An intelligent system and method for monitoring and regulating the operation of cooling tower fans

Abstract

The present invention discloses an intelligent system and method for monitoring and regulating the operation of a cooling tower fan, belonging to the technical field of cooling tower fan control, comprising two temperature sensors, a pressure sensor, a PLC controller, several frequency converters, several soft starters, several fans and a central server, solving the technical problem of improving cooling efficiency by formulating a control strategy according to the cooling effect of each fan through a multivariate linear regression model, reducing unnecessary energy consumption and fan wear, ensuring its effectiveness in the long term, dividing the fans into main fans and standby fans, ensuring redundancy when the main fan fails, maintaining consistent cooling performance, predicting cooling time and adjusting fan priority through a regression model, improving the system's ability to predict and respond to changes, enhancing overall performance, and ensuring stable system operation.

Description

Technical Field

[0001] The invention belongs to the technical field of cooling tower fan control, and in particular relates to an intelligent system and method for monitoring and regulating the operation of a cooling tower fan. Background Art

[0002] In a cooling tower, the fan is one of the important cooling equipment, and its operating status directly affects the cooling effect.

[0003] Traditional cooling tower fan control methods are mostly manual control, which requires manual operation to start and stop the fan, and cannot achieve automatic control. It also has high energy consumption and low cooling efficiency. After replacing the fan, it is impossible to make a digital judgment on the cooling effect of the fan, nor can the fan control strategy be adjusted according to the cooling effect of the fan. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent system and method for monitoring and regulating the operation of cooling tower fans, which solves the technical problem of improving cooling efficiency by formulating control strategies according to the cooling effects of each fan through a multivariate linear regression model.

[0005] To achieve the above object, the present invention adopts the following technical solution:

[0006] An intelligent system for monitoring and regulating the operation of a cooling tower fan comprises two temperature sensors, a pressure sensor, a PLC controller, several frequency converters, several soft starters, several fans and a central server. All frequency converters and all soft starters are connected to the PLC controller, each frequency converter is connected to a fan, each soft starter is connected to a fan, the PLC controller communicates with the central server via a network cable, all temperature sensors are connected to the PLC controller, and the pressure sensor is connected to the PLC controller.

[0007] Preferably, the model of the PLC controller is Siemens S7-1200, the model of the temperature sensor is BSR-VD+RS, the model of the pressure sensor is Rosemount 3051, the model of the frequency converter is ABB ACS580, and the model of the soft starter is Schneider Electric Altistart 22.

[0008] Preferably, two temperature sensors and pressure sensors are installed at the water outlet of the cooling tower, one temperature sensor is used to measure the water temperature data of the cooling water, the other temperature sensor is used to measure the ambient temperature data, and the pressure sensor is used to measure the water pressure data of the cooling water.

[0009] Preferably, the number of the frequency converters is three, the number of the soft starters is three, each frequency converter is used to control a fan, and each soft starter is used to control a fan.

[0010] An intelligent method for monitoring and adjusting the operation of a cooling tower fan comprises the following steps:

[0011] Step 1: Establish an intelligent system for monitoring and regulating the operation of cooling tower fans. The PLC controller collects field data, including water temperature data, ambient temperature data, water pressure data and the speed of each fan. The PLC controller collects field data and sends the field data to the central server in real time. The central server receives and saves the field data.

[0012] Set the warning temperature in the central server, and the central server sends the warning temperature to the PLC controller;

[0013] Step 2: In the PLC controller, the fans are classified into a main fan group and an auxiliary fan group. The three fans of the three inverter controllers constitute the main fan group, and the fans controlled by the three soft starters are all auxiliary fans. In the main fan group, one fan is selected as the main fan, and the other two fans are used as standby fans. The PLC controller generates a classification document A based on the classification method of the fans, and presets the default priority of the auxiliary fans in the classification document A.

[0014] The PLC sends the classified document A to the central server, which saves it as classified document B.

[0015] Step 3: Set the control strategy in the PLC controller, modify the temperature threshold in the control strategy according to the warning temperature, and dispatch and control the main fan unit and the auxiliary fan unit according to the control strategy;

[0016] Record the temperature change curve of each auxiliary fan after it is turned on under the scheduling control of the control strategy, generate cooling effect data of each fan according to the classification document A and the temperature change curve, and send the cooling effect data to the central server;

[0017] The cooling effect data is the cooling time required for the auxiliary fan to reach the preset target temperature under certain field data conditions.

[0018] Step 4: Construct an optimized wind turbine combination strategy model in the central server based on the multivariate linear regression model, with the goal of reordering the priority of the auxiliary motors;

[0019] Step 5: The central server saves the cooling effect data, presets the length of the data set, constructs a training set based on the cooling effect data and its corresponding field data, and trains the optimized fan combination strategy model through the training set to obtain a trained optimized fan combination strategy model;

[0020] Step 6: The central server regularly adjusts the priority of the auxiliary fans through the trained optimized fan combination strategy model and updates the classification document B;

[0021] The central server sends the updated classification document B to the PLC controller, and the PLC controller updates the classification document A stored in itself according to the classification document B;

[0022] Step 7: The PLC controller re-formulates the control strategy according to the updated classification document A, and adjusts the order of turning on or off the auxiliary fans according to the priority of the auxiliary fans;

[0023] Step 8: The PLC controller dispatches and controls all soft starters and all frequency converters according to the control strategy re-established in step 7, specifically setting the inspection time and timing the inspection. After the preset inspection time is reached, the main fan or the standby fan in the main fan unit is turned on according to the control strategy. At the same time, according to the priority of the auxiliary fan, the auxiliary fans are turned on or off one by one to adjust the speed of the main fan, the standby fan or the auxiliary fan.

[0024] Step 9: The PLC controller continues to send cooling effect data to the central server according to the method of step 3.

[0025] Preferably, when executing step 3, the control strategy is as follows:

[0026] The fans controlled by the three frequency converters include fan 1#, fan 2#, and fan 3#, and the fans controlled by the soft starter controller include fan 4#, fan 5#, and fan 6#;

[0027] Strategy 1: Fan 1#, Fan 2# and Fan 3# form the main fan unit, where Fan 1# is the main fan, Fan 2# and Fan 3# are standby fans. When Fan 1# is damaged, Fan 2# is activated; when both Fan 1# and Fan 2# are damaged, Fan 3# is activated;

[0028] Strategy 2: Fan 4#, Fan 5# and Fan 6# form an auxiliary fan group. Fan 4#, Fan 5# and Fan 6# have default priorities in the control strategy. In the default priorities, Fan 4# has the highest priority, that is, Fan 4# is turned on first, followed by Fan 5#, and finally Fan 6#;

[0029] The default priority is modified as the category document B is updated by the central server;

[0030] Strategy 3: Set the inspection time. When the inspection time arrives, execute the start condition judgment function, as follows:

[0031] Condition 1: If the water temperature reaches the temperature threshold of 30 degrees Celsius and does not reach the temperature threshold of 40 degrees Celsius, the speed of all fans in the on state will increase by 10%; if the water temperature does not reach the temperature threshold of 30 degrees Celsius, the speed of all fans in the on state will return to normal speed;

[0032] Condition 2: If the water temperature reaches the temperature threshold of 40 degrees Celsius, a new auxiliary fan is turned on according to the priority of the auxiliary fans;

[0033] Condition 3: If the water temperature does not reach the temperature threshold of 40 degrees Celsius, one auxiliary fan is turned off according to the priority of the auxiliary fans;

[0034] Condition 4: If the water pressure is less than 1.0 bar, the speed of fan 1# remains unchanged; otherwise, it increases by 10%;

[0035] Reset the timer, continue timing and determine whether the inspection time has been reached.

[0036] Preferably, when executing step 4, the specific optimization wind turbine combination strategy model is shown in the following formula:

[0037]

[0038] in, is the cooling time of the ith auxiliary fan; T env is the ambient temperature; T water is the water temperature; P is the water pressure; is the speed of the fan; α i , β i1 , β i2 , β i3 , β i4 It is the parameter to be estimated of the model, and its value is obtained by training from the training set;

[0039] The goal of the fan combination strategy optimization model is to predict the cooling time required for each auxiliary fan to reach the preset temperature based on the input water temperature, water pressure, and ambient temperature.

[0040] Comparison of cooling time of each auxiliary fan Reprioritize auxiliary fans: Cooling time Small, high priority.

[0041] Preferably, when executing step 1, the speed of the fan is acquired by a frequency converter controller or a soft start controller and collected in a PLC controller for storage.

[0042] The intelligent system and method for monitoring and regulating the operation of cooling tower fans described in the present invention solve the technical problem of formulating control strategies based on the cooling effects of each fan through a multivariate linear regression model to improve cooling efficiency. The system can reduce unnecessary energy consumption and fan wear by optimizing fan priority and adjusting fan speed based on real-time data. It allows control strategies and priority settings to be adjusted according to changing conditions and accumulated data to ensure their effectiveness in the long term. Fans are divided into main fans and backup fans to ensure redundancy in the event of a main fan failure and maintain consistent cooling performance. The cooling time is predicted and fan priority is adjusted through a regression model, which improves the system's ability to predict and respond to changes and enhances overall performance. Continuous monitoring and real-time data processing enable the system to quickly respond to deviations from expectations and ensure stable system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a system architecture diagram of the present invention;

[0044] Figure 2 It is the main flow chart of the present invention;

[0045] Figure 3 It is a flow chart of the control strategy of the present invention. DETAILED DESCRIPTION

[0046] Embodiment 1:

[0047] Depend on Figure 1 An intelligent system for monitoring and regulating the operation of a cooling tower fan is shown, comprising two temperature sensors, a pressure sensor, a PLC controller, several frequency converters, several soft starters, several fans and a central server. All frequency converters and all soft starters are connected to the PLC controller, each frequency converter is connected to a fan, each soft starter is connected to a fan, the PLC controller communicates with the central server via a network cable, all temperature sensors are connected to the PLC controller, and the pressure sensor is connected to the PLC controller.

[0048] Two temperature sensors and pressure sensors are installed at the outlet of the cooling tower. One temperature sensor is used to measure the water temperature data of the cooling water, the other temperature sensor is used to measure the ambient temperature data, and the pressure sensor is used to measure the water pressure data of the cooling water.

[0049] The number of the frequency converters is three, the number of the soft starters is three, each frequency converter is used to control a fan, and each soft starter is used to control a fan.

[0050] In this embodiment, the fans are divided into main fan groups and auxiliary fan groups, and a clear priority strategy is set. This design logic is reasonable, ensuring that the system can effectively respond to changing conditions. The system can also clearly define control strategies based on temperature thresholds and water pressures. Through the inspection time (inspection time) and the mechanism of gradually starting and stopping fans, the stability of the cooling system is effectively maintained. The central server can regularly update the fan priority and send it to the PLC to ensure efficient operation of the system.

[0051] The model of the PLC controller is Siemens S7-1200, the model of the temperature sensor is BSR-VD+RS, the model of the pressure sensor is Rosemount 3051, the model of the frequency converter is ABB ACS580, and the model of the soft starter is Schneider Electric Altistart 22.

[0052] In this embodiment, the components selected for the system architecture (such as Siemens S7-1200 PLC, BSR-VD+RS temperature sensor, Rosemount 3051 pressure sensor, ABB ACS580 inverter and Schneider Electric Altistart 22 soft starter) have strong compatibility and modular design, can be applied to various field environments, and are easy to update and maintain.

[0053] Embodiment 2:

[0054] like Figure 2-Figure 3 The intelligent method for monitoring and adjusting the operation of a cooling tower fan described in the embodiment 2 is implemented on the basis of the intelligent system for monitoring and adjusting the operation of a cooling tower fan described in the embodiment, and comprises the following steps:

[0055] Step 1: Establish an intelligent system for monitoring and regulating the operation of cooling tower fans. The PLC controller collects field data, including water temperature data, ambient temperature data, water pressure data and the speed of each fan. The PLC controller collects field data and sends the field data to the central server in real time. The central server receives and saves the field data.

[0056] Set the warning temperature in the central server, and the central server sends the warning temperature to the PLC controller;

[0057] In this embodiment, two warning temperatures should be set, one is a low temperature warning temperature, which is represented by the temperature threshold of 30 degrees Celsius in the control strategy in step 3; the other is a high temperature warning temperature, which is represented by the temperature threshold of 40 degrees Celsius in the control strategy in step 3.

[0058] After reaching different temperature thresholds, different strategies are adopted.

[0059] The fan speed is collected by the frequency converter controller or soft start controller and collected to the PLC controller for storage.

[0060] Step 2: In the PLC controller, the fans are classified into a main fan group and an auxiliary fan group. The three fans of the three inverter controllers constitute the main fan group, and the fans controlled by the three soft starters are all auxiliary fans. In the main fan group, one fan is selected as the main fan, and the other two fans are used as standby fans. The PLC controller generates a classification document A based on the classification method of the fans, and presets the default priority of the auxiliary fans in the classification document A.

[0061] The PLC sends the classified document A to the central server, which saves it as classified document B.

[0062] Step 3: Set the control strategy in the PLC controller, modify the temperature threshold in the control strategy according to the warning temperature, and dispatch and control the main fan unit and the auxiliary fan unit according to the control strategy;

[0063] Record the temperature change curve of each auxiliary fan after it is turned on under the scheduling control of the control strategy, generate cooling effect data of each fan according to the classification document A and the temperature change curve, and send the cooling effect data to the central server;

[0064] The cooling effect data is the cooling time required for the auxiliary fan to reach the preset target temperature under certain field data conditions.

[0065] When executing step 3, the control strategy is as follows:

[0066] The fans controlled by the three frequency converters include fan 1#, fan 2#, and fan 3#, and the fans controlled by the soft starter controller include fan 4#, fan 5#, and fan 6#;

[0067] Strategy 1: Fan 1#, Fan 2# and Fan 3# form the main fan unit, where Fan 1# is the main fan, Fan 2# and Fan 3# are standby fans. When Fan 1# is damaged, Fan 2# is activated; when both Fan 1# and Fan 2# are damaged, Fan 3# is activated;

[0068] Strategy 2: Fan 4#, Fan 5# and Fan 6# form an auxiliary fan group. Fan 4#, Fan 5# and Fan 6# have default priorities in the control strategy. In the default priorities, Fan 4# has the highest priority, that is, Fan 4# is turned on first, followed by Fan 5#, and finally Fan 6#;

[0069] The default priority is modified as the category document B is updated by the central server;

[0070] Strategy 3: Set the inspection time. When the inspection time arrives, execute the start condition judgment function, as follows:

[0071] Condition 1: If the water temperature reaches the temperature threshold of 30 degrees Celsius and does not reach the temperature threshold of 40 degrees Celsius, the speed of all fans in the on state will increase by 10%; if the water temperature does not reach the temperature threshold of 30 degrees Celsius, the speed of all fans in the on state will return to normal speed;

[0072] Condition 2: If the water temperature reaches the temperature threshold of 40 degrees Celsius, a new auxiliary fan is turned on according to the priority of the auxiliary fans;

[0073] Condition 3: If the water temperature does not reach the temperature threshold of 40 degrees Celsius, one auxiliary fan is turned off according to the priority of the auxiliary fans;

[0074] Condition 4: If the water pressure is less than 1.0 bar, the speed of fan 1# remains unchanged; otherwise, it increases by 10%;

[0075] Reset the timer, continue timing and determine whether the inspection time has been reached.

[0076] In this embodiment, the inspection method is adopted to schedule the fans regularly. For example, when the inspection time is up and the water temperature reaches 40 degrees Celsius, an auxiliary fan that is in the off state is turned on according to the auxiliary fan priority. For example, when it is still in the default priority state, fan 4# is in the off state (according to the default priority setting, fan 5# and fan 6# must be in the off state), and only fan 4# is turned on;

[0077] When the next inspection time arrives, the water temperature is judged again. If the water temperature drops to 35 degrees Celsius, fan 4# is turned off at this time; if it is still above 40 degrees Celsius, fan 5# is turned on according to the priority setting; and so on. After each inspection time arrives, the auxiliary fan is dispatched and controlled.

[0078] When the default priority is updated, the auxiliary fans are scheduled according to the updated priority.

[0079] Step 4: Construct an optimized wind turbine combination strategy model in the central server based on the multivariate linear regression model, with the goal of reordering the priority of the auxiliary motors;

[0080] The specific optimization wind turbine combination strategy model is shown in the following formula:

[0081]

[0082] in, is the cooling time of the ith auxiliary fan; T env is the ambient temperature; T water is the water temperature; P is the water pressure; is the speed of the fan; α i , β i1 , β i2 , β i3 , β i4 are the parameters to be estimated of the model, and their values ​​are obtained through training in the training set. Specifically, when estimating these parameters, the least squares method (OLS) can be used for estimation. i is a constant term representing the cooling time when all input variables are zero, β i1 , β i2 , β i3 , β i4 It is the coefficient of each input variable, indicating the influence of each variable on the cooling time.

[0083] The goal of the fan combination strategy optimization model is to predict the cooling time required for each auxiliary fan to reach the preset temperature based on the input water temperature, water pressure, and ambient temperature.

[0084] Comparison of cooling time of each auxiliary fan Reprioritize auxiliary fans: Cooling time Small, high priority.

[0085] After training, the optimized fan combination strategy model will be applied to the prediction of real-time data. By inputting the real-time ambient temperature, water temperature, water pressure and fan speed, the model will predict the time required for each fan to reach the preset temperature and adjust the fan priority accordingly.

[0086] Step 5: The central server saves the cooling effect data, presets the length of the data set, constructs a training set based on the cooling effect data and its corresponding field data, and trains the optimized fan combination strategy model through the training set to obtain a trained optimized fan combination strategy model;

[0087] Step 6: The central server regularly adjusts the priority of the auxiliary fans through the trained optimized fan combination strategy model and updates the classification document B;

[0088] The central server sends the updated classification document B to the PLC controller, and the PLC controller updates the classification document A stored in itself according to the classification document B;

[0089] Step 7: The PLC controller re-formulates the control strategy according to the updated classification document A, and adjusts the order of turning on or off the auxiliary fans according to the priority of the auxiliary fans;

[0090] Step 8: The PLC controller dispatches and controls all soft starters and all frequency converters according to the control strategy re-established in step 7, specifically setting the inspection time and timing the inspection. After the preset inspection time is reached, the main fan or the standby fan in the main fan unit is turned on according to the control strategy. At the same time, according to the priority of the auxiliary fan, the auxiliary fans are turned on or off one by one to adjust the speed of the main fan, the standby fan or the auxiliary fan.

[0091] Step 9: The PLC controller continues to send cooling effect data to the central server according to the method of step 3.

[0092] In this embodiment, after the PLC controller re-formulates the control strategy according to the updated classification document A, it immediately re-schedules the auxiliary fan according to the priority after the inspection time arrives, and changes the start-up mode of the auxiliary fan.

[0093] The specific operations are as follows:

[0094] Step S1: After receiving the updated classification document B from the central server, the PLC controller first checks the priority ranking of the auxiliary fans; for example, in the new ranking, the priority of fan 5# has been adjusted to the highest;

[0095] Step S2: The PLC controller checks the current operating status of all fans, especially the status of fan 4# and fan 5#;

[0096] Step S3: Execute according to the following three situations:

[0097] Case 1: Fan 4# is running, but fan 5# is not running:

[0098] The PLC controller first gradually reduces the speed of fan 4# according to the control specifications of the soft start controller until it reaches a safe stopping point;

[0099] After the speed of fan 4# drops to the minimum and stops, the PLC controller will start fan 5# after a short time interval, and fan 5# will run at a higher speed according to the new priority.

[0100] Case 2: Fan 4# and Fan 5# are not running:

[0101] The PLC controller will directly skip fan 4#, start fan 5# directly according to the new priority order, and increase the speed of fan 5# according to the strategy to achieve the required cooling effect.

[0102] Case 3: Fan 5# is already running:

[0103] If fan 5# is already running, the PLC controller will no longer start fan 4# (when shutting down, the fans with lower priority will be shut down first, that is, after adjustment, fan 4# will be shut down first), and continue to adjust the speed of fan 5# according to the control strategy to maintain or enhance the cooling effect.

[0104] Step S4: According to the new priority order, the PLC controller will start other auxiliary fans one by one when needed (Fan 5#>Fan 4#>Fan 6#), or after the water temperature reaches the preset temperature, shut down the auxiliary fans one by one (Fan 6#>Fan 4#>Fan 5#), and continue to prioritize starting Fan 5# when needed.

[0105] Step S5: The PLC controller sends the new fan operating status, adjustment process and cooling effect data to the central server in real time to further update and optimize the strategy.

[0106] The intelligent system and method for monitoring and regulating the operation of cooling tower fans described in the present invention solve the technical problem of formulating control strategies based on the cooling effects of each fan through a multivariate linear regression model to improve cooling efficiency. The system can reduce unnecessary energy consumption and fan wear by optimizing fan priority and adjusting fan speed based on real-time data. It allows control strategies and priority settings to be adjusted according to changing conditions and accumulated data to ensure their effectiveness in the long term. Fans are divided into main fans and backup fans to ensure redundancy in the event of a main fan failure and maintain consistent cooling performance. The cooling time is predicted and fan priority is adjusted through a regression model, which improves the system's ability to predict and respond to changes and enhances overall performance. Continuous monitoring and real-time data processing enable the system to quickly respond to deviations from expectations and ensure stable system operation.

Claims

1. An intelligent method for monitoring and adjusting the operation of a cooling tower fan, characterized in that: The steps include: Step 1: Establish an intelligent system for monitoring and regulating the operation of cooling tower fans, including two temperature sensors, a pressure sensor, a PLC controller, several frequency converters, several soft starters, several fans and a central server. All frequency converters and all soft starters are connected to the PLC controller, each frequency converter is connected to a fan, each soft starter is connected to a fan, the PLC controller communicates with the central server through a network cable, all temperature sensors are connected to the PLC controller, and the pressure sensor is connected to the PLC controller; The model of the PLC controller is Siemens S7-1200, the model of the temperature sensor is BSR-VD+RS, the model of the pressure sensor is Rosemount 3051, the model of the frequency converter is ABB ACS580, and the model of the soft starter is Schneider Electric Altistart 22; Two temperature sensors and pressure sensors are installed at the outlet of the cooling tower. One temperature sensor is used to measure the water temperature data of the cooling water, the other temperature sensor is used to measure the ambient temperature data, and the pressure sensor is used to measure the water pressure data of the cooling water; The number of the frequency converters is three, the number of the soft starters is three, each frequency converter is used to control a fan, and each soft starter is used to control a fan; The PLC controller collects field data, including water temperature data, ambient temperature data, water pressure data and the speed of each fan. The PLC controller collects field data and sends the field data to the central server in real time. The central server receives and saves the field data. Set the warning temperature in the central server, and the central server sends the warning temperature to the PLC controller; Step 2: In the PLC controller, the fans are classified into a main fan group and an auxiliary fan group. The three fans of the three inverter controllers constitute the main fan group, and the fans controlled by the three soft starters are all auxiliary fans. In the main fan group, one fan is selected as the main fan, and the other two fans are used as standby fans. The PLC controller generates a classification document A based on the classification method of the fans, and presets the default priority of the auxiliary fans in the classification document A. The PLC sends the classified document A to the central server, which saves it as classified document B. Step 3: Set the control strategy in the PLC controller, modify the temperature threshold in the control strategy according to the warning temperature, and dispatch and control the main fan unit and the auxiliary fan unit according to the control strategy; Record the temperature change curve of each auxiliary fan after it is turned on under the scheduling control of the control strategy, generate cooling effect data of each fan according to the classification document A and the temperature change curve, and send the cooling effect data to the central server; The cooling effect data is the cooling time required for the auxiliary fan to reach the preset target temperature under certain field data conditions. ; Step 4: Construct an optimized wind turbine combination strategy model in the central server based on the multivariate linear regression model, with the goal of reordering the priority of the auxiliary motors; Step 5: The central server saves the cooling effect data, presets the length of the data set, constructs a training set based on the cooling effect data and its corresponding field data, and trains the optimized fan combination strategy model through the training set to obtain a trained optimized fan combination strategy model; Step 6: The central server regularly adjusts the priority of the auxiliary fans through the trained optimized fan combination strategy model and updates the classification document B; The central server sends the updated classification document B to the PLC controller, and the PLC controller updates the classification document A stored in itself according to the classification document B; Step 7: The PLC controller re-formulates the control strategy according to the updated classification document A, and adjusts the order of turning on or off the auxiliary fans according to the priority of the auxiliary fans; Step 8: The PLC controller dispatches and controls all soft starters and all frequency converters according to the control strategy re-established in step 7, specifically setting the inspection time and timing the inspection. After the preset inspection time is reached, the main fan or the standby fan in the main fan unit is turned on according to the control strategy. At the same time, according to the priority of the auxiliary fan, the auxiliary fans are turned on or off one by one to adjust the speed of the main fan, the standby fan or the auxiliary fan. Step 9: The PLC controller continues to send cooling effect data to the central server according to the method of step 3.

2. An intelligent method for monitoring and adjusting the operation of a cooling tower fan according to claim 1, characterized in that: When executing step 3, the control strategy is as follows: The fans controlled by the three frequency converters include fan 1#, fan 2#, and fan 3#, and the fans controlled by the soft starter controller include fan 4#, fan 5#, and fan 6#; Strategy 1: Fan 1#, Fan 2# and Fan 3# form the main fan unit, where Fan 1# is the main fan, Fan 2# and Fan 3# are standby fans. When Fan 1# is damaged, Fan 2# is activated; when both Fan 1# and Fan 2# are damaged, Fan 3# is activated; Strategy 2: Fan 4#, Fan 5# and Fan 6# form an auxiliary fan group. Fan 4#, Fan 5# and Fan 6# have default priorities in the control strategy. In the default priorities, Fan 4# has the highest priority, that is, Fan 4# is turned on first, followed by Fan 5#, and finally Fan 6#; The default priority is modified as the category document B is updated by the central server; Strategy 3: Set the inspection time. When the inspection time arrives, execute the start condition judgment function, as follows: Condition 1: If the water temperature reaches the temperature threshold of 30 degrees Celsius and does not reach the temperature threshold of 40 degrees Celsius, the speed of all fans in the on state will increase by 10%; if the water temperature does not reach the temperature threshold of 30 degrees Celsius, the speed of all fans in the on state will return to normal speed; Condition 2: If the water temperature reaches the temperature threshold of 40 degrees Celsius, a new auxiliary fan is turned on according to the priority of the auxiliary fans; Condition 3: If the water temperature does not reach the temperature threshold of 40 degrees Celsius, one auxiliary fan is turned off according to the priority of the auxiliary fans; Condition 4: If the water pressure is less than 1.0 bar, the speed of fan 1# remains unchanged; otherwise, it increases by 10%; Reset the timer, continue timing and determine whether the inspection time has been reached.

3. An intelligent method for monitoring and adjusting the operation of a cooling tower fan according to claim 1, characterized in that: When executing step 4, the specific optimization wind turbine combination strategy model is shown in the following formula: ； in, is the cooling time of the ith auxiliary fan; is the ambient temperature; It is the water temperature; It’s the water pressure; is the speed of the fan; It is the parameter to be estimated of the model, and its value is obtained by training from the training set; The goal of the fan combination strategy optimization model is to predict the cooling time required for each auxiliary fan to reach the preset temperature based on the input water temperature, water pressure, and ambient temperature. ; Comparison of cooling time of each auxiliary fan , re-prioritize auxiliary fans: cooling time Small, high priority.

4. An intelligent method for monitoring and adjusting the operation of a cooling tower fan according to claim 1, characterized in that: When executing step 1, the speed of the fan is acquired by the inverter controller or the soft start controller and collected to the PLC controller for storage.

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