CN117665572A - Synchronous motor rotor conducting bar state evaluation method and system - Google Patents
Synchronous motor rotor conducting bar state evaluation method and system Download PDFInfo
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract
The invention discloses a synchronous motor rotor bar state evaluation method and a system, wherein the evaluation method comprises the following steps: collecting voltage and current data from the starting of the synchronous motor to a steady running state; calculating fundamental wave frequency and active power of the motor; dividing a working state warehouse; judging the data quantity in each working state warehouse, and carrying out normalization processing; dividing time intervals of data in each working state warehouse; constructing a characteristic value space threshold database according to the time interval; and judging the abnormality of the motor rotor conducting bar according to the characteristic value space threshold database. The method is suitable for monitoring the state of the rotor conducting bar of the synchronous motor, and can monitor and alarm the state of the rotor conducting bar of the synchronous motor only by collecting instantaneous data of three-phase voltage and current in the starting process of the synchronous motor when the synchronous motor is normal; the invention does not need data in a fault state, and uses non-stable data in a starting process, thereby overcoming the problem that the data is required to be stable by the conventional method.
Description
Technical Field
The invention belongs to the field of mechanical fault diagnosis, and particularly relates to a synchronous motor rotor bar state evaluation method and system.
Background
The motor is a very important device in the current industrial and mining enterprises, and most of the device operation is driven by the motor. Synchronous motors have high precision, but are complex in construction, high in manufacturing cost and relatively difficult to maintain. The failure of the synchronous motor not only damages the machine body itself, but also affects the whole transmission system. In complex production lines, if an important motor fails but maintenance cannot be found in time, the product quality of the whole production line is affected, and even serious production accidents and economic losses are caused.
The state evaluation of the motor in the current industry is generally concentrated in an asynchronous motor, more research is conducted on a rotor of the asynchronous motor, in the state evaluation of a rotor conducting bar in the asynchronous motor, a frequency spectrum analysis method is generally adopted, when the rotor conducting bar is abnormal such as broken bar, specific fault characteristic frequency components appear in the frequency spectrum, and the rotor conducting bar of the asynchronous motor is evaluated by observing the fault characteristic frequency components related to the broken bar of the rotor; or the rotor bars of the motor are subjected to diagnostic evaluation by a machine learning method.
For example, in the prior art, an invention patent with application number CN201710264311 discloses a system and a method for diagnosing a broken bar fault of an asynchronous motor rotor, which specifically include the following steps: step 1: the Hall current sensor acquires a stator current analog signal from the asynchronous motor and sends the stator current analog signal to the analog-to-digital conversion module: step 2: the analog-to-digital conversion module converts the stator current analog signal into a stator current digital signal and sends the stator current digital signal to the given point signal processor; step 3: the fixed-point signal processor divides the stator current digital signals into short-time signals with equal length, overlaps between adjacent short-time signals, and performs windowing and shortening treatment on the short-time signals to obtain windowing signals; step 4: the fixed point signal processor calculates the frequency spectrum of the windowed signal by utilizing the short-time Fourier transform of the sequence to obtain a power spectrum density matrix, converts the power spectrum density matrix into a power spectrum density column vector, and utilizes the power spectrum density column vector to obtain the variance of the power spectrum density vector according to a vector variance calculation formula; step 5: the fixed point signal processor compares the variance of the power spectrum density vector with the variance of the power spectrum density vector of the normal asynchronous motor, and if the result of the ratio of the variance of the power spectrum density vector to the variance of the power spectrum density vector of the normal asynchronous motor is more than 0 and less than 2, the rotor of the asynchronous motor is judged to have no broken bar fault; and if the result of the ratio of the variance of the power spectrum density vector to the variance of the power spectrum density vector of the normal asynchronous motor is more than or equal to 2, judging that the asynchronous motor rotor has a broken bar fault.
Another patent application number CN201510070172 discloses a method for diagnosing faults of broken bars of an asynchronous motor rotor for engineering machinery internet of things, which comprises the following steps: step one: the method comprises the steps that a current sensor and a speed measuring motor are used for respectively collecting starting rotation speed of an asynchronous motor in engineering machinery and starting current of any phase of a stator end, collected data are respectively sent to an ARM processor, the ARM processor compresses the collected data through compressed sensing, and the compressed data are sent to a remote monitoring center through a GPRS module through a wireless network; step two: the remote monitoring center receives the compressed data sent by the GPRS module, reconstructs the received compressed data by utilizing the compressed sensing, and performs fault diagnosis on the reconstructed starting current data by utilizing a second-order discrete polynomial phase transformation method to obtain a fault diagnosis result of the rotor broken bar of the asynchronous motor.
Another patent application number CN202310656378 discloses a rotor bar breakage detection system, which comprises: acquiring induction current according to the abnormal signal, monitoring the induction current on a rotor in real time through an electromagnetic sensor, acquiring the sequence of time of the induction current, dequantizing the induction current, and drawing an abnormal graph; the method comprises the steps of importing an abnormal graph into a pre-trained abnormal graph library, calculating the similarity value of the abnormal graph and an abnormal comparison graph in the abnormal graph library through a similarity measurement algorithm, selecting a group of abnormal comparison graphs with the maximum similarity value, and extracting defect marks of the abnormal comparison graphs to be used as defect detection conclusion.
However, the rotor bars of the synchronous machine act similarly to the bars of the asynchronous machine during starting, but after starting is completed, the rotor bars will no longer function. Therefore, the state evaluation of the rotor bars of the synchronous machine is performed during the start-up phase of the synchronous machine. And less attention and less research are paid to the synchronous motor, particularly to the rotor conducting bars of the synchronous motor, and an effective and reliable synchronous motor rotor breakage state evaluation method is lacked. Therefore, on the basis of the current edge computing technology, a simple, rapid and accurate fault diagnosis method is designed specifically for the state evaluation of the synchronous motor rotor guide bar.
In summary, in the prior art, on one hand, a method for evaluating the state of a rotor bar of a synchronous motor is currently lacking; on the other hand, the conventional state evaluation of the rotor bars of the asynchronous motor is based on the premise that the steady operation state of the motor is assumed, and the rotor bars of the synchronous motor mainly play a role in the starting process, and the state evaluation of the rotor bars of the synchronous motor cannot be referred to in the state evaluation of the rotor bars of the synchronous motor by the conventional spectrum analysis method because the operation state of the rotor bars in the starting process is a non-steady state; the rotor bar of the motor can be evaluated by a machine learning method without being influenced by a non-stable working condition, but the method needs to acquire data with a fault tag in advance, which is generally not easy to acquire under field conditions; and the motor may be in different running states due to different production processes, and misjudgment may be caused by adopting a single index to evaluate the state of the rotor bar.
Disclosure of Invention
The following presents a simplified summary of embodiments of the invention in order to provide a basic understanding of some aspects of the invention. It should be understood that the following summary is not an exhaustive overview of the invention. It is not intended to identify key or critical elements of the invention or to delineate the scope of the invention. Its purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is discussed later.
According to one aspect of the present application, there is provided a synchronous motor rotor bar state evaluation method, including:
step 1: collecting voltage data U and current data I from the starting of the synchronous motor to the steady running state as starting process data;
step 2: judging the starting process state and the steady state; the starting process state comprises a starting moment point and a starting ending moment point;
step 3: calculating fundamental frequencyThe actual power P of the motor;
step 4: dividing a working state warehouse: the collected starting process data is based on the fundamental frequencyDividing starting process data into different working state warehouses according to different actual power P and voltage data U amplitude values;
Step 5: judging the data quantity in each working state warehouse and carrying out normalization processing on the data in the working state warehouse;
step 6: carrying out time interval division on the normalized data in each working state warehouse;
step 7: constructing a characteristic value space threshold value database: calculating the characteristic value of each time interval in each working state warehouse, and adaptively calculating the characteristic value threshold interval, wherein the characteristic value and the characteristic value threshold interval form a characteristic value space threshold database;
step 8: judging the abnormal state of the motor guide bar according to the characteristic value and the characteristic value threshold value interval of the characteristic value space threshold value database, specifically comparing the characteristic value of each time interval with the characteristic value threshold value interval, and if the characteristic values of all the time intervals are in the characteristic value threshold value interval, making the motor guide bar normal; otherwise, the motor conducting bar is abnormal, and an alarm is sent out.
Further, the step 1 specifically includes: when the synchronous motor starts to run each time, the three-phase voltage and the three-phase current of the synchronous motor are respectively collected by utilizing a voltage sensor and a current sensor;
further, in the step 2, the starting process state and the steady state are judged, specifically, the collected three-phase voltage and current data are processed, and the identification data are in the starting process or the steady state; the specific discrimination process is as follows:
The n data points in the starting process data are marked as x 1 ,x 2 ,x 3 ,……,x n Starting from a first data point, adding a Hanning window with the length of m (0.ltoreq.m.ltoreq.n) to the data, sliding rightward at the overlapping rate of a preset value (for example, 0.5), calculating the frequency spectrum of each windowed data respectively, marking the maximum value in the frequency spectrum amplitude, taking the maximum value in the frequency spectrum amplitude as a characteristic value, and marking the maximum value as R respectively 1 ,R 2 ,R 3 ,……,R M ；
Then, the start-up procedure start state is discriminated as follows: suppose slave（0≤N 1 M) is not more than, the difference between 4 consecutive characteristic values and the previous characteristic value is larger than the starting sensitivity threshold TH 1 Then it is determined that the start-up procedure begins from the nth 1 Starting the characteristic values, and calculating a starting point of the starting process according to the characteristic values;
the end state of the starting process is judged as follows: suppose from the first（N 1 ≤N 2 M) feature values are not more than, and the absolute value of the difference value between the continuous 4 feature values and the previous 1 feature value is smaller than the start end sensitivity threshold TH 2 And the absolute value of the difference between the following 4 continuous characteristic values and the first 1 characteristic value is smaller than the stable sensitivity threshold TH 3 Determining that the starting process is finished to be Nth 2 The +3 eigenvalues end, from which the starting process end point is calculated.
Wherein TH is that 1 ，TH 2 ，TH 3 The start-up sensitivity threshold is respectively set,the starting end sensitivity threshold and the stable sensitivity threshold can be preset on the device by a user according to the actual situation of the measuring motor.
Further, the calculating the fundamental frequency in the step 3 specifically includes: firstly, carrying out Fourier transformation on stable data to obtain a frequency spectrum, finding a rough fundamental frequency in the frequency spectrum, then carrying out frequency spectrum refinement analysis near the fundamental frequency, determining a more accurate fundamental frequency, and recording as。
Further, the step 3 of calculating the actual power of the motor is to calculate the actual power of the motor by using voltage and current data after the motor reaches a stable state, and the calculation formula is as follows:
；
wherein,U a ，I a ，U b ，I b ，U c ，I c respectively the instantaneous values of the voltage and current assigned to each phase of the motor.
Further, the step 4 of dividing the working state warehouse is to perform state binning processing on collected starting process data (including three-phase voltage and three-phase current data) according to voltage amplitude, fundamental wave frequency and actual power, and specifically includes:
step 41: to fluctuate within a small range (e.g. fluctuation interval of 0.02)U n ) Is considered to be in the same state for a predetermined range of voltage data U (e.g., 0.9U n -U n ) Segmenting based on small range fluctuations, e.g. 0.9 amplitudeU n -U n Voltage data U of 0.02U n Segmenting;
step 42: the voltage data U (for example, the amplitude is 0.9) of each segment interval (i.e. under the same state) U n -0.92U n Interval of 0.92U n -0.94U n Interval of，……，0.98U n -U n Interval), carrying out state binning treatment on the collected starting process data according to fundamental wave frequency and actual power, and dividing the interval by a proper frequency range and a proper power range; dividing all voltage data U states in a preset range into bins to obtain a space state warehouse; the space state warehouse is a working state warehouse and stores starting process data in all normal states; the starting process data in the warehouses in different states are stored in a database.
Further, in step 5, the data amount in each working state warehouse is judged, specifically: and monitoring the data volume in each working state warehouse in real time, and carrying out normalization processing on the data of the current working state warehouse when the data volume in a certain working state warehouse meets the requirements.
Further, in step 5, normalization processing is performed on data in the working state warehouse, specifically, length normalization is performed on data belonging to the same state, which specifically includes:
step 51: in each interval of the working state warehouse, calculating the length of each piece of starting process data, and selecting the starting process data with the longest length as standard length data;
Step 52: and selecting the maximum value in the standard length data, taking the index of the subscript as a reference point, aligning the index of the subscript of the maximum value of other data (namely data except the standard length data) in the working state warehouse interval with the reference point, taking the data of the maximum value as the reference, and carrying out interpolation or truncation processing on the data except the maximum value point, so that all the data are processed into the same length as the standard length data, and thus, normalizing the data length.
Further, the step 6 performs time interval division on the normalized data in each working state warehouse, and specifically includes:
step 61: acquiring the number of segments of configuration options in a system;
step 62: and dividing the normalized length data in the same working state warehouse into time sequence data with the same number of segments.
Further, step 7 builds a feature value space threshold database, specifically including:
step 71: assume a narrow segment of data for a time intervalWherein a is n (x) The method is characterized in that narrow segment data with the same segment number label and divided by different starting process curves in the same working state warehouse are represented, and x is a time point or a subscript of a data point; n represents the number of data samples in the time interval;
Step 72: for each sampleI is more than or equal to 1 and less than or equal to n, respectively calculating preset characteristic indexes to form a characteristic vector matrix X n×W Wherein n is the number of samples, and W is the number of preset characteristic indexes;
for example, if the feature index is shown in Table 1, a feature vector X is formed 1×W 、X 2×W … …, finally form the feature matrix。
Step 73: for the obtained characteristic vector matrix X n×W Feature vector matrix X by Kernel Principal Component Analysis (KPCA) method n×W Feature fusion is carried out to construct one-dimensional feature indexesWherein n is the number of samples;
step 74: respectively calculating characteristic indexesMean value of>And standard deviation->：
；
；
Step 75: the threshold value interval of the characteristic value in the time interval is；
Step 76: repeating the steps 71-75, and calculating the current narrow-section data in each time interval to obtain a characteristic value threshold value interval of each time interval;
step 77: and establishing a characteristic value space threshold value library with the same size as the working state warehouse, and storing the calculated characteristic value threshold value interval of each time interval into a corresponding characteristic value space threshold value library.
According to another aspect of the present application, there is provided a synchronous motor rotor bar state evaluation system, comprising:
A starting process data acquisition module: the method comprises the steps of collecting voltage data and current data from starting to steady running state of the synchronous motor as starting process data;
the starting state judging module is used for: the method is used for judging the starting process state and the steady state; the starting process state comprises a starting moment point and a starting ending moment point;
and a data calculation module: for calculating fundamental frequencyThe actual power P of the motor;
the working state warehouse dividing module: for dividing working-state warehouse, in particular to collect starting-process data according to fundamental frequencyDividing starting process data into different working state warehouses according to different actual power P and voltage data U amplitude values;
the working state warehouse data processing module: the data normalization processing module is used for judging the data quantity in each working state warehouse and normalizing the data in the working state warehouse;
the time interval dividing module is used for dividing time intervals of the normalized data in each working state warehouse;
the eigenvalue space threshold database construction module: the method comprises the steps of calculating a characteristic value of each time interval in each working state warehouse, and adaptively calculating a characteristic value threshold interval of the characteristic value, wherein the characteristic value and the characteristic value threshold interval form a characteristic value space threshold database;
The abnormal judgment module of the motor conducting bar: the method is used for judging the abnormal state of the motor guide bar according to the characteristic value and the characteristic value threshold value interval of the characteristic value space threshold value database, specifically comparing the characteristic value of each time interval with the characteristic value threshold value interval, and if the characteristic values of all the time intervals are in the characteristic value threshold value interval, the motor guide bar is normal; otherwise, the motor conducting bar is abnormal, and an alarm is sent out.
In actual implementation, the synchronous motor rotor bar state evaluation system can realize automatic judgment of abnormal states of sample data to be tested, and the specific process is as follows:
when the synchronous motor is started again, the starting process data acquisition module acquires instantaneous three-phase voltage and current data;
the starting state judging module determines starting time and starting ending time of the motor;
the data calculation module calculates fundamental frequency of the current data and calculates actual power of the motor by adopting voltage and current data after the motor reaches a steady state;
the working state warehouse dividing module divides the collected starting data according to fundamental wave frequencyActual power->Dividing starting data of a sample to be detected into corresponding working state warehouses according to different magnitudes of the voltage data U;
The working state warehouse data processing module normalizes the divided starting data of the sample to be tested;
the time interval dividing module divides time intervals of the normalized sample starting data to be tested;
the characteristic value space threshold value database construction module takes the starting data of the sample to be detected and the data in the same time interval of the same warehouse for constructing the characteristic value threshold value interval as a whole, and performs step 71-step 74 calculation to obtain the magnitude of the characteristic value in each time zone corresponding to the starting data of the sample to be detected;
the abnormal judgment module of the motor guide bar compares the characteristic value of each time interval with the characteristic value threshold value interval, and if the characteristic values of all the time intervals are within the characteristic value threshold value interval, the motor guide bar is normal; otherwise, the motor conducting bar is abnormal, and an alarm is sent out.
The invention provides an effective state evaluation method for the synchronous motor rotor conducting bar through the scheme, and compared with the prior art, the method has the following advantages:
1. in order to solve the dependence of the conventional method on the stability of data, the method evaluates the state of the synchronous motor rotor bar by non-stable data in the starting process;
2. In order to solve the dependence of the machine learning method on the requirement of fault data, the scheme only needs to start process data under the normal state of the synchronous motor;
3. in order to solve the problem that the industrial field synchronous motor affects the starting characteristics of the synchronous motor due to voltage data amplitude, voltage data frequency, load and the like, the state evaluation algorithm of the scheme is applicable to any working condition.
Drawings
The invention may be better understood by referring to the following description in conjunction with the accompanying drawings in which like or similar reference numerals are used to indicate like or similar elements throughout the several views. The accompanying drawings, which are included to provide a further illustration of the preferred embodiments of the invention and together with a further understanding of the principles and advantages of the invention, are incorporated in and constitute a part of this specification. In the drawings:
FIG. 1 is a flow chart of a synchronous motor rotor bar state evaluation method of the present invention;
FIG. 2 shows the present inventionA bin example diagram under the voltage amplitude condition;
fig. 3 is a schematic diagram of a spatial state warehouse of the present invention.
Detailed Description
Embodiments of the present invention will be described below with reference to the accompanying drawings. Elements and features described in one drawing or embodiment of the invention may be combined with elements and features shown in one or more other drawings or embodiments. It should be noted that the illustration and description of components and processes known to those skilled in the art, which are not relevant to the present invention, have been omitted in the drawings and description for the sake of clarity.
The starting characteristics of the synchronous motor are affected by the voltage data amplitude U, the voltage data frequency f and the load size (i.e. the power P), and can be expressed as the following formula:
I=F(U,f,P)；
wherein I is motor starting current; u is the voltage data amplitude; p is the power of the motor after stabilization.
The above parts are coupled together to affect the starting characteristics of the synchronous motor together, and when any part of the parts changes, the starting characteristics of the synchronous motor change, so that all influence factors need to be decoupled, and then the starting characteristics of the synchronous motor are evaluated. When the rotor bars of the synchronous motor are abnormal, the starting characteristics of the synchronous motor can be obviously changed. Based on the above, the invention provides a state evaluation system and a state evaluation method for a synchronous motor rotor bar.
Example 1
The embodiment provides a synchronous motor rotor conducting bar state evaluation system which comprises a starting process data acquisition module, a starting state judging module, a data calculation module, a working state warehouse dividing module, a working state warehouse data processing module, a time interval dividing module, a characteristic value space threshold value database construction module and a motor conducting bar abnormality judging module.
The starting process data acquisition module is used for acquiring voltage data and current data from the starting start of the synchronous motor to the steady running state as starting process data.
The starting state judging module is used for: the method is used for judging the starting process state and the steady state; the start-up procedure state comprises a start-up start time point and a start-up end time point.
And a data calculation module: for calculating fundamental frequencyAnd the actual power P of the motor.
The working state warehouse dividing module: for dividing working-state warehouse, in particular to collect starting-process data according to fundamental frequencyThe actual power P and the voltage data U are different in magnitude, and the starting process data are divided into different working state warehouses.
The working state warehouse data processing module: the method is used for judging the data quantity in each working state warehouse and normalizing the data in the working state warehouse.
And the time interval dividing module is used for dividing the time interval of the normalized data in each working state warehouse.
The eigenvalue space threshold database construction module: the method is used for calculating the characteristic value of each time interval in each working state warehouse and adaptively calculating the characteristic value threshold interval thereof, and the characteristic value threshold interval form a characteristic value space threshold database.
The abnormal judgment module of the motor conducting bar: the method is used for judging the abnormal state of the motor guide bar according to the characteristic value and the characteristic value threshold value interval of the characteristic value space threshold value database, specifically comparing the characteristic value of each time interval with the characteristic value threshold value interval, and if the characteristic values of all the time intervals are in the characteristic value threshold value interval, the motor guide bar is normal; otherwise, the motor conducting bar is abnormal, and an alarm is sent out.
Specifically, the synchronous motor rotor bar state evaluation system comprises two parts, namely a space characteristic value threshold value database is constructed, and synchronous motor test data are automatically judged. The construction process of the spatial characteristic value threshold database is as follows:
(1) The data acquisition module acquires voltage and current data from the starting of the synchronous motor to the steady running state;
(2) The starting state judging module judges the starting process state (comprising judging the starting moment point and the starting ending moment point) and judges the steady state;
(3) The data calculation module calculates the fundamental frequency and the active power of the motor,
(4) The working state warehouse dividing module divides the working state warehouse;
(5) The working state warehouse data processing module judges the data quantity in each state warehouse and normalizes the data in the working state warehouse;
(6) The time interval dividing module divides the time interval of each current starting curve in each warehouse; the current starting curve is current data acquired by the sensor;
(7) And adaptively constructing a characteristic value space threshold value database.
The automatic judging of the synchronous motor test data is mainly that the motor conducting bar abnormality judging module is used for automatically judging the data to be tested, when the abnormal state of the motor rotor conducting bar is judged, an alarm is sent out, and otherwise, no alarm is given.
The system is at least suitable for monitoring the state of the rotor conducting bars of the synchronous motor, and can be used for monitoring and alarming the state of the rotor conducting bars of the synchronous motor.
Example 2
The embodiment provides a synchronous motor rotor bar state evaluation method, which is shown in fig. 1, and mainly comprises the steps of data acquisition, data state identification, calculation division of a working state warehouse, normalization of data length in the warehouse, time interval division in the working state warehouse, establishment of a characteristic value space threshold database, rotor bar state evaluation and the like.
Step 1, data acquisition:
when the synchronous motor starts to run each time, the three-phase voltage and the three-phase current of the synchronous motor are respectively acquired by utilizing a voltage sensor and a current sensor, and acquired data are sent to an intelligent monitoring terminal;
and 2, the electric signals are used for identifying the starting process state and the steady operation state of the motor:
the starting state judging module processes the collected three-phase voltage and current data, identifies that the data is in a starting process or a stable state, and classifies the data. The specific calculation steps are as follows:
taking a data acquisition sample of a phase current as an example, assume that there are n data points for a start-up procedure, denoted as x 1 ,x 2 ,x 3 ,……,x n From the first point of the data, adding a Hanning window with the length of m to the data, sliding to the right at an overlapping rate of 0.5, calculating the frequency spectrum of each windowed data, marking the maximum value in the frequency spectrum amplitude, and marking the maximum value as a characteristic value as R respectively 1 ,R 2 ,R 3 ,……,R M M is a natural number.
The n data points in the starting process are recorded as first data points, a Hanning window with the length of m (0 is less than or equal to m is less than or equal to n) is added to the data, then the data slide rightwards at the overlapping rate of a preset value (for example, 0.5), the frequency spectrums of the windowed data are respectively calculated, the maximum value in the frequency spectrum amplitude is marked, and the maximum value in the frequency spectrum amplitude is used as a characteristic value and is recorded as the characteristic value;
then, the start-up procedure start state is discriminated as follows: suppose slave（0≤N 1 M) is not more than, the difference between 4 consecutive characteristic values and the previous characteristic value is larger than the starting sensitivity threshold TH 1 Then it is determined that the start-up procedure begins from the nth 1 Starting the characteristic values, and calculating a starting point of the starting process according to the characteristic values;
the end state of the starting process is judged as follows: suppose from the first（N 1 ≤N 2 ≤M）（ >) Starting from the characteristic value, the absolute value of the difference between the 4 continuous characteristic values and the first 1 characteristic value is smaller than the starting end sensitivity threshold TH 2 And the absolute value of the difference between the following 4 continuous characteristic values and the first 1 characteristic value is smaller than the stable sensitivity threshold TH 3 Determining that the starting process is finished to be Nth 2 The +3 eigenvalues end, from which the starting process end point is calculated.
Wherein TH is that 1 ，TH 2 ，TH 3 The starting sensitive threshold, the starting sensitive threshold and the stable sensitive threshold are respectively set in advance by a user on the device according to the actual condition of the measuring motor.
Step 3, fundamental wave frequency calculation:
the data calculation module calculates a fundamental frequency of the current data. The method comprises the following specific steps: firstly, carrying out Fourier transformation on stable data to obtain a frequency spectrum, finding a rough fundamental frequency in the frequency spectrum, then carrying out frequency spectrum refinement analysis near the fundamental frequency, determining a more accurate fundamental frequency, and recording as。
Step 4, power calculation:
the data calculation module calculates the actual power of the motor by adopting voltage and current data after the motor reaches a stable state. The calculation formula is as follows:
；
wherein,respectively the instantaneous values of the voltage and current assigned to each phase of the motor.
Step 5, calculating and dividing the working state warehouse:
the working state warehouse dividing module divides the collected starting data according to fundamental wave frequencyActual power->And dividing the starting data into different working state warehouses according to the different amplitude values of the voltage data U.
Due to the fundamental frequencyActual power->And the amplitude of the voltage data U have influence on the starting characteristics of the synchronous motor, and the three conditions are mutually coupled together, so that the analysis of the starting characteristics of the synchronous motor is not facilitated. Therefore, the three influencing factors are decoupled through the idea of controlling the variables, so that the analysis of the starting characteristics of the synchronous motor is possible.
Controlling one of the variables, e.g. assuming that the amplitude of the voltage data U remains unchanged, in practice it may be assumed that the amplitude of the voltage data U fluctuates within a small range, ignoring the effect thereof, thus considering the voltage data U fluctuating within a small range as being in the same state, e.g. the small range fluctuation interval is set to 0.02U n 0.96U n -0.98U n Considered as a voltage state, 0.94U n -0.96U n Then it is another state. At this time, only the fundamental frequency remainsAnd the actual power P has an influence on the start-up characteristics of the synchronous motor.
For a preset range of voltage data U (e.g., 0.9U n -U n ) Segmenting based on small range fluctuations, e.g. 0.9 amplitudeU n -U n Voltage data U of 0.02U n Segmenting; then, the voltage data U of each segment interval (namely under the same state) is subjected to state binning. For example of amplitude According to the fundamental wave frequency and the actual power, carrying out state binning processing on the collected starting process data, dividing the intervals by a proper frequency range and a proper power range, carrying out the same processing on the three-phase current data, and storing the result in a database. As shown in fig. 2, the dark area is a bin with a power of 85% -90% of rated power and a fundamental wave frequency of 44% -46 hz, and the sizes of the bins with power and frequency can be adjusted according to practical situations.
The same applies to the voltage data U (0.9U n -0.98U n ) The same processing is carried out, so that a space state warehouse shown in fig. 3 is obtained, and all starting process data in a normal state are distributed and stored in the space state warehouse (namely, a working state warehouse). The dark cuboid portion of FIG. 3 shows the voltage data U amplitude asThe frequency is 48-50 Hz, and the power is +.>Is provided, the start-up characteristics in this warehouse are substantially consistent. The working state warehouse is composed of a plurality of state warehouse intervals, and each state warehouse interval contains a plurality of starting process data (three-phase voltage and current).
The present example shows only for voltage data amplitude U, frequency Power->The smaller range of the three factors is divided, and the range of voltage data amplitude, frequency and power is covered as much as possible according to the running condition of the synchronous motor on site.
Step 6, judging the data quantity in the warehouse by a module:
the data quantity discriminating module in the warehouse monitors the data quantity in each state warehouse in real time, and when the data quantity in a certain state warehouse meets the requirement, the follow-up steps are carried out in the current warehouse.
Step 7, normalizing the data length in the warehouse:
and the time interval dividing module in the state warehouse performs length normalization on the data belonging to the same state. The method comprises the following specific steps:
1) And in each state warehouse interval, calculating the length of each piece of starting process data, and selecting the data with the longest length as standard length data.
2) And selecting the maximum value of the standard length data, taking the index of the subscript of the maximum value as a reference point, aligning the index of the subscript of the maximum value of other data with the reference point, taking the data of the standard length as the reference point, carrying out interpolation or truncation processing on other data points except the maximum value point, processing all the data into the length identical with the standard data, and normalizing the data length.
Step 8, dividing data time intervals in the warehouse:
the time interval dividing module in the state warehouse divides the time interval of the normalized length data in each state warehouse. The method comprises the following specific steps:
1) Acquiring the number of segments of configuration options in a system;
2) The normalized length data in the same state warehouse is divided into time sequence data with the same segment number.
Step 9, calculating a threshold value interval of the characteristic value in the time interval:
the characteristic value space threshold value database construction module has self-learning capability, can adaptively calculate the characteristic value of each time interval in each state warehouse, and adaptively calculate the threshold value interval. The method comprises the following specific steps:
1) Assume a narrow segment of data for a time intervalN represents the number of data samples in the time interval.
2) For each sampleThe characteristic indexes shown in Table 1 are calculated respectively to form characteristic vectors +.>Finally, a feature matrix is formed>Where n is the number of samples.
Table 1 signal characteristic index:
；
3) For the obtained characteristic matrixFeature matrix by Kernel Principal Component Analysis (KPCA) method>Feature fusion is carried out to construct a one-dimensional feature index +.>Where n is the number of samples.
4) Respectively calculating characteristic indexesMean value of>And standard deviation->. Wherein,
；
；
5) The threshold value interval of the characteristic value in the time interval is；
6) Carrying out 1) to 5) step calculation on the characteristic value of the narrow-segment data in each time interval;
7) And establishing a characteristic value space threshold value library with the same size as the state warehouse, and storing the calculated characteristic value threshold value interval of each time interval into a corresponding characteristic value space threshold value library.
Step 10, rotor bar state evaluation: and the motor conducting bar abnormality judging module is used for automatically judging the state of the sample data to be detected.
When the synchronous motor is started again, instantaneous three-phase voltage and current data are collected. The starting state judging module determines starting time and starting ending time of the motor. The data calculation module calculates fundamental frequency of the current data and calculates actual power of the motor by adopting voltage and current data after the motor reaches a steady state. The collected start data is based on the fundamental frequencyActual power->And dividing the starting data of the sample to be tested into corresponding working state warehouses according to the difference of the amplitude values of the voltage data U. And carrying out normalization processing on the starting data of the sample to be detected, and carrying out time interval division. Taking the data of the sample to be detected and the data in the same time interval of the same warehouse with the construction characteristic value threshold value interval as a whole, and carrying out the step 1) -4) calculation in the step 7) to obtain the characteristic value in each time interval corresponding to the sample to be detected. Comparing the characteristic value of each time interval with a characteristic value threshold value region If the characteristic values of all the time intervals are within the characteristic value threshold value interval, the motor guide bar is normal; otherwise, the motor conducting bar is abnormal, and an alarm is sent out. />
The method only needs to collect instantaneous data of three-phase voltage and current in the normal starting process of the synchronous motor, does not need data in a fault state, and uses non-stable data in the starting process, so that the problem that the conventional method requires stable data is solved, and the method is more in line with actual application conditions on site;
the method is not affected by the change of the working condition of the motor: the method overcomes the influence of different starting characteristics of the motor caused by different working conditions when the motor is started due to different voltages and loads on site, and has self-adaptability;
the method is based on a self-learning method, and the problem of state evaluation index failure caused by the fact that the motor is under different working conditions is solved by adaptively constructing a characteristic value space threshold database through three-phase voltage and current data in the normal starting process of the synchronous motor.
The method can be deployed in intelligent monitoring terminal equipment, and can realize automatic assessment of the state of the synchronous motor rotor conducting bar through the function of edge calculation, so that manual intervention is reduced, manual assessment cost is reduced, and assessment objectivity and accuracy are improved. And respectively acquiring three-phase voltage and three-phase current when the synchronous motor is started by using a voltage sensor and a current sensor, and transmitting the acquired data to the intelligent monitoring terminal. And automatically judging the abnormality of the motor guide bar by the intelligent monitoring terminal and outputting a result.
In the foregoing description of specific embodiments of the invention, features that are described and/or illustrated with respect to one embodiment may be used in the same or a similar way in one or more other embodiments in combination with or instead of the features of the other embodiments.
It should be emphasized that the term "comprises/comprising" when used herein is taken to specify the presence of stated features, elements, steps or components, but does not preclude the presence or addition of one or more other features, elements, steps or components.
Furthermore, the methods of the present invention are not limited to being performed in the time sequence described in the specification, but may be performed in other time sequences, in parallel or independently. Therefore, the order of execution of the methods described in the present specification does not limit the technical scope of the present invention.
While the invention has been disclosed in the context of specific embodiments, it should be understood that all embodiments and examples described above are illustrative rather than limiting. Various modifications, improvements, or equivalents of the invention may occur to persons skilled in the art and are within the spirit and scope of the following claims. Such modifications, improvements, or equivalents are intended to be included within the scope of this invention.
Claims (10)
1. A synchronous motor rotor bar state evaluation method is characterized in that: comprising the following steps:
step 1: collecting voltage data and current data from the starting of the synchronous motor to the steady running state as starting process data;
step 2: judging the starting process state and the steady state; the starting process state comprises a starting moment point and a starting ending moment point;
step 3: calculating fundamental frequencyThe actual power P of the motor;
step 4: dividing a working state warehouse: the collected starting process data is based on the fundamental frequencyDividing starting process data into different working state warehouses according to different actual power P and voltage data U amplitude values;
step 5: judging the data quantity in each working state warehouse and carrying out normalization processing on the data in the working state warehouse;
step 6: carrying out time interval division on the normalized data in each working state warehouse;
step 7: constructing a characteristic value space threshold value database: calculating the characteristic value of each time interval in each working state warehouse, and adaptively calculating the characteristic value threshold interval, wherein the characteristic value and the characteristic value threshold interval form a characteristic value space threshold database;
Step 8: judging the abnormal state of the motor guide bar according to the characteristic value and the characteristic value threshold value interval of the characteristic value space threshold value database, specifically comparing the characteristic value of each time interval with the characteristic value threshold value interval, and if the characteristic values of all the time intervals are in the characteristic value threshold value interval, making the motor guide bar normal; otherwise, the motor conducting bar is abnormal, and an alarm is sent out.
2. The synchronous motor rotor bar state evaluation method according to claim 1, wherein: the step 1 specifically includes: when the synchronous motor starts to run each time, the three-phase voltage and the three-phase current of the synchronous motor are respectively acquired by utilizing a voltage sensor and a current sensor.
3. The synchronous motor rotor bar state evaluation method according to claim 1, wherein: in the step 2, the starting process state and the steady state are judged, specifically, the collected three-phase voltage and current data are processed, and the identification data are in the starting process or the steady state; the specific discrimination process is as follows:
the n data points of the start-up procedure data are denoted as x 1 ,x 2 ,x 3 ,……,x n From the first data point, adding a Hanning window with the length of m (0.ltoreq.m.ltoreq.n) to the data, sliding rightwards at the overlapping rate of a preset value, respectively calculating the frequency spectrums of the windowed data, marking the maximum value in the frequency spectrum amplitude, taking the maximum value in the frequency spectrum amplitude as a characteristic value, and respectively marking the maximum value as R 1 ,R 2 ,R 3 ,……,R M ；
Then, the start-up procedure start state is discriminated as follows: suppose slave（0≤N 1 M) is not more than, the difference between 4 consecutive characteristic values and the previous characteristic value is larger than the starting sensitivity threshold TH 1 Then it is determined that the start-up procedure begins from the nth 1 Starting the characteristic values, and calculating a starting point of the starting process according to the characteristic values;
the end state of the starting process is judged as follows: suppose from the first（N 1 ≤N 2 M) feature values are not more than, and the absolute value of the difference value between the continuous 4 feature values and the previous 1 feature value is smaller than the start end sensitivity threshold TH 2 And the absolute value of the difference between the following 4 continuous characteristic values and the first 1 characteristic value is smaller than the stable sensitivity threshold TH 3 Determining that the starting process is finished to be Nth 2 The +3 eigenvalues end, from which the starting process end point is calculated.
4. The synchronous motor rotor bar state evaluation method according to claim 1, wherein: and 3, calculating the actual power of the motor by adopting voltage and current data after the motor reaches a stable state, wherein the actual power of the motor is calculated according to the following calculation formula:
；
wherein,U a ，I a ，U b ，I b ，U c ，I c respectively the instantaneous values of the voltage and current assigned to each phase of the motor.
5. The synchronous motor rotor bar state evaluation method according to claim 1, wherein: the step 4 of dividing the working state warehouse specifically comprises the following steps:
Step 41: the voltage data U fluctuating in a small range are regarded as being in the same state, and the voltage data U in a preset range is segmented according to the fluctuation in the small range;
step 42: carrying out state binning processing on the collected starting process data according to fundamental wave frequency and actual power on the voltage data U of each segmented interval, and dividing the intervals in a proper frequency range and a proper power range; dividing all voltage data U states in a preset range into bins to obtain a space state warehouse; the space state warehouse is a working state warehouse and stores all starting process data in a normal state; the starting process data in the warehouses in different states are stored in a database.
6. The synchronous motor rotor bar state evaluation method according to claim 1, wherein: in step 5, the data volume in each working state warehouse is judged, specifically: and monitoring the data volume in each working state warehouse in real time, and carrying out normalization processing on the data of the current working state warehouse when the data volume in a certain working state warehouse meets the requirements.
7. The synchronous motor rotor bar state evaluation method according to claim 1, wherein: in step 5, the normalization processing is performed on the data in the working state warehouse, specifically, the length normalization is performed on the data belonging to the same state, and the method specifically includes:
Step 51: in each interval of the working state warehouse, calculating the length of each piece of starting process data, and selecting the starting process data with the longest length as standard length data;
step 52: and selecting the maximum value in the standard length data, taking the index of the subscript as a reference point, aligning the index of the subscript of the maximum value of other data in the state warehouse interval with the reference point, taking the data of the standard length as the reference point except for the maximum value point, and performing interpolation or truncation processing, so that all the data are processed into the same length as the standard length data, and the data length is normalized.
8. The synchronous motor rotor bar state evaluation method according to claim 1, wherein: step 6 is to divide time intervals of the normalized data in each working state warehouse, and specifically includes:
step 61: acquiring the number of segments of configuration options in a system;
step 62: and dividing the normalized length data in the same working state warehouse into time sequence data with the same number of segments.
9. The synchronous motor rotor bar state evaluation method according to claim 1, wherein: step 7, constructing a characteristic value space threshold database, which specifically comprises the following steps:
Step 71: assume a narrow segment of data for a time interval，a n (x) Narrow section data with the same section number label and divided by different starting process curves in the same working state warehouse, wherein x is a time point or a subscript of a data point; n represents the number of data samples in the time interval;
step 72: for each sampleI is more than or equal to 1 and less than or equal to n, respectively calculating preset characteristic indexes to form a characteristic vector matrix X n×W Wherein n is the number of samples, and W is the number of preset characteristic indexes;
step 73: for the obtained characteristic vector matrix X n×W Feature vector matrix X by Kernel Principal Component Analysis (KPCA) method n×W Feature fusion is carried out to construct one-dimensional feature indexesWherein n is the number of samples;
step 74: respectively calculating characteristic indexesMean value of>And standard deviation->：
；
；
Step 75: the threshold value interval of the characteristic value in the time interval is；
Step 76: repeating the steps 71-75, and calculating the current narrow-section data in each time interval to obtain a characteristic value threshold value interval of each time interval;
step 77: and establishing a characteristic value space threshold value library with the same size as the working state warehouse, and storing the calculated characteristic value threshold value interval of each time interval into a corresponding characteristic value space threshold value library.
10. A synchronous motor rotor bar state evaluation system is characterized in that: comprising the following steps:
a starting process data acquisition module: the method comprises the steps of collecting voltage data and current data from starting to steady running state of the synchronous motor as starting process data;
the starting state judging module is used for: the method is used for judging the starting process state and the steady state; the starting process state comprises a starting moment point and a starting ending moment point;
and a data calculation module: for calculating fundamental frequencyThe actual power P of the motor;
working state warehouse dividing moduleAnd (3) block: for dividing working-state warehouse, in particular to collect starting-process data according to fundamental frequencyDividing starting process data into different working state warehouses according to different actual power P and voltage data U amplitude values;
the working state warehouse data processing module: the data normalization processing module is used for judging the data quantity in each working state warehouse and normalizing the data in the working state warehouse;
the time interval dividing module is used for dividing time intervals of the normalized data in each working state warehouse;
the eigenvalue space threshold database construction module: the method comprises the steps of calculating a characteristic value of each time interval in each working state warehouse, and adaptively calculating a characteristic value threshold interval of the characteristic value, wherein the characteristic value and the characteristic value threshold interval form a characteristic value space threshold database;
The abnormal judgment module of the motor conducting bar: the method is used for judging the abnormal state of the motor guide bar according to the characteristic value and the characteristic value threshold value interval of the characteristic value space threshold value database, specifically comparing the characteristic value of each time interval with the characteristic value threshold value interval, and if the characteristic values of all the time intervals are in the characteristic value threshold value interval, the motor guide bar is normal; otherwise, the motor conducting bar is abnormal, and an alarm is sent out.
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| CN118393351A (en) * | 2024-06-26 | 2024-07-26 | 叁零陆(江苏)动力部件有限公司 | Method and device for processing and detecting abnormal information of motor rotor |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4698756A (en) * | 1985-07-16 | 1987-10-06 | Westinghouse Electric Corp. | Generator stator winding diagnostic system |
| CN102279342A (en) * | 2011-07-23 | 2011-12-14 | 华北电力大学（保定） | Cage asynchronous motor rotor broken-bar fault detection method based on multi-signal classification technology (MUSIC) and simulation annealing algorithm (SAA) |
| CN105318961A (en) * | 2014-07-29 | 2016-02-10 | 上海宝钢工业技术服务有限公司 | Vibration-state on-line monitoring method of high-voltage motor driving conveying belt |
| CN112258039A (en) * | 2020-10-23 | 2021-01-22 | 贵州电网有限责任公司 | Intelligent scheduling method for defective materials of power system based on reinforcement learning |
-
2024
- 2024-01-31 CN CN202410132762.0A patent/CN117665572B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4698756A (en) * | 1985-07-16 | 1987-10-06 | Westinghouse Electric Corp. | Generator stator winding diagnostic system |
| CN102279342A (en) * | 2011-07-23 | 2011-12-14 | 华北电力大学（保定） | Cage asynchronous motor rotor broken-bar fault detection method based on multi-signal classification technology (MUSIC) and simulation annealing algorithm (SAA) |
| CN105318961A (en) * | 2014-07-29 | 2016-02-10 | 上海宝钢工业技术服务有限公司 | Vibration-state on-line monitoring method of high-voltage motor driving conveying belt |
| CN112258039A (en) * | 2020-10-23 | 2021-01-22 | 贵州电网有限责任公司 | Intelligent scheduling method for defective materials of power system based on reinforcement learning |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118393351A (en) * | 2024-06-26 | 2024-07-26 | 叁零陆(江苏)动力部件有限公司 | Method and device for processing and detecting abnormal information of motor rotor |
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