CN115600700A - Cooling tower energy efficiency diagnosis method based on big data analysis - Google Patents
Cooling tower energy efficiency diagnosis method based on big data analysis Download PDFInfo
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Abstract
The invention provides a cooling tower energy efficiency diagnosis method based on big data analysis, which comprises the steps of obtaining operation data of a cooling tower; calculating the conveying coefficient of the cooling tower; judging the quantitative relation between the conveying coefficient of the cooling tower and the conveying coefficient of the preset cooling tower; if the conveying coefficient of the cooling tower is smaller than the preset value, entering the next step; if the conveying coefficient of the cooling tower is larger than the preset value, judging that the cooling energy efficiency is normal; step three, calculating the ratio of the operating power and the rated power of the cooling tower; calculating the ratio of the operating frequency and the rated frequency of the cooling tower; step four, calculating an operation parameter interval; and (6) judging a fault. The method solves the problems of unreasonable operation, low system energy efficiency and the like in the existing cooling tower, and promotes the technical progress of the high-efficiency machine room air conditioning system.
Description
Technical Field
The invention belongs to the field of hoisting tools, and particularly relates to a cooling tower energy efficiency diagnosis method based on big data analysis.
Background
At present, the conventional cooling tower is generally controlled at a fixed frequency, the energy consumption is high, meanwhile, the field monitoring data are few, an energy efficiency diagnosis method is lacked, and the system energy efficiency problem is caused because the cooling tower is abnormal and is difficult to discover. The energy efficiency of the cooling tower is directly influenced by the conveying coefficient of the cooling tower, the water outlet temperature of the cooling tower is influenced by the unreasonable control of the cooling tower, and the energy efficiency of a cold machine is further influenced, so that the energy efficiency of the whole central air-conditioning system is reduced.
When the cooling tower fan is used, the causes of unbalanced inertia, looseness, corrosion and the like of the fan cause equipment abrasion aggravation and vibration increase, and if the causes cannot be found and processed in time, serious accidents such as bearing burning, part damage, even blade fracture, transmission shaft throwing-out and the like can be caused. Therefore, in order to ensure the reliable operation of the fan, the effective monitoring of parameters such as fan blades, a transmission shaft, a gear box temperature, an oil level and the like is necessary. With the gradual development of high-efficiency machine rooms, the energy consumption of the cooling tower in the whole air-conditioning system is higher and higher, the equipment fault of the cooling tower is diagnosed in time and reasonably controlled, the operation energy efficiency of the cooling tower and the cold machine can be effectively improved, and the method has important significance for improving the operation energy-saving level of the whole central air-conditioning system. According to the nature of the current cooling tower operation condition, in the energy efficiency diagnosis process, the influence factors are more, and multiple factors can cause the same problem, so that great difficulty is brought to diagnosis.
In view of this, it is at present necessary to provide a cooling tower energy efficiency diagnosis method based on big data analysis to implement energy efficiency diagnosis of a cooling tower.
Disclosure of Invention
Therefore, the invention provides a cooling tower energy efficiency diagnosis method based on big data analysis, which solves the problems of unreasonable operation, low system energy efficiency and the like in the existing cooling tower and promotes the technical progress of an efficient machine room air conditioning system.
The method comprises the following steps:
step one, acquiring operation data of a cooling tower;
step two, calculating the conveying coefficient of the cooling tower; judging the quantitative relation between the conveying coefficient of the cooling tower and the conveying coefficient of the preset cooling tower;
if the conveying coefficient of the cooling tower is smaller than the preset value, entering the next step;
if the conveying coefficient of the cooling tower is larger than the preset value, judging that the cooling energy efficiency is normal;
step three, calculating the ratio epsilon of the operating power and the rated power of the cooling tower; calculating the ratio xi of the operating frequency and the rated frequency of the cooling tower;
step four, calculating boundary conditions about epsilon and xi to obtain an upper boundary epsilon u and a lower boundary epsilon d of the epsilon and xi so as to obtain three operation parameter intervals of (epsilon u, 1), (epsilon d, epsilon u) and (0, epsilon d) to obtain the percentage u%, m% and d% of epsilon u, 1), (epsilon d, epsilon u) and (0, epsilon d);
step five, if u% + m% >1, executing the next step;
judging a fault if u% + m% < 1;
step six, if u% is larger than 2, the energy efficiency of the cooling tower is normal;
u% >2, judging a fault;
and step seven, if the fault is judged in the step five or the step six, diagnosing the fault type.
Preferably, the specific process of calculating the boundary condition about epsilon and ξ in step four is as follows:
determining the optimal operation interval epsilon = xi ^3 of the cooling tower;
calculating the upper and lower boundaries of the operating parameter interval:
εu＝(1+Δ％)ξ^3,
εd＝(1-Δ％)ξ^3。
further preferably, the fault types in the step seven include:
the step five diagnoses that the impeller fault is included, and the output interval is small; the air outlet is failed, and the air quantity is small; the elevation angle of the impeller is not adjusted or is larger when leaving the factory; air inlet blockage or belt looseness;
the diagnosis of step six includes the current is larger; the output torque becomes large; the slip force becomes smaller; the voltage of the motor does not reach the rated voltage; phase loss, phase loss or three-phase imbalance; motor shaft failure or mechanical failure.
Further preferably, the conveying coefficient of the cooling tower is calculated by the following formula:
wherein the content of the first and second substances,
α i acquiring the instantaneous conveying coefficient of the ith cooling tower in a time interval;
q is the heat dissipation capacity of the cooling tower in the collection time interval;
n is the total number of the cooling towers in the collection time interval;
P i，j in order to collect the power consumption of the jth fan of the ith cooling tower in the time interval.
Further preferably, after the step one, the method further comprises:
and (4) data preprocessing, namely, eliminating negative values, missing values and unstable values in the operation process in the operation data.
Further preferably, the data collected in the first step includes:
the number N of running cooling towers, the running power Pij and the frequency fij of the fan, the rated fan power Pe and the rated frequency fe, and the temperature difference delta T of the supply water and the return water of the cooling main pipe.
The invention further provides a cooling tower energy efficiency diagnosis system based on big data analysis, which comprises:
the data preprocessing module is used for acquiring data in the operation process of the cooling tower and preprocessing the data;
the data correlation analysis module is used for receiving the data from the data preprocessing module and calculating an optimal parameter interval and an operation parameter interval related to the operation of the cooling tower;
and the diagnosis module receives the data from the data correlation analysis module and carries out energy efficiency diagnosis according to comparison of preset parameters.
The invention also provides a computer-readable storage medium, which stores a computer program for executing the cooling tower energy efficiency diagnosis method based on big data analysis.
The present invention also provides an electronic device, including:
a processor;
a memory for storing the processor-executable instructions;
the processor is used for executing the cooling tower energy efficiency diagnosis method based on big data analysis.
Compared with the prior art, the technical scheme of the invention has the following advantages:
according to the invention, the transmission coefficient of the cooling tower is diagnosed, the power and the frequency of the fan of the cooling tower are analyzed by utilizing the big data analysis platform, the reasons of equipment failure and unreasonable control in the operation process of the cooling tower are diagnosed, and the operation parameters are regulated and controlled, so that the efficient operation of the cooling tower is realized, the heat exchange efficiency of the cooling tower is further improved, the energy consumption of the whole air conditioning system is greatly reduced, and the energy-saving level of the central air conditioning system is improved.
Drawings
FIG. 1 is a schematic flow diagram of a method provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of an operating region of a method provided by an embodiment of the invention;
FIG. 3 is a schematic diagram illustrating a workflow of a data processing module of the system according to an embodiment of the present invention;
FIG. 4 is a schematic workflow diagram of a data association analysis module of the system according to an embodiment of the present invention;
FIG. 5 is a schematic workflow diagram of a diagnostic module provided by an embodiment of the present invention;
fig. 6 is a schematic block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The method of this embodiment, as shown in fig. 1, is implemented as follows.
Firstly, the energy efficiency of the cooling tower is diagnosed, and the reason that the conveying coefficient of the cooling tower is low is judged.
Collecting parameters such as the number N of running towers, the running power Pij and the frequency fij of a fan, the rated fan power Pe, the frequency fe, the temperature difference delta T of supply and return water of a cooling main pipe and the like by using a data transmission platform, carrying out data preprocessing, and processing a negative value, a missing value and an unstable value in the process of starting equipment;
calculating the transport coefficient of a cooling tower
Wherein alpha is i Acquiring the instantaneous conveying coefficient of the ith cooling tower in a time interval; q is the heat dissipation capacity of the cooling side in the acquisition time interval, unit: kW; n is the total number of the cooling towers in the collection time interval;
P i，j in order to collect the power consumption of the jth fan of the ith cooling tower in a time interval, the unit is as follows: kW.
Judging the conveying coefficient alpha of the cooling tower i Whether or not toSatisfies alpha i >δ, wherein δ is a set value of a cooling tower conveying coefficient, and can be set according to different working conditions, if yes, the output cooling tower operation parameters are normal, debugging is not needed, and if not, the next step is carried out;
inputting the operating power Pi and the corresponding rated power Pe of the fan of the cooling tower, the operating frequency fi and the corresponding rated frequency fe of the fan of the cooling tower, calculating epsilon = Pi/Pe,
xi = fi/fe, epsilon and xi are dimensionless constants;
dividing an epsilon value interval by utilizing a big data relevance analysis module, calculating epsilon = xi ^3, determining an optimal parameter interval for the operation of a cooling tower, calculating that epsilon u = (1 + delta%) xi ^3 and epsilon d = (1-delta%) xi ^3, wherein epsilon u and epsilon d are respectively the conditions of the upper boundary and the lower boundary of a normal operation interval of the power of the cooling tower, and counting the percentage of three intervals (epsilon u, 1), (epsilon d, epsilon u), (0, epsilon d) which are respectively u%, m% and d%, wherein u, m and d are all a numerical constant.
Big data diagnosis and analysis are carried out on the operation power and the operation frequency, the failure of the cooling tower equipment and the reason of unreasonable control are diagnosed in time, and therefore the operation data of the equipment are adjusted, the heat exchange efficiency of the cooling tower is improved, and the energy-saving level of the whole system is improved.
And judging whether the condition meets the set value 1 of u% + m% > or not through a diagnosis module, if so, carrying out the next step, and if not, diagnosing the fault: the impeller has faults, the output interval is small, the air inlet has faults, and the air quantity is small. The output reason is as follows: the elevation angle of the impeller is not adjusted or is larger when leaving the factory, and an air inlet is blocked or a belt is loosened;
and judging whether the u% > set value is 2, if so, outputting the normal operation parameters of the cooling tower without debugging. If not, diagnosing the fault: the current is bigger, the output torque is bigger, the slip force is smaller, and the output reason is as follows: insufficient motor voltage, phase loss or three-phase imbalance, motor shaft failure or mechanical failure;
the energy efficiency diagnosis is carried out on the cooling tower, whether the conveying coefficient is small or not is judged, and then the self-diagnosis is carried out on the operation parameters of the cooling tower based on big data, so that the reason that the heat exchange efficiency of the cooling tower is low or the equipment fault is judged in time, the maintenance personnel can repair the cooling tower in time, the operation energy consumption of the cooling tower is effectively reduced, the energy conservation of the cooling tower is improved, and the energy-saving operation level of a central air-conditioning system is further improved.
The embodiment also provides a system, which includes a data preprocessing module, configured to acquire data during operation of the cooling tower and preprocess the data, where an operation process is as shown in fig. 3.
And the data correlation analysis module receives the data from the data preprocessing module, calculates an optimal parameter interval and an operation parameter interval related to the operation of the cooling tower, and the working process is shown in figure 4.
And the diagnosis module receives the data from the data correlation analysis module and performs energy efficiency diagnosis according to comparison of preset parameters, wherein the working process is as shown in fig. 5.
According to the embodiment, the power and the frequency of the fan of the cooling tower are analyzed by diagnosing the conveying coefficient of the cooling tower and utilizing the big data analysis platform, so that the reasons of equipment failure and unreasonable control in the operation process of the cooling tower are diagnosed, and then the operation parameters are regulated and controlled, so that the efficient operation of the cooling tower is realized, the heat exchange efficiency of the cooling tower is further improved, the energy consumption of the whole air conditioning system is greatly reduced, and the energy-saving level of the central air conditioning system is improved. The technical scheme that this patent provided has solved current operation unreasonable in the cooling tower, has caused system efficiency low grade difficult problem, has promoted high-efficient computer lab air conditioning system's technological progress.
Next, an electronic apparatus according to an embodiment of the present application is described with reference to fig. 6. The electronic device may be either or both of the first device and the second device, or a stand-alone device separate from them, which stand-alone device may communicate with the first device and the second device to receive the acquired input signals therefrom.
FIG. 6 illustrates a block diagram of an electronic device in accordance with an embodiment of the present application.
As shown in fig. 6, the electronic device 10 includes one or more processors 11 and memory 12.
The processor 11 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.
Memory 12 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer readable storage medium and executed by the processor 11 to implement the video generation method supporting multi-protocol video live broadcast of the various embodiments of the present application described above and/or other desired functions. Various contents such as an input signal, a signal component, a noise component, etc. may also be stored in the computer-readable storage medium.
In one example, the electronic device 10 may further include: an input device 13 and an output device 14, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
When the electronic device is a stand-alone device, the input means 13 may be a communication network connector for receiving the acquired input signals from the first device and the second device.
The input device 13 may also include, for example, a keyboard, a mouse, and the like.
The output device 14 may output various information including the determined distance information, direction information, and the like to the outside. The output devices 14 may include, for example, a display, speakers, a printer, and a communication network and its connected remote output devices, among others.
Of course, for simplicity, only some of the components of the electronic device 10 relevant to the present application are shown in fig. 6, and components such as buses, input/output interfaces, and the like are omitted. In addition, the electronic device 10 may include any other suitable components depending on the particular application.
In addition to the above-described methods and apparatus, embodiments of the present application may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in a video generation method supporting multi-protocol video live according to various embodiments of the present application described in the above-mentioned "exemplary methods" section of this specification.
The computer program product may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages, for carrying out operations according to embodiments of the present application. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present application may also be a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform the steps in a video generation method supporting multi-protocol video live broadcast according to various embodiments of the present application described in the "exemplary methods" section above in this specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The basic principles of the present application have been described above with reference to specific embodiments, but it should be noted that advantages, effects, etc. mentioned in the present application are only examples and are not limiting, and the advantages, effects, etc. must not be considered to be possessed by various embodiments of the present application. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the foregoing disclosure is not intended to be exhaustive or to limit the disclosure to the precise details disclosed. It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. This need not be, nor should it be exhaustive of all embodiments. And obvious variations or modifications derived therefrom are intended to be within the scope of the invention.
Claims (9)
1. A cooling tower energy efficiency diagnosis method based on big data analysis is characterized by comprising the following steps:
step one, acquiring operation data of a cooling tower;
step two, calculating the conveying coefficient of the cooling tower; judging the quantitative relation between the conveying coefficient of the cooling tower and the conveying coefficient of a preset cooling tower;
if the conveying coefficient of the cooling tower is smaller than the preset value, entering the next step;
if the conveying coefficient of the cooling tower is larger than the preset value, judging that the cooling energy efficiency is normal;
step three, calculating the ratio epsilon of the operating power and the rated power of the cooling tower; calculating the ratio xi of the operating frequency and the rated frequency of the cooling tower;
step four, calculating boundary conditions about epsilon and xi to obtain an upper boundary epsilon u and a lower boundary epsilon d of epsilon and xi so as to obtain three operation parameter intervals of (epsilon u, 1), (epsilon d, epsilon u) and (0, epsilon d) to obtain the percentage u%, m% and d% of epsilon u, 1), (epsilon d, epsilon u) and (0, epsilon d);
step five, if u% + m% >1, executing the next step;
judging a fault if u% + m% < 1;
step six, if u% is larger than 2, the energy efficiency of the cooling tower is normal;
u% >2, judging a fault;
and step seven, if the fault is judged in the step five or the step six, diagnosing the fault type.
2. The cooling tower energy efficiency diagnosis method based on big data analysis according to claim 1, characterized in that the specific process of calculating the boundary conditions about epsilon and ξ in the fourth step is as follows:
determining an optimal operation interval epsilon = xi ^3 of the cooling tower;
calculating the upper and lower boundaries of the operating parameter interval:
εu＝(1+Δ％)ξ^3,
εd＝(1-Δ％)ξ^3。
3. the cooling tower energy efficiency diagnosis method based on big data analysis according to claim 2, wherein the fault type in the seventh step comprises:
the step five diagnoses that the impeller fault is included, and the output interval is small; the air outlet is in failure, and the air volume is small; the elevation angle of the impeller is not adjusted or is larger when leaving the factory; air inlet blockage or belt looseness;
the diagnosis of step six includes the current is larger; the output torque becomes large; the slip force becomes smaller; the voltage of the motor does not reach the rated voltage; phase loss, phase loss or three-phase imbalance; motor shaft failure or mechanical failure.
4. The big data analysis-based cooling tower energy efficiency diagnosis method according to claim 1, wherein the transportation coefficient of the cooling tower is calculated by the following formula:
wherein the content of the first and second substances,
α i acquiring the instantaneous conveying coefficient of the ith cooling tower in a time interval;
q is the heat dissipation capacity of the cooling tower in the collection time interval;
n is the total number of the cooling towers in the collection time interval;
P i，j in order to collect the power consumption of the jth fan of the ith cooling tower in the time interval.
5. The cooling tower energy efficiency diagnosis method based on big data analysis according to claim 4, characterized by further comprising, after the step one:
and (4) data preprocessing, namely, eliminating negative values, missing values and unstable values in the operation process in the operation data.
6. The cooling tower energy efficiency diagnosis method based on big data analysis according to claim 1, wherein the data collected in the first step comprises:
the number N of running cooling towers, the running power Pij and the frequency fij of the fan, the rated fan power Pe and the rated frequency fe, and the temperature difference delta T of the supply water and the return water of the cooling main pipe.
7. A cooling tower energy efficiency diagnostic system based on big data analysis is characterized by comprising:
the data preprocessing module is used for acquiring data in the operation process of the cooling tower and preprocessing the data;
the data correlation analysis module is used for receiving the data from the data preprocessing module and calculating an optimal parameter interval and an operation parameter interval related to the operation of the cooling tower;
and the diagnosis module is used for receiving the data from the data correlation analysis module and performing energy efficiency diagnosis according to comparison of preset parameters.
8. A computer-readable storage medium storing a computer program for executing the big data analysis-based cooling tower energy efficiency diagnosis method according to any one of claims 1 to 6.
9. An electronic device, the electronic device comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor is used for executing the cooling tower energy efficiency diagnosis method based on big data analysis according to any one of the claims 1 to 6.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116242197A (en) * | 2023-05-12 | 2023-06-09 | 浙江弗尔德驱动科技有限公司 | Permanent magnet semi-direct-drive motor special for cooling tower |
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2022
- 2022-10-17 CN CN202211269095.8A patent/CN115600700A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116242197A (en) * | 2023-05-12 | 2023-06-09 | 浙江弗尔德驱动科技有限公司 | Permanent magnet semi-direct-drive motor special for cooling tower |
| CN116242197B (en) * | 2023-05-12 | 2023-08-29 | 浙江弗尔德驱动科技有限公司 | Operation monitoring system of special permanent magnet semi-direct-drive motor for cooling tower |
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