CN118137926A - Frequency modulation control method for three-phase asynchronous motor based on electromechanical signal analysis - Google Patents
Frequency modulation control method for three-phase asynchronous motor based on electromechanical signal analysis Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P27/00—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage
- H02P27/04—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage
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Abstract
The invention discloses a frequency modulation control method of a three-phase asynchronous motor based on electromechanical signal analysis, which relates to the technical field of motor control and comprises the following steps: acquiring electric power data of a target three-phase asynchronous motor through an electric power internet of things, and acquiring electric power basic data; generating a first analog signal set, wherein the first analog signal set is obtained by performing analog operation of a target three-phase asynchronous motor based on electric power basic data and collecting a plurality of operation electromechanical signals in the analog operation process; synchronizing the first analog signal set to a fault prediction evaluation model to perform fault prediction and fault evaluation, and obtaining a fault prediction evaluation result; updating the plurality of operation electromechanical signals according to the fault prediction evaluation result to generate a second analog signal set; and performing frequency modulation control on the target three-phase asynchronous motor according to the second analog signal set. The invention solves the technical problems of insufficient accuracy and efficiency of frequency modulation control in the prior art, and achieves the technical effect of improving the accuracy and efficiency of frequency modulation control.
Description
Technical Field
The invention relates to the technical field of motor control, in particular to a frequency modulation control method of a three-phase asynchronous motor based on electromechanical signal analysis.
Background
In the running process of the motor, the three-phase asynchronous motor is easy to fail due to various reasons such as overload, overheat, electrical faults and the like. These faults can lead to reduced motor performance, equipment damage and even downtime, which can have serious consequences for production and life. Currently, fault detection and prediction for three-phase asynchronous motors relies mainly on periodic maintenance and inspection. However, these methods generally require manual operations, observe and record the operation state of the motor, and then make a failure judgment empirically. Due to subjectivity and errors of manual operation, fault judgment is inaccurate. If the fault determination is inaccurate, the frequency modulation control may deviate from the correct adjustment direction, resulting in unstable motor operation or other problems. Therefore, the prior art has the technical problems of insufficient accuracy and efficiency of frequency modulation control.
Disclosure of Invention
The application effectively solves the technical problems of insufficient accuracy and efficiency of frequency modulation control in the prior art by providing the frequency modulation control method of the three-phase asynchronous motor based on electromechanical signal analysis, and achieves the technical effect of improving the accuracy and efficiency of frequency modulation control.
The application provides a frequency modulation control method of a three-phase asynchronous motor based on electromechanical signal analysis, which comprises the following steps:
In a first aspect, an embodiment of the present application provides a frequency modulation control method for a three-phase asynchronous motor based on electromechanical signal analysis, where the method is applied to a frequency modulation control system for a three-phase asynchronous motor, and the frequency modulation control system for a three-phase asynchronous motor is connected to an electric power internet of things in a communication manner, and the method includes:
acquiring electric power data of a target three-phase asynchronous motor through the electric power Internet of things, and acquiring electric power basic data;
Generating a first analog signal set, wherein the first analog signal set is obtained by performing analog operation of a target three-phase asynchronous motor based on the electric power basic data and collecting a plurality of operation electromechanical signals in the analog operation process;
Synchronizing the first analog signal set to a fault prediction evaluation model to predict and evaluate faults of the target three-phase asynchronous motor, and obtaining a fault prediction evaluation result;
Updating the plurality of operation electromechanical signals according to the fault prediction evaluation result to generate a second simulation signal set;
and performing frequency modulation control on the target three-phase asynchronous motor according to the second analog signal set.
In a second aspect, an embodiment of the present application provides a frequency modulation control system for a three-phase asynchronous motor based on electromechanical signal analysis, the system comprising:
The power basic data acquisition module is used for acquiring power data of the target three-phase asynchronous motor through the power internet of things and acquiring power basic data;
The first analog signal set generation module is used for generating a first analog signal set, wherein the first analog signal set is obtained by performing analog operation of a target three-phase asynchronous motor based on the electric power basic data and collecting a plurality of operation electromechanical signals in the analog operation process;
the fault prediction evaluation result acquisition module is used for synchronizing the first analog signal set to a fault prediction evaluation model to perform fault prediction and fault evaluation on the target three-phase asynchronous motor, so as to acquire a fault prediction evaluation result;
The second analog signal set generation module is used for updating the plurality of operation electromechanical signals according to the fault prediction evaluation result to generate a second analog signal set;
and the frequency modulation control module is used for performing frequency modulation control on the target three-phase asynchronous motor according to the second analog signal set.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
According to the method, electric power data of a target three-phase asynchronous motor are acquired through an electric power internet of things, electric power basic data are acquired, simulation operation of the target three-phase asynchronous motor is carried out based on the electric power basic data, a plurality of operation electromechanical signals are acquired in the simulation operation process, a first simulation signal set is generated, the first simulation signal set is synchronized to a fault prediction evaluation model to carry out fault prediction and fault evaluation on the target three-phase asynchronous motor, a fault prediction evaluation result is acquired, the plurality of operation electromechanical signals are updated according to the fault prediction evaluation result, a second simulation signal set is generated, and finally frequency modulation control is carried out on the target three-phase asynchronous motor according to the second simulation signal set. The technical problems of insufficient accuracy and efficiency of frequency modulation control in the prior art are effectively solved, and the technical effect of improving the accuracy and efficiency of frequency modulation control is achieved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a frequency modulation control method of a three-phase asynchronous motor based on electromechanical signal analysis according to an embodiment of the present application;
FIG. 2 is a flowchart of a method for generating an operation indication signal according to an embodiment of the present application;
FIG. 3 is a flowchart of a method for obtaining a failure prediction evaluation result according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a frequency modulation control system of a three-phase asynchronous motor based on electromechanical signal analysis according to an embodiment of the present application.
Reference numerals illustrate: the system comprises an electric power basic data acquisition module 1, a first analog signal set generation module 2, a fault prediction evaluation result acquisition module 3, a second analog signal set generation module 4 and a frequency modulation control module 5.
Detailed Description
The application provides a frequency modulation control method of a three-phase asynchronous motor based on electromechanical signal analysis, which is used for solving the technical problems of accuracy and efficiency deficiency of frequency modulation control in the prior art.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that the terms "comprises" and "comprising," along with any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus, but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
As shown in fig. 1, the invention provides a frequency modulation control method of a three-phase asynchronous motor based on electromechanical signal analysis, the method is applied to a frequency modulation control system of the three-phase asynchronous motor, the frequency modulation control system of the three-phase asynchronous motor is in communication connection with an electric power internet of things, the method is used for improving accuracy and efficiency of frequency modulation control, and the method comprises the following steps:
The electric power Internet of things is connected, and is a network for transmitting, measuring, controlling and monitoring data of an electric power system, so that remote monitoring and management of electric power equipment can be realized. Acquiring power data of a target three-phase asynchronous motor through the power internet of things, wherein the target three-phase asynchronous motor is a motor needing fault prediction and frequency modulation control, and the risk of the motor on faults is increased due to factors such as load change and voltage fluctuation of the motor, so that the frequency modulation control is needed to be carried out on the target three-phase asynchronous motor, so that the occurrence of the faults is reduced, and the stability of the motor is improved; the power data includes voltage, current, power, phase difference, etc., which constitute power base data.
After the electric power basic data are acquired, the operation of the target three-phase asynchronous motor is simulated by using motor simulation software, the electric power basic data such as voltage, current, frequency and the like are input in the simulation operation, and then the operation state and performance of the motor are observed. At the same time, during the simulation operation, a plurality of operation electromechanical signals including the rotation speed, the torque, the temperature, the vibration and the like of the motor are collected by using the sensor and the measuring equipment, and the operation electromechanical signals are recorded to generate the first simulation signal set.
The first set of analog signals is input into a fault prediction assessment model, which is a software or algorithm specific for the prediction and assessment of faults of a three-phase asynchronous motor, is a model based on machine learning and data analysis, and can predict future behaviour and possible faults of the motor (the fault prediction) and assess the risk and severity of these faults (the fault assessment) based on the input set of analog signals. After the first analog signal set is synchronized to the fault prediction evaluation model, the model analyzes various analog signals, recognizes abnormal behaviors of the motor, predicts possible faults of the motor based on the abnormal behaviors, and outputs fault prediction evaluation results, wherein the fault prediction evaluation results comprise information such as types (power failure, short circuit and the like), positions, severity, risk levels and the like of potential faults.
And analyzing the failure prediction evaluation result output by the model to find out the operation electromechanical signals related to the potential failure, namely determining which operation electromechanical signals cause the failure of the target three-phase asynchronous motor. And updating the operation electromechanical signals related to the faults according to the analysis results, including adjusting the values, the ranges or the trends of the signals, and the like. The updated operating electromechanical signals are combined to form a new signal set, the second analog signal set, which reflects the presence and impact of the potential fault.
And analyzing the second analog signal set to determine the running state and performance of the motor, wherein the running state and performance of the motor comprise parameters such as the rotating speed, the torque and the temperature of the motor are evaluated. And (3) formulating a corresponding frequency modulation control strategy according to an analysis result, wherein the frequency modulation control strategy comprises operations of adjusting the frequency of a power supply, changing the rotating speed of a motor, adjusting a load and the like, so that the motor is ensured to normally operate, and meanwhile, potential faults are avoided or the influence of the faults is reduced. And performing frequency modulation control on the target three-phase asynchronous motor according to the formulated frequency modulation control strategy, wherein the frequency modulation control comprises the step of adjusting the power frequency of the motor through a frequency converter or other control equipment so as to realize stable operation and performance optimization of the motor. After the frequency modulation control is implemented, the running state and performance of the motor are monitored in real time, if a new abnormality or potential fault is found, the second analog signal set is re-analyzed and the frequency modulation control strategy is adjusted, and the process can be repeatedly performed to ensure that the motor runs in the optimal state and avoid the occurrence of the fault. According to the embodiment of the application, the fault occurrence area of the motor is accurately positioned through the analog signal set and the fault prediction evaluation model, and the abnormal area is subjected to frequency modulation control in a targeted manner based on the fault occurrence area, so that the technical effect of improving the efficiency and accuracy of frequency modulation control is achieved.
In a preferred implementation manner provided by the embodiment of the present application, the method for acquiring power basic data by acquiring power data of a target three-phase asynchronous motor through the power internet of things includes:
According to the characteristics and monitoring requirements of the target three-phase asynchronous motor, a plurality of wireless sensors are selected, wherein the wireless sensors consist of sensors and wireless communication modules, and the wireless sensors are connected with the electric power Internet of things through a wireless communication technology, so that measurement data can be transmitted to a network in real time. The wireless sensor comprises a current sensor, a voltage sensor and a power grid frequency sensor. According to the structure and the running environment of the motor, the node positions of the sensors are determined by combining the factors such as the precision, the stability and the reliability of each sensor, so that the corresponding sensors can accurately monitor the relevant parameters of the motor at each sensing node.
And based on the wireless sensors installed at the plurality of node positions, acquiring current data in a circuit through the current sensor, acquiring voltage data in the circuit through the voltage sensor, and acquiring frequency data of a power grid through the power grid frequency sensor. And carrying out fusion processing on data acquired by different sensing nodes through collaborative sensing algorithms such as Kalman filtering, particle filtering and the like so as to generate more accurate and comprehensive sensing information. And extracting current parameter information, voltage parameter information and power grid frequency parameter information from the fused data, wherein the parameter information reflects the running state and performance of the target three-phase asynchronous motor.
And adding the current parameter information, the voltage parameter information and the grid frequency parameter information to power basic data. The current parameter information comprises the current magnitude, direction, waveform and the like of the motor, and reflects the running state and load condition of the motor; the voltage parameter information comprises the voltage magnitude, waveform, phase and the like of the motor, and reflects the voltage fluctuation and stability of the motor; the power grid frequency parameter information comprises the frequency of the power grid and the fluctuation condition, and reflects the running state and the stability of the power grid. These parameter information are used to provide comprehensive data support for fault prediction and frequency modulation control. According to the preferred embodiment, through data fusion and complementation of the plurality of sensors, measurement errors are reduced, and therefore the technical effects of improving accuracy and comprehensiveness of data acquisition are achieved.
In another preferred implementation manner provided by the embodiment of the present application, the method includes:
And extracting measured values of current, voltage and grid frequency from the electric power basic data, and generating corresponding operation indication signals including a start signal, a stop signal, a speed regulation signal, a protection signal and the like according to the extracted current, voltage and grid frequency parameters and in combination with the operation characteristics and control requirements of the motor, wherein the operation indication signals are used for guiding the operation and control of the motor.
And using motor simulation software or a simulator to simulate the operation of the target three-phase asynchronous motor according to the operation indication signal. In the simulation operation process, a plurality of operation electromechanical signals are collected through the measurement function of the simulator. Wherein the plurality of operating electromechanical signals includes a feedback signal, a temperature signal, a pressure signal, and a trigger signal. The feedback signal is used for monitoring the operation information of the three-phase asynchronous motor and feeding back the operation information to the control system; the temperature signal is used for monitoring the temperature of the three-phase asynchronous motor; the pressure signal is used for monitoring the pressure of the three-phase asynchronous motor; the trigger signal is a trigger point for a particular action or operation, and when certain conditions are met, the trigger signal is generated and communicated to a control system or other device.
The method comprises the steps of adjusting the rotating speed of a target three-phase asynchronous motor according to a feedback signal, a temperature signal, a pressure signal and a trigger signal, and specifically judging the running state and the load condition of the motor by analyzing the numerical value and the change trend of the feedback signal; by monitoring the temperature signal, the overload or overheat condition of the motor is judged according to the change condition of the temperature; judging the pressure state and the load condition of the motor according to the value and the change trend of the pressure signal by evaluating the pressure signal; and judging the starting and stopping states of the motor according to the numerical value and the change trend of the trigger signal by analyzing the trigger signal. Based on the analysis result, parameters to be adjusted, such as rotation speed, current, voltage, etc., are determined. The adjustment parameters are combined with the operating electromechanical signals to generate a first set of analog signals reflecting the operating state and adjustment of the motor. According to the preferred embodiment, the first analog signal set is determined through the adjustment parameters, the actual running state of the motor is reflected by the obtained running electromechanical signals more accurately after the adjustment parameters, and the signals are input into the fault prediction model, so that a more accurate and more reliable fault prediction result can be provided, and the technical effect of improving the accuracy of fault prediction is achieved.
As shown in fig. 2, in another preferred implementation manner provided by the embodiment of the present application, an operation indication signal is generated based on the current parameter information, the voltage parameter information, and the grid frequency parameter, and the method includes:
Judging whether the running speed of the target three-phase asynchronous motor meets a preset speed interval or not based on current parameter information, voltage parameter information and power grid frequency parameter respectively, and specifically, analyzing the current magnitude, direction and waveform of the motor for the current parameter information so as to judge the load condition and running state of the motor; for the voltage parameter information, analyzing the voltage, waveform and phase of the motor to judge the output power and operation stability of the motor; and analyzing the frequency and fluctuation conditions of the power grid for the power grid frequency parameters so as to judge the stability of the power grid and the influence on the operation of the motor, and according to the analysis result, combining the design parameters and the operation characteristics of the motor to obtain the operation speed information of the motor.
Judging whether the running speed of the motor is in a preset speed interval, wherein the preset speed interval refers to a preset speed range according to design parameters, running characteristics and application requirements of the motor, and is used for ensuring normal running and performance of the motor and avoiding faults or abnormal conditions caused by overhigh or overlow speed. If the running speed of the motor exceeds a preset speed interval, overheat, overload or accelerated wear of the motor can be caused, so that the service life of the motor is shortened; if the running speed of the motor is lower than the preset speed interval, the motor efficiency is low, the output power is insufficient or the application requirement cannot be met.
If the running speed of the motor does not meet the preset speed interval, a stop indication signal is generated, and the stop indication signal is used for controlling the motor to stop running, so that potential faults or problems are avoided. And after the stop indication signal is generated, stopping the target three-phase asynchronous motor through corresponding control equipment or a corresponding system. After the motor is shut down, abnormal parameter information including current, voltage and/or grid frequency is retrieved, the abnormal condition of the motor occurs in the running process is reflected by the parameter information, and the current parameter information, the voltage parameter information and/or the grid frequency parameter are updated according to the retrieved abnormal parameter information, for example, if the current parameter information of the motor shows abnormal condition, the abnormal condition may be caused by overload or underload of the motor, and in this case, the voltage and/or the current parameter of the motor can be adjusted to reduce the load of the motor and enable the motor to resume normal running.
And after updating the abnormal parameter information, performing secondary judgment, namely, based on the updated parameter information, re-evaluating whether the running speed of the motor meets a preset speed interval. And if the running speed of the motor meets the preset speed interval, generating a running indicating signal, wherein the running indicating signal is used for guiding the normal running of the motor and ensuring the running of the motor under safe and stable conditions. After the operation indication signal is generated, starting operation is carried out on the target three-phase asynchronous motor through corresponding control equipment or a corresponding system, and the operation state of the target three-phase asynchronous motor is prompted through corresponding display equipment or a corresponding system. According to the preferred embodiment, through judgment and adjustment based on current, voltage and power grid frequency parameters, stable operation of the motor in a preset speed interval is ensured, potential faults caused by overspeed or underspeed are avoided, and therefore the technical effect of improving the stability and safety of operation of the motor is achieved.
As shown in fig. 3, in another preferred implementation manner provided by the embodiment of the present application, the first analog signal set is synchronized to a fault prediction evaluation model to perform fault prediction and fault evaluation on the target three-phase asynchronous motor, so as to obtain a fault prediction evaluation result, where the method includes:
A plurality of analog signal records are collected from analog operation or historical data, including signals of voltage, temperature, etc. of the motor. Fault prediction records corresponding to the analog signal record data are collected, including known fault types, locations, severity, etc.
The method comprises the steps of taking a plurality of analog signal record data as input training data, training a fault prediction evaluation model, specifically, firstly cleaning, normalizing and normalizing the analog signal record data to enable the analog signal record data to be suitable for inputting of a neural network, then constructing a BP neural network with a plurality of hidden layers, wherein the number of nodes of the input layer corresponds to the characteristic number of the analog signal record data, the number of nodes of the output layer corresponds to the dimension of a fault prediction result, further randomly initializing the weight and bias parameters of the neural network, then using a training data set, and iteratively updating the parameters of the neural network through a back propagation algorithm to enable the network to gradually learn the mapping relation from the analog signal to the fault prediction.
And taking a part of fault prediction record data as a verification data set for evaluating the performance of the model, inputting an analog signal of the verification data set into a trained neural network to obtain a fault prediction result, and then comparing the fault prediction result with an actual fault record to calculate a fault prediction evaluation error parameter such as Mean Square Error (MSE), accuracy and the like.
According to the size and the change trend of the error parameters, super parameters such as the learning rate, the batch size and the like of the neural network are adjusted, the training and verification processes are repeated, and the parameters of the neural network are updated continuously. When the error parameter of the verification data set meets the preset requirement (for example, reaches the preset threshold or does not obviously drop any more) in the iteration of the continuous preset times, the model is considered to be converged.
And carrying out preprocessing operation which is the same as that of training data on the first analog signal set, inputting the processed first analog signal set into a converged neural network, and acquiring a fault prediction evaluation result from an output layer of the neural network, wherein the fault prediction evaluation result comprises information such as potential fault type, position, severity and the like. According to the preferred embodiment, the BP neural network is adopted to construct a fault prediction evaluation model, and the strong learning and training capacity of the BP neural network is utilized to capture the complex nonlinear relation between the analog signal and the fault, so that the technical effect of improving the fault prediction accuracy is achieved.
In another preferred implementation manner provided by the embodiment of the present application, the updating of the plurality of operation electromechanical signals according to the failure prediction evaluation result generates a second analog signal set, and the method includes:
The fault prediction evaluation result is a multi-dimensional data structure, and comprises information such as fault type, position, severity and the like. The failure prediction evaluation result is decomposed to generate a failure information set containing a plurality of failure location information describing a specific location where the failure occurs, for example, which part or component has a failure condition, and a plurality of failure cause information describing a cause of the failure, for example, failure due to overheat, overload, or other factors, etc.
The relevance evaluation is carried out on the positioning information and the cause information of each fault through methods such as similarity calculation, causal relation analysis and the like, namely the relevance between the fault positioning information and the fault cause information is determined, in other words, the positions where a specific fault cause can cause faults are evaluated, for example, the faults of the coils and windings of the motor, the bearings and bearing chambers, the cooling system and the like can be caused by overheating. Based on the correlation evaluation result, a fault correlation factor is generated, which describes the strength of the relationship between each fault cause and each fault location.
And weighting the plurality of operation electromechanical signals according to the fault correlation factors, wherein the weights are distributed based on the values of the correlation factors, namely, signals with higher correlation degree with a certain fault can obtain higher weights, and signals with lower correlation degree with a certain fault can obtain lower weights. And marking or classifying each operation electromechanical signal according to the result of the weighted calculation, updating the operation electromechanical signals according to the result and the identification of the weighted calculation, wherein the updating mode comprises adding, reducing or modifying the numerical value of the signals, and the like, and combining the updated operation electromechanical signals to generate a second simulation signal set, wherein the signal set reflects the state of the electromechanical signals considering the fault prediction evaluation result. According to the preferred embodiment, the relation between the operation electromechanical signal and the fault is reflected more accurately through the weighted calculation based on the fault correlation factor, so that the signal can be updated more accurately, and the technical effect of improving the accuracy and reliability of signal updating is achieved.
In another preferred implementation manner provided by the embodiment of the present application, the method includes:
And analyzing the second analog signal set to determine which areas or time periods have abnormal running states of the motor, namely possible faults, and positioning fault related signals in the second analog signal set by using a fault positioning algorithm, such as a signal processing-based method, a pattern recognition technology and the like. By analyzing these signals, the approximate area where the fault occurred is determined. For example, comparing the amplitude, waveform, frequency, etc. of a signal with a signal in a normal state, if the amplitude, frequency, or waveform of a certain signal is abnormal, this may mean that the area has a fault.
After the fault occurrence areas are determined, the frequencies of the areas are monitored, whether frequency modulation abnormality exists or not is detected by analyzing parameters such as the rotating speed, the current and the voltage of the motor, when the frequency modulation abnormality is detected, corresponding deviation frequency data including information such as the deviation frequency, duration time and amplitude are collected, the collected deviation frequency data are organized into files, and the frequency modulation abnormality condition of the fault occurrence areas is recorded.
Traversing the generated deviation frequency data file, analyzing the abnormal frequency modulation condition of the fault occurrence area, and formulating a corresponding deviation correction control strategy according to the analysis result of the deviation frequency data file, for example, correcting the abnormal frequency modulation by adjusting the controller parameters of the motor, changing the power supply frequency and the like. And loading the target three-phase asynchronous motor to execute operation according to the deviation rectification control strategy, and monitoring the operation state of the motor in real time in the execution process to ensure the effectiveness of deviation rectification control. According to the preferred embodiment, the fault occurrence area is positioned through the second analog signal set, so that the accuracy and reliability of fault positioning are improved, frequency modulation control is performed on an abnormal area in a targeted manner, and the technical effect of improving the efficiency and accuracy of frequency modulation control is achieved.
Example two
Based on the same inventive concept as the frequency modulation control method of the three-phase asynchronous motor based on the electromechanical signal analysis in the foregoing embodiment, as shown in fig. 4, the present application provides a frequency modulation control system of the three-phase asynchronous motor based on the electromechanical signal analysis, the system comprising:
The power basic data acquisition module 1 is used for acquiring power data of a target three-phase asynchronous motor through the power internet of things and acquiring power basic data;
The first analog signal set generation module 2 is used for generating a first analog signal set, wherein the first analog signal set is obtained by performing analog operation of the target three-phase asynchronous motor based on the electric power basic data and collecting a plurality of operation electromechanical signals in the analog operation process;
The fault prediction evaluation result acquisition module 3 is used for synchronizing the first analog signal set to a fault prediction evaluation model to perform fault prediction and fault evaluation on the target three-phase asynchronous motor, so as to acquire a fault prediction evaluation result;
The second analog signal set generating module 4 is configured to update the plurality of operation electromechanical signals according to the fault prediction evaluation result, and generate a second analog signal set;
and the frequency modulation control module 5 is used for performing frequency modulation control on the target three-phase asynchronous motor according to the second analog signal set.
Further, the power base data acquisition module 1 is configured to perform the following method:
setting a plurality of sensing nodes for a target three-phase asynchronous motor by using a wireless sensor through the electric power Internet of things;
based on the plurality of sensing nodes, performing cooperative sensing to generate a plurality of sensing information, wherein the plurality of sensing information comprises current parameter information, voltage parameter information and power grid frequency parameter information;
and adding the current parameter information, the voltage parameter information and the grid frequency parameter information to power basic data.
Further, the first analog signal set generating module 2 is configured to perform the following method:
generating an operation indication signal based on the current parameter information, the voltage parameter information and the power grid frequency parameter;
Performing simulation operation on the target three-phase asynchronous motor according to the operation indication signal, and simultaneously collecting a plurality of operation electromechanical signals, wherein the plurality of operation electromechanical signals comprise feedback signals, temperature signals, pressure signals and trigger signals;
and adjusting the rotating speed of the target three-phase asynchronous motor according to the feedback signal, the temperature signal, the pressure signal and the trigger signal, and determining the first analog signal set according to a plurality of adjustment parameters.
Further, the first analog signal set generating module 2 is configured to perform the following method:
judging whether the running speed of the target three-phase asynchronous motor meets a preset speed interval or not based on the current parameter information, the voltage parameter information and the power grid frequency parameter respectively;
if not, generating a stop indication signal, controlling the target three-phase asynchronous motor to stop running according to the stop indication signal, simultaneously searching abnormal parameter information, updating the current parameter information and/or the voltage parameter information and/or the power grid frequency parameter, and performing secondary judgment;
if yes, generating an operation indication signal to prompt the operation state of the target three-phase asynchronous motor.
Further, the failure prediction evaluation result obtaining module 3 is configured to execute the following method:
acquiring a plurality of analog signal record data and fault prediction record data;
Taking the plurality of analog signal record data as input training data to train the fault prediction evaluation model;
Performing accuracy evaluation on the output result of the fault prediction evaluation model through the fault prediction record data to obtain a fault prediction evaluation error parameter;
Performing iterative training on the fault prediction evaluation model according to the fault prediction evaluation error parameters, and considering that the fault prediction evaluation model converges when the fault prediction evaluation error parameters of continuous preset times meet preset requirements;
And inputting the first analog signal set into the converged fault prediction evaluation model to obtain the fault prediction evaluation result.
Further, the second analog signal set generating module 4 is configured to perform the following method:
Decomposing the fault prediction evaluation result to generate a fault information set, wherein the fault information set comprises a plurality of fault positioning information and a plurality of fault cause information;
performing fault relevance assessment on the plurality of fault locating information and the plurality of fault cause information to generate a fault relevance factor;
and carrying out weighted calculation on the plurality of operation electromechanical signals according to the fault correlation factor, and identifying the plurality of operation electromechanical signals to update according to a calculation result to generate the second simulation signal set.
Further, the frequency modulation control module 5 is configured to perform the following method:
Positioning a fault occurrence area of the target three-phase asynchronous motor through the second analog signal set;
performing frequency modulation abnormality monitoring based on the fault occurrence area to generate a deviation frequency data file;
And traversing the deviation frequency data file to carry out frequency modulation deviation correction control, and loading the target three-phase asynchronous motor according to deviation correction control data to execute operation.
It should be noted that the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the application. In some cases, the acts or steps recited in the present application may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.
The specification and figures are merely exemplary illustrations of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, the present application is intended to include such modifications and alterations insofar as they come within the scope of the application or the equivalents thereof.
Claims (8)
1. The frequency modulation control method for the three-phase asynchronous motor based on the electromechanical signal analysis is characterized by being applied to a frequency modulation control system of the three-phase asynchronous motor based on the electromechanical signal analysis, wherein the frequency modulation control system of the three-phase asynchronous motor is in communication connection with an electric power internet of things, and the method comprises the following steps:
acquiring electric power data of a target three-phase asynchronous motor through the electric power Internet of things, and acquiring electric power basic data;
Generating a first analog signal set, wherein the first analog signal set is obtained by performing analog operation of a target three-phase asynchronous motor based on the electric power basic data and collecting a plurality of operation electromechanical signals in the analog operation process;
Synchronizing the first analog signal set to a fault prediction evaluation model to predict and evaluate faults of the target three-phase asynchronous motor, and obtaining a fault prediction evaluation result;
Updating the plurality of operation electromechanical signals according to the fault prediction evaluation result to generate a second simulation signal set;
and performing frequency modulation control on the target three-phase asynchronous motor according to the second analog signal set.
2. The method of claim 1, wherein the power data of the target three-phase asynchronous motor is collected through the power internet of things, and power basic data is obtained, the method comprising:
setting a plurality of sensing nodes for a target three-phase asynchronous motor by using a wireless sensor through the electric power Internet of things;
based on the plurality of sensing nodes, performing cooperative sensing to generate a plurality of sensing information, wherein the plurality of sensing information comprises current parameter information, voltage parameter information and power grid frequency parameter information;
and adding the current parameter information, the voltage parameter information and the grid frequency parameter information to power basic data.
3. The method of claim 2, wherein the method comprises:
generating an operation indication signal based on the current parameter information, the voltage parameter information and the power grid frequency parameter;
Performing simulation operation on the target three-phase asynchronous motor according to the operation indication signal, and simultaneously collecting a plurality of operation electromechanical signals, wherein the plurality of operation electromechanical signals comprise feedback signals, temperature signals, pressure signals and trigger signals;
and adjusting the rotating speed of the target three-phase asynchronous motor according to the feedback signal, the temperature signal, the pressure signal and the trigger signal, and determining the first analog signal set according to a plurality of adjustment parameters.
4. A method according to claim 3, wherein generating an operation indication signal based on the current parameter information, the voltage parameter information, the grid frequency parameter, the method comprising:
judging whether the running speed of the target three-phase asynchronous motor meets a preset speed interval or not based on the current parameter information, the voltage parameter information and the power grid frequency parameter respectively;
if not, generating a stop indication signal, controlling the target three-phase asynchronous motor to stop running according to the stop indication signal, simultaneously searching abnormal parameter information, updating the current parameter information and/or the voltage parameter information and/or the power grid frequency parameter, and performing secondary judgment;
if yes, generating an operation indication signal to prompt the operation state of the target three-phase asynchronous motor.
5. The method of claim 1, wherein synchronizing the first set of analog signals to a fault prediction assessment model performs fault prediction and fault assessment on the target three-phase asynchronous motor, and obtains a fault prediction assessment result, the method comprising:
acquiring a plurality of analog signal record data and fault prediction record data;
Taking the plurality of analog signal record data as input training data to train the fault prediction evaluation model;
Performing accuracy evaluation on the output result of the fault prediction evaluation model through the fault prediction record data to obtain a fault prediction evaluation error parameter;
Performing iterative training on the fault prediction evaluation model according to the fault prediction evaluation error parameters, and considering that the fault prediction evaluation model converges when the fault prediction evaluation error parameters of continuous preset times meet preset requirements;
And inputting the first analog signal set into the converged fault prediction evaluation model to obtain the fault prediction evaluation result.
6. The method of claim 1, wherein updating the plurality of operating electromechanical signals based on the fault prediction assessment results generates a second set of analog signals, the method comprising:
Decomposing the fault prediction evaluation result to generate a fault information set, wherein the fault information set comprises a plurality of fault positioning information and a plurality of fault cause information;
performing fault relevance assessment on the plurality of fault locating information and the plurality of fault cause information to generate a fault relevance factor;
and carrying out weighted calculation on the plurality of operation electromechanical signals according to the fault correlation factor, and identifying the plurality of operation electromechanical signals to update according to a calculation result to generate the second simulation signal set.
7. The method of claim 1, wherein the method comprises:
Positioning a fault occurrence area of the target three-phase asynchronous motor through the second analog signal set;
performing frequency modulation abnormality monitoring based on the fault occurrence area to generate a deviation frequency data file;
And traversing the deviation frequency data file to carry out frequency modulation deviation correction control, and loading the target three-phase asynchronous motor according to deviation correction control data to execute operation.
8. Three-phase asynchronous motor frequency modulation control system based on electromechanical signal analysis, characterized in that, the system includes:
The power basic data acquisition module is used for acquiring power data of the target three-phase asynchronous motor through the power internet of things and acquiring power basic data;
The first analog signal set generation module is used for generating a first analog signal set, wherein the first analog signal set is obtained by performing analog operation of a target three-phase asynchronous motor based on the electric power basic data and collecting a plurality of operation electromechanical signals in the analog operation process;
the fault prediction evaluation result acquisition module is used for synchronizing the first analog signal set to a fault prediction evaluation model to perform fault prediction and fault evaluation on the target three-phase asynchronous motor, so as to acquire a fault prediction evaluation result;
The second analog signal set generation module is used for updating the plurality of operation electromechanical signals according to the fault prediction evaluation result to generate a second analog signal set;
and the frequency modulation control module is used for performing frequency modulation control on the target three-phase asynchronous motor according to the second analog signal set.
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