Variable-load energy-saving operation control method for cross-flow fan

Abstract

The invention relates to the technical field of fluid machinery control, in particular to a cross flow fan variable load energy-saving operation control method which comprises the steps that 1, operation parameters of a cross flow fan are collected in real time; 2, the real-time load coefficient of the cross flow fan is calculated based on the collected operation parameters, and the calculation process comprises the steps that the actual output power of the fan is calculated, the theoretical output power of the fan is calculated, the ratio of the actual output power to the theoretical output power serves as the power ratio, and the real-time load coefficient is obtained; 3, querying a speed-load mapping table according to the real-time load coefficient to determine the optimal running speed of the cross flow fan; 4, the running speed of the cross flow fan is adjusted to the optimal running speed; and 5, repeating the steps 1 to 4 to form closed-loop control. Through a real-time monitoring system formed by various sensors, synchronous acquisition of multiple parameters such as current, airflow pressure, airflow temperature and environment temperature is realized.

Description

Technical Field

[0001] This invention relates to the field of fluid machinery control technology, and in particular to a variable load energy-saving operation control method for crossflow fans. Background Technology

[0002] Crossflow fans, as highly efficient gas conveying devices, are widely used in air conditioning systems, ventilation equipment, industrial cooling and drying devices, and other fields. Their unique airflow path design allows for compact structure and low noise while providing large air volumes. However, in actual operation, crossflow fans often face dynamically changing load conditions. For example, in air conditioning systems, the aerodynamic load of the fan changes in real time due to factors such as indoor and outdoor temperature fluctuations, changes in personnel activity, or equipment start-up and shutdown. In existing technologies, the control methods for crossflow fans mainly rely on fixed-speed operation modes or adjustment methods based on simple threshold feedback. Fixed-speed operation modes cannot adjust the fan power when the load changes, resulting in significant energy waste under low-load conditions. While control methods based on simple threshold feedback, such as triggering fan speed switching through a single temperature sensor, can achieve basic adjustment functions, they have slow response speeds, low control accuracy, and cannot track continuous load changes in real time.

[0003] Further analysis reveals several key issues with existing technologies: First, traditional control methods lack comprehensive monitoring of the fan's operating status, typically relying on a single parameter (such as temperature or pressure) for decision-making, neglecting the synergistic effects of key parameters like current, airflow pressure, and multi-point temperatures. This leads to low fan efficiency under varying load conditions, preventing the fan from maintaining its high-efficiency operating range. Second, existing methods often employ preset fixed parameters (such as speed levels or thresholds), failing to dynamically adjust the control strategy according to the actual operating environment. For example, in scenarios with frequent load fluctuations, fan speed adjustments lag, easily causing system oscillations or overshooting, increasing energy consumption and shortening equipment lifespan. Furthermore, while some advanced control algorithms (such as fuzzy control or neural network control) have been proposed, they are computationally complex, costly to implement, and require extensive historical data for training, making them difficult to promote in general industrial applications.

[0004] Another prominent issue is the inadequacy of existing control methods in parameter acquisition and processing. For example, data from current and pressure sensors are often processed independently, lacking integrated analysis, leading to inaccurate load assessment. Simultaneously, the impact of ambient temperature changes on fan efficiency is not fully considered, causing control decisions to deviate from actual needs. Regarding energy saving, traditional methods cannot identify the fan's optimal efficiency point in real time, especially under partial load conditions, where fan operating efficiency is far below design values. Therefore, there is an urgent need in the field for a method that can adapt to load changes in real time, precisely control the operation of crossflow fans, and simultaneously achieve energy savings. This method should be based on a feasible hardware platform, using efficient data acquisition and processing flows to dynamically adjust fan parameters, ensuring efficient and stable operation under varying load conditions. This invention addresses the above problems by proposing an innovative control method that achieves energy-saving goals through real-time monitoring of multiple parameters and dynamic speed adjustment. Summary of the Invention

[0005] To achieve the above objectives, the present invention provides a variable load energy-saving operation control method for crossflow fans, comprising the following steps:

[0006] Step 1: Collect the operating parameters of the crossflow fan in real time. The operating parameters include the current value of the fan motor, the airflow pressure value at the fan outlet, the airflow temperature value at the fan inlet, and the ambient temperature value at which the fan operates.

[0007] Step 2: Calculate the real-time load factor of the crossflow fan based on the collected operating parameters. The calculation process includes: calculating the actual output power of the fan based on the current value of the fan motor and the preset motor efficiency curve; calculating the theoretical output power of the fan based on the airflow pressure value at the fan outlet and the airflow temperature value at the fan inlet, combined with the fan characteristic equation; using the ratio of the actual output power to the theoretical output power as the power ratio; and correcting the power ratio based on the ambient temperature value to obtain the real-time load factor.

[0008] Step 3: Determine the optimal operating speed of the crossflow fan by querying the speed-load mapping table based on the real-time load factor;

[0009] Step 4: Adjust the operating speed of the crossflow fan to the optimal operating speed;

[0010] Step 5: Repeat steps 1 to 4 to form a closed-loop control.

[0011] Preferably, the specific process of real-time acquisition of the operating parameters of the crossflow fan in step 1 includes:

[0012] The current value of the wind turbine motor is acquired by a Hall effect current sensor, which is connected in series in the power supply circuit of the wind turbine motor.

[0013] The airflow pressure value at the fan outlet is collected by a microelectromechanical system (MEMS) piezoresistive pressure sensor, which is installed in the airflow channel at the fan outlet.

[0014] The airflow temperature value at the fan inlet is collected by a platinum resistance temperature sensor, which is installed at the fan inlet airflow.

[0015] The ambient temperature value of the fan is collected by a thermistor temperature sensor, which is installed on the surface of the fan casing.

[0016] All sensor output signals are converted into digital signals by a 16-bit analog-to-digital converter. The processing unit reads the converted digital signals at a sampling frequency of 10 times per second, and takes the arithmetic mean of each parameter after sampling it 5 times continuously as the valid value at that sampling time.

[0017] Preferably, the process of establishing the preset motor efficiency curve in step 2 includes:

[0018] After the fan assembly is completed, a load test is performed on the crossflow fan on the test platform. The test process includes: at least 20 test points are evenly set up from no-load to full-load under the rated voltage of the fan. At each test point, the input current value of the fan motor and the actual torque value of the fan output shaft are recorded synchronously. The actual output power is calculated based on the actual torque value and the fan speed. The current value of each test point is fitted with the corresponding actual output power data using a cubic polynomial to obtain the functional relationship between the current value and the actual output power. This functional relationship is stored as the motor efficiency curve in the flash memory of the processing unit.

[0019] Preferably, the process of establishing the wind turbine characteristic equation in step 2 includes:

[0020] After the fan assembly is completed, the aerodynamic performance of the crossflow fan is tested under a standard test environment. The test process includes: at the rated speed of the fan, the fan load is changed by adjusting the opening of the outlet damper. At least 15 operating points are selected within the stable operating range of the fan. At each operating point, the airflow pressure value at the fan outlet, the airflow temperature value at the fan inlet, and the fan output power are measured simultaneously. A fan characteristic equation is established using a multiple linear regression method, with the airflow pressure value and airflow temperature value as independent variables and the output power as the dependent variable. The fan characteristic equation is in the form of a second-order polynomial equation. The established fan characteristic equation is stored in the flash memory of the processing unit.

[0021] Preferably, the specific process of correcting the power ratio based on the ambient temperature value in step 2 includes:

[0022] A temperature correction coefficient table is established. The establishment process includes: conducting temperature characteristic tests on the crossflow fan in an environmental test chamber. The test process includes: setting temperature test points at 5-degree intervals within an ambient temperature range of -10 degrees Celsius to 50 degrees Celsius; at each temperature test point, running the fan under rated load; measuring the deviation between the actual power ratio and the power ratio at the standard temperature; generating a temperature correction coefficient table based on the deviation values; and storing the correction coefficients corresponding to different ambient temperatures using a piecewise linear interpolation method.

[0023] The correction process includes: querying the temperature correction coefficient table based on the current ambient temperature value to obtain the corresponding correction coefficient, multiplying the power ratio by the correction coefficient to obtain the corrected power ratio, and normalizing the corrected power ratio to the range of 0 to 1 as the real-time load coefficient.

[0024] Preferably, the process of establishing the speed-load mapping table in step 3 includes:

[0025] After the fan assembly is completed, the crossflow fan is tested for energy efficiency on the test platform. The test process includes: setting load test points in increments of 0.05 within a real-time load factor range from 0 to 1; adjusting the fan operating speed at each load test point; measuring the input power of the fan at different operating speeds; and finding the operating speed that minimizes the fan input power as the optimal operating speed under that load factor.

[0026] A mapping relationship is established between each load test point and the corresponding optimal operating speed. A continuous speed-load mapping curve is generated using the cubic spline interpolation method. The speed-load mapping curve is discretized and stored in the form of a two-dimensional lookup table. The index of the two-dimensional lookup table is the real-time load coefficient, and the value is the optimal operating speed.

[0027] Preferably, the specific process of determining the optimal operating speed of the crossflow fan by querying the speed-load mapping table in step 3 includes:

[0028] The real-time load factor is compared with the index of the speed-load mapping table. When the real-time load factor matches a certain index value exactly, the corresponding optimal running speed is read directly.

[0029] When the real-time load factor is between two index values, the optimal running speed is calculated using a linear interpolation method. The linear interpolation calculation process includes: taking two adjacent index values ​​of the real-time load factor and their corresponding optimal running speeds, and calculating the optimal running speed according to the interpolation formula.

[0030] When the real-time load factor exceeds the index range of the speed-load mapping table, the optimal running speed at the index boundary is taken as the output value.

[0031] Preferably, the specific process of adjusting the operating speed of the crossflow fan to the optimal operating speed in step 4 includes:

[0032] The processing unit converts the optimal operating speed into a speed control signal, which is a pulse width modulation signal. The duty cycle of the pulse width modulation signal is linearly related to the optimal operating speed.

[0033] The pulse width modulation signal is sent to the motor drive circuit through the pulse width modulation output interface of the processing unit. The motor drive circuit is a half-bridge inverter circuit composed of insulated gate bipolar transistors.

[0034] The motor drive circuit adjusts the average voltage output to the fan motor according to the duty cycle of the pulse width modulation signal, thereby controlling the fan's operating speed;

[0035] During speed adjustment, the motor drive circuit uses a ramp function to control the rate of speed change. The slope of the ramp function is preset based on the fan's moment of inertia and the motor's torque characteristics to ensure that the fan speed smoothly transitions from the current value to the target optimal operating speed.

[0036] Preferably, the specific process of repeatedly executing steps 1 to 4 to form closed-loop control in step 5 includes:

[0037] Set a fixed execution cycle, which is 1 second;

[0038] At the beginning of each cycle, the processing unit first checks the validity of the data from each sensor. The data validity check process includes: comparing the difference between the current sampled value and the historical sampled value. When the difference exceeds a preset threshold, it is determined to be data abnormal.

[0039] When an abnormal data is detected, the calculation is performed using the last valid sampled value, and a fault alarm signal is triggered simultaneously.

[0040] If the data is valid, execute steps 1 to 4 sequentially to complete one control loop.

[0041] Preferably, it also includes a process for recording and analyzing historical data of operating parameters:

[0042] A historical database of operating parameters is established in the external memory of the processing unit, recording the real-time load coefficient, optimal operating speed, and actual operating speed once per minute;

[0043] Historical data is statistically analyzed weekly. The statistical analysis process includes: calculating the average and standard deviation of the real-time load factor and assessing the stability of the wind turbine's operating status.

[0044] The speed-load mapping table is automatically updated based on the statistical analysis results. The update process includes: when the standard deviation of the real-time load coefficient continues to be large, the energy efficiency test is re-performed and a new speed-load mapping table is established.

[0045] By regularly updating the speed-load mapping table, the system adapts to changes in fan performance over time, maintaining optimal energy-saving performance of the control method.

[0046] The beneficial effects of this invention are:

[0047] 1. This invention utilizes a real-time monitoring system comprised of multiple sensors, including a Hall effect current sensor, a piezoresistive pressure sensor, a platinum resistance temperature sensor, and a thermistor temperature sensor, to simultaneously acquire multiple parameters such as current, airflow pressure, airflow temperature, and ambient temperature. Through 16-bit A / D conversion and multiple averaging processes, the stability and accuracy of the data are improved. This design enables more precise load characteristic calculations, accurately reflecting the fan's operating status and providing a reliable basis for subsequent control decisions.

[0048] 2. This invention establishes motor efficiency curves and fan characteristic equations to calculate the actual and theoretical power of the fan in real time, obtaining a real-time load factor. The power ratio is then corrected using a temperature correction coefficient table, ensuring the accuracy of the control strategy under different ambient temperatures. This dynamic calculation mechanism replaces traditional static parameter settings, achieving adaptive control that changes with environmental and load variations.

[0049] 3. This invention establishes a speed-load mapping table, accurately determines the optimal operating speed through interpolation calculations, and uses a PWM signal to drive a half-bridge inverter circuit to control the motor voltage, achieving linearly adjustable fan speed. Combined with a ramp function speed limiting strategy, the speed adjustment process is smoothly transitioned, reducing mechanical shocks and current surges, improving the smoothness and reliability of the system response, and extending the equipment's service life.

[0050] 4. This invention employs parametric modeling and lookup table control, implementing complex aerodynamic characteristics and power relationships using interpolation functions and lookup tables. This reduces computational load, simplifies hardware implementation, and is suitable for general embedded processing units. This solution significantly reduces implementation costs and computational burden while maintaining control accuracy.

[0051] 5. This invention adds a historical data recording and statistical analysis module. The system periodically performs statistical analysis on real-time load coefficients and operating speeds, assesses operational stability, and dynamically updates the speed-load mapping table based on the analysis results, thereby achieving self-learning and performance self-adaptation. This mechanism can compensate for the deviation in wind turbine performance over time, maintaining long-term energy-saving effects and efficient operation. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in this invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0053] Figure 1 This is a flowchart of the steps of the method of the present invention;

[0054] Figure 2 This is a flowchart illustrating the steps involved in establishing the speed-load mapping table in the method of this invention.

[0055] Figure 3 This is a flowchart illustrating the steps involved in recording and analyzing historical data of the operating parameters of the method of the present invention. Detailed Implementation

[0056] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0057] Please see Figures 1-3 This invention provides a variable load energy-saving operation control method for a crossflow fan. In step 1, the operating status of the fan is comprehensively monitored by deploying multiple sensors. A current sensor collects the current value of the fan motor in real time, while an airflow pressure sensor and a temperature sensor collect the airflow pressure value at the fan outlet, the airflow temperature value at the fan inlet, and the ambient temperature value, respectively. These multi-dimensional operating data comprehensively reflect the operating status of the fan and provide an accurate basis for subsequent control decisions.

[0058] In step 2, based on the collected current value and using a pre-set motor efficiency curve, the actual output power of the fan motor can be calculated. Then, combining the airflow pressure at the fan outlet and the airflow temperature at the inlet, the theoretical output power is calculated using the fan characteristic equation. The power ratio is obtained by comparing the actual output power with the theoretical output power. Further adjustments to the power ratio are made using an ambient temperature correction factor, ultimately yielding the real-time load factor. This process accurately assesses the fan's operating load under current environmental conditions, thereby achieving precise control.

[0059] In step 3, based on the real-time calculated load factor, the optimal operating speed for the fan under the current load conditions can be quickly determined by consulting a preset speed-load mapping table. This mapping table, based on historical operating data and fan characteristics, accurately reflects the optimal speed of the fan under different load conditions, thereby ensuring that the fan always operates at high efficiency.

[0060] In step 4, the system precisely adjusts the speed of the fan motor using a PWM (Pulse Width Modulation) control signal, enabling it to quickly and smoothly reach the optimal operating speed obtained from the query. This step ensures that the fan maintains its high-efficiency range during load fluctuations, avoiding inefficient operation.

[0061] In step 5, this control method is a dynamic closed-loop control system that executes steps 1 to 4 in real time, continuously adjusting the fan's operating status. This closed-loop control ensures that the fan can automatically adjust its operating parameters according to real-time load changes during operation, avoiding efficiency losses or energy waste caused by fixed parameters.

[0062] Through the embodiments of this invention, crossflow fans can maintain a highly efficient and stable operating state under varying load conditions, greatly reducing energy waste and extending the service life of the equipment. Especially in application environments with frequent load fluctuations, it can significantly improve the system's response speed and control accuracy, avoiding the excessive energy consumption problems caused by response lag in traditional control methods. Therefore, this technology has significant energy-saving effects and high promotional value in industrial applications.

[0063] In one possible implementation, a Hall effect current sensor is used to collect the current value of the wind turbine motor. The sensor is installed in series in the power supply circuit of the wind turbine motor, and senses the current through the Hall effect principle, outputting a signal proportional to the current magnitude. This sensor has high anti-interference capability, strong adaptability, and can work stably in complex electromagnetic environments.

[0064] Microelectromechanical systems (MEMS) piezoresistive pressure sensors are used to monitor the airflow pressure at the fan outlet in real time. They are installed inside the airflow channel at the fan outlet. MEMS piezoresistive pressure sensors can provide high-precision airflow pressure data and have a fast response time, making them suitable for highly dynamic systems such as fans.

[0065] Platinum resistance temperature sensors are used to measure the airflow temperature at the inlet of a fan. The sensor is installed at the fan inlet airflow point. Platinum resistance sensors have excellent stability and accuracy, maintaining stable performance during long-term operation and ensuring accurate temperature measurements.

[0066] Thermistor temperature sensors are used to collect temperature data of the operating environment of wind turbines and are installed on the surface of the wind turbine casing. Thermistor sensors are inexpensive, have a fast response time, and can effectively measure ambient temperature and transmit real-time data.

[0067] All sensor output signals are converted into digital signals via a 16-bit analog-to-digital converter (ADC). This converter features high precision and high resolution, ensuring the accuracy of the acquired data. The processing unit reads the converted digital signals at a sampling frequency of 10 times per second, ensuring the timeliness and continuity of the data. Within each sampling period, the sensor samples five times consecutively. The data after each sampling is smoothed by calculating the arithmetic mean to reduce errors caused by instantaneous fluctuations, ensuring that the effective values ​​acquired each time are more stable and reliable.

[0068] Through the above implementation steps, the system can efficiently and accurately collect key operating parameters of the fan. The combined use of Hall effect current sensors, piezoresistive pressure sensors, platinum resistance temperature sensors, and thermistor temperature sensors ensures accurate monitoring and comprehensive coverage of different types of data. Furthermore, the high-precision conversion using a 16-bit ADC and the averaging method after multiple sampling greatly improves data accuracy and reduces errors caused by environmental changes or short-term sensor fluctuations. This high-precision data acquisition method provides strong support for subsequent load calculations and control strategy implementation, ultimately achieving energy saving and optimized operation of the crossflow fan.

[0069] In one possible implementation, after the wind turbine is assembled, a suitable test platform is selected for load testing. This platform is capable of accurately recording current values ​​and output power, and can simulate the wind turbine's operating state under different loads.

[0070] The test began with the fan under no-load conditions, gradually increasing the load until it reached full load. To ensure the comprehensiveness and accuracy of the test data, at least 20 test points were evenly distributed across the load range. These test points covered all operating conditions from no-load to full-load, ensuring that the motor's performance under different load conditions was fully tested.

[0071] At each test point, the system synchronously records the input current value of the fan motor and the actual torque value of the fan output shaft. By combining the actual torque value and the fan speed, the actual output power of the fan under that load is calculated. The specific calculation formula is: Actual output power = Torque × Speed, ensuring that the calculated output power accurately reflects the actual operating state of the fan.

[0072] The current values ​​at each test point were fitted with the corresponding actual output power data using a cubic polynomial. This cubic polynomial fitting method effectively describes the complex nonlinear relationship between current and output power. The fitted function reflects the efficiency characteristics of the wind turbine motor under different current values, i.e., the motor efficiency curve.

[0073] The fitted motor efficiency curve is stored in the flash memory of the processing unit as the basis for subsequent real-time calculations. This efficiency curve not only provides accurate reference data for the performance analysis of the wind turbine, but also enables dynamic adjustment of the control strategy during actual operation, ensuring that the wind turbine always operates at high efficiency.

[0074] Through the above testing and fitting process, the motor efficiency curve of the crossflow fan can be accurately obtained, and this curve can reflect the actual operating efficiency of the fan under different load conditions. The cubic polynomial fitting method effectively captures the nonlinear relationship between current and output power, improving the fitting accuracy of the efficiency curve. Using the motor efficiency curve stored in the processing unit, the system can query and apply the curve in real time to calculate and adjust operating parameters according to the fan's real-time load status, achieving optimal energy-saving effects. This invention not only improves the working efficiency of the crossflow fan but also extends equipment lifespan and reduces energy waste, resulting in significant economic and environmental benefits.

[0075] In one possible implementation, after the fan is assembled, it is placed in a standard testing environment to ensure the stability of environmental conditions during testing. The aerodynamic performance of the fan in this environment can accurately reflect its performance in actual operation.

[0076] At the fan's rated speed, the fan load is changed by adjusting the opening of the outlet damper. By controlling the damper opening, the fan's operating state under different load conditions can be accurately simulated. This operation ensures that the fan's performance can be tested within a stable operating range.

[0077] Within the stable operating range of the wind turbine, at least 15 different operating points were selected for testing. These operating points cover the wind turbine's operating state under different loads and can reflect the wind turbine's aerodynamic performance under various actual operating conditions. At each operating point, the airflow pressure at the wind turbine outlet, the airflow temperature at the wind turbine inlet, and the wind turbine's output power were measured and recorded in real time. These data will be used to establish the wind turbine's aerodynamic characteristic equations.

[0078] The collected test data were analyzed using multiple linear regression, with airflow pressure and temperature as independent variables and fan output power as the dependent variable. Through regression analysis, a mathematical model was obtained describing how airflow pressure and temperature jointly affect the fan output power.

[0079] Ultimately, the resulting wind turbine characteristic equation is a second-order polynomial equation that accurately represents the relationship between airflow pressure and airflow temperature on output power. This equation can be stored in the processing unit using flash memory for later retrieval and application during real-time operation.

[0080] Through the embodiments of this invention, a set of accurate fan characteristic equations can be established, accurately reflecting the output power characteristics of the fan under different airflow pressure and temperature conditions. This equation not only provides a theoretical basis for subsequent energy-saving operation control but also effectively predicts the fan's performance under various operating conditions. By acquiring real-time airflow pressure and temperature data from the fan and combining it with the stored fan characteristic equations, the fan's operating state can be dynamically adjusted to maintain high-efficiency operation under different conditions, thereby achieving energy-saving control and optimized operation. The embodiments of this invention help improve the energy utilization efficiency of fans, reduce energy consumption, extend equipment lifespan, and have significant economic and environmental benefits.

[0081] In one possible implementation, temperature characteristic tests are conducted inside an environmental test chamber, with the temperature range set from -10 degrees Celsius to 50 degrees Celsius, and a temperature test point set every 5 degrees Celsius. This covers different environmental temperatures, ensuring the comprehensiveness and representativeness of the test data.

[0082] At each designated temperature test point, ensure the fan operates stably under rated load, and measure the actual power ratio of the fan at different ambient temperatures. The actual power ratio is the ratio of the fan's actual output power to its rated power. By comparing the power ratio at a standard temperature, calculate the deviation value for each test point.

[0083] A temperature correction factor table is generated based on the deviation values ​​at each temperature point. This table is stored using a piecewise linear interpolation method to ensure that the corresponding correction factor can be obtained under different ambient temperatures. Through interpolation, the system can quickly find and apply the correction factor in practical applications.

[0084] During the operation of the wind turbine, the established temperature correction coefficient table is consulted based on the current ambient temperature value to obtain the correction coefficient corresponding to the current temperature.

[0085] The corrected power ratio is obtained by multiplying the obtained correction factor by the current power ratio of the fan. This compensates for the influence of ambient temperature on fan performance, ensuring that the fan can maintain optimal operating conditions under different ambient temperatures.

[0086] To ensure the corrected power ratio meets actual operating requirements, it is normalized to a range of 0 to 1. This normalized value serves as a real-time load factor, acting as an input signal for the subsequent control system to further adjust the fan load and energy efficiency.

[0087] Through the aforementioned correction process, the fan can automatically adjust its operating state according to different ambient temperatures, effectively coping with performance fluctuations caused by temperature changes. A temperature correction coefficient table is established, and combined with piecewise linear interpolation, ensuring that the temperature correction coefficients can be obtained quickly and accurately in practical applications, improving system response speed and control precision. Finally, by correcting the power ratio and performing normalization, the system can optimize the fan's load operation under different temperature conditions, achieving energy-saving effects. The implementation of this method not only improves the stability of the fan in extreme environments but also effectively reduces energy consumption and extends the fan's service life, demonstrating significant economic and environmental benefits.

[0088] In one possible implementation, after the wind turbine is assembled, it is placed on a test platform for energy efficiency testing. The key objective of the test is to determine the optimal operating speed of the wind turbine under different load conditions to ensure maximum energy efficiency.

[0089] During testing, the real-time load factor is set from 0 to 1 in a step of 0.05 to ensure that the test covers multiple operating conditions from minimum to maximum load. Each load test point corresponds to a specific real-time load factor, representing different load conditions that the wind turbine may experience during operation.

[0090] At each load test point, the fan's input power was measured at different speeds by adjusting its operating speed. Analysis of this power data determined the optimal operating speed for each load factor, i.e., the speed at which the fan's input power is minimized. This testing provides precise data support for subsequent control systems.

[0091] By analyzing each load test point and its corresponding optimal operating speed, a mapping relationship between the load factor and the optimal operating speed is established. This mapping relationship clearly indicates what operating speed the wind turbine should select under different loads to ensure optimal energy efficiency.

[0092] To ensure a smooth transition of the wind turbine to different load conditions during actual operation, a cubic spline interpolation method is used to generate a continuous speed-load mapping curve. This method can generate smooth and accurate curves, avoiding abrupt changes or discontinuities between different load points.

[0093] The generated speed-load mapping curve is discretized and stored in a two-dimensional lookup table. The index of the lookup table is the real-time load coefficient, and its value is the optimal operating speed. In this way, during actual wind turbine operation, the corresponding optimal operating speed can be quickly retrieved based on the current load coefficient, thereby achieving highly efficient energy control.

[0094] By establishing a speed-load mapping table, the wind turbine can adjust its speed in real time during operation to minimize input power and ensure optimal energy efficiency under various load conditions. Cubic spline interpolation provides a smooth and accurate transition curve, avoiding the breakpoint problem in traditional methods and resulting in smoother turbine operation. Storage in the form of a two-dimensional lookup table makes lookup and real-time control more efficient, significantly improving turbine operating efficiency, reducing energy waste, extending equipment lifespan, and ultimately achieving the goals of energy saving and reduced operating costs.

[0095] In one possible implementation, when the real-time load factor perfectly matches an index value in the speed-load mapping table, the system can directly read the optimal operating speed corresponding to that index value. This process is very simple and efficient, eliminating the need for further calculations and enabling rapid response to the real-time operating needs of the wind turbine. This allows for timely adjustment of the wind turbine speed when the load changes, reducing unnecessary calculation delays and improving the overall response speed of the control system.

[0096] When the real-time load factor lies between two index values, the system uses linear interpolation to calculate the optimal operating speed. Specifically, the system first determines two adjacent index values ​​corresponding to the real-time load factor, representing the low and high values ​​of the load factor. Next, the system finds the optimal operating speed corresponding to these two load factors and calculates a new optimal operating speed based on the position of the real-time load factor using linear interpolation. This process ensures a smooth transition in the fan speed between the two index values, thus avoiding sudden changes in operating speed and guaranteeing stable fan operation and energy-saving performance.

[0097] If the real-time load factor exceeds the index range of the speed-load mapping table, the system will select the optimal operating speed corresponding to the lowest or highest load factor in the mapping table as the output value. Specifically, when the real-time load factor is lower than the minimum value in the mapping table, the system will use the optimal operating speed corresponding to the minimum load value in the table; when the real-time load factor is higher than the maximum load value in the table, the system will use the optimal operating speed corresponding to the maximum load value in the table. This process ensures that the fan can still operate at a reasonable speed even when the load exceeds the preset range, avoiding damage to the equipment due to overload or underload operation.

[0098] Through the above query and calculation process, the crossflow fan can flexibly adjust its operating speed according to the real-time load factor, maintaining optimal energy efficiency under different load conditions. In the case of a perfect match, the optimal operating speed is directly read, improving response speed and avoiding unnecessary calculations. For cases of incomplete matching, linear interpolation ensures a smooth transition in fan operation, avoiding sudden fluctuations in energy efficiency. Furthermore, when the speed exceeds the index range, boundary values ​​are used as the output operating speed, ensuring safe operation of the system under all circumstances. This control method achieves significant energy savings by optimizing fan operating efficiency, reducing energy consumption and operating costs, and improving system stability and reliability.

[0099] In one possible implementation, the processing unit first converts the previously calculated optimal operating speed into a speed control signal. This control signal is output using pulse width modulation (PWM). The duty cycle of the PWM signal is linearly related to the optimal operating speed; that is, a larger duty cycle indicates a higher target operating speed, and vice versa. In this way, the fan's operating speed can be precisely adjusted, achieving accurate control.

[0100] The pulse width modulation (PWM) signal generated by the processing unit is sent to the motor drive circuit via the PWM output interface. The motor drive circuit typically consists of a half-bridge inverter circuit composed of insulated-gate bipolar transistors (iGBTs). This circuit provides the necessary voltage signals to the motor based on the received PWM signal. The average value of these voltage signals determines the drive voltage of the fan motor, thereby controlling the fan's operating speed.

[0101] The motor drive circuit controls the average voltage delivered to the fan motor by adjusting the duty cycle of the PWM signal. Specifically, a larger duty cycle results in a higher voltage received by the motor, increasing the fan's operating speed; conversely, a smaller duty cycle results in a lower voltage received by the motor, decreasing the fan's operating speed. In this way, the motor drive circuit can precisely regulate the fan's operating speed, ensuring that the fan always operates at its optimal state.

[0102] During the adjustment of the fan's operating speed, the motor drive circuit also employs a ramp function to control the rate of speed change. The slope of the ramp function is preset based on the fan's moment of inertia and the motor's torque characteristics. Specifically, the ramp function controls the smoothness of speed changes, preventing sudden changes or vibrations during speed adjustment, thus ensuring a smooth transition from the current speed to the target optimal operating speed. This process effectively avoids fan overload or unstable operation, extending the equipment's service life.

[0103] Through this invention, the operating speed adjustment of a crossflow fan can be achieved precisely and smoothly. Pulse width modulation (PWM) signals can accurately control the operating state of the fan motor, ensuring the fan is always operating at its most energy-efficient point, thereby significantly reducing energy consumption. Furthermore, the application of a ramp function to control the rate of change of speed avoids drastic fluctuations during speed adjustments, protecting the fan from mechanical damage and reducing equipment maintenance costs. Overall, this control method optimizes the fan's energy-saving effect, improves operating efficiency, and enhances system stability and reliability through smooth speed transitions.

[0104] In one possible implementation, the cycle time is set to 1 second. This means that every second, the system will perform a complete control process to ensure that the wind turbine can respond promptly to load changes and adjust its operating speed to achieve optimal energy efficiency. This fixed periodic execution ensures the real-time and continuous operation of the control system, thereby constantly adjusting the wind turbine's operating state and preventing energy efficiency fluctuations.

[0105] At the start of each cycle, the processing unit first checks the validity of the data from each sensor. This data validity check involves comparing the current sampled value with historical sampled values. If the difference between the current and historical sampled values ​​exceeds a preset threshold, the system determines the data to be abnormal. This method effectively filters out erroneous data caused by sensor malfunctions or interference, ensuring the accuracy and reliability of the control system when processing data.

[0106] When an anomaly is detected, the control system will use the last valid sampled value for calculations, ensuring that the control system can continue to operate even with data problems, preventing shutdowns or malfunctions. Simultaneously, the system will trigger a fault alarm signal to promptly notify maintenance personnel of sensor anomalies or data issues, preventing system failure due to data problems. This anomaly handling mechanism reduces operational instability caused by sensor malfunctions or abnormal data.

[0107] When the sensor data is valid, the processing unit executes steps 1 to 4 sequentially according to a predetermined order. First, it determines the optimal operating speed by consulting the speed-load mapping table based on the current load factor. Then, it converts the optimal operating speed into a pulse width modulation signal, which controls the operation of the fan motor through the motor drive circuit. Finally, the system smoothly adjusts the fan speed using a ramp function, transitioning from the current value to the target operating speed. Each control cycle ensures that the fan is always in optimal operating condition and dynamically adjusts according to load changes.

[0108] By employing a fixed 1-second cycle and data validity checks, this control method ensures that the wind turbine can make real-time adjustments in each cycle, improving the system's response speed and stability. The data anomaly detection and processing mechanism effectively avoids control failures caused by sensor malfunctions or data errors, ensuring the reliability of wind turbine operation. The system's closed-loop control, through continuous feedback and adjustment, maximizes energy-efficient operation of the wind turbine, reduces energy consumption, maintains system stability, and extends equipment lifespan.

[0109] In one possible implementation, the processing unit first establishes a historical database of operating parameters via external memory. This database records key operating parameters such as real-time load factor, optimal operating speed, and actual operating speed every minute. In this way, detailed data on the wind turbine during long-term operation can be recorded, providing crucial information for subsequent analysis and optimization. Recording every minute ensures the system can fully reflect the dynamic changes of the wind turbine, thereby improving the accuracy of the analysis.

[0110] Within a weekly cycle, the system performs statistical analysis on data in the historical database. The specific statistical analysis process includes calculating the average and standard deviation of the real-time load factor. Calculating the average value reveals the common level of the wind turbine's load factor within a week; while the standard deviation reflects the magnitude of load fluctuations and is an important indicator for assessing the stability of wind turbine operation. A large standard deviation of the load factor indicates unstable wind turbine operation, potentially suggesting energy efficiency losses or other problems.

[0111] Based on statistical analysis, the system can automatically update the speed-load mapping table. The key to this process is that when the standard deviation of the real-time load coefficient remains consistently large, the system initiates an energy efficiency test and rebuilds a new speed-load mapping table. This new mapping table more accurately reflects the current operating status and load characteristics of the wind turbine, thus providing updated reference data for the wind turbine's control system. By regularly updating the speed-load mapping table, the system can adapt to changes in wind turbine performance over time, ensuring that the energy-saving effect of the control method remains optimal.

[0112] By regularly recording, analyzing, and updating data, the system can adapt to changes in the performance of the wind turbine under different operating environments and time periods. For example, the operating efficiency of the wind turbine may be affected by the external environment, equipment aging, or other factors. By continuously adjusting the speed-load mapping table, the system can make precise adjustments in real time based on the latest operating status of the wind turbine. This adaptive adjustment ensures that the wind turbine maintains a highly efficient and stable operating state throughout long-term operation, thereby minimizing energy consumption and optimizing the energy-saving effect of the wind turbine.

[0113] The introduction of this historical data recording and analysis process enables the wind turbine control system to dynamically adapt to changes in wind turbine performance and maintain optimal energy efficiency during long-term operation. Weekly statistical analysis and standard deviation assessments allow for timely identification of potential problems in wind turbine operation, facilitating targeted adjustments by updating the mapping table. This continuous optimization not only improves system stability but also ensures that wind turbine energy efficiency remains at an ideal level over the long term, extending equipment lifespan while reducing maintenance and energy costs. This intelligent and adaptive control method further enhances the overall effectiveness of energy management.

[0114] Example

[0115] This embodiment takes a crossflow fan in a central air conditioning system of a commercial building as the application scenario. In this scenario, the crossflow fan is used to deliver regulated air to each room. Due to factors such as fluctuations in indoor and outdoor temperatures, changes in personnel activity, and equipment start-up and shutdown, the aerodynamic load of the fan will change in real time, requiring an energy-saving control method to adapt to variable load conditions.

[0116] The crossflow fan system used in this embodiment includes:

[0117] Crossflow fan body: rated power 1.5kW, rated speed 1500RPM, impeller diameter 300mm, number of blades 36.

[0118] Sensor group: including Hall effect current sensor (range 0-20A), microelectromechanical system piezoresistive pressure sensor (range 0-100kPa), platinum resistance temperature sensor (measurement range -50℃ to 150℃) and thermistor temperature sensor (measurement range -40℃ to 125℃).

[0119] Processing unit: It adopts an STM32F407 microcontroller with a built-in 16-bit analog-to-digital converter (ADC) and flash memory.

[0120] Drive circuit: a half-bridge inverter circuit based on insulated gate bipolar transistor (iGBT) to receive pulse width modulation signals to control motor speed.

[0121] All sensor signals are converted into digital signals via an ADC, the processing unit executes the control algorithm, and the drive circuit adjusts the fan speed.

[0122] Step 1: Real-time acquisition of operating parameters of the crossflow fan:

[0123] In this embodiment, the operating parameters include the current value of the fan motor, the airflow pressure value at the fan outlet, the airflow temperature value at the fan inlet, and the ambient temperature value during fan operation. The data collection process is as follows:

[0124] A Hall effect current sensor is connected in series in the power supply circuit of the wind turbine motor to measure the motor's operating current in real time. The sensor outputs a voltage signal proportional to the current (e.g., 2V for a 10A current), which is read through ADC channel 1. The ADC reference voltage is 3.3V, and the resolution is 16 bits, therefore the voltage measurement accuracy is approximately 3.3V / 65536≈0.05mV, corresponding to a current accuracy of approximately 0.0005A.

[0125] A microelectromechanical system (MEMS) piezoresistive pressure sensor is installed in the airflow channel at the fan outlet to measure the static pressure of the airflow. The sensor outputs a voltage signal proportional to the pressure (e.g., 2.5V at 50kPa), which is read through ADC channel 2.

[0126] The platinum resistance temperature sensor is installed at the airflow inlet of the fan to measure the airflow temperature. A three-wire connection is used to reduce errors. The resistance value is read through ADC channel 3 and converted into a temperature value according to the PT100 resistance-temperature relationship table (e.g., a resistance of 110Ω corresponds to a temperature of 25℃).

[0127] A thermistor temperature sensor is mounted on the surface of the fan casing to measure the ambient temperature. The resistance value is read via ADC channel 4 and converted to a temperature value according to the Steinhart-Hart equation for NTC thermistors. Where A, B, and C are sensor constants, and T is the Kelvin temperature.

[0128] The processing unit reads all ADC channels at a sampling frequency of 10 times per second. For each parameter, it samples 5 times consecutively, and the arithmetic mean is taken as the valid value at that sampling moment. For example, the current value sampled 5 times is [10.01A, 10.02A, 9.99A, 10.00A, 10.01A], and the average value is 10.006A. This averaging process suppresses random noise and ensures data stability.

[0129] Step 2: Calculate the real-time load factor of the crossflow fan based on the collected operating parameters:

[0130] Real-time load factor calculation involves three steps:

[0131] Step 2.1: Calculate the actual output power of the fan.

[0132] The actual output power is obtained by querying a preset motor efficiency curve. The process of establishing the motor efficiency curve is as follows:

[0133] After the wind turbine was assembled, a load test was conducted on the test platform. During the test, the wind turbine operated at its rated voltage of 220V, with 20 test points evenly distributed between no-load (0A current) and full-load (15A current). At each test point, a torque sensor (model TQ-201) was used to measure the actual torque value of the wind turbine output shaft, and the current value was recorded. The actual output power was calculated using the following formula:

[0134]

[0135] The unit for torque is N·m, and the unit for rotational speed is RPM.

[0136] The current values ​​and actual output power data from 20 test points were fitted with a cubic polynomial to obtain the motor efficiency curve equation: P actual =aI 3 +bI 2 +cI+d;

[0137] Where I is the current value, and a, b, c, and d are fitting coefficients. In this embodiment, the fitting coefficients are calculated using the least squares method: a = 0.0012, b = -0.035, c = 1.2, d = 0.05. This equation is stored in the flash memory of the processing unit.

[0138] In actual operation, the processing unit substitutes the current current value into the equation to calculate the actual output power.

[0139] Step 2.2: Calculate the theoretical output power of the fan:

[0140] The theoretical output power is calculated using the wind turbine characteristic equation. The process of establishing the wind turbine characteristic equation is as follows:

[0141] The aerodynamic performance of the crossflow fan was tested under standard test conditions (temperature 25℃, humidity 50%). During the test, the fan operated at its rated speed of 1500 RPM. The load was varied by adjusting the outlet damper opening, and 15 operating points were selected within the stable operating range. At each operating point, the airflow pressure at the fan outlet, the airflow temperature at the fan inlet, and the fan output power (using a power analyzer) were measured simultaneously.

[0142] The characteristic equations of the wind turbine were established using the multiple linear regression method. The equations are in the form of second-order polynomials:

[0143]

[0144] Where Pr is the airflow pressure value (unit: kPa), T iHere, α represents the airflow temperature (in °C), and β, γ, δ, ε, and ζ are regression coefficients. In this embodiment, the regression coefficients are calculated using the MATLAB regression toolbox: α = 0.005, β = 0.001, γ = -0.002, δ = 0.1, ε = 0.05, and ζ = 0.5. This equation is stored in the flash memory of the processing unit.

[0145] In actual operation, the processing unit substitutes the current airflow pressure and airflow temperature values ​​into the equation to calculate the theoretical output power.

[0146] Step 2.3: Calculate the power ratio and perform temperature correction;

[0147] First, calculate the power ratio:

[0148] Then, the power ratio is corrected based on the ambient temperature. The process of establishing the temperature correction factor table is as follows:

[0149] Temperature characteristics of the crossflow fan were tested in an environmental test chamber. During testing, 13 temperature test points were set at 5°C intervals, ranging from -10°C to 50°C. At each temperature test point, the fan was operated under rated load, and the deviation between the actual power ratio and the power ratio at a standard temperature (25°C) was measured. For example, at -10°C, the power ratio deviation was -0.05; at 50°C, the deviation was 0.03.

[0150] A temperature correction coefficient table is generated based on the deviation values ​​and stored using a piecewise linear interpolation method. The table is in the form of a two-dimensional array, with the first column being the ambient temperature value and the second column being the correction coefficient. For example: [-10,0.95],[5,0.98],[25,1.00],[35,1.02],[50,1.03].

[0151] Correction process: The temperature correction factor table is consulted based on the current ambient temperature value. If the temperature value exists directly in the table, the correction factor is read directly; otherwise, linear interpolation is used for calculation. For example, if the ambient temperature is 30℃, which is between 25℃ and 35℃, the correction factor = 1.00 + (1.02 - 1.00) × (30 - 25) / (35 - 25) = 1.01.

[0152] The final real-time load factor = power ratio × correction factor, and normalized to the range of 0 to 1. For example, power ratio 0.561 × correction factor 1.01 = 0.566, which is normalized to 0.566 (because 0.566 is already in the range of 0-1).

[0153] Step 3: Determine the optimal operating speed of the crossflow fan based on the real-time load factor;

[0154] Optimal operating speed is obtained by querying the speed-load mapping table. The process of creating the speed-load mapping table is as follows:

[0155] Energy efficiency tests were conducted on the crossflow fan on the test platform. During testing, 21 load test points were set with a real-time load factor ranging from 0 to 1 in increments of 0.05. At each load test point, the fan operating speed was adjusted from 500 RPM to 2000 RPM, and the fan input power was measured (using a power meter). The operating speed that minimized the input power was identified as the optimal operating speed. For example, at a load factor of 0.5, test speeds of 800 RPM, 1000 RPM, and 1200 RPM corresponded to input power values ​​of 300W, 280W, and 320W, respectively; therefore, the optimal operating speed was 1000 RPM.

[0156] A mapping relationship is established between each load test point and the optimal operating speed, and a continuous curve is generated using cubic spline interpolation. Cubic spline interpolation ensures curve smoothness, and the interpolation formula is a piecewise cubic polynomial: S(x) = a i (xx i ) 3 +b i (xx i ) 2 +c i (xx i )+d i Where x is the real-time load factor, x i For load test points, a i b i c i d i These are the interpolation coefficients.

[0157] The curve is discretized and stored as a two-dimensional lookup table, with the index being the real-time load coefficient (0, 0.05, 0.10, ..., 1.00), and the value is set to the optimal running speed.

[0158] Query process:

[0159] The processing unit compares the current real-time load coefficient with the lookup table index. If there is a perfect match, the speed value is read directly.

[0160] If there is a mismatch, linear interpolation is used for calculation. For example, if the load factor is 0.52, which is between 0.50 and 0.55, corresponding to speeds of 1000 RPM and 1050 RPM, then the optimal operating speed = 1000 + (1050 - 1000) × (0.52 - 0.50) / (0.55 - 0.50) = 1000 + 50 × 0.4 = 1020 RPM.

[0161] If the load factor is out of range (e.g., less than 0 or greater than 1), take the boundary value (the speed corresponding to 0 or 1).

[0162] Step 4: Adjust the operating speed of the crossflow fan to the optimal operating speed;

[0163] The adjustment process is as follows:

[0164] The processing unit converts the optimal operating speed into a pulse-width modulation (PWM) signal. The duty cycle of the PWM signal is linearly related to the optimal operating speed. The minimum speed is 500 RPM and the maximum speed is 2000 RPM.

[0165] The pulse width modulation signal is sent to the iGBT half-bridge inverter circuit through the timer output interface of the processing unit. The signal frequency is set to 10kHz to ensure smooth drive.

[0166] The iGBT drive circuit adjusts the average voltage output to the fan motor according to the duty cycle, thereby controlling the speed. For example, a duty cycle of 34.67% corresponds to an average voltage of approximately 76V (assuming a power supply voltage of 220V).

[0167] Speed ​​adjustment uses a ramp function to control the rate of change, avoiding impact. The ramp function slope is preset based on the fan's moment of inertia and motor torque characteristics. The moment of inertia is obtained through testing (e.g., 0.1 kg·m). 2 The torque characteristics are given by the motor parameters (e.g., maximum torque 10 N·m). The adjustment time is set according to the acceleration limit, which is 2 seconds in this embodiment. For example, when adjusting from the current speed of 1000 RPM to 1020 RPM, the slope is (1020-1000) / 2 = 10 RPM / s. The processing unit updates the speed setting value every 0.1 seconds to ensure a smooth transition.

[0168] Step 5: Repeat steps 1 to 4 to form a closed-loop control;

[0169] Closed-loop control process:

[0170] The fixed cycle time is set to 1 second. At the beginning of each cycle, the processing unit checks the validity of the sensor data.

[0171] Data validity check: Compare the current sampled value with the historical sampled values ​​(the most recent 10 values). If the difference exceeds a preset threshold, it is considered abnormal. The threshold is set according to the parameter type: current threshold 10% (i.e., change exceeding 10%), pressure threshold 5%, temperature threshold 2℃. For example, if the current is 10A, the historical average is 9.5A, the difference is 5.26%, which is less than the threshold and is valid.

[0172] If the data is abnormal, the previous valid value is used for calculation, and a fault alarm signal is triggered through the GPiO interface of the processing unit (such as outputting a high level to the LED indicator).

[0173] If the data is valid, execute steps 1 to 4 sequentially to complete one control loop.

[0174] Historical data recording and analysis process:

[0175] A historical database of operating parameters is established on the external SD card of the processing unit, recording real-time load coefficients, optimal operating speeds, and actual operating speeds every minute. The data is stored in CSV format, including timestamps and parameter values.

[0176] Perform weekly statistical analysis on historical data: calculate the average and standard deviation of the real-time load factor. For example, the average load factor for a week is 0.6, and the standard deviation is 0.1.

[0177] The speed-load mapping table is automatically updated based on statistical analysis results. If the standard deviation of the real-time load coefficient remains greater than 0.15 (indicating unstable operation), the energy efficiency test is repeated, and a new speed-load mapping table is established. The update process is implemented by the processing unit calling the test program, which controls the drive circuit and sensors to perform the energy efficiency test in step 3.

[0178] By regularly updating the system, the system can adapt to changes in wind turbine performance (such as blade wear) and maintain energy efficiency.

[0179] This embodiment was tested in a commercial building air conditioning system. Compared with traditional fixed speed control, it saves more than 25% energy and the system operates stably without oscillation.

[0180] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0181] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A cross-flow fan variable load energy saving operation control method, characterized by, The method comprises the following steps: Step 1: Real-time acquisition of the operating parameters of the crossflow fan, including the current value of the fan motor, the airflow pressure value at the fan outlet, the airflow temperature value at the fan inlet, and the ambient temperature value during the operation of the fan; Step 2: Calculation of the real-time load coefficient of the crossflow fan based on the acquired operating parameters, including: calculation of the actual output power of the fan according to the current value of the fan motor and the preset motor efficiency curve, calculation of the theoretical output power of the fan according to the airflow pressure value at the fan outlet and the airflow temperature value at the fan inlet combined with the fan characteristic equation, taking the ratio of the actual output power to the theoretical output power as the power ratio, and correcting the power ratio according to the ambient temperature value to obtain the real-time load coefficient; Step 3: Determination of the optimal operating speed of the crossflow fan according to the real-time load coefficient and the speed-load mapping table; Step 4: Adjustment of the operating speed of the crossflow fan to the optimal operating speed; Step 5: Repeated execution of steps 1 to 4 to form a closed-loop control.

2. A variable load energy saving operation control method of a crossflow fan according to claim 1, characterized by, The specific process of real-time acquisition of the operating parameters of the crossflow fan in step 1 includes: Acquisition of the current value of the fan motor through a Hall effect current sensor connected in series in the power supply circuit of the fan motor; Acquisition of the airflow pressure value at the fan outlet through a micro-electro-mechanical system piezoresistive pressure sensor installed in the airflow channel at the fan outlet; Acquisition of the airflow temperature value at the fan inlet through a platinum resistance temperature sensor installed at the airflow at the fan inlet; Acquisition of the ambient temperature value during the operation of the fan through a thermistor temperature sensor installed on the surface of the fan housing; All sensor output signals are converted into digital signals by a 16-bit analog-to-digital converter, and the processing unit reads the converted digital signals at a sampling frequency of 10 times per second, and takes the arithmetic mean value after continuous sampling of each parameter for 5 times as the effective value at that sampling time.

3. A variable load energy saving operation control method of a crossflow fan according to claim 2, characterized in that, The establishment process of the preset motor efficiency curve in step 2 includes: After the fan is assembled, load testing of the crossflow fan is performed on a test platform, and the testing process includes: under the rated voltage of the fan, at least 20 test points are uniformly set from the no-load state to the full-load state, the input current value of the fan motor and the actual torque value of the fan output shaft are recorded synchronously at each test point, the actual output power is calculated according to the actual torque value and the fan speed, and the current value at each test point is fitted with the corresponding actual output power data by a cubic polynomial to obtain the functional relationship between the current value and the actual output power, and the functional relationship is stored in the flash memory of the processing unit as the motor efficiency curve.

4. The variable load energy saving operation control method of a crossflow fan according to claim 3, characterized by, The establishment process of the fan characteristic equation in step 2 includes: After the fan is assembled, the aerodynamic performance of the cross-flow fan is tested in a standard test environment. The testing process includes: at the rated speed of the fan, the load of the fan is changed by adjusting the opening of the outlet baffle, at least 15 working points are selected in the stable operation range of the fan, the airflow pressure value at the outlet of the fan, the airflow temperature value at the inlet of the fan and the output power of the fan are measured synchronously at each working point, a fan characteristic equation is established by using a multiple linear regression method, the fan characteristic equation takes the airflow pressure value and the airflow temperature value as independent variables and takes the output power as a dependent variable, the form of the fan characteristic equation is a second-order polynomial equation, and the established fan characteristic equation is stored in the flash memory of the processing unit.

5. A variable load energy saving operation control method of a crossflow fan according to claim 4, characterized in that, The specific process of correcting the power ratio according to the ambient temperature value in step 2 includes: A temperature correction coefficient table is established, and the establishment process includes: temperature characteristic testing of the cross-flow fan is performed in an environmental test chamber, the testing process includes: temperature test points are set at intervals of 5 degrees Celsius in the range from minus 10 degrees Celsius to plus 50 degrees Celsius, the fan is operated at the rated load at each temperature test point, the deviation value of the actual power ratio from the power ratio at the standard temperature is measured, and a temperature correction coefficient table is generated according to the deviation value, the temperature correction coefficient table stores correction coefficients corresponding to different ambient temperatures by using a piecewise linear interpolation method; The correction process includes: the corresponding correction coefficient is obtained by querying the temperature correction coefficient table according to the current ambient temperature value, the power ratio is multiplied by the correction coefficient to obtain a corrected power ratio, and the corrected power ratio is normalized to the range of 0 to 1 as a real-time load coefficient.

6. A variable load energy saving operation control method of a crossflow fan according to claim 5, characterized by, The establishment process of the speed-load mapping table in step 3 includes: After the fan is assembled, the energy efficiency of the cross-flow fan is tested on a test platform, and the testing process includes: load test points are set at intervals of 0.05 in the range from 0 to 1, the operating speed of the fan is adjusted at each load test point, the input power of the fan at different operating speeds is measured, and the operating speed that minimizes the input power of the fan is found as the optimal operating speed at the load coefficient. A mapping relationship is established between each load test point and the corresponding optimal operating speed, a continuous speed-load mapping curve is generated by using a cubic spline interpolation method, the speed-load mapping curve is discretized and stored in the form of a two-dimensional lookup table, the index of the two-dimensional lookup table is the real-time load coefficient, and the value is the optimal operating speed.

7. A variable load energy saving operation control method of a crossflow fan according to claim 6, characterized in that, The specific process of querying the speed-load mapping table to determine the optimal operating speed of the cross-flow fan in step 3 includes: The real-time load coefficient is compared with the index of the speed-load mapping table, when the real-time load coefficient completely matches a certain index value, the corresponding optimal operating speed is directly read; When the real-time load coefficient is between two index values, the optimal operating speed is calculated by using a linear interpolation method, and the linear interpolation calculation process includes: the two index values adjacent to the real-time load coefficient and the corresponding optimal operating speeds are taken, and the optimal operating speed is calculated according to an interpolation formula; When the real-time load coefficient is out of the index range of the speed-load mapping table, the optimal operating speed at the index boundary is taken as the output value.

8. A variable load energy saving operation control method of a crossflow fan according to claim 7, characterized in that, The specific process of adjusting the operating speed of the cross-flow fan to the optimal operating speed in step 4 includes: The processing unit converts the optimal operating speed into a speed control signal, which is a pulse width modulation signal, and the duty cycle of the pulse width modulation signal is linearly related to the optimal operating speed; The pulse width modulation signal is sent to the motor drive circuit through the pulse width modulation output interface of the processing unit, and the motor drive circuit is a half-bridge inverter circuit composed of insulated gate bipolar transistors; The motor drive circuit adjusts the average voltage output to the fan motor according to the duty cycle of the pulse width modulation signal, thereby controlling the fan operating speed; During the speed adjustment process, the motor drive circuit uses a ramp function to control the speed change rate, and the slope of the ramp function is pre-set according to the fan moment of inertia and motor torque characteristics to ensure smooth transition of the fan speed from the current value to the target optimal operating speed.

9. A variable load energy saving operation control method of a crossflow fan according to claim 8, characterized by, The specific process of repeatedly executing steps 1 to 4 to form a closed-loop control in step 5 includes: A fixed cycle execution period is set, which is 1 second; At the beginning of each cycle period, the processing unit first checks the validity of the sensor data, and the data validity check process includes comparing the difference between the current sampling value and the historical sampling value, and determining that the data is abnormal when the difference exceeds the pre-set threshold; When data anomalies are detected, the last valid sampling value is used for calculation, and a fault alarm signal is triggered at the same time; In the case of data validity, steps 1 to 4 are sequentially executed to complete a control cycle.

10. A variable load energy saving operation control method of a crossflow fan according to claim 9, wherein It also includes a historical data recording and analysis process of operating parameters: A running parameter history database is established in the external memory of the processing unit to record real-time load coefficients, optimal operating speeds and actual operating speeds at a frequency of once per minute; Statistical analysis of historical data is performed every week, and the statistical analysis process includes calculating the mean and standard deviation of the real-time load coefficient to evaluate the stability of the fan operating state; The speed-load mapping table is automatically updated according to the statistical analysis results, and the updating process includes re-testing the energy efficiency when the standard deviation of the real-time load coefficient is consistently large, and establishing a new speed-load mapping table; By regularly updating the speed-load mapping table, the changes in fan performance over time are adapted, and the optimal energy-saving effect of the control method is maintained.

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