A fan group control system, an intelligent adjusting method and a control terminal thereof

Abstract

This invention discloses a group control system for a wind turbine group, its intelligent adjustment method, and a control terminal. The system includes a group control host device and multiple turbine units. The group control host device includes a communication module, a data acquisition module, a control module, a monitoring module, and an intelligent adjustment module. Each turbine unit includes multiple wind turbines. The intelligent adjustment method includes: S1. The monitoring module determines that several wind turbines in the unit have failed; S2. The intelligent adjustment module determines the total air output Q of the unit before any wind turbines fail. total The total air volume Q is calculated. total The required remaining fan speed N'; S3. The intelligent adjustment module compares whether the speed N' is less than the maximum fan speed N. max If yes, proceed to S4; if no, proceed to S5; S4. The intelligent adjustment module adjusts the speed of the remaining fan to N'; S5. The intelligent adjustment module adjusts the speed of the remaining fan to N. max The control terminal includes this system. This invention can simultaneously control multiple groups of wind turbines in real time.

Description

Technical Field

[0001] This invention belongs to the field of wind turbine control, specifically relating to a group control system for wind turbine clusters, its intelligent adjustment method, and control terminal. Background Technology

[0002] The industrial sector today faces increasingly demanding requirements for ventilation systems, particularly in terms of air quality, energy consumption, and intelligence, calling for innovative solutions. Traditional AHU (Air Handling Units) and FFU (Fan Filter Units) fan filter units typically employ independent control, requiring each unit to have its own control system, resulting in high energy consumption and low efficiency. With the rapid development of smart technology, group control technology is gradually becoming an important direction for optimizing ventilation systems as a highly efficient management method. However, currently, there is no comprehensive solution on the market for group control of AHU and FFU fan filter units that can simultaneously achieve intelligence, energy saving and emission reduction, remote monitoring, and equipment self-diagnosis. With the rapid rise of IoT technology, information exchange between devices has become increasingly convenient. However, intelligent control methods for large-scale equipment clusters remain relatively weak in industrial ventilation systems. Currently, many industrial sites still use traditional independent control methods, leading to fragmented control systems and making it difficult to achieve efficient and unified control of multiple fan units.

[0003] Furthermore, energy conservation in current ventilation systems is a major concern. Traditional ventilation system control methods often lack dynamic adjustment of real-time environmental parameters, making it difficult to optimize energy consumption. How to intelligently control the operating status of fans in real time to achieve maximum energy saving and emission reduction is one of the urgent problems to be solved in the optimization of current ventilation systems. On the other hand, troubleshooting and maintenance of ventilation systems is also a significant issue. Traditional troubleshooting methods require manual intervention and are difficult to respond to in a timely manner, which seriously affects the stability and reliability of the ventilation system. In summary, current ventilation systems still face a series of problems that urgently need to be addressed in terms of control methods, energy-saving performance, and troubleshooting.

[0004] Patent application CN202211121429.7 discloses a safe and efficient intelligent group control system for FFUs (Fan Filter Units). It uses TCP network and RS485 communication to complete data transmission between the human-machine interface unit, the regional host, the gateway, and the FFU fans. A fine particle monitoring device installed in the cleanroom monitors the particulate matter concentration in real time, and the FFU controller receives the monitoring information and adjusts the fan speed accordingly. However, this patent application cannot intelligently adjust the speed of the remaining fans when several fans in the FFU fan group fail. Summary of the Invention

[0005] The purpose of this invention is to provide a group control system for a group of fans, its intelligent adjustment method and control terminal, to achieve efficient, intelligent, energy-saving and reliable centralized control of AHU air walls and FFU fan filter units.

[0006] To achieve the above-mentioned objectives, the technical solution adopted by the present invention is as follows:

[0007] A group control system for a wind turbine group includes a group control host device and multiple turbine units; the group control host device includes a communication module, a data acquisition module, a control module, a monitoring module, and an intelligent adjustment module; each turbine unit includes multiple wind turbines;

[0008] The communication module is used to enable the data acquisition module, the control module, the monitoring module, and the intelligent adjustment module to establish communication with all the generator units;

[0009] The data acquisition module is used to acquire the operating data of all the units in real time;

[0010] The control module is used to analyze and calculate the corresponding operation data based on the operation data acquired by the data acquisition module, and generate a corresponding control strategy to be implemented in the corresponding unit.

[0011] The monitoring module is used to determine whether the fan in the unit has malfunctioned based on the operating data acquired by the data acquisition module.

[0012] The intelligent adjustment module is used to adjust the speed of the non-faulty fans in the unit when the monitoring module detects that several fans in the unit have failed.

[0013] This invention, through real-time monitoring and intelligent adjustment of multiple units, ensures that in the event of several fan failures, the system can automatically adjust the operating status of other fans to meet the overall system's performance requirements and maintain the overall ventilation effect. This mechanism provides a strong guarantee for the continuity and reliability of system operation, while the alarm module further improves the efficiency of maintenance response.

[0014] Preferably, the data acquisition module includes multiple environmental sensors; the multiple environmental sensors are installed inside the unit; the multiple environmental sensors include a temperature sensor, a humidity sensor, and a pressure sensor; the multiple units include multiple air handling units and multiple fan filter units.

[0015] Preferably, the calculation data obtained by the control module includes the deviation e(t) when the wind turbine runs for time t;

[0016] The control module includes an intelligent algorithm module; the intelligent algorithm module includes a PID control algorithm and sets a proportional coefficient Kp, an integral coefficient Ki, and a derivative coefficient Kd.

[0017] The PID control algorithm calculates the control quantity u(t) of the fan, which satisfies the following equation:

[0018] ;

[0019] in This represents the steady-state error of the wind turbine.

[0020] Preferably, the data acquisition module acquires the cross-sectional area A of the fan outlet, the resistance coefficient K, and the real-time wind speed v;

[0021] The control module also includes an intelligent variable air volume (VAV) algorithm; the intelligent VAV algorithm includes calculating the airflow Q of the fan. n The formula is as follows: Q n = A v ;

[0022] The intelligent variable air volume algorithm also includes the following formula for calculating the air resistance P experienced by the fan: .

[0023] This invention also provides an intelligent adjustment method for a group control system of a wind turbine group, applied to the aforementioned group control system of a wind turbine group; the intelligent adjustment method includes the following steps:

[0024] Step S1. When the monitoring module determines that several fans in the unit have failed, it generates a request for adjustment and sends it to the intelligent adjustment module.

[0025] Step S2. The intelligent adjustment module adjusts the total air volume Q of the unit before any fan malfunctions. total The total air volume Q is calculated. total The required remaining fan speed N';

[0026] Step S3. The intelligent adjustment module compares whether the rotational speed N' is less than the maximum rotational speed N of the fan. max If yes, proceed to step S4; otherwise, proceed to step S5.

[0027] Step S4. The intelligent adjustment module adjusts the speed of the remaining fan to N';

[0028] Step S5. The intelligent adjustment module adjusts the speed of the remaining fan to N. max .

[0029] Preferably, when the monitoring module determines that no fan has failed, assuming the number of fans in the unit is n, then the rotational speed of all n fans in the unit is N; the intelligent adjustment module further includes calculating the total air volume Q of the unit at this time. total =n·N;

[0030] When the monitoring module determines that x fans in the unit have failed, the number of remaining fans that have not failed is nx.

[0031] The intelligent adjustment module calculates the required rotational speed N' of the remaining fans according to the following formula: .

[0032] Preferably, the intelligent adjustment module adjusts the fan speed within a range of 10%N. max ~100%N max ;

[0033] When the intelligent adjustment module adjusts the speed of the remaining fan to N max When the unit is in fault condition, the total output air volume Q' satisfies the following formula: Q' = (nx)·N max .

[0034] Preferably, the group control host device further includes an alarm module; the alarm module sets the air output alarm threshold Q of the unit. c The alarm module is used when the total fault-state air volume Q' of the unit is less than the air volume alarm threshold Q. c An alarm signal will be issued at any time.

[0035] The present invention also provides a control terminal for a group control system of a wind turbine group, including the aforementioned group control system for a wind turbine group; the control terminal further includes a Bluetooth module; the control terminal communicates with the group control host device through the Bluetooth module.

[0036] Preferably, the control terminal further includes a human-computer interaction module and a query module; the human-computer interaction module is used to interact with the group control host device through the Bluetooth module; the query module is used to query the data of the group control host device through the Bluetooth module.

[0037] Beneficial effects:

[0038] 1. This invention provides a group control system for a fan group, its intelligent adjustment method, and control terminal. Through real-time monitoring and intelligent adjustment of multiple units, it ensures that when several fans fail, the system can automatically adjust the operating status of other fans to meet the overall system's performance requirements and maintain the overall system's ventilation effect. This mechanism provides a strong guarantee for the continuity and reliability of system operation, while the alarm module further improves the efficiency of maintenance response.

[0039] 2. The present invention provides a group control system for a wind turbine group, its intelligent adjustment method, and control terminal.

[0040] It can simultaneously control multiple groups of fans in real time to achieve efficient, intelligent, energy-saving and reliable centralized control of AHU air walls and FFU fan filter units, significantly improving control efficiency, stability and reliability. Attached Figure Description

[0041] Figure 1 The diagram shown is a structural block diagram of a group control system for a wind turbine group according to Embodiment 1.

[0042] Figure 2 The diagram shown is a flowchart of an intelligent adjustment method for a group control system of a wind turbine group according to Embodiment 2.

[0043] Figure 3 The diagram shown is a schematic diagram of the architecture of the control terminal of a group control system for a wind turbine group according to Embodiment 3. Detailed Implementation

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings and other implementation methods can be obtained based on these drawings without any creative effort.

[0045] The technical solution of the present invention will be described in detail below with specific embodiments.

[0046] Example 1

[0047] like Figure 1 As shown, this embodiment of a wind turbine group control system includes a group control host device and multiple turbine units; the group control host device includes a communication module, a data acquisition module, a control module, a monitoring module, and an intelligent adjustment module; each turbine unit includes multiple wind turbines;

[0048] The communication module is used to enable the data acquisition module, control module, monitoring module, and intelligent adjustment module to establish communication with all units;

[0049] The data acquisition module is used to acquire the operating data of all units in real time;

[0050] The control module is used to analyze and calculate the corresponding operation data based on the operation data obtained by the data acquisition module, and generate the corresponding control strategy to be implemented in the corresponding unit.

[0051] The monitoring module is used to determine whether the fans in the unit have malfunctioned based on the operating data obtained by the data acquisition module.

[0052] The intelligent adjustment module is used to adjust the speed of the non-faulty fans in the unit when the monitoring module detects that several fans in the unit have failed.

[0053] Preferably, the data acquisition module includes multiple environmental sensors; the multiple environmental sensors are installed inside the unit; the multiple environmental sensors include temperature sensors, humidity sensors, and pressure sensors; the multiple units include multiple air handling units and multiple fan filter units.

[0054] Preferably, the calculation data obtained by the control module includes the deviation e(t) when the fan runs for time t;

[0055] The control module includes an intelligent algorithm module; the intelligent algorithm module includes a PID control algorithm and sets the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd;

[0056] The control quantity u(t) of the fan calculated by the PID control algorithm satisfies the following equation:

[0057] ;

[0058] in This represents the steady-state error of the wind turbine.

[0059] Preferably, the data acquisition module collects the cross-sectional area A of the fan outlet, the resistance coefficient K, and the real-time wind speed v;

[0060] The control module also includes an intelligent variable air volume (VAV) algorithm; the intelligent VAV algorithm includes calculating the airflow rate Q of the fan. n The formula is as follows: Q n = A v ;

[0061] The intelligent variable air volume algorithm also includes the following formula for calculating the air resistance P experienced by the fan: .

[0062] Specifically, in this embodiment, the environmental sensors transmit real-time data of environmental parameters such as temperature, humidity, and wind pressure to the group control host device, enabling the system to monitor and analyze this data in real time. Timely feedback of sensor data allows the ventilation system to dynamically adjust its operating status according to current environmental conditions, thereby maintaining stable and efficient ventilation. Furthermore, it enables automatic closed-loop control, which relies on sensing technology such as environmental sensors for temperature, humidity, pressure, and wind speed. These sensors monitor the surrounding environment in real time and feed the data back to the control system. Simultaneously, by monitoring equipment status, such as fan speed and power, a comprehensive understanding of the entire system and equipment's operating status can be obtained.

[0063] Based on data collected by sensors, the control system calculates the optimal adjustment scheme in real time according to preset parameters, algorithms, and strategies. In high-temperature environments, the system automatically adjusts the fan speed to enhance ventilation; in low-humidity conditions, it can adjust the humidity control equipment accordingly to maintain a suitable humidity level.

[0064] Specifically, in the automatic closed-loop control of wind turbines, the PID control algorithm is applied as follows:

[0065] 1. Application of the proportional term (P): In fan control, the proportional term corresponds to adjusting the fan's operating state according to the magnitude of the current deviation. For example, when the ambient temperature rises, the control system will increase the fan speed through the proportional term to improve ventilation and ensure that the ambient temperature is maintained within a suitable range.

[0066] 2. Application of the integral term (I): The integral term is used to eliminate the steady-state error of the system, that is, the deviation under steady-state conditions. In fan control, the integral term can ensure that the steady-state error of the system is effectively reduced during long-term operation, thereby ensuring the stability of the ventilation system.

[0067] 3. Application of the derivative term (D): The derivative term suppresses system overshoot and oscillation, making the control process smoother. In fan control, the derivative term adjusts the fan's operating speed to prevent system instability caused by drastic changes during the control process.

[0068] Specifically, in the automatic closed-loop control of the wind turbine, the control system also includes the following implementation method of fuzzy control algorithm: (Fuzzy control algorithm realizes the control of the system by transforming fuzzy rules into mathematical operations.)

[0069] 1. Fuzzification: Input signals (such as environmental parameters and equipment status) are transformed into fuzzy sets through fuzzification, and fuzzy inputs are transformed into a set of fuzzy control rules.

[0070] 2. Fuzzy Rule Base: Establish a set of fuzzy rules that describe the relationship between input signals and output control quantities. For example, "If the temperature is high and the humidity is low, increase the fan speed."

[0071] 3. Inference and Defuzzification: Based on the current input signal, fuzzy control quantities are obtained through fuzzy inference. Then, the fuzzy control quantities are converted into specific values ​​through a defuzzification process, serving as the system's output control quantities.

[0072] Furthermore, fuzzy control algorithms, by processing fuzzy input signals and a fuzzy rule base, achieve intelligent adjustment of fan control, making them suitable for complex situations where precise mathematical models are difficult to establish. For example, when facing complex environmental conditions, such as simultaneously considering multiple factors like temperature, humidity, and wind speed, the fuzzy control algorithm can quickly react and adjust the fan's operating state based on the settings in the fuzzy rule base to meet actual needs.

[0073] Specifically, in the automatic closed-loop control of the fan, the intelligent variable air volume (VAV) algorithm enables the system to adjust the fan's airflow in real time based on environmental parameters to maintain stable system operation. Under different operating conditions, the ventilation system can dynamically adjust the airflow based on real-time data to adapt to different ventilation needs. The intelligent VAV algorithm also allows the ventilation system to respond more flexibly to the needs under different operating conditions, thereby optimizing energy saving and energy efficiency. By precisely controlling the airflow, the system can reduce energy consumption and improve energy efficiency while ensuring ventilation effects.

[0074] Specifically, the intelligent variable air volume (VAV) algorithm includes a feedback loop that compares sensor data with a set target value to determine whether the fan's airflow needs adjustment. The intelligent VAV algorithm also employs variable frequency speed control (VFD) technology: adjusting the fan's rotational speed to regulate airflow and achieve precise airflow control. Simultaneously, the intelligent VAV algorithm utilizes a PID control strategy for precise fan speed control. The PID control algorithm calculates the corresponding control input based on real-time environmental parameters and setpoints, thereby ensuring stable system operation.

[0075] Example 2

[0076] This embodiment provides an intelligent adjustment method for a group control system of a wind turbine group, applied to a group control system of a wind turbine group in Embodiment 1; such as Figure 2 As shown, the intelligent adjustment method in this embodiment includes the following steps:

[0077] Step S1. When the monitoring module determines that several fans in the unit have failed, it generates a request for adjustment and sends it to the intelligent adjustment module.

[0078] Step S2. The intelligent adjustment module adjusts the total air volume Q of the unit before any fan malfunctions. total The total air volume Q is calculated. total The required remaining fan speed N';

[0079] Step S3. The intelligent adjustment module compares whether the rotational speed N' is less than the maximum rotational speed N of the fan. max If yes, proceed to step S4; otherwise, proceed to step S5.

[0080] Step S4. The intelligent adjustment module adjusts the speed of the remaining fan to N';

[0081] Step S5. The intelligent adjustment module adjusts the speed of the remaining fan to N. max .

[0082] Preferably, when the monitoring module determines that no fan has failed, assuming the number of fans in the unit is n, then the rotational speed of all n fans in the unit is N; the intelligent adjustment module also includes calculating the total air volume Q of the unit at this time. total =n·N;

[0083] When the monitoring module determines that x fans in the unit have failed, the number of remaining fans that have not failed is nx.

[0084] The intelligent adjustment module calculates the required rotational speed N' of the remaining fans according to the following formula: .

[0085] Preferably, the intelligent adjustment module adjusts the fan speed within a range of 10%N. max ~100%N max ;

[0086] When the intelligent adjustment module adjusts the speed of the remaining fan to N max When the unit is in fault condition, the total output air volume Q' satisfies the following formula: Q' = (nx)·N max .

[0087] Preferably, the group control host device also includes an alarm module; the alarm module sets the unit's airflow alarm threshold Q. c The alarm module is used when the total air volume Q' of the unit in a fault state is less than the air volume alarm threshold Q. c An alarm signal will be issued at any time.

[0088] Specifically, the intelligent adjustment module enables stepless speed regulation of the fan, with a speed regulation range from 10% to 100%, meeting the precise control of air volume under different environmental requirements.

[0089] Specifically, through the intelligent adjustment method of this embodiment, once one or more fans in the system fail, the device can automatically adjust the operating status of other fans: by adjusting the speed and air volume, it can meet the performance requirements of the overall system and ensure the normal operation of the ventilation system. At the same time, the device is equipped with a complete alarm module to monitor the operating status of all fans in real time. When the fan failure is so severe that the total air volume of the unit falls below the air volume alarm threshold, an alarm will be issued in a timely manner to remind the staff.

[0090] Specifically, the unit in this embodiment consists of a 2*2 matrix fan. Under normal circumstances, each of the four fans operates at 50% RPM, with a total air volume of 200%. When one fan fails, the intelligent adjustment module will automatically adjust the remaining three fans to operate at 67% RPM (200% / 3) to ensure that the system can still meet the environmental operating requirements.

[0091] If two fans fail, the intelligent control module will adjust the remaining two fans to operate at 100% RPM (200% / 2). If all three fans fail, the intelligent control system will adjust the remaining fan to operate at 100% RPM (200% / 1). At this time, the total air volume of the unit will fall below the air volume alarm threshold, indicating that the unit cannot meet the environmental operating requirements, and the system will enter a critical state. In this state, the fan will run at full speed and the alarm module will issue an alarm.

[0092] Example 3

[0093] like Figure 3 As shown, this embodiment provides a control terminal for a group control system of a wind turbine group, including a group control system for a wind turbine group as described in Embodiment 1; the control terminal also includes a Bluetooth module; the control terminal communicates with the group control host device through the Bluetooth module.

[0094] Preferably, the control terminal further includes a human-machine interaction module and a query module; the human-machine interaction module is used to interact with the group control host device via a Bluetooth module; the query module is used to query data from the group control host device via a Bluetooth module.

[0095] Specifically, this embodiment presents a group control system for a fan cluster, which is an application topology based on an HMI (Human Machine Interface) group control intelligent fan management system. The system integrates the equipment layer, group control layer, cloud layer, and client layer to achieve efficient, intelligent, energy-saving, and reliable centralized control of AHU (Air Hull Unit) and FFU (Fan Filter Unit) air walls and filters. The key technology lies in utilizing advanced communication technology and intelligent algorithms to uniformly manage the ventilation equipment cluster, thereby solving the problems of high energy consumption and low efficiency caused by independent control methods in traditional ventilation systems.

[0096] Specifically, the group control host device in this embodiment is the core of the group control layer, possessing rich functional features. The human-machine interface module makes operation more intuitive and user-friendly, allowing users to perform various settings and monitoring through an intuitive interface, improving the device's ease of use and convenience. Secondly, through RS485 network data communication among multiple fan groups, the device can efficiently handle the control needs of large-scale equipment. The group control host device, in conjunction with a communication gateway, allows one gateway to support 8 fan groups, with each group supporting up to 31 units. Simultaneously, up to 8 gateways can be connected for networking, ensuring the device's scalability and stability. By grouping and managing the fans, parameter settings and start / stop control can be implemented for multiple groups, single groups, and individual fans, making centralized management of the ventilation system more efficient.

[0097] Specifically, to ensure the stable operation of the entire system, the group control host device also has multiple protection mechanisms, including overvoltage, undervoltage, stall, and overtemperature protection functions. It also supports automatic vibration damping to ensure safe operation of the equipment. Furthermore, the Bluetooth module enables firmware upgrades and remote OTA upgrades, ensuring the device is always up-to-date and updated.

[0098] Specifically, the group control host device has a work log recording function, which can record the operating status and parameter settings of each fan. These records provide important data support for system operation analysis. Furthermore, through the control terminal's mobile APP or PC software, users can view the historical data recorded by the group control host device at any time via the query module, including the operating status of each fan, environmental conditions, operation records, and fault records, facilitating the analysis of system operation status. Simultaneously, it supports recording the operating power of multiple groups, a single group, and a single fan, providing data support for energy consumption analysis and energy-saving optimization. By analyzing historical data, the system can perform energy efficiency assessments to identify areas for optimization. Users can make corresponding adjustments based on historical data trends to improve the overall performance of the ventilation system.

[0099] Furthermore, by analyzing and learning from historical data such as temperature, power, speed, and fault status, the system can achieve pre-diagnosis and self-repair functions for system equipment. By comparing historical data, the system can detect potential fault signs in advance, thereby reducing equipment downtime.

[0100] Specifically, when equipment failure occurs in the system, the group control host device can automatically diagnose and record the fault information. Simultaneously, the control terminal's mobile app or PC software will automatically send fault notifications to the user, reminding them to address the fault promptly. Furthermore, in the event of a fault, the system can automatically activate backup equipment, achieving automatic equipment switching and ensuring the continuous and stable operation of the system.

[0101] The embodiments of the group control system for wind turbine groups, its intelligent adjustment method, and control terminal provided by the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention, and the descriptions of the embodiments above are only for the purpose of helping to understand the core ideas of the present invention. It should be noted that those skilled in the art can make several improvements and modifications to the present invention without departing from the principles of the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims

1. A group control system for a wind turbine group, characterized in that, It includes a group control host device and multiple units; the group control host device includes a communication module, a data acquisition module, a control module, a monitoring module, and an intelligent adjustment module; each unit includes multiple fans; The communication module is used to enable the data acquisition module, the control module, the monitoring module, and the intelligent adjustment module to establish communication with all the generator units; The data acquisition module is used to acquire the operating data of all the units in real time; The control module is used to analyze and calculate the corresponding operation data based on the operation data acquired by the data acquisition module, and generate a corresponding control strategy to be implemented in the corresponding unit. The monitoring module is used to determine whether the fan in the unit has malfunctioned based on the operating data acquired by the data acquisition module. The intelligent adjustment module is used to adjust the speed of the non-faulty fans in the unit when the monitoring module detects that several fans in the unit have failed. The calculation data obtained by the control module includes the deviation e(t) when the wind turbine runs for time t; The control module includes an intelligent algorithm module; the intelligent algorithm module includes a PID control algorithm and sets a proportional coefficient K. p Integral coefficient K i Differential coefficient K d ; The PID control algorithm calculates the control quantity u(t) of the fan, which satisfies the following equation: ; in This represents the steady-state error of the wind turbine. The data acquisition module collects the cross-sectional area A of the fan outlet, the resistance coefficient K, and the real-time wind speed v; The control module also includes an intelligent variable air volume (VAV) algorithm; the intelligent VAV algorithm includes calculating the airflow Q of the fan. n The formula is as follows: Q n = A v ; The intelligent variable air volume algorithm also includes the following formula for calculating the air resistance P experienced by the fan: .

2. The group control system according to claim 1, characterized in that, The data acquisition module includes multiple environmental sensors; these multiple environmental sensors are installed within the unit; the multiple environmental sensors include temperature sensors, humidity sensors, and pressure sensors; the multiple units include multiple air handling units and multiple fan filter units.

3. An intelligent adjustment method for a group control system of wind turbine clusters, characterized in that, The intelligent adjustment method is applied to a group control system for a wind turbine group as described in any one of claims 1 to 2, and includes the following steps: Step S1. When the monitoring module determines that several fans in the unit have failed, it generates a request for adjustment and sends it to the intelligent adjustment module. Step S2. The intelligent adjustment module adjusts the total air volume Q of the unit before any fan malfunctions. total The total air volume Q is calculated. total The required remaining fan speed N'; Step S3. The intelligent adjustment module compares whether the rotational speed N' is less than the maximum rotational speed N of the fan. max If yes, proceed to step S4; otherwise, proceed to step S5. Step S4. The intelligent adjustment module adjusts the speed of the remaining fan to N'; Step S5. The intelligent adjustment module adjusts the speed of the remaining fan to N. max .

4. The intelligent adjustment method according to claim 3, characterized in that, When the monitoring module determines that no fan has malfunctioned, assuming the number of fans in the unit is n, then the rotational speed of each of the n fans in the unit is N; the intelligent adjustment module also includes calculating the total air output Q of the unit at this time. total =n·N; When the monitoring module determines that x fans in the unit have failed, the number of remaining fans that have not failed is nx. The intelligent adjustment module calculates the required rotational speed N' of the remaining fans according to the following formula: .

5. The intelligent adjustment method according to claim 4, characterized in that, The intelligent adjustment module adjusts the fan speed range to 10%N. max ~100%N max ; When the intelligent adjustment module adjusts the speed of the remaining fan to N max When the unit is in fault condition, the total output air volume Q' satisfies the following formula: Q' = (nx)·N max .

6. The intelligent adjustment method according to claim 5, characterized in that, The group control host device also includes an alarm module; the alarm module sets the air volume alarm threshold Q of the unit. c The alarm module is used when the total fault-state air volume Q' of the unit is less than the air volume alarm threshold Q. c An alarm signal will be issued at any time.

7. A control terminal for a group control system of a wind turbine group, characterized in that, The system includes a group control system for a wind turbine group as described in any one of claims 1 to 2; the control terminal further includes a Bluetooth module; the control terminal communicates with the group control host device through the Bluetooth module.

8. The control terminal according to claim 7, characterized in that, The control terminal also includes a human-computer interaction module and a query module; the human-computer interaction module is used to interact with the group control host device through the Bluetooth module; the query module is used to query the data of the group control host device through the Bluetooth module.

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