CN1255653C - Method for fuzzy expected controlling cold water system of central air conditioner - Google Patents

Method for fuzzy expected controlling cold water system of central air conditioner Download PDF

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CN1255653C
CN1255653C CN 200410040667 CN200410040667A CN1255653C CN 1255653 C CN1255653 C CN 1255653C CN 200410040667 CN200410040667 CN 200410040667 CN 200410040667 A CN200410040667 A CN 200410040667A CN 1255653 C CN1255653 C CN 1255653C
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蔡小兵
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GUIZHOU HUITONG HUACHENG CO., LTD.
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GUIZHOU HUICHENG SCIENCE AND TECHNOLOGY Co Ltd
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Abstract

The present invention discloses a fuzzy anticipation control method and a device of a cooling water system of a central air conditioner, which belongs to the technology for controlling the energy saving of the air conditioner. The present invention aims to provide an energy-saving control method and a device of the central air conditioner. The method comprises operation parameter collection, data processing, optimized correction value prediction, fuzzy reasoning, and control variable output and control. By collecting the operation parameters of the temperature, the temperature difference, the flow rate, the pressure difference, etc. of supply water and return water of the cooling water system, the average values of corresponding operation parameters for n periods are computed. According to the variation trend of the average values, optimized correction values are predicted, and then, optimizing control quantity can be obtained through the fuzzy reasoning; the rotation speed of a refrigeration water pump is controlled through a frequency converter. The device comprises a fuzzy controller, the frequency converter, a water temperature sensor, a flow meter, a pressure difference sensor of water flows, and an intelligent control unit, wherein the sensor and the flow meter are connected with the intelligent control unit; the water pump is connected with the intelligent control unit through the frequency converter; the intelligent control unit is connected with the fuzzy controller.

Description

Freezing water system of central air conditioner fuzzy expection control method and device
Technical field: the present invention relates to a kind of energy-saving control method of air conditioning water system, relate in particular to the fuzzy expection of a kind of freezing water system of central air conditioner control method; The invention still further relates to a kind of device of realizing this method.
Background technology: central air conditioner system is widely adopted in modern heavy construction, is one of facility of energy consumption maximum in the modern building.Increasingly serious along with the world energy sources supply and demand, people more and more pay attention to the exploitation and the application of central air conditioner system energy-conserving control technology.
In recent years, in order to reduce the energy waste of central air conditioner system, people begin to adopt universal frequency converter to control the chilled water pump of air-conditioning system.Control method commonly used has PID or PI control, and its control parameter is the temperature or the pressure of chilled water, i.e. constant difference or constant-pressure drop control; Control is according to being the operational parameter value in the current a certain moment, and control mode is for following control.This PID or PI control based on mathematical models, principle is simple, easy to use, cost is lower, also can reach certain energy-saving effect, but its limitation is:
A. central air-conditioning be a kind of time lag, the time become, the very strong complication system of coupling between non-linear, many reference amounts and parameter, be difficult for obtaining more accurate Mathematical Modeling, approximate or coarse model is difficult to achieve effective control;
During B.PID regulates, proportionality coefficient K P, integration time constant T IWith derivative time constant T d, can not follow the variation of controlled parameter and adjust automatically in case after selected, if the people does not go to regulate, it is changeless etc. most important engineering parameter.That is to say, after engineering parameter is adjusted, just remove to tackle various operating condition, therefore can not reach best energy-saving effect with same kind of parameter.Its analysis of causes is as follows:
Central air conditioner system realizes the process of refrigeration, with chilled water as carrying cold medium in the realization that constantly circulates between air conditioner refrigerating device evaporimeter and the end equipments such as fan coil, air-conditioning unit.Because heavy construction air-conditioning area is big, water-system pipeline by air complex structure, path length, the water yield of possessing of chilled water system reaches hundreds of m 3, cause its thermal capacity and thermal inertia very big, be a typical big inertia system; On the other hand, be tens of minutes owing to finish a chilled water circulation required time, so it is again a Large-lag System.For this big inertia, large time delay and have dynamic mutation and highly nonlinear system, the maximum difficult point of control is to control the uncertainty and the height mutability of effect.Mainly show as: after control instruction is sent, response that water cycle period does not finish as yet or controlled parameter also is not able to do in time, variation may just take place again in system running environment or part throttle characteristics; After energy-saving control device collects new variation, can send new control instruction again, thereby cause the disorderly or vibration of system's operation, the chilled water return water temperature is fluctuateed in a big way, the long-time stable state that all can not reach setting is difficult to obtain desired control effect.
Summary of the invention: in order to overcome the deficiency that existing PID or PI energy-conserving control technology exist, the present invention aims to provide the fuzzy expection of a kind of freezing water system of central air conditioner control method; This method can effectively solve owing to chilled water system self characteristics such as intrinsic big inertia, large time delay cause the difficult problem of the disorderly or vibration of system's operation, realize the accurate control of chilled water return water temperature, thereby both can realize accurate control to air conditioning terminal service quality, can excavate the delivery energy saving space of chilled water system again to greatest extent, reach best energy-saving effect.The present invention also aims to provide a kind of device of realizing this method.
Technical scheme of the present invention comprises that correction valve prediction, fuzzy reasoning are handled, optimized to operational factor collection, data, controlled quentity controlled variable is exported and control; By collection to chilled water system supply and return water temperature, the temperature difference, flow, four operational factors of pressure reduction, calculate the n cycle mean value of corresponding operational factor, predict its optimization correction value according to the variation tendency of this mean value, then by the fuzzy reasoning controlled quentity controlled variable that is optimized, and, chilled-water flow is carried out corresponding adjusting by Frequency Converter Control chilled water pump rotating speed;
Concrete grammar is as follows:
1. operational factor collection is gathered the supply and return water temperature of chilled water system, the flow in the water supply line, these four operational factors of current pressure reduction between the confession water return pipeline by the cooling-water temperature sensor that is installed in central air conditioner system low-temperature receiver side, flowmeter and current differential pressure pickup;
2. data are handled, and intelligent control unit the air conditioning terminal k period according to the above-mentioned data computation of being gathered, i.e. required refrigerating capacity of current period:
Q k＝λ·w·ΔT
In the formula, Q k---k period, the i.e. required refrigerating capacity of air conditioning terminal of current period;
λ---with relevant constants such as chilled water specific heat, density;
W---chilled-water flow collection value;
Δ T---the temperature difference collection value between chilled water confession, the backwater;
Calculate the mean value of above-mentioned four operational factors of chilled water in nearest n chilled water cycle period simultaneously; Described mean value is the addition gained and that get divided by the number n in the cycle one by one value of the value of relevant parameters in nearest n chilled water cycle period; Its formula is:
x nk - = Σ i = 1 n x i n
In the formula,
Figure C20041004066700081
---certain parameter k period, i.e. the n cycle mean value of current period;
x i---certain parameter is in the value in i cycle;
N---the number of chilled water cycle period, n is 〉=5 integer;
Intelligent control unit is with required refrigerating capacity Q kDynamically update and send to fuzzy controller with the n cycle mean value of described four operational factors;
3. optimize correction valve prediction, fuzzy controller calculates it and optimizes correction value---deviation E according to the n cycle mean value of Δ T kAnd deviation variation rate
Figure C20041004066700082
E k = ΔT ‾ nk - Δ T o
In the formula, E k---k period, the i.e. temperature difference deviation of chilled water system of current period;
---k period, i.e. the n cycle mean temperature difference value of chilled water system of current period;
Δ T 0---the temperature difference setting value of chilled water system;
E · k = E k - E k - 1
In the formula,
Figure C20041004066700086
Period, the i.e. rate of change of the chilled water system temperature difference deviation of current period:
E K-1---the temperature difference deviation of the preceding 1 period chilled water system of k;
4. fuzzy reasoning, fuzzy controller is optimized correction value, i.e. deviation E with temperature difference T kAnd deviation variation rate Carry out the A/D conversion and make Fuzzy processing, obtain fuzzy control quantity u through fuzzy reasoning again k, this fuzzy control quantity is carried out sharpening processing and D/A conversion, the controlled quentity controlled variable that is optimized U kAnd pass to intelligent control unit;
Deviation E k, deviation variation rate
Figure C20041004066700088
With optimal control amount u kFuzzy subset's domain get:
{ E k } = { E · k } = { u k } = { - 3 , - 2 , - 1,0,1,2,3 }
Control law is expressed as:
u k = - [ α E k + ( 1 - α ) E · k ] E k＝±1，0
u k = - [ β E k + ( 1 - β ) E · k ] E k＝±2，±3
In the formula, α, β ∈ (0,1)
5. the output of controlled quentity controlled variable and control, intelligent control unit is with optimal control amount U kConvert the control signal of frequency converter to, regulate chilled water water circulating pump rotating speed to change the flow of chilled water system by frequency converter.
In order to realize said method, device of the present invention comprises fuzzy controller, frequency converter, cooling-water temperature sensor, flowmeter, current differential pressure pickup and the intelligent control unit that is made of control processor circuit, memory circuitry, network communication interface circuit, simulated measurement input circuit, analogue quantity output circuit and power circuit; The current differential pressure pickup is installed at two ends at by-passing valve, in the water side of secondary water pump flowmeter is installed, and flowmeter is installed on bypass pipe, and cooling-water temperature sensor, backwater end installation cooling-water temperature sensor are installed in the chilled water water side of air conditioner refrigerating unit; Current differential pressure pickup, flowmeter, cooling-water temperature sensor are connected with intelligent control unit by order wire respectively; No. one time water pump is connected with intelligent control unit respectively with order wire by frequency converter with the secondary water pump, and by-passing valve is connected with intelligent control unit by order wire; Intelligent control unit is connected with fuzzy controller by order wire respectively.
Compared with the prior art, the operational parameter value addition of continuous n the chilled water cycle period that the present invention will collect draws corresponding mean value, has reflected the mean parameter in some cycles.Because the parameter of institute's statistical computation so the mean parameter that calculates also is a dynamic parameter, can reflect the variation of process along with process moves preferably; On the other hand, owing to gathered the parameter value in n cycle, because of its accumulated time is far longer than the time in 1 cycle, so can eliminate the harmful effect of system's inertia and hysteresis preferably.
The present invention has significant effect and practical significance for the stability and the adaptive capacity of the energy-conservation and raising system operation that realizes freezing water system of central air conditioner.
Description of drawings:
Fig. 1 is the fuzzy expection of a freezing water system of central air conditioner control device schematic diagram;
Fig. 2 is the circuit diagram of intelligent control unit;
Fig. 3 is a chilled water return water temperature variation diagram in the prior art;
Fig. 4 is a chilled water return water temperature variation diagram of the present invention.
In the accompanying drawing: No. 10 water pumps of fuzzy controller 1 intelligent control unit, 2,3 frequency converter 4 by-passing valves, 5 current differential pressure pickup 6 flowmeters, 7,13 secondary water pump 8 cooling-water temperature sensors, 9,11 air conditioner refrigerating units 12 control processor circuits 14 memory circuitries 15 network communication interface circuit 16 simulated measurement input circuits 17 analogue quantity output circuits 18 power circuits 19
The specific embodiment: the invention will be further described below in conjunction with accompanying drawing and specific embodiment:
The fuzzy expection of freezing water system of central air conditioner provided by the present invention control method is achieved in that
1. operational factor collection, when the air conditioning terminal load variations, the operational factors such as current pressure reduction between the supply and return water temperature in the chilled water system, water supply flow and the confession water return pipeline are gathered by the cooling-water temperature sensor, flowmeter and the differential pressure pickup that are installed in the chilled water system.
2. data are handled, and intelligent control unit the air conditioning terminal k period, promptly current required refrigerating capacity according to the data computation of being gathered:
Q k＝λ·w·ΔT
In the formula, Q k---k period, the required refrigerating capacity of promptly current air conditioning terminal;
λ---with relevant constants such as chilled water specific heat, density:
W---chilled-water flow collection value;
Δ T---the temperature difference collection value between chilled water confession, the backwater:
Calculate the mean values of operational factor in nearest n chilled water cycle period such as the chilled water temperature difference, pressure reduction or flow simultaneously.Described mean value is the addition gained and that get divided by the number n in the cycle one by one value of the value of relevant parameters in nearest n chilled water cycle period: its mathematical formulae is:
x ‾ nk = Σ i = 1 n x i n
In the formula, ---the n cycle mean value of certain parameter k period (promptly current);
x i---certain parameter is in the value in i cycle;
N---the number of chilled water cycle period, n is 〉=5 integer;
Can obtain the relevant operational factor of chilled water with said method, as: n cycle mean values such as the temperature difference, pressure reduction or flow.As time goes on, these n cycle mean values also will be along with change.Therefore be also referred to as n cycle moving average; Certainly, also it can be indicated in respectively with time is on the figure of abscissa or in the form, obtains the Moving Average or the rolling average table of relevant parameter.Intelligent control unit is with required refrigerating capacity Q kDynamically update and send to fuzzy controller with the n cycle moving average of relevant operational factor.
8. optimize correction valve prediction, at required refrigerating capacity Q kUnder the situation about determining,, can regulate, promptly be optimized correction the operational factor of chilled water system in order to seek the maximum energy-saving running status.Because the required refrigerating capacity Q of air conditioning terminal kBe the function of flow w and temperature difference T: Q 0=f (w, Δ T) therefore, can realize the energy-saving run of chilled water system by the reasonable adjusting to flow W and temperature difference T; Simultaneously, in order to realize accurate control, can realize: for this reason, choose Δ T as the control parameter by control temperature difference T to the chilled water return water temperature.Fuzzy controller calculates its optimization correction value, that is: deviation E according to the n cycle moving average of Δ T kAnd deviation variation rate
Figure C20041004066700121
E k = ΔT ‾ nk - ΔT 0
In the formula, E k---k period, the i.e. temperature difference deviation of current chilled water system;
---k period, i.e. the n cycle rolling average temperature approach of current chilled water system;
Δ T 0---the temperature difference setting value of chilled water system.
E · k = E k - E k - 1
In the formula,
Figure C20041004066700125
Period, the rate of change of promptly current chilled water system temperature difference deviation;
E K-1---the temperature difference deviation of the preceding 1 period chilled water system of k.
Work as E k＞0, the expression k period, promptly the n cycle rolling average temperature approach of current chilled water system is greater than setting value;
Work as E k＜0, the expression k period, promptly the n cycle rolling average temperature approach of current chilled water system is less than setting value:
Work as E k=0, the expression k period, promptly the n cycle rolling average temperature approach of current chilled water system equals setting value;
The time length of described period is got the several times in the sampling period or the cycle of system operational parameters.
4. fuzzy reasoning, fuzzy controller is optimized correction value, i.e. deviation E with temperature difference T kAnd deviation variation rate Carry out A/D conversion and Fuzzy processing, obtain fuzzy control quantity u through fuzzy reasoning again k, this fuzzy control quantity is carried out sharpening processing and D/A conversion, the controlled quentity controlled variable that is optimized U kAnd pass to intelligent control unit.In order to realize the self-adjusting of fuzzy control rule in the fuzzy controller, system is provided with two and adjusts factor-alpha and β, can be implemented under the different conditions deviation E in the control law kAnd deviation variation rate
Figure C20041004066700132
The adjustment of weighting degree.As deviation E kHour, system is near stable state, and the main task of controller is to reduce overshoot to make system stable as early as possible; Just require deviation variation rate in the control law
Figure C20041004066700133
Effect big, promptly to deviation variation rate Weighting more greatly, control law is adjusted by α; As deviation E kWhen big, the main task of controller is to eliminate deviation, at this moment deviation E kWeighting in control law should be adjusted by β greatly, can eliminate deviation as early as possible.
Deviation E k, deviation variation rate With optimal control amount u kFuzzy subset's domain get:
{ E k } = { E · k } = { u k } = { - 3 , - 2 , - 1,0,1,2,3 }
Control law is expressed as:
u k = - [ α E k + ( 1 - α ) E · k ] E k＝±1，0
u k = - [ β E k + ( 1 - β ) E · k ] E k＝±2，±3
In the formula, α, β ∈ (0,1)
5. the output of controlled quentity controlled variable and control is with optimal control amount U kConvert the control signal of frequency converter to, regulate chilled water water circulating pump rotating speed to change the flow of chilled water system, to satisfy the required refrigerating capacity Q of air conditioning terminal by frequency converter kNeeds, realize the accurate control of chilled water return water temperature simultaneously, make chilled water system be in optimum capacity dispensing duty all the time, realize energy-saving and cost-reducing purpose.
The principle of device of the fuzzy expection of realization freezing water system of central air conditioner provided by the present invention control method is shown in Fig. 1～2, current differential pressure pickup 6 is installed at two ends at by-passing valve 5, flowmeter 7 is installed in water side at secondary water pump 8, flowmeter 13 is installed on bypass pipe, cooling-water temperature sensor 9,11 is installed respectively at the water side and the backwater end of air conditioner refrigerating unit 10 chilled waters.The sensor is used to gather operational factors such as water temperature, flow, pressure reduction.Current differential pressure pickup 6, flowmeter 7 and 13, cooling-water temperature sensor 9 are connected with intelligent control unit 2 by order wire respectively with 11; No. one time water pump 12, secondary water pump 8 are connected with intelligent control unit 3 respectively with order wire by frequency converter 4, and by-passing valve 5 is connected with intelligent control unit 3 by order wire. Intelligent control unit 2,3 is connected with fuzzy controller 1 respectively by order wire. Intelligent control unit 2,3 constitutes by control processor circuit 14, memory circuitry 15, network communication interface circuit 16, simulated measurement input circuit 17, analogue quantity output circuit 18 and power circuit 19.
As shown in Figure 3, Figure 4, the present invention can realize the accurate control to the chilled water return water temperature, and the return water temperature fluctuations amplitude of chilled water is less; And PID or PI controlling party rule can not realize the accurate control to the chilled water return water temperature, and the return water temperature fluctuations amplitude of chilled water is bigger.

Claims (2)

1, the fuzzy expection of a kind of freezing water system of central air conditioner control method comprises that correction valve prediction, fuzzy reasoning are handled, optimized to operational factor collection, data, controlled quentity controlled variable is exported and control; It is characterized in that: by collection chilled water system supply and return water temperature, the temperature difference, flow, four operational factors of pressure reduction, calculate the n cycle mean value of corresponding operational factor, predict its optimization correction value according to the variation tendency of this mean value, then by the fuzzy reasoning controlled quentity controlled variable that is optimized, and, chilled-water flow is carried out corresponding adjusting by Frequency Converter Control chilled water pump rotating speed; Concrete grammar is as follows:
1. operational factor collection is gathered the supply and return water temperature of chilled water system, the flow in the water supply line, these four operational factors of current pressure reduction between the confession water return pipeline by the cooling-water temperature sensor that is installed in central air conditioner system low-temperature receiver side, flowmeter and current differential pressure pickup;
2. data are handled, and intelligent control unit the air conditioning terminal k period according to the above-mentioned data computation of being gathered, i.e. required refrigerating capacity of current period:
Q k＝λ·w·ΔT
In the formula, Q k---k period, the i.e. required refrigerating capacity of air conditioning terminal of current period;
λ---with relevant constants such as chilled water specific heat, density;
W---chilled-water flow collection value;
Δ T---the temperature difference collection value between chilled water confession, the backwater;
Calculate the mean value of above-mentioned four operational factors of chilled water in nearest n chilled water cycle period simultaneously; Described mean value is the addition gained and that get divided by the number n in the cycle one by one value of the value of relevant parameters in nearest n chilled water cycle period; Its formula is:
x nk ‾ = Σ i = 1 n x i n
In the formula,
Figure C2004100406670003C2
---certain parameter k period, i.e. the n cycle mean value of current period;
x i---certain parameter is in the value in i cycle;
N---the number of chilled water cycle period, n is 〉=5 integer;
Intelligent control unit is with required refrigerating capacity Q kDynamically update and send to fuzzy controller with the n cycle mean value of described four operational factors;
3. optimize correction valve prediction, fuzzy controller calculates it and optimizes correction value---deviation E according to the n cycle mean value of Δ T kAnd deviation variation rate
E k = ΔT nk ‾ - Δ T 0
In the formula, E k---k period, the i.e. temperature difference deviation of chilled water system of current period;
---k period, i.e. the n cycle mean temperature difference value of chilled water system of current period;
Δ T 0---the temperature difference setting value of chilled water system;
E · k = E k - E k - 1
In the formula,
Figure C2004100406670003C7
---k period, the i.e. rate of change of the chilled water system temperature difference deviation of current period;
E K-1---the temperature difference deviation of the preceding 1 period chilled water system of k;
4. fuzzy reasoning, fuzzy controller is optimized correction value, i.e. deviation E with temperature difference T kAnd deviation variation rate Carry out the A/D conversion and make Fuzzy processing, obtain fuzzy control quantity u through fuzzy reasoning again k, this fuzzy control quantity is carried out sharpening processing and D/A conversion, the controlled quentity controlled variable that is optimized U kAnd pass to intelligent control unit;
Deviation E k, deviation variation rate With optimal control amount u kFuzzy subset's domain get:
{ E k } = { E · k } = { u k } = { - 3 , - 2 , - 1,0,1,2,3 }
Control law is expressed as:
u k = - [ α E k + ( 1 - α ) E · k ] E k＝±1，0
u k = - [ β E k + ( 1 - β ) E · k ] E k＝±2，±3
In the formula, α, β ∈ (0,1)
5. the output of controlled quentity controlled variable and control, intelligent control unit is with optimal control amount U kConvert the control signal of frequency converter to, regulate chilled water water circulating pump rotating speed to change the flow of chilled water system by frequency converter.
2, a kind of device of realizing the fuzzy expection of the described freezing water system of central air conditioner of claim 1 control method comprises fuzzy controller, frequency converter, cooling-water temperature sensor, flowmeter, current differential pressure pickup and the intelligent control unit that is made of control processor circuit, memory circuitry, network communication interface circuit, simulated measurement input circuit, analogue quantity output circuit and power circuit; It is characterized in that: current differential pressure pickups (6) are installed at the two ends at by-passing valve (5), flowmeter (7) is installed in water side at secondary water pump (8), flowmeter (13) is installed on bypass pipe, cooling-water temperature sensor (9), backwater end installation cooling-water temperature sensor (11) are installed in the chilled water water side of air conditioner refrigerating unit (10); Current differential pressure pickup (6), flowmeter (7) and (13), cooling-water temperature sensor (9) are connected with intelligent control unit (2) by order wire respectively with (11); A water pump (12) is connected with intelligent control unit (3) respectively with order wire by frequency converter (4) with secondary water pump (8), and by-passing valve (5) is connected with intelligent control unit (3) by order wire; Intelligent control unit (2), (3) are connected with fuzzy controller (1) by order wire respectively.
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