A control method for a traction asynchronous motor

Abstract

The present invention discloses a control method for a traction asynchronous motor, which includes monitoring the signal data and operation data of the traction asynchronous motor, performing data preprocessing, comprehensively analyzing and evaluating the operation performance of the traction asynchronous motor, constructing an objective function for the optimal performance of the motor and outputting and implementing a motor regulation scheme. By monitoring the traction asynchronous motor and performing data preprocessing and comprehensive analysis, the present invention first preliminarily evaluates the electrical quality and operation risk status of the motor, then deeply evaluates the operation performance of the traction asynchronous motor, and realizes comprehensive and timely diagnosis and early warning of the operation performance state of the motor in actual application from local to whole, ensuring the comprehensiveness of data processing and the accuracy of early warning prompts, thereby guaranteeing the operation safety of the motor, and then outputting a motor regulation scheme. According to the actual application state of the motor and its performance attenuation characteristics, it adaptively tends to the optimal operation performance of the traction asynchronous motor, ensuring the efficient, green and reliable operation of the motor system.

Description

Technical Field

[0001] The present invention relates to the technical field of motor control, and in particular, to a control method for a traction asynchronous motor. Background Art

[0002] The asynchronous motor is one of the most widely used types of electric motors in industrial production. It has a simple structure and low maintenance costs. However, due to various possible fault problems during its operation, it poses a threat to the stability and continuity of industrial production. When the traction motor is the main component for locomotive traction and braking, its safe and reliable operation is equally crucial;

[0003] Existing traction asynchronous motors may have abnormal fault problems in actual application scenarios. Due to the lack of comprehensiveness in motor operation monitoring and processing and the inaccuracy of early warning prompts, it is difficult to conduct timely fault risk regulation and management, which will lead to a decline in the operating safety of the motor. Moreover, the performance of the motor will continue to decay under long-term use, further resulting in a poor operating state of the motor, unable to achieve the optimal operating performance of the traction asynchronous motor, causing an increase in the consumption of motor operating resources and usage costs, and unable to meet the energy-saving and reliability requirements of actual production applications;

[0004] In view of the above technical deficiencies, a solution is now proposed. Summary of the Invention

[0005] The purpose of the present invention is to solve the technical deficiencies in the prior art, where the comprehensiveness of traction asynchronous motor faults and the accuracy of diagnostic prompts are insufficient, resulting in a decline in the operating safety of the motor and a poor operating state, causing an increase in the consumption of motor operating resources and usage costs, and being unable to meet the requirements of actual production. The present invention realizes comprehensive and timely diagnosis and early warning of the actual application operating performance state of the motor from local to overall, ensuring the comprehensiveness of data processing and the accuracy of early warning prompts, thereby guaranteeing the operating safety of the motor, and then adaptively tending towards the optimal operating performance of the traction asynchronous motor, ensuring the efficient, green, and reliable operation of the motor system.

[0006] In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0007] A control method for a traction asynchronous motor, comprising the following steps:

[0008] Step 1, monitoring the signal data and operating data of the traction asynchronous motor: The signal data includes power supply signal parameters, stator signal parameters, and rotor signal parameters; the operating data includes temperature parameters, vibration parameters, and torque parameters;

[0009] Step 2, preprocess the signal data and operation data of the traction asynchronous motor: By preprocessing the signal data, evaluate the electrical quality of the traction asynchronous motor; By preprocessing the operation data, analyze the vibration risk level, temperature risk level and traction load performance of the traction asynchronous motor, so as to monitor the operation status of the traction asynchronous motor;

[0010] Step 3, comprehensively analyze and evaluate the operation performance of the traction asynchronous motor: By combining the electrical quality and traction load performance of the traction asynchronous motor, comprehensively evaluate the operation performance of the traction asynchronous motor; Then, by combining the vibration risk level and temperature risk level of the traction asynchronous motor, evaluate the comprehensive influence degree of the motor performance;

[0011] Step 4, construct the objective function of the optimal motor performance and output the motor control scheme: By establishing the correlation curve between the motor operation performance index and the motor performance influence index, and generating the objective function of the optimal motor performance, so as to generate and output the corresponding control scheme for the traction asynchronous motor;

[0012] Step 5, receive the control scheme of the traction asynchronous motor and perform corresponding processing.

[0013] Furthermore, the specific process of preprocessing the signal data and operation data of the traction asynchronous motor is as follows:

[0014] A1, by preprocessing the signal data, evaluate the electrical quality of the traction asynchronous motor;

[0015] The signal data includes power supply signal parameters, stator signal parameters and rotor signal parameters. Through signal processing of the power supply signal parameters, stator signal parameters and rotor signal parameters, obtain the power supply electrical coefficient, stator electrical coefficient and rotor electrical coefficient, analyze the module structure electrical characteristics of the power supply module, stator module and rotor module of the traction asynchronous motor, and then through the power supply electrical coefficient, stator electrical coefficient and rotor electrical coefficient, integrate and generate the electrical quality index to comprehensively evaluate the electrical quality of the traction asynchronous motor;

[0016] A2, by preprocessing the operation data, analyze the vibration risk level, temperature risk level and traction load performance of the traction asynchronous motor, so as to monitor the operation status of the traction asynchronous motor;

[0017] The operation data includes temperature parameters, vibration parameters and torque parameters. Through data processing of the temperature parameters, vibration parameters and torque parameters, obtain the temperature risk coefficient, vibration risk coefficient and traction load coefficient, analyze the temperature risk level, vibration risk level and traction load performance of the traction asynchronous motor, so as to comprehensively evaluate the operation status of the traction asynchronous motor.

[0018] Further, the specific process of comprehensively evaluating the operating performance of the traction asynchronous motor is as follows:

[0019] B1. By combining the electrical quality and traction load performance of the traction asynchronous motor and conducting a timing analysis, the electrical quality index and the traction load coefficient are combined to obtain the motor operating performance index, and the operating performance of the traction asynchronous motor is comprehensively evaluated.

[0020] B2. By analyzing the vibration risk level and temperature risk level of the traction asynchronous motor, the vibration risk coefficient and the temperature risk coefficient are combined to generate the motor performance impact index, and the comprehensive impact degree of the motor performance is evaluated.

[0021] Further, the specific process of preprocessing the signal data is as follows:

[0022] The signal data includes power supply signal parameters, stator signal parameters, and rotor signal parameters.

[0023] A1-1. The power supply signal parameters include the power supply voltage Uem and the power supply current Iem; the stator signal parameters include the stator voltage Ust and the stator current Ist; the rotor signal parameters include the rotor electrical angular velocity ωrt and the rotor current Irt.

[0024] A1-2. A parameter processing model is constructed to analyze and process the power supply signal parameters, stator signal parameters, and rotor signal parameters. The specific process of the parameter processing model is as follows:

[0025] The input signal parameter G is input into the parameter processing model. The signal parameter includes n0 data indicators. Any one of the data indicators is marked as g, and the data value of the data indicator g is marked as Mg.

[0026] The standard interval of the data indicator g is set as [Qg1, Qg2]. When the data value Mg of the data indicator g is within the standard interval, it indicates that the data indicator g is normal; otherwise, it indicates that the data indicator g is abnormal.

[0027] The deviation coefficient δg of the data indicator g is output: ;

[0028] When the deviation coefficient δg is greater than 0, it is determined that the data indicator g is abnormal; otherwise, it is determined that the data indicator g is normal.

[0029] The parameter processing model outputs the deviation coefficients corresponding to the n0 data indicators of the signal parameter G.

[0030] A1-3. The standard interval of the power supply signal parameters is set, and the power supply signal parameters and their standard intervals are substituted into the parameter processing model to obtain the deviation coefficients of the power supply voltage Uem and the power supply current Iem, which are respectively marked as the power supply voltage deviation coefficient δuem and the power supply current deviation coefficient δiem.

[0031] By combining the power supply voltage deviation coefficient δuem, the power supply current deviation coefficient δiem, the power supply voltage Uem, and the power supply current Iem(t), the power supply electrical coefficient EMe is obtained;

[0032] A1-4. Set the standard range of the stator signal parameters, substitute the stator signal parameters and their standard range into the parameter processing model, obtain the deviation coefficients of the stator voltage Ust and the stator current Ist, and mark them as the stator voltage deviation coefficient δust and the stator current deviation coefficient δist respectively;

[0033] By combining the stator voltage deviation coefficient δust, the stator current deviation coefficient δist, the stator voltage Ust, and the stator current Ist(t), the stator electrical coefficient STe is obtained;

[0034] A1-5. Set the standard range of the rotor signal parameters, substitute the rotor signal parameters and their standard range into the parameter processing model, obtain the deviation coefficients of the rotor electrical angular velocity ωrt and the rotor current Irt, and mark them as the rotor speed deviation coefficient δωrt and the rotor current deviation coefficient δirt respectively;

[0035] By combining the rotor speed deviation coefficient δωrt, the rotor current deviation coefficient δirt, the rotor electrical angular velocity ωrt, and the rotor current Irt, the rotor electrical coefficient RTe is obtained;

[0036] A1-6. Furthermore, by combining the power supply electrical coefficient EMe, the stator electrical coefficient STe, and the rotor electrical coefficient RTe, the electrical quality index EQ is generated.

[0037] Furthermore, the specific process of preprocessing the operation data is as follows:

[0038] A2-1. The temperature parameters include the power supply module temperature Wem, the stator module temperature Wst, and the rotor module temperature Wrt; the vibration parameters include the vibration speed value Vzd, the vibration amplitude value Azd, and the vibration frequency value Fzd; the torque parameters include the starting torque Τstart, the maximum torque Τmax, and the slip torque Τslip;

[0039] A2-2. Through the power supply module temperature Wem, the stator module temperature Wst, and the rotor module temperature Wrt for data processing, obtain the temperature risk coefficient Ψw, evaluate the temperature risk degree of the traction asynchronous motor, and generate the corresponding temperature risk prompt signal;

[0040] A2-3. Through the vibration parameters for data processing, set the standard ranges of the vibration speed value Vzd, the vibration amplitude value Azd, and the vibration frequency value Fzd respectively, and substitute them into the parameter processing model, output the deviation coefficients of the corresponding data indicators, and mark them as the vibration speed deviation value , vibration amplitude deviation value , vibration frequency deviation value , and then comprehensively obtain the vibration risk coefficient Ψzd, evaluate the vibration risk degree of the traction asynchronous motor, and generate the corresponding vibration risk prompt signal;

[0041] A2-4, through the starting torque Τstart, maximum torque Τmax and slip torque Τslip for data processing, obtain the traction load coefficient Ψτ, evaluate the traction load performance of the traction asynchronous motor, and generate the corresponding load performance prompt signal;

[0042] A2-5, integrate the temperature risk prompt signal, vibration risk prompt signal and load performance prompt signal into an operation prompt signal group, so as to monitor the operation state of the traction asynchronous motor.

[0043] Furthermore, the specific process of comprehensively evaluating the operation performance of the traction asynchronous motor is as follows:

[0044] B1-1, through the electrical quality and traction load performance of the traction asynchronous motor and combined with time series analysis, combine the electrical quality index EQ and the traction load coefficient Ψτ, set the data measurement period Ts to measure the electrical quality index EQ and the traction load coefficient Ψτ regularly, extract the electrical quality index EQ and the traction load coefficient Ψτ corresponding to n1 data measurement periods Ts, and construct the historical matrix H;

[0045] B1-101, establish a matrix analysis model, obtain the vertical vector fluctuation coefficient σx by calculating the variance of the vertical vector of the historical matrix H; and calculate the average value of the difference between adjacent vertical vectors to obtain the vertical vector growth average coefficient Kx; then calculate the standard deviation of the difference between adjacent vertical vectors to obtain the vertical vector growth change coefficient σk; furthermore, combine the vertical vector fluctuation coefficient σx, the vertical vector growth average coefficient Kx and the vertical vector growth change coefficient σk to obtain the evaluation index φx of the xth group of vertical vectors;

[0046] B1-102, mark any electrical quality index EQ as , and substitute it into the matrix analysis model, output the corresponding evaluation index and mark it as the electrical quality evaluation index φ1;

[0047] B1-103, mark any traction load coefficient Ψτ as , and substitute it into the matrix analysis model, output the corresponding evaluation index and mark it as the traction load evaluation index φ2;

[0048] By combining the electrical quality evaluation index φ1 and the traction load evaluation index φ2, the motor operation performance index RUNem is obtained, the operation performance of the traction asynchronous motor is evaluated, and the corresponding motor performance prompt signal is generated.

[0049] Further, the specific process of evaluating the comprehensive influence degree of the motor performance is as follows:

[0050] B2. By analyzing the vibration risk degree and temperature risk degree of the traction asynchronous motor, combining the vibration risk coefficient Ψzd and the temperature risk coefficient Ψw, generating the motor performance influence index INFem, evaluating the comprehensive influence degree of the motor performance risk, and generating the corresponding motor performance risk signal.

[0051] Further, the specific process of generating the objective function of the optimal motor performance and obtaining the regulation scheme of the traction asynchronous motor is as follows:

[0052] By establishing the correlation curve S0 between the motor operation performance index RUNem and the motor performance influence index INFem, and fitting the correlation curve S0, generating the objective function Fopt of the optimal motor performance;

[0053] By constructing a dynamic curve through the objective function Fopt and extracting the maximum value Fmax of the objective function Fopt, taking the signal data corresponding to the maximum value Fmax of the objective function Fopt as the optimal performance state parameters of the traction asynchronous motor, thereby generating and outputting the corresponding regulation scheme of the traction asynchronous motor.

[0054] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are as follows:

[0055] The present invention monitors the signal data and operation data of the traction asynchronous motor, preprocesses and comprehensively analyzes the data, initially evaluates the electrical quality and operation risk state of the motor, then deeply evaluates the operation performance of the traction asynchronous motor, and comprehensively and timely diagnoses and warns the actual application operation performance state of the motor from local to overall, ensuring the comprehensiveness of data processing and the accuracy of warning prompts, thereby ensuring the operation safety of the motor, and further constructing the objective function of the optimal motor performance and outputting the motor regulation scheme for processing, so as to dynamically respond according to the actual application state of the motor and its performance attenuation characteristics, adaptively tend to the optimal operation performance of the traction asynchronous motor, and ensure the efficient, green and reliable operation of the motor system. Description of the Drawings

[0056] Figure 1 Shows the step schematic diagram of the overall scheme flow of the present invention;

[0057] Figure 2 Shows the flow schematic diagram of the data processing of the present invention. Detailed Embodiments

[0058] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0059] Embodiment 1:

[0060] As Figure 1 - Figure 2 shown, a traction asynchronous motor control method includes the following steps:

[0061] S1, monitor the signal data and operation data of the traction asynchronous motor: the signal data includes power supply signal parameters, stator signal parameters, and rotor signal parameters; the operation data includes temperature parameters, vibration parameters, and torque parameters;

[0062] S2, preprocess the signal data and operation data of the traction asynchronous motor: by preprocessing the signal data, the electrical quality of the traction asynchronous motor is evaluated; by preprocessing the operation data, the vibration risk degree, temperature risk degree, and traction load performance of the traction asynchronous motor are analyzed, so as to monitor the operation state of the traction asynchronous motor. The specific process is as follows:

[0063] A1, evaluate the electrical quality of the traction asynchronous motor by preprocessing the signal data;

[0064] The signal data includes power supply signal parameters, stator signal parameters, and rotor signal parameters. Through signal processing of the power supply signal parameters, stator signal parameters, and rotor signal parameters, power supply electrical coefficients, stator electrical coefficients, and rotor electrical coefficients are obtained, and the module structure electrical characteristics of the power supply module, stator module, and rotor module of the traction asynchronous motor are analyzed. Then, through the power supply electrical coefficient, stator electrical coefficient, and rotor electrical coefficient, an electrical quality index is integrated and generated to comprehensively evaluate the electrical quality of the traction asynchronous motor;

[0065] A1-1, the power supply signal parameters include power supply voltage Uem and power supply current Iem; the stator signal parameters include stator voltage Ust and stator current Ist; the rotor signal parameters include rotor electrical angular velocity ωrt and rotor current Irt; the signal data is collected through existing Hall sensors and power meters;

[0066] A1-2, construct a parameter processing model to analyze and process the power supply signal parameters, stator signal parameters, and rotor signal parameters. The specific process of the parameter processing model is as follows:

[0067] Input the signal parameter G into the parameter processing model. The signal parameter includes n0 data indicators. Mark any one of the data indicators as g, and mark the data value of the data indicator g as Mg;

[0068] Set the standard interval of the data indicator g as [Qg1, Qg2]. When the data value Mg of the data indicator g is within the standard interval, it indicates that the data indicator g is normal; otherwise, it indicates that the data indicator g is abnormal;

[0069] Output the deviation coefficient δg of the data indicator g: ;

[0070] When the deviation coefficient δg is greater than 0, it is determined that the data indicator g is abnormal; otherwise, it is determined that the data indicator g is normal;

[0071] The parameter processing model outputs the deviation coefficients corresponding to the n0 data indicators of the signal parameter G;

[0072] A1 - 3, set the standard interval of the power supply signal parameter, substitute the power supply signal parameter and its standard interval into the parameter processing model, obtain the deviation coefficients of the power supply voltage Uem and the power supply current Iem, and mark them as the power supply voltage deviation coefficient δuem and the power supply current deviation coefficient δiem respectively;

[0073] By combining the power supply voltage deviation coefficient δuem, the power supply current deviation coefficient δiem, the power supply voltage Uem, and the power supply current Iem(t), obtain the power supply electrical coefficient EMe:

[0074] ;

[0075] where t is the current time node, refers to the change rate of the power supply current Iem, is the inductance evaluation coefficient of the power supply module. α0, α1, and α2 are the weight coefficients of the power supply voltage Uem, the power supply voltage deviation coefficient δuem, and the power supply current deviation coefficient δiem respectively, and α0, α1, and α2 are all greater than 0. The weight coefficients are preset after being calculated through a large amount of experimental data. The specific preset values are combined with the actual parameters of the traction asynchronous motor and only need to meet the corresponding associated influence proportional relationship; when is higher, and the power supply voltage deviation coefficient δuem and the power supply current deviation coefficient δiem are lower, then the power supply electrical coefficient EMe is higher, indicating that the structural inductance of the power supply module is lower, indicating that the current delay time constant is lower, indicating that the power supply module responds faster, and the deviation degree of the power supply voltage Uem and the power supply current Iem is lower, indicating that the abnormal degree of the power supply signal parameter is lower. Furthermore, the comprehensive evaluation of the electrical characteristic state of the power supply module is better;

[0076] A1-4. Set the standard range of the stator signal parameters, substitute the stator signal parameters and their standard range into the parameter processing model to obtain the deviation coefficients of the stator voltage Ust and the stator current Ist, and mark them as the stator voltage deviation coefficient δust and the stator current deviation coefficient δist respectively;

[0077] Combine the stator voltage deviation coefficient δust, the stator current deviation coefficient δist, the stator voltage Ust and the stator current Ist(t) to obtain the stator electrical coefficient STe:

[0078] ;

[0079] Among them, is the rate of change of the stator current Ist, is the evaluation formula for the stator module inductance, and β0, β1 and β2 are the weight coefficients of the stator voltage Ust, the stator voltage deviation coefficient δust and the stator current deviation coefficient δist respectively, and β0, β1 and β2 are all greater than 0; when is higher and the stator voltage deviation coefficient δust and the stator current deviation coefficient δist are lower, the stator electrical coefficient STe is higher, and the comprehensive evaluation of the electrical characteristic state of the stator module is better;

[0080] A1-5. Set the standard range of the rotor signal parameters, substitute the rotor signal parameters and their standard range into the parameter processing model to obtain the deviation coefficients of the rotor electrical angular velocity ωrt and the rotor current Irt, and mark them as the rotor speed deviation coefficient δωrt and the rotor current deviation coefficient δirt respectively;

[0081] Combine the rotor speed deviation coefficient δωrt, the rotor current deviation coefficient δirt, the rotor electrical angular velocity ωrt and the rotor current Irt to obtain the rotor electrical coefficient RTe:

[0082] ;

[0083] Among them, is the rate of change of the rotor current Irt. When is higher, it means that the delay time of the rotor current Irt within a certain time length is lower, indicating that the rotor module structure responds faster and has better performance. γ0, γ1 and γ2 are the weight coefficients of the rotor electrical angular velocity ωrt, the rotor speed deviation coefficient δωrt and the rotor current deviation coefficient δirt respectively, and γ0, γ1 and γ2 are all greater than 0. When the rotor electrical angular velocity ωrt is higher and the rotor speed deviation coefficient δωrt and the rotor current deviation coefficient δirt are lower, the rotor electrical coefficient RTe is higher, and the comprehensive evaluation of the electrical characteristic state of the rotor module is better;

[0084] A1 - 6, and then through the combination of the power supply electrical coefficient EMe, the stator electrical coefficient STe, and the rotor electrical coefficient RTe, an electrical quality index EQ is generated: ;

[0085] Among them, ω1, ω2, and ω3 are the conversion coefficients of the power supply electrical coefficient EMe, the stator electrical coefficient STe, and the rotor electrical coefficient RTe respectively. The conversion coefficients are preset after being calculated through a large amount of data, and the preset ω1, ω2, and ω3 are all greater than 1; the higher the power supply electrical coefficient EMe, the stator electrical coefficient STe, and the rotor electrical coefficient RTe, the higher the electrical quality index EQ, indicating that the electrical characteristics of the module structures of the power supply module, the stator module, and the rotor module of the traction asynchronous motor are better, and thus the electrical quality of the traction asynchronous motor is comprehensively evaluated to be better;

[0086] A2, by preprocessing the operation data, analyzing the vibration risk degree, temperature risk degree, and traction load performance of the traction asynchronous motor, so as to monitor the operation state of the traction asynchronous motor;

[0087] The operation data includes temperature parameters, vibration parameters, and torque parameters. Through data processing of the temperature parameters, vibration parameters, and torque parameters, a temperature risk coefficient, a vibration risk coefficient, and a traction load coefficient are obtained, and the temperature risk degree, vibration risk degree, and traction load performance of the traction asynchronous motor are analyzed, so as to comprehensively evaluate the operation state of the traction asynchronous motor;

[0088] A2 - 1, the temperature parameters include the power supply module temperature Wem, the stator module temperature Wst, and the rotor module temperature Wrt;

[0089] The vibration parameters include the vibration speed value Vzd, the vibration amplitude value Azd, and the vibration frequency value Fzd;

[0090] The torque parameters include the starting torque Τstart, the maximum torque Τmax, and the slip torque Τslip;

[0091] Among them, the vibration parameters are comprehensively evaluated by collecting the vibration conditions of any point inside the motor structure. The starting torque is the torque generated by the motor during the starting process, usually greater than the rated torque of the motor, which helps the motor start smoothly; the maximum torque is the maximum torque that the motor can reach when the slip is zero; the slip torque is the torque generated by the motor during operation;

[0092] A2 - 2, through data processing of the power supply module temperature Wem, the stator module temperature Wst, and the rotor module temperature Wrt, a temperature risk coefficient Ψw is obtained:

[0093] ;

[0094] Among them, refers to the average value of the power module temperature Wem, the stator module temperature Wst, and the rotor module temperature Wrt. When the average value is higher, and the standard deviation among the power module temperature Wem, the stator module temperature Wst, and the rotor module temperature Wrt is higher, it indicates that the overall temperature of the motor is high and the temperature difference between structures is large. High temperature and uneven temperature distribution will cause rapid loss of the motor's life during use, resulting in poor motor performance;

[0095] Set the evaluation interval of the temperature risk coefficient Ψw, evaluate the temperature risk degree of the traction asynchronous motor through interval comparison, and generate the corresponding temperature risk prompt signal;

[0096] A2-3. Perform data processing through vibration parameters. Respectively set the standard intervals of the vibration speed value Vzd, the vibration amplitude value Azd, and the vibration frequency value Fzd, and substitute them into the parameter processing model to output the deviation coefficients of the corresponding data indicators, and mark them as the vibration speed deviation value , the vibration amplitude deviation value , and the vibration frequency deviation value respectively. Then comprehensively obtain the vibration risk coefficient Ψzd:

[0097] ;

[0098] Among them, η1, η2, and η3 are the proportionality coefficients of the vibration speed deviation value , the vibration amplitude deviation value , and the vibration frequency deviation value respectively, and η1, η2, and η3 are all greater than 0. When the vibration speed deviation value , the vibration amplitude deviation value , and the vibration frequency deviation value are higher, the vibration risk coefficient Ψzd is higher, indicating that the degree of deviation of each data indicator of the vibration parameter from the preset standard interval is higher, and the evaluation of the vibration risk degree of the traction asynchronous motor is higher;

[0099] Set the evaluation interval of the vibration risk coefficient Ψzd, evaluate the vibration risk degree of the traction asynchronous motor through interval comparison, and generate the corresponding vibration risk prompt signal;

[0100] A2-4. Perform data processing through the starting torque Τstart, the maximum torque Τmax, and the slip torque Τslip to obtain the traction load coefficient Ψτ:

[0101] ;

[0102] Among them, ε1, ε2, and ε3 are the logarithms of the starting torque Τstart, the maximum torque Τmax, and the slip torque Τslip respectively, and it is preset that ε1, ε2, and ε3 are all greater than 1. When the starting torque Τstart, the maximum torque Τmax, and the slip torque Τslip are higher, the traction load coefficient Ψτ is higher, indicating that the comprehensive traction ability of the motor is better, and the evaluation and analysis of the traction load performance of the traction asynchronous motor are better;

[0103] Set the evaluation interval of the traction load coefficient Ψτ, evaluate and analyze the traction load performance of the traction asynchronous motor through interval comparison, and generate a corresponding load performance prompt signal;

[0104] A2-5, integrate the temperature risk prompt signal, the vibration risk prompt signal, and the load performance prompt signal into an operation prompt signal group, so as to monitor the operation state of the traction asynchronous motor;

[0105] S3, comprehensively analyze and evaluate the operation performance of the traction asynchronous motor: combine the electrical quality and the traction load performance of the traction asynchronous motor to comprehensively evaluate the operation performance of the traction asynchronous motor; then combine the vibration risk degree and the temperature risk degree of the traction asynchronous motor to evaluate the comprehensive influence degree of the motor performance. The specific process is as follows:

[0106] B1, through the electrical quality and the traction load performance of the traction asynchronous motor and combined with time series analysis, combine the electrical quality index and the traction load coefficient to obtain the motor operation performance index, and comprehensively evaluate the operation performance of the traction asynchronous motor. The specific process is as follows:

[0107] B1-1, through the electrical quality and the traction load performance of the traction asynchronous motor and combined with time series analysis, combine the electrical quality index EQ and the traction load coefficient Ψτ, set the data measurement period Ts to measure the electrical quality index EQ and the traction load coefficient Ψτ regularly, extract the electrical quality index EQ and the traction load coefficient Ψτ corresponding to n1 data measurement periods Ts, and construct the historical matrix H:

[0108] ;

[0109] Among them, the two column vectors of the historical matrix H represent the electrical quality index EQ and the traction load coefficient Ψτ respectively, and the n1 row vectors of the historical matrix H represent n1 data measurement periods Ts;

[0110] B1-101, establish a matrix analysis model, calculate the variance through the column vectors of the historical matrix H, and obtain the column vector fluctuation coefficient σx: , where, is any value of the xth group of column vectors, is the average value of the n1 data of the xth group of column vectors;

[0111] Furthermore, calculate the average value of the difference in adjacent vertical vectors to obtain the average vertical vector growth coefficient Kx:

[0112] , where is the adjacent value of the x-th group of vertical vectors;

[0113] Then, calculate the standard deviation of the difference in adjacent vertical vectors to obtain the vertical vector growth variation coefficient σk:

[0114] ;

[0115] Furthermore, combine the vertical vector fluctuation coefficient σx, the average vertical vector growth coefficient Kx, and the vertical vector growth variation coefficient σk to obtain the evaluation index φx of the x-th group of vertical vectors:

[0116] , where refers to the data value corresponding to the last data measurement period Ts of the x-th group of vertical vectors. p1 and p2 are the weight indices of the vertical vector fluctuation coefficient σx and the vertical vector growth variation coefficient σk, respectively. It is preset that both p1 and p2 are greater than 0 and p1 + p2 = 1. When the vertical vector fluctuation coefficient σx and the vertical vector growth variation coefficient σk are lower, it indicates that the historical data stability of the x-th group of vertical vectors is better; when and the average vertical vector growth coefficient Kx are higher, it indicates that the growth trend of the x-th group of vertical vectors is more obvious. Furthermore, when the evaluation index φx of the x-th group of vertical vectors is higher, it indicates that the evaluation state of the x-th group of vertical vectors is better;

[0117] The matrix analysis model outputs the evaluation index φx of the x-th group of vertical vectors;

[0118] B1-102: Mark any electrical quality index EQ as , and substitute the first group of vertical vectors into the matrix analysis model, output the corresponding evaluation index and mark it as the electrical quality evaluation index φ1;

[0119] B1-103: Mark any traction load coefficient Ψτ as , and substitute the second group of vertical vectors into the matrix analysis model, output the corresponding evaluation index and mark it as the traction load evaluation index φ2;

[0120] B1-2, combine the electrical quality evaluation index φ1 and the traction load evaluation index φ2 to obtain the motor operating performance index RUNem: ;

[0121] When the electrical quality evaluation index φ1 and the traction load evaluation index φ2 are higher, the motor operation performance index RUNem is higher, and the comprehensive evaluation of the operation performance of the traction asynchronous motor is better;

[0122] Set the evaluation interval of the motor operation performance index RUNem, evaluate the operation performance of the traction asynchronous motor through interval comparison, and generate corresponding motor performance prompt signals;

[0123] B2. By analyzing the vibration risk degree and temperature risk degree of the traction asynchronous motor, combining the vibration risk coefficient and the temperature risk coefficient, generate a motor performance influence index, and evaluate the comprehensive influence degree of the motor performance. The specific process is as follows:

[0124] By analyzing the vibration risk degree and temperature risk degree of the traction asynchronous motor, combine the vibration risk coefficient Ψzd and the temperature risk coefficient Ψw to generate a motor performance influence index INFem:

[0125] ;

[0126] Among them, q1 and q2 are the weight indexes of the vibration risk coefficient Ψzd and the temperature risk coefficient Ψw respectively. It is preset that both q1 and q2 are greater than 0 and q1 + q2 = 1. When the vibration risk coefficient Ψzd and the temperature risk coefficient Ψw are higher, the motor performance influence index INFem is higher, and the comprehensive influence degree of evaluating the motor performance risk is higher;

[0127] Set the evaluation interval of the motor performance influence index INFem, evaluate the comprehensive influence degree of the motor performance risk through interval comparison, and generate corresponding motor performance risk signals;

[0128] It is preset that there are Y evaluation intervals of the motor performance influence index INFem. Mark any one of the evaluation intervals as Qy. When the motor performance influence index INFem is within the evaluation interval Qy, the comprehensive influence degree of evaluating the motor performance risk is Ny level, and an Ny-level motor performance risk signal is generated;

[0129] S4. Construct the objective function of the optimal motor performance and output the motor control scheme: By establishing the correlation curve between the motor operation performance index and the motor performance influence index, and generating the objective function of the optimal motor performance, so as to generate and output the corresponding control scheme of the traction asynchronous motor. The specific process is as follows:

[0130] By establishing the correlation curve S0 between the motor operation performance index RUNem and the motor performance influence index INFem, and through mathematical model fitting of the correlation curve S0, generate the objective function Fopt of the optimal motor performance: , when the motor operation performance index RUNem is higher and the motor performance influence index INFem is lower, the objective function Fopt is higher;

[0131] Construct a dynamic curve through the objective function Fopt and extract the maximum value Fmax of the objective function Fopt. Take the signal data corresponding to the maximum value Fmax of the objective function Fopt as the optimal performance state parameters of the traction asynchronous motor, and integrate the optimal performance state parameters into the regulation target of the traction asynchronous motor, so as to generate and output the corresponding traction asynchronous motor regulation scheme;

[0132] S5. Receive the traction asynchronous motor regulation scheme and perform corresponding processing:

[0133] By receiving the traction asynchronous motor regulation scheme and performing corresponding processing, the power signal parameters, stator signal parameters, and rotor signal parameters of the signal data are dynamically controlled to continuously approach the regulation target of the traction asynchronous motor. Moreover, the performance of the traction asynchronous motor in the load application scenario will decay over time, and the objective function Fopt is in the process of timed measurement and dynamic development. Therefore, the maximum value Fmax will be continuously refreshed, so as to adapt to the actual application state of the traction asynchronous motor and its performance decay characteristics, and adaptively seek the optimal operation performance under the existing state of the motor, reduce the energy consumption cost of the motor, and ensure the efficient, green, and reliable operation of the motor system.

[0134] In summary, the present invention monitors the signal data and operation data of the traction asynchronous motor, preprocesses and comprehensively analyzes the data, initially evaluates the electrical quality and operation risk state of the motor, then deeply evaluates the operation performance of the traction asynchronous motor, and realizes a comprehensive and timely diagnosis and early warning of the actual application operation performance state of the motor from the local to the whole, ensuring the comprehensiveness of data processing and the accuracy of early warning prompts, thereby guaranteeing the operation safety of the motor. Furthermore, an objective function for the optimal performance of the motor is constructed, and a motor regulation scheme is output for processing, so as to adaptively tend to the optimal operation performance of the traction asynchronous motor according to the actual application state of the motor and its performance decay characteristics, and ensure the efficient, green, and reliable operation of the motor system.

[0135] The setting of the interval and the size of the threshold is for the convenience of comparison. Regarding the size of the threshold, it depends on the amount of sample data and the base quantity set by those skilled in the art for each group of sample data; as long as it does not affect the proportional relationship between the parameters and the quantified values.

[0136] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation;

[0137] The above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes should be covered within the protection scope of the present invention.

Claims

1. A traction asynchronous motor control method, characterized in that: The following steps are involved: Step 1: monitor the signal data and operation data of the traction asynchronous motor: the signal data includes power signal parameters, stator signal parameters and rotor signal parameters; the operation data includes temperature parameters, vibration parameters and torque parameters; Step 2, preprocessing the signal data and operation data of the traction asynchronous motor: by preprocessing the signal data, wherein the power signal parameters include the power supply voltage Uem and the power supply current Iem; the stator signal parameters include the stator voltage Ust and the stator current Ist; the rotor signal parameters include the rotor electrical angular velocity ωrt and the rotor current Irt; by analyzing and processing the power signal parameters, the stator signal parameters and the rotor signal parameters, the power supply electrical coefficient EMe, the stator electrical coefficient STe and the rotor electrical coefficient RTe are obtained in turn, and then the power supply electrical coefficient EMe, the stator electrical coefficient STe and the rotor electrical coefficient RTe are combined to generate the electrical quality index EQ, so as to evaluate the electrical quality of the traction asynchronous motor; by preprocessing the operation data, the vibration risk degree, the temperature risk degree and the traction load performance of the traction asynchronous motor are analyzed, so as to monitor the operation status of the traction asynchronous motor; Step 3: Comprehensively analyze and evaluate the operating performance of the traction asynchronous motor: comprehensively evaluate the operating performance of the traction asynchronous motor by combining the electrical quality of the traction asynchronous motor and the traction load performance; and then evaluate the comprehensive impact of the motor performance by combining the vibration risk level and temperature risk level of the traction asynchronous motor; Step 4: construct the objective function of the motor's optimal performance and output the motor control plan: by establishing a correlation curve between the motor's operating performance index and the motor's performance impact index, and generating the objective function of the motor's optimal performance, the corresponding traction asynchronous motor control plan is generated and output; Step 5, receiving the traction asynchronous motor control plan and performing corresponding processing; The specific process of preprocessing signal data is as follows: The signal data includes power signal parameters, stator signal parameters and rotor signal parameters; A1-1, power signal parameters include power supply voltage Uem and power supply current Iem; stator signal parameters include stator voltage Ust and stator current Ist; rotor signal parameters include rotor electrical angular velocity ωrt and rotor current Irt; A1-2, construct a parameter processing model to analyze and process the power signal parameters, stator signal parameters and rotor signal parameters. The specific process of the parameter processing model is as follows: Input signal parameter G to the parameter processing model, the signal parameter includes n0 data indicators, any data indicator is marked as g, and the data value of data indicator g is marked as Mg; The standard interval of the data indicator g is set to [Qg1, Qg2]. When the data value Mg of the data indicator g is within the standard interval, it means that the data indicator g is normal; otherwise, it means that the data indicator g is abnormal. Deviation coefficient δg of output data index g: ; When the deviation coefficient δg is greater than 0, the data index g is judged to be abnormal; otherwise, the data index g is judged to be normal; Deviation coefficients corresponding to n0 data indicators of the output signal parameter G of the parameter processing model; A1-3, set the standard range of the power signal parameter, substitute the power signal parameter and its standard range into the parameter processing model, obtain the deviation coefficients of the power voltage Uem and the power current Iem, and mark them as the power voltage deviation coefficient δuem and the power current deviation coefficient δiem respectively; The power supply electrical coefficient EMe is obtained by combining the power supply voltage deviation coefficient δuem, the power supply current deviation coefficient δiem, the power supply voltage Uem and the power supply current Iem (t): ； Among them, t is the current time node, It refers to the rate of change of the power supply current Iem. is the inductance evaluation coefficient of the power module, α0, α1 and α2 are the weight coefficients of the power supply voltage Uem, the power supply voltage deviation coefficient δuem and the power supply current deviation coefficient δiem respectively, and α0, α1 and α2 are all greater than 0. The weight coefficients are preset and obtained after calculating a large amount of experimental data; A1-4, set the standard interval of the stator signal parameters, substitute the stator signal parameters and their standard intervals into the parameter processing model, obtain the deviation coefficients of the stator voltage Ust and the stator current Ist, and mark them as the stator voltage deviation coefficient δust and the stator current deviation coefficient δist respectively; The stator electrical coefficient STe is obtained by combining the stator voltage deviation coefficient δust, the stator current deviation coefficient δist, the stator voltage Ust and the stator current Ist (t): ； in, is the rate of change of the sub-current Ist, is the stator module inductance evaluation formula, β0, β1 and β2 are the weight coefficients of the stator voltage Ust, the stator voltage deviation coefficient δust and the stator current deviation coefficient δist, respectively, and β0, β1 and β2 are all greater than 0; A1-5, set the standard interval of the rotor signal parameter, substitute the rotor signal parameter and its standard interval into the parameter processing model, obtain the deviation coefficient of the rotor electrical angular velocity ωrt and the rotor current Irt, and mark them as the rotor speed deviation coefficient δωrt and the rotor current deviation coefficient δirt respectively; The rotor electrical coefficient RTe is obtained by combining the rotor speed deviation coefficient δωrt, the rotor current deviation coefficient δirt, the rotor electrical angular velocity ωrt and the rotor current Irt: ； in, It refers to the rate of change of the rotor current Irt, γ0, γ1 and γ2 are the weight coefficients of the rotor electrical angular velocity ωrt, the rotor speed deviation coefficient δωrt and the rotor current deviation coefficient δirt, respectively, and γ0, γ1 and γ2 are all greater than 0; A1-6, and then generate the electrical quality index EQ by combining the power supply electrical coefficient EMe, stator electrical coefficient STe and rotor electrical coefficient RTe: ; Among them, ω1, ω2 and ω3 are conversion coefficients of the power supply electrical coefficient EMe, the stator electrical coefficient STe and the rotor electrical coefficient RTe respectively. The conversion coefficients are preset and obtained after a large amount of data calculation, and the preset ω1, ω2 and ω3 are all greater than 1; The electrical quality of the traction asynchronous motor is evaluated by the electrical quality index EQ; The specific process of preprocessing the running data is as follows: A2-1, temperature parameters include power module temperature Wem, stator module temperature Wst and rotor module temperature Wrt; vibration parameters include vibration speed value Vzd, vibration amplitude value Azd and vibration frequency value Fzd; torque parameters include starting torque Τstart, maximum torque Τmax and slip torque Tslip; A2-2, through the power module temperature Wem, stator module temperature Wst and rotor module temperature Wrt data processing, obtain the temperature risk coefficient Ψw: ； in, It refers to the average value of the power module temperature Wem, the stator module temperature Wst and the rotor module temperature Wrt; Set the evaluation interval of the temperature risk coefficient Ψw, evaluate the temperature risk level of the traction asynchronous motor by comparing the intervals, and generate a corresponding temperature risk warning signal; A2-3, perform data processing through vibration parameters, set the standard intervals of vibration velocity value Vzd, vibration amplitude value Azd and vibration frequency value Fzd respectively, substitute them into the parameter processing model, output the deviation coefficient of the corresponding data index, and mark them as vibration velocity deviation value respectively , vibration amplitude deviation value , vibration frequency deviation value , and then comprehensively obtain the vibration risk coefficient Ψzd: ; Among them, η1, η2 and η3 are vibration velocity deviation values ​​respectively , vibration amplitude deviation value And vibration frequency deviation The proportionality coefficient is , and η1, η2 and η3 are all preset to be greater than 0; Set the evaluation interval of the vibration risk coefficient Ψzd, evaluate the vibration risk level of the traction asynchronous motor by comparing the intervals, and generate a corresponding vibration risk warning signal; A2-4, through the starting torque Τstart, maximum torque Τmax and slip torque Tslip data processing, obtain the traction load coefficient Ψτ: ; Wherein, ε1, ε2 and ε3 are the logarithmic bases of the starting torque Tstart, the maximum torque Tmax and the slip torque Tslip, respectively, and ε1, ε2 and ε3 are all preset to be greater than 1; An evaluation interval of the traction load coefficient Ψτ is set, the traction load performance of the traction asynchronous motor is evaluated by interval comparison, and a corresponding load performance prompt signal is generated; A2-5, integrates the temperature risk warning signal, vibration risk warning signal and load performance warning signal into an operation warning signal group, so as to monitor the operation status of the traction asynchronous motor; The specific process of comprehensively evaluating the operating performance of traction asynchronous motors is as follows: B1-1, through the electrical quality and traction load performance of the traction asynchronous motor and combined with timing analysis, the electrical quality index EQ and the traction load coefficient Ψτ are combined, and the data calculation period Ts is set to perform regular calculations on the electrical quality index EQ and the traction load coefficient Ψτ, and the electrical quality index EQ and the traction load coefficient Ψτ corresponding to n1 data calculation periods Ts are extracted to construct the history matrix H; B1-101, establish a matrix analysis model, calculate the variance of the longitudinal quantity of the historical matrix H, and obtain the longitudinal quantity fluctuation coefficient σx; and calculate the average value of the adjacent longitudinal quantity difference to obtain the longitudinal quantity growth average coefficient Kx; then calculate the standard deviation of the adjacent longitudinal quantity difference to obtain the longitudinal quantity growth change coefficient σk; and then combine the longitudinal quantity fluctuation coefficient σx, the longitudinal quantity growth average coefficient Kx and the longitudinal quantity growth change coefficient σk to obtain the evaluation index φx of the xth group of longitudinal quantities: ,in, It refers to the data value corresponding to the last data measurement period Ts of the x-th group of longitudinal quantities. p1 and p2 are the weight indexes of the longitudinal quantity fluctuation coefficient σx and the longitudinal quantity growth change coefficient σk, respectively. It is preset that p1 and p2 are both greater than 0 and p1+p2=1; B1-102, mark any electrical quality index EQ as , and substitute it into the matrix analysis model, output the corresponding evaluation index and mark it as the electrical quality evaluation index φ1; B1-103, mark any traction load factor Ψτ as , and substitute it into the matrix analysis model, output the corresponding evaluation index and mark it as the traction load evaluation index φ2; The motor running performance index RUNem is obtained by combining the electrical quality evaluation index φ1 and the traction load evaluation index φ2: ; The running performance of the traction asynchronous motor is evaluated through the motor running performance index RUNem, and a corresponding motor performance prompt signal is generated; The specific process of evaluating the comprehensive impact of motor performance is: B2, by analyzing the vibration risk level and temperature risk level of the traction asynchronous motor, the vibration risk coefficient Ψzd and the temperature risk coefficient Ψw are combined to generate the motor performance impact index INFem: ; Wherein, q1 and q2 are weight indexes of vibration risk coefficient Ψzd and temperature risk coefficient Ψw, respectively. It is preset that q1 and q2 are both greater than 0 and q1+q2=1; The motor performance impact index INFem is used to evaluate the comprehensive impact of motor performance risks and generate corresponding motor performance risk signals.

2. A traction asynchronous motor control method according to claim 1, characterized in that: The specific process of preprocessing the signal data and operation data of the traction asynchronous motor is as follows: A1, by preprocessing the signal data, the electrical quality of the traction asynchronous motor is evaluated; The signal data includes power signal parameters, stator signal parameters and rotor signal parameters. Signal processing is performed through the power signal parameters, stator signal parameters and rotor signal parameters to obtain the power electrical coefficient, stator electrical coefficient and rotor electrical coefficient, analyze the module structural electrical characteristics of the power module, stator module and rotor module of the traction asynchronous motor, and then integrate and generate the electrical quality index through the power electrical coefficient, stator electrical coefficient and rotor electrical coefficient to comprehensively evaluate the electrical quality of the traction asynchronous motor; A2, by pre-processing the operating data, analyzing the vibration risk level, temperature risk level and traction load performance of the traction asynchronous motor, so as to monitor the operating status of the traction asynchronous motor; The operating data includes temperature parameters, vibration parameters and torque parameters. The temperature risk coefficient, vibration risk coefficient and traction load coefficient are obtained through data processing of the temperature parameters, vibration parameters and torque parameters. The temperature risk degree, vibration risk degree and traction load performance of the traction asynchronous motor are analyzed, so as to comprehensively evaluate the operating status of the traction asynchronous motor.

3. A traction asynchronous motor control method according to claim 2, characterized in that: The specific process of comprehensively evaluating the operating performance of traction asynchronous motors is as follows: B1, through the electrical quality and traction load performance of the traction asynchronous motor and combined with timing analysis, the electrical quality index and traction load factor are combined to obtain the motor operation performance index, and the operation performance of the traction asynchronous motor is comprehensively evaluated; B2, by analyzing the vibration risk level and temperature risk level of the traction asynchronous motor, the vibration risk coefficient and the temperature risk coefficient are combined to generate a motor performance impact index to evaluate the comprehensive impact level of the motor performance.

4. A traction asynchronous motor control method according to claim 3, characterized in that: The specific process of generating the objective function of the motor's optimal performance and obtaining the traction asynchronous motor control scheme is as follows: By establishing a correlation curve S0 between the motor running performance index RUNem and the motor performance influence index INFem, and fitting the correlation curve S0, the objective function Fopt of the motor optimal performance is generated; A dynamic curve is constructed through the objective function Fopt and the maximum value Fmax of the objective function Fopt is extracted. The signal data corresponding to the maximum value Fmax of the objective function Fopt is used as the optimal performance state parameter of the traction asynchronous motor, thereby generating and outputting the corresponding traction asynchronous motor control scheme.

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