Energy-saving control method for production line vacuum pump based on vacuum degree feedback and PI algorithm

Abstract

The application belongs to the technical field of vacuum pump control, and relates to a production line vacuum pump energy-saving control method based on vacuum degree feedback and a PI algorithm. The method collects vacuum degree, vibration intensity and body temperature, calculates a vacuum change rate and a temperature influence coefficient, quantifies dynamic characteristics of working conditions, constructs a stability index division system state based on the vacuum change rate, an effective deviation and the vibration intensity, and dynamically adjusts proportional and integral gains of the PI algorithm according to the system state, the effective deviation and the temperature influence coefficient, so that control parameters can be self-adaptive to different states of the system and temperature interference, the problem of response lag or overshoot of a traditional fixed parameter PI algorithm when working conditions fluctuate is solved, and adaptability of working conditions is improved. Finally, the integral term is adjusted to avoid overshoot, and an energy efficiency optimization constraint is superimposed, so that unnecessary high-frequency operation is limited, and redundant energy consumption under stable working conditions is reduced on the premise of meeting the surge and temperature safety threshold.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of vacuum pump control, and more particularly to a production line vacuum pump energy-saving control method based on vacuum degree feedback and PI algorithm. BACKGROUND

[0002] In modern industrial production lines such as semiconductor manufacturing, precision electronics, pharmaceuticals, and food packaging, a vacuum environment is a core condition for process precision and product quality. As the power source of the vacuum system, the energy consumption of the vacuum pump accounts for 20%-35% of the total energy consumption of the production line, making it a key object of industrial energy saving. To maintain a stable process vacuum degree, the vacuum pump usually uses closed-loop feedback control. Proportional-integral control, with its simple structure and strong robustness, has become the mainstream control strategy.

[0003] The current production line vacuum pump control system drives the variable frequency motor to adjust the speed through fixed proportional gain and integral gain to achieve stable vacuum degree. However, in actual industrial scenarios, the vacuum degree requirement of the production line process varies dynamically with batch and process. The traditional PI controller uses fixed parameters, which is difficult to match the load characteristics of different vacuum degree intervals. For example, when the vacuum degree switches from low vacuum to high vacuum, a fixed high proportional gain can easily cause the motor speed to surge, causing the vacuum degree to overshoot, which requires additional energy consumption to pull back the vacuum degree through high-speed operation. In low load, a fixed integral gain can cause the integral term to accumulate continuously, resulting in the motor maintaining a higher speed than necessary, causing low load and high energy consumption. SUMMARY

[0004] The present application provides a production line vacuum pump energy-saving control method based on vacuum degree feedback and PI algorithm, which aims to solve the technical problems of poor working condition adaptability and serious energy consumption redundancy caused by the current vacuum pump control method.

[0005] The production line vacuum pump energy-saving control method based on vacuum degree feedback and PI algorithm includes the following steps:

[0006] S1. Collect the current signal of the vacuum gauge tube, the voltage signal of the vibration sensor, and the pump body temperature signal, and preprocess the collected signals to obtain the actual vacuum degree, vibration intensity, and pump body temperature.

[0007] S2. Dynamically adjust the dead zone width according to the deviation between the preset vacuum degree and the actual vacuum degree, and then extract the effective deviation based on the absolute value of the deviation between the actual vacuum degree and the set vacuum degree and the dead zone width. Then, calculate the vacuum change rate based on the current actual vacuum degree and the actual vacuum degree of the previous period, and calculate the temperature influence coefficient based on the body temperature.

[0008] S3. Weighted sum of the absolute value of the vacuum change rate, the absolute value of the effective deviation, and the vibration intensity to obtain the stability index, and divide the system state based on the stability index.

[0009] S4. dynamically adjusting the proportional gain and the integral gain based on the divided system state, the effective deviation, and the temperature influence coefficient;

[0010] S5. adjusting the integral term according to the effective deviation size and the integral cumulative value state, and then summing the proportional term and the integral term to obtain a preliminary output frequency;

[0011] S6. constructing a surge protection constraint, a temperature protection constraint, and an energy efficiency optimization constraint to constrain the preliminary calculation frequency and obtain a final execution frequency.

[0012] The application avoids invalid control actions under small deviations by collecting the vacuum degree, vibration intensity, and body temperature to realize real-time sensing of the current working condition deviation, and calculates the vacuum change rate and the temperature influence coefficient to quantize the dynamic characteristics of the working condition and improve the sensing accuracy of complex working conditions. Secondly, the stability index is constructed based on the vacuum change rate, the effective deviation, and the vibration intensity to divide the system state, and the proportional and integral gains of the PI algorithm are dynamically adjusted according to the system state, the effective deviation, and the temperature influence coefficient, so that the control parameters can adapt to different states and temperature disturbances such as system stability / dynamic adjustment. The problem of response lag or overshoot of the traditional fixed parameter PI algorithm under working condition fluctuation is solved, and the working condition adaptability is improved. Finally, the integral term is adjusted to avoid overshoot, and the energy efficiency optimization constraint is superimposed to limit unnecessary high-frequency operation under the premise of meeting the surge and temperature safety threshold, reduce the redundant energy consumption under stable working conditions, and reduce the frequent adjustment under small deviations through the design of the dynamic dead zone, further reducing the invalid energy consumption.

[0013] Preferably, obtaining the actual vacuum degree comprises the following steps:

[0014] The current signal collected by the vacuum gauge is subjected to analog-digital conversion to obtain a vacuum degree original value, and then the vacuum degree original value is subjected to Kalman filtering to obtain a filtered vacuum degree value. During the Kalman filtering process, the process noise is increased when the vacuum degree change rate increases, and the observation noise is increased when the vibration intensity increases.

[0015] The original vacuum degree values of the last n sampling points are subjected to arithmetic averaging to obtain a trend value of the original signal. If the vibration intensity is less than or equal to a preset threshold, the Kalman filtered vacuum degree value is directly used as the actual vacuum degree output. If the vibration intensity is greater than the preset threshold, the Kalman filtered vacuum degree value and the trend value of the original signal are subjected to weighted summation to obtain a fused vacuum degree as the actual vacuum degree output.

[0016] Preferably, the dynamic adjustment of the dead zone width comprises:

[0017] The dead zone width is based on a basic dead zone width plus a deviation absolute value multiplied by an adjustment coefficient; wherein the deviation absolute value is an absolute value of a difference between the current actual vacuum degree and the set vacuum degree.

[0018] Preferably, the extracting effective deviation comprises the following steps:

[0019] If the deviation absolute value of the actual vacuum degree and the set vacuum degree is less than the dead zone width, it is considered that the deviation is within the allowable range, and the effective deviation is set to 0; otherwise, the effective deviation is equal to the set vacuum degree minus the actual vacuum degree, and a negative value indicates that the vacuum degree is insufficient, and a positive value indicates that the vacuum is excessive.

[0020] Preferably, the system state comprises a stable state, a disturbance state and a high-risk state;

[0021] When the stability index is less than a first stability threshold, the system state is the stable state; when the stability index is less than a second stability threshold, the system state is the disturbance state, wherein the first stability threshold is less than the second stability threshold;

[0022] When the stability index is greater than or equal to the second stability threshold, the system state is the high-risk state, wherein the high-risk state is divided into a surge risk and a mutation state;

[0023] If the vibration intensity is greater than the intensity threshold and the absolute value of the vacuum change rate is greater than the change rate threshold, the system state is the surge risk, otherwise, the system is the mutation state.

[0024] Preferably, the step S4 comprises the following steps:

[0025] When the system state is the stable state, the proportional gain is reduced and the integral gain is increased;

[0026] When the system state is the disturbance state, the proportional gain is increased and the integral gain is reduced;

[0027] When the system state is the surge risk, the proportional gain is reduced by a fixed proportion and the integral gain is minimized;

[0028] When the system state is the mutation state, the proportional gain is increased and the maximum value of the integral gain is limited;

[0029] Based on the corresponding system state, a corresponding proportional integral calculation mode is selected to obtain a preliminary adjusted proportional gain and an integral gain; then the preliminary proportional gain is multiplied by a temperature influence coefficient to obtain an adjusted proportional gain, and the preliminary integral gain is divided by a temperature coefficient to obtain an adjusted integral gain.

[0030] Preferably, the adjustment integral term is to disable the integral when the deviation is large, to decay the integral value when the integral is saturated, and to normally update the integral under normal conditions.

[0031] When the absolute value of the effective deviation is greater than the threshold value, the large deviation is obtained, and the use of the integral term is stopped;

[0032] When the absolute value of the history integral is greater than the threshold value, the integral saturation is indicated, and the integral value decay operation is performed;

[0033] If the above conditions are not met, it indicates that it is in a normal condition, and the update integral is obtained by accumulating the product of the current deviation and the time step;

[0034] The output frequency is calculated based on the determined integral term and the proportional term, and a preliminary output frequency is obtained.

[0035] Preferably, the surge protection constraint sets the frequency base lower limit by the vibration intensity, and the absolute minimum frequency is obtained by adding the frequency base lower limit and the safety margin value;

[0036] Temperature protection constraint: limit the maximum operating frequency based on the body temperature, and set a temperature threshold value, when the pump body temperature is less than or equal to the temperature threshold value, the frequency is the upper limit of the working frequency; if the pump body temperature is greater than the temperature threshold value, the maximum frequency decreases by nHz for every 1 degree Celsius increase; based on this, the maximum frequency under temperature constraint is obtained;

[0037] Energy efficiency optimization constraint: according to the deviation proportion of the set vacuum degree and the actual vacuum degree, the theoretical frequency meeting the vacuum demand is calculated, and the energy efficiency optimization frequency upper limit is obtained by adding the predetermined proportion of the margin to the theoretical frequency.

[0038] Preferably, the final execution frequency is obtained based on the following steps:

[0039] Based on the preliminary output frequency and the absolute minimum frequency, the maximum value is selected to obtain a first candidate value;

[0040] Based on the maximum frequency under temperature constraint and the energy efficiency optimization frequency upper limit, the minimum value is obtained to obtain a second candidate value;

[0041] The theoretical demand frequency is taken as a third candidate value;

[0042] The first, second and third candidate values are sorted, and the middle value is taken as the final execution frequency.

[0043] The beneficial effects of the present application include:

[0044] This invention uses data collection of vacuum level, vibration intensity, and body temperature to perceive current operating condition deviations in real time, avoiding ineffective control actions under small deviations. It also calculates the vacuum change rate and temperature influence coefficient to quantify dynamic characteristics of operating conditions, improving the perception accuracy for complex conditions. Secondly, it constructs stability indices based on vacuum change rate, effective deviation, and vibration intensity to classify system states. The proportional and integral gains of the PI algorithm are dynamically adjusted according to the system state, effective deviation, and temperature influence coefficient, enabling control parameters to adapt to different states such as system stability / dynamic adjustment and temperature disturbances. This solves the problem of lag or overshoot in traditional fixed-parameter PI algorithms during operating condition fluctuations, improving adaptability. Finally, it avoids overshoot by adjusting the integral term and superimposes energy efficiency optimization constraints. While meeting surge and temperature safety thresholds, it limits unnecessary high-frequency operation, reducing redundant energy consumption under stable conditions. Simultaneously, the design of a dynamic dead zone reduces frequent adjustments under small deviations, further reducing ineffective energy consumption. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is an overall step diagram provided for an embodiment of the present invention.

[0047] Figure 2 A flowchart of step S1 provided in an embodiment of the present invention. Detailed Implementation

[0048] To make the technical problems, technical solutions, and beneficial effects to be solved by this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and are not intended to limit the scope of this application.

[0049] See Figure 1 As shown, the energy-saving control method for production line vacuum pumps based on vacuum feedback and PI algorithm includes the following steps:

[0050] S1. Collect the current signal of the vacuum gauge tube, the voltage signal of the vibration sensor, and the temperature signal of the pump body. Preprocess the collected signals to obtain the actual vacuum level, vibration intensity, and pump body temperature.

[0051] See Figure 2As shown, in the present embodiment, the signal collected by the vacuum gauge tube, the vibration sensor and the PT100 platinum resistance sensor is first converted into a vacuum degree original value, a vibration intensity and a pump body temperature, respectively; wherein the vibration sensor is used to collect the vibration signal of the pump body.

[0052] In the present embodiment, Kalman filtering is used to filter the vacuum degree original value to obtain a smoothed vacuum degree filtered value, specifically as follows:

[0053] Suppose that the vacuum degree change is a first-order inertia process, the state quantity , represents the current vacuum degree, the state transition matrix , the process noise , the state equation is as follows: ; in the formula: represents the state quantity at the current time, and represents the vacuum degree at the k time; represents the state quantity at the last time; represents the state transition matrix; process noise;

[0054] The observation equation is that the observation value is , the observation matrix , the observation noise : in the formula: represents the observation value at the k time; represents the observation matrix;

[0055] represents the observation noise;

[0056] In the present embodiment, based on the vacuum degree change rate , the process noise covariance is dynamically adjusted, and the greater the vacuum degree change rate, the greater the process noise covariance: ;;

[0057] Then, according to the vibration intensity , the observation noise covariance is adjusted, and the greater the vibration intensity, the greater the observation noise covariance: ;

[0058] In the present embodiment, the vacuum degree change rate can refer to step S2, and since steps S1 to S6 of the present application are continuously iterated, the vacuum change rate here can be the vacuum degree change rate obtained in step S2 in the last iteration.

[0059] Based on the above Kalman filtering, the filtered vacuum degree is obtained, and when the vibration intensity exceeds the threshold value, the confidence of the Kalman filtering result needs to be reduced, and the moving average of the original signal is introduced as an auxiliary to avoid distortion of the single filtering result, as follows:

[0060] The vacuum degree original value is subjected to sliding window averaging (window size N=5, i.e., the average of the last 5 sampling points, and the sampling interval is determined by the timestamp t): ;

[0061] In the formula: represents the vacuum degree original value of the i-th time window; represents the trend value of the original signal;

[0062] When the vibration intensity , the filtered vacuum degree output by the Kalman filter is directly used as the actual vacuum degree;

[0063] When the vibration intensity , the filtered vacuum degree output by the Kalman filter and the trend value of the signal are weighted and summed, as follows: ;

[0064] In the formula: represents the fusion vacuum degree, which is used as the actual vacuum degree; represents the vacuum degree output by the Kalman filter.

[0065] S2. According to the deviation size of the preset vacuum degree and the actual vacuum degree, the dead zone width is dynamically adjusted, and then based on the deviation absolute value of the actual vacuum degree and the set vacuum degree and the dead zone width, the effective deviation is extracted; then the vacuum change rate is calculated according to the current actual vacuum degree and the actual vacuum degree of the last period, and the temperature influence coefficient is calculated based on the body temperature;

[0066] Dead zone width calculation: ;

[0067] In the formula: represents the dynamic dead zone width; is the set basic dead zone width; is the dead zone scaling factor; represents the set vacuum degree; represents the actual vacuum degree;

[0068] The dead zone width is dynamically adjusted to avoid the control system being too sensitive when the deviation is small, and to appropriately relax the dead zone to enhance stability when the deviation is large. The dead zone width increases with the increase of the deviation absolute value, which meets the engineering requirement that the control action should be reduced when the deviation is large, for example, to prevent oscillation caused by sudden change of the vacuum degree.

[0069] Based on the dynamic dead zone, the effective control deviation is calculated, when the deviation is in the dead zone, the zero deviation is returned, avoiding the micro fluctuation triggering the control action, otherwise, the real deviation is returned for subsequent PI control, the effective deviation expression is as follows:

[0070] ;

[0071] In the formula: The effective deviation is represented;

[0072] The rate of change of the vacuum degree, the calculation formula is as follows: ;

[0073] In the formula: The rate of change of the vacuum degree is represented; The actual vacuum degree at the current time point is represented; The actual vacuum degree at the last sampling time point is represented;

[0074] The temperature influence coefficient is used to quantify the influence of the pump body temperature on the control gain, for fuzzy self-adaptive temperature compensation, when the temperature rises, the system gain changes, the temperature coefficient is used as the compensation coefficient, to ensure that the PI parameters adapt to the temperature change, wherein the calculation formula of the temperature influence coefficient is as follows: ;

[0075] In the formula: The temperature influence coefficient is represented; The pump body temperature is represented; 40 is the set reference temperature.

[0076] S3. The absolute values of the vacuum change rate, the effective deviation and the vibration intensity are weighted and summed to obtain a stability index, and the system state is divided based on the stability index;

[0077] The calculation formula of the stability index is as follows: ;

[0078] In the formula: The stability index is represented; The weight coefficient of the vacuum change rate is 0.6; The weight coefficient of the effective deviation is 0.3; The weight coefficient of the vibration intensity is 0.1;

[0079] The current state of the system is determined based on the value of the stability index, as follows:

[0080] If the value of the stability index is less than 0.8, the system is in a stable state, indicating that the fluctuation, deviation and vibration are small, and the system runs smoothly;

[0081] If the value of the stability index is less than 2.5, the system is in a perturbed state, indicating a moderate level of fluctuation and deviation, the system receives interference but is not out of control;

[0082] If the value of the stability index is greater than or equal to 2.5, the system is in a high-risk state, and if it is in a high-risk state, it needs to be determined whether the system is in a surge risk or a catastrophe state, as follows:

[0083] If the vibration intensity is greater than 40 and is greater than 5, the system is in a surge risk, where surge is a dangerous state of the pump system involving severe pressure and flow oscillation; otherwise, the system is in a catastrophe state, indicating that the system parameters change rapidly or substantially, but do not meet the surge condition.

[0084] S4. Dynamically adjusting the proportional gain and integral gain based on the divided system state, effective deviation, and temperature influence coefficient;

[0085] In this embodiment, a fuzzy-PI fusion mechanism is introduced, and through the fuzzy rule base and temperature compensation, the PI parameters are adapted to different working conditions, thereby improving the response speed, stability, and robustness of the system, as follows:

[0086] Four states are included in the fuzzy rule base, namely, stable state, perturbed state, surge risk, and catastrophe state.

[0087] When the system is in a stable state, the proportional gain is reduced to reduce overshoot, and the integral gain is increased to eliminate steady-state error, wherein the adjustment formula of the proportional gain is as follows: ;

[0088] In the formula: represents the proportional gain before temperature compensation; represents the basic proportional gain, with a value of 0.5; represents the effective deviation;

[0089] The adjustment formula of the integral gain is as follows: ;

[0090] In the formula: represents the integral gain before temperature compensation; represents the basic integral gain, with a value of 0.1;

[0091] When the system is in a perturbed state, the proportional gain is increased to quickly suppress deviation, and the integral gain is reduced to avoid integral saturation, wherein the adjustment formula of the proportional gain is as follows: ;

[0092] The adjustment formula of the integral gain is as follows: ;

[0093] In this embodiment, when the system is in the state of disturbance, the integral gain is directly set to 60% of the basic integral gain to reduce the integral effect and prevent oscillation under disturbance.

[0094] When the system is in the state of surge risk, conservative parameters are adopted to avoid triggering surge, wherein the adjustment formula of the proportional gain is as follows: ;

[0095] By setting the proportional gain to 20% of the basic integral gain, the proportional effect is greatly reduced, and the control is more gentle;

[0096] The adjustment formula of the integral gain is as follows: ;

[0097] By setting the integral gain to 10% of the basic integral gain, the integral effect is minimized to avoid rapid accumulation of deviation;

[0098] When the system is in the state of mutation, the proportional gain is increased while the integral gain is limited to prevent overshoot, as follows:

[0099] Proportional gain calculation and limitation: ;

[0100] Integral gain calculation and limitation: ;

[0101] In this embodiment, the response is enhanced by 2.5 basic gain, and the instability caused by too large is avoided by limiting to 3.0; secondly, the basic integral effect is ensured by the minimum integral gain to avoid being too large.

[0102] After the above steps, the preliminary adjusted proportional gain and integral gain are obtained, and further adjustment is made through the temperature influence coefficient to obtain the final proportional gain and integral gain, wherein the adjustment formula of the proportional gain is as follows: ;

[0103] In the formula: is the dynamically adjusted proportional gain; represents the temperature influence coefficient; when the temperature is greater than 40℃, is greater than 1, increases, and the control strength at high temperature is enhanced; when the temperature is less than 40℃, is less than 1, decreases to avoid overshoot at low temperature;

[0104] The adjustment formula of the integral gain is as follows: ;

[0105] In the formula: is the dynamically adjusted integral gain;

[0106] In this embodiment, at high temperature reduce, prevent too fast integration, low temperature increase, accelerate error elimination.

[0107] S5. According to the effective deviation size and the integral cumulative value state, adjust the integral term, and then sum the proportional term and the integral term to obtain a preliminary output frequency;

[0108] In this embodiment, the integral term is dynamically adjusted according to the deviation size, that is, the integral is disabled when the deviation is large, the integral value is attenuated when the integral is saturated, and the integral is updated under normal conditions. The specific process is as follows:

[0109] Condition 1: When the absolute value of the effective deviation is greater than the threshold value (5 kPa), it indicates that the deviation is too large, at this time the integral term is disabled to avoid integral leading to overshoot or oscillation;

[0110] Condition 2: When the absolute historical integral is greater than the preset threshold value (50 kPa·s), it indicates that the integral is saturated, at this time the integral value is attenuated to avoid the control system being rigid; wherein the absolute historical integral is the cumulative integral of the deviation , the specific expression is as follows:

[0111] ; ; ;

[0112] In the formula: represents the historical integral value; represents the effective integral value; 0.8 represents the attenuation factor;

[0113] When conditions 1 and 2 are not met, it indicates that the deviation is small and the integral is not saturated, then the integral term is normally updated, the specific expression is as follows:

[0114] :

[0115] In the formula: represents the time step, taking 0.2; represents the current deviation;

[0116] According to the determined integral term update mode, the determined integral term is brought into the PI control formula to calculate and obtain a preliminary output frequency, the specific expression is as follows: ;

[0117] In the formula: represents the preliminary output frequency.

[0118] S6. Build surge protection constraints, temperature protection constraints and energy efficiency optimization constraints to constrain the preliminary calculation frequency and obtain the final execution frequency.

[0119] Surge protection constraints: dynamically adjust the lower limit of the frequency according to the vibration intensity to prevent the pump from surging. When the vibration is too high, the minimum frequency is increased to enhance the stability of the system. The specific steps are as follows:

[0120] First, calculate the basic frequency lower limit:

[0121] ;

[0122] In the formula: represents the basic frequency lower limit;

[0123] That is, when the vibration intensity is greater than 40, it is a high vibration risk, at which time the basic frequency lower limit is set to 30Hz, and the lower limit is increased to avoid surging; otherwise, the basic frequency lower limit is set to 25Hz;

[0124] Absolute minimum frequency calculation ; represents the absolute minimum frequency; wherein 5 is a safety margin.

[0125] Temperature protection constraints: limit the maximum frequency based on the pump body temperature to prevent overheating damage. When the temperature exceeds the threshold, the frequency is linearly reduced to control the heat load. The specific calculation formula is as follows: ;

[0126] In the formula: represents the pump body temperature limit maximum frequency; represents the pump body temperature;

[0127] Energy efficiency optimization constraints: optimize the frequency based on the vacuum demand to improve energy efficiency. Through the theoretical demand frequency and the upper limit of energy efficiency, the system is ensured to meet the performance while reducing energy consumption. Specifically, the calculation formula of the theoretical demand frequency is as follows: ;

[0128] In the formula: represents the theoretical demand frequency; represents the set vacuum degree; represents the fusion vacuum degree, i.e. the actual vacuum degree;

[0129] Based on the theoretical demand frequency, the upper limit of the energy efficiency optimization frequency is determined: ;

[0130] In the formula: represents the upper limit of the energy efficiency optimization frequency;

[0131] Based on the above three constraints and the frequency output in step S5, the final execution frequency is generated by fusing three candidate frequencies, as follows:

[0132] Candidate frequency 1: ;

[0133] Candidate frequency 2: ;

[0134] Candidate frequency 3: ;

[0135] Median fusion: ;

[0136] The candidate frequencies 1 to 3 are sorted, and the middle value is taken as the final execution frequency.

[0137] The application collects the vacuum degree, vibration intensity and body temperature, real-time perceives the current working condition deviation, avoids invalid control action under small deviation, calculates the vacuum change rate and temperature influence coefficient, quantifies the working condition dynamic characteristics, improves the perception accuracy of complex working conditions, secondly, constructs a stability index division system state based on the vacuum change rate, effective deviation and vibration intensity, and dynamically adjusts the proportional and integral gain of the PI algorithm according to the system state, effective deviation and temperature influence coefficient, so that the control parameters can adapt to different states such as system stability / dynamic adjustment and temperature interference, solve the problem of response lag or overshoot of traditional fixed parameter PI algorithm under working condition fluctuation, and improve the working condition adaptability; finally, by adjusting the integral term to avoid overshoot, and superimposing energy efficiency optimization constraints, under the premise of meeting the surge and temperature safety threshold, unnecessary high-frequency operation is limited, and redundant energy consumption under stable working condition is reduced; at the same time, the design of dynamic dead zone reduces frequent adjustment under small deviation, further reduces invalid energy consumption.

[0138] The above is only a preferred embodiment of the present application, and does not limit the present application, any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A line vacuum pump energy-saving control method based on vacuum degree feedback and PI algorithm, characterized in that, The method comprises the following steps: S1. Collecting the current signal of the vacuum gauge tube, the voltage signal of the vibration sensor and the pump body temperature signal, and pre-processing the collected signals to obtain the actual vacuum degree, vibration intensity and pump body temperature; S2. Dynamically adjusting the dead zone width according to the deviation between the set vacuum degree and the actual vacuum degree, and then extracting the effective deviation based on the absolute value of the deviation between the actual vacuum degree and the set vacuum degree and the dead zone width; then calculating the vacuum change rate according to the current actual vacuum degree and the actual vacuum degree of the last period, and calculating the temperature influence coefficient based on the pump body temperature; S3. Weighted sum of the absolute value of the vacuum change rate, the absolute value of the effective deviation and the vibration intensity to obtain the stability index, and dividing the system state based on the stability index; The system state includes stable state, disturbance state and high-risk state; When the stability index is less than the first stability threshold, the system state is stable; when the stability index is greater than or equal to the first stability threshold and less than the second stability threshold, the system state is the disturbance state, wherein the first stability threshold is less than the second stability threshold; When the stability index is greater than or equal to the second stability threshold, the system state is the high-risk state, wherein the high-risk state is divided into surge risk and mutation state; If the vibration intensity is greater than the intensity threshold and the absolute value of the vacuum change rate is greater than the change rate threshold, the system state is the surge risk, otherwise, the system is the mutation state; S4. Dynamically adjusting the proportional gain and the integral gain based on the divided system state, the effective deviation and the temperature influence coefficient; The step S4 comprises the following steps: When the system state is stable, reduce the proportional gain and increase the integral gain; When the system state is the disturbance state, increase the proportional gain and reduce the integral gain; When the system state is the surge risk, reduce the proportional gain by a fixed proportion and minimize the integral gain; When the system state is the mutation state, increase the proportional gain and limit the maximum value of the integral gain; Selecting the corresponding proportional integral calculation mode based on the corresponding system state to obtain the preliminary adjusted proportional gain and integral gain; then multiplying the preliminary proportional gain by the temperature influence coefficient to obtain the adjusted proportional gain, and dividing the preliminary integral gain by the temperature influence coefficient to obtain the adjusted integral gain; S5. Adjusting the integral term according to the effective deviation and the integral cumulative value state, and then summing the proportional term and the integral term to obtain the preliminary output frequency; S6. Constructing the surge protection constraint, the temperature protection constraint and the energy efficiency optimization constraint to constrain the preliminary calculation frequency and obtain the final execution frequency.

2. The energy-saving control method for production line vacuum pumps based on vacuum degree feedback and PI algorithm according to claim 1, characterized in that, The actual vacuum degree comprises the following steps: Analog-to-digital conversion is performed on the collected current signal of the vacuum gauge tube to obtain a vacuum degree raw value; then Kalman filtering is performed on the vacuum degree raw value to obtain a filtered vacuum degree value; during the Kalman filtering process, when the vacuum degree change rate increases, the process noise increases; when the vibration intensity increases, the observation noise increases; The trend value of the original signal is obtained by taking an arithmetic mean of the original values of the vacuum degree of the last n sampling points; if the vibration intensity is less than or equal to a preset threshold value, the vacuum degree value after Kalman filtering is directly used as the actual vacuum degree output; if the vibration intensity is greater than the preset threshold value, the vacuum degree value after Kalman filtering and the trend value of the original signal are weighted and summed to obtain a fusion vacuum degree as the actual vacuum degree output.

3. The energy saving control method of the production line vacuum pump based on vacuum degree feedback and PI algorithm according to claim 1, characterized in that, The dynamic adjustment of the dead zone width comprises: The dead zone width is obtained by adding a bias absolute value multiplied by an adjustment coefficient to a basic dead zone width; wherein the bias absolute value is the absolute value of the deviation of the current actual vacuum degree from the set vacuum degree.

4. The energy-saving control method of a production line vacuum pump based on vacuum degree feedback and PI algorithm according to claim 1, characterized in that, The extraction of the effective bias comprises the following steps: If the absolute value of the deviation of the actual vacuum degree from the set vacuum degree is less than the dead zone width, it is considered that the deviation is within the allowed range, and the effective bias is set to 0; otherwise, the effective bias is equal to the set vacuum degree minus the actual vacuum degree, and a negative value indicates that the vacuum degree is insufficient, and a positive value indicates that the vacuum is excessive.

5. The energy saving control method of line vacuum pump based on vacuum degree feedback and PI algorithm according to claim 1, characterized in that, The adjustment of the integral term is to disable the integral when the deviation is large, to attenuate the integral value when the integral is saturated, and to normally update the integral under normal conditions; When the absolute value of the effective bias is greater than a threshold value, it is considered that the deviation is large, and the use of the integral term is stopped; When the absolute value of the historical integral is greater than a threshold value, it is considered that the integral is saturated, and the integral value is attenuated; If it does not belong to the above conditions, it is considered that it is under normal conditions, and the integral is updated by accumulating the product of the current deviation and the time step; The output frequency is calculated based on the determined integral term and the proportional term to obtain a preliminary output frequency.

6. The method for energy saving control of a production line vacuum pump based on vacuum degree feedback and PI algorithm according to claim 1, characterized in that, The surge protection constraint dynamically sets a lower limit of the basic frequency based on the vibration intensity, and adds the lower limit of the basic frequency and a safety margin value to obtain an absolute minimum frequency; The temperature protection constraint limits the maximum operating frequency based on the pump body temperature, and sets a temperature threshold value, when the pump body temperature is less than or equal to the temperature threshold value, the frequency is the upper limit of the power frequency; if the pump body temperature is greater than the temperature threshold value, the maximum frequency decreases by nHz for every 1 degree Celsius increase; based on this, the maximum frequency under temperature constraint is obtained; The energy efficiency optimization constraint calculates a theoretical demand frequency that meets the vacuum demand according to the deviation proportion of the set vacuum degree and the actual vacuum degree, and adds a predetermined proportion of margin to the theoretical demand frequency to obtain an energy efficiency optimization upper limit frequency.

7. The energy-saving control method of a production line vacuum pump based on vacuum degree feedback and PI algorithm according to claim 6, characterized in that, The final execution frequency is obtained based on the following steps: Based on the preliminary output frequency and the absolute minimum frequency, the maximum value is selected to obtain a first candidate value; Based on the maximum frequency under temperature constraint and the energy efficiency optimization upper limit frequency, the minimum value is selected to obtain a second candidate value; The theoretical demand frequency is taken as a third candidate value; The first, second and third candidate values are sorted, and the middle value is taken as the final execution frequency.

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