CN115201678A - Method for BLDC control and fault diagnosis based on gray prediction model - Google Patents

Method for BLDC control and fault diagnosis based on gray prediction model Download PDF

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CN115201678A
CN115201678A CN202210991261.9A CN202210991261A CN115201678A CN 115201678 A CN115201678 A CN 115201678A CN 202210991261 A CN202210991261 A CN 202210991261A CN 115201678 A CN115201678 A CN 115201678A
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万军
郭露露
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Abstract

The invention relates to the technical field of control and fault diagnosis, in particular to a BLDC control and fault diagnosis method based on a gray prediction model, which comprises the steps of collecting a rotating speed value, a bus voltage, a bus current and a temperature value; inputting the original sequence data into a grey prediction DGM (1,1) model, and respectively predicting a speed value, a bus voltage value, a bus current value and a temperature value at the next moment; searching a proportional coefficient and an integral coefficient of a speed PI ring by using an HPO algorithm; and respectively calculating the probabilities of undervoltage, overvoltage, overcurrent, undercurrent and overtemperature according to the maximum value and the minimum value of the voltage, the current and the temperature of the BLDC bus, thereby judging whether a fault occurs. The method takes the collected bus current value, voltage and temperature as historical data, predicts the bus current, voltage and temperature values at the future time through a grey prediction model, combines the predicted values and the actual values, and jointly diagnoses whether the motor has faults or not, thereby greatly reducing the possibility of misdiagnosis.

Description

Method for BLDC control and fault diagnosis based on gray prediction model
Technical Field
The invention relates to the technical field of control and fault diagnosis, in particular to a method for controlling BLDC and diagnosing faults based on a grey prediction model.
Background
For the fault aspect of the brushless direct current motor, the efficiency of the motor is highest when the motor works at rated voltage, and when the voltage is too high, the no-load exciting current of the motor can be increased, so that the operation of the motor is not favorable, and the efficiency of the motor is reduced. When the voltage is too low, the output power of the motor can be reduced, overload is caused, and rotation blockage occurs when the voltage is serious. And the three-bus current of the BLDC is abnormally increased due to overlarge load, short circuit of a winding, open circuit of the winding, imbalance of three-phase voltage and the like, so that the damage to the motor is caused. When the current of the motor is too low, the load requirement cannot be met, and overload is easy to occur. The temperature of the motor is required to be within a normal range during operation, and the aging of the winding coil is easily caused by overhigh temperature, so that the service life of the motor is shortened.
The invention discloses an electric drive system open-circuit fault-tolerant control method based on a gray prediction theory (CN 111740682B), which predicts a three-phase current value at the next moment by using a gray prediction model, calculates a difference value between prediction data of the three-phase current and original data, and compares the difference value with a set threshold value so as to diagnose the occurrence of a fault. The method needs to detect the phase current of the motor in real time, can quickly diagnose a fault signal when a fault occurs, but when the load of the motor is suddenly changed in a normal range, the predicted value of the three-phase current of the motor is still developed according to the original trend, the actual current is increased, and the difference value between the three-phase current and the actual current may exceed a set threshold value, so that misdiagnosis is caused.
In addition, when the motor operates at an excessively high temperature, on one hand, an insulating layer on the outer surface of a motor winding is aged, short circuit risks can be caused for a long time, and on the other hand, electronic elements on a motor control board are damaged, so that motor faults are caused.
Disclosure of Invention
Aiming at the defects of the existing algorithm, the collected bus current value is taken as historical data, the future bus current value is predicted through a grey prediction model, the predicted value and the actual value are combined together, whether the motor has the risk of overcurrent or undercurrent or not is jointly diagnosed, and therefore the possibility of misdiagnosis is greatly reduced; the motor overcurrent diagnosis method can diagnose that the motor is about to overcurrent or just has overcurrent, and is quicker in detection compared with the traditional method; meanwhile, the invention also diagnoses the overvoltage and undervoltage faults; and by acquiring historical temperature data values and predicting the temperature value of the motor at the next moment by using a grey prediction model, whether the motor has the risk of over-temperature is diagnosed.
The technical scheme adopted by the invention is as follows: a method for BLDC control and fault diagnosis based on a gray prediction model includes the steps of:
step one, collecting a rotating speed value, a bus voltage, a bus current and a temperature value, and storing a plurality of collecting results as an original sequence of a gray prediction model;
inputting the original sequence data into a grey prediction DGM (1,1) model, and respectively predicting a speed value, a bus voltage value, a bus current value and a temperature value at the next moment;
further, the predicted values of the grey predictive DGM (1,1) model are:
Figure BDA0003804040400000021
wherein, a is the development coefficient, b is the gray effect quantity, and x (0) (1) Is the first value of the original sequence and,
Figure BDA0003804040400000022
the (k + 1) th predicted value of the accumulated sequence;
compared with the traditional gray GM (1,1), the gray prediction DGM (1,1) model has the advantages of being more applicable to swing or oscillation development sequences and better in prediction effect, and the prediction sequences targeted by the invention are oscillation sequences, so that the gray prediction DGM (1,1) model is more suitable.
Step three, establishing a self-adaptive speed PI model, and searching a proportional coefficient and an integral coefficient of a speed PI ring by using an HPO algorithm;
further, the method specifically comprises the following steps:
respectively subtracting the predicted rotating speed value and the current rotating speed value from the target rotating speed value to obtain a predicted error and an actual error which are used as the input of an adaptive speed PI loop, and adjusting an error item participating in speed PI control according to the predicted error and the actual error;
searching the optimal values of the proportional coefficient and the integral coefficient of the speed PI ring by using an HPO algorithm;
respectively calculating the probabilities of undervoltage, overvoltage, overcurrent, undercurrent and overtemperature according to the maximum value and the minimum value of the voltage, the current and the temperature of the BLDC bus, thereby judging whether a fault occurs;
further, the judgment process of undervoltage and overvoltage is as follows:
setting maximum value U of BLDC bus voltage max And minimum value U min 0.5[ U (k + 1) + U (k) ]]And U max 、U min Comparing and calculating the probability gamma of overvoltage or undervoltage fault 1 Probability gamma 1 The formula of (1) is:
Figure BDA0003804040400000031
wherein U =0.5[ U (k + 1) + U (k) ], U (k + 1) is a bus voltage value at the next time, and U (k) is a bus voltage value at the present time;
when U > U max ，γ 1 If the voltage is more than 5%, indicating that the BLDC can have overvoltage faults;
when U is less than U min ，γ 1 When the voltage is more than 3%, the BLDC is prompted to have the possibility of undervoltage fault;
further, the judgment process of the over-current and under-current is as follows:
setting maximum value I of BLDC bus current max And a minimum value I min 2, 0.5[ 2 ], [ I (k + 1) + I (k)]And I max 、I min Comparing and calculating the probability gamma of overcurrent or undercurrent fault 2 Probability gamma 2 The formula of (1) is:
Figure BDA0003804040400000041
wherein, I =0.5[ I (k + 1) + I (k) ], I (k + 1) is a bus current value at the next time, and I (k) is a bus current value at the present time;
when I > I max ，γ 2 When the current is more than 9%, indicating that the BLDC can generate overcurrent fault;
when I < I min ，γ 2 > 6%, suggesting that the BLDC may have an undercurrent fault.
Further, the process of judging the over-temperature is as follows:
setting BLDC temperature maximum T max 0.5[ 2 ], [ T (k + 1) + T (k)]And T max Comparing and calculating the probability gamma of the over-temperature fault 3 Probability gamma 3 The formula of (1) is:
Figure BDA0003804040400000042
wherein, T =0.5[ T (k + 1) + T (k) ], T (k + 1) is a bus bar temperature value at the next time, and T (k) is a bus bar temperature value at the present time;
when gamma is 3 > 8%, it is suggested that the BLDC may have an over-temperature fault.
The invention has the beneficial effects that:
1. compared with the existing method, the method has high reliability, can improve the BLDC response speed and reduce overshoot;
2. when the motor runs, whether the motor has over-current, under-current, over-voltage, under-voltage and over-temperature faults or not can be detected.
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FIG. 1 is a flow chart of a method of the present invention for BLDC control and fault diagnosis based on a gray prediction model;
FIG. 2 is a schematic diagram of the DGM (1,1) model structure of the present invention;
FIG. 3 is a schematic flow chart of the HPO algorithm of the present invention;
FIG. 4 is a tachograph of the HPO control of the present invention;
FIG. 5 is a best fit value of the present invention;
FIG. 6 is an iteration graph of Kp and Ki of the present invention;
FIG. 7 is a graph illustrating the effectiveness of the overvoltage fault detection of the present invention;
FIG. 8 is a graph of the overcurrent fault detection effect of the present invention;
fig. 9 is a diagram showing the effect of the over-temperature fault detection of the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings and examples, which are simplified schematic drawings and which illustrate only the basic structure of the invention and, therefore, only show the structures associated with the invention.
As shown in fig. 1, a method for BLDC control and fault diagnosis based on a gray prediction model includes the steps of:
the method comprises the following steps of firstly, collecting a motor rotating speed value, a bus voltage, a bus current and a temperature value;
respectively passing through a speed acquisition module, a voltage acquisition module, a current acquisition module and a temperature acquisition module at equal time intervals T s Collecting a motor rotating speed value, a bus voltage value, a bus current value and a collected temperature value, and storing 4-bit collection results as an original sequence of a gray prediction model;
step two, establishing a grey prediction model;
and taking the sampling values stored by each acquisition module as an original sequence, and respectively predicting the speed value, the bus voltage value, the bus current value and the temperature value at the next moment by using a gray prediction DGM (1,1) model.
The specific prediction process is as follows, and a schematic diagram is shown in fig. 2.
The n raw data for a known sample are noted as:
x (0) ＝{x (0) (1),x (0) (2),x (0) (3)…x (0) (n)}
to reduce the effect of random numbers, the raw data is accumulated to generate a new sequence:
x (1) ＝{x (1) (1),x (1) (2),x (1) (3)…x (1) (n)}
wherein
Figure BDA0003804040400000061
Establishing a whitening equation of a DGM (1,1) model:
Figure BDA0003804040400000062
where a is called the coefficient of development and b is the amount of grey contribution.
Order to
Figure BDA0003804040400000063
From the least squares parameter estimation:
Figure BDA0003804040400000064
wherein
Figure BDA0003804040400000065
Figure BDA0003804040400000066
Accumulation sequence x (1) The predicted value of (A) is:
Figure BDA0003804040400000071
original sequence x (0) The predicted value of (A) is:
Figure BDA0003804040400000072
in the example, each prediction item adopts rolling prediction, and the original sequence value is continuously updated and iterated when the sampling storage value is predicted once in every 4 samples;
step three, establishing a self-adaptive speed PI module;
respectively comparing the predicted rotating speed value n (k + 1) and the current rotating speed value n (k) with the target rotating speed value n ref Making a difference to obtain a prediction error
Figure BDA0003804040400000073
And an actual error e (k) based on
Figure BDA0003804040400000074
And e (k) adjusting an error term e participating in speed PI control * (k)，e * (k) The calculation of (a) is as follows:
Figure BDA0003804040400000075
searching for the optimal values of the proportional coefficient and the integral coefficient of the speed PI ring by using an HPO algorithm to achieve the optimal control effect, wherein the principle of the HPO algorithm is as follows, and the flow chart is as shown in FIG. 3;
the initial population is randomly set to:
Figure BDA0003804040400000076
the position of each member in the initial population is:
x i ＝rand(1,d).*(ub-lb)+lb
wherein x is i Is the location of the hunter or prey, ub is the maximum value of the problem variable, lb is the minimum value of the problem variable, d is the dimension of the problem variable;
ub＝[ub 1 ,ub 2 ,ub 3 …ub d ]
lb＝[lb 1 ,lb 2 ,lb 3 …lb d ]
the mathematical model of hunter search mechanism is:
x i,j (t+1)＝x i,j (t)+0.5[(2CZP pos(j) -x i,j (t))+(2(1-C)Zu (j) -x i,j (t))]
where x (t) is the current hunter position, x (t + 1) is the hunter's next iteration position, P pos Is the location of the prey, μ is the average of all locations, Z is the adaptive parameter, the calculation formula is as follows:
Figure BDA0003804040400000081
Figure BDA0003804040400000082
wherein the content of the first and second substances,
Figure BDA0003804040400000083
and
Figure BDA0003804040400000084
is [0,1]Random vector of inner, P is
Figure BDA0003804040400000085
Index value of R 2 Is [0,1]The random number in (i) is IDX which is a vector satisfying the condition (P = = 0)
Figure BDA0003804040400000086
C is a balance parameter between exploration and development, the value of which decreases from 1 to 0.02 in an iterative process, the calculation formula is as follows:
Figure BDA0003804040400000087
wherein it is the current iteration number, and MaxIt is the maximum iteration number;
the average of all individual positions is calculated as:
Figure BDA0003804040400000088
the Euclidean distance formula for calculating the individual from the average position is as follows:
Figure BDA0003804040400000089
the search agent furthest from the mean position is considered the prey (P) pos )；
According to a hunting scenario, when a predator catches a game, the game dies and the predator moves to a new game location the next time the predator is moved to this problem, a decrementing mechanism is considered as follows:
kbest＝round(C×N)
wherein N is the number of search agents, at the start of the algorithm, the value of k equals N, the last search agent furthest from the mean position μ is selected as the prey and captured by the prey; assuming that the optimal safe position is the global optimal position, where the prey would have a better chance of survival, the predator would select another prey, and the prey position update formula is:
x i,j (t+1)＝T pos(j) +CZcos(2πR 4 )×(T pos(j) -x i,j (t))
where x (T) is the current position of the prey, x (T + 1) is the next iteration position of the prey, T pos Is the global optimum position, Z is the adaptive parameter, R 4 Is the range [ -1,1]The random number in C, the balance parameter between exploration and development, the value of which is reduced in the iterative process of the algorithm, and the cos function and the input parameter thereof allow the global optimal position of the next prey position at different radiuses and angles;
the formula for selecting hunters and prey is:
Figure BDA0003804040400000091
wherein R is 5 Is [0,1]Random number within the range, β is a tuning parameter set to 0.1; if R is 5 If beta is less than beta, the search population is considered as a hunter, and the search for the next position is updated by the first formula in the above formula; otherwise, the search population is regarded as prey, and the next position is searched and updated by using the second formula in the above formula;
simulating the BLDC under the control of the double closed loops based on the HPO algorithm, wherein relevant parameters of the BLDC are as follows:
TABLE 1 BLDC parameter Table
Figure BDA0003804040400000092
Based on the rotation speed diagram, the optimal adaptive value, the Kp and the Ki iteration of the BLDC under the control of the HPO algorithm, the obtained BLDC under the control of the HPO algorithm has no overshoot of two speed mutations, the response speed is high, and the K of the speed PI ring is shown in figures 4-6 p And K i Can achieve stability in less iteration times, and embodies the characteristics of high convergence rate and strong optimization capability of the HPO algorithm.
Step four, establishing a fault diagnosis model;
setting the predicted bus voltage value U (k + 1) at the next moment and the bus voltage value U (k) at the current moment to a fault diagnosis module, and setting the maximum value U of the BLDC bus voltage max And minimum value U min 0.5[ U (k + 1) + U (k) ]]And U max 、U min Comparing and calculating the probability gamma of overvoltage or undervoltage fault 1 When is γ 1 When the set threshold value is exceeded, the BLDC is prompted to have overvoltage or undervoltage faults;
let U =0.5[ U (k + 1) + U (k) ]
Figure BDA0003804040400000101
When U is more than U max ，γ 1 If the voltage is more than 5%, indicating that the BLDC can have overvoltage faults;
when U is less than U min ，γ 1 If the voltage is more than 3%, indicating that the BLDC can have undervoltage fault;
setting the predicted bus current value I (k + 1) at the next time and the bus current value I (k) at the current time to a fault diagnosis module, and setting the maximum value I of the BLDC bus current max And a minimum value I min 2, 0.5[ 2 ], [ I (k + 1) + I (k)]And I max 、I min Comparing, calculating the probability gamma of over-current or under-current fault 2 When is γ 2 When the set threshold value is exceeded, the BLDC is prompted to possibly sendOver-current or under-current faults occur.
Let I =0.5[ I (k + 1) + I (k) ]
Figure BDA0003804040400000111
When I > I max ，γ 2 > 9%, indicating that the BLDC may have an overcurrent fault.
When I < I min ，γ 2 > 6%, suggesting that the BLDC may have an undercurrent fault.
Setting the predicted temperature value T (k + 1) at the next moment and the current temperature value T (k) to the fault diagnosis module, and setting the maximum temperature value T of the BLDC max 0.5[ 2 ], T (k + 1) + T (k)]And T max Comparing and calculating the probability gamma of over-temperature fault 3 When is γ 3 When the set threshold value is exceeded, the BLDC is indicated to be possible to generate over-temperature fault. Let T =0.5[ T (k + 1) + T (k ]]
Figure BDA0003804040400000112
When gamma is 3 > 8%, it is suggested that the BLDC may have an over-temperature fault.
The BLDC model is simulated based on MATLAB software, and three fault diagnosis results are shown as follows: as can be seen from fig. 7, when the overvoltage fault occurs to the motor at 0.12s, the predicted value of the bus current reaches the maximum value first, then the average value of the predicted value and the actual value reaches the maximum value, and the actual value reaches the maximum value last, when the value of γ is reached 1 And when the voltage is more than 5%, the motor is prompted to have an overvoltage fault. The method can early warn the overvoltage fault in advance, reduce the fault duration and play a certain protection role on the motor;
as can be seen from fig. 8, when the overcurrent fault occurs in the motor at 0.12s, the predicted value of the bus current reaches the maximum value first, then the average value of the predicted value and the actual value reaches the maximum value, and the actual value reaches the maximum value last, when γ is reached 2 And when the current is more than 9%, the motor is prompted to have overcurrent fault. The method can early warn overcurrent faults, reduce fault duration time and protect the motor to a certain extentThe preparation method comprises the following steps of (1) using;
as can be seen from fig. 9, the temperature is low when the motor starts to operate and is close to the ambient temperature, when the motor works abnormally or the ambient temperature is too high, the predicted value of the temperature reaches the maximum value first, then the average value of the predicted value and the actual value reaches the maximum value, the actual value reaches the maximum value last, and when γ is reached 3 When the current is more than 8%, prompting that the motor has an over-temperature fault; the method can early warn the over-temperature fault in advance, reduce the fault duration and play a certain protection role on the motor.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.

Claims (9)

1. A method for BLDC control and fault diagnosis based on a gray predictive model, comprising the steps of:
acquiring a rotating speed value, a bus voltage, a bus current and a temperature value, and storing a plurality of acquisition results as an original sequence of a gray prediction model;
inputting the original sequence data into a grey prediction DGM (1,1) model, and respectively predicting a speed value, a bus voltage value, a bus current value and a temperature value at the next moment;
step three, establishing a self-adaptive speed PI model, and searching a proportional coefficient and an integral coefficient of a speed PI ring by using an HPO algorithm;
and fourthly, respectively calculating the probabilities of undervoltage, overvoltage, overcurrent, undercurrent and overtemperature according to the maximum value and the minimum value of the voltage, the current and the temperature of the BLDC bus, thereby judging whether a fault occurs.
2. The method for BLDC control and fault diagnosis based on gray prediction model of claim 1, wherein the predicted values of gray prediction DGM (1,1) model are:
Figure FDA0003804040390000011
wherein a is a development coefficient, b is a gray effect amount, and x (0) (1) For the first value of the original sequence,
Figure FDA0003804040390000012
is the (k + 1) th predictor of the accumulated sequence.
3. The method for BLDC control and fault diagnosis based on gray predictive model as claimed in claim 1, wherein step three specifically comprises:
respectively subtracting the predicted rotating speed value and the current rotating speed value from the target rotating speed value to obtain a predicted error and an actual error which are used as the input of an adaptive speed PI loop, and adjusting an error item participating in speed PI control according to the predicted error and the actual error;
the optimum values of the proportional coefficient and the integral coefficient of the speed PI loop are searched for using the HPO algorithm.
4. The method for BLDC control and fault diagnosis based on gray predictive models of claim 1, wherein calculating the probabilities of under-voltage, over-current, under-current, and over-temperature comprises:
setting maximum value U of BLDC bus voltage max And minimum value U min 0.5[ U (k + 1) + U (k) ]]And U max 、U min Comparing and calculating the probability gamma of overvoltage or undervoltage fault 1 Probability gamma 1 The formula of (1) is:
Figure FDA0003804040390000021
wherein U =0.5[ U (k + 1) + U (k) ], U (k + 1) being a bus voltage value at the next time, U (k) is the bus voltage value at the present time.
5. According to the rightThe method for BLDC control and fault diagnosis based on gray prediction model as claimed in claim 4, wherein the probability γ 1 > 5% and U > U max When the BLDC fails, over-voltage faults occur; probability gamma 1 Greater than 3% and U < U min When the BLDC is under-voltage failed.
6. The method for BLDC control and fault diagnosis based on gray predictive models of claim 1, wherein calculating the probabilities of under-voltage, over-current, under-current, and over-temperature further comprises:
setting maximum value I of BLDC bus current max And a minimum value I min 2, 0.5[ 2 ], [ I (k + 1) + I (k)]And I max 、I min Comparing and calculating the probability gamma of overcurrent or undercurrent fault 2 Probability gamma 2 The formula of (1) is:
Figure FDA0003804040390000022
where I =0.5[ I (k + 1) + I (k) ], I (k + 1) is a bus current value at the next time, and I (k) is a bus current value at the present time.
7. The method for BLDC control and fault diagnosis based on gray prediction model as claimed in claim 6, wherein the probability γ 2 > 9% and I > I max When the BLDC is in overcurrent fault; probability gamma 2 Greater than 6% and I < I min When the BLDC fails under-current.
8. The method for BLDC control and fault diagnosis based on gray predictive models of claim 1, wherein calculating the probabilities of under-voltage, over-current, under-current, and over-temperature further comprises:
setting BLDC temperature maximum T max 0.5[ 2 ], [ T (k + 1) + T (k)]And T max Comparing and calculating the probability gamma of over-temperature fault 3 Probability gamma 3 The formula of (1) is:
Figure FDA0003804040390000031
wherein, T =0.5[ T (k + 1) + T (k) ], T (k + 1) is a bus bar temperature value at the next time, and T (k) is a bus bar temperature value at the present time.
9. The method for BLDC control and fault diagnosis based on gray prediction model as claimed in claim 8, wherein the probability γ 3 Above 8%, the BLDC fails over-temperature.
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CN117496133A (en) * 2024-01-03 2024-02-02 山东工商学院 Closed bus R-CNN temperature fault monitoring method based on multi-mode data

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Publication number Priority date Publication date Assignee Title
CN115903577A (en) * 2022-10-26 2023-04-04 淮阴工学院 Environmental balance adjusting equipment based on HPO algorithm
CN117496133A (en) * 2024-01-03 2024-02-02 山东工商学院 Closed bus R-CNN temperature fault monitoring method based on multi-mode data
CN117496133B (en) * 2024-01-03 2024-03-22 山东工商学院 Closed bus R-CNN temperature fault monitoring method based on multi-mode data

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