Explosion-proof induction motor control method, device and system for liquid supply of emulsion pump station

Abstract

The invention discloses an anti-explosion induction motor control method, device and system for liquid supply of an emulsion pump station. The method carries out vector control on the motor according to a given rotating speed and a feedback rotating speed. Firstly, the given rotating speed is determined through a hydraulic stability control algorithm, and hysteresis control is conducted through the algorithm according to the real-time hydraulic pressure supply intensity and the unloading valve emulsion flow; secondly, the feedback rotating speed is obtained through an improved high-performance rotating speed estimation algorithm, a discrete model is constructed through a second-order Runge-Kutta method in the algorithm, a particle swarm algorithm is constructed to optimize PI parameters in the rotating speed self-adaption law, the previous historical optimization algorithm process is considered in the improved particle swarm algorithm, and a sliding window method and an exponential decay weighting method are introduced; according to the method, the precision, the dynamic performance and the parameter adaptive capacity of the rotating speed identification algorithm are improved, and the intelligence, the energy-saving performance and the reliability of the liquid supply system of the emulsion pump station are improved.

Description

Technical Field

[0001] The invention relates to the field of AC motor frequency conversion control and mine emulsion pump station control, and in particular to an explosion-proof induction motor control method, device and system for supplying liquid in an emulsion pump station. Background Art

[0002] As the power source of mine hydraulic supports, mine emulsion pumps are generally driven by high-power explosion-proof induction motors. To ensure the safe operation of the fully mechanized mining face, the emulsion pump station must ensure that the system hydraulic pressure is maintained within the set range. In recent years, variable frequency control strategies have been gradually introduced into the control system of emulsion pump stations, but there are still problems such as unclear constant pressure variable frequency control mechanism and slow response speed.

[0003] As a mine explosion-proof motor, it is difficult to install a speed sensor on the high-power induction motor for emulsion pumps, so a speed sensorless vector control strategy must be adopted. This control strategy uses negative feedback to make the feedback speed closely follow the given speed. The given speed is obtained by the hydraulic stability control logic. The current hydraulic stability control logic generally only determines the speed setting signal based on the measured hydraulic pressure feedback. When the unloading valve is opened, the speed adjustment cannot control the pressure, that is, there is a control blind spot, and there is still a lot of loss at this time. The feedback speed is obtained by the speed estimation algorithm. The full-order adaptive observer is a widely used speed estimation algorithm, which mainly includes the design of feedback matrix and adaptive law. In the traditional algorithm, the PI parameters of the adaptive law are configured by trial and error. Not only is the process cumbersome, but it is also difficult to automatically adjust according to the actual working conditions, which has a great impact on the estimation performance. In addition, due to the large power and the energy loss limit of the switching device, the switching frequency in the control system of the high-power induction motor for emulsion pumps is relatively small compared to that of the small-power motor. Therefore, the traditional first-order discretization method is not accurate enough and will affect the design of the feedback matrix.

[0004] Therefore, the hydraulic stability control logic and algorithm of the emulsion pump station must be improved to enhance the intelligence and energy saving level of the emulsion pump station. In addition, in order to ensure the fast, stable and accurate response of the system to meet the needs of safe production, the speed estimation algorithm must also be further improved in terms of accuracy and dynamic convergence performance. Summary of the invention

[0005] The purpose of the present invention is to provide an explosion-proof induction motor control method, device and system for emulsion pump station fluid supply. The method includes an emulsion pump station hydraulic stability control algorithm and a high-performance speed estimation algorithm, which can realize variable frequency control of the emulsion pump motor, save energy loss while automatically maintaining the hydraulic pressure required by the system, and the motor's speed sensorless variable frequency speed regulation process has good accuracy and dynamic performance.

[0006] The technical solution provided by the present invention is as follows:

[0007] In a first aspect, the present invention provides a method for controlling an explosion-proof induction motor for supplying liquid in an emulsion pump station, the method comprising:

[0008] Initialize parameters, measure the initial unloading valve emulsion flow value under the unloading valve opening state corresponding to each speed, and the hydraulic pressure value for opening and closing the unloading valve;

[0009] Real-time measurement of hydraulic pressure and emulsion flow value of unloading valve;

[0010] A third-order Butterworth filter is constructed to filter the measured hydraulic pressure and emulsion flow rate signals of the unloading valve;

[0011] Obtain the motor's given speed and issue a fault alarm based on the real-time hydraulic pressure and the emulsion flow rate of the unloading valve;

[0012] A mathematical model of the speed identification system based on a full-order adaptive observer is established to obtain the motor feedback speed, and a discretized model is constructed using the second-order Runge-Kutta method.

[0013] A particle swarm algorithm model and fitness function for optimizing adaptive law parameters are constructed, and multi-objective optimization is performed on the PI parameters in the speed adaptive law.

[0014] The induction motor is controlled by double closed loop according to the motor given speed and feedback speed.

[0015] In one implementation, the third-order Butterworth filter transfer function is:

[0016]

[0017] Among them, s is the Laplace operator, ω c is the cut-off frequency, a1, a2, a3 are the correlation coefficients, which can be obtained by looking up the table. Written in the form of state equation:

[0018]

[0019] In the formula, C = [1 0 0],

[0020] In one embodiment, the step of obtaining a given motor speed according to the real-time hydraulic pressure and the emulsion flow rate of the unloading valve comprises the following steps:

[0021] When the unloading valve emulsion flow F r <(F cmin_0 -F0) is considered that the unloading valve is in the closed state, otherwise it is in the open state; where F cmin_0is the flow value of the unloading valve when the unloading valve is opened at the lowest speed during initialization, and F0 is the flow threshold introduced to reduce the influence of flow measurement error;

[0022] When the unloading valve is in the open state, the speed setting value ω is directly * Increase or decrease identifier L a Set to 0;

[0023] When the unloading valve is in a closed state, the speed and the pressure of the hydraulic supply are subjected to hysteresis control; the hysteresis control of the speed and the pressure includes:

[0024] If the hydraulic pressure value P<P min , then L a Set to 1 until the hydraulic pressure value P>P max , so that L a Set to 0; then until P < P min , so that L a Set to 1 and repeat this process; where P min Should be less than the hydraulic pressure value P when the unloading valve is closed cl , P max Should be less than the hydraulic pressure value P when the unloading valve is opened op ;

[0025] If L a If it is 1, the speed set value increases in a ramp form. If it is 0, the speed set value decreases in a ramp form.

[0026] In one implementation, the fault alarm includes:

[0027] During the normal operation of the system, the actual flow value F of the unloading valve when it is open is detected in real time. cω_r , denoted by ΔF c =F cω_r -F cω_0 , set the flow difference F caused by the allowable blockage a , when ΔF c >F a or ΔF c <-F a When the fault alarm is triggered,

[0028] In one implementation, the mathematical model of the speed identification system based on the full-order adaptive observer includes:

[0029] Full-order adaptive observer state equation:

[0030]

[0031] Among them, R s , R r are stator and rotor resistance, L m, L s , L r , L l They are mutual inductance, stator inductance, rotor inductance, and leakage inductance;

[0032] A 11 =aI,A 12 =cI-cT r ω r J,A 21 =dI,A 22 =-I / T+ω r J,B=[b 0] T , are the stator current, rotor flux and stator voltage in the two-phase stationary coordinate system respectively. “^” represents the estimated value;

[0033] The speed adaptation law is:

[0034] in, k p , k i is the PI coefficient;

[0035] The observer poles are configured at the motor poles shifted to the left by δ, and the feedback matrix is:

[0036]

[0037] Among them, a r11 =-R s / σL s -(1-σ) / στ r ,a r12 =L m / (σL s L r τ r ), a r21 =L m / τ r ,a r22 =-1 / τ r ,a i12 =a r12 / τ r .

[0038] In one embodiment, the second-order Runge-Kutta method discrete model is:

[0039]

[0040] in: T is the discretization period, λ is the weight coefficient, h is the sampling coefficient, 0≤λ≤1,0≤h≤1.

[0041] In one embodiment, the particle swarm model is:

[0042]

[0043] Among them, the particle dimension d is 2, representing k p , k i , w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers between [0,1]; is the distance between the current position of the i-th particle and its historical optimal position, is the distance between the optimal value of the historical position of all particles and the current position of the i-th particle, is the k+1th updated speed of the ith particle, is the k+1th updated position of the ith particle.

[0044] Introduce a linearly decreasing inertia weight:

[0045]

[0046] Among them, N is the maximum number of iterations of the particle, k is the current number of iterations, and w start 、w end are the initial and final weights respectively;

[0047] In addition to the speed estimation system, another independent full-order adaptive observer and adaptive law are paralleled to perform fitness calculation. The fitness function is established as:

[0048]

[0049] Where j is the number of program cycles, To measure the current, is the estimated current of the parallel full-order adaptive observer.

[0050] In one implementation, the multi-objective optimization of the PI parameters in the speed adaptive law includes:

[0051] When calculating the fitness function, the k in the particle p , k i Input the parallel adaptive law, and then wait for the adaptive law and the full-order adaptive observer to run Q discrete cycles, and output the fitness function. The smaller the fitness function value, the better the particle.

[0052] Assume that the final historical optimal fitness of the i-th particle in the previous n-th particle swarm optimization algorithm is The historical optimal fitness of the i-th particle after the k-th update in this particle swarm optimization algorithm is Then the historical optimal fitness of the i-th particle used in the k-th update in this particle swarm optimization algorithm is:

[0053]

[0054] Among them, α n =β·b N-n ,0<b<1,1≤n≤M, M is the window length, and its corresponding particle position is That is, the historical optimal position of the i-th particle applied in the k-th update.

[0055] In one implementation, the operation sequence of the particle swarm algorithm is:

[0056] After each particle swarm optimization algorithm reaches the maximum number of iterations, the optimal PI value is output for use by the adaptive law, the particle swarm optimization algorithm is terminated, and then the next particle swarm optimization algorithm is performed. In the initial population of the next optimization, the initial position of the i-th particle is the optimal position of the i-th particle in the previous optimization, and the optimal value of the initial historical position of all particles is the optimal value of the historical position of all particles in the previous optimization. This process is repeated during the operation of the system.

[0057] In a second aspect, the present invention provides an explosion-proof induction motor control device for emulsion pump station liquid supply, the device comprising a hydraulic stability control module, a high-performance speed estimation module and an induction motor vector control module, wherein:

[0058] The hydraulic stability control module is used to determine the given speed of the motor, and includes an initialization module, a signal measurement and filtering module, a fault detection and alarm module, and a hysteresis control module; wherein,

[0059] An initialization module is used to measure the initial unloading valve emulsion flow value and the hydraulic pressure value for opening and closing the unloading valve under the corresponding unloading valve opening state at each speed through initialization parameters;

[0060] The signal measurement and filtering module is used to measure the supply pressure and the emulsion flow value of the unloading valve in real time, and to construct a third-order Butterworth filter to filter the measured supply pressure and emulsion flow signals of the unloading valve;

[0061] Fault detection and alarm module, used to monitor the actual unloading valve emulsion flow value when the unloading valve is open in real time and compare it with the initial unloading valve emulsion flow value, and issue an unloading valve fault alarm when the difference is large;

[0062] The hysteresis control module is used to obtain the motor set speed according to the real-time hydraulic pressure and the emulsion flow of the unloading valve;

[0063] The high-performance speed estimation module is used to estimate the motor feedback speed, including a speed identification system discretization mathematical model module, a particle swarm algorithm optimization module and a fitness function calculation module, wherein:

[0064] The speed identification system discretization mathematical model module is used to establish the speed identification system mathematical model based on the full-order adaptive observer, obtain the motor feedback speed, and construct the discretization model using the second-order Runge-Kutta method;

[0065] The particle swarm algorithm optimization module and fitness function calculation module are used to build a particle swarm algorithm model and fitness function for optimizing the parameters of the adaptive law, and to perform multi-objective optimization on the PI parameters in the speed adaptive law;

[0066] The induction motor vector control module is used to perform double closed-loop control on the induction motor according to the motor given speed and feedback speed.

[0067] In a third aspect, the present invention provides an explosion-proof induction motor control system for emulsion pump station liquid supply, the system comprising the above-mentioned explosion-proof induction motor control device for emulsion pump station liquid supply.

[0068] The beneficial effects of the present invention are:

[0069] 1. The hydraulic stability control algorithm of the emulsion pump station proposed by the present invention adjusts the motor speed control signal according to the system pressure and the unloading valve flow, and performs frequency conversion control on the high-power explosion-proof induction motor used for the emulsion pump, which can reduce energy consumption while stabilizing the system hydraulic pressure within the set range, and can reduce energy loss even when the unloading valve is opened, and can detect whether the unloading valve is blocked in real time;

[0070] 2. A high-performance speed estimation algorithm proposed in the present invention uses the second-order Runge-Kutta method to construct a discretization model, which improves the problem of large discretization error caused by large switching cycles in high-power motor drive algorithms, and can expand the value range of the feedback matrix, avoiding the problem of poor stability in high-speed sections due to large switching cycles when using the first-order Euler discretization method;

[0071] 3. The invention proposes a high-performance speed estimation algorithm, which combines the observer model to build a particle swarm algorithm model. The particle swarm algorithm is used to optimize the PI parameters in the speed adaptive law, and the PI parameters are updated in real time, which greatly reduces the cumbersomeness of parameter selection and improves the accuracy and dynamic performance of the speed estimation algorithm;

[0072] 4. The proposed particle swarm algorithm takes into account the previous M historical particle swarm optimization algorithm processes, introduces the sliding window method and exponential decay weighted method, improves the accuracy of the algorithm optimization, and reduces the impact of interference on the optimization process. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The accompanying drawings are part of the present invention and are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but do not constitute an improper limitation of the present invention. Obviously, the accompanying drawings described below are only some embodiments. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0074] Figure 1 This is a schematic diagram of the emulsion pump liquid supply system;

[0075] Figure 2 A flow chart of a method for controlling an explosion-proof induction motor for supplying liquid in an emulsion pump station provided by an embodiment of the present invention;

[0076] Figure 3 A schematic diagram of a speed hysteresis control provided in one embodiment of the present invention;

[0077] Figure 4 A flow chart of a hydraulic stability control algorithm for an emulsion pump station provided in one embodiment of the present invention;

[0078] Figure 5 A schematic diagram of a rotation speed estimation system provided in one embodiment of the present invention;

[0079] Figure 6 A flow chart of a particle swarm algorithm provided in one embodiment of the present invention;

[0080] Figure 7 A timing diagram of a particle swarm optimization algorithm provided in one embodiment of the present invention;

[0081] Figure 8 This is a structural diagram of a speed sensorless vector control system of an induction motor for an emulsion pump provided in one embodiment of the present invention.

[0082] It should be noted that these drawings and textual descriptions are not intended to limit the conceptual scope of the present invention in any way, but are intended to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0083] In order to deepen the recognition and understanding of the present invention, the technical solution of the present invention is further introduced below in conjunction with the accompanying drawings and specific implementation methods. The following embodiments are explanations of the present invention and the present invention is not limited to the following embodiments.

[0084] Figure 1The schematic diagram of the emulsion pump supply system is given. The emulsion pump is driven by a high-power explosion-proof induction motor to draw emulsion. When the unloading valve is closed, the emulsion is pumped into the high-pressure supply system, and when the unloading valve is opened, the emulsion is pumped into the return liquid passage. When the hydraulic support needs to be raised, the high-pressure supply system supplies liquid to the hydraulic support. At this time, the high-pressure supply system will cause the system pressure to decrease due to the release of part of the emulsion; when it needs to be lowered, the hydraulic support releases the emulsion through its own valve. In order to ensure the safe and stable operation of the hydraulic support, it is necessary to ensure that the pressure value of the high-pressure supply system is stable within the required range.

[0085] Due to mechanical inertia, the pressure values ​​corresponding to the opening and closing of the unloading valve are different, which is a hysteresis response process, such as Figure 2 As shown. When the pressure is greater than P op When the pressure drops below P, the unloading valve opens and remains open until the pressure drops below P cl After that, the unloading valve is closed.

[0086] The high-power induction motor used to drive the emulsion pump must be explosion-proof to meet the production requirements of the mine. It is difficult to install a speed sensor to obtain speed information, so a speed sensorless control algorithm must be used for control.

[0087] The present disclosure provides an explosion-proof induction motor control method for liquid supply in an emulsion pump station, such as Figure 2 As shown in FIG. 1 , the method consists of two parts: an emulsion pump station hydraulic stability control algorithm and an improved high-performance speed estimation algorithm, and specifically includes the following steps:

[0088] Step S100: Initializing parameters, measuring the initial unloading valve emulsion flow value under the unloading valve opening state corresponding to each rotation speed, and the hydraulic pressure value for opening and closing the unloading valve.

[0089] Specifically, in order to avoid the impact of high current shock caused by frequent motor start-up, and considering that the equipment lubricating fluid maintenance system is also powered by the motor, the minimum speed of the motor is set to min , set the maximum speed limit of the motor ω max . Record the flow value F when the unloading valve is opened at each speed cω_0 , where F cmin_0 is the flow value of the unloading valve when the unloading valve is opened at the lowest speed during initialization, and records the pressure value P that makes the unloading valve open. op and the closed pressure value P cl .

[0090] Step S200: Real-time measurement of the hydraulic pressure and the emulsion flow value of the unloading valve.

[0091] Step S300: construct a third-order Butterworth filter to filter the measured supply pressure and unloading valve emulsion flow signals.

[0092] After collecting the hydraulic pressure of the system and the flow signal of the unloading valve, a third-order Butterworth filter is used for filtering. The frequency response in the passband is relatively flat and the phase delay is small. The stopband attenuates rapidly and has a strong ability to suppress clutter and noise. The above advantages are conducive to the rapid response of the system.

[0093] Furthermore, the filter transfer function is:

[0094]

[0095] Among them, s is the Laplace operator, ω c is the cut-off frequency, a1, a2, a3 are the correlation coefficients, which can be obtained by looking up the table. Written in the form of state equation:

[0096]

[0097] In the formula, C = [1 0 0],

[0098] Step S400: Obtaining the motor set speed and performing a fault alarm according to the real-time hydraulic pressure and the emulsion flow of the unloading valve.

[0099] Since the pressure of the hydraulic supply can only be increased by controlling the motor speed, the pressure will only drop when the load such as the hydraulic support is in motion. Therefore, hysteresis control is used to adjust the speed given signal to maintain the pressure at [P min P max ] range. Among them, P min , P max are the minimum and maximum values ​​of the pressure hysteresis control respectively. Figure 3 shown.

[0100] Further, fault alarm includes:

[0101] During the normal operation of the system, the actual flow value F of the unloading valve when it is open is detected in real time. cω_r , denoted by ΔF c =F cω_r -F cω_0 When the unloading valve is not blocked, ΔF c ≈0, when blockage occurs, ΔF c ≠0. Set the flow difference F caused by the allowable blockage a , when ΔF c >F a or ΔF c <-F a When the fault alarm is triggered,

[0102] In the embodiment of the present application, obtaining the given speed of the motor according to the real-time hydraulic pressure and the emulsion flow of the unloading valve includes the following steps:

[0103] When the unloading valve emulsion flow F r <(F cmin_0 -F0) is considered that the unloading valve is in the closed state, otherwise it is in the open state; where F cmin_0 is the flow value of the unloading valve when the unloading valve is opened at the lowest speed during initialization, and F0 is the flow threshold introduced to reduce the influence of flow measurement error;

[0104] When the unloading valve is in the open state, the speed setting value ω is directly * Increase or decrease identifier L a Set to 0;

[0105] When the unloading valve is in a closed state, the speed and the pressure of the hydraulic supply are subjected to hysteresis control; the hysteresis control of the speed and the pressure includes:

[0106] If the hydraulic pressure value P<P min , then L a Set to 1 until the hydraulic pressure value P>P max , so that L a Set to 0; then until P < P min , so that L a Set to 1 and repeat this process; where P min Should be less than the hydraulic pressure value P when the unloading valve is closed cl , P max Should be less than the hydraulic pressure value P when the unloading valve is opened op ;

[0107] If L a If it is 1, the speed set value increases in a ramp form. If it is 0, the speed set value decreases in a ramp form.

[0108] Therefore, when the pressure reaches the upper limit, the speed decreases, and the speed starts to increase again after the pressure drops to the lower limit, thus realizing hysteresis control.

[0109] The specific implementation process of the hydraulic stability control algorithm of the emulsion pump station is as follows: Figure 4 shown.

[0110] Step S500: Establish a mathematical model of the speed identification system based on a full-order adaptive observer, obtain the motor feedback speed, and construct a discretized model using the second-order Runge-Kutta method.

[0111] In a two-phase stationary coordinate system, combined with the mathematical model of the induction motor, a mathematical model of the speed identification system based on the full-order adaptive observer is established, including:

[0112] The state equation of the full-order adaptive observer is:

[0113]

[0114] Among them, R s , R r are stator and rotor resistance, L m , L s , L r , L l They are mutual inductance, stator inductance, rotor inductance, and leakage inductance;

[0115] A 11 =aI,A 12 =cI-cT r ω r J,A 21 =dI,A 22 =-I / T+ω r J,B=[b 0] T , are the stator current, rotor flux and stator voltage in the two-phase stationary coordinate system. “^” represents an estimated value.

[0116] The speed adaptation law is:

[0117] in, k p , k i is the PI coefficient.

[0118] In all working conditions of the emulsion pump station, the motor is in forward rotation and the load is in positive direction, so there will be no low-speed power generation state. When designing the feedback matrix, there is no need to consider the problem of low-speed power generation instability. In order to improve the convergence speed of the observer, the observer pole is configured at the motor pole shifted to the left by δ, which satisfies:

[0119] s oi =s mi -δ

[0120] where s oi is the observer pole, s mi The motor pole.

[0121] According to the relationship between the observer and the characteristic polynomial of the induction motor, the feedback matrix can be obtained as:

[0122]

[0123] Among them, a r11 =-R s / σL s -(1-σ) / στ r ,ar12 =L m / (σL s L r τ r ), a r21 =L m / τ r ,a r22 =-1 / τ r ,a i12 =a r12 / τ r .

[0124] By using the above feedback matrix, the observer pole can be configured on the left side of the motor pole. Since the motor itself is a stable system, the observer is also a stable system, and its convergence speed is faster than that of the motor itself.

[0125] In a discrete system, the observer state equation is:

[0126]

[0127] Furthermore, the second-order Runge-Kutta method discrete model is:

[0128]

[0129] in: T is the discretization period, λ is the weight coefficient, h is the sampling coefficient, 0≤λ≤1,0≤h≤1.

[0130] By adopting the above discretization method, the condition for ensuring the stability of the observer after discretization is that the real part of the poles of the flux observer based on the continuous model is less than 0. This condition is consistent with the stability condition under the continuous state, avoiding the problem of poor stability in the high-speed section due to the large switching cycle when using the first-order Euler discretization method. Therefore, the left shift distance of the feedback matrix should be designed to satisfy k>0 to ensure the stability of the observer.

[0131] Step S600: constructing a particle swarm algorithm model and fitness function for optimizing the parameters of the adaptive law, and performing multi-objective optimization on the PI parameters in the speed adaptive law.

[0132] Furthermore, the particle swarm algorithm model is:

[0133]

[0134] In the embodiment of the present invention, the particle dimension d is 2, which represents k p , k i . w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers between [0,1]; is the distance between the current position of the i-th particle and its historical optimal position, is the distance between the optimal value of the historical position of all particles and the current position of the i-th particle. is the k+1th updated speed of the ith particle, is the k+1th updated position of the ith particle.

[0135] Introduce a linearly decreasing inertia weight:

[0136]

[0137] Among them, N is the maximum number of iterations of the particle, k is the current number of iterations, and w start 、w end are the initial and final weights respectively.

[0138] The particle swarm algorithm is used to optimize the parameter k in the adaptive law. p , k i Perform multi-objective optimization. Figure 6 The particle swarm algorithm process is given. The extreme values ​​and optimal positions of individuals and groups are updated according to the fitness function F, and then the particle positions and speeds are updated according to the proposed particle swarm model. After the termination condition is met, the optimal particle value is output for use by the speed estimation system. In the embodiment of the present invention, the termination condition is to reach the maximum number of iterations N.

[0139] Figure 5 The schematic diagram of the speed estimation system is shown in Figure 2. In order to calculate the fitness, another independent full-order adaptive observer and adaptive law are paralleled outside the speed estimation system. In the proposed particle swarm optimization algorithm, the fitness function is:

[0140]

[0141] Where j is the number of program cycles, To measure the current, is the estimated current of the parallel full-order adaptive observer.

[0142] In the embodiment of the present application, a multi-objective optimization is performed on the PI parameters in the speed adaptive law, including:

[0143] When calculating the fitness function, the k in the particle p , k i Input the parallel adaptive law, and then wait for the adaptive law and the full-order adaptive observer to run Q discrete cycles, and output the fitness function. The smaller the fitness function value, the better the particle.

[0144] The sliding window method and exponential weighted algorithm are introduced. The proposed optimal historical position of the i-th particle and the optimal historical position of all particles not only consider the current particle swarm optimization algorithm, but also consider the previous M historical particle swarm optimization algorithms.

[0145] Assume that the final optimal fitness of all particles in the previous n-th particle swarm optimization algorithm is The historical optimal fitness of all particles after the kth update in this particle swarm optimization algorithm is Then the historical optimal fitness of all particles used in the kth update in this particle swarm optimization algorithm is:

[0146]

[0147] Among them, α n =β·b N-n ,0<b<1,1≤n≤M, M is the window length, and its corresponding particle position is That is, the optimal historical position of all particles applied in the kth update. n The larger it is, the less influence the optimization results from a long time ago will have on this optimization process.

[0148] Similarly, let the final historical optimal fitness of the i-th particle in the previous n-th particle swarm optimization algorithm be The historical optimal fitness of the i-th particle after the k-th update in this particle swarm optimization algorithm is Then the historical optimal fitness of the i-th particle used in the k-th update in this particle swarm optimization algorithm is:

[0149]

[0150] The corresponding particle position is That is, the historical optimal position of the i-th particle applied in the k-th update.

[0151] The particle swarm optimization algorithm is performed according to the set timing cycle. Figure 7 As shown, a timing diagram of the particle swarm optimization algorithm according to an embodiment of the present invention is given.

[0152] Furthermore, the operation sequence of the particle swarm algorithm is as follows: after each particle swarm optimization algorithm reaches the maximum number of iterations, the optimal PI value is output for use by the adaptive law, the particle swarm optimization algorithm is terminated, and then the next particle swarm optimization algorithm is performed. In the initial population of the next optimization, the initial position of the i-th particle is the optimal position of the i-th particle in the previous optimization, and the optimal value of the initial historical positions of all particles is the optimal value of the historical positions of all particles in the previous optimization. This process is repeated during the operation of the system.

[0153] Each time the particle swarm optimization algorithm performs N iterative updates, the fitness function calculation in each iterative update lasts for Q discrete cycles, and the sliding window length is M, that is, the calculation of the optimal value of the historical position in each iterative update not only considers the current particle swarm optimization algorithm, but also considers the previous M historical particle swarm optimization algorithms.

[0154] Step S700: performing double closed-loop control on the induction motor according to the motor given speed and feedback speed.

[0155] Specifically, a given speed signal and a feedback speed signal are input into a vector control system to perform speed sensorless vector control of the induction motor.

[0156] The following is an embodiment of an explosion-proof induction motor control device for emulsion pump station liquid supply of the present invention, which can be used to execute an embodiment of an explosion-proof induction motor control method for emulsion pump station liquid supply of the present invention. For details not disclosed in the embodiment of an explosion-proof induction motor control device for emulsion pump station liquid supply of the present invention, please refer to the embodiment of an explosion-proof induction motor control method for emulsion pump station liquid supply of the present invention.

[0157] In one embodiment, an explosion-proof induction motor control device for emulsion pump station liquid supply is proposed, the device includes a hydraulic stability control module, a high-performance speed estimation module and an induction motor vector control module, wherein:

[0158] The hydraulic stability control module is used to determine the given speed of the motor, and includes an initialization module, a signal measurement and filtering module, a fault detection and alarm module, and a hysteresis control module; wherein,

[0159] An initialization module is used to measure the initial unloading valve emulsion flow value and the hydraulic pressure value for opening and closing the unloading valve under the corresponding unloading valve opening state at each speed through initialization parameters;

[0160] The signal measurement and filtering module is used to measure the supply pressure and the emulsion flow value of the unloading valve in real time, and to construct a third-order Butterworth filter to filter the measured supply pressure and emulsion flow signals of the unloading valve;

[0161] Fault detection and alarm module, used to monitor the actual unloading valve emulsion flow value when the unloading valve is open in real time and compare it with the initial unloading valve emulsion flow value, and issue an unloading valve fault alarm when the difference is large;

[0162] The hysteresis control module is used to obtain the motor set speed according to the real-time hydraulic pressure and the emulsion flow of the unloading valve;

[0163] The high-performance speed estimation module is used to estimate the motor feedback speed, including a speed identification system discretization mathematical model module, a particle swarm algorithm optimization module and a fitness function calculation module, wherein:

[0164] The speed identification system discretization mathematical model module is used to establish the speed identification system mathematical model based on the full-order adaptive observer, obtain the motor feedback speed, and construct the discretization model using the second-order Runge-Kutta method;

[0165] The particle swarm algorithm optimization module and fitness function calculation module are used to build a particle swarm algorithm model and fitness function for optimizing the parameters of the adaptive law, and to perform multi-objective optimization on the PI parameters in the speed adaptive law;

[0166] The induction motor vector control module is used to perform double closed-loop control on the induction motor according to the motor given speed and feedback speed.

[0167] In one embodiment, an explosion-proof induction motor control system for emulsion pump station supply is proposed, referring to Figure 8 The system includes the above-mentioned explosion-proof induction motor control device for emulsion pump station liquid supply.

[0168] For a detailed description of the explosion-proof induction motor control device used for emulsion pump station liquid supply, please refer to the description of the same or similar parts mentioned above, which will not be repeated here.

[0169] The system uses a double closed-loop control strategy to control the feedback speed to closely follow the given speed, wherein the given speed is obtained by the hydraulic stability control algorithm of the emulsion pump station provided by the embodiment of the present invention, and the feedback speed is obtained by the high-performance speed estimation algorithm provided by the embodiment of the present invention.

[0170] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0171] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for controlling an explosion-proof induction motor for supplying liquid in an emulsion pump station, characterized in that: The method comprises: Initialize parameters, measure the initial unloading valve emulsion flow value under the unloading valve opening state corresponding to each speed, and the hydraulic pressure value for opening and closing the unloading valve; Real-time measurement of hydraulic pressure and emulsion flow value of unloading valve; A third-order Butterworth filter is constructed to filter the measured hydraulic pressure and emulsion flow rate signals of the unloading valve; Obtain the motor's given speed and issue a fault alarm based on the real-time hydraulic pressure and the emulsion flow rate of the unloading valve; A mathematical model of the speed identification system based on a full-order adaptive observer is established to obtain the motor feedback speed, and a discretized model is constructed using the second-order Runge-Kutta method. A particle swarm algorithm model and fitness function for optimizing adaptive law parameters are constructed, and multi-objective optimization is performed on the PI parameters in the speed adaptive law. The induction motor is controlled by double closed loop according to the motor given speed and feedback speed.

2. The explosion-proof induction motor control method for emulsion pump station liquid supply according to claim 1 is characterized in that: The third-order Butterworth filter transfer function is: Among them, s is the Laplace operator, ω c is the cut-off frequency, a1, a2, a3 are the correlation coefficients, which can be obtained by looking up the table and written in the form of state equation: In the formula, 3. The explosion-proof induction motor control method for emulsion pump station liquid supply according to claim 1 is characterized in that: The method of obtaining a given motor speed according to the real-time hydraulic pressure and the emulsion flow rate of the unloading valve comprises the following steps: When the unloading valve emulsion flow F r <(F cmin_0 -F0) is considered that the unloading valve is in the closed state, otherwise it is in the open state; where F cmin_0 is the flow value of the unloading valve when the unloading valve is opened at the lowest speed during initialization, and F0 is the flow threshold introduced to reduce the influence of flow measurement error; When the unloading valve is in the open state, the speed setting value ω is directly * Increase or decrease identifier L a Set to 0; When the unloading valve is in a closed state, the speed and the pressure of the hydraulic supply are subjected to hysteresis control; the hysteresis control of the speed and the pressure includes: If the hydraulic pressure value P<P min , then L a Set to 1 until the hydraulic pressure value P>P max , so that L a Set to 0; then until P < P min , so that L a Set to 1 and repeat this process; where P min Should be less than the hydraulic pressure value P when the unloading valve is closed cl , P max Should be less than the hydraulic pressure value P when the unloading valve is opened op ; If L a If it is 1, the speed set value increases in a ramp form. If it is 0, the speed set value decreases in a ramp form.

4. The explosion-proof induction motor control method for emulsion pump station liquid supply according to claim 1 is characterized in that: The fault alarm includes: During the normal operation of the system, the actual flow value F of the unloading valve when it is open is detected in real time. cω_r , denoted by ΔF c =F cω_r -F cω_0 , set the flow difference F caused by the allowable blockage a , when ΔF c >F a or ΔF c <-F a When the fault alarm is triggered, 5. The explosion-proof induction motor control method for emulsion pump station liquid supply according to claim 1, characterized in that: The mathematical model of the speed identification system based on the full-order adaptive observer includes: Full-order adaptive observer state equation: Among them, R s , R r are stator and rotor resistance, L m , L s , L r , L l They are mutual inductance, stator inductance, rotor inductance, and leakage inductance; A 11 =aI,A 12 =cI-cT r ω r J,A 21 =dI,A 22 =-I / T+ω r J, are the stator current, rotor flux and stator voltage in the two-phase stationary coordinate system respectively. "^" represents the estimated value; The speed adaptation law is: in, k p , k i is the PI coefficient; The observer poles are configured at the motor poles shifted to the left by δ, and the feedback matrix is: among them, a r11 =-R s / σL s -(1-s) / f r ,a r12 =L m / (σL s L r t r ),a r21 =L m / t r ,a r22 =-1 / t r ,a i12 =a r12 / t r 。 6. The explosion-proof induction motor control method for emulsion pump station liquid supply according to claim 1, characterized in that: The second-order Runge-Kutta method discrete model is: in: T is the discretization period, λ is the weight coefficient, h is the sampling coefficient, 0≤λ≤1,0≤h≤1.

7. The explosion-proof induction motor control method for emulsion pump station liquid supply according to claim 1 or 6, characterized in that: The particle swarm model is: Among them, the particle dimension d is 2, representing k p , k i , w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers between [0,1]; is the distance between the current position of the i-th particle and its historical optimal position, is the distance between the optimal value of the historical position of all particles and the current position of the i-th particle, is the k+1th updated speed of the ith particle, is the k+1th updated position of the ith particle; Introduce a linearly decreasing inertia weight: Among them, N is the maximum number of iterations of the particle, k is the current number of iterations, and w start 、w end are the initial and final weights respectively; In addition to the speed estimation system, another independent full-order adaptive observer and adaptive law are paralleled to perform fitness calculation and establish the fitness function as follows: Where j is the number of program cycles, To measure the current, is the estimated current of the parallel full-order adaptive observer.

8. The explosion-proof induction motor control method for emulsion pump station liquid supply according to claim 7, characterized in that: The multi-objective optimization of the PI parameters in the speed adaptive law includes: When calculating the fitness function, the k in the particle p , k i Input the parallel adaptive law, and then wait for the adaptive law and the full-order adaptive observer to run Q discrete cycles, and output the fitness function. The smaller the fitness function value, the better the particle. Assume that the final historical optimal fitness of the i-th particle in the previous n-th particle swarm optimization algorithm is The historical optimal fitness of the i-th particle after the k-th update in this particle swarm optimization algorithm is Then the historical optimal fitness of the i-th particle used in the k-th update in this particle swarm optimization algorithm is: in, M is the window length, and its corresponding particle position is That is, the historical optimal position of the i-th particle applied in the k-th update.

9. The explosion-proof induction motor control method for emulsion pump station liquid supply according to claim 8, characterized in that: The operation sequence of the particle swarm algorithm is: After each particle swarm optimization algorithm reaches the maximum number of iterations, the optimal PI value is output for use by the adaptive law, the particle swarm optimization algorithm is terminated, and then the next particle swarm optimization algorithm is performed. In the initial population of the next optimization, the initial position of the i-th particle is the optimal position of the i-th particle in the previous optimization, and the optimal value of the initial historical position of all particles is the optimal value of the historical position of all particles in the previous optimization. This process is repeated during the operation of the system.

10. An explosion-proof induction motor control device for emulsion pump station liquid supply, characterized in that: The device includes a hydraulic stability control module, a high-performance speed estimation module and an induction motor vector control module, wherein: The hydraulic stability control module is used to determine the given speed of the motor, and includes an initialization module, a signal measurement and filtering module, a fault detection and alarm module, and a hysteresis control module; wherein, An initialization module is used to measure the initial unloading valve emulsion flow value and the hydraulic pressure value for opening and closing the unloading valve under the corresponding unloading valve opening state at each speed through initialization parameters; The signal measurement and filtering module is used to measure the supply pressure and the emulsion flow value of the unloading valve in real time, and to construct a third-order Butterworth filter to filter the measured supply pressure and emulsion flow signals of the unloading valve; Fault detection and alarm module, used to monitor the actual unloading valve emulsion flow value when the unloading valve is open in real time and compare it with the initial unloading valve emulsion flow value, and issue an unloading valve fault alarm when the difference is large; The hysteresis control module is used to obtain the motor set speed according to the real-time hydraulic pressure and the emulsion flow of the unloading valve; The high-performance speed estimation module is used to estimate the motor feedback speed, including a speed identification system discretization mathematical model module, a particle swarm algorithm optimization module and a fitness function calculation module, wherein: The speed identification system discretization mathematical model module is used to establish the speed identification system mathematical model based on the full-order adaptive observer, obtain the motor feedback speed, and construct the discretization model using the second-order Runge-Kutta method; The particle swarm algorithm optimization module and fitness function calculation module are used to build a particle swarm algorithm model and fitness function for optimizing the parameters of the adaptive law, and to perform multi-objective optimization on the PI parameters in the speed adaptive law; The induction motor vector control module is used to perform double closed-loop control on the induction motor according to the motor given speed and feedback speed.

11. An explosion-proof induction motor control system for emulsion pump station supply, characterized in that: The system includes the explosion-proof induction motor control device for emulsion pump station liquid supply as described in claim 10.

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