Integrated digital fan energy-saving control system

Abstract

The present invention belongs to the technical field of fan energy-saving control, specifically an energy-saving control system for an integrated digital fan, which dynamically separates and quantifies the basic load and additional load in the operation of the fan, intercepts the historical load data of the previous time window, analyzes the deviation characteristics of the basic load and the process target, and the fluctuation law characteristics of the additional load, so as to predict the load change trend of the subsequent process demand, break through the limitations of the existing load mixing processing and the lag of real-time monitoring, and then dynamically adjust the operating frequency of the inverter, greatly improving the energy efficiency optimization accuracy. At the same time, the fan operation energy efficiency ratio data during the adjustment process is collected in real time, and the frequency conversion control optimization instructions are generated based on the energy efficiency ratio, and fed back to the frequency conversion control module to form an adaptive optimization loop. It can not only accurately match the real-time energy efficiency requirements, realize the intelligent calibration and dynamic tuning of the fan operating frequency, but also significantly improve the system's adaptability to multiple working conditions.

Description

Technical Field

[0001] The present invention belongs to the technical field of fan energy-saving control, and in particular relates to an integrated digital fan energy-saving control system. Background Art

[0002] The energy-saving control of fans has a direct and critical relationship with the precise execution of fan inverters, which is also a necessary condition for achieving energy saving. Through dynamic speed regulation, precise control and multi-objective optimization of inverters, it transforms fan energy consumption from "extensive" to "on-demand supply", which has significant economic benefits and environmental value in industries such as industry and construction.

[0003] In the prior art, there are also some solutions related to energy saving through fan inverter regulation. For example, China Patent Publication No. CN117249570A is a fan frequency conversion energy-saving control system and control method in a ventilation and air-conditioning system. It finds the branch pipe at the air outlet that is in working state and farthest from the fan, and obtains the total pressure value at the installation position of the air volume control valve on it. The branch pipe with the smallest total pressure value is screened and determined as the most unfavorable branch pipe. The fan inverter is controlled to change the fan speed, and the total pressure value at the installation position of the air volume control valve on the most unfavorable branch pipe is reduced to meet the minimum working total pressure value of the air volume control valve on it. It does not affect the normal operation of the air volume control valve and reduces the energy consumption of the fan, thereby achieving the purpose of energy saving.

[0004] Another Chinese patent publication number is CN119146536A, which is an adaptive fan variable frequency air volume control method. It predicts the required air volume and detects the actual air volume based on the actual working conditions of each area, calculates the total required air volume and combines it with the fan characteristic curve to obtain the initial frequency of the fan. Then, based on the difference between the total required air volume and the total measured air volume and the terminal damper opening, the initial frequency of the fan is corrected according to the fan frequency correction rule, realizing adaptive control of the fan frequency, achieving energy-saving effects while taking into account the air volume demand of the most unfavorable terminal.

[0005] Although the above schemes propose some energy-saving solutions for fan inverter control, the existing technology still has the following limitations, specifically: 1. The existing technology relies on an immediate or delayed control mechanism of real-time monitoring values ​​in inverter control, and lacks the ability to predict the change trend of fan load changes in a forward-looking manner, resulting in the control action always lagging behind the actual working conditions. It is impossible to adjust the inverter parameters in advance before load fluctuations occur, which also causes energy saving to lag.

[0006] 2. The existing technology does not analyze the composition characteristics of the fan load, confuses the basic load and the additional load, and ignores the differences between the two in dynamic response and energy consumption ratio, resulting in insufficient control accuracy. In addition, the existing technology has a single inverter control strategy and only relies on total air volume data, which can easily limit energy efficiency optimization and pose a risk of exceeding operating conditions. Summary of the Invention

[0007] In order to overcome the shortcomings of the background technology, the embodiments of the present invention provide an integrated digital wind turbine energy-saving control system, which can effectively solve the problems involved in the above background technology.

[0008] The purpose of the present invention can be achieved through the following technical solutions: an integrated digital fan energy-saving control system includes: a load quantization module, a load prediction module, a frequency conversion control module and a dynamic optimization module.

[0009] The load quantification module is connected to the load prediction module, the load prediction module is connected to the frequency conversion control module, and the frequency conversion control module is connected to the dynamic optimization module.

[0010] The load quantification module dynamically separates and quantifies the basic load and additional load during fan operation. The basic load is determined based on real-time air volume monitoring data and pipe network resistance characteristics, and the additional load is generated based on real-time calculation of the dynamic pressure difference between the inlet and outlet, the flow oscillation amplitude, and the medium density change.

[0011] The load prediction module takes the quantified results of the basic load and the additional load as input, intercepts the historical load data of the preceding time window including the current moment, analyzes the deviation characteristics of the basic load and the process target and the fluctuation law characteristics of the additional load, and predicts the load change trend in combination with the process requirements of the subsequent time window.

[0012] The frequency conversion control module dynamically adjusts the operating frequency of the inverter according to the load change trend output by the load prediction module.

[0013] The dynamic optimization module collects the fan operation energy efficiency ratio data during the execution of the variable frequency control module in real time, generates variable frequency control optimization instructions based on the energy efficiency comparison results, and feeds the optimization instructions back to the variable frequency control module to form an adaptive optimization loop.

[0014] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention intercepts the historical load data of the previous time window, analyzes the deviation characteristics of the basic load and the process target and the regular characteristics of the additional load fluctuation, breaks through the lag of real-time monitoring, and realizes forward prediction of load change trends in combination with subsequent process requirements, ensuring that the control action precedes the actual working condition changes.

[0015] (2) The present invention breaks through the limitations of the existing technology of mixed processing by dynamically separating and quantifying the basic load and additional load during the operation of the fan. In the subsequent dynamic adjustment process of the inverter operating frequency, the reference frequency is generated based on the predicted basic load, and a reasonable operating frequency range is formed by combining the predicted additional load normalization correction, highlighting the targeted differences in different load characteristics and effectively improving the accuracy of energy efficiency optimization.

[0016] (3) The present invention generates dual-mode control instructions of voltage stabilization and frequency limiting control and frequency optimization through energy efficiency comparison, constructs a dynamic adaptive frequency conversion optimization mechanism, and feeds back to the frequency conversion control module for continuous self-learning optimization. It can not only accurately match the real-time energy efficiency requirements and realize intelligent calibration and dynamic optimization of the fan operating frequency, but also significantly improve the system's adaptability to multiple working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of module connections provided by the first embodiment of the present invention.

[0019] Figure 2 This is a specific logic diagram of generating variable frequency control optimization instructions based on energy efficiency comparison results in the first embodiment of the present invention.

[0020] Figure 3 A schematic structural diagram of a device provided in accordance with the second embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] Example 1

[0023] Reference Figure 1 As shown, the first embodiment of the present invention provides an integrated digital wind turbine energy-saving control system, including: a load quantization module, a load prediction module, a frequency conversion control module and a dynamic optimization module.

[0024] The load quantification module is connected to the load prediction module, the load prediction module is connected to the frequency conversion control module, and the frequency conversion control module is connected to the dynamic optimization module.

[0025] The load quantification module dynamically separates and quantifies the basic load and additional load during fan operation. The basic load is determined based on real-time air volume monitoring data and pipe network resistance characteristics, and the additional load is generated based on real-time calculation of the dynamic pressure difference between the inlet and outlet, the flow oscillation amplitude, and the medium density change.

[0026] It should be noted that the base load is the inherent load that the fan must bear to meet the air volume and pressure requirements of the process system design, and is directly related to the core parameters of the process settings.

[0027] The additional load is the dynamic load that the fan bears because the actual operating environment deviates from the ideal operating conditions (such as inlet and outlet pressure difference, flow fluctuation, and differences in medium characteristics).

[0028] In addition, the above flow rate and air volume refer to the volume of gas passing through the fan per unit time.

[0029] In a preferred embodiment of the present invention, the basic load refers to the following quantification process: the first air volume data of the fan inlet and the second air volume data of the air outlet are collected in real time through the built-in air volume sensor of the fan, and the first and second air volume data are arithmetic averaged to obtain the real-time effective air volume of the fan.

[0030] The ratio of the real-time effective air volume of the fan to the pre-stored equivalent cross-sectional area of ​​the fan flow channel is used as the real-time effective flow velocity inside the fan.

[0031] The real-time effective flow velocity inside the fan is substituted into the preset pipe network resistance formula to obtain the pipe resistance overcome by the fan based on the real-time effective flow velocity inside.

[0032] It should be noted that the above-mentioned preset pipe network resistance formula is specifically the Darcy-Weisbach formula, and its input parameters also include fluid density, pipe friction coefficient and equivalent length-diameter ratio, which are all derived from the pre-set data of engineers during the fan pipe network design stage.

[0033] The product of the pipe resistance overcome by the fan's real-time effective air volume and its synchronous effective flow velocity is used as the numerator, and the pre-stored fan design efficiency is used as the denominator to carry out the ratio operation, and the ratio operation result is used as the basic load quantification value.

[0034] It should be noted that the above-mentioned pre-stored equivalent cross-sectional area of ​​the fan flow channel is based on the flow channel geometric topology structure specification corresponding to the fan model, or is dynamically updated through laser scanning calibration experiments, and the pre-stored fan design efficiency is specifically extracted with reference to the fan factory performance manual guide specification.

[0035] In a preferred embodiment of the present invention, the additional load refers to the following quantification process: real-time acquisition of the inlet and outlet pressure difference time domain signal, and obtaining the dynamic pressure difference fluctuation intensity of the fan inlet and outlet by analyzing the root mean square value of the time domain signal in the sliding window.

[0036] The signal of the built-in air volume sensor of the fan is subjected to frequency band filtering, and the oscillation amplitude within the pre-calibrated frequency band of the fan process characteristics is used as the fan flow oscillation amplitude.

[0037] The dynamic pressure difference fluctuation intensity and flow oscillation amplitude at the fan inlet and outlet are comprehensively considered, and a preset flow resistance coefficient is introduced to obtain the transient flow resistance component.

[0038] The medium density change is obtained in real time through the built-in gas density sensor of the fan. The change is the difference between the current density value and the reference working density value. Combined with the real-time effective air volume of the fan, a preset inertia correction coefficient is introduced to obtain the medium inertia component.

[0039] It should be noted that the aforementioned transient flow resistance component is composed of the cumulative intensity of the dynamic pressure differential fluctuation at the fan inlet and outlet, the flow oscillation amplitude, and the preset flow resistance coefficient. The medium inertia component is the product of the change in medium density and the preset inertia correction coefficient. Both the preset flow resistance coefficient and the preset inertia correction coefficient are obtained through experimental calibration: the former is obtained by measuring and fitting the corresponding relationship between the dynamic pressure differential fluctuation intensity, flow oscillation amplitude, and actual flow resistance under baseline operating conditions (stable flow, constant density) by varying the fan speed or load. The latter is obtained by measuring the relationship between density change and load response and performing regression analysis in a density-controllable experimental environment (e.g., when filled with gases of varying densities). Both experimental calibration processes are performed by professionals in the early stages of system development and pre-stored in a cloud database for direct retrieval and use.

[0040] The transient flow resistance component is superimposed on the medium inertia component to generate an additional load.

[0041] The load prediction module takes the quantified results of the basic load and the additional load as input, intercepts the historical load data of the preceding time window including the current moment, analyzes the deviation characteristics of the basic load and the process target and the fluctuation law characteristics of the additional load, and predicts the load change trend in combination with the process requirements of the subsequent time window.

[0042] In a preferred embodiment of the present invention, the deviation characteristics of the basic load and the process target refer to the following analysis process: the process requirement parameters of each sampling moment in the previous time window are read from the fan process control log, and based on the set air volume and associated pipeline flow rate requirements in the process requirement parameters, the basic load corresponding to the process target at each sampling moment in the previous time window is quantified.

[0043] It should be noted that the process requirement parameters include but are not limited to set air volume, associated pipeline flow rate requirements, set air pressure, valve opening and medium temperature.

[0044] The analysis logic of the basic load corresponding to the process target at each sampling moment in the preceding time window and the subsequent time window is consistent with the basic load quantification logic in the above-mentioned load quantification module. The calculation process is as follows: substitute the associated pipeline flow rate requirement value in the process demand parameter into the preset pipeline resistance formula to obtain the theoretical pipeline resistance that the fan overcomes based on the pipeline flow rate requirement.

[0045] The product of the set air volume in the process demand parameters and the theoretical pipeline resistance overcome under the synchronous pipeline flow rate requirement is used as the numerator, and the pre-stored fan design efficiency is used as the denominator to carry out the ratio operation, and the ratio operation result is used as the basic load of the corresponding process target.

[0046] The actual base load at each sampling moment in the previous time window is subtracted from the process target base load to generate a timing deviation sequence.

[0047] The slope of the time series deviation sequence is analyzed by linear regression to determine the deviation direction feature, and the ratio of the cumulative absolute deviation of the time series deviation sequence to the length of the previous time window is used as the deviation intensity feature.

[0048] The product of the deviation direction feature and the deviation intensity feature constitutes the deviation feature between the basic load and the process target in the preceding time window.

[0049] In a preferred embodiment of the present invention, the deviation direction feature is determined by the slope of the timing deviation sequence according to the following process: if the slope of the timing deviation sequence is non-negative, the deviation direction feature value is determined to be 1, otherwise the deviation direction feature value is determined to be -1.

[0050] In a preferred embodiment of the present invention, the fluctuation law characteristics of the additional load refer to the following analysis process: whether the relative change rate of the process requirement parameters at adjacent sampling moments exceeds the preset threshold is used as the judgment basis, and the previous time window is divided into a parameter change interval and a parameter steady-state interval.

[0051] It should be noted that the relative change rate of the process requirement parameters at adjacent sampling moments (a certain sampling moment and its previous sampling moment) specifically refers to the maximum relative change rate of the variables in the process requirement parameters at adjacent sampling moments, the parameter change interval specifically includes each sampling moment in the previous time window where the relative change rate of the process requirement parameters at the previous sampling moment exceeds the preset threshold, and the parameter steady-state interval specifically includes each sampling moment in the previous time window where the relative change rate of the process requirement parameters at the previous sampling moment does not exceed the preset threshold.

[0052] A dynamic mapping relationship between the change in process requirement parameters and the change in additional load is established for the parameter change interval, and the additional load change rate characteristics are generated by iteratively updating the pre-stored historical calibration correlation coefficients between process requirement parameters and additional loads.

[0053] It should be noted that the historical calibration correlation coefficient between the above-mentioned pre-stored process demand parameters and the additional load is based on historical operating data (such as fan control logs, sensor records, etc.). The quantitative correlation coefficient between each variable in the process demand parameter (such as set air volume, pipeline flow rate, valve opening, etc.) and the additional load is calculated through statistical regression analysis to reflect the impact weight of a unit change in a parameter variable on the additional load.

[0054] The specific process for iteratively updating the pre-stored historical calibration correlation coefficients between process requirement parameters and additional loads is as follows: Retrieve process requirement parameter variables whose relative change rates at sampling moments within the parameter change interval exceed a preset threshold, mark them as significantly changed variables, associate the significantly changed variables with the additional load changes at the corresponding sampling moments (which can be recorded in coordinate form), integrate the correlation sequence sets of each significantly changed variable and the additional load variable within the parameter change interval, and, based on the correlation sequence data, use the Pearson correlation coefficient or gray correlation method to obtain the real-time correlation coefficient between the change value of the significantly changed variable and the additional load change at the corresponding sampling moment. This is then compared with the historical calibration correlation coefficients between the significantly changed variable and the additional load, and a weighted fusion method is used (requiring that the preset weights of the historical calibration correlation coefficients be greater than the preset weights of the real-time correlation coefficients, exemplified by 0.7 and 0.3) to iteratively update the historical calibration correlation coefficients between the significantly changed variable and the additional load. Special note: The relative change rates of the process requirement parameters at each sampling moment within the parameter change interval exceed the preset threshold, and the number of significantly changed variables is at least one.

[0055] The standard deviation of the actual additional load at each sampling moment is calculated for the parameter steady-state interval to generate the additional load fluctuation rate characteristics.

[0056] It should be noted that the above standard deviation is calculated by constructing a sequence containing the actual additional load at each sampling moment within the steady-state interval of the parameters and using the existing standard deviation formula for calculation. Since the existing standard deviation formula is a mature statistical tool and has formed a recognized standard expression, it is directly used here and the details are not repeated here.

[0057] The additional load change rate feature and the fluctuation rate feature are integrated to form the additional load fluctuation regularity feature of the previous time window.

[0058] In a preferred embodiment of the present invention, the load change trend refers to the following prediction process: based on the set air volume and associated pipeline flow rate requirements in the process demand parameters at each sampling moment in the subsequent time window, the basic load corresponding to the process target is calculated, and calibrated by superimposing the basic load deviation characteristics of the previous time window to obtain the predicted benchmark load at each sampling moment in the subsequent time window.

[0059] It should be noted that the calibration by superimposing the basic load deviation characteristics of the preceding time window specifically refers to accumulating the basic load and basic load deviation characteristics corresponding to the process target at each sampling moment in the subsequent time window.

[0060] Compare the process demand parameters at the current moment and at each sampling moment in the subsequent time window. When the parameters are consistent, the median of the volatility confidence interval of the additional load at the current moment is used as the predicted additional load. When the parameters are inconsistent, the current additional load is dynamically corrected based on the process parameter deviation value and the correlation coefficient to obtain the predicted additional load.

[0061] The predicted basic load and predicted additional load at each sampling moment in the subsequent time window are sorted out to generate the load change trend prediction results.

[0062] The embodiment of the present invention intercepts the historical load data of the previous time window, analyzes the deviation characteristics of the basic load and the process target and the regular characteristics of the additional load fluctuation, breaks through the lag of real-time monitoring, and realizes forward prediction of load change trends in combination with subsequent process requirements, ensuring that the control action precedes the actual working condition changes.

[0063] The frequency conversion control module dynamically adjusts the operating frequency of the inverter according to the load change trend output by the load prediction module.

[0064] In a preferred embodiment of the present invention, the inverter operating frequency refers to the following dynamic adjustment process: according to the predicted basic load at each sampling moment in the subsequent time window, the preset load-frequency mapping curve is matched to generate the reference operating frequency.

[0065] The predicted additional load at each sampling moment in the subsequent time window is extracted, and normalized with the preset additional load limit minimum value and limit maximum value respectively to obtain the load minimum correction ratio and maximum correction ratio. The frequency sensitivity factor is introduced and multiplied by the two load correction ratios to obtain the operating frequency correction minimum value and maximum value, which are superimposed on the reference operating frequency to form a reasonable operating frequency range.

[0066] It should be noted that the above-mentioned frequency sensitivity factor is a dimensionless proportional coefficient with a specific physical unit, which is used to quantify the sensitivity of the additional load change to the fan operating frequency adjustment demand. Its core function is to convert the normalized load correction ratio (minimum and maximum values) into the corresponding operating frequency correction amount, ensuring that the frequency adjustment and load fluctuation are consistent in physical logic and system response characteristics. Under stable working conditions, a known additional load step change can be artificially applied and the corresponding operating frequency response can be recorded. The slope of the additional load step change and the operating frequency response can be fitted through linear regression of multiple sets of experimental data to serve as the frequency sensitivity factor.

[0067] The reasonable working frequency intervals of each sampling moment in the subsequent time window are integrated and the frequency distribution statistics of the intervals are performed, and the continuous sub-intervals with the highest overlapping frequency are selected as the effective adjustment range and the lower limit of the effective adjustment range is the inverter working frequency.

[0068] The embodiment of the present invention breaks through the limitations of the existing technology of mixed processing by dynamically separating and quantifying the basic load and additional load during the operation of the wind turbine. In the subsequent dynamic adjustment process of the inverter operating frequency, a reference frequency is generated based on the predicted basic load, and a reasonable operating frequency range is formed by combining the normalization correction of the predicted additional load, highlighting the targeted differences in different load characteristics and effectively improving the accuracy of energy efficiency optimization.

[0069] The dynamic optimization module collects the fan operation energy efficiency ratio data during the execution of the variable frequency control module in real time, generates variable frequency control optimization instructions based on the energy efficiency comparison results, and feeds the optimization instructions back to the variable frequency control module to form an adaptive optimization loop.

[0070] Reference Figure 2 As shown, in a preferred embodiment of the present invention, the frequency conversion control optimization instruction is generated based on the energy efficiency comparison result, referring to the following process: if the fan operation energy efficiency ratio exceeds or reaches the energy-saving standard energy efficiency ratio threshold at the subsequent time window sampling moment, a frequency optimization instruction is generated, and the operating frequency is gradually increased according to the unit gradient step within the effective adjustment range, and the fan operation energy efficiency ratio before and after the increase is compared. If the comparison result is greater than, the increased operating frequency is retained to approach the optimal operating frequency.

[0071] If the fan operating energy efficiency ratio continues to be lower than the energy-saving standard energy efficiency ratio threshold for a preset period of time at the subsequent time window sampling moment, a voltage stabilization and frequency limiting control instruction is generated, and the backup parameter group is switched to perform voltage stabilization and frequency limiting control. The backup parameter group includes a preset safety curve for limiting the frequency fluctuation amplitude, a voltage fluctuation suppression coefficient, and a forced power upper limit value.

[0072] In a preferred embodiment of the present invention, the wind turbine operating energy efficiency ratio is the ratio of the wind turbine's effective output power to its input electrical power.

[0073] It should be noted that the above-mentioned effective output power of the fan refers to the mechanical power actually transmitted by the fan to the fluid, that is, the useful work power used to overcome system resistance (such as pipe friction, static pressure difference, etc.) and drive gas flow. Its calculated value is consistent with the specific quantified value of the basic load in the fan operation in the above-mentioned load quantification module. The input electric power refers to the electric energy consumed by the fan from the power grid, including the sum of motor loss, transmission loss and mechanical power of the fan. The basis for the inverter operating frequency to affect the fan operation energy efficiency ratio to show energy saving is: by matching the load demand through variable frequency speed regulation, the fan can operate in the high-efficiency area, helping to maximize the fan operation energy efficiency ratio.

[0074] The embodiment of the present invention generates dual-mode control instructions of voltage stabilization and frequency limiting control and frequency optimization through energy efficiency comparison, constructs a dynamic and adaptive frequency conversion optimization mechanism, and feeds back to the frequency conversion control module for continuous self-learning optimization. It can not only accurately match real-time energy efficiency requirements, realize intelligent calibration and dynamic tuning of the fan operating frequency, but also significantly improve the system's adaptability to multiple working conditions.

[0075] Example 2

[0076] Reference Figure 3 As shown, a second embodiment of the present invention provides an apparatus comprising: a processor, a memory, and a communication bus. The memory stores a computer-readable program executable by the processor. The communication bus enables communication between the processor and the memory. When the processor executes the computer-readable program, it can implement the energy-saving control system of the integrated digital wind turbine.

[0077] Specifically, the above-mentioned memory and processor can be general-purpose memory and processor, which are not specifically limited here. When the processor runs the computer-readable program stored in the memory, it can execute the above-mentioned system-related steps.

[0078] The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above system may be completed by hardware integrated logic circuits in the processor or by software instructions. The processor may be a general-purpose processor, including a central processing unit (CPU), a network processor, etc. It may also be a digital signal processor, an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (PGM), or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The methods, steps, and logic block diagrams disclosed in the embodiments of this application may be implemented or executed. The general-purpose processor may be a microprocessor or any conventional processor. The system-related method steps disclosed in conjunction with the embodiments of this application may be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules may be located in a storage medium well-known in the art, such as random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), electrically erasable programmable memory (EEPM), registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above system.

[0079] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. The integrated digital fan energy-saving control system is characterized by: The system includes: The load quantification module dynamically separates and quantifies the base load and additional load during fan operation. The base load is determined based on real-time air volume monitoring data and pipe network resistance characteristics, while the additional load is generated based on the real-time calculation of the dynamic pressure difference between the inlet and outlet, the flow oscillation amplitude, and the medium density change. The load prediction module uses the quantified results of the basic load and the additional load as input, intercepts the historical load data of the preceding time window including the current moment, analyzes the deviation characteristics of the basic load and the process target and the fluctuation characteristics of the additional load, and predicts the load change trend in combination with the process demand of the subsequent time window; A frequency conversion control module dynamically adjusts the operating frequency of the inverter according to the load change trend output by the load prediction module; The dynamic optimization module collects the fan operation energy efficiency ratio data during the execution of the variable frequency control module in real time, generates variable frequency control optimization instructions based on the energy efficiency comparison results, and feeds the optimization instructions back to the variable frequency control module to form an adaptive optimization loop.

2. The integrated digital fan energy-saving control system according to claim 1 is characterized in that: The basic load is quantified as follows: first air volume data of the fan inlet and second air volume data of the fan outlet are collected in real time by a built-in air volume sensor of the fan, and the first and second air volume data are arithmetic averaged to obtain the real-time effective air volume of the fan; The ratio of the real-time effective air volume of the fan to the pre-stored equivalent cross-sectional area of ​​the fan flow channel is used as the real-time effective flow velocity inside the fan; Substituting the real-time effective flow velocity inside the fan into the preset pipe network resistance formula to obtain the pipe resistance overcome by the fan based on the real-time effective flow velocity inside; The product of the pipe resistance overcome by the fan's real-time effective air volume and its synchronous effective flow velocity is used as the numerator, and the pre-stored fan design efficiency is used as the denominator to carry out the ratio operation, and the ratio operation result is used as the basic load quantification value.

3. The integrated digital fan energy-saving control system according to claim 1 is characterized in that: The additional load is quantified as follows: real-time acquisition of the inlet and outlet pressure difference time domain signal, and analysis of the root mean square value of the time domain signal within the sliding window to obtain the dynamic pressure difference fluctuation intensity of the fan inlet and outlet; Perform frequency band filtering on the built-in air volume sensor signal of the fan, and use the oscillation amplitude within the pre-calibrated frequency band of the fan process characteristics as the fan flow oscillation amplitude; The dynamic pressure difference fluctuation intensity and flow oscillation amplitude of the fan inlet and outlet are comprehensively considered, and a preset flow resistance coefficient is introduced to obtain the transient flow resistance component; The gas density sensor built into the fan is used to obtain the density change of the medium in real time. The density change is the difference between the current density value and the density value under the reference working condition. Combined with the real-time effective air volume of the fan, a preset inertia correction coefficient is introduced to obtain the inertia component of the medium. The transient flow resistance component is superimposed on the medium inertia component to generate an additional load.

4. The integrated digital fan energy-saving control system according to claim 1 is characterized in that: The deviation characteristics of the base load and the process target are shown in the following analysis process: the process requirement parameters at each sampling time in the preceding time window are read from the fan process control log, and the base load corresponding to the process target at each sampling time in the preceding time window is quantified based on the set air volume and the associated pipeline flow rate requirements in the process requirement parameters; The actual base load at each sampling moment in the previous time window is subtracted from the process target base load to generate a time series deviation sequence; The slope of the time series deviation sequence is analyzed by linear regression to determine the deviation direction feature, and the ratio of the cumulative absolute deviation of the time series deviation sequence to the length of the previous time window is used as the deviation intensity feature; The product of the deviation direction feature and the deviation intensity feature constitutes the deviation feature between the basic load and the process target in the preceding time window.

5. The integrated digital fan energy-saving control system according to claim 4 is characterized in that: The process of determining the deviation direction feature by the slope of the time series deviation sequence is as follows: if the slope of the time series deviation sequence is non-negative, the deviation direction feature value is determined to be 1, otherwise the deviation direction feature value is determined to be -1.

6. The integrated digital fan energy-saving control system according to claim 1 is characterized in that: The fluctuation regularity characteristics of the additional load refer to the following analysis process: based on whether the relative change rate of the process requirement parameters at adjacent sampling moments exceeds a preset threshold, the preceding time window is divided into a parameter change interval and a parameter steady-state interval; A dynamic mapping relationship between the process requirement parameter change and the additional load change is established for the parameter change interval, and the additional load change rate feature is generated by iteratively updating the pre-stored historical calibration correlation coefficient between the process requirement parameter and the additional load; The standard deviation of the actual additional load at each sampling moment is calculated for the parameter steady-state interval to generate the additional load fluctuation rate characteristics; The additional load change rate feature and the fluctuation rate feature are integrated to form the additional load fluctuation regularity feature of the previous time window.

7. The integrated digital fan energy-saving control system according to claim 6, characterized in that: The load change trend is shown in the following prediction process: based on the set air volume and associated pipeline flow rate requirements in the process demand parameters at each sampling moment in the subsequent time window, the basic load corresponding to the process target is calculated, and the basic load deviation characteristics of the previous time window are superimposed and calibrated to obtain the predicted benchmark load at each sampling moment in the subsequent time window; Compare the process demand parameters at the current moment with those at each sampling moment in the subsequent time window. When the parameters are consistent, the median of the confidence interval of the fluctuation rate of the additional load at the current moment is used as the predicted additional load. When the parameters are inconsistent, the current additional load is dynamically corrected based on the process parameter deviation value and the correlation coefficient to obtain the predicted additional load. The predicted basic load and predicted additional load at each sampling moment in the subsequent time window are sorted out to generate the load change trend prediction results.

8. The integrated digital fan energy-saving control system according to claim 7 is characterized in that: The inverter operating frequency refers to the following dynamic adjustment process: according to the predicted basic load at each sampling moment in the subsequent time window, the preset load-frequency mapping curve is matched to generate the reference operating frequency; Extract the predicted additional load at each sampling moment in the subsequent time window, normalize it with the preset additional load limit minimum value and limit maximum value, obtain the load minimum correction ratio and maximum correction ratio, introduce a frequency sensitivity factor and multiply the two load correction ratios to obtain the operating frequency correction minimum value and maximum value, and superimpose them on the reference operating frequency to form a reasonable operating frequency range; The reasonable working frequency intervals of each sampling moment in the subsequent time window are integrated and the frequency distribution statistics of the intervals are performed, and the continuous sub-intervals with the highest overlapping frequency are selected as the effective adjustment range and the lower limit of the effective adjustment range is the inverter working frequency.

9. The integrated digital fan energy-saving control system according to claim 8, characterized in that: The generation of the variable frequency control optimization instruction based on the energy efficiency comparison result refers to the following process: if the fan operation energy efficiency ratio exceeds or reaches the energy efficiency ratio threshold of the energy-saving standard at the sampling moment of the subsequent time window, a frequency optimization instruction is generated, and the operating frequency is gradually increased according to the unit gradient step within the effective adjustment range, and the fan operation energy efficiency ratio before and after the increase is compared. If the comparison result is greater than, the increased operating frequency is retained to approach the optimal operating frequency; If the fan operating energy efficiency ratio continues to be lower than the energy-saving standard energy efficiency ratio threshold for a preset period of time at the subsequent time window sampling moment, a voltage stabilization and frequency limiting control instruction is generated, and the backup parameter group is switched to perform voltage stabilization and frequency limiting control. The backup parameter group includes a preset safety curve for limiting the frequency fluctuation amplitude, a voltage fluctuation suppression coefficient, and a forced power upper limit value.

10. The integrated digital fan energy-saving control system according to claim 9, characterized in that: The fan operating energy efficiency ratio is the ratio of the fan's effective output power to its input electrical power.

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