A method of BLDC control and fault diagnosis based on grey prediction model

Abstract

The present invention relates to the technical field of control and fault diagnosis, and in particular to a method for BLDC control and fault diagnosis based on a grey prediction model, including collecting speed values, bus voltage, bus current and temperature values; inputting original sequence data into a grey prediction DGM (1,1) model to respectively predict the speed value, bus voltage value, bus current value and temperature value at the next moment; and using an HPO algorithm to search for the proportional coefficient and integral coefficient of the speed PI loop; and calculating the probability of undervoltage, overvoltage, overcurrent, undercurrent and overtemperature according to the maximum and minimum values ​​of the BLDC bus voltage, current and temperature, so as to judge whether a fault occurs. The present invention uses the collected bus current value, voltage and temperature as historical data, predicts the bus current, voltage and temperature values ​​at future moments through a grey prediction model, combines the predicted value with the actual value, and jointly diagnoses whether the motor is faulty, thereby greatly reducing the possibility of misdiagnosis.

Description

Technical Field

[0001] The present invention relates to the technical field of control and fault diagnosis, and in particular to a method for BLDC control and fault diagnosis based on a grey prediction model. Background Art

[0002] As for the failure of brushless DC motors, the motor is most efficient when it works at rated voltage. When the voltage is too high, the motor's no-load excitation current will increase, which is not conducive to the operation of the motor and causes the motor efficiency to decrease. When the voltage is too low, the motor's output power will decrease, causing overload, and in severe cases, stalling. Excessive load, winding short circuit, winding open circuit, three-phase voltage imbalance and other reasons can cause the three busbar currents of BLDC to increase abnormally, causing harm to the motor. When the motor current is too low, it cannot meet the load demand and is prone to overload. The temperature of the motor is also required to be within the normal range when it is running. Excessive temperature can easily cause aging of the winding coil and shorten the service life of the motor.

[0003] The invention patent "Open-circuit fault-tolerant control method for electric drive system based on grey prediction theory" (CN111740682B) discloses an open-circuit fault-tolerant control method for electric drive system based on grey prediction theory. The method uses a grey prediction model to predict the three-phase current value at the next moment, obtains the difference between the predicted data and the original data of the three-phase current, and compares it with the set threshold value to diagnose the occurrence of the fault. This method requires real-time detection of the phase current of the motor, and can quickly diagnose the fault signal when a fault occurs. However, when the motor load suddenly changes within the normal range, the predicted value of the three-phase current of the motor is still developing according to the original trend, while the actual current has increased. The difference between the two at this time may exceed the set threshold, causing misdiagnosis.

[0004] In addition, when the temperature of the motor is too high during operation, on the one hand, the insulation layer on the outer surface of the motor winding will age, which may cause the risk of short circuit over a long period of time. On the other hand, it will damage the electronic components on the motor control board and cause motor failure. Summary of the invention

[0005] In view of the shortcomings of the existing algorithms, the present invention uses the collected bus current values ​​as historical data, predicts the bus current values ​​at future moments through a grey prediction model, combines the predicted values ​​with the actual values, and jointly diagnoses whether the motor has a risk of overcurrent or undercurrent, thereby greatly reducing the possibility of misdiagnosis; it can diagnose when the motor is about to have an overcurrent or has just had an overcurrent, which is faster than the traditional detection method; at the same time, the present invention also diagnoses overvoltage and undervoltage faults; and by collecting historical temperature data values, the grey prediction model is used to predict the temperature value of the motor at the next moment, so as to diagnose whether the motor has a risk of overheating.

[0006] The technical solution adopted by the present invention is: a method for BLDC control and fault diagnosis based on a grey prediction model comprises the following steps:

[0007] Step 1: collecting speed values, bus voltage, bus current and temperature values, and storing several collected results as the original sequence of the grey prediction model;

[0008] Step 2: Input the original sequence data into the grey prediction DGM (1,1) model to predict the speed value, bus voltage value, bus current value and temperature value at the next moment respectively;

[0009] Furthermore, the prediction value of the grey prediction DGM (1,1) model is:

[0010]

[0011] Among them, a is the development coefficient, b is the gray action, x (0) (1) is the first value of the original sequence, is the k+1th predicted value of the cumulative sequence;

[0012] The advantage of the grey prediction DGM (1,1) model over the traditional grey GM (1,1) model is that it is more applicable to swing or oscillating development sequences and has better prediction effect. The prediction sequences targeted by the present invention are all oscillating sequences, so the grey prediction DGM (1,1) model is more suitable.

[0013] Step 3: Establish an adaptive speed PI model and use the HPO algorithm to search for the proportional coefficient and integral coefficient of the speed PI loop;

[0014] Further, it specifically includes:

[0015] The predicted speed value and the current speed value are respectively subtracted from the target speed value to obtain the predicted error and the actual error, which are used as the input of the adaptive speed PI loop, and the error term involved in the speed PI control is adjusted according to the predicted error and the actual error;

[0016] The HPO algorithm is used to search for the optimal values ​​of the proportional coefficient and integral coefficient of the speed PI loop;

[0017] Step 4: Calculate the probability of undervoltage, overvoltage, overcurrent, undercurrent and overtemperature according to the maximum and minimum values ​​of the BLDC bus voltage, current and temperature, so as to determine whether a fault occurs;

[0018] Furthermore, the judgment process of undervoltage and overvoltage is as follows:

[0019] Set the maximum value of BLDC bus voltage U max and the minimum value U min , compare 0.5[U(k+1)+U(k)] with Umax , U min By comparison, the probability γ1 of overvoltage or undervoltage fault is calculated. The formula of probability γ1 is:

[0020]

[0021] Among them, U = 0.5 [U (k + 1) + U (k)], U (k + 1) is the bus voltage value at the next moment, and U (k) is the bus voltage value at the current moment;

[0022] When U>U max , when γ1>5%, it indicates that the BLDC may have an overvoltage fault;

[0023] When U<U min , when γ1>3%, it indicates that the BLDC may have an undervoltage fault;

[0024] Furthermore, the judgment process of overcurrent and undercurrent is as follows:

[0025] Set the maximum value of BLDC bus current I max and minimum value I min , compare 0.5[I(k+1)+I(k)] with I max ,I min By comparison, the probability γ2 of overcurrent or undercurrent fault is calculated. The formula of probability γ2 is:

[0026]

[0027] Where, I = 0.5 [I (k + 1) + I (k)], I (k + 1) is the bus current value at the next moment, and I (k) is the bus current value at the current moment;

[0028] When I>I max , when γ2>9%, it indicates that the BLDC may have an overcurrent fault;

[0029] When I<I min When γ2>6%, it indicates that the BLDC may have an undercurrent fault.

[0030] Furthermore, the over-temperature judgment process is as follows:

[0031] Set the maximum BLDC temperature T max , 0.5[T(k+1)+T(k)] and T max By comparison, the probability of over-temperature failure γ3 is calculated. The formula for probability γ3 is:

[0032]

[0033] Wherein, T = 0.5 [T (k + 1) + T (k)], T (k + 1) is the bus temperature value at the next moment, and T (k) is the bus temperature value at the current moment;

[0034] When γ3>8%, it indicates that the BLDC may have an over-temperature fault.

[0035] Beneficial effects of the present invention:

[0036] 1. Compared with the existing methods, it has high reliability, can improve the BLDC response speed and reduce the overshoot;

[0037] 2. While the motor is running, it can detect whether the motor has overcurrent, undercurrent, overvoltage, undervoltage and overtemperature faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of the method for BLDC control and fault diagnosis based on the grey prediction model of the present invention;

[0039] Figure 2 It is a schematic diagram of the DGM (1,1) model structure of the present invention;

[0040] Figure 3 It is a schematic diagram of the HPO algorithm flow of the present invention;

[0041] Figure 4 is a rotation speed diagram of the HPO control of the present invention;

[0042] Figure 5 is the best adaptation value of the present invention;

[0043] Figure 6 It is the Kp and Ki iteration graph of the present invention;

[0044] Figure 7 This is an effect diagram of overvoltage fault detection of the present invention;

[0045] Figure 8 This is an overcurrent fault detection effect diagram of the present invention;

[0046] Fig. 9 It is an effect diagram of over-temperature fault detection of the present invention. DETAILED DESCRIPTION

[0047] The present invention is further described below in conjunction with the accompanying drawings and embodiments. This figure is a simplified schematic diagram, which only illustrates the basic structure of the present invention in a schematic manner, and therefore it only shows the components related to the present invention.

[0048] like Figure 1 As shown, a method for BLDC control and fault diagnosis based on a grey prediction model includes the following steps:

[0049] Step 1: Collect motor speed value, bus voltage, bus current and temperature value;

[0050] The speed acquisition module, voltage acquisition module, current acquisition module and temperature acquisition module are used to collect data at equal time intervals T. s Collect the motor speed value, bus voltage value, bus current value and temperature value, and store the 4-bit collection results as the original sequence of the grey prediction model;

[0051] Step 2: Establish a grey prediction model;

[0052] The sampling values ​​stored in each acquisition module are taken as the original sequence, and the grey prediction DGM (1,1) model is used to predict the speed value, bus voltage value, bus current value and temperature value at the next moment.

[0053] The specific prediction process is as follows, and the schematic diagram is as follows Figure 2 shown.

[0054] The n original data of known sampling are recorded as:

[0055] x (0) ={x (0) (1),x (0) (2),x (0) (3)…x (0) (n)}

[0056] In order to reduce the impact of random numbers, the original data is accumulated to generate a new sequence:

[0057] x (1) ={x (1) (1),x (1) (2),x (1) (3)…x (1) (n)}

[0058] in

[0059]

[0060] Establish the whitening equation of the DGM(1,1) model:

[0061]

[0062] Among them, a is called the development coefficient and b is called the grey action.

[0063] make From the least squares parameter estimation, we can get:

[0064]

[0065] in

[0066]

[0067]

[0068] Accumulate sequence x (1) The predicted value of is:

[0069]

[0070] Original sequence x (0) The predicted value of is:

[0071]

[0072] In this example, each forecast item adopts rolling forecast, and the sample storage value is predicted once every 4, and the original sequence value is continuously updated and iterated;

[0073] Step 3: Establish an adaptive speed PI module;

[0074] The predicted speed value n(k+1) and the current speed value n(k) are respectively compared with the target speed value n ref The prediction error is obtained by subtracting and the actual error e(k), according to and e(k) to adjust the error term e involved in speed PI control * (k), e * (k) is obtained as follows:

[0075]

[0076] The HPO algorithm is used to search for the optimal values ​​of the proportional coefficient and integral coefficient of the speed PI loop to achieve the optimal control effect. The principle of the HPO algorithm is shown below, and its flow chart is as follows Figure 3 As shown;

[0077] Set the initial population random to:

[0078]

[0079] The position of each member in the initial population is:

[0080] x i =rand(1,d).*(ub-lb)+lb

[0081] Among them, x i is the position of the hunter or prey, ub is the maximum value of the problem variable, lb is the minimum value of the problem variable, and d is the dimension of the problem variable;

[0082] ub=[ub1,ub2,ub3…ub d ]

[0083] lb=[lb1,lb2,lb3…lb d ]

[0084] The mathematical model of the hunter search mechanism is:

[0085] 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))]

[0086] Among them, x(t) is the current position of the hunter, x(t+1) is the next iteration position of the hunter, and P pos is the position of the prey, μ is the average of all positions, and Z is the adaptive parameter, which is calculated as follows:

[0087]

[0088]

[0089] in, and is a random vector in [0,1], and P is The index value, R2 is a random number in [0,1], and IDX is a vector that satisfies the condition (P == 0) The index value of , C is the balance parameter between exploration and development, and its value decreases from 1 to 0.02 during the iteration process. The calculation formula is as follows:

[0090]

[0091] Among them, it is the current iteration number, MaxIt is the maximum iteration number;

[0092] The formula for calculating the average value of all individual positions is:

[0093]

[0094] The formula for calculating the Euclidean distance between an individual and the average position is:

[0095]

[0096] The search agent farthest from the average position is considered prey (P pos );

[0097] According to the hunting scenario, when the predator captures the prey, the prey will die, and the next time, the predator will move to a new prey location. To solve this problem, consider a decreasing mechanism as follows:

[0098] kbest = round(C×N)

[0099] Where N is the number of search agents. At the beginning of the algorithm, the value of k is equal to N. The last search agent farthest from the average position μ is selected as the prey and captured by the hunter. Assuming that the best safe position is the global best position, the prey will have a better chance of survival at this time, and the predator may choose another prey. The prey position update formula is:

[0100] x i,j (t+1)=T pos(j) +CZcos(2πR4)×(T pos(j) -x i,j (t))

[0101] Among them, x(t) is the current position of the prey, x(t+1) is the next iteration position of the prey, and T pos is the global optimal position, Z is an adaptive parameter, R4 is a random number in the range [-1,1], C is a balance parameter between exploration and exploitation, whose value decreases during the iteration of the algorithm, and the cosine function and its input parameters allow the next prey position to be at the global optimal position at different radii and angles;

[0102] The calculation formula for selecting hunters and prey is:

[0103]

[0104] Where R5 is a random number in the range of [0,1], and β is a tuning parameter set to 0.1; if R5 < β, the search population will be regarded as a hunter, and the search for the next position will be updated using the first formula in the above formula; otherwise, the search population will be regarded as a prey, and the search for the next position will be updated using the second formula in the above formula;

[0105] Based on the HPO algorithm, the BLDC under dual closed-loop control is simulated, and the BLDC related parameters are as follows:

[0106] Table 1 BLDC parameter table

[0107]

[0108] The speed diagram, optimal adaptation value, Kp, Ki iteration of BLDC under HPO algorithm control are as follows: Figure 4-6 As shown in the figure, the BLDC under the control of the HPO algorithm has no overshoot in the two speed mutations and has a fast response speed. p and K i It can achieve stability within a relatively small number of iterations, which shows that the HPO algorithm has the characteristics of fast convergence speed and strong optimization ability.

[0109] Step 4: Establish a fault diagnosis model;

[0110] The predicted bus voltage value U(k+1) at the next moment and the bus voltage value U(k) at the current moment are given to the fault diagnosis module, and the maximum value U of the BLDC bus voltage is set. max and the minimum value U min , compare 0.5[U(k+1)+U(k)] with U max , U min By comparison, the probability of overvoltage or undervoltage fault occurrence γ1 is calculated. When γ1 exceeds the set threshold, it indicates that the BLDC may have an overvoltage or undervoltage fault.

[0111] Let U = 0.5[U(k+1)+U(k)]

[0112]

[0113] When U>U max , when γ1>5%, it indicates that the BLDC may have an overvoltage fault;

[0114] When U<U min , when γ1>3%, it indicates that the BLDC may have an undervoltage fault;

[0115] The predicted bus current value I(k+1) at the next moment and the bus current value I(k) at the current moment are given to the fault diagnosis module, and the maximum value I of the BLDC bus current is set. max and minimum value I min , compare 0.5[I(k+1)+I(k)] with I max ,I min By comparison, the probability γ2 of overcurrent or undercurrent fault is calculated. When γ2 exceeds the set threshold, it indicates that the BLDC may have an overcurrent or undercurrent fault.

[0116] Let I = 0.5[I(k+1)+I(k)]

[0117]

[0118] When I>I max When γ2>9%, it indicates that the BLDC may have an overcurrent fault.

[0119] When I<I min When γ2>6%, it indicates that the BLDC may have an undercurrent fault.

[0120] The predicted temperature value T(k+1) at the next moment and the current temperature value T(k) are given to the fault diagnosis module, and the maximum BLDC temperature T is set. max, 0.5[T(k+1)+T(k)] and T max By comparison, the probability of over-temperature fault γ3 is calculated. When γ3 exceeds the set threshold, it indicates that BLDC may have over-temperature fault. Let T = 0.5[T(k+1)+T(k)]

[0121]

[0122] When γ3>8%, it indicates that the BLDC may have an over-temperature fault.

[0123] The BLDC model is simulated based on MATLAB software, and the three fault diagnosis results are as follows: Figure 7 It can be seen that at 0.12s, the motor has an overvoltage fault, the predicted value of the bus current reaches the maximum value first, followed by the average of the predicted value and the actual value, and the actual value reaches the maximum value last. When γ1>5%, it indicates that the motor has an overvoltage fault. This method can warn of overvoltage faults in advance, reduce the duration of faults, and play a certain protective role on the motor;

[0124] Depend on Figure 8 It can be seen that at 0.12s, the motor has an overcurrent fault, the predicted value of the bus current reaches the maximum value first, followed by the average of the predicted value and the actual value, and the actual value reaches the maximum value last. When γ2>9%, it indicates that the motor has an overcurrent fault. This method can warn of overcurrent faults in advance, reduce the duration of faults, and play a certain protective role on the motor;

[0125] Depend on Fig. 9 It can be seen that when the motor starts running, the temperature is low and 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, followed by the average of the predicted value and the actual value reaching the maximum value, and the actual value reaches the maximum value last. When γ3>8%, it indicates that the motor has an over-temperature fault. This method can warn of over-temperature faults in advance, reduce the duration of faults, and play a certain protective role for the motor.

[0126] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A method for BLDC control and fault diagnosis based on grey prediction model, characterized in that: The following steps are involved: Step 1: collecting speed values, bus voltage, bus current and temperature values, and storing several collected results as the original sequence of the grey prediction model; Step 2: Input the original sequence data into the grey prediction DGM (1,1) model to predict the speed value, bus voltage value, bus current value and temperature value at the next moment respectively; The predicted value of the grey prediction DGM(1,1) model is: Among them, a is the development coefficient, b is the grey action, x( 0 )(1) is the first value of the original sequence, is the k+1th predicted value of the cumulative sequence; Step 3: Establish an adaptive speed PI model and use the HPO algorithm to search for the proportional coefficient and integral coefficient of the speed PI loop; Step three specifically includes: The predicted speed value and the current speed value are respectively subtracted from the target speed value to obtain the predicted error and the actual error, which are used as the input of the adaptive speed PI loop, and the error term involved in the speed PI control is adjusted according to the predicted error and the actual error; The HPO algorithm is used to search for the optimal values ​​of the proportional coefficient and integral coefficient of the speed PI loop; Step 4: Calculate the probability of undervoltage, overvoltage, overcurrent, undercurrent and overtemperature according to the maximum and minimum values ​​of the BLDC bus voltage, current and temperature, so as to determine whether a fault occurs; Calculation of the probability of undervoltage, overvoltage, overcurrent, undercurrent and overtemperature includes: Set the maximum value of BLDC bus voltage U max and the minimum value U min , compare 0.5[U(k+1)+U(k)] with U max , U min By comparison, the probability γ1 of overvoltage or undervoltage fault is calculated. The formula of probability γ1 is: Among them, U=0.5[U(k+1)+U(k)], U(k+1) is the bus voltage value at the next moment, and U(k) is the bus voltage value at the current moment.

2. The method for BLDC control and fault diagnosis based on grey prediction model according to claim 1, characterized in that: Probability γ1>5% and U>U max When , BLDC overvoltage fault occurs; probability γ1>3% and U<U min When , the BLDC has an undervoltage fault.

3. The method for BLDC control and fault diagnosis based on grey prediction model according to claim 1, characterized in that: Calculation of the probabilities of undervoltage, overvoltage, overcurrent, undercurrent and overtemperature also includes: Set the maximum value of BLDC bus current I max and minimum value I min , compare 0.5[I(k+1)+I(k)] with I max ,I min By comparison, the probability γ2 of overcurrent or undercurrent fault is calculated. The formula of probability γ2 is: Among them, I=0.5[I(k+1)+I(k)], I(k+1) is the bus current value at the next moment, and I(k) is the bus current value at the current moment.

4. The method for BLDC control and fault diagnosis based on grey prediction model according to claim 3 is characterized in that: Probability γ2>9% and I>I max When , BLDC has an overcurrent fault; probability γ2>6% and I<I min When , an undercurrent fault occurs in the BLDC.

5. The method for BLDC control and fault diagnosis based on grey prediction model according to claim 1, characterized in that: Calculation of the probabilities of undervoltage, overvoltage, overcurrent, undercurrent and overtemperature also includes: Set the maximum BLDC temperature T max , 0.5[T(k+1)+T(k)] and T max By comparison, the probability of over-temperature failure γ3 is calculated. The formula for probability γ3 is: Wherein, T=0.5[T(k+1)+T(k)], T(k+1) is the bus temperature value at the next moment, and T(k) is the bus temperature value at the current moment.

6. The method for BLDC control and fault diagnosis based on grey prediction model according to claim 5 is characterized in that: When the probability γ3>8%, the BLDC will have an over-temperature fault.

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