Method for dynamically evaluating refrigeration performance of water-cooled inter-row air conditioning based on operation data analysis

Abstract

The application discloses a water-cooled inter-car air conditioner refrigeration performance dynamic evaluation method based on operation data analysis, and method steps are as follows: S1: dividing a load state category of a refrigeration equipment end operation, and dividing into a corresponding category according to a RH value of the inter-car air conditioner; S2: determining a PR parameter range of the air conditioner; S3: if the PR parameter of the air conditioner is in interval I i , it is judged that the operation is normal, otherwise, S4 is executed; S4: performing clustering analysis based on electric energy consumption; S5: further checking according to a CV parameter of the inter-car air conditioner; S6: calculating and evaluating a lag influence condition; S7: performing real-time evaluation on operation performance of each inter-car air conditioner equipment according to a time interval period; and S8: updating the PR parameter in S2. The application can timely find out the inter-car air conditioner equipment with operation performance out of a group, low performance, a performance deterioration trend or severe performance jitter, and provides quantitative data support for improving end refrigeration system performance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of air conditioning refrigeration performance evaluation, and particularly relates to a water-cooled inter-car air conditioner refrigeration performance dynamic evaluation method based on operation data analysis. BACKGROUND

[0002] The inter-car air conditioner is tested for theoretical performance when product design and manufacturing are performed, but in actual operation environment, its operation state is always dynamically changed, and it is not appropriate to evaluate with static data at a moment, so there is still a lack of an objective and operable method in evaluating whether the operation performance is normal. SUMMARY

[0003] Based on the technical problems existing in the background technology, the present application provides a water-cooled inter-car air conditioner refrigeration performance dynamic evaluation method based on operation data analysis, which can timely find the inter-car air conditioner equipment with performance deterioration trend or performance jittering, and provide quantitative data support for improving the performance of the terminal refrigeration system.

[0004] The water-cooled inter-car air conditioner refrigeration performance dynamic evaluation method based on operation data analysis provided by the present application has the following method steps:

[0005] S1: dividing the load state category of the refrigeration equipment end operation, and dividing into the corresponding category according to the ratio RH of the operation electric power consumption of the inter-car air conditioner to the rated power consumption;

[0006] S2: determining the ratio PR parameter range of the operation electric power consumption of the host computer with load to the operation electric power consumption of the air conditioner corresponding to the normal operation performance of the air conditioner;

[0007] S3: if the PR parameter of the air conditioner is in interval I i , it is judged to be normal, otherwise, S4 is executed;

[0008] S4: performing clustering analysis based on electric power consumption;

[0009] S5: further checking according to the related coefficient CV of the supply and return air temperature difference and the inlet and outlet water temperature difference of the inter-car air conditioner and the change before and after;

[0010] S6: calculating the evaluation lag influence situation;

[0011] S7: repeating the above S3-S6 steps according to the time interval period s0 to the next moment t+s0, and real-time evaluating the operation performance of each inter-car air conditioner equipment;

[0012] S8: update the PR parameter in S2, after time interval t0, execute S2, recalculate PR value for each type of air conditioner, and execute S3-S7 for analysis.

[0013] Preferably, the classification in S1 includes no load [0, 0.05], light load (0.05, 0.40], half load (0.40, 0.70], heavy load (0.70, 0.90], and full load (0.90, 1.00].

[0014] Preferably, the method for determining the PR parameter range in S2 is to calculate the PR parameter of all inter-row air conditioners in each type i (i = 1,..., N) respectively, take iPR = maxi (PR i ) as the air conditioner refrigeration performance reference of the load state of the type, and determine I i = [a*iPR, iPR] as the normal range of the refrigeration performance of the inter-row air conditioners of the type, where 0.7≤a<1.0. i

[0015] Preferably, when analyzing the refrigeration performance of the machine room level or micro-module level, for all inter-row air conditioners in each type i (i = 1,..., N), the average value PR ij of the parameter PR i of each inter-row air conditioner j (j = 1,..., J) in the machine room or micro-module is calculated respectively, iPR = maxi (PR i ) is taken as the air conditioner refrigeration performance reference of the load state of the type.

[0016] Preferably, the method for clustering analysis in S4 is to perform automatic clustering analysis on all operation data consisting of host load operation electric power Ph and air conditioner operation electric power Pc within the air conditioner t1 time, aggregate into K types, and classify each operation data into one type (k = 1,..., K); if the number of operation data in the type where the current operation data of the air conditioner is located is less than n0, exit, and after n0 data periods, re-enter S3; otherwise, enter S5 for analysis and confirmation.

[0017] Preferably, the troubleshooting method in S5 is to calculate the CV parameter value corresponding to the supply and return air temperature difference and the inlet and return water temperature difference in the type where the current operation data of the air conditioner is located; if the value is located in the interval C i = [-1.00, -0.70], indicating significant negative correlation, it is determined to be normal operation; otherwise, calculate the CV parameter value corresponding to the front and back change amount of the supply and return air temperature difference and the front and back change amount of the inlet and return water temperature difference, if the value is located in the interval C i ​, indicating that there is a significant negative correlation, then it is determined that the operation is normal; otherwise, the explicit refrigerating capacity value Cc_B of the air conditioner under the current working condition is obtained by referring to the refrigerating performance parameter table of the air conditioning equipment, and whether the main machine load operation electric power consumption Ph value at the current time is in the refrigerating capacity range [b*Cc_B, Cc_B] of the better refrigerating performance, wherein b is taken as 0.8, if yes, it is determined that the operation is normal; otherwise, S6 is entered for analysis and confirmation.

[0018] Preferably, the method for calculating and evaluating the hysteresis effect in S6 is as follows: for the class in which the current time data of the air conditioning equipment is located, the influence distribution of the cross correlation coefficient CF of the supply and return air temperature difference of the inter-row air conditioner and the inlet and return water temperature difference and its change before and after is calculated, if the hysteresis effect is within the time range of n1 monitoring data update periods s1, it is determined that the operation is normal; otherwise, it is determined that the inter-row air conditioner has abnormal operation performance, and the administrator is reminded to alarm, and the inter-row air conditioner is adjusted and improved by the administrator after manual troubleshooting and analysis.

[0019] The beneficial technical effects of the present application are as follows:

[0020] (1) The three performance indicators proposed in the present application can quantify the operation performance of the inter-row air conditioner, so that the inter-row air conditioners can be compared with good feasibility.

[0021] (2) The dynamic evaluation method of the water-cooled inter-row air conditioner refrigerating performance proposed in the present application can timely find the inter-row air conditioner with performance out of range or low performance, performance deterioration trend or performance jittering, for the inter-row air conditioner in operation state, combined with its recent or historical operation data, and provides quantitative data support for improving the performance of the terminal refrigeration system. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 The flow chart of the water-cooled inter-row air conditioner refrigerating performance dynamic evaluation method based on operation data analysis proposed in the present application is shown in the figure.

[0023] Figure 2 The troubleshooting result based on the PR parameter proposed in the present application is shown in the figure.

[0024] Figure 3 The troubleshooting result based on the CV parameter proposed in the present application is shown in the figure. DETAILED DESCRIPTION

[0025] The present application will be further described below in combination with specific embodiments.

[0026] Embodiment 1

[0027] Referring to Figure 1 The water-cooled inter-row air conditioner refrigerating performance dynamic evaluation method based on operation data analysis proposed in the present application has the following method steps:

[0028] S1: divide the load state category of the refrigeration equipment end operation, including no load [0, 0.05], light load (0.05, 0.40], half load (0.40, 0.70], heavy load (0.70, 0.90], full load (0.90, 1.00], the division basis is the relevant standards of the country, industry, etc., if there is no clear standard, the experience can be divided based on the operation data, wherein the load state is defined by the continuous operation for more than 5 minutes; and the column air conditioner is divided into the corresponding category according to the ratio RH of the running electric power consumption and the rated power consumption;

[0029] S2: determine the ratio PR parameter range of the running electric power consumption of the host computer with the load state of the refrigeration equipment and the running electric power consumption of the air conditioner under the normal operation performance of the air conditioner; for all column air conditioners in each category i (i = 1,..., N), calculate its PR i parameter respectively, take iPR = maxi (PR i ) as the air conditioner refrigeration performance benchmark of this load state, and determine I i = [a*iPR, iPR] as the normal range of the column air conditioner refrigeration performance, wherein 0.7 ≤ a < 1.0, which can be taken as 0.9; for each column air conditioner j i (j i = 1,..., M i ) in each category i (i = 1,..., N):

[0030] S3: if the PR parameter of the air conditioner is in the interval I i , it is judged to be running normally, otherwise S4 is executed;

[0031] S4: clustering analysis based on electric power consumption; after automatic clustering analysis of all operation data corresponding to the host computer load running electric power Ph and the air conditioner running electric power Pc within t1 (which can be taken as 4h) time, it is aggregated into K categories and each operation data is classified into one category (k = 1,..., K); if the number of operation data in the category where the current operation data of the air conditioner is located is less than n0, it is exited, and after n0 data periods, it reenters S3; otherwise, it enters S5 for analysis and confirmation. The value of n0 is set according to the refrigeration response speed of the air conditioner equipment, such as n0 = 5;

[0032] S5: further investigate according to the related coefficient CV of the supply and return air temperature difference and the inlet and return water temperature difference and its change before and after; for the category where the current operation data of the air conditioner equipment is located, calculate the CV parameter value corresponding to the supply and return air temperature difference and the inlet and return water temperature difference; if the value is in the interval C i= [-1.00, -0.70] indicates a significant negative correlation, which is considered normal operation; otherwise, calculate the CV parameter values ​​corresponding to the changes in supply and return air temperature difference and the changes in inlet and return water temperature difference between the two steps. If this value is within the interval C... i If a significant negative correlation is found, the system is considered to be operating normally. Otherwise, the system is compared with the cooling performance parameter table of the air conditioning equipment (provided by the equipment manufacturer) to obtain the explicit cooling capacity value Cc_B of the air conditioner under the current operating conditions (generally including the supply and return air temperature difference, chilled water inlet temperature, etc.). The system is then compared with the current host load operating power consumption Ph value to see if it is within the optimal cooling capacity range [b*Cc_B, Cc_B], where b is taken as 0.8. If it is within the range, the system is considered to be operating normally. Otherwise, the system proceeds to S6 for analysis and confirmation.

[0033] S6: Calculate and assess the lag effect; for the class containing the current data of the air conditioning equipment, calculate the influence distribution of the cross-correlation coefficient CF between the supply and return air temperature difference and the inlet and return water temperature difference and their changes before and after. If the lag effect (i.e., the absolute value of the CF parameter is greater than c0) is within the time range of n1 monitoring data update cycles of s1, it is determined to be operating normally; otherwise, it is determined to be an abnormal operation of the air conditioning equipment, and an alarm is sent to the administrator. The administrator will then conduct manual investigation and analysis and adjust and improve the air conditioning unit. Among them, n1 is related to the response time and load status of the air conditioning equipment, and can be taken as 5; c0 is a positive real number between 0 and 1, and can be taken as 0.3.

[0034] S7: Repeat steps S3-S6 at the next time interval t+s0 to evaluate the operating performance of each in-row air conditioning unit in real time. S0 should be greater than or equal to s1 but much less than t0, and can be taken as 3 minutes. The time interval should be determined in combination with the update cycle s1 of the monitoring data and the change of the host load Ph.

[0035] S8: Update the PR parameters in S2. After time interval t0, execute S2 to recalculate the PR value for each type of air conditioner, and then execute S3-S7 for analysis.

[0036] Example 2

[0037] When analyzing the cooling performance at the data center or micro-module level, step S2 specifically involves: for all inter-row air conditioners in each class i (i = 1, ..., N), calculating the parameter PR for each inter-row air conditioner j (j = 1, ..., J) within that data center or micro-module. ij average PR i Take iPR = maxi(PR) i This serves as a benchmark for the air conditioning cooling performance within the computer room or micro-module under this type of load condition. The remaining steps are the same as in Example 1.

[0038] Application Example 1

[0039] Reference Figure 1 For a computer room G1, which has 6 micro-modules G1A, G1B, G1C, G1D, G1E, G1F, each of which is composed of several computer cabinets and water-air heat exchange type row air conditioners. From a certain time (marked as 0 time), the running condition of the computer room is monitored, and it is found that the computer cabinets are basically in heavy load state. Then the PRA, PRB, PRC, PRD, PRE, PRF values of each micro-module are calculated and plotted, and it is found that the PRD value of micro-module D is the largest, and the value is about in the range [16.0, 18.0]. Taking PRD as the air conditioner reference performance iPR, Ii = [0.9*iPR, iPR] is determined as the normal range of the cooling performance of this type of row air conditioner. Comparing the PR values of other micro-modules with the normal range, it is found that they are all lower than the normal range, which may be abnormal.

[0040] In order to quickly solve the problem, the row air conditioners are directly checked. After checking, it is found that the row air conditioners with abnormal PR values are improperly configured, so optimization is carried out, and then the air conditioner running performance of the micro-module is monitored. After subsequent monitoring, it is found that (from 15 o'clock, about 5 minutes at each time) the running performance of the row air conditioners of these micro-modules is improved, and basically in the range of [15.0, 18.0], which is within the above normal range.

[0041] Application Example 2

[0042] For a micro-module A in a computer room G2, which has 6 row air conditioners AKT01d, AKT02d, AKT04d, AKT06d, AKT07d, AKT10d running. From a certain time (marked as 0 time), the running condition of the row air conditioners of the micro-module is monitored, and it is found that AKT01d, AKT02d, AKT04d, AKT06d, AKT07d are basically in heavy load state, and AKT10d is in light load state. Then the CV parameter values CV01, CV02, CV04, CV06, CV07 corresponding to the air conditioner's supply and return air temperature difference and the inlet and return water temperature difference of AKT01d, AKT02d, AKT04d, AKT06d, AKT07d are calculated and plotted, and it is found that the CV values of AKT01d, AKT02d, AKT06d are basically in the range of C = [-1.0, -0.8], which have strong negative correlation and belong to normal state; and it is found that the CV values of AKT04d, AKT07d deviate far from the normal range, and some of the values are positive, which are most likely abnormal.

[0043] For quick solution, direct to the column air conditioning AKT04d, AKT07d were investigated; after investigation, found that the two air conditioning monitoring point setting is improper, so the monitoring point adjustment. After adjustment and continue to monitor the CV showed a strong negative correlation, normal operation.

Claims

1. A dynamic evaluation method for refrigeration performance of water-cooled inter-row air conditioning based on operation data analysis, characterized in that, The method steps are as follows: S1: divide the load state category of the refrigeration equipment end operation, and divide into the corresponding category according to the ratio RH of the running electric power consumption of the inter-row air conditioner to the rated power consumption; S2: determine the range of the ratio PR parameter of the running electric power consumption of the host computer with the load state of the refrigeration equipment to the running electric power consumption of the air conditioner under the normal operation performance of the air conditioner; S3: If the PR parameter of the air conditioner is in interval I i , it is judged that the operation is normal, otherwise S4 is executed; S4: perform clustering analysis based on the electric power consumption; S5: further investigate according to the correlation coefficient CV of the supply and return air temperature difference of the inter-row air conditioner and the inlet and return water temperature difference and the change before and after; S6: calculate and evaluate the hysteresis influence; S7: repeat the above S3-S6 steps every time interval period s0 to the next time t+s0, and real-time evaluate the operation performance of each inter-row air conditioner; S8: update the PR parameter in S2, execute S2 after a time interval t0, recalculate the PR value for each type of air conditioner, and perform S3-S7 for analysis; The investigation method of S5 is: for the class in which the current time operation data of the air conditioner equipment is located, the CV parameter value corresponding to the supply and return air temperature difference and the inlet and return water temperature difference is calculated; If the value is in the interval C i =[-1.00, -0.70], which indicates a significant negative correlation, then it is determined that the operation is normal; Otherwise, the CV parameter value corresponding to the change amount of the supply-return air temperature difference between the two steps before and after and the change amount of the supply-return water temperature difference between the two steps before and after are calculated. If the value is located in the interval C i , indicating a significant negative correlation, it is determined that the operation is normal. Otherwise, the explicit refrigeration capacity value Cc_B of the air conditioner under the current working condition is obtained by referring to the refrigeration performance parameter table of the air conditioner, and whether the host computer load running electric power consumption Ph value at the current time is in the refrigeration capacity range [b*Cc_B, Cc_B] of the optimal refrigeration performance is compared, wherein b is taken as 0.8, if yes, it is determined that the operation is normal; otherwise, enter S6 for analysis and confirmation.

2. The method according to claim 1, wherein, The classification in S1 includes no load [0, 0.05], light load (0.05, 0.40], half load (0.40, 0.70], heavy load (0.70, 0.90], and full load (0.90, 1.00].

3. The method according to claim 1, wherein, S2: Determine the PR parameter range: For all inter-row air conditioners in each class i, calculate its PR parameter respectively, take iPR= maxi(PR i ) as the air conditioner refrigeration performance benchmark of this class load state, and determine i I i =[a*iPR, iPR] as the normal range of the refrigeration performance of the inter-row air conditioner in this class, where 0.7≤a<1.0, i=1,...,N.

4. The water-cooled inter-row air conditioning refrigeration performance dynamic evaluation method based on operation data analysis according to claim 3, characterized in that, When analyzing the cooling performance at the data center or micro-module level, for all inter-row air conditioners in each category i, calculate the parameter PR of each inter-row air conditioner j within that data center or micro-module. ij average PR i Take iPR=maxi(PR) i ) serves as the benchmark for the air conditioning cooling performance of the computer room or micro-module under this type of load condition, where j=1,...,J.

5. The method according to claim 1, wherein, The clustering analysis method in S4 is: after automatic clustering analysis of all running data composed of the host computer load running electric power consumption Ph and the air conditioner running electric power consumption Pc within t1 time of the air conditioner, K classes are aggregated and each running data is classified into one class k, wherein k=1,...,K; if the number of running data in the class in which the current time running data of the air conditioner is located is less than n0, exit, and after n0 data periods, re-enter S3; Otherwise, enter S5 for analysis and confirmation.

6. The method according to claim 1, wherein, The method for calculating and evaluating the hysteresis influence in S6 is: for the class in which the current time data of the air conditioner equipment is located, the influence distribution of the cross-correlation coefficient CF of the supply and return air temperature difference and the inlet and return water temperature difference of the inter-row air conditioner and the change before and after is calculated, if the hysteresis influence is within the time range of n1 monitoring data update periods s1, it is determined that the operation is normal; otherwise, it is determined that the operation performance of the inter-row air conditioner equipment is abnormal, and the administrator is reminded to alarm, and the inter-row air conditioner is adjusted and improved after manual investigation and analysis by the administrator.

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