Multi-connected control system for low-temperature cascade water chilling unit

Abstract

The invention relates to the technical field of water chilling unit control, in particular to a multi-connected control system for a low-temperature cascade water chilling unit. The system comprises a data acquisition module used for acquiring historical and real-time refrigeration parameter sequences; the refrigeration demand analysis module screens an abnormal period based on the volatility of different period parameter sequences, and calculates a historical refrigeration demand degree in combination with normal period data; the adjustment degree analysis module identifies abnormal units through parameter deviation and gives an alarm, non-abnormal units are sorted according to the parameter deviation degree to generate a demand sequence, and the pressure adjustment degree of the units is dynamically calculated in combination with liquid level distribution; and the adjustment control module performs pressure control on the unit according to the demand sequence and the pressure adjustment degree. According to the method, the historical data and the real-time parameters are fused, the abnormity is responded in time, the unit pressure is dynamically adjusted, the influence of the abnormal unit on the cooperative operation stability of the multiple units under the complex condition is reduced, and the refrigeration effect of the low-temperature refrigeration system meets the high reliability requirement.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water chiller control, and particularly relates to a multi-connection control system for a low-temperature cascade water chiller. BACKGROUND

[0002] The low-temperature cascade water chiller is widely used in chemical industry, pharmaceutical industry, food industry and other industries with strict requirements on low-temperature environment because it can provide efficient refrigeration in an ultra-low temperature environment. The current industry is developing towards automation, intelligence and energy saving and environmental protection, and the application of intelligent control technology improves the intelligent and automatic level of the unit.

[0003] In actual operation, when the refrigerating capacity needs to be adjusted according to different working conditions, the single-unit independent operation mode cannot flexibly adapt to the refrigerating capacity, resulting in serious waste of resources. Therefore, the control system of the low-temperature cascade water chiller usually adopts a single-unit independent operation model or a multi-unit linkage control mode. The multi-unit operation mode collects the operation parameters of each unit through a central controller, and issues control instructions according to a preset threshold to realize basic distribution of refrigerating capacity.

[0004] However, the refrigeration cycle structure, operation condition and environmental load of different units are different, and the cooperative coupling parameters will cause insufficient stability of the system. When a low-temperature cascade water chiller fails, inappropriate cooperative adjustment and distribution among multiple units will affect the accurate control of the low-temperature environment, and further affect the environmental stability of the production process. SUMMARY

[0005] In order to solve the technical problem that inappropriate cooperative adjustment and distribution among multiple units affect the accurate control of the low-temperature environment in the prior art, the purpose of the present application is to provide a multi-connection control system for a low-temperature cascade water chiller, and the technical solution adopted is as follows:

[0006] The present application provides a multi-connection control system for a low-temperature cascade water chiller, which comprises:

[0007] A data acquisition module is configured to acquire each refrigeration parameter sequence of each unit in each period in a historical period, and the refrigeration parameter sequence and the liquid level data of the refrigeration liquid of each unit in the current operation; the refrigeration parameters include pressure data of the compressor and temperature data of the condenser evaporator;

[0008] A refrigeration demand analysis module is configured to determine an abnormal period from all periods according to the fluctuation degree between different refrigeration parameter sequences in each period for each unit; analyze the approximate stability between each refrigeration parameter sequence in each abnormal period and non-abnormal period, and determine the historical refrigeration demand degree of each unit in each refrigeration parameter.

[0009] The adjustment degree analysis module is configured to screen out abnormal units and give a warning according to the deviation between the different refrigeration parameters and the historical refrigeration demand degree of the units at the current time, and sort the non-abnormal units according to the deviation to obtain a demand sequence; the units in the demand sequence are traversed, the pressure adjustment degree of the units is determined according to the predicted growth of the different refrigeration parameter sequences at the current time and in combination with the distribution of the liquid level data at the current time.

[0010] The adjustment control module is configured to perform pressure control on each unit at the current time according to the pressure adjustment degree based on the order of the demand sequence.

[0011] Further, the method for obtaining the abnormal period comprises:

[0012] For any unit, the correlation degree between the pressure data sequence and the temperature data sequence in each period is calculated to obtain a correlation index of each period.

[0013] In all periods of the unit, the period with a correlation index less than a preset abnormal threshold is regarded as an abnormal period of the unit.

[0014] Further, the method for obtaining the historical refrigeration demand degree comprises:

[0015] For any refrigeration parameter on each unit, the normal index of each abnormal period in the refrigeration parameter is obtained according to the correlation degree between the refrigeration parameter sequence of each abnormal period and all non-abnormal periods and the distribution stability degree of the refrigeration parameter in the abnormal period.

[0016] The normal period of the refrigeration parameter is determined according to the size of the normal index; the data mean of the refrigeration parameter in the normal period is calculated to obtain the historical refrigeration demand degree of the refrigeration parameter.

[0017] Further, the method for obtaining the normal index comprises:

[0018] For any abnormal period, the correlation degree between the refrigeration parameter sequence of the abnormal period and each non-abnormal period is calculated to obtain the similarity of the abnormal period and each non-abnormal period in the refrigeration parameter; the similarity mean of the abnormal period and all non-abnormal periods in the refrigeration parameter is calculated to obtain the fluctuation approximation degree of the abnormal period in the refrigeration parameter.

[0019] The variance of all data of the refrigeration parameter in the abnormal period is negatively correlated to obtain the continuous stability degree of the abnormal period in the refrigeration parameter.

[0020] The normal index of the refrigeration parameter in the abnormal period is obtained in combination with the fluctuation approximation degree and the continuous stability degree of the refrigeration parameter in the abnormal period.

[0021] Further, the method for obtaining the normal period comprises:

[0022] When the normal index of all refrigeration parameters in the abnormal period is less than the preset normal threshold, the abnormal period is recorded as the abnormal period of each refrigeration parameter;

[0023] Otherwise, the abnormal period is recorded as the abnormal period of the refrigeration parameter corresponding to the minimum normal index, and as the normal period of another refrigeration parameter; all non-abnormal periods are recorded as the normal period of each refrigeration parameter.

[0024] Further, the method for obtaining the demand sequence comprises:

[0025] For any unit, according to the difference between each refrigeration parameter of the unit at the current time and the historical refrigeration demand degree, the deviation degree of each refrigeration parameter is obtained; when the deviation degree of the refrigeration parameter of the unit exceeds the preset expected threshold, the unit is recorded as an abnormal unit and an alarm is given;

[0026] In combination with the arrangement sequence number of each non-abnormal unit in the sequence of the deviation degree of each refrigeration parameter, the demand sequence number of each non-abnormal unit is obtained; all non-abnormal units are arranged in order of demand sequence number to obtain a demand sequence.

[0027] Further, the method for obtaining the demand sequence number comprises:

[0028] For any refrigeration parameter, all non-abnormal units are arranged in order of the deviation degree of the refrigeration parameter from large to small to obtain a unit sequence of the refrigeration parameter;

[0029] For any non-abnormal unit, the sequence number of the unit in the unit sequence corresponding to each refrigeration parameter is obtained; the average of the sequence numbers of the unit in all unit sequences is calculated and rounded up to obtain the demand sequence number of the unit.

[0030] Further, the method for obtaining the pressure adjustment degree comprises:

[0031] For any unit in the demand sequence, each refrigeration parameter at the next time in the current operation is predicted, and in combination with the growth degree of all refrigeration parameters relative to the predicted situation, the demand change degree of the unit is obtained;

[0032] According to the proportion of the liquid level data of the unit at the current time in all units, the demand supply index of the unit is obtained;

[0033] In combination with the demand supply index and the demand change degree of the unit, the pressure adjustment degree of the unit is obtained.

[0034] Further, the method for obtaining the demand change degree comprises:

[0035] Based on the current running each kind of refrigeration parameter sequence of the unit, the predicted refrigeration parameter of the next time is obtained;

[0036] For any kind of refrigeration parameter, the numerical difference between the refrigeration parameter at the current time and the predicted refrigeration parameter, the proportion in the historical refrigeration demand degree of the refrigeration parameter, is taken as the growth rate of the refrigeration parameter, and the product of the growth rate of the refrigeration parameter and the preset growth time is taken as the growth index of the refrigeration parameter.

[0037] The mean value of the growth index of all refrigeration parameters at the current time is normalized to obtain the demand change degree of the unit.

[0038] Further, the pressure control of each unit at the current time is carried out based on the demand sequence order and the pressure adjustment degree, comprising:

[0039] According to the order of the units in the demand sequence, the product of the pressure adjustment degree of each unit and the preset adjustment parameter is taken as the adjustment value of the unit, and the pressure data of the unit at the current time is increased by the adjustment value to obtain the adjusted pressure data of the unit.

[0040] The present application has the following beneficial effects:

[0041] The present application screens abnormal periods based on the volatility of different period parameter sequences, calculates the historical refrigeration demand degree combined with normal period data, reflects the long-term operation law of the unit, avoids the misjudgment of cold load demand caused by short-term fluctuations or noise interference, and improves the reliability of demand prediction. Through demand and current parameter deviation analysis, abnormal units are identified and an alarm is triggered, and the non-abnormal units are sorted according to the parameter deviation degree to generate a demand sequence, and the pressure adjustment degree of the unit is dynamically calculated combined with the liquid level distribution, to ensure that the adjustment strategy considers the current running state and takes into account the physical constraints of refrigerant inventory, to avoid system imbalance caused by excessive adjustment. Finally, according to the demand sequence order and the pressure adjustment degree, the pressure parameter of the unit is optimized one by one, to realize the dynamic redistribution of refrigeration capacity, to maintain the system refrigeration capacity through the collaborative compensation of the remaining units after the isolation of abnormal units, to reduce the impact of faults on the overall performance. The present application fuses historical data and real-time parameters, responds to abnormalities in time and dynamically adjusts the pressure of the unit, reduces the impact of abnormal units on the stability of multi-unit cooperative operation in complex situations, and makes the refrigeration effect of the low-temperature refrigeration system meet the high reliability requirements. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, below will briefly introduce the drawings needed in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative effort.

[0043] Figure 1 A structure diagram of a multi-connected control system for a low-temperature cascade water chiller provided by an embodiment of the present application;

[0044] Figure 2 A flow chart of a method for obtaining a pressure adjustment degree provided by an embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined inventive purpose, below, the specific embodiments, structure, features and effects of a multi-connected control system for a low-temperature cascade water chiller according to the present application are described in detail in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0047] Below, the specific scheme of a multi-connected control system for a low-temperature cascade water chiller provided by the present application is specifically described in combination with the drawings.

[0048] The low-temperature cascade water chiller group is composed of multiple low-temperature cascade water chillers, each of which is composed of multiple independent refrigeration cycles using different refrigerants and connected through condensers and evaporators. Each low-temperature cascade water chiller is equipped with an independent local controller. The local controller receives instructions from the central control unit on one hand, and collects real-time operating parameters of the local chiller, such as the pressure, temperature and liquid level of the compressor and condenser, on the other hand, and uploads these data to the central control unit through a data communication network.

[0049] The data communication network adopts a combination of industrial Ethernet and wireless communication to ensure high speed, stability and reliability of data transmission. For chillers close to the central control unit, industrial Ethernet is used for connection to ensure low delay and high bandwidth of data transmission. For chillers located remotely or difficult to wire, wireless communication modules are used for connection to enhance the flexibility and scalability of the system.

[0050] The central control unit is the main part of the control system, responsible for collecting the operation data of each unit and analyzing the control requirements, and sending control instructions to each unit to realize the coordinated operation of multiple units through the control system. Figure 1 Fig. 1 shows a structure diagram of a multi-unit control system for a low-temperature cascade cold water unit according to an embodiment of the present application, which includes a data acquisition module 101, a refrigeration demand analysis module 102, an adjustment degree analysis module 103, and an adjustment control module 104.

[0051] The data acquisition module 101 is configured to acquire each refrigeration parameter sequence of each unit in each period in a historical period, and the refrigeration parameter sequence and the refrigerant level data of each unit in the current operation; the refrigeration parameters include the pressure data of the compressor and the temperature data of the condenser.

[0052] In the embodiment of the present application, during the working process of the low-temperature cascade cold water unit, the local controller of each low-temperature cascade cold water unit acquires the pressure data of the compressor, the time sequence temperature data of the condenser, and the refrigerant level data in the condenser. Since the lower the evaporation temperature of the low-temperature cascade cold water unit is during the working process, the greater the refrigeration load demand is, and the compressor power needs to be increased to meet the refrigeration demand. Therefore, the demand degree of the cold load is different under different working conditions.

[0053] To ensure the cooperation of the multiple units in the current operation, the historical demand is calibrated and analyzed, and the historical period of 7 days under the same working condition is analyzed, in which the collection frequency is once every 10 seconds, and each day is taken as a period to reflect the operation. Under normal circumstances, the operation in the period should be consistent, in which the compressor pressure and power of the low-temperature cascade cold water unit are positively correlated, and the greater the pressure is, the greater the corresponding power is. Therefore, the abnormality caused by the temperature and the pressure is analyzed to analyze the demand adjustment of the multiple units, and the pressure data and the temperature data are recorded as different refrigeration parameters for subsequent acquisition and analysis.

[0054] The refrigeration demand analysis module 102 is configured to determine the abnormal period from all periods according to the fluctuation degree between different refrigeration parameter sequences in each period for each unit; analyze the approximate stability between each abnormal period and non-abnormal period in each refrigeration parameter sequence to determine the historical refrigeration demand degree of each unit in each refrigeration parameter.

[0055] In the normal stable period, the stable trend change of temperature is the same as pressure, so that the abnormal period with poor correlation is screened out through the correlation fluctuation of temperature data and pressure data in time sequence, and does not participate in the demand situation analysis. Considering that there are two different situations of temperature or pressure in abnormal situation, in order to improve the calibration comprehensiveness of historical data, the approximation degree of temperature and pressure between abnormal and non-abnormal is analyzed respectively, so as to screen out more complete data in the normal trend period, and obtain the historical demand.

[0056] Preferably, in the embodiment of the present application, the abnormal period acquisition method comprises:

[0057] For any unit, the correlation degree between the pressure data sequence and the temperature data sequence in each period is calculated to obtain the correlation index of each period. When the correlation degree is larger, that is, the correlation index is larger, the cooperative change of pressure data and temperature data is more consistent, and the fluctuation of the cooling load demand of the unit is more normal. It should be noted that the correlation degree calculation between sequences is a well-known technical means familiar to those skilled in the art, such as using DTW algorithm or Pearson correlation coefficient method, which will not be repeated here.

[0058] In all periods of the unit, the period with a correlation index less than a preset abnormal threshold is regarded as an abnormal period of the unit. In the embodiment of the present application, the preset abnormal threshold is set to 0.5, and the specific value can be adjusted by the implementer. When the correlation index is smaller, the cooling load demand fluctuation between temperature and pressure is more abnormal, and the abnormal period is preliminarily screened out for further analysis of which refrigeration parameter causes the abnormal situation.

[0059] By analyzing the abnormal performance of each refrigeration parameter in the abnormal period, all normal periods are screened out as much as possible to determine the historical demand more accurately. Preferably, in the embodiment of the present application, the acquisition method of the historical refrigeration demand degree comprises:

[0060] For any refrigeration parameter on each unit, according to the correlation degree between each abnormal period and all non-abnormal periods in the sequence of the refrigeration parameter, and the distribution stability degree of the refrigeration parameter in the abnormal period, the normal index of each abnormal period in the refrigeration parameter is obtained. Through the similarity analysis between non-abnormal periods and the change trend of the parameter itself tends to be normal, the normal degree of the refrigeration parameter in the abnormal period is reflected, and in the embodiment of the present application, the acquisition method of the normal index comprises:

[0061] Firstly, for any one abnormal period, the correlation between the abnormal period and the sequence of the refrigeration parameter of each non-abnormal period is calculated to obtain the similarity of the abnormal period and each non-abnormal period in the refrigeration parameter. The non-abnormal period is a period with normal data change trend, and the similarity is calculated by correlation with the non-abnormal period. The higher the similarity is, the closer the change in the abnormal period is to normal.

[0062] Further combining the similarity analysis results between the abnormal period and all normal periods, the similarity average of the abnormal period and all non-abnormal periods in the refrigeration parameter is calculated to obtain the fluctuation approximation degree of the abnormal period in the refrigeration parameter. The higher the fluctuation approximation degree is, the more similar the data change between the abnormal period and the overall normal period is, and the closer the performance of the refrigeration parameter in the abnormal period is to the normal period.

[0063] Then, the variance of all data of the refrigeration parameter in the abnormal period is negatively correlated to obtain the continuous stability of the abnormal period in the refrigeration parameter. The fluctuation stability of the refrigeration parameter in the abnormal period is reflected by the variance calculation. The smaller the variance is, the more stable the sequence fluctuation of the refrigeration parameter is, so the higher the continuous stability is, which indicates that the refrigeration period performs more normally and stably in the abnormal period.

[0064] Finally, the normal index of the refrigeration parameter in the abnormal period is obtained by combining the fluctuation approximation degree and the continuous stability of the refrigeration parameter in the abnormal period. In the embodiment of the present application, the product of the fluctuation approximation degree and the continuous stability of the refrigeration parameter in the abnormal period is calculated to obtain the normal index of the refrigeration parameter in the abnormal period. The higher the normal index is, the more normal the performance of the refrigeration parameter in the abnormal period is.

[0065] Therefore, the normal period of the refrigeration parameter is determined according to the size of the abnormal index. In the embodiment of the present application, when the normal index of all refrigeration parameters in the abnormal period is less than the preset normal threshold, it indicates that the normal representation of temperature and pressure is small, and at this time, both parameters may be abnormal. Therefore, the abnormal period is recorded as the abnormal period of each refrigeration parameter. The preset normal threshold is set to 0.6, and the specific value can be adjusted according to the specific implementation.

[0066] Otherwise, it indicates that there is a higher normal representation of parameters in the abnormal period. The abnormal period is recorded as the abnormal period of the refrigeration parameter corresponding to the minimum normal index, and as the normal period of another refrigeration parameter. Since there is at least one refrigeration parameter representing abnormality in the abnormal period, the abnormal period is an abnormal performance period for the refrigeration parameter with smaller normal index, and a normal performance period for another refrigeration parameter. At the same time, all the non-abnormal periods preliminarily screened are recorded as the normal period of each refrigeration parameter.

[0067] Finally, for each refrigeration parameter, the data of the abnormal performance period is screened out and does not participate in the historical result analysis, and the reliability of the historical analysis is improved. The mean value of the data of the refrigeration parameter in the normal period is calculated, and the historical refrigeration demand degree of the refrigeration parameter is obtained, reflecting the required temperature data and pressure data size under the historical unit operation condition.

[0068] The adjustment degree analysis module 103 is used for screening out abnormal units and warning according to the deviation condition between the current time of the unit and the historical refrigeration demand degree of different refrigeration parameters, and sequencing the demand sequence of the non-abnormal units according to the deviation condition; the units in the demand sequence are traversed, and the pressure adjustment degree of the unit is determined according to the predicted growth condition of the current time of different refrigeration parameter sequences and the distribution of the current time liquid level data.

[0069] In the multi-unit cooperative regulation, if a single unit appears data deviation and the like, due to the coupling of multiple data, interlocking abnormalities are easily generated, in order to ensure the timely stability of the temperature fault-tolerant control, other units need to be re-distributed refrigeration capacity to maintain the basic refrigeration demand of the system. Therefore, after screening the abnormality through the deviation condition, further demand sequencing is performed to ensure that the unit deviating from the target state can be adjusted preferentially, and the risk of system overall performance decline or fault increase is reduced.

[0070] Preferably, in the embodiment of the application, the method for obtaining the demand sequence comprises:

[0071] First, the abnormal unit condition is screened, for any unit, the deviation degree of each refrigeration parameter is obtained according to the difference between each refrigeration parameter of the unit at the current time and the historical refrigeration demand degree, reflecting the deviation degree from the historical state. When the deviation degree of the refrigeration parameter of the unit exceeds the preset expected threshold, the unit is marked as an abnormal unit and an alarm is given, and for temperature and pressure, when the deviation of any refrigeration parameter of the unit is high, it means that a high abnormality may occur during operation, and the unit is marked as an abnormal unit. In the embodiment of the application, the preset expected threshold can be 10, and the implementer can adjust it according to the specific implementation, and the expected threshold of different refrigeration parameters can be inconsistent, which is not limited herein.

[0072] For the abnormal unit that may have a fault, protection measures need to be taken immediately by the local controller to prevent the fault from expanding, and the fault alarm information of the abnormal low-temperature cascade cold water chiller unit can be sent to the maintenance personnel through the data communication network, including the fault unit number, fault type and the like. Detailed information, so as to facilitate the maintenance personnel to repair in time. The refrigeration capacity of other normal units is re-distributed to maintain the basic refrigeration demand of the system.

[0073] And consider the size of the deviation of demand adjustment demand sequence, for large deviation priority adjustment to quickly match the cold load demand, if the difference is small unit priority adjustment, may lead to high deviation unit is not timely corrected, delay adjustment may cause failure, the system deviates from the target state, reduce the energy efficiency and stability.

[0074] Therefore further combined with the deviation degree size sequence of each non- abnormal unit in each refrigeration parameter, obtain the demand sequence number of each non- abnormal unit, in the embodiment of the application, for any kind of refrigeration parameter, according to the deviation degree of the refrigeration parameter from big to small sequence will be arranged, obtain the unit sequence of the refrigeration parameter, the deviation size of each unit under temperature and pressure is sorted from big to small, when the serial number is smaller, the deviation degree of the unit in the remaining units is higher, and the priority adjustment demand is higher.

[0075] Therefore, for any one non- abnormal unit, obtain the serial number of the unit in the unit sequence corresponding to each refrigeration parameter, calculate the average of the serial number of the unit in all unit sequences, and round up to obtain the demand sequence number of the unit. The smaller the demand sequence number, the higher the deviation degree of the unit in the remaining units, and the higher the priority demand. It should be noted that the rounding processing is a technical means familiar to those skilled in the art, which will not be repeated here.

[0076] Finally, all non- abnormal units are arranged in order of demand sequence number to obtain the demand sequence. By arranging in order from small to large, when the demand sequence number is the same, multiple units can be arranged in priority in all unit sequences with smaller sequence numbers, so that the subsequent unit dynamic optimization operating parameters can be more quickly reduced. The overall deviation ensures that the system quickly converges to a stable state.

[0077] Traverse the units in the demand sequence, analyze the degree of adjustment required by the possible growth of the deviation of each unit at the next time, and obtain the final suitable pressure adjustment degree of the unit combined with the available refrigeration capacity constraint. Preferably, in the embodiment of the application, the method for obtaining the pressure adjustment degree is described in Figure 2 , which shows a flow chart of a method for obtaining a pressure adjustment degree provided by an embodiment of the application. The method comprises the following steps:

[0078] S311: for any unit in the demand sequence, predict each refrigeration parameter at the next time in the current operation, and obtain the demand change degree of the unit combined with the growth degree of all refrigeration parameters relative to the predicted situation.

[0079] The energy consumption fluctuation caused by the predicted reduction of the lag response is reduced, when the deviation degree of the parameter growth in the next moment in operation is higher, the adjustment intensity required by the unit is higher, to ensure the stability of the system at the current moment, in the embodiment of the application, the predicted refrigeration parameter at the next moment is obtained based on each refrigeration parameter sequence of the unit in the current operation, it should be noted that the sequence data prediction is a well-known technical means familiar to those skilled in the art, such as AIRMA method, etc., which is not described and limited here.

[0080] For any kind of refrigeration parameter, the numerical difference between the refrigeration parameter at the current moment and the predicted refrigeration parameter, the proportion in the historical refrigeration demand degree of the refrigeration parameter, is taken as the growth rate of the refrigeration parameter, if the deviation between the numerical value of the parameter at the current moment and the predicted parameter is higher, the possible deviation growth is greater, the ratio of the numerical difference to the historical refrigeration demand degree reflects the proportion of the growth, and the growth rate is obtained.

[0081] In order to facilitate the growth of temperature and pressure parameters in the subsequent combination, the product of the growth rate of the refrigeration parameter and the preset growth time is taken as the growth index of the refrigeration parameter, and the preset growth time is set to 3 in the embodiment of the application, and the specific value can be adjusted by the implementer.

[0082] The mean value of the growth index of all refrigeration parameters at the current moment is normalized to obtain the demand change degree of the unit, and the demand change degree is obtained by combining the growth deviation of temperature and pressure. When the demand change degree is higher, the adjustment degree is higher. It should be noted that normalization is a well-known technical means for those skilled in the art, and the selection of normalization can be linear normalization or standard normalization, and the specific normalization method is not limited here.

[0083] S312: obtaining the demand supply index of the unit according to the proportion of the liquid level data of the unit in all units at the current moment.

[0084] The liquid level is higher, indicating that the low-temperature cascade cold water unit can provide more refrigeration capacity in the subsequent, and vice versa. Therefore, the unit with higher liquid level in the low-temperature cascade cold water unit can provide more refrigeration capacity in the subsequent. In the embodiment of the application, the ratio of the liquid level data of the unit at the current moment to the sum of the liquid level data of all units in the demand sequence is taken as the demand supply index.

[0085] S313: combining the demand supply index and the demand change degree of the unit as the pressure adjustment degree of the unit.

[0086] The pressure adjustment degree of the unit is obtained by combining the liquid level condition with the demand change degree, and in the embodiment of the present application, the product of the demand supply index of the unit and the demand change degree is taken as the pressure adjustment degree of the unit, and the greater the pressure adjustment degree of the unit, the greater the refrigeration adjustment degree required by the unit.

[0087] The adjustment control module 104 is used for performing pressure control on each unit at the current time according to the pressure adjustment degree based on the sequence of the demand sequence.

[0088] All units are adjusted in sequence in the demand sequence, so that the low-temperature cascade water chiller unit with high cold demand can be processed as quickly as possible, and the basic refrigeration demand of the system can be maintained to a higher degree. In the embodiment of the present application, the product of the pressure adjustment degree of each unit and the preset adjustment parameter is taken as the adjustment value of the unit according to the sequence of the unit in the demand sequence, wherein the preset adjustment parameter is set to 5, and the maximum degree of adjustable amount is controlled by the preset adjustment parameter, which is not limited herein.

[0089] Further, the pressure data of the unit at the current time is increased by the adjustment value, and the pressure value of the low-temperature cascade water chiller unit is continuously increased by the adjustment value on the basis of the existing pressure, so as to obtain the adjusted pressure data of the unit, and realize dynamic collaborative control of multiple units.

[0090] It should be noted that, in order to facilitate calculation, all index data involved in the operation in the embodiment of the present application are subjected to data preprocessing, and then the dimension influence is cancelled. The means for removing the dimension influence is a technical means familiar to those skilled in the art, which is not limited herein.

[0091] In summary, the present application screens abnormal periods based on the fluctuation of different period parameter sequences, calculates the historical refrigeration demand degree based on normal period data, reflects the long-term operation law of the unit, avoids misjudgment of cold load demand caused by short-term fluctuation or noise interference, and improves the reliability of demand prediction. Through demand and current parameter deviation analysis, abnormal units are identified and an alarm is triggered. At the same time, the non-abnormal units are sorted according to the parameter deviation degree to generate a demand sequence, and the pressure adjustment degree of the unit is dynamically calculated based on the liquid level distribution, so as to ensure that the adjustment strategy considers the current operating state and takes into account the physical constraints of refrigerant inventory, avoiding system imbalance caused by excessive adjustment. Finally, the pressure parameter of the unit is optimized one by one according to the sequence of the demand sequence and the pressure adjustment degree, so as to realize dynamic redistribution of refrigeration capacity, maintain the refrigeration capacity of the system through the collaborative compensation of the remaining units after isolation of the abnormal unit, and reduce the influence of the fault on the overall performance. The present application responds to the abnormality in time through the fusion of historical data and real-time parameters, dynamically adjusts the pressure of the unit, reduces the influence of the abnormal unit on the stability of the collaborative operation of multiple units under complex conditions, and makes the refrigeration effect of the low-temperature refrigeration system meet the high reliability requirement.

[0092] It is to be noted that the sequential order of the above-described embodiments of the present application only for the purpose of description, but not the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0093] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments.

Claims

1. A multi-connected control system for a low-temperature cascade chiller, characterized in that: The system comprises: The data acquisition module is used to obtain each refrigeration parameter sequence of each unit in each cycle in the historical period, as well as the refrigeration parameter sequence and refrigerant level data of each unit in current operation; the refrigeration parameters include: compressor pressure data and condenser evaporator temperature data; The cooling demand analysis module is used to determine the abnormal cycle from all cycles for each unit based on the degree of fluctuation between different cooling parameter sequences in each cycle; analyze the approximate stability of each abnormal cycle and non-abnormal cycle between each cooling parameter sequence, and determine the historical cooling demand of each unit for each cooling parameter; The adjustment degree analysis module is used to screen out abnormal units and issue warnings based on the deviation between the unit's current cooling parameters and historical cooling demand. It also sorts non-abnormal units based on the deviation to obtain a demand sequence. It traverses the units in the demand sequence and determines the unit's pressure adjustment degree based on the predicted growth of the different cooling parameter sequences at the current moment and the distribution of the current liquid level data. The adjustment control module is used to control the pressure of each unit at the current moment according to the pressure adjustment degree based on the demand sequence order.

2. A multi-connected control system for a low-temperature cascade chiller according to claim 1, characterized in that: The method for obtaining the abnormal period includes: For any unit, calculate the correlation between the pressure data series and the temperature data series in each cycle to obtain the correlation index of each cycle; Among all the cycles of the unit, the cycle in which the relevant indicator is less than the preset abnormal threshold is regarded as the abnormal cycle of the unit.

3. The multi-control system for a low-temperature cascade chiller according to claim 1, characterized in that: The method for obtaining the historical refrigeration demand degree includes: For any refrigeration parameter on each unit, the normal index of each abnormal cycle in the refrigeration parameter sequence is obtained based on the correlation between each abnormal cycle and all non-abnormal cycles, as well as the distribution stability of the refrigeration parameter in the abnormal cycle. The normal cycle of the refrigeration parameter is determined according to the size of the normal index; the data mean of the refrigeration parameter in the normal cycle is calculated to obtain the historical refrigeration demand of the refrigeration parameter.

4. A multi-connected control system for a low-temperature cascade chiller according to claim 3, characterized in that: The method for obtaining the normal indicators includes: For any abnormal cycle, calculate the correlation between the abnormal cycle and the cooling parameter sequence of each non-abnormal cycle to obtain the similarity between the abnormal cycle and each non-abnormal cycle in the cooling parameter; calculate the mean similarity between the abnormal cycle and all non-abnormal cycles in the cooling parameter to obtain the fluctuation approximation of the abnormal cycle in the cooling parameter; Perform negative correlation mapping on the variance of all data of the refrigeration parameter in the abnormal period to obtain the continuous stability of the refrigeration parameter in the abnormal period; The normal index of the refrigeration parameter in the abnormal period is obtained by combining the fluctuation approximation and the continuous stability of the refrigeration parameter in the abnormal period.

5. The multi-control system for a low-temperature cascade chiller according to claim 3, characterized in that: The method for obtaining the normal cycle includes: When the normal indicators of all cooling parameters in the abnormal period are less than the preset normal threshold, the abnormal period is recorded as an abnormal period for each cooling parameter; Otherwise, the abnormal cycle is recorded as the abnormal cycle of the corresponding refrigeration parameter when the normal index is minimum, and recorded as the normal cycle of another refrigeration parameter; all non-abnormal cycles are recorded as normal cycles of each refrigeration parameter.

6. The multi-control system for a low-temperature cascade chiller according to claim 1, characterized in that: The method for obtaining the demand sequence includes: For any unit, the deviation of each cooling parameter is obtained based on the difference between the current cooling parameter and the historical cooling demand of the unit. When the deviation of the cooling parameter of the unit exceeds the preset expected threshold, the unit is marked as an abnormal unit and an alarm is issued. Combine the arrangement sequence number of each non-abnormal unit in the deviation size sequence of each refrigeration parameter to obtain the demand sequence number of each non-abnormal unit; arrange all non-abnormal units in the order of demand sequence number to obtain the demand sequence.

7. A multi-control system for a low-temperature cascade chiller according to claim 6, characterized in that: The method for obtaining the requirement sequence number includes: For any cooling parameter, arrange all non-abnormal units in descending order of the deviation of the cooling parameter to obtain the unit sequence of the cooling parameter; For any non-abnormal unit, obtain the sequence number of the unit in the unit sequence corresponding to each cooling parameter; calculate the average of the sequence numbers of the unit in all unit sequences and round it up to obtain the required sequence number of the unit.

8. The multi-control system for a low-temperature cascade chiller according to claim 1, characterized in that: The method for obtaining the pressure adjustment degree includes: For any unit in the demand sequence, predict each cooling parameter at the next moment in the current operation, and combine the growth degree of all cooling parameters relative to the predicted situation to obtain the demand change degree of the unit; According to the proportion of the liquid level data of the unit in all units at the current moment, the demand and supply index of the unit is obtained; The demand supply index and demand change degree of the unit are combined to serve as the pressure adjustment degree of the unit.

9. A multi-connected control system for a low-temperature cascade chiller according to claim 8, characterized in that: The method for obtaining the demand change degree includes: Based on the current operation of each cooling parameter sequence of the unit, the predicted cooling parameters at the next moment are obtained; For any cooling parameter, the difference between the current cooling parameter and the predicted cooling parameter, as well as its percentage in the historical cooling demand of the cooling parameter, is used as the growth rate of the cooling parameter. The product of the growth rate of the cooling parameter and the preset growth time is used as the growth index of the cooling parameter. Normalize the mean of the growth indexes of all cooling parameters at the current moment to obtain the demand change degree of the unit.

10. The multi-control system for a low-temperature cascade chiller according to claim 1, characterized in that: The pressure control of each unit at the current moment is performed based on the demand sequence and the pressure adjustment degree, including: According to the order of the units in the demand sequence, the product of the pressure adjustment degree of each unit and the preset adjustment parameter is taken as the adjustment value of the unit; the pressure data of the unit at the current moment is increased by the adjustment value to obtain the pressure data of the unit after adjustment.

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