CN102012077A - Energy-saving control system and control method of central air conditioning freezing station - Google Patents

Abstract

The invention relates to an energy-saving control system and control method of a central air conditioning freezing station. The control system is an energy conservation system with a self-learning function, and comprises a relational database, an energy consumption self-optimization controller, a rolling optimization controller, a signal acquisition control cabinet, a cooling tower control cabinet, a cooling pump control cabinet, a refrigeration pump control cabinet, and a plurality of sensors. Through relational database modeling for each main current consuming device in the central air conditioning freezing station and rolling optimization of the relational database, energy consumption associated information of each main current consuming device is obtained, and through further energy consumption self-optimization course, an optimization parameter of the operation of devices in the freezing station is obtained, thus the integral central air conditioning freezing station operates at a high-efficient and energy-saving state.

Description

A kind of central air-conditioning refrigeration station energy-saving control system and control method

Technical field:

The invention belongs to central air-conditioning control technology field, particularly relate to a kind of central air-conditioning refrigeration station energy-saving control system and control method.

Background technology:

Low-carbon economy development has in recent years become a social hotspots, building energy conservation has been subjected to unprecedented attention, and the central air-conditioning energy consumption has occupied the significant proportion of whole building energy consumption, and therefore, the energy consumption that how to reduce central air conditioner system becomes an important subject.

In system of central air-conditioning refrigeration station, the energy-saving and frequency-variable technology of water pump is used at present morely, as a kind of centrifugation apparatus, the power consumption of water pump is directly proportional with the cube of rotating speed, and it is significant to the energy consumption that reduces water system therefore to regulate pump rotary speed by frequency converter.In addition, the frequency conversion centrifugal refrigerating machines has also also had some application cases at home and abroad, by the converting operation to centrifugal refrigerating machines, can reach remarkable energy saving effect under part load condition.

Because input variable and output variable that the system of refrigeration station need control are numerous, and have non-linear, the time change, time lag, coupling, inertia characteristics, and the supply of material equipment performance difference of each manufacturer, coupling and changeable mutually between the operational factor, be difficult to find out accurate dynamic mathematical models, thereby the control difficulty is bigger.

Conventional central air-condition freezing station control method mostly is single-point control, and for example, constant-pressure drop control is controlled for backwater pressure reduction water system; Constant difference control is controlled the water system supply backwater temperature difference.Employed typical single argument control model is the PID control model, and as one of control strategy that grows up the earliest, control algolithm is simple, versatility good and the reliability advantages of higher because it has, and is widely used in industrial process control.Exist at PID control vibrate easily, the problem of less stable, some improved PID control methods, fuzzy control etc. also are applied to during the central air-conditioning refrigeration station controls.

But, the subject matter that these control technologys exist is certain specific installation or local mini system energy-conservation in the central air-conditioning refrigeration station emphatically only, do not consider entire system is energy-conservation, 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.For example, only close the water injecting pump economize on electricity, ignored the cold function that may occur and consumed the system energy consumption rising of rising and causing; Chilled water circulation and cooling water loop control are relatively independent, can not realize the complex optimum control of system effectiveness.

Summary of the invention:

The objective of the invention is to overcome the above-mentioned deficiency of prior art, a kind of central air-conditioning refrigeration station energy-saving control system is provided, this system is by the linked database model that adopts rolling optimization, direct self-optimization method at the whole energy consumption of refrigeration station, realization is to the monitoring and the optimization of central air-condition freezing station system operational parameters, improve the overall operation efficiency of system of central air-conditioning refrigeration station, reach energy-saving and cost-reducing effect.

Another object of the present invention is to provide the control method of a kind of central air-conditioning refrigeration station energy-saving control system.

Above-mentioned purpose of the present invention is achieved by following technical solution:

A kind of central air-conditioning refrigeration station energy-saving control system comprises linked database, energy consumption self-optimizing control device, rolling optimization controller, signals collecting switch board, cooling tower switch board, coolant pump switch board, refrigerating water pump switch board and plurality of sensors, wherein:

Linked database: carry out the linked database modeling of central air-conditioning refrigeration station, form the linked database model, and in real time received signal is gathered the state parameter of switch board output, described linked database model parameter and described state parameter is exported to energy consumption self-optimizing control device carry out handling from optimizing; Also described linked database model parameter is exported to the rolling optimization controller simultaneously and carry out real-time rolling optimization computing, and receive the optimization parameter that the rolling optimization controller returns, finish the renewal of linked database model;

Energy consumption self-optimizing control device: the linked database model parameter and the state parameter that receive linked database output, receive the state parameter of cooling tower switch board, coolant pump switch board, refrigerating water pump switch board, central air-conditioning refrigeration station handpiece Water Chilling Units and the output of each electrically operated valve simultaneously, carry out handling from optimizing, obtain best energy consumption operational factor, and described best energy consumption operational factor is exported to rolling optimization controller, cooling tower switch board, coolant pump switch board, refrigerating water pump switch board, central air-conditioning refrigeration station handpiece Water Chilling Units and each electrically operated valve respectively;

Rolling optimization controller: the linked database model parameter that receives linked database output, receive the best energy consumption operational factor of energy consumption self-optimizing control device output, in each searching process, carry out the rolling optimization computing, and the parameter that will finish the rolling optimization computing returns to linked database, finishes the renewal of linked database model;

Signals collecting switch board: gather central air-conditioning refrigeration station grey water system operational factor and ambient parameter that plurality of sensors is measured, and described parameter is exported to linked database;

Cooling tower switch board: the cooling tower running state parameter is exported to linked database in real time, and receive the best energy consumption operational factor of energy consumption self-optimizing control device output in real time, according to the operation of described best energy consumption operational factor VFC cooling tower;

Coolant pump switch board: the coolant pump running state parameter is exported to linked database in real time, and receive the best energy consumption operational factor of energy consumption self-optimizing control device output in real time, according to the operation of described best energy consumption operational factor VFC coolant pump;

Refrigerating water pump switch board: the refrigerating water pump running state parameter is exported to linked database in real time, and receive the best energy consumption operational factor of energy consumption self-optimizing control device output in real time, according to the operation of described best energy consumption operational factor VFC refrigerating water pump;

Sensor: be used to measure water system operational factor and ambient parameter in the central air-conditioning refrigeration station, and described parameter exported to the signals collecting switch board, its grey water system operational factor comprises the terminal pressure reduction of chilled-water flow, chilled water leaving water temperature, chilled water return water temperature, cooling water flow, cooling water leaving water temperature, cooling water return water temperature, handpiece Water Chilling Units power and chilled water, and ambient parameter comprises outdoor wet-bulb temperature.

In above-mentioned central air-conditioning refrigeration station energy-saving control system, in linked database, carry out the linked database modeling, the concrete grammar that forms the linked database model is as follows:

(1) refrigerating capacity with the output of central air-conditioning refrigeration station disperses in its cold scope, obtains exporting the m of cold ₁Item discrete function Q _n:

$Q_n=Q_{\min }+\frac{(n-1)(Q_{\max }-Q_{\min })}{m_1-1}$

Wherein:

Q _Min: the minimum refrigerating capacity that refrigeration station exports under typical condition

Q _Max: the specified refrigerating capacity that refrigeration station exports under typical condition

1≤n≤m ₁, and n, m ₁∈ N, Q _nThe accuracy of value is by m ₁Value determines that its minimum resolution is:

(2) wet-bulb temperature with outdoor environment disperses in the wet-bulb temperature working range of refrigeration station, obtains the m of wet-bulb temperature ₂Item discrete function TW _l:

${\mathrm{TW}}_l={\mathrm{TW}}_{\min }+\frac{(l-1)({\mathrm{TW}}_{\max }-{\mathrm{TW}}_{\min })}{m_2-1}$

Wherein:

TW _Min: the minimum wet-bulb temperature of the outdoor environment of refrigeration station work

TW _Max: the highest wet-bulb temperature of outdoor environment of refrigeration station work

1≤l≤m ₂, and l, m ₂∈ N, TW _lThe accuracy of value is by m ₂Value determines that its minimum resolution is:

The discrete function Q of (3) output colds _nDiscrete function TW with an outdoor environment wet-bulb temperature _lCorresponding refrigeration station initial operational parameter array, corresponding m altogether ₁* m ₂The individual initial operational parameter array (Q of refrigeration station _n, TW _l), to m ₁* m ₂The individual initial operational parameter array (Q of refrigeration station _n, TW _l) in the default initial value of each parameter, form the linked database model.

In above-mentioned central air-conditioning refrigeration station energy-saving control system, energy consumption self-optimizing control device receives linked database model parameter and each state parameter of linked database output, carries out handling from optimizing, and the concrete grammar that obtains best energy consumption operational factor is as follows:

(1) calculate the current output cold Q of refrigeration station by the refrigeration station running state parameter,

Q＝C·F _CH·ΔT＝C·F _CH·(T _CHI-T _CHO)

Wherein, F _CH: chilled-water flow, T _CHI: chilled water return water temperature, T _CHO: chilled water leaving water temperature, C: specific heat of water, Δ T: the temperature difference of chilled water backwater and water outlet;

(2) the energy loss-rate f of central air-conditioning refrigeration station optimizing starting point under the calculating current working _B,

$f_B={\mathrm{COP}}_B=\frac{Q}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+P_{\mathrm{CB}}+P_{\mathrm{TB}}},$

Wherein: P _WCU: handpiece Water Chilling Units power, P _CHB: chilled water pump power, P _CB: cooling water pump power, P _TB: blower fan of cooling tower power;

(3) establishing the optimizing step-length is Δ P, and calculating at chilled water pump power respectively is P _CHB± Δ P, cooling water pump power are P _CB± Δ P, blower fan of cooling tower power are P _TBCentral air-conditioning refrigeration station under the ± Δ P situation can loss-rate f ₁, f ₂, f ₃, f ₄, f ₅, f ₆, and the maximum of getting wherein is labeled as f _Max, wherein:

$f_1={\mathrm{COP}}_1=\frac{Q_n}{P_{\mathrm{WCU}}+(P_{\mathrm{CHB}}+\mathrm{\ΔP})+P_{\mathrm{CB}}+P_{\mathrm{TB}}};$

$f_2={\mathrm{COP}}_2=\frac{Q_n}{P_{\mathrm{WCU}}+(P_{\mathrm{CHB}}-\mathrm{\ΔP})+P_{\mathrm{CB}}+P_{\mathrm{TB}}};$

$f_3={\mathrm{COP}}_3=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+(P_{\mathrm{CB}}+\mathrm{\ΔP})+P_{\mathrm{TB}}};$

$f_4={\mathrm{COP}}_4=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+(P_{\mathrm{CB}}-\mathrm{\ΔP})+P_{\mathrm{TB}}};$

${\mathrm{<figure-callout\; id="5"\; label="f"\; filenames="HSA00000381206400021.png"\; state="\{\{state\}\}">f</figure-callout>}}_5={\mathrm{<figure-callout\; id="5"\; label="COP"\; filenames="HSA00000381206400021.png"\; state="\{\{state\}\}">COP</figure-callout>}}_5=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+P_{\mathrm{CB}}+(P_{\mathrm{TB}}+\mathrm{\&Delta;P})};$

$f_6={\mathrm{COP}}_6=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+P_{\mathrm{CB}}+(P_{\mathrm{TB}}-\mathrm{\&Delta;P})};$

(4) with the energy loss-rate f of optimizing starting point _BWith maximum energy loss-rate f _MaxCompare,

If f _Max＞f _B, then make f _B=f _Max, searching process is continued in repeating step (3), (4);

Otherwise, set a constant ε, 0＜ε＜1,

If | f _Max-f _B| 〉=ε then makes

Searching process is continued in repeating step (3), (4);

If | f _Max-f _B|＜ε then makes f _O=f _B, with f _OCorresponding operational factor from the optimizing operational factor, finishes searching process as the energy consumption of the best.

In above-mentioned central air-conditioning refrigeration station energy-saving control system, the rolling optimization controller receives the linked database model parameter of linked database output, receive the best energy consumption operational factor of energy consumption self-optimizing control device output, the concrete grammar that carries out the rolling optimization computing is as follows:

(1) the rolling optimization controller receives the linked database model parameter of linked database output: the chilled water pump power P _CHB, the cooling water pump power P _CB, the blower fan of cooling tower power P _TB

(2) the rolling optimization controller receives the best energy consumption operational factor of energy consumption self-optimizing control device output: the chilled water pump power P _CHO, the cooling water pump power P _CO, the blower fan of cooling tower power P _TO

(3) chilled water pump power P _CHX, the cooling water pump power P _CX, the blower fan of cooling tower power P _TXFor finishing the parameter of rolling optimization computing, its computational methods are as follows:

$P_{\mathrm{CHX}}=\frac{(n-1)P_{\mathrm{CHB}}}{n}+\frac{P_{\mathrm{CHO}}}{n};$

$P_{\mathrm{CX}}=\frac{(n-1)P_{\mathrm{CB}}}{n}+\frac{P_{\mathrm{CO}}}{n};$

$P_{\mathrm{TX}}=\frac{(n-1)P_{\mathrm{TB}}}{n}+\frac{P_{\mathrm{TO}}}{n};$

(4) with parameter P _CHX, P _CX, P _TXReturn to linked database, upgrade linked database parameter P respectively _CHB, P _CB, P _TB

In above-mentioned central air-conditioning refrigeration station energy-saving control system, also comprise man-machine interface, be used for operational factor to refrigeration station and component devices thereof and show and manage that wherein operational factor comprises coefficient of energy dissipation parameter and running state parameter etc.

The control method of a kind of central air-conditioning refrigeration station energy-saving control system comprises the steps:

(1) linked database carries out the linked database modeling of central air-conditioning refrigeration station, forms the linked database model;

(2) the signals collecting switch board is gathered central air-conditioning refrigeration station grey water system operational factor and the ambient parameter that plurality of sensors is measured, and described parameter is exported to linked database;

(3) the real-time received signal of linked database is gathered the state parameter of switch board output, linked database model parameter and described state parameter is exported to energy consumption self-optimizing control device carry out handling from optimizing; Also the linked database model parameter is exported to the rolling optimization controller simultaneously and carried out real-time rolling optimization computing;

(4) energy consumption self-optimizing control device receives the linked database model parameter and the state parameter of linked database output, receive the state parameter of cooling tower switch board, coolant pump switch board, refrigerating water pump switch board, central air-conditioning refrigeration station handpiece Water Chilling Units and the output of each electrically operated valve simultaneously, carry out handling from optimizing, obtain best energy consumption operational factor, and described best energy consumption operational factor is exported to rolling optimization controller, cooling tower switch board, coolant pump switch board, refrigerating water pump switch board, central air-conditioning refrigeration station handpiece Water Chilling Units and each electrically operated valve respectively;

(5) the cooling tower switch board receives the best energy consumption operational factor of energy consumption self-optimizing control device output in real time, according to the operation of described best energy consumption operational factor VFC cooling tower; The coolant pump switch board receives the best energy consumption operational factor of energy consumption self-optimizing control device output in real time, according to the operation of described best energy consumption operational factor VFC coolant pump; The refrigerating water pump switch board receives the best energy consumption operational factor of energy consumption self-optimizing control device output in real time, according to the operation of described best energy consumption operational factor VFC refrigerating water pump;

(6) the rolling optimization controller receives the linked database model parameter of linked database output, receive the best energy consumption operational factor of energy consumption self-optimizing control device output, in each searching process, carrying out the rolling optimization computing, and the parameter that will finish the rolling optimization computing returns to linked database;

(7) linked database receives the optimization parameter that the rolling optimization controller returns, and finishes the renewal of linked database model.

In the control method of above-mentioned central air-conditioning refrigeration station energy-saving control system, linked database carries out the linked database modeling of central air-conditioning refrigeration station in the step (1), and the concrete grammar that forms the linked database model is as follows:

(1) refrigerating capacity with the output of central air-conditioning refrigeration station disperses in its cold scope, obtains exporting the m of cold ₁Item discrete function Q _n:

$Q_n=Q_{\min }+\frac{(n-1)(Q_{\max }-Q_{\min })}{m_1-1}$

Wherein:

Q _Min: the minimum refrigerating capacity that refrigeration station exports under typical condition

Q _Max: the specified refrigerating capacity that refrigeration station exports under typical condition

1≤n≤m ₁, and n, m ₁∈ N, Q _nThe accuracy of value is by m ₁Value determines that its minimum resolution is:

(2) wet-bulb temperature with outdoor environment disperses in the wet-bulb temperature working range of refrigeration station, obtains the m of wet-bulb temperature ₂Item discrete function TW _l:

${\mathrm{TW}}_l={\mathrm{TW}}_{\min }+\frac{(l-1)({\mathrm{TW}}_{\max }-{\mathrm{TW}}_{\min })}{m_2-1}$

Wherein:

TW _Min: the minimum wet-bulb temperature of the outdoor environment of refrigeration station work

TW _Max: the highest wet-bulb temperature of outdoor environment of refrigeration station work

1≤l≤m ₂, and l, m ₂∈ N, TW _lThe accuracy of value is by m ₂Value determines that its minimum resolution is:

The discrete function Q of (3) output colds _nDiscrete function TW with an outdoor environment wet-bulb temperature _lCorresponding refrigeration station initial operational parameter array, corresponding m altogether ₁* m ₂The individual initial operational parameter array (Q of refrigeration station _n, TW _l), to m ₁* m ₂The individual initial operational parameter array (Q of refrigeration station _n, TW _l) in the default initial value of each parameter, form the linked database model.

In the control method of above-mentioned central air-conditioning refrigeration station energy-saving control system, energy consumption self-optimizing control device receives linked database model parameter and each state parameter of linked database output in the step (4), carry out handling from optimizing, the concrete grammar that obtains best energy consumption operational factor is as follows:

(1) calculate the current output cold Q of refrigeration station by the refrigeration station running state parameter,

Q＝C·F _CH·ΔT＝C·F _CH·(T _CHI-T _CHO)

Wherein, F _CH: chilled-water flow, T _CHI: chilled water return water temperature, T _CHO: chilled water leaving water temperature, C: specific heat of water, Δ T: the temperature difference of chilled water backwater and water outlet;

(2) the energy loss-rate f of central air-conditioning refrigeration station optimizing starting point under the calculating current working _B,

$f_B={\mathrm{COP}}_B=\frac{Q}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+P_{\mathrm{CB}}+P_{\mathrm{TB}}},$

Wherein: P _WCU: handpiece Water Chilling Units power, P _CHB: chilled water pump power, P _CB: cooling water pump power, P _TB: blower fan of cooling tower power;

(3) establishing the optimizing step-length is Δ P, and calculating at chilled water pump power respectively is P _CHB± Δ P, cooling water pump power are P _CB± Δ P, blower fan of cooling tower power are P _TBCentral air-conditioning refrigeration station under the ± Δ P situation can loss-rate f ₁, f ₂, f ₃, f ₄, f ₅, f ₆, and the maximum of getting wherein is labeled as f _Max, wherein:

$f_1={\mathrm{COP}}_1=\frac{Q_n}{P_{\mathrm{WCU}}+(P_{\mathrm{CHB}}+\mathrm{\&Delta;P})+P_{\mathrm{CB}}+P_{\mathrm{TB}}};$

$f_2={\mathrm{COP}}_2=\frac{Q_n}{P_{\mathrm{WCU}}+(P_{\mathrm{CHB}}-\mathrm{\&Delta;P})+P_{\mathrm{CB}}+P_{\mathrm{TB}}};$

$f_3={\mathrm{COP}}_3=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+(P_{\mathrm{CB}}+\mathrm{\&Delta;P})+P_{\mathrm{TB}}};$

$f_4={\mathrm{COP}}_4=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+(P_{\mathrm{CB}}-\mathrm{\&Delta;P})+P_{\mathrm{TB}}};$

${\mathrm{<figure-callout\; id="5"\; label="f"\; filenames="HSA00000381206400021.png"\; state="\{\{state\}\}">f</figure-callout>}}_5={\mathrm{<figure-callout\; id="5"\; label="COP"\; filenames="HSA00000381206400021.png"\; state="\{\{state\}\}">COP</figure-callout>}}_5=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+P_{\mathrm{CB}}+(P_{\mathrm{TB}}+\mathrm{\&Delta;P})};$

$f_6={\mathrm{COP}}_6=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+P_{\mathrm{CB}}+(P_{\mathrm{TB}}-\mathrm{\&Delta;P})};$

(4) with the energy loss-rate f of optimizing starting point _BWith maximum energy loss-rate f _MaxCompare,

If f _Max＞f _B, then make f _B=f _Max, searching process is continued in repeating step (3), (4);

Otherwise, set a constant ε, 0＜ε＜1,

If | f _Max-f _B| 〉=ε then makes

Searching process is continued in repeating step (3), (4);

If | f _Max-f _B|＜ε then makes f _O=f _B, with f _OCorresponding operational factor from the optimizing operational factor, finishes searching process as the energy consumption of the best.

In the control method of above-mentioned central air-conditioning refrigeration station energy-saving control system, the rolling optimization controller receives the linked database model parameter of linked database output in the step (6), receive the best energy consumption operational factor of energy consumption self-optimizing control device output, the concrete grammar that carries out the rolling optimization computing is as follows:

(1) the rolling optimization controller receives the linked database model parameter of linked database output: the chilled water pump power P _CHB, the cooling water pump power P _CB, the blower fan of cooling tower power P _TB

(2) the rolling optimization controller receives the best energy consumption operational factor of energy consumption self-optimizing control device output: the chilled water pump power P _CHO, the cooling water pump power P _CO, the blower fan of cooling tower power P _TO

(3) chilled water pump power P _CHX, the cooling water pump power P _CX, the blower fan of cooling tower power P _TXFor finishing the parameter of rolling optimization computing, its computational methods are as follows:

$P_{\mathrm{CHX}}=\frac{(n-1)P_{\mathrm{CHB}}}{n}+\frac{P_{\mathrm{CHO}}}{n};$

$P_{\mathrm{CX}}=\frac{(n-1)P_{\mathrm{CB}}}{n}+\frac{P_{\mathrm{CO}}}{n};$

$P_{\mathrm{TX}}=\frac{(n-1)P_{\mathrm{TB}}}{n}+\frac{P_{\mathrm{TO}}}{n};$

(4) with parameter P _CHX, P _CX, P _TXReturn to linked database, upgrade linked database parameter P respectively _CHB, P _CB, P _TB

The present invention compared with prior art has following advantage:

(1) energy-saving control system of the present invention is by carrying out the linked database modeling to each main current consuming apparatus in the central air-condition freezing station, and adopt energy consumption self-optimizing control device to carry out energy consumption from searching process, obtain the optimization parameter of refrigeration station equipment operation, adopt the rolling optimization controller that linked database is carried out rolling optimization again, realize dynamically updating of linked database, make the variation that whole central air-conditioning refrigeration station can adaptation condition, always work in energy-efficient state;

(2) energy-saving control system of the present invention is at first by carrying out parametric optimization to the associated data database data, determine the current basic operating conditions of refrigeration station, pass through then in handpiece Water Chilling Units, chilled water pump, cooling water pump, installation power sensor in the main current consuming apparatus supply line such as cooling tower, monitor the operation power consumption situation of each main consumer, and by further online energy consumption from searching process, operational factor after each equipment energy consumption optimization is transferred to the cooling tower switch board, the cooling water pump switch board, the chilled water pump switch board, regulate handpiece Water Chilling Units, chilled water pump, cooling water pump, the control parameter of cooling tower, reach the minimum purpose of the whole energy consumption of refrigeration station, rather than a certain equipment energy consumption is minimum;

(3) the associated data database data in the central air-conditioning of the present invention refrigeration station control method is a rolling optimization, be that parameter optimization is not that disposable off-line is finished, but online repeatedly carry out, in central air-conditioning refrigeration station running, the chilled water flow quantity sensor, the cooling water flow quantity sensor, chilled water supply and return water temperature sensor, cooling water supply and return water temperature sensor, outdoor wet bulb temperature sensor, power sensor, the measurement data of differential pressure pickup is through online updating linked database behind the algorithm optimization, central air-conditioning refrigeration station device performance parameters can be adjusted along with the variation of equipment performance, aging or more behind the exchange device, this control method still can guarantee the energy-efficient operation at whole freezing station when equipment operation;

(4) control method of the present invention is a kind of central air-conditioning refrigeration station energy-saving control method with self-learning function, realization is to the monitoring and the optimization of central air-condition freezing station system operational parameters, improve the overall operation efficiency of system of central air-conditioning refrigeration station, reach energy-saving and cost-reducing effect, have stronger practicality.

Description of drawings

Fig. 1 is central air-conditioning of the present invention refrigeration station energy-saving control system schematic diagram;

Fig. 2 is that energy consumption self-optimizing control device of the present invention is from the optimizing workflow diagram.

The specific embodiment

The present invention is described in further detail below in conjunction with the drawings and specific embodiments:

Be illustrated in figure 1 as central air-conditioning of the present invention refrigeration station energy-saving control system schematic diagram, this central air-conditioning refrigeration station energy-saving control system comprises linked database, energy consumption self-optimizing control device, rolling optimization controller, signals collecting switch board, cooling tower switch board, coolant pump switch board, refrigerating water pump switch board, plurality of sensors and man-machine interface as seen from the figure.Sensor comprises temperature sensor, wet bulb temperature sensor, flow sensor, power sensor, specifically be divided into outdoor wet bulb temperature sensor, cooling water return water temperature sensor, cooling water leaving water temperature sensor, the cooling water flow quantity sensor, terminal differential pressure pickup, the chilled water flow quantity sensor, chilled water leaving water temperature sensor, chilled water return water temperature sensor etc., the cooling tower switch board is monitored the blower fan of cooling tower operation of central air-conditioning, the cooling water pump switch board is monitored the cooling water pump operation of central air-conditioning, and the chilled water pump switch board is monitored the chilled water pump operation of central air-conditioning.

Linked database carries out the linked database modeling of central air-conditioning refrigeration station, forms the linked database model, and concrete grammar is as follows:

(1) refrigerating capacity with the output of central air-conditioning refrigeration station disperses in its cold scope, obtains exporting the m of cold ₁Item discrete function Q _n:

$Q_n=Q_{\min }+\frac{(n-1)(Q_{\max }-Q_{\min })}{m_1-1}---\left(1\right)$

Wherein:

Q _Min: the minimum refrigerating capacity that refrigeration station exports under typical condition

Q _Max: the specified refrigerating capacity that refrigeration station exports under typical condition

1≤n≤m ₁, and n, m ₁∈ N, Q _nThe accuracy of value is by m ₁Value determines that its minimum resolution is:

(2) wet-bulb temperature with outdoor environment disperses in the wet-bulb temperature working range of refrigeration station, obtains the m of wet-bulb temperature ₂Item discrete function TW _l:

${\mathrm{TW}}_l={\mathrm{TW}}_{\min }+\frac{(l-1)({\mathrm{TW}}_{\max }-{\mathrm{TW}}_{\min })}{m_2-1}---\left(2\right)$

Wherein:

TW _Min: the minimum wet-bulb temperature of the outdoor environment of refrigeration station work

TW _Max: the highest wet-bulb temperature of outdoor environment of refrigeration station work

1≤l≤m ₂, and l, m ₂∈ N, TW _lThe accuracy of value is by m ₂Value determines that its minimum resolution is:

The discrete function Q of (3) output colds _nDiscrete function TW with an outdoor environment wet-bulb temperature _lCorresponding refrigeration station initial operational parameter array, corresponding m altogether ₁* m ₂The individual initial operational parameter array (Q of refrigeration station _n, TW _l), as to set the specified refrigerating capacity of a certain central air-conditioning refrigeration station be Q _Max, minimum refrigerating capacity is 10% of a specified refrigerating capacity, outdoor wet-bulb temperature working range is 23 ℃≤TW _l≤ 32 ℃, m ₁=m ₂=10, be that (n l), sets up linked database model such as following table 1 to D with the array representation in the linked database.

Table 1

To m ₁* m ₂The individual initial operational parameter array (Q of refrigeration station _n, TW _l) in the default initial value of each parameter, form the linked database model.

Each initial operational parameter array (Q of refrigeration station wherein _n, TW _l) comprise following parameter:

Chilled-water flow F _CH, chilled water return water temperature T _CHI, chilled water leaving water temperature T _CHO, the chilled water pump rotational speed omega _CH, the chilled water pump power P _CH, the terminal pressure differential deltap P of chilled water, cooling water flow F _C, cooling water return water temperature T _CI, cooling water leaving water temperature T _CO, the cooling water pump rotational speed omega _C, the cooling water pump power P _C, the blower fan of cooling tower rotational speed omega _T, the blower fan of cooling tower power P _T, the handpiece Water Chilling Units power P _WCU, the refrigeration station refrigeration efficiency compares COP.

Wherein to the chilled water pump power P _CH, the cooling water pump power P _CWith the blower fan of cooling tower power P _TOperational factor behind the default initial value is the chilled water pump power P _CHB, the cooling water pump power P _CBWith the blower fan of cooling tower power P _TB

The real-time received signal of linked database is gathered the state parameter of switch board output, linked database model parameter and each state parameter is exported to energy consumption self-optimizing control device carry out handling from optimizing; Also the linked database model parameter is exported to the rolling optimization controller simultaneously and carried out real-time rolling optimization computing, and receive the optimization parameter that the rolling optimization controller returns, finish the renewal of linked database model.

Energy consumption self-optimizing control device receives linked database model parameter and each state parameter of linked database output, receive the state parameter of cooling tower switch board, coolant pump switch board, refrigerating water pump switch board, central air-conditioning refrigeration station handpiece Water Chilling Units and the output of each electrically operated valve simultaneously, carry out handling from optimizing, obtain best energy consumption operational factor, be illustrated in figure 2 as energy consumption self-optimizing control device of the present invention from the optimizing workflow diagram, as follows from the optimizing detailed process:

(1) calculate the current output cold Q of refrigeration station by the refrigeration station running state parameter,

Q＝C·F _CH·ΔT＝C·F _CH·(T _CHI-T _CHO) (3)

Wherein, F _CH: chilled-water flow, T _CHI: chilled water return water temperature, T _CHO: chilled water leaving water temperature, C: specific heat of water, Δ T: the temperature difference of chilled water backwater and water outlet;

(2) with the discrete Q that turns to of Q _n, and with parameter array (Q in the linked database _n, TW _l) corresponding operational factor preset value exports to cooling tower switch board, coolant pump switch board, refrigerating water pump switch board, central air-conditioning refrigeration station handpiece Water Chilling Units and each electrically operated valve respectively.

Calculate (Q _n, TW _l) the energy loss-rate f of corresponding central air-conditioning refrigeration station optimizing starting point _B,

$f_B={\mathrm{COP}}_B=\frac{Q}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+P_{\mathrm{CB}}+P_{\mathrm{TB}}},$

Wherein: P _WCU: handpiece Water Chilling Units power, P _CHB: chilled water pump power, P _CB: cooling water pump power, P _TB: blower fan of cooling tower power;

(3) establishing the optimizing step-length is Δ P, and calculating at chilled water pump power respectively is P _CHB± Δ P, cooling water pump power are P _CB± Δ P, blower fan of cooling tower power are P _TBCentral air-conditioning refrigeration station under the ± Δ P situation can loss-rate f ₁, f ₂, f ₃, f ₄, f ₅, f ₆, and the maximum of getting wherein is labeled as f _Max, wherein:

$f_1={\mathrm{COP}}_1=\frac{Q_n}{P_{\mathrm{WCU}}+(P_{\mathrm{CHB}}+\mathrm{\&Delta;P})+P_{\mathrm{CB}}+P_{\mathrm{TB}}};$

$f_3={\mathrm{COP}}_3=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+(P_{\mathrm{CB}}+\mathrm{\&Delta;P})+P_{\mathrm{TB}}};$

$f_4={\mathrm{COP}}_4=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+(P_{\mathrm{CB}}-\mathrm{\&Delta;P})+P_{\mathrm{TB}}};$

${\mathrm{<figure-callout\; id="5"\; label="f"\; filenames="HSA00000381206400021.png"\; state="\{\{state\}\}">f</figure-callout>}}_5={\mathrm{<figure-callout\; id="5"\; label="COP"\; filenames="HSA00000381206400021.png"\; state="\{\{state\}\}">COP</figure-callout>}}_5=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+P_{\mathrm{CB}}+(P_{\mathrm{TB}}+\mathrm{\&Delta;P})};$

$f_6={\mathrm{COP}}_6=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+P_{\mathrm{CB}}+(P_{\mathrm{TB}}-\mathrm{\&Delta;P})};---\left(4\right)$

(4) with the energy loss-rate f of optimizing starting point _BWith maximum energy loss-rate f _MaxCompare,

If f _Max＞f _B, then make f _B=f _Max, searching process is continued in repeating step (3), (4);

Otherwise, set a constant ε, 0＜ε＜1,

If | f _Max-f _B| 〉=ε then makes

Searching process is continued in repeating step (3), (4);

If | f _Max-f _B|＜ε then makes f _O=f _B, with f _OCorresponding operational factor from the optimizing operational factor, finishes searching process as the energy consumption of the best.

For a specific example:

For example maximum is f in first round searching process ₅, and f ₅＞f _B, then make f _B=f ₅, promptly

Reenter step (3) and carry out second and take turns optimizing, calculate f ₁, f ₂, f ₃, f ₄, f ₅, f ₆, and the maximum of getting wherein is labeled as f _Max, at this moment:

$f_1={\mathrm{COP}}_1=\frac{Q_n}{P_{\mathrm{WCU}}+(P_{\mathrm{CHB}}+\mathrm{\&Delta;P})+P_{\mathrm{CB}}+(P_{\mathrm{TB}}+\mathrm{\&Delta;P})};$

$f_2={\mathrm{COP}}_2=\frac{Q_n}{P_{\mathrm{WCU}}+(P_{\mathrm{CHB}}-\mathrm{\&Delta;P})+P_{\mathrm{CB}}+(P_{\mathrm{TB}}+\mathrm{\&Delta;P})};$

$f_3={\mathrm{COP}}_3=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+(P_{\mathrm{CB}}+\mathrm{\&Delta;P})+(P_{\mathrm{TB}}+\mathrm{\&Delta;P})};$

$f_4={\mathrm{COP}}_4=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+(P_{\mathrm{CB}}-\mathrm{\&Delta;P})+(P_{\mathrm{TB}}+\mathrm{\&Delta;P})};$

${\mathrm{<figure-callout\; id="5"\; label="f"\; filenames="HSA00000381206400021.png"\; state="\{\{state\}\}">f</figure-callout>}}_5={\mathrm{<figure-callout\; id="5"\; label="COP"\; filenames="HSA00000381206400021.png"\; state="\{\{state\}\}">COP</figure-callout>}}_5=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+P_{\mathrm{CB}}+(P_{\mathrm{TB}}+2\mathrm{\&Delta;P})};$

$f_6={\mathrm{COP}}_6=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+P_{\mathrm{CB}}+P_{\mathrm{TB}}};---\left(5\right)$

If second takes turns f in the optimizing ₃Be maximum, and f ₃＞f _B, then make f _B=f ₃, promptly

Reenter step (3) and carry out the third round optimizing, calculate f ₁, f ₂, f ₃, f ₄, f ₅, f ₆, and the maximum of getting wherein is labeled as f _Max

Make ε=0.5, if f in the third round optimizing _Max≤ f _BAnd | f _Max-f _B| 〉=ε then makes

Repeating step (3) carries out the four-wheel optimizing, calculates f ₁, f ₂, f ₃, f ₄, f ₅, f ₆, and the maximum of getting wherein is labeled as f _Max

$f_1={\mathrm{COP}}_1=\frac{Q_n}{P_{\mathrm{WCU}}+(P_{\mathrm{CHB}}+\frac{1}{2}\mathrm{\&Delta;P})+(P_{\mathrm{CB}}+\mathrm{\&Delta;P})+(P_{\mathrm{TB}}+\mathrm{\&Delta;P})}$

$f_2={\mathrm{COP}}_2=\frac{Q_n}{P_{\mathrm{WCU}}+(P_{\mathrm{CHB}}-\frac{1}{2}\mathrm{\&Delta;P})+(P_{\mathrm{CB}}+\mathrm{\&Delta;P})+(P_{\mathrm{TB}}+\mathrm{\&Delta;P})};$

$f_3={\mathrm{COP}}_3=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+(P_{\mathrm{CB}}+\mathrm{\&Delta;P}+\frac{1}{2}\mathrm{\&Delta;P})+(P_{\mathrm{TB}}+\mathrm{\&Delta;P})}$

$f_4={\mathrm{COP}}_4=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+(P_{\mathrm{CB}}+\mathrm{\&Delta;P}-\frac{1}{2}\mathrm{\&Delta;P})+(P_{\mathrm{TB}}+\mathrm{\&Delta;P})};$

${\mathrm{<figure-callout\; id="5"\; label="f"\; filenames="HSA00000381206400021.png"\; state="\{\{state\}\}">f</figure-callout>}}_5={\mathrm{<figure-callout\; id="5"\; label="COP"\; filenames="HSA00000381206400021.png"\; state="\{\{state\}\}">COP</figure-callout>}}_5=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+(P_{\mathrm{CB}}+\mathrm{\&Delta;P})+(P_{\mathrm{TB}}+\mathrm{\&Delta;P}+\frac{1}{2}\mathrm{\&Delta;P})}$

$f_6={\mathrm{COP}}_6=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+(P_{\mathrm{CB}}+\mathrm{\&Delta;P})+(P_{\mathrm{TB}}+\mathrm{\&Delta;P}-\frac{1}{2}\mathrm{\&Delta;P})}---\left(6\right)$

If f in the four-wheel optimizing _Max≤ f _BAnd | f _Max-f _B|＜ε then makes f _O=f _B,

With f _OCorresponding operational factor from the optimizing operational factor, finishes searching process as the energy consumption of the best.

Searching process is exported to rolling optimization controller, cooling tower switch board, coolant pump switch board, refrigerating water pump switch board, central air-conditioning refrigeration station handpiece Water Chilling Units and each electrically operated valve respectively with best energy consumption operational factor after finishing.

The cooling tower switch board is exported to linked database in real time with the cooling tower running state parameter, and receives the best energy consumption operational factor of energy consumption self-optimizing control device output in real time, according to the operation of best energy consumption operational factor VFC cooling tower.

The coolant pump switch board is exported to linked database in real time with the coolant pump running state parameter, and receives the best energy consumption operational factor of energy consumption self-optimizing control device output in real time, according to the operation of best energy consumption operational factor VFC coolant pump.

The refrigerating water pump switch board is exported to linked database in real time with the refrigerating water pump running state parameter, and receives the best energy consumption operational factor of energy consumption self-optimizing control device output in real time, according to the operation of best energy consumption operational factor VFC refrigerating water pump.

The rolling optimization controller receives the linked database model parameter of linked database output, receive the best energy consumption operational factor of energy consumption self-optimizing control device output, carry out the rolling optimization computing in each searching process, the concrete grammar that carries out the rolling optimization computing is as follows:

(1) the rolling optimization controller receives the linked database model parameter of linked database output: the chilled water pump power P _CHB, the cooling water pump power P _CB, the blower fan of cooling tower power P _TB

(2) the rolling optimization controller receives the best energy consumption operational factor of energy consumption self-optimizing control device output: the chilled water pump power P _CHO, the cooling water pump power P _CO, the blower fan of cooling tower power P _TO

(3) chilled water pump power P _CHX, the cooling water pump power P _CX, the blower fan of cooling tower power P _TXFor finishing the parameter of rolling optimization computing, its computational methods are as follows:

$P_{\mathrm{CHX}}=\frac{(n-1)P_{\mathrm{CHB}}}{n}+\frac{P_{\mathrm{CHO}}}{n};$

$P_{\mathrm{CX}}=\frac{(n-1)P_{\mathrm{CB}}}{n}+\frac{P_{\mathrm{CO}}}{n};$

$P_{\mathrm{TX}}=\frac{(n-1)P_{\mathrm{TB}}}{n}+\frac{P_{\mathrm{TO}}}{n};---\left(7\right)$

(4) with parameter P _CHX, P _CX, P _TXReturn to linked database, upgrade linked database parameter P respectively _CHB, P _CB, P _TB

Water system operational factor and ambient parameter in the sensor measurement central air-conditioning refrigeration station, the above-mentioned parameter that signals collecting switch board pick-up transducers is measured, and above-mentioned parameter exported to linked database, its grey water system operational factor comprises the terminal pressure reduction of chilled-water flow, chilled water leaving water temperature, chilled water return water temperature, cooling water flow, cooling water leaving water temperature, cooling water return water temperature, handpiece Water Chilling Units power and chilled water, and ambient parameter comprises outdoor wet-bulb temperature.

Man-machine interface is used for operational factor to refrigeration station and component devices thereof and shows and manage that wherein operational factor comprises coefficient of energy dissipation parameter and running state parameter etc.

It is as follows to adopt above-mentioned central air-conditioning refrigeration station energy-saving control system to carry out the process of Energy Saving Control:

Step 1, linked database carry out the linked database modeling of central air-conditioning refrigeration station, form the linked database model.

Step 2, signals collecting switch board are gathered central air-conditioning refrigeration station grey water system operational factor and the ambient parameter that plurality of sensors is measured, and described parameter is exported to linked database;

The real-time received signal of step 3, linked database is gathered the state parameter of switch board output, linked database model parameter and described state parameter is exported to energy consumption self-optimizing control device carry out handling from optimizing; Also the linked database model parameter is exported to the rolling optimization controller simultaneously and carried out real-time rolling optimization computing.

Step 4, energy consumption self-optimizing control device receive linked database model parameter and each state parameter of linked database output, receive the state parameter of cooling tower switch board, coolant pump switch board, refrigerating water pump switch board, central air-conditioning refrigeration station handpiece Water Chilling Units and the output of each electrically operated valve simultaneously, carry out handling from optimizing, obtain best energy consumption operational factor, and described best energy consumption operational factor is exported to rolling optimization controller, cooling tower switch board, coolant pump switch board, refrigerating water pump switch board, central air-conditioning refrigeration station handpiece Water Chilling Units and each electrically operated valve respectively.

Step 5, cooling tower switch board receive the best energy consumption operational factor of energy consumption self-optimizing control device output in real time, according to the operation of best energy consumption operational factor VFC cooling tower; The coolant pump switch board receives the best energy consumption operational factor of energy consumption self-optimizing control device output in real time, according to the operation of best energy consumption operational factor VFC coolant pump; The refrigerating water pump switch board receives the best energy consumption operational factor of energy consumption self-optimizing control device output in real time, according to the operation of best energy consumption operational factor VFC refrigerating water pump.

Step 6, rolling optimization controller receive the linked database model parameter of linked database output, receive the best energy consumption operational factor of energy consumption self-optimizing control device output, in each searching process, carrying out the rolling optimization computing, and the parameter that will finish the rolling optimization computing returns to linked database.

Step 7, linked database receive the optimization parameter that the rolling optimization controller returns, and finish the renewal of linked database model.

Through above-mentioned steps existing monitoring and optimization to central air-condition freezing station system operational parameters, improve the overall operation efficiency of system of central air-conditioning refrigeration station, reach energy-saving and cost-reducing effect.

The above; only be the specific embodiment of the best of the present invention, but protection scope of the present invention is not limited thereto, anyly is familiar with those skilled in the art in the technical scope that the present invention discloses; the variation that can expect easily or replacement all should be encompassed within protection scope of the present invention.

The content that is not described in detail in the specification of the present invention belongs to this area professional and technical personnel's known technology.

Claims (9)

1. central air-conditioning refrigeration station energy-saving control system, it is characterized in that: comprise linked database, energy consumption self-optimizing control device, rolling optimization controller, signals collecting switch board, cooling tower switch board, coolant pump switch board, refrigerating water pump switch board and plurality of sensors, wherein:

Linked database: carry out the linked database modeling of central air-conditioning refrigeration station, form the linked database model, and in real time received signal is gathered the state parameter of switch board output, described linked database model parameter and described state parameter is exported to energy consumption self-optimizing control device carry out handling from optimizing; Also described linked database model parameter is exported to the rolling optimization controller simultaneously and carry out real-time rolling optimization computing, and receive the optimization parameter that the rolling optimization controller returns, finish the renewal of linked database model;

Energy consumption self-optimizing control device: the linked database model parameter and the described state parameter that receive linked database output, receive the state parameter of cooling tower switch board, coolant pump switch board, refrigerating water pump switch board, central air-conditioning refrigeration station handpiece Water Chilling Units and the output of each electrically operated valve simultaneously, carry out handling from optimizing, obtain best energy consumption operational factor, and described best energy consumption operational factor is exported to rolling optimization controller, cooling tower switch board, coolant pump switch board, refrigerating water pump switch board, central air-conditioning refrigeration station handpiece Water Chilling Units and each electrically operated valve respectively;

Rolling optimization controller: the linked database model parameter that receives linked database output, receive the best energy consumption operational factor of energy consumption self-optimizing control device output, in each searching process, carry out the rolling optimization computing, and the parameter that will finish the rolling optimization computing returns to linked database, finishes the renewal of linked database model;

Signals collecting switch board: gather central air-conditioning refrigeration station grey water system operational factor and ambient parameter that plurality of sensors is measured, and described parameter is exported to linked database;

Cooling tower switch board: the cooling tower running state parameter is exported to linked database in real time, and receive the best energy consumption operational factor of energy consumption self-optimizing control device output in real time, according to the operation of described best energy consumption operational factor VFC cooling tower;

Coolant pump switch board: the coolant pump running state parameter is exported to linked database in real time, and receive the best energy consumption operational factor of energy consumption self-optimizing control device output in real time, according to the operation of described best energy consumption operational factor VFC coolant pump;

Refrigerating water pump switch board: the refrigerating water pump running state parameter is exported to linked database in real time, and receive the best energy consumption operational factor of energy consumption self-optimizing control device output in real time, according to the operation of described best energy consumption operational factor VFC refrigerating water pump;

Sensor: be used to measure water system operational factor and ambient parameter in the central air-conditioning refrigeration station, and described parameter exported to the signals collecting switch board, its grey water system operational factor comprises the terminal pressure reduction of chilled-water flow, chilled water leaving water temperature, chilled water return water temperature, cooling water flow, cooling water leaving water temperature, cooling water return water temperature, handpiece Water Chilling Units power and chilled water, and ambient parameter comprises outdoor wet-bulb temperature.

2. a kind of central air-conditioning according to claim 1 refrigeration station energy-saving control system is characterized in that: describedly carry out the linked database modeling in linked database, the concrete grammar that forms the linked database model is as follows:

(1) refrigerating capacity with the output of central air-conditioning refrigeration station disperses in its cold scope, obtains exporting the m of cold ₁Item discrete function Q _n:

$Q_n=Q_{\min }+\frac{(n-1)(Q_{\max }-Q_{\min })}{m_1-1}$

Wherein:

Q _Min: the minimum refrigerating capacity that refrigeration station exports under typical condition

Q _Max: the specified refrigerating capacity that refrigeration station exports under typical condition

1≤n≤m ₁, and n, m ₁∈ N, Q _nThe accuracy of value is by m ₁Value determines that its minimum resolution is:

(2) wet-bulb temperature with outdoor environment disperses in the wet-bulb temperature working range of refrigeration station, obtains the m of wet-bulb temperature ₂Item discrete function TW _l:

${\mathrm{TW}}_l={\mathrm{TW}}_{\min }+\frac{(l-1)({\mathrm{TW}}_{\max }-{\mathrm{TW}}_{\min })}{m_2-1}$

Wherein:

TW _Min: the minimum wet-bulb temperature of the outdoor environment of refrigeration station work

TW _Max: the highest wet-bulb temperature of outdoor environment of refrigeration station work

1≤l≤m ₂, and l, m ₂∈ N, TW _lThe accuracy of value is by m ₂Value determines that its minimum resolution is:

The discrete function Q of (3) output colds _nDiscrete function TW with an outdoor environment wet-bulb temperature _lCorresponding refrigeration station initial operational parameter array, corresponding m altogether ₁* m ₂The individual initial operational parameter array (Q of refrigeration station _n, TW _l), to m ₁* m ₂The individual initial operational parameter array (Q of refrigeration station _n, TW _l) in the default initial value of each parameter, form the linked database model.

3. a kind of central air-conditioning according to claim 1 refrigeration station energy-saving control system, it is characterized in that: described energy consumption self-optimizing control device receives linked database model parameter and each state parameter of linked database output, carry out handling from optimizing, the concrete grammar that obtains best energy consumption operational factor is as follows:

(1) calculate the current output cold Q of refrigeration station by the refrigeration station running state parameter,

Q＝C·F _CH·ΔT＝C·F _CH·(T _CHI-T _CHO)

Wherein, F _CH: chilled-water flow, T _CHI: chilled water return water temperature, T _CHO: chilled water leaving water temperature, C: specific heat of water, Δ T: the temperature difference of chilled water backwater and water outlet;

(2) the energy loss-rate f of central air-conditioning refrigeration station optimizing starting point under the calculating current working _B,

$f_B={\mathrm{COP}}_B=\frac{Q}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+P_{\mathrm{CB}}+P_{\mathrm{TB}}},$

Wherein: P _WCU: handpiece Water Chilling Units power, P _CHB: chilled water pump power, P _CB: cooling water pump power, P _TB: blower fan of cooling tower power;

(3) establishing the optimizing step-length is Δ P, and calculating at chilled water pump power respectively is P _CHB± Δ P, cooling water pump power are P _CB± Δ P, blower fan of cooling tower power are P _TBCentral air-conditioning refrigeration station under the ± Δ P situation can loss-rate f ₁, f ₂, f ₃, f ₄, f ₅, f ₆, and the maximum of getting wherein is labeled as f _Max, wherein:

$f_1={\mathrm{COP}}_1=\frac{Q_n}{P_{\mathrm{WCU}}+(P_{\mathrm{CHB}}+\mathrm{\&Delta;P})+P_{\mathrm{CB}}+P_{\mathrm{TB}}};$

$f_2={\mathrm{COP}}_2=\frac{Q_n}{P_{\mathrm{WCU}}+(P_{\mathrm{CHB}}-\mathrm{\&Delta;P})+P_{\mathrm{CB}}+P_{\mathrm{TB}}};$

$f_3={\mathrm{COP}}_3=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+(P_{\mathrm{CB}}+\mathrm{\&Delta;P})+P_{\mathrm{TB}}};$

$f_4={\mathrm{COP}}_4=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+(P_{\mathrm{CB}}-\mathrm{\&Delta;P})+P_{\mathrm{TB}}};$

$f_5={\mathrm{COP}}_5=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+P_{\mathrm{CB}}+(P_{\mathrm{TB}}+\mathrm{\&Delta;P})};$

$f_6={\mathrm{COP}}_6=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+P_{\mathrm{CB}}+(P_{\mathrm{TB}}-\mathrm{\&Delta;P})};$

(4) with the energy loss-rate f of optimizing starting point _BWith maximum energy loss-rate f _MaxCompare,

If f _Max＞f _B, then make f _B=f _Max, searching process is continued in repeating step (3), (4);

Otherwise, set a constant ε, 0＜ε＜1,

If | f _Max-f _B| 〉=ε then makes Searching process is continued in repeating step (3), (4);

If | f _Max-f _B|＜ε then makes f _O=f _B, with f _OCorresponding operational factor from the optimizing operational factor, finishes searching process as the energy consumption of the best.

4. a kind of central air-conditioning according to claim 1 refrigeration station energy-saving control system, it is characterized in that: described rolling optimization controller receives the linked database model parameter of linked database output, receive the best energy consumption operational factor of energy consumption self-optimizing control device output, the concrete grammar that carries out the rolling optimization computing is as follows:

(1) the rolling optimization controller receives the linked database model parameter of linked database output: the chilled water pump power P _CHB, the cooling water pump power P _CB, the blower fan of cooling tower power P _TB

(2) the rolling optimization controller receives the best energy consumption operational factor of energy consumption self-optimizing control device output: the chilled water pump power P _CHO, the cooling water pump power P _CO, the blower fan of cooling tower power P _TO

(3) chilled water pump power P _CHX, the cooling water pump power P _CX, the blower fan of cooling tower power P _TXFor finishing the parameter of rolling optimization computing, its computational methods are as follows:

$P_{\mathrm{CHX}}=\frac{(n-1)P_{\mathrm{CHB}}}{n}+\frac{P_{\mathrm{CHO}}}{n};$

$P_{\mathrm{CX}}=\frac{(n-1)P_{\mathrm{CB}}}{n}+\frac{P_{\mathrm{CO}}}{n};$

$P_{\mathrm{TX}}=\frac{(n-1)P_{\mathrm{TB}}}{n}+\frac{P_{\mathrm{TO}}}{n};$

(4) with parameter P _CHX, P _CX, P _TXReturn to linked database, upgrade linked database parameter P respectively _CHB, P _CB, P _TB

5. a kind of central air-conditioning according to claim 1 refrigeration station energy-saving control system, it is characterized in that: also comprise man-machine interface, be used for operational factor to refrigeration station and component devices thereof and show and manage that wherein operational factor comprises coefficient of energy dissipation parameter and running state parameter.

6. the control method of a kind of central air-conditioning according to claim 1 refrigeration station energy-saving control system is characterized in that: comprise the steps:

(1) linked database carries out the linked database modeling of central air-conditioning refrigeration station, forms the linked database model;

(2) the signals collecting switch board is gathered central air-conditioning refrigeration station grey water system operational factor and the ambient parameter that plurality of sensors is measured, and described parameter is exported to linked database;

(3) the real-time received signal of linked database is gathered the state parameter of switch board output, linked database model parameter and described state parameter is exported to energy consumption self-optimizing control device carry out handling from optimizing; Also the linked database model parameter is exported to the rolling optimization controller simultaneously and carried out real-time rolling optimization computing;

(4) energy consumption self-optimizing control device receives the linked database model parameter and the described state parameter of linked database output, receive the state parameter of cooling tower switch board, coolant pump switch board, refrigerating water pump switch board, central air-conditioning refrigeration station handpiece Water Chilling Units and the output of each electrically operated valve simultaneously, carry out handling from optimizing, obtain best energy consumption operational factor, and described best energy consumption operational factor is exported to rolling optimization controller, cooling tower switch board, coolant pump switch board, refrigerating water pump switch board, central air-conditioning refrigeration station handpiece Water Chilling Units and each electrically operated valve respectively;

(5) the cooling tower switch board receives the best energy consumption operational factor of energy consumption self-optimizing control device output in real time, according to the operation of described best energy consumption operational factor VFC cooling tower; The coolant pump switch board receives the best energy consumption operational factor of energy consumption self-optimizing control device output in real time, according to the operation of described best energy consumption operational factor VFC coolant pump; The refrigerating water pump switch board receives the best energy consumption operational factor of energy consumption self-optimizing control device output in real time, according to the operation of described best energy consumption operational factor VFC refrigerating water pump;

(6) the rolling optimization controller receives the linked database model parameter of linked database output, receive the best energy consumption operational factor of energy consumption self-optimizing control device output, in each searching process, carrying out the rolling optimization computing, and the parameter that will finish the rolling optimization computing returns to linked database;

(7) linked database receives the optimization parameter that the rolling optimization controller returns, and finishes the renewal of linked database model.

7. the control method of a kind of central air-conditioning according to claim 6 refrigeration station energy-saving control system, it is characterized in that: linked database carries out the linked database modeling of central air-conditioning refrigeration station in the described step (1), and the concrete grammar that forms the linked database model is as follows:

(1) refrigerating capacity with the output of central air-conditioning refrigeration station disperses in its cold scope, obtains exporting the m of cold ₁Item discrete function Q _n:

$Q_n=Q_{\min }+\frac{(n-1)(Q_{\max }-Q_{\min })}{m_1-1}$

Wherein:

Q _Min: the minimum refrigerating capacity that refrigeration station exports under typical condition

Q _Max: the specified refrigerating capacity that refrigeration station exports under typical condition

1≤n≤m ₁, and n, m ₁∈ N, Q _nThe accuracy of value is by m ₁Value determines that its minimum resolution is:

(2) wet-bulb temperature with outdoor environment disperses in the wet-bulb temperature working range of refrigeration station, obtains the m of wet-bulb temperature ₂Item discrete function TW _l:

${\mathrm{TW}}_l={\mathrm{TW}}_{\min }+\frac{(l-1)({\mathrm{TW}}_{\max }-{\mathrm{TW}}_{\min })}{m_2-1}$

Wherein:

TW _Min: the minimum wet-bulb temperature of the outdoor environment of refrigeration station work

TW _Max: the highest wet-bulb temperature of outdoor environment of refrigeration station work

1≤l≤m ₂, and l, m ₂∈ N, TW _lThe accuracy of value is by m ₂Value determines that its minimum resolution is:

The discrete function Q of (3) output colds _nDiscrete function TW with an outdoor environment wet-bulb temperature _lCorresponding refrigeration station initial operational parameter array, corresponding m altogether ₁* m ₂The individual initial operational parameter array (Q of refrigeration station _n, TW _l), to m ₁* m ₂The individual initial operational parameter array (Q of refrigeration station _n, TW _l) in the default initial value of each parameter, form the linked database model.

8. the control method of a kind of central air-conditioning according to claim 6 refrigeration station energy-saving control system, it is characterized in that: energy consumption self-optimizing control device receives linked database model parameter and each state parameter of linked database output in the described step (4), carry out handling from optimizing, the concrete grammar that obtains best energy consumption operational factor is as follows:

(1) calculate the current output cold Q of refrigeration station by the refrigeration station running state parameter,

Q＝C·F _CH·ΔT＝C·F _CH·(T _CHI-T _CHO)

Wherein, F _CH: chilled-water flow, T _CHI: chilled water return water temperature, T _CHO: chilled water leaving water temperature, C: specific heat of water, Δ T: the temperature difference of chilled water backwater and water outlet;

(2) the energy loss-rate f of central air-conditioning refrigeration station optimizing starting point under the calculating current working _B,

$f_B={\mathrm{COP}}_B=\frac{Q}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+P_{\mathrm{CB}}+P_{\mathrm{TB}}},$

Wherein: P _WCU: handpiece Water Chilling Units power, P _CHB: chilled water pump power, P _CB: cooling water pump power, P _TB: blower fan of cooling tower power;

(3) establishing the optimizing step-length is Δ P, and calculating at chilled water pump power respectively is P _CHB± Δ P, cooling water pump power are P _CB± Δ P, blower fan of cooling tower power are P _TBCentral air-conditioning refrigeration station under the ± Δ P situation can loss-rate f ₁, f ₂, f ₃, f ₄, f ₅, f ₆, and the maximum of getting wherein is labeled as f _Max, wherein:

$f_1={\mathrm{COP}}_1=\frac{Q_n}{P_{\mathrm{WCU}}+(P_{\mathrm{CHB}}+\mathrm{\&Delta;P})+P_{\mathrm{CB}}+P_{\mathrm{TB}}};$

$f_2={\mathrm{COP}}_2=\frac{Q_n}{P_{\mathrm{WCU}}+(P_{\mathrm{CHB}}-\mathrm{\&Delta;P})+P_{\mathrm{CB}}+P_{\mathrm{TB}}};$

$f_3={\mathrm{COP}}_3=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+(P_{\mathrm{CB}}+\mathrm{\&Delta;P})+P_{\mathrm{TB}}};$

$f_4={\mathrm{COP}}_4=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+(P_{\mathrm{CB}}-\mathrm{\&Delta;P})+P_{\mathrm{TB}}};$

$f_5={\mathrm{COP}}_5=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+P_{\mathrm{CB}}+(P_{\mathrm{TB}}+\mathrm{\&Delta;P})};$

$f_6={\mathrm{COP}}_6=\frac{Q_n}{P_{\mathrm{WCU}}+P_{\mathrm{CHB}}+P_{\mathrm{CB}}+(P_{\mathrm{TB}}-\mathrm{\&Delta;P})};$

(4) with the energy loss-rate f of optimizing starting point _BWith maximum energy loss-rate f _MaxCompare,

If f _Max＞f _B, then make f _B=f _Max, searching process is continued in repeating step (3), (4);

Otherwise, set a constant ε, 0＜ε＜1,

If | f _Max-f _B| 〉=ε then makes

Searching process is continued in repeating step (3), (4);

If | f _Max-f _B|＜ε then makes f _O=f _B, with f _OCorresponding operational factor from the optimizing operational factor, finishes searching process as the energy consumption of the best.

9. the control method of a kind of central air-conditioning according to claim 6 refrigeration station energy-saving control system, it is characterized in that: the rolling optimization controller receives the linked database model parameter of linked database output in the described step (6), receive the best energy consumption operational factor of energy consumption self-optimizing control device output, the concrete grammar that carries out the rolling optimization computing is as follows:

(1) the rolling optimization controller receives the linked database model parameter of linked database output: the chilled water pump power P _CHB, the cooling water pump power P _CB, the blower fan of cooling tower power P _TB

(2) the rolling optimization controller receives the best energy consumption operational factor of energy consumption self-optimizing control device output: the chilled water pump power P _CHO, the cooling water pump power P _CO, the blower fan of cooling tower power P _TO

(3) chilled water pump power P _CHX, the cooling water pump power P _CX, the blower fan of cooling tower power P _TXFor finishing the parameter of rolling optimization computing, its computational methods are as follows:

$P_{\mathrm{CHX}}=\frac{(n-1)P_{\mathrm{CHB}}}{n}+\frac{P_{\mathrm{CHO}}}{n};$

$P_{\mathrm{CX}}=\frac{(n-1)P_{\mathrm{CB}}}{n}+\frac{P_{\mathrm{CO}}}{n};$

$P_{\mathrm{TX}}=\frac{(n-1)P_{\mathrm{TB}}}{n}+\frac{P_{\mathrm{TO}}}{n};$

(4) with parameter P _CHX, P _CX, P _TXReturn to linked database, upgrade linked database parameter P respectively _CHB, P _CB, P _TB

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