阅读说明：本技术 一种空调冷却水系统控制方法及装置 (Control method and device for air conditioner cooling water system ) 是由 宋阳光 王志强 朱小磊 冯三龙 于 2021-09-30 设计创作，主要内容包括：本发明涉及中央空调冷却水系统节能控制技术领域,公开了一种空调冷却水系统控制方法及装置,包括S1：启动默认数量的冷热源主机、冷却水循环泵和冷却塔；S2：记录累计运行时间t；S3：判断设备运行状态是否有变化,若是,则重新执行S2,若否,则执行S4；S4：采集关键参数；S5：判断历史数据中是否存在与S4中采集的数据相同的数据,若是,则执行S6,若否,则执行S7；S6：根据历史数据确定最优控制参数；S7：确定冷却水散热量和冷却水系统设备能耗；S8：确定最优控制参数；S9：根据最优控制参数调节各设备的运行状态；该方法经过多参数、多变量、拟合函数关系式分析得到最优控制参数,大大降低空调系统整体能源消耗。(The invention relates to the technical field of energy-saving control of a cooling water system of a central air conditioner, and discloses a control method and a control device of the cooling water system of the air conditioner, which comprise the following steps of S1: starting up the default number of cold and heat source hosts, the cooling water circulating pumps and the cooling tower; s2: recording the accumulated running time t; s3: judging whether the running state of the equipment is changed, if so, executing S2 again, and if not, executing S4; s4: collecting key parameters; s5: judging whether the historical data has the same data as the data collected in the S4, if so, executing S6, and if not, executing S7; s6: determining optimal control parameters according to historical data; s7: determining the heat dissipation capacity of cooling water and the energy consumption of equipment of a cooling water system; s8: determining an optimal control parameter; s9: adjusting the running state of each device according to the optimal control parameters; the method obtains optimal control parameters through multi-parameter, multivariable and fitting function relational expression analysis, and the overall energy consumption of the air conditioning system is greatly reduced.)

1. A control method for an air conditioner cooling water system is characterized by comprising the following steps:

s1: starting a default number of cold and heat source hosts, cooling water circulating pumps and cooling towers, wherein the cooling tower circulating pumps and fans in the cooling towers run at a default frequency;

s2: recording the accumulated running time t;

s3: judging whether the running state of the equipment is changed, if so, executing S2 again, and if not, executing S4; the running state of the equipment comprises the number of running chilled water circulating pumps, the number of running cooling water circulating pumps and the number of running cold and heat source units;

s4: the accumulated running time t reaches the default running time t_MRIn time, key parameters are collected, wherein the key parameters comprise outdoor air real-time parameter data and an air conditioning systemThe system comprises an air conditioning system, a cooling water system and a control system, wherein the air conditioning system comprises a cooling water system and a cooling water system, the air conditioning system comprises an air conditioner, a cooling water system and a control system, the air conditioner comprises equipment running state data and cooling water system equipment configuration information data, the outdoor air real-time parameter data comprise dry bulb temperature and relative humidity of outdoor air, the air conditioning system equipment running state data comprise start-stop states and load rates of cold and heat source hosts, fans in a cooling tower, cooling water pumps and cooling water pumps, water inlet temperature and outlet temperature of cooling water, water inlet temperature and outlet temperature of chilled water and actual frequency of the cooling water pumps, and the cooling water system equipment configuration information data comprise serial numbers and actual frequency of the started cooling water pumps and fans in a cold area tower;

s5: comparing the data collected in the step S4 with the historical data, judging whether the historical data has the same data as the data collected in the step S4, if so, executing the step S6, and if not, executing the step S7;

s6: determining optimal control parameters according to the optimal control parameters corresponding to the same data in the historical data;

s7: determining the heat dissipation capacity of the cooling water and the energy consumption of the cooling water system equipment according to a preset prediction model of the heat dissipation capacity of the cooling water and a preset prediction model of the energy consumption of the cooling water system equipment;

s8: determining optimal control parameters according to the heat dissipation capacity of cooling water and the energy consumption of cooling water system equipment;

s9: and adjusting the running state of each device according to the optimal control parameters.

2. The control method of claim 1, wherein the prediction model of the cooling water heat dissipation capacity is

Q_{Heat dissipation}＝Q_{Cold quantity}+Q_{Main unit}＝C_{Chilled water}m_{Chilled water}Δt+Q_{Main unit}；

Wherein Q is_{Heat dissipation}Represents the heat dissipation capacity of cooling water, Q_{Cold quantity}Indicating the cooling capacity of the cold and heat source main unit, Q_{Main unit}Indicating the heat dissipation of the cold and heat source main unit, C_{Chilled water}Represents the specific heat capacity of chilled water, m_{Chilled water}The mass flow of the chilled water is shown, and delta t represents the temperature difference between the supply water and the return water of the chilled water.

3. The control method of the cooling water system of the air conditioner as claimed in claim 1, wherein the predictive energy consumption model of the cooling water system equipment is

W_min＝W_{Cold and heat source host}+W_{Cooling water circulating pump}+W_{Cooling tower group}；

Wherein, W_minRepresents the minimum energy consumption, W, of the cooling water system equipment_{Cold and heat source host}Represents the energy consumption of the cold and heat source main unit, W_{Cooling water circulating pump}Represents the energy consumption of the cooling water circulation pump, W_{Cooling tower group}Represents the energy consumption of the cooling tower group.

4. The control method for the cooling water system of the air conditioner as claimed in claim 3, wherein the energy consumption W of the main machine of the cold and heat source is_{Cold and heat source host}Determined by its device model selection report or determined by fitting a functional relationship of

W_{Cold and heat source host}＝f(t₁、t₂、t₃、t₄、η、V₁、V₂)；

Wherein, t₁、t₂、t₃、t₄Respectively showing chilled water inlet, chilled water outlet, cooling water inlet and cooling water outlet temperatures, eta shows the load factor of the cold and heat source main machine, V₁And V₂The chilled water flow and chilled water flow are indicated separately.

5. The control method of an air-conditioning cooling water system as claimed in claim 3, wherein the energy consumption W of the cooling water circulating pump_{Cooling water circulating pump}Is calculated by the formula

Or

Wherein f is_{Cooling pump}Representing the actual frequency of the cooling water circulation pump, n_{Cooling pump}Indicating the number of cooling water circulation pumps that have been activated; p_{Cooling pump}Indicating rated power, V, of cooling water circulating pump₂And V_2.dRespectively representing the cooling water flow and the rated flow of the cooling water pump.

6. The method as claimed in claim 3, wherein the energy consumption W of the cooling tower group_{Cooling tower group}Is calculated by the formula

Wherein f is_{Cooling tower}Representing the actual frequency, P, of the fans in the cooling tower_{Cooling tower}Representing the rated power, n, of the fans in the cooling tower group_{Cooling tower}Indicating the number of fans in the cooling tower cluster that are activated.

7. The control method of the air-conditioning cooling water system as claimed in claim 1, further comprising:

s10: when the accumulated operation time t reaches the update period t_UpdatingUpdating a cooling water heat dissipation capacity prediction model and a cooling water system equipment energy consumption prediction model;

the updating of the cooling water heat dissipation capacity prediction model and the cooling water system equipment energy consumption prediction model comprises:

s101: extracting parameters required by the cooling water heat dissipation capacity prediction model, removing abnormal data in the parameters, calculating a predicted value of the cooling water heat dissipation capacity, comparing the predicted value and an actual measured value of the cooling water heat dissipation capacity, and taking the original cooling water heat dissipation capacity prediction model as a new cooling water heat dissipation capacity prediction model if the error between the predicted value and the actual measured value is smaller than a preset threshold value; if the error between the actual measured value and the measured value is larger than or equal to the preset threshold value, substituting the actual measured value of the cooling water heat dissipation capacity and the actual measured value of each data in the cooling water heat dissipation capacity prediction model into the original cooling water heat dissipation capacity prediction model, calculating the updated value of the parameters which cannot be actually measured, and obtaining a new formula as the cooling water heat dissipation capacity prediction model;

s102: extracting parameters required by the energy consumption prediction model of the cooling water system equipment, removing abnormal data in the parameters, calculating a predicted value of the energy consumption of the cooling water system equipment, comparing the predicted value with an actually measured value of the energy consumption of the cooling water system equipment, and if the error between the predicted value and the actually measured value is smaller than a preset threshold value, taking the original energy consumption prediction model of the cooling water system equipment as a new energy consumption prediction model of the cooling water system equipment; if the error between the two values is larger than or equal to the preset threshold value, substituting the measured value of the energy consumption of the cooling water system equipment and the measured value of each data in the energy consumption prediction model of the cooling water system equipment into the original energy consumption prediction model of the cooling water system equipment, calculating the updated value of the parameter which can not be measured in the model, and obtaining a new formula as the energy consumption prediction model of the cooling water system equipment.

8. The control method of the air-conditioning cooling water system as claimed in claim 1, further comprising:

s11: determining key parameters of a prediction time period according to the key parameters of the current time period and historical data, and determining optimal control parameters of the prediction time period according to the key parameters of the prediction time period and an energy consumption prediction model of cooling water system equipment;

s12: and adjusting the running state of each device in the prediction period according to the optimal control parameter in the prediction period.

9. An air conditioner cooling water system control device, characterized by comprising:

the operation module is used for starting a default number of cooling water circulating pumps and cooling towers, and fans in the cooling tower circulating pumps and the cooling towers operate at a default frequency;

the recording module is used for recording the accumulated running time t;

the operation state judgment module is used for judging whether the operation state of the equipment is changed; the running state of the equipment comprises the number of running chilled water circulating pumps, the number of running cooling water circulating pumps and the number of running cold and heat source units;

a data acquisition module for accumulating the running time t to reach the default running time t_MRThe method comprises the steps that when the air conditioner system equipment is started, real-time outdoor air parameter data, running state data of the air conditioner system equipment and configuration information data of cooling water system equipment are collected, the real-time outdoor air parameter data comprise dry bulb temperature and relative humidity of outdoor air, the running state data of the air conditioner system equipment comprise start-stop states and load rates of cold and heat source hosts, fans in a cooling tower, cooling water pumps and cooling water pumps, water inlet temperature and water outlet temperature of cooling water and actual frequency of the cooling water pumps, and the configuration information data of the cooling water system equipment comprise serial numbers and actual frequency of the started cooling water pumps and fans in a cold area tower;

the data storage module is used for storing historical data;

the data comparison and judgment module is used for comparing the currently acquired data with the historical data and judging whether the historical data has the same data as the currently acquired data;

the optimal control parameter determining module is used for determining optimal control parameters according to the optimal control parameters corresponding to the same data in the historical data or determining the optimal control parameters according to the cooling water heat dissipation capacity and the cooling water system equipment energy consumption;

the prediction module is used for determining the cooling water heat dissipation capacity and the cooling water system equipment energy consumption according to a preset cooling water heat dissipation capacity prediction model and a preset cooling water system equipment energy consumption prediction model;

and the adjusting module is used for adjusting the running state of each device according to the optimal control parameter.

10. An air conditioning cooling water system control device as claimed in claim 9, further comprising:

an update module for updating the running time t when the accumulated running time t reaches the update period t_UpdatingModel for predicting heat dissipation capacity of cooling water and cooling water system equipmentUpdating the energy consumption prediction model;

the historical prediction module is used for determining key parameters of a prediction time period according to the key parameters of the current time period and historical data, and determining optimal control parameters of the prediction time period according to the key parameters of the prediction time period and the energy consumption prediction model of the cooling water system equipment;

the history adjusting module is used for adjusting the running state of each device in the prediction period according to the optimal control parameter in the prediction period;

the update module includes:

the cooling water heat dissipation capacity prediction model updating submodule is used for extracting parameters required by the cooling water heat dissipation capacity prediction model calculation, eliminating abnormal data in the parameters, calculating a predicted value of the cooling water heat dissipation capacity, comparing the predicted value and an actually measured value of the cooling water heat dissipation capacity, and if the error between the predicted value and the actually measured value is smaller than a preset threshold value, taking the original cooling water heat dissipation capacity prediction model as a new cooling water heat dissipation capacity prediction model; if the error between the actual measured value and the measured value is larger than or equal to the preset threshold value, substituting the actual measured value of the cooling water heat dissipation capacity and the actual measured value of each data in the cooling water heat dissipation capacity prediction model into the original cooling water heat dissipation capacity prediction model, calculating the updated value of the parameters which cannot be actually measured, and obtaining a new formula as the cooling water heat dissipation capacity prediction model;

the cooling water system equipment energy consumption prediction model updating submodule is used for extracting parameters required by the cooling water system equipment energy consumption prediction model calculation, eliminating abnormal data in the parameters, calculating a predicted value of the cooling water system equipment energy consumption, comparing the predicted value with an actually measured value of the cooling water system equipment energy consumption, and if the error between the predicted value and the actually measured value is smaller than a preset threshold value, taking the original cooling water system equipment energy consumption prediction model as a new cooling water system equipment energy consumption prediction model; if the error between the two values is larger than or equal to the preset threshold value, substituting the measured value of the energy consumption of the cooling water system equipment and the measured value of each data in the energy consumption prediction model of the cooling water system equipment into the original energy consumption prediction model of the cooling water system equipment, calculating the updated value of the parameter which can not be measured in the model, and obtaining a new formula as the energy consumption prediction model of the cooling water system equipment.

Technical Field

The invention relates to the technical field of energy-saving control of a cooling water system of a central air conditioner, in particular to a control method and a control device of the cooling water system of the air conditioner.

Background

At present, the high-efficiency operation of a cooling water system is influenced, and three problems generally exist: firstly, the selection type of central air-conditioning cooling water system equipment (comprising a cold and heat source host, a cooling water pump, a cooling tower and the like) is generally larger; secondly, in the whole section operation process, the equipment is operated under the low-load state in most of time, so that the equipment can have unfavorable operation conditions under the low-load condition, and the energy consumption of the whole central air-conditioning system is increased, thereby causing a great deal of energy waste. Thirdly, the cooling water system generally adopts a manual control operation mode, and the system has strong hysteresis and cannot meet the variable heat dissipation requirement. If the phenomenon is to realize the efficient operation of the system with low cost, the requirement for intelligent regulation and control of the system operation process is very high, so that the refined intelligent regulation of a cooling water system cannot be avoided.

According to statistics, most of the existing central air-conditioning systems are in a manual control state, the adjustment of equipment depends on manual intervention, and meanwhile, the attention degree of operation and maintenance personnel on the operation of a cooling water system is not high in the operation process, so that the important role of the cooling water system in air-conditioning energy conservation cannot be fully understood. Most projects are provided with cooling tower fan frequency converters, but the cooling tower fan frequency converters still run in a manual adjustment or even non-adjustment mode in the actual use process, namely, the cooling tower fan frequency converters run at power frequency; meanwhile, the cooling tower which does not run is not isolated independently, so that a large amount of water flow bypass phenomenon is caused, and the whole system level, linkage, intelligent and digital regulation and control measures are lacked, so that the energy efficiency of the air conditioning system is low, and the energy waste is high.

Disclosure of Invention

Aiming at the defects in the prior art, the invention aims to provide a control method and a control device for an air-conditioning cooling water system.

In order to achieve the above purpose, the invention provides the following technical scheme:

a control method for an air conditioner cooling water system is characterized by comprising the following steps:

s1: starting a default number of cold and heat source hosts, cooling water circulating pumps and cooling towers, wherein the cooling tower circulating pumps and fans in the cooling towers run at a default frequency; s2: recording the accumulated running time t; s3: judging whether the running state of the equipment is changed, if so, executing S2 again, and if not, executing S4; the running state of the equipment comprises the number of running chilled water circulating pumps, the number of running cooling water circulating pumps and the number of running cold and heat source units; s4: the accumulated running time t reaches the default running time t_MRThe method comprises the steps that when the air conditioner system is started, key parameters are collected, the key parameters comprise outdoor air real-time parameter data, air conditioner system equipment running state data and cooling water system equipment configuration information data, the outdoor air real-time parameter data comprise dry bulb temperature and relative humidity of outdoor air, the air conditioner system equipment running state data comprise start-stop states and load rates of cold and heat source hosts, fans in a cooling tower, cooling water pumps and cooling water pumps, water inlet temperature and water outlet temperature of cooling water, water inlet temperature and water outlet temperature of chilled water and actual frequency of the cooling water pumps, and the cooling water system equipment configuration information data comprise serial numbers and actual frequency of the started cooling water pumps and fans in a cooling area tower; s5: comparing the data collected in the step S4 with the historical data, judging whether the historical data has the same data as the data collected in the step S4, if so, executing the step S6, and if not, executing the step S7; s6: determining optimal control parameters according to the optimal control parameters corresponding to the same data in the historical data; s7: prediction model and method according to preset cooling water heat dissipation capacityThe energy consumption prediction model of the cooling water system equipment determines the heat dissipation capacity of cooling water and the energy consumption of the cooling water system equipment; s8: determining optimal control parameters according to the heat dissipation capacity of cooling water and the energy consumption of cooling water system equipment; s9: and adjusting the running state of each device according to the optimal control parameters.

In the present invention, preferably, the cooling water heat dissipation amount prediction model is

Q_{Heat dissipation}＝Q_{Cold quantity}+Q_{Main unit}＝C_{Chilled water}m_{Chilled water}Δt+Q_{Main unit}；

Wherein Q is_{Heat dissipation}Represents the heat dissipation capacity of cooling water, Q_{Cold quantity}Indicating the cooling capacity of the cold and heat source main unit, Q_{Main unit}Indicating the heat dissipation capacity of the heat source and the host, C_{Chilled water}Represents the specific heat capacity of chilled water, m_{Chilled water}The mass flow of the chilled water is shown, and delta t represents the temperature difference between the supply water and the return water of the chilled water.

In the invention, preferably, the energy consumption prediction model of the cooling water system equipment is

W_min＝W_{Cold and heat source host}+W_{Cooling water circulating pump}+W_{Cooling tower group}；

Wherein, W_minRepresents the minimum energy consumption, W, of the cooling water system equipment_{Cold and heat source host}Represents the energy consumption of the main machine of the cold and heat source, W_{Cooling water circulating pump}Represents the energy consumption of the cooling water circulation pump, W_{Cooling tower group}Represents the energy consumption of the cooling tower group.

In the present invention, it is preferable that the energy consumption amount W of the cold/heat source main unit_{Cold and heat source host}Determined by its device-type report or determined by fitting a functional relationship of

W_{Cold and heat source host}＝f(t₁、t₂、t₃、t₄、η、V₁、V₂)；

Wherein, t₁、t₂、t₃、t₄Respectively showing the temperature of chilled water inlet, chilled water outlet, cooling water inlet and cooling water outlet, wherein eta shows cold and hotSource host load factor, V₁And V₂The chilled water flow and chilled water flow are indicated separately.

In the present invention, it is preferable that the cooling water circulation pump consumes energy W_{Cooling water circulating pump}Is calculated by the formula

Wherein f is_{Cooling pump}Representing the actual frequency of the cooling water circulation pump, n_{Cooling pump}Indicating the number of cooling water circulation pumps that have been activated; p_{Cooling pump}Indicating rated power, V, of cooling water circulating pump₂And V_2.dRespectively representing the cooling water flow and the rated flow of the cooling water pump.

In the present invention, it is preferable that the energy consumption W of the cooling tower group_{Cooling tower group}Is calculated by the formula

Wherein f is_{Cooling tower}Representing the actual frequency, P, of the fans in the cooling tower_{Cooling tower}Representing the rated power, n, of the fans in the cooling tower group_{Cooling tower}Indicating the number of fans in the cooling tower cluster that are activated.

In the present invention, it is preferable that the method further comprises:

s10: when the accumulated operation time t reaches the update period t_UpdatingUpdating a cooling water heat dissipation capacity prediction model and a cooling water system equipment energy consumption prediction model; the updating of the cooling water heat dissipation capacity prediction model and the cooling water system equipment energy consumption prediction model comprises: s101: extracting parameters required for calculation of cooling water heat dissipation prediction model, eliminating abnormal data therein, and calculating cooling water heat dissipationComparing the predicted value and the measured value of the cooling water heat dissipation capacity, and if the error between the predicted value and the measured value is smaller than a preset threshold value, taking the original cooling water heat dissipation capacity prediction model as a new cooling water heat dissipation capacity prediction model; if the error between the measured value and the measured value is larger than or equal to a preset threshold value, substituting the measured value of the cooling water heat dissipation capacity and the measured value of each data in the cooling water heat dissipation capacity prediction model into the original cooling water heat dissipation capacity prediction model, calculating an updated value of a parameter which cannot be measured actually, and obtaining a formula as a new cooling water heat dissipation capacity prediction model; s102: extracting parameters required by the energy consumption prediction model of the cooling water system equipment, removing abnormal data in the parameters, calculating a predicted value of the energy consumption of the cooling water system equipment, comparing the predicted value with an actual value of the energy consumption of the cooling water system equipment, and taking the original energy consumption prediction model of the cooling water system equipment as a new energy consumption prediction model of the cooling water system equipment if the error between the predicted value and the actual value is smaller than a preset threshold value; if the error between the actual value and the measured value is larger than or equal to the preset threshold value, substituting the actual measured value of the energy consumption of the cooling water system equipment and the actual measured value of each data in the energy consumption prediction model of the cooling water system equipment into the original energy consumption prediction model of the cooling water system equipment, calculating the updated value of the parameter which can not be actually measured, and obtaining a formula which is a new energy consumption prediction model of the cooling water system equipment.

In the present invention, it is preferable that the method further comprises: s11: determining key parameters of a prediction time period according to the key parameters of the current time period and historical data, and determining optimal control parameters of the prediction time period according to the key parameters of the prediction time period and an energy consumption prediction model of cooling water system equipment; s12: and adjusting the running state of each device in the prediction period according to the optimal control parameter in the prediction period.

An air conditioner cooling water system control device, comprising: the operation module is used for starting a default number of cooling water circulating pumps and cooling towers, and fans in the cooling tower circulating pumps and the cooling towers operate at a default frequency; the recording module is used for recording the accumulated running time t; the operation state judgment module is used for judging whether the operation state of the equipment is changed; the running state of the equipment comprises the number of running chilled water circulating pumps, the number of running cooling water circulating pumps and the runningThe number of cold and heat source units; a data acquisition module for accumulating the running time t to reach the default running time t_MRThe method comprises the steps that when the outdoor air is used, outdoor air real-time parameter data, air conditioning system equipment running state data and cooling water system equipment configuration information data are collected, wherein the outdoor air real-time parameter data comprise dry bulb temperature and relative humidity of outdoor air, the air conditioning system equipment running state data comprise start-stop states and load rates of cold and heat source hosts, fans in a cooling tower, cooling water pumps and cooling water pumps, water inlet temperature and water outlet temperature of cooling water, water inlet temperature and water outlet temperature of chilled water and actual frequency of the chilled water pumps, and the cooling water system equipment configuration information data comprise serial numbers and actual frequency of the started cooling water pumps and fans in a cold area tower; the data storage module is used for storing historical data; the data comparison and judgment module is used for comparing the currently acquired data with the historical data and judging whether the historical data has the same data as the currently acquired data; the optimal control parameter determining module is used for determining optimal control parameters according to the optimal control parameters corresponding to the same data in the historical data or determining the optimal control parameters according to the cooling water heat dissipation capacity and the cooling water system equipment energy consumption; the prediction module is used for determining the cooling water heat dissipation capacity and the cooling water system equipment energy consumption according to a preset cooling water heat dissipation capacity prediction model and a preset cooling water system equipment energy consumption prediction model; and the adjusting module is used for adjusting the running state of each device according to the optimal control parameter.

In the present invention, it is preferable that the method further comprises: an update module for updating the running time t when the accumulated running time t reaches the update period t_UpdatingUpdating a cooling water heat dissipation capacity prediction model and a cooling water system equipment energy consumption prediction model; the historical prediction module is used for determining key parameters of a prediction time period according to the key parameters of the current time period and historical data, and determining optimal control parameters of the prediction time period according to the key parameters of the prediction time period and the energy consumption prediction model of the cooling water system equipment; the history adjusting module is used for adjusting the running state of each device in the prediction time period according to the optimal control parameter in the prediction time period; the update module includes: cooling water heat dissipation capacity prediction modelThe type updating submodule is used for extracting parameters required by the cooling water heat dissipation capacity prediction model, eliminating abnormal data in the parameters, calculating a predicted value of the cooling water heat dissipation capacity, comparing the predicted value and an actually measured value of the cooling water heat dissipation capacity, and if the error between the predicted value and the actually measured value is smaller than a preset threshold value, taking the original cooling water heat dissipation capacity prediction model as a new cooling water heat dissipation capacity prediction model; if the error between the actual measured value and the measured value is larger than or equal to the preset threshold value, substituting the actual measured value of the cooling water heat dissipation capacity and the actual measured value of each data in the cooling water heat dissipation capacity prediction model into the original cooling water heat dissipation capacity prediction model, calculating the updated value of the parameters which cannot be actually measured, and obtaining a new formula as the cooling water heat dissipation capacity prediction model; the cooling water system equipment energy consumption prediction model updating submodule is used for extracting parameters required by the cooling water system equipment energy consumption prediction model calculation, eliminating abnormal data in the parameters, calculating a predicted value of the cooling water system equipment energy consumption, comparing the predicted value with an actual measured value of the cooling water system equipment energy consumption, and if the error between the predicted value and the actual measured value is smaller than a preset threshold value, taking the original cooling water system equipment energy consumption prediction model as a new cooling water system equipment energy consumption prediction model; if the error between the measured value and the energy consumption of the cooling water system equipment is larger than or equal to the preset threshold value, substituting the measured value of the energy consumption of the cooling water system equipment and the measured value of each data in the energy consumption prediction model of the cooling water system equipment into the original energy consumption prediction model of the cooling water system equipment, calculating the updated value of the parameter which can not be measured in the model, and obtaining a formula which is a new energy consumption prediction model of the cooling water system equipment.

Compared with the prior art, the invention has the beneficial effects that:

the method and the device take main equipment operation parameters, equipment operation states, configuration information, temperature sensors, temperature and humidity sensors, cold heat meters, flow meters, frequency converters, electric valves and other equipment parameters of the air conditioning system as key parameters, obtain optimal control parameters through a built-in cooling water heat dissipation capacity prediction model and a cooling water system energy consumption model according to the key parameters, take equipment operation data and the operation states as the basis, take cooling water heat dissipation capacity as a medium and optimal energy consumption as a target, and obtain the optimal control parameters through multi-parameter, multivariable and fitting function relational analysis, and send the optimal control parameters to corresponding specified equipment for automatic adjustment, thereby ensuring that the air conditioning cooling water system is in a high-efficiency operation state and greatly reducing the overall energy resource consumption of the air conditioning system.

Drawings

FIG. 1 is a flow chart of an embodiment of a control method for an air conditioning cooling water system.

FIG. 2 is a flow chart of another embodiment of a control method for an air conditioning cooling water system.

FIG. 3 is a flow chart of updating a cooling water heat dissipation prediction model and a cooling water system equipment energy consumption prediction model.

FIG. 4 is a flow chart of another embodiment of a control method for an air conditioning cooling water system.

Fig. 5 is a schematic structural diagram of an embodiment of the control device of the air-conditioning cooling water system.

Fig. 6 is a schematic structural diagram of another embodiment of the control device of the air-conditioning cooling water system.

Fig. 7 is a schematic structural diagram of an update module.

In the drawings: the system comprises a 1-operation module, a 2-recording module, a 3-operation state judgment module, a 4-data acquisition module, a 5-data storage module, a 6-data comparison judgment module, a 7-optimal control parameter determination module, an 8-prediction module, a 9-adjustment module, a 10-update module, a 101-cooling water heat dissipation prediction model update sub-module, a 102-cooling water system equipment energy consumption prediction model update sub-module, an 11-history prediction module and a 12-history adjustment module.

Detailed Description

The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. When a component is referred to as being "disposed on" another component, it can be directly on the other component or intervening components may also be present. The terms "vertical," "horizontal," "left," "right," and the like as used herein are for illustrative purposes only.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

Referring to fig. 1 to 4, a preferred embodiment of the present invention provides a method for controlling an air conditioning cooling water system, including:

s1: and starting the cold and heat source host machines, the cooling water circulating pumps and the cooling tower in the default number, wherein the fans in the cooling tower circulating pumps and the cooling tower run at the default frequency.

The cooling water circulating pump and the fans in the cooling tower are all frequency conversion motors, so that the starting and stopping states and the running frequency of the cooling water circulating pump and the fans can be monitored and controlled through circuits, and the cooling water circulating pump and the fans enter an initial state running mode. The default number and the default frequency can be set and adjusted manually.

S2: the accumulated running time t is recorded.

The accumulated running time refers to the time for which the cooling water circulating pump and the cooling tower are kept in a start-stop state and the running frequency.

S3: judging whether the running state of the equipment is changed, if so, executing S2 again, and if not, executing S4; the running state of the equipment comprises the number of running chilled water circulating pumps, the number of running cooling water circulating pumps and the number of running cold and heat source units.

And monitoring whether the running state of main equipment of the air conditioning system changes, if so, adding a chilled water circulating pump, a cooling water circulating pump or a cold and heat source unit, if the monitoring result is changed, resetting the accumulated running time t to zero, namely, executing S2 again, and if the detection result is unchanged, entering the next step S4.

S4: the accumulated running time t reaches the default running time t_MRThe method comprises the steps of collecting key parameters, wherein the key parameters comprise outdoor air real-time parameter data, air conditioning system equipment running state data and cooling water system equipment configuration information data, the outdoor air real-time parameter data comprise dry bulb temperature and relative humidity of outdoor air, the air conditioning system equipment running state data comprise cold and heat source hosts, fans in a cooling tower, start-stop states and load rates of a cooling water pump and the cooling water pump, the water inlet temperature and the water outlet temperature of cooling water, the water inlet temperature and the water outlet temperature of freezing water and the actual frequency of the freezing water pumps, and the cooling water system equipment configuration information data comprise serial numbers and actual frequencies of the started cooling water pumps and fans in a cold area tower.

Default running time duration t_MRThe adjustment can be made according to the actual conditions of the project, for example, 5 min.

S5: comparing the data collected in the step S4 with the historical data, and determining whether the historical data includes data identical to the data collected in the step S4, if so, performing the step S6, and if not, performing the step S7.

If the currently collected data is the same as the corresponding data in a certain time period in the historical data, that is, the outdoor air real-time parameter data, the air conditioning system equipment operation state data and the cooling water system equipment configuration information data are the same values in history, the current air conditioning system can be adjusted by referring to the corresponding optimal control parameters in the historical data, that is, S6 is executed.

S6: and determining the optimal control parameters according to the optimal control parameters corresponding to the same data in the historical data.

The step is to use the optimal control parameters corresponding to the same historical data as the optimal control parameters corresponding to the currently acquired data.

S7: and determining the heat dissipation capacity of the cooling water and the energy consumption of the cooling water system equipment according to a preset prediction model of the heat dissipation capacity of the cooling water and a preset prediction model of the energy consumption of the cooling water system equipment.

The cooling water heat dissipation capacity prediction model is used for theoretically predicting the heat dissipation capacity required by the air conditioner cooling system, and the cooling water heat dissipation capacity can be calculated by using the model; the model for predicting the energy consumption of the cooling water system equipment is used for theoretically predicting the energy consumed by each equipment in the air-conditioning cooling system under the current start-stop state and frequency, and the model can be used for calculating the energy consumption of the cooling water system equipment.

Optionally, the cooling water heat dissipation capacity prediction model is

Q_{Heat dissipation}＝Q_{Cold quantity}+Q_{Main unit}＝C_{Chilled water}m_{Chilled water}Δt+Q_{Main unit}；

Wherein Q is_{Heat dissipation}Represents the heat dissipation capacity of cooling water in kWh; q_{Cold quantity}The refrigerating capacity of the cold and heat source host is shown, and the single-unit kWh can be obtained by calculation by utilizing the obtained flow and temperature drop value of the chilled water; q_{Main unit}Indicating the heat dissipation of the cold and heat source main unit, C_{Chilled water}The specific heat capacity of the chilled water is expressed in kJ/(kg. k), m_{Chilled water}The flow rate of the frozen water is expressed in units of kg/s, and delta t represents the temperature difference between the supply water and the return water of the frozen water in units of ℃. Due to the difference of the types of the cold and heat source host machines, the types of the used energy sources are not limited to electric power, and gas, steam and the like are possible, and are converted into equivalent electric power consumption according to the correlation coefficient when the energy consumption is calculated.

Optionally, the energy consumption prediction model of the cooling water system equipment is

W_min＝W_{Cold and heat source host}+W_{Cooling water circulating pump}+W_{Cooling tower group}；

Wherein, W_minRepresents the minimum energy consumption of cooling water system equipment, and has a unit of kWh, W_{Cold and heat source host}Represents the energy consumption of the cold and heat source main unit, W_{Cooling water circulating pump}Represents the energy consumption of the cooling water circulation pump, W_{Cooling tower group}Represents the energy consumption of the cooling tower group.

Optionally, the energy consumption W of the cold and heat source host_{Cold and heat source host}The method mainly comprises two modes, namely, according to an equipment model selection report provided by an equipment manufacturer, data expansion is carried out by combining with actual project requirements, the granularity of the data is reduced, and the improved data is written into an analysis system in a data matrix form; in the second way, through data analysis, part of the key parameters are fitted into a relation or a relation group, and the relation is written into the analysis system, and the fitting function relation is

W_{Cold and heat source host}＝f(t₁、t₂、t₃、t₄、η、V₁、V₂)；

Wherein, t₁、t₂、t₃、t₄Respectively representing the water inlet temperature, the water outlet temperature, the water inlet temperature and the water outlet temperature of chilled water in unit ℃; eta represents the load rate of the cold and heat source main unit, V₁And V₂Respectively showing the flow rates of the freezing water and the cooling water in m³/h。

Optionally, the energy consumption W of the cooling water circulation pump_{Cooling water circulating pump}Is calculated by the formula

Or

Wherein f is_{Cooling pump}Representing the actual frequency of the cooling water circulation pump in Hz, n_{Cooling pump}Indicating the number of cooling water circulation pumps that have been activated; p_{Cooling pump}The rated power of the cooling water circulating pump is expressed in kW; v₂And V_2.dRespectively showing the cooling water flow and the rated flow of a cooling water pump, and 50 shows the electricity frequency in China.

Optionally, based on calculation of cooling water heat dissipationObtaining a corresponding cooling tower fan frequency parameter f by combining the formula_{Cooling tower}：

Q_{Heat dissipation}＝f(t₃,t₄,t₅,Δt_{Approach to temperature difference}，V₂,V_2d,f_{Cooling tower})；

Energy consumption W_{Cooling tower group}Is calculated by the formula

Wherein f is_{Cooling tower}Representing the actual frequency of the fans in the cooling tower group in Hz; t is t₃、t₄、t₅Respectively showing the water inlet temperature of cooling water, the water outlet temperature of the cooling water and the outdoor air wet bulb temperature; Δ t_{Approach to temperature difference}The difference between the outlet water temperature of the cooling water and the outdoor wet bulb temperature is expressed, the default value is 2 ℃ (the general value is 2-4 ℃), and the temperature can be manually set; p_{Cooling tower}The rated power of a fan in the cooling tower group is represented in kW; n is_{Cooling tower}Indicating the number of fans in the cooling tower cluster that are activated.

S8: and determining optimal control parameters according to the heat dissipation capacity of the cooling water and the energy consumption of the cooling water system equipment.

Fitting the plurality of cooling water heat dissipation capacity and the cooling water system equipment energy consumption values into a curve which changes along with time, dividing the fitted curve of the cooling water heat dissipation capacity by the fitted curve of the cooling water system equipment energy consumption to obtain a curve which represents the cooling water system equipment energy consumption efficiency, searching a point with the highest efficiency in the curve, and determining the configuration parameter of each equipment of the air-conditioning cooling water system corresponding to the point as the optimal control parameter.

In the step, fans of the same type in the controlled cooling tower group can operate under the same frequency through the optimal control parameters obtained through data processing and analysis, the condition that the efficiency of the cooling tower is seriously uneven due to the frequency difference of the fans and hydraulic imbalance is avoided, and the fans can operate according to the same wind-water ratio index on the basis of similarity of outdoor air environment after static hydraulic balance adjustment is carried out at the beginning of the construction of an air-conditioning cooling water system through the regulation and control measure.

S9: and adjusting the running state of each device according to the optimal control parameters.

In the step, a control instruction is sent to specific equipment according to the optimal control parameter obtained by analysis, adjustment is carried out, and finally, the adjustment and prediction process is completed.

Optionally, the control method of the air-conditioning cooling water system further comprises:

s10: when the accumulated operation time t reaches the update period t_UpdatingAnd updating the cooling water heat dissipation capacity prediction model and the cooling water system equipment energy consumption prediction model.

Wherein, the period t is updated_UpdatingThe setting may be made as needed, for example, one day, one week, one month, one year, or the like.

Specifically, updating the cooling water heat dissipation capacity prediction model and the cooling water system equipment energy consumption prediction model comprises:

s101: extracting parameters required by the cooling water heat dissipation capacity prediction model, removing abnormal data in the parameters, calculating a predicted value of the cooling water heat dissipation capacity, comparing the predicted value and an actual measured value of the cooling water heat dissipation capacity, and taking the original cooling water heat dissipation capacity prediction model as a new cooling water heat dissipation capacity prediction model if the error between the predicted value and the actual measured value is smaller than a preset threshold value; if the error between the two is larger than or equal to the preset threshold value, the measured value of the cooling water heat dissipation capacity and the measured value of each data in the cooling water heat dissipation capacity prediction model are substituted into the original cooling water heat dissipation capacity prediction model, the updating value of the parameters which can not be measured in the model is calculated, and the obtained formula is the new cooling water heat dissipation capacity prediction model.

The parameters required by the cooling water heat dissipation capacity prediction model at least comprise outdoor dry bulb temperature, humidity, wet bulb temperature, heat dissipation capacity of a cold and heat source host, real-time cooling capacity of air conditioner chilled water, real-time flow of air conditioner cooling water, cooling water inlet temperature, outdoor last-time temperature and outdoor last-time humidity.

S102: extracting parameters required by the energy consumption prediction model of the cooling water system equipment, removing abnormal data in the parameters, calculating a predicted value of the energy consumption of the cooling water system equipment, comparing the predicted value with an actually measured value of the energy consumption of the cooling water system equipment, and if the error between the predicted value and the actually measured value is smaller than a preset threshold value, taking the original energy consumption prediction model of the cooling water system equipment as a new energy consumption prediction model of the cooling water system equipment; if the error between the two values is larger than or equal to the preset threshold value, substituting the measured value of the energy consumption of the cooling water system equipment and the measured value of each data in the energy consumption prediction model of the cooling water system equipment into the original energy consumption prediction model of the cooling water system equipment, calculating the updated value of the parameter which can not be measured in the model, and obtaining a new formula as the energy consumption prediction model of the cooling water system equipment.

The parameters required by the energy consumption prediction model of the cooling water system equipment at least comprise cold and heat source host equipment data, cooling water circulating pump equipment data and cooling tower equipment data.

Optionally, the control method of the air-conditioning cooling water system further comprises:

s11: and determining the key parameters of the prediction time period according to the key parameters of the current time period and historical data, and determining the optimal control parameters of the prediction time period according to the key parameters of the prediction time period and the energy consumption prediction model of the cooling water system equipment.

Comparing the collected key parameters of the current time period with the historical data, finding out the historical key parameters in the historical data, which are the same as the key parameters of the current time period, then finding out the key parameters of the next time period of the historical key parameters in the historical data, taking the key parameters as the key parameters of the prediction time period, and then substituting the key parameters of the prediction time period into the energy consumption prediction model of the cooling water system equipment, so that a group of optimal control parameters can be calculated, namely the optimal control parameters of the prediction time period.

S12: and adjusting the running state of each device in the prediction period according to the optimal control parameter in the prediction period.

And sending a control instruction to specific equipment for regulation according to the key control parameters of the prediction time period obtained by analysis, namely completing a pre-regulation process.

The invention also provides a control device of the air-conditioning cooling water system, which comprises: the device comprises an operation module 1, a recording module 2, an operation state judgment module 3, a data acquisition module 4, a data storage module 5, a data comparison judgment module 6, an optimal control parameter determination module 7, a prediction module 8 and an adjustment module 9.

The operation module 1 can be arranged on the central processing unit and used for starting the cooling water circulating pumps and the cooling tower with default quantities, and the fans in the cooling tower circulating pumps and the cooling tower operate at default frequency. The recording module 2 may employ a clock circuit for recording the accumulated running time t. The operation state determining module 3 may be disposed on the central processing unit, and is configured to determine whether the operation state of the apparatus changes. The data acquisition module 4 can comprise transmitter equipment such as a temperature sensor, a humidity sensor, a cold heat meter, a flowmeter, a frequency converter, an electric valve and the like, the transmitter equipment is connected with analog or digital input and output circuits, the input and output circuits are connected with certain acquisition and amplification circuits, and then the central processing unit is connected for accumulating the running time t to reach the default running time t_MRThe method comprises the steps of collecting real-time parameter data of outdoor air, running state data of air conditioning system equipment and configuration information data of cooling water system equipment, wherein the real-time parameter data of the outdoor air comprises dry bulb temperature and relative humidity of the outdoor air, the running state data of the air conditioning system equipment comprises a cold and heat source host, fans in a cooling tower, start-stop states and load rates of a cooling water pump and a cooling water pump, water inlet temperature and water outlet temperature of cooling water, water inlet temperature and water outlet temperature of the cooling water, actual frequency of the cooling water pump, and the configuration information data of the cooling water system equipment comprises serial numbers and actual frequency of the started cooling water pump and fans in a cold area tower. The data storage module 5 may include ROM, RAM, etc. and is used for storing historical data, and also for the functions of data analysis program operation, data reading and writing, etc. The data comparison and judgment module 6 may be disposed on the central processing unit, and is configured to compare currently acquired data with historical data, and judge whether data identical to the currently acquired data exists in the historical data. The optimal control parameter determining module 7 can be disposed on the central processing unit and used for determining the corresponding data according to the same data in the historical dataThe optimal control parameter is determined or the optimal control parameter is determined according to the heat dissipation capacity of the cooling water and the energy consumption of the cooling water system equipment. The prediction module 8 can be disposed on the central processing unit, and is configured to determine the cooling water heat dissipation capacity and the cooling water system equipment energy consumption according to a preset cooling water heat dissipation capacity prediction model and a preset cooling water system equipment energy consumption prediction model. The adjusting module 9 may be disposed on the central processing unit, and is configured to adjust the operation state of each device according to the optimal control parameter.

Optionally, the air-conditioning cooling water system control device further comprises an updating module 10, a history predicting module 118 and a history adjusting module 129.

Wherein, the update module 10 can be disposed on the cpu for updating the update period t when the accumulated running time t reaches the update period t_UpdatingAnd updating the cooling water heat dissipation capacity prediction model and the cooling water system equipment energy consumption prediction model. The history prediction module 118 may be disposed on the central processing unit, and configured to determine a key parameter of the prediction time period according to the key parameter of the current time period and the history data, and determine an optimal control parameter of the prediction time period according to the key parameter of the prediction time period and the energy consumption prediction model of the cooling water system device. The history adjusting module 129 may be disposed on the central processing unit, and is configured to adjust the operation status of each device in the prediction period according to the optimal control parameter in the prediction period.

The update module 10 includes a cooling water heat dissipation prediction model update sub-module 101 and a cooling water system equipment energy consumption prediction model update sub-module 102, both of which may be disposed on the central processing unit. The cooling water heat dissipation capacity prediction model updating submodule 101 is used for extracting parameters required by the cooling water heat dissipation capacity prediction model calculation, eliminating abnormal data in the parameters, calculating a predicted value of the cooling water heat dissipation capacity, comparing a predicted value and an actual measured value of the cooling water heat dissipation capacity, and if the error between the predicted value and the actual measured value is smaller than a preset threshold value, taking the original cooling water heat dissipation capacity prediction model as a new cooling water heat dissipation capacity prediction model; if the error between the actual measured value and the measured value is larger than or equal to the preset threshold value, substituting the actual measured value of the cooling water heat dissipation capacity and the actual measured value of each data in the cooling water heat dissipation capacity prediction model into the original cooling water heat dissipation capacity prediction model, calculating the updated value of the parameters which cannot be actually measured, and obtaining a new formula as the cooling water heat dissipation capacity prediction model. The cooling water system equipment energy consumption prediction model updating submodule 102 is used for extracting parameters required by the cooling water system equipment energy consumption prediction model calculation, eliminating abnormal data in the parameters, calculating a predicted value of the cooling water system equipment energy consumption, comparing the predicted value with an actual measured value of the cooling water system equipment energy consumption, and if the error between the predicted value and the actual measured value is smaller than a preset threshold value, taking the original cooling water system equipment energy consumption prediction model as a new cooling water system equipment energy consumption prediction model; if the error between the two values is larger than or equal to the preset threshold value, substituting the measured value of the energy consumption of the cooling water system equipment and the measured value of each data in the energy consumption prediction model of the cooling water system equipment into the original energy consumption prediction model of the cooling water system equipment, calculating the updated value of the parameter which can not be measured in the model, and obtaining a new formula as the energy consumption prediction model of the cooling water system equipment.

The above description is intended to describe in detail the preferred embodiments of the present invention, but the embodiments are not intended to limit the scope of the claims of the present invention, and all equivalent changes and modifications made within the technical spirit of the present invention should fall within the scope of the claims of the present invention.