense cycle actual measurement air conditioner load value.
After obtaining the load prediction value, described intelligent controller is controlled the operation of corresponding cooling water pump switch board, chilled water pump switch board and cooling tower switch board according to the load prediction results that draws.
In specific implementation process, can operating scheme be proposed according to intelligence database and the load prediction that obtains value, each component devices of system is implemented control, effect comprises following three aspects:
1, team control: according to the user's who analyzes demand, optimum organization start and stop operational outfit makes it in efficient district's work, brings into play each equipment potential to greatest extent, and system effectiveness is the highest:
Central air-conditioning generally is made up of many handpiece Water Chilling Units and supporting water pump and cooling tower, and air conditioner load is changing at any time, handpiece Water Chilling Units cooling amount is also adjusted thereupon, because every unit performance indications under each load condition have very big-difference, promptly along with the variation of loading, the pairing efficient of unit is different, and every unit all has fixing efficient district.In running, adjust cold source equipment all the time in its efficient district work by the optimum organization arrangement, just can reduce the cooling energy consumption significantly.
The utility model embodiment can be according to the air conditioner load value of prediction, and the configuration unit is in the operation of efficient district, and the unit's of making cooling energy consumption minimum has been optimized the use of the energy greatly.
2, optimize host configuration: according to the customer charge demand and the environmental analysis that draw, control handpiece Water Chilling Units leaving water temperature and cold in-water temperature guarantee main frame under the prerequisite that meets the demands, efficient operation:
The standard service condition of central air-conditioning handpiece Water Chilling Units is 7 ℃/12 ℃ of cold water temperatures, 30 ℃/35 ℃ of cooling water temperatures, this group parameter can satisfy all kinds of demands of air-conditioning system, but, central air-conditioning load change at random amplitude is bigger, if according to fixing operational factor cooling, will cause the phenomenon that wastes energy, calculate and site-test analysis according to theory, the handpiece Water Chilling Units leaving water temperature is whenever heightened 1 ℃, its cooling energy consumption can reduce about 3%, because the unit leaving water temperature is by the decision of air-conditioning latent heat load, and latent heat load mainly is to be caused by indoor occupant and new wind, for the bigger commercial building of flow of personnel, meeting room, user's latent heat load random fluctuations such as stadiums are very big, therefore to make operation adjustment in good time, whole air-conditioning system energy consumption level is greatly improved.The utility model embodiment can contrast according to detecting data and intelligence database according to aforementioned load prediction, provides prioritization scheme, dynamically adjusts the handpiece Water Chilling Units leaving water temperature, has saved energy consumption.
And 1 ℃ of the every reduction of handpiece Water Chilling Units coolant water temperature equally also can reduce by 3% left and right sides energy consumption, and coolant water temperature is relevant with the cooling tower operation with environment wet-bulb temperature.The utility model embodiment can adjust cooling blower rotating speed or increase and decrease cooling tower operation quantity according to the load value that calculates in good time, makes the main frame efficient operation.
3, equipment coordination operation: according to the maximum principle of comprehensive COP, control chilled water pump, cooling water pump, blower fan of cooling tower running frequency make system energy consumption minimum:
Central air-conditioning cooling, chilled water pump, blower fan of cooling tower variable-frequency control technique are according to advancing the backwater temperature difference differential pressure, proportion of utilization integral differential (Proportion Integral Derivative, PID) or proportional integral (Proportion Integral,) the PI method implements variable frequency regulating speed control to equipment, to reach the variable air rate variable water volume purpose that cuts down the consumption of energy, this technology is widely used in the reducing energy consumption engineering, and obtains effect preferably.But a deficiency of this technology is only to consider to regulate and control single devices, and central air-conditioning is a complication system, and the individual equipment energy-saving effect is not necessarily best to system, also negative benefit can occur under certain situation.As in the variable water volume adjustment process, water pump is regulated can be energy-conservation, and host manager opinion and actual measurement prove that all energy consumption increases to some extent, therefore how will see the entire system effect.The comprehensive COP notion of the utility model embodiment drawing-in system is defined as:
Comprehensively
In the formula: Q----air-conditioning cooling amount
N
j------main frame power consumption
N
l------cooling water pump power consumption
N
d------chilled water pump power consumption
N
t------cooling tower power consumption
See also Fig. 2 according to comprehensive COP variation of actual measurement and equipment power consumption:
Wherein, the crosspoint of main frame energy consumption and pump energy consumption is the extreme point of comprehensive COP, its system synthesis benefit maximum.The utility model intelligent data library storage mass data can find this extreme point in conjunction with on-the-spot detection, reaches optimal result.
See also Fig. 3, this figure is the course of work of the central air-conditioning intelligent optimization control device that provides of the utility model.
In step 01, obtain the trip information of each system.
In step 02,, the user is carried out the dynamic load analyses and prediction with cold demand according to multiple parameter and the service data that described sensor obtains;
According to multiple parameter and the service data that described sensor obtains, when the user is carried out the dynamic analysis prediction with cold demand, use following formula:
Q(t)＝cpGtΔTt
Q(t+dt)＝Q(t)+[Q(t)-Q(t-dt)]，
Wherein, Q (t) is a t air conditioner load constantly;
Cp is the water specific heat at constant pressure;
Gt is a t chilled-water flow constantly;
Δ Tt is the t freezing water temperature difference of sending back to constantly;
The load prediction value is: Q (t+dt)=Q (t)+[Q (t)-Q (t-dt)]
In the formula: Q (t+dt)---next sense cycle air conditioner load predicted value
Q (t-dt)---last sense cycle actual measurement air conditioner load value.
In step 03, according to the current running status of judgement system that predicts the outcome.When positive trend appears in described load, carry out step 04; When described load occurs bearing trend, carry out step 05; When described load is constant, carry out step 06;
The utility model embodiment is by the trip information of each system of pick-up transducers, by intelligent controller the user is carried out the dynamic analysis prediction with cold demand, and predict the outcome, equipment operational factor etc. utilized intelligence database analysis, propose to optimize operating scheme and carry out the control system equipment control module, make the central air-conditioning energy potentiality obtain maximum performance, obtain maximum economic benefit.
The above only is preferred embodiment of the present utility model; not in order to restriction the utility model; all any modifications of within spirit of the present utility model and principle, being done, be equal to and replace and improvement etc., all should be included within the protection domain of the present utility model.
Claims (4)
1, a kind of central air-conditioning intelligent optimization control device is characterized in that described device comprises sensor, intelligent controller and system equipment control module, wherein,
Described sensor obtains the trip information of each system, and described sensor is connected with described intelligent controller, and the trip information that described intelligent controller obtains according to described sensor carries out the dynamic load analyses and prediction to the user with cold demand,
Described intelligent controller also is connected with described system equipment control module, and described intelligent controller is controlled described system equipment control module according to the load prediction results that draws.
2, central air-conditioning intelligent optimization control device according to claim 1 is characterized in that described sensor is connected with intelligent controller by communication interface.
3, central air-conditioning intelligent optimization control device according to claim 1 is characterized in that described sensor also includes data module, and described data module is connected with described intelligent controller by communication interface.
4, central air-conditioning intelligent optimization control device according to claim 1 is characterized in that, described system equipment control module comprises: cooling water pump switch board, chilled water pump switch board and cooling tower switch board.
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| Application Number | Priority Date | Filing Date | Title |
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| CNU2008202140779U CN201335488Y (en) | 2008-11-29 | 2008-11-29 | Intelligently optimized control device for central air conditioner |
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| CNU2008202140779U CN201335488Y (en) | 2008-11-29 | 2008-11-29 | Intelligently optimized control device for central air conditioner |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102367980A (en) * | 2011-10-19 | 2012-03-07 | 深圳市奥宇控制系统有限公司 | Central air-conditioning multi-dimension integrating optimization control system and method |
| CN104571034A (en) * | 2014-12-29 | 2015-04-29 | 中国机械工业企业管理协会 | Intelligent management and control equipment for cold and hot joint supply of heating ventilating and air conditioning system and control method thereof |
| CN105240993A (en) * | 2015-09-11 | 2016-01-13 | 董锐 | Fine energy-saving control system of central air conditioner and achieving method of fine energy-saving control system |
| CN106545930A (en) * | 2016-11-04 | 2017-03-29 | 广东汉维科技有限公司 | A kind of energy-efficient central air conditioner room and power-economizing method |
| CN107763799A (en) * | 2017-11-27 | 2018-03-06 | 中山路得斯空调有限公司 | A kind of building air conditioning flexible control system |
| CN111256294A (en) * | 2020-01-17 | 2020-06-09 | 深圳市得益节能科技股份有限公司 | Model prediction-based optimization control method for combined operation of water chilling unit |
| WO2021187423A1 (en) * | 2020-03-16 | 2021-09-23 | 三菱電機株式会社 | Air conditioning system |
-
2008
- 2008-11-29 CN CNU2008202140779U patent/CN201335488Y/en not_active Expired - Lifetime
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102367980A (en) * | 2011-10-19 | 2012-03-07 | 深圳市奥宇控制系统有限公司 | Central air-conditioning multi-dimension integrating optimization control system and method |
| CN102367980B (en) * | 2011-10-19 | 2014-04-30 | 深圳市奥宇控制系统有限公司 | Central air-conditioning multi-dimension integrating optimization control system and method |
| CN104571034A (en) * | 2014-12-29 | 2015-04-29 | 中国机械工业企业管理协会 | Intelligent management and control equipment for cold and hot joint supply of heating ventilating and air conditioning system and control method thereof |
| CN104571034B (en) * | 2014-12-29 | 2017-05-03 | 中国机械工业企业管理协会 | Intelligent management and control equipment and control method thereof |
| CN105240993A (en) * | 2015-09-11 | 2016-01-13 | 董锐 | Fine energy-saving control system of central air conditioner and achieving method of fine energy-saving control system |
| CN105240993B (en) * | 2015-09-11 | 2018-06-19 | 董锐 | Become more meticulous energy-saving control system and its implementation of a kind of central air-conditioning |
| CN106545930A (en) * | 2016-11-04 | 2017-03-29 | 广东汉维科技有限公司 | A kind of energy-efficient central air conditioner room and power-economizing method |
| CN106545930B (en) * | 2016-11-04 | 2019-09-27 | 广东汉维科技有限公司 | A kind of energy-efficient central air conditioner room and power-economizing method |
| CN107763799A (en) * | 2017-11-27 | 2018-03-06 | 中山路得斯空调有限公司 | A kind of building air conditioning flexible control system |
| CN111256294A (en) * | 2020-01-17 | 2020-06-09 | 深圳市得益节能科技股份有限公司 | Model prediction-based optimization control method for combined operation of water chilling unit |
| WO2021187423A1 (en) * | 2020-03-16 | 2021-09-23 | 三菱電機株式会社 | Air conditioning system |
| JP7414958B2 (en) | 2020-03-16 | 2024-01-16 | 三菱電機株式会社 | air conditioning system |
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Legal Events
| Date | Code | Title | Description |
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| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| C56 | Change in the name or address of the patentee |
Owner name: SHENZHEN AUTO-UNION ENERGY-SAVING TECHNOLOGY CO., Free format text: FORMER NAME: AOYU CONTORL SYSTEM CO., LTD., SHENZHEN CITY |
|
| CP03 | Change of name, title or address |
Address after: Shenzhen City, Nanshan District high tech Industrial Park in Guangdong province 518000 Shenzhen Software Park Building 7, 201, 202 Patentee after: Ao Yu power-saving technology limited company of Shenzhen Address before: 518000 Guangdong city of Shenzhen province Nanshan District high tech Industrial Park District Technology in 2 Road 7 Shenzhen Software Park Building 2 floor Patentee before: Aoyu Contorl System Co., Ltd., Shenzhen City |
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| CX01 | Expiry of patent term |
Granted publication date: 20091028 |
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| CX01 | Expiry of patent term |