CN114593534B - Control method of transcritical carbon dioxide heat pump system - Google Patents
Control method of transcritical carbon dioxide heat pump system Download PDFInfo
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- CN114593534B CN114593534B CN202011402038.3A CN202011402038A CN114593534B CN 114593534 B CN114593534 B CN 114593534B CN 202011402038 A CN202011402038 A CN 202011402038A CN 114593534 B CN114593534 B CN 114593534B
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- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 title claims abstract description 70
- 229910002092 carbon dioxide Inorganic materials 0.000 title claims abstract description 35
- 239000001569 carbon dioxide Substances 0.000 title claims abstract description 35
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000004458 analytical method Methods 0.000 claims abstract description 11
- 230000003068 static effect Effects 0.000 claims abstract description 6
- 238000006243 chemical reaction Methods 0.000 claims abstract description 4
- 238000010586 diagram Methods 0.000 claims description 22
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 abstract description 12
- 230000007613 environmental effect Effects 0.000 description 13
- 230000008859 change Effects 0.000 description 8
- 238000005338 heat storage Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000013461 design Methods 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000010438 heat treatment Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005485 electric heating Methods 0.000 description 1
- 239000008236 heating water Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000000513 principal component analysis Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000007711 solidification Methods 0.000 description 1
- 230000008023 solidification Effects 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25B—REFRIGERATION MACHINES, PLANTS OR SYSTEMS; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS
- F25B9/00—Compression machines, plants or systems, in which the refrigerant is air or other gas of low boiling point
- F25B9/002—Compression machines, plants or systems, in which the refrigerant is air or other gas of low boiling point characterised by the refrigerant
- F25B9/008—Compression machines, plants or systems, in which the refrigerant is air or other gas of low boiling point characterised by the refrigerant the refrigerant being carbon dioxide
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25B—REFRIGERATION MACHINES, PLANTS OR SYSTEMS; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS
- F25B49/00—Arrangement or mounting of control or safety devices
- F25B49/02—Arrangement or mounting of control or safety devices for compression type machines, plants or systems
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- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Mechanical Engineering (AREA)
- Thermal Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The invention discloses a control method of a transcritical carbon dioxide heat pump system, which comprises the following steps: step A: static analysis of a transcritical carbon dioxide heat pump system; specifically, the method comprises the following steps: and (B) step (B): the control state conversion judgment of the transcritical carbon dioxide heat pump system is carried out, and whether dynamic control is needed or not is determined by analyzing historical data; step C: system parameters are collected and analyzed for dynamic control. The invention can improve the traditional energy supply efficiency, in particular to the CO with better parameters in all aspects which are recognized at present 2 A heat pump water heater, or a hybrid system incorporating the same.
Description
Technical Field
The invention belongs to the technical field of mechanical energy, and particularly relates to a transcritical carbon dioxide heat pump system control method.
Background
Many conventional energy sources currently offer this means, which has been or will be limited worldwide due to the high greenhouse effect potential. CO 2 The heat pump water heater is a heat pump water heater which is currently recognized and superior to the conventional working medium in all aspects. Studies of national learners such as Austrian, norway and Japan show that the annual average COP value of the heat pump heat system can reach more than 3, hot water at 90 ℃ can be provided under the environment of-20 ℃, and compared with an electric heating or gas water heater, the heat pump heat system has the advantage that the energy consumption is reduced by 75%. Because the critical temperature of the carbon dioxide is lower and is 31.1 ℃ (the critical pressure is 73.8 bar), when the carbon dioxide is applied to systems such as an air-cooled air conditioner or a heat pump with higher ambient temperature, the high pressure side of the system is the supercritical pressure, and the whole cycle operates in a transcritical region. CO 2 Compared with the traditional gas water heater and electric water heater, the heat pump water heater has the characteristics of low energy consumption and environmental friendliness; compared with the traditional heat pump water heater, the water heater has the advantages of wide heating water temperature range and capability of providing high-temperature hot water. A large number of heating systems mainly based on carbon dioxide heat pump water heaters have been established in many areas. With the increase of heated terminals and the increase of areas in the system, the number of heat pumps is correspondingly increased, the types of heat pumps in the system are various due to history and development reasons, the topological connection relation and connection modes between the heat pumps and the heated terminals in the system are also various, and in such a case, the design and the use of the heat pump system are not optimal. On the other hand, as the degree of automation of the heat pump system increasesHigh, many systems are self-contained with simple acquisition and control systems, and how to utilize limited acquisition and control resources to improve the performance of these heat pump systems is a problem not considered to be solved in the prior art. Under the condition that a transcritical carbon dioxide heat pump system is relatively fixed, the system is controlled through data acquisition, analysis and detection, so that the overall performance of the system is kept at a better level, a solution idea of dynamic control is provided for a system with relatively fixed structure and difficulty in adjustment, an automatic control basis is provided through simple correspondence between a connection diagram and an entity system, and the adjustment of equipment can be simply realized through node attribute adjustment; big data analysis is introduced in the judging stage, so that the current system can gradually approach to a better system control mode in a larger range, and meanwhile, the historical control experience of the current system is referred in the control stage, so that the control mode is convenient to realize; the invention is applicable not only to carbon dioxide heat pump systems, but also to some hybrid heat pump systems.
Disclosure of Invention
In order to solve the above-mentioned problems in the prior art, the present invention proposes a transcritical carbon dioxide heat pump system control method, the method comprising:
step A: static analysis of a transcritical carbon dioxide heat pump system; specifically, the method comprises the following steps:
and (B) step (B): the control state conversion judgment of the transcritical carbon dioxide heat pump system is carried out, and whether dynamic control is needed or not is determined by analyzing historical data;
step C: system parameters are collected and analyzed for dynamic control.
Further, the step S1 specifically includes:
step SA1: obtaining a topological connection relation of a transcritical carbon dioxide heat pump system, and representing the topological connection relation as a connection diagram;
step SA2: the connection graph is analyzed to obtain an adjustable node.
Further, the step S2 is specifically to acquire historical data of system operation and acquire a plurality of target parameters based on the historical data; calculating a first target parameter according to the plurality of target parameters, acquiring a first target threshold value based on the connection diagram, and determining that dynamic control is not needed when all the first target parameters are better than the target threshold value; when all the first target parameters are worse than the target threshold value, determining that dynamic control is required and entering the next step; otherwise, calculating a plurality of second target parameters associated with different environment parameters based on the plurality of target parameters, acquiring a second target threshold according to the connection diagram, and comparing the plurality of second parameters with the second target threshold to determine whether dynamic control is required.
Further, the node types in the connection diagram are a heat receiving end, a heat pump compressor and an electromagnetic valve.
Further, the node parameters include node identification, node type, node running state, and node running parameters.
Furthermore, the adjustable node can set its working state according to different dynamic control modes.
Further, the method is applied to a transcritical carbon dioxide heat pump system.
Further, the transcritical carbon dioxide heat pump system is provided with an acquisition and control module for dynamic control.
Further, attributes of edges between nodes are used to identify attribute information of the connection relationship.
Further, the number of heat pump/compressors in the system is 2 or more.
The beneficial effects of the invention include: under the condition that a transcritical carbon dioxide heat pump system is relatively fixed, the system is controlled through data acquisition, analysis and detection, so that the overall performance of the system is kept at a better level, a solution idea of dynamic control is provided for a system with relatively fixed structure and difficulty in adjustment, an automatic control basis is provided through simple correspondence between a connection diagram and an entity system, and the adjustment of equipment can be simply realized through node attribute adjustment; big data analysis is introduced in the judging stage, so that the current system can gradually approach to a better system control mode in a larger range, and meanwhile, the historical control experience of the current system is referred in the control stage, so that the control mode is convenient to realize; the invention is applicable not only to carbon dioxide heat pump systems, but also to some hybrid heat pump systems. The invention combines static control and dynamic control, and performs state change control by taking the connection result of the system into consideration, and introduces a series of target values to perform relatively accurate judgment under the control critical state, thereby ensuring accuracy without excessive system overhead.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate and together with the description serve to explain the invention, if necessary:
fig. 1 is a schematic diagram of a control method of a transcritical carbon dioxide heat pump system according to the present invention.
Detailed Description
The present invention will now be described in detail with reference to the drawings and the specific embodiments thereof, wherein the exemplary embodiments and the description are for the purpose of illustrating the invention only and are not to be construed as limiting the invention.
In the transcritical carbon dioxide heat pump system applied by the invention, the heat pump installation time in the system is different and the types are also various, the topological connection relation and the connection mode between the heat pump and the heated terminal in the system are also various, and the connection and the topology are not the optimal design and are limited by various factors such as expenses, physical positions, human factors and the like. In such cases, neither the design nor the use of the heat pump system itself is optimal, and how to improve the performance of these systems is a significant issue. In addition, because the operation performance of the carbon dioxide air source heat pump is greatly influenced by the climate conditions and the operation parameters, the optimal working mode is dynamically changed, and the control of the system is required according to the dynamically changed information. Indeed, the method of the present invention is applicable not only to carbon dioxide heat pump systems, but also to some hybrid heat pump systems; the present invention has been made in view of the above-mentioned considerations.
The control method of the transcritical carbon dioxide heat pump system comprises the following steps:
step A: static analysis of a transcritical carbon dioxide heat pump system; specifically, the method comprises the following steps:
step SA1: obtaining a topological connection relation of a transcritical carbon dioxide heat pump system, and representing the topological connection relation as a connection diagram; wherein: the node types in the connection diagram are a heated end, a heat pump compressor, an electromagnetic valve and the like; edges are the connection relations between nodes; the node parameters comprise node identification, node type, node running state, node running parameters and the like; wherein: the running state of the node is related to the node working mode; the information of the system is represented and stored by means of a connection diagram.
Preferably: attributes of edges between nodes are used to identify attribute information of connection relationships.
Step SA2: analyzing the connection graph to obtain an adjustable node; specific: analyzing the node type to determine if the operational state of the node is adjustable; for example: the working states of the electromagnetic valve comprise closing and opening, and the working states of the electromagnetic valve of a specific type also comprise opening degree and the like; according to the type of the node, the operation state of the node is adjustable; the working state of the heat pump compressor is also adjustable according to the different working modes; the type of the heated end can be considered as non-adjustable, if a heat storage device such as a heat storage box and the like is arranged in the system, the heat storage device can determine whether the heat storage device is an adjustable node according to whether the working mode of the heat storage device is fixed; in the control process, adjustment is required according to the current node operation state.
The adjustable node can set its working state according to different dynamic control modes.
The static analysis is prepared for the subsequent dynamic analysis, the execution of the step A and the execution of the steps B and C are located in different modules, the execution of the steps B and C is completed by an acquisition and control module of the transcritical carbon dioxide heat pump system, and the execution of the step A is executed by a third party server or an analysis terminal.
And (B) step (B): the control state conversion judgment of the transcritical carbon dioxide heat pump system is carried out, and whether dynamic control is needed or not is determined by analyzing historical data; specific: acquiring historical data of system operation, and acquiring a plurality of target parameters based on the historical data; calculating a first target parameter according to the plurality of target parameters, acquiring a first target threshold value based on the connection diagram, and determining that dynamic control is not needed when all the first target parameters are better than the target threshold value; when all the first target parameters are worse than the target threshold value, determining that dynamic control is required and entering the next step; otherwise, calculating a plurality of second target parameters associated with different environment parameters based on the plurality of target parameters, acquiring a second target threshold according to the connection diagram, and comparing the plurality of second parameters with the second target threshold to determine whether dynamic control is required.
In the prior art, a one-knife cutting mode is often adopted for control judgment, and even the prior art does not consider the need or the possibility of dynamic control; in practice, however, for the heat pump system, the target parameters and the external environment are closely related to each other, and many factors must be fully considered to start the control system when appropriate; wherein: the first target parameter is irrelevant to the environment parameter and the change condition of the system load; the second target parameter is related to the dynamic change of the environmental parameter and the system load; the frequency of the preliminary judgment is very high, and the requirement on accuracy is limited; the matching method is that the step B is realized by adopting hardware solidification, so that the judging speed is increased, and whether the subsequent judgment needs to be carried out or not is simply determined by an AND OR gate.
The historical data of the system operation comprises various types of data such as target parameters and the like, and the target parameters needing to be compared are extracted from the data, for example: heating power, coefficient of performance (COP) of the system and the like; the target parameters of the system are related to the external environment of the system and the load dynamic, and the time complexity is increased by considering the external environment (the external environment is actually related to time), but the software and hardware burden of the control system is obviously increased if the comparison of the second target parameters is started for each analysis; processing the target parameter to be independent of the environmental parameter, dynamic change of system load to be the first target parameter, for example: calculating a mean value of the target parameters and the like; such that for each class of target parameters the first target parameter is a value; in fact, from a big data point of view, it is a good compromise between accuracy and speed to obtain the threshold value for comparison directly from the connection graph, irrespective of the specific target parameters and environment.
The obtaining the first target threshold based on the connection diagram specifically includes: acquiring node types and corresponding numbers thereof in a connection graph, and inquiring a standard server based on the node types and the corresponding numbers thereof to acquire a first target threshold; big data of a transcritical carbon dioxide heat pump system are stored in a standard server, and connection diagrams and target parameters of different systems are analyzed to obtain node types, corresponding numbers of the node types and corresponding relations of a first target threshold; for example: (N1.4, N2.8, N3.7) corresponds to a first target threshold value of (y 1, y 2); N1-N3 are node types, 4,8,7 are the number of corresponding node types; y1 and y2 are two first target threshold values, respectively; the first target threshold value is one or more; similarly, a simple method of calculating the first target threshold is to calculate an average value of the first target parameters corresponding to the same type of connection graph.
Alternatively, comparing the graphs according to the connection graphs, and finding a first standard threshold value corresponding to the similar connection graph; since many systems are constructed based on similar templates or templates, such connection graph-based comparisons are feasible and accurate pairs are greatly improved.
Preferably: the first target threshold value is obtained based on the connection graph, and the graph considering the node attribute is compared according to the connection graph.
Preferably: when the system topology connection relation is detected to be updated, acquiring node types and corresponding quantity corresponding to the updated connection diagram, inquiring a corresponding first target threshold value, and writing the first target threshold value into a hardware storage device of the system; the first target threshold value is actively pushed to the internal storage of the system, and the dynamic change of the control mode is directly triggered by hardware.
The calculating the plurality of second target parameters associated with different environment parameters based on the plurality of target parameters specifically comprises: determining a specific environment parameter, and determining a second target parameter corresponding to the specific environment parameter based on the target parameter; the specific environmental parameter is an environmental parameter when the environmental parameter is a specific value, for example, when the external environment is winter, or the target parameter value of the system under the environmental parameter corresponding to the range of a plurality of environmental temperature intervals; for example: acquiring a target parameter when the external temperature is 20 ℃, and calculating a second target parameter based on the target parameter; one simple way of calculating is a weighted average; in practice, the second target parameter value at the plurality of specific environmental parameters constitutes a hashed sampling point of the second parameter value and the specific environmental parameters, but no fitting is needed at this time to increase accuracy.
The obtaining a second target threshold according to the connection diagram specifically includes: acquiring node types and corresponding numbers thereof in the connection graph, inquiring a standard server based on the node types and the corresponding numbers thereof to acquire target parameters corresponding to the same type of connection graph, selecting target parameter values under specific environment parameters, fitting the target parameter values to obtain a fitting function between the environment parameters and the target parameters, and solving the target parameter values corresponding to the specific environment parameters based on the fitting function to serve as a second target threshold; since the specific environmental parameter is one or more, the corresponding second target threshold is also one or more.
The comparing the plurality of second target parameters with the second target threshold to determine whether dynamic control is needed, specifically: determining whether the proportion of times of the second target parameter exceeding a second target threshold exceeds a proportion threshold, if so, determining that dynamic control is not needed, otherwise, determining that dynamic control is needed; wherein: the proportional threshold is a preset value; the ratio is obtained by dividing the number of times of the superiority by the number of the second target parameters; the problem that the analysis of big data is not accurate enough due to the fact that the number of samples is not approximate enough under some environmental parameters is overcome through curve fitting and the like, a second target threshold value which is accurate in combination is obtained, system control is only carried out under the condition that the second target threshold value is necessary, and system control overhead is reduced.
Step C: collecting and analyzing system parameters for dynamic control; specifically, the method comprises the following steps:
step C1: collecting and analyzing system parameters to obtain a dynamic control mode list; specific: collecting environment parameters and system load parameters, and searching a dynamic control mode list corresponding to the combination of the environment parameters and the system load parameters; the corresponding relation between the environment parameters and the system load parameters and the dynamic control modes is stored in advance, a set of similar dynamic control modes corresponding to the environment parameters and the system load parameters is searched based on the current environment parameters and the system load parameters, and the dynamic control modes are ordered according to the similarity degree to obtain a dynamic control mode list; the number of the searched similar (environmental parameters and system load parameters) is one or more through the change of the similar degree, so that a control mode can be also assembled; the combination for which the search is directed can be expanded, and is not limited to two system parameters, namely an environment parameter and a system load parameter; simplification can also be achieved by principal component analysis.
Preferably: the corresponding target parameters of the dynamic control modes in the dynamic control mode set are superior to the target parameters acquired and calculated by the system.
The corresponding relation is obtained according to the historical operation data of the current system, a control mode with better target parameters is selected from the historical operation data for storage, and/or the corresponding relation is obtained through simulation by creating a simulation model for the system, and/or the corresponding relation is set according to experience.
The system parameters comprise environment parameters, system load parameters, running state parameters, running parameters and target parameters; the environment parameters comprise system environment parameters such as outdoor environment temperature and the like; the system load includes various types of heated terminals and the like; the operation state includes the operation modes of various nodes in the connection graph, for example: on, off, etc.; the operating parameters include parameter values at each node acquired by the sensor during operation of the system, such as: an outlet temperature value, an inlet temperature, etc. at node a; the target parameter is a target of dynamic control, for example: thermodynamic performance, carbon emissions, operating consumption, etc. over a cycle time; the number of each type of parameter is one or more, so that the characteristic description is performed from multiple angles; the above parameters are merely examples and may be extended and reduced depending on the acquisition means or limitations of hardware resources.
Preferably: and deleting the dynamic control mode which cannot be realized according to whether the nodes which need to change the working state are adjustable nodes in the new control mode.
Step C2: selecting a new control mode from the dynamic control mode list according to the operation parameters and/or the operation state parameters; specific: selecting a new control mode from the dynamic control mode list, wherein the difference between the running state parameter and/or the running parameter corresponding to the new control mode and the current running state parameter and/or the running parameter is the smallest; therefore, from the perspective of the system, the damage of equipment is avoided, and the repeated adjustment of old equipment is also avoided.
Preferably: in the selected new control mode, the nodes needing to change the working state are all adjustable nodes in the connection diagram; if the node itself is adjustable, but adjustment is difficult, the node may be set to be non-adjustable.
Step C3, performing system control according to the new control mode; specific: adjusting based on the operation state parameters and/or the operation parameters corresponding to the new control mode; the object of adjustment here is an adjustable node in the connection graph.
Preferably: and collecting the operation parameters in real time and judging whether the operation parameters are consistent with the operation parameters expected by the new control mode, if so, completing the new control mode.
And acquiring the working states of the adjustable nodes or the adjustable node groups in the connection diagram from the new control mode, and adjusting the working states of the adjustable nodes/the adjustable node groups.
Through the cyclic execution of the step A and the step B, the dynamic control of the system can be carried out when the load or the environment changes or changes greatly, so that the system is kept in an optimal working state; the system is controlled to learn between continuous and big data according to the judgment of whether the system needs to be adjusted or not, so that a better control mode is continuously sought, meanwhile, the control mode is based on the actual condition of the current system and is more suitable for the current system, and through remote learning and local implementation, the learning and implementation can be combined, so that the applicability is stronger.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In this specification, each embodiment is described in a related manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments in part.
Those of ordinary skill in the art will appreciate that implementing all or part of the steps in the above-described method embodiments may be accomplished by programming instructions in a computer readable storage medium, such as: ROM/RAM, magnetic disks, optical disks, etc.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.
Claims (5)
1. A method of transcritical carbon dioxide heat pump system control, the method comprising:
step A: static analysis of a transcritical carbon dioxide heat pump system; specifically, the method comprises the following steps:
step SA1: obtaining a topological connection relation of a transcritical carbon dioxide heat pump system, and representing the topological connection relation as a connection diagram; the node types in the connection diagram are a heated end, a heat pump compressor and an electromagnetic valve; edges are the connection relations between nodes; the node parameters comprise node identification, node type, node running state and node running parameters;
step SA2: analyzing the connection graph to obtain an adjustable node: analyzing the node type to determine if the operational state of the node is adjustable;
and (B) step (B): the control state conversion judgment of the transcritical carbon dioxide heat pump system is carried out, and whether dynamic control is needed or not is determined by analyzing historical data;
acquiring historical data of system operation, and acquiring a plurality of target parameters based on the historical data; calculating a first target parameter according to the plurality of target parameters, acquiring a first target threshold value based on the connection diagram, and determining that dynamic control is not needed when all the first target parameters are better than the target threshold value; when all the first target parameters are worse than the target threshold value, determining that dynamic control is required and entering the next step; otherwise, calculating a plurality of second target parameters associated with different environment parameters based on the plurality of target parameters, acquiring a second target threshold according to the connection diagram, and comparing the plurality of second parameters with the second target threshold to determine whether dynamic control is needed;
step C: system parameters are collected and analyzed for dynamic control.
2. The method of claim 1, wherein the adjustable node is configured to set its operating state according to a different dynamic control mode.
3. The method of claim 2, wherein the transcritical carbon dioxide heat pump system has an acquisition and control module for dynamic control.
4. A transcritical carbon dioxide heat pump system control method according to claim 3, wherein attributes of edges between nodes are used to identify attribute information of the connection relationship.
5. The method of claim 4, wherein the number of heat pump/compressors in the system is 2 or more.
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