A belt speed re-throwing method with zero voltage vector multi-pulse

Abstract

This invention discloses a zero-voltage vector multi-pulse belt speed re-engagement method, which relates to the field of motor control technology. This method involves calculating the winding short-circuit coefficient through real-time acquisition of multiple parameters such as current, voltage, temperature, and leakage charge. This method uses a three-stage process: continuous monitoring during normal operation; in the event of an interturn short circuit, an adaptive pulse width dynamic compensation algorithm is used to adjust the faulty phase voltage to restore inductance symmetry. The inflection point (i.e., evolution value) of the inductance trend model is analyzed using second-order derivatives to predict the risk of fault escalation; and in the event of an interphase short circuit, the machine is immediately shut down. Through multi-parameter fusion diagnosis and dynamic compensation strategies, this invention improves the accuracy of belt speed re-engagement detection and provides early warning of serious short-circuit risks.

Description

Technical Field

[0001] The present invention relates to the technical field of motor control, in particular to a zero voltage vector multi-pulse belt speed re-investment method. Background Art

[0002] Permanent magnet synchronous motors are a type of high-efficiency and energy-saving motor that are widely used in rail transit, electric vehicles, industrial drives, aerospace, and household appliances. They convert electrical energy into mechanical energy to drive equipment. Their core advantages lie in high power density, small size, high efficiency, and precise speed control capabilities. They are particularly suitable for scenarios with stringent requirements on performance and energy efficiency, such as high-speed rail traction systems, new energy vehicle powertrains, CNC machine tool servo systems, and variable-frequency air-conditioning compressors.

[0003] When the motor is in the idling state (still rotating) due to a fault or power outage, multiple short zero-voltage vector pulses (i.e., momentary short circuits) are applied to the motor stator windings. The changing pattern of the short-circuit current is used to quickly and accurately estimate the real-time motor speed and rotor position, thereby achieving a safe restart without a position sensor.

[0004] The motor's stator and rotor are both wound with coils, or windings. When powered on, a magnetic field is generated to drive the motor to rotate. The wires of the coils are separated by insulating material. However, if the insulation layer of the coils is damaged, the originally isolated coils will collide with each other, causing the windings to short-circuit. On this basis, the accuracy of estimating the real-time motor speed and rotor position based on the change law of the short-circuit current is reduced. Summary of the Invention

[0005] Technical problems solved

[0006] In response to the shortcomings of the existing technology, the present invention provides a zero-voltage vector multi-pulse speed re-investment method, which solves the problem of short circuit between the stator and rotor windings of the motor due to insulation damage, interference with the short-circuit current change law, and reduction in the accuracy of the motor's real-time speed and rotor position estimation.

[0007] Technical Solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a zero voltage vector multi-pulse belt speed re-casting method, comprising the following specific steps: Step 1: real-time acquisition of current data, voltage data, power supply frequency data, resistance, thermal imaging, high-frequency instantaneous voltage at the leakage point and capacitance and comprehensive calculation to obtain the winding short-circuit coefficient; Step 2: three situations are judged based on the winding short-circuit coefficient to determine whether the winding is short-circuited. The first situation is that the winding is normal, and the above operation is repeated in step 1. Then, the zero voltage vector multi-pulse is used for detection to obtain the evolution value. The second situation is that the short circuit is between turns, and the adaptive pulse width dynamic compensation algorithm is first used to compensate for the short circuit in one of the three phases. Phase inductance dynamic compensation is performed, and then zero voltage vector multi-pulse is used for detection, and the evolution of inter-turn short circuit into inter-phase short circuit is predicted, and the process returns to step one to repeat the above operation. If the third judgment is inter-phase short circuit, the motor is stopped directly and a prompt for maintenance is issued; Step three: Predict whether the inter-turn short circuit will evolve into an inter-phase short circuit based on the evolution value. If the predicted trend is an inter-turn short circuit, dynamic compensation is first performed through the adaptive pulse width dynamic compensation algorithm, and then zero voltage vector multi-pulse is used for detection, and the evolution of inter-turn short circuit into inter-phase short circuit is predicted, and the process returns to step one to repeat the above operation. If the predicted trend is an inter-phase short circuit, the motor is stopped directly and a prompt for maintenance is issued.

[0009] Furthermore, in step one, the current data, voltage data, and power frequency data are cleaned, and the current data and voltage data are calculated by the volt-ampere method to obtain the resistance. The current data, voltage data, power frequency data, and resistance are averaged and then calculated by the inductance phase difference method to obtain the average inductance. The average inductance is normalized and comprehensively calculated over time to obtain the inductance anomaly coefficient.

[0010] Furthermore, in step one, the thermal image is filtered and denoised, and the color in the thermal image is converted to RGB value using a color conversion RGB value tool. The RGB value includes a red channel value, a green channel value, and a blue channel value. The RGB value is normalized and comprehensively calculated over time to obtain a temperature anomaly coefficient.

[0011] Furthermore, in step one, the high-frequency instantaneous voltage is subjected to data cleaning processing, and the product of the high-frequency instantaneous voltage and the capacitance is calculated according to the capacitor energy storage formula to obtain the charge at the leakage point, and the inductance anomaly coefficient, the temperature anomaly coefficient and the charge at the leakage point are normalized and comprehensively calculated to obtain the winding short-circuit coefficient.

[0012] Furthermore, a specific method for obtaining the winding short-circuit coefficient is: RX=WY-DY+DH; wherein RX represents the winding short-circuit coefficient, WY represents the temperature anomaly coefficient, DY represents the inductance anomaly coefficient, and DH represents the charge at the leakage point.

[0013] Furthermore, in step 2, the inter-turn short-circuit interval is set according to historical experimental data. If the winding short-circuit coefficient is less than the lower limit of the inter-turn short-circuit interval, it is judged that the motor is normal. If the winding short-circuit coefficient is within the range of the winding short-circuit coefficient, it is judged to be an inter-turn short-circuit. If the winding short-circuit coefficient is greater than the upper limit of the inter-turn short-circuit interval, it is judged to be a phase short-circuit.

[0014] Furthermore, the specific steps of dynamically compensating the real-time speed and real-time position of the motor with inter-turn short circuit and the rotor through the adaptive pulse width dynamic compensation algorithm are as follows: comparing the inductances of the three phases respectively, judging the phase with reduced inductance as the fault phase according to the inductance phase difference method, and increasing the voltage of this phase in real time for the fault phase, calculating the current data through the variance method to obtain the current stability value, summarizing the current data of the normal operation of the motor to obtain the current stability dynamic threshold, comparing the current stability value with the current stability dynamic threshold, if the current stability value is within the current stability dynamic threshold, stop increasing the voltage of this phase, if the current stability value is not within the current stability dynamic threshold, continue to increase the voltage of this phase until the current stability value is within the current stability dynamic threshold.

[0015] Furthermore, the specific method for obtaining the evolution value is as follows: mathematical modeling is performed based on the characteristic of the accelerated inductance decrease trend to obtain an inductance trend model, and the second-order derivative of the inductance trend model is performed. When the second-order derivative of this model is equal to zero, the evolution value is obtained.

[0016] Furthermore, in step three, a real-time comparison is performed based on the current inductance and the evolution value. If the current inductance is less than the evolution value, the predicted trend is turn-to-turn short circuit. If the current inductance is greater than or equal to the evolution value, the predicted trend is phase-to-phase short circuit.

[0017] Beneficial effects

[0018] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0019] 1. By real-time monitoring of multi-dimensional parameters such as current, voltage, temperature, and leakage charge, the winding short-circuit coefficient is calculated. Combined with an adaptive pulse width dynamic compensation algorithm, the inductance of the faulty and normal phases is restored to symmetry, eliminating the interference of current fluctuations on zero voltage vector multi-pulse detection, and improving the estimation accuracy of the motor's real-time speed and rotor position.

[0020] 2. By leveraging the accelerating trend of inductance decrease, a monotonically decreasing convex-concave function model is established. The second-order derivative of the inductance trend model is then taken to predict whether a turn-to-turn short circuit will evolve into a phase-to-phase short circuit. This helps workers repair the motor in advance, preventing serious damage, reducing repair costs, and mitigating safety risks.

[0021] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The present invention is a flow chart of a zero-voltage vector multi-pulse belt speed re-investment method. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] It should be noted that, in this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include," "comprise," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0025] like Figure 1 As shown, the embodiment of the present invention provides a zero voltage vector multi-pulse belt speed re-casting method, which includes the following specific steps:

[0026] Step 1: Obtain current data in real time through a current sensor, obtain voltage data in real time through a voltage sensor, and obtain power frequency data in real time through a frequency meter. Clean the current data, voltage data, and power frequency data to remove redundant information in the current data, voltage data, and power frequency data to improve data accuracy. Calculate the current data and voltage data through the volt-ampere method, quantify the resistance through the linear relationship between the voltage data and the current data, and obtain the resistance. Since industrial motors have three phase differences, the current data, voltage data, power frequency data, and resistance of each phase are independent. Therefore, the current data, voltage data, power frequency data, and resistance are averaged and then calculated through the inductance phase difference method. The phase difference between voltage and current in alternating current is utilized, and the power frequency data and resistance are quantified to obtain the average inductance. Since the insulation layer of the coil is damaged, resulting in a short circuit between turns, the number of turns is reduced, and the average inductance decreases. The average inductance is normalized and comprehensively calculated over time to obtain the inductance anomaly coefficient.

[0027]

[0028] Where DY represents the inductance abnormal coefficient, reflecting whether the average inductance decreases, t represents time, DG t+1 Denotes the average inductance at time t+1, DG t It represents the average inductance at time t. If the average inductance decreases, the inductance abnormality coefficient is negative and gradually decreases.

[0029] Acquire thermal images in real time using a thermal imager and filter and reduce noise on them, which helps improve their quality. Use the color conversion tool to convert the colors in the filtered and noise-reduced thermal images to RGB values. RGB values ​​include red, green, and blue channel values, and each channel value ranges from 0 to 255. A short-circuited winding reduces resistance, causing a surge in current. According to the Joule heating effect, the square of the current is proportional to the amount of heat. Therefore, a short-circuited winding causes a gradual increase in temperature, which in turn increases the proportion of red in the thermal image and the red channel value. Over time, the RGB values ​​are normalized and comprehensively calculated to obtain the temperature anomaly coefficient.

[0030]

[0031] Among them, WY represents the temperature anomaly coefficient, reflecting whether the temperature is abnormal, t represents time, HT t+1 Indicates the red channel value at time t+1, VT t+1 Indicates the green channel value at time t+1, LT t+1 Indicates the blue channel value at time t+1, HT t Indicates the red channel value at time t, VT t Indicates the green channel value at time t, LT t Represents the blue channel value at time t. Since the red channel value gradually increases with time, the ratio of the red channel value to the red channel value, green channel value, and blue channel value at time t+1 is greater than the ratio of the red channel value to the red channel value, green channel value, and blue channel value at time t. Therefore Greater than 1, so the temperature anomaly coefficient is greater than zero and gradually increases.

[0032] Due to the damage of the insulation layer, leakage occurs at the damaged part. The high-frequency instantaneous voltage at the leakage point is obtained in real time through a high-frequency voltage pulse sensor. Data cleaning processing of the high-frequency instantaneous voltage helps to remove redundancy in the high-frequency instantaneous voltage. The capacitance is obtained from the motor specifications, and the product of the high-frequency instantaneous voltage and capacitance is calculated according to the capacitor energy storage formula to obtain the amount of charge at the leakage point. The more serious the damage to the insulation layer, the greater the amount of charge at the leakage point.

[0033] The inductance anomaly coefficient, temperature anomaly coefficient and charge at the leakage point are normalized and calculated comprehensively to obtain the winding short-circuit coefficient;

[0034] RX=WY-DY+DH;

[0035] Among them, RX represents the winding short-circuit coefficient, which reflects whether the motor winding is short-circuited and the severity of the insulation layer damage; WY represents the temperature anomaly coefficient, which reflects whether the temperature is abnormal; DY represents the inductance anomaly coefficient, which reflects whether the average inductance decreases. Since the inductance anomaly coefficient is negative, the negative sign is taken as positive; DH represents the amount of charge at the leakage point.

[0036] Step 2: Determine whether the winding is short-circuited based on the winding short-circuit coefficient. Winding short-circuit includes turn-to-turn short-circuit and phase-to-phase short-circuit. Turn-to-turn short-circuit means that part of the winding is short-circuited, which reduces the motor's operating performance and increases the probability of evolving into a phase-to-phase short-circuit over time. Phase-to-phase short-circuit means a short-circuit across phases, that is, industrial motors are three-phase motors. The voltage of the three phase differences allows the motor rotor to rotate on its own without an additional starting device. Phase-to-phase short-circuit will cause a momentary disconnection, which is much more serious than a turn-to-turn short-circuit. Therefore, a turn-to-turn short-circuit interval is set based on historical experimental data. If the winding short-circuit coefficient is less than the lower limit of the turn-to-turn short-circuit interval, the motor is judged to be normal. Subsequently, the real-time speed of the motor and the real-time speed of the rotor are detected by zero-voltage vector multi-pulse. The position is detected and the above operation is repeated. If the winding short-circuit coefficient is within the range of the winding short-circuit coefficient, it is determined to be an inter-turn short-circuit. The real-time speed of the motor and the real-time position of the rotor with the inter-turn short-circuit are dynamically compensated by the adaptive pulse width dynamic compensation algorithm. Subsequently, the real-time speed of the motor and the real-time position of the rotor are detected by zero voltage vector multi-pulse and the severity of the inter-turn short-circuit is predicted to obtain the evolution value, and the above operation is repeated. After the motor has an inter-turn short-circuit and has to run, the inter-turn short-circuit is prevented from evolving into an inter-phase short-circuit. If the winding short-circuit coefficient is greater than the upper limit of the inter-turn short-circuit interval, it is determined to be an inter-phase short-circuit. The motor is stopped directly and a maintenance prompt is issued.

[0037] The specific steps for dynamically compensating the real-time speed and rotor position of the motor with inter-turn short circuit using the adaptive pulse width dynamic compensation algorithm are as follows:

[0038] Due to the short circuit between turns, the number of turns decreases, and a fault phase appears, that is, the phase with a fault in the three phases, which causes the average inductance to decrease, resulting in asymmetric inductance between the fault phase and the normal phase in the three phases, which in turn causes the current in the motor to be unstable. The current fluctuation and noise superimposed increase the error between the real-time speed of the motor and the real-time position of the rotor detected by zero voltage vector multi-pulse. The inductances of the three phases are compared respectively, and the phase with reduced inductance is judged as the fault phase according to the inductance phase difference method. The voltage of the fault phase is increased in real time for the fault phase, and the inductance of the phase is increased. The current data is calculated by the variance method to obtain the current. The current stability value is obtained by summarizing the current data of the normal operation of the motor to obtain the current stability dynamic threshold. The current stability value is compared with the current stability dynamic threshold. If the current stability value is within the current stability dynamic threshold, the voltage of this phase is stopped from being increased. If the current stability value is not within the current stability dynamic threshold, the voltage of this phase is continued to be increased until the current stability value is within the current stability dynamic threshold. This helps to improve the accuracy of increasing the fault phase voltage and make the inductance of the fault phase and the normal phase in the three phases symmetrical, thereby avoiding the reduction of the accuracy of detecting the real-time speed of the motor and the real-time position of the rotor through zero voltage vector multi-pulse detection.

[0039] The specific method of obtaining the evolution value is as follows:

[0040] Since turn-to-turn short circuit has a probability of evolving into phase-to-phase short circuit over time, if the turn-to-turn short circuit evolves into the phase-to-phase short circuit, the severity of the winding short circuit increases and the trend of inductance decrease accelerates. That is, mathematical modeling is performed based on this characteristic. This mathematical model decreases monotonically and goes from slow to fast. The slow part is convex and the fast part is concave, resulting in an inductance trend model. Therefore, the inductance trend model is second-order differentiated. When the second-order derivative of this model is equal to zero, the inductance is at an inflection point, that is, the evolution value.

[0041] Step 3: Determine whether the turn-to-turn short circuit will evolve into a phase-to-phase short circuit over time based on the evolution value, and perform real-time comparison between the current inductance and the evolution value. If the current inductance is less than the evolution value, it is determined to be a turn-to-turn short circuit, that is, the winding short-circuit coefficient is within the range of the winding short-circuit coefficient. According to step 2, the real-time speed of the motor and the real-time position of the rotor with the turn-to-turn short circuit are dynamically compensated through the adaptive pulse width dynamic compensation algorithm. Subsequently, the real-time speed and real-time position of the motor are detected through zero voltage vector multi-pulses and the above operation is repeated in step 1. If the current inductance is greater than or equal to the evolution value, it is determined to be a phase-to-phase short circuit. According to step 2, the motor is directly stopped and a maintenance reminder is issued. Predicting that the motor will evolve from a turn-to-turn short circuit to a phase-to-phase short circuit will help workers repair the motor in advance, avoid serious damage to the motor, reduce repair costs and reduce safety risks.

[0042] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A zero voltage vector multi-pulse belt speed re-investment method, characterized by: The specific steps include: Step 1: Obtain current data, voltage data, power frequency data, resistance, thermal imaging, high-frequency instantaneous voltage at the leakage point, and capacitance in real time and perform comprehensive calculations to obtain the winding short-circuit coefficient; Step 2: Based on the winding short-circuit coefficient, three situations are judged to determine whether the winding is short-circuited. In the first case, if the winding is normal, the process returns to step 1 and repeats the operation. Then, zero voltage vector multi-pulse detection is performed to obtain an evolution value. In the second case, if it is a turn-to-turn short circuit, the inductance dynamic compensation algorithm is firstly performed on one of the three phases, and then zero voltage vector multi-pulse detection is performed. The evolution of the turn-to-turn short circuit into a phase-to-phase short circuit is predicted, and the process returns to step 1 and repeats the operation. In the third case, if it is a phase-to-phase short circuit, the motor is directly stopped and a maintenance prompt is issued. Step 3: Predict whether the turn-to-turn short circuit will evolve into a phase-to-phase short circuit based on the evolution value. If the predicted trend is a turn-to-turn short circuit, first perform dynamic compensation using the adaptive pulse width dynamic compensation algorithm, then perform detection using the zero voltage vector multi-pulse algorithm, and predict whether the turn-to-turn short circuit will evolve into a phase-to-phase short circuit. Return to step 1 and repeat the above operations. If the predicted trend is a phase-to-phase short circuit, directly stop the motor and issue a maintenance reminder. In step 1, the current data, voltage data, and power frequency data are cleaned, and the current data and voltage data are calculated using the volt-ampere method to obtain the resistance. The current data, voltage data, power frequency data, and resistance are averaged and then calculated using the inductance phase difference method to obtain the average inductance. The average inductance is normalized over time and comprehensively calculated to obtain the inductance anomaly coefficient. In step 1, the thermal image is filtered and denoised. The color in the thermal image is converted to RGB value using a color conversion RGB value tool. The RGB value includes the red channel value, the green channel value, and the blue channel value. The RGB values ​​are normalized and comprehensively calculated over time to obtain the temperature anomaly coefficient. In step 1, the high-frequency instantaneous voltage is cleaned, and the product of the high-frequency instantaneous voltage and the capacitance is calculated according to the capacitor energy storage formula to obtain the charge at the leakage point. The inductance anomaly coefficient, temperature anomaly coefficient, and charge at the leakage point are normalized and comprehensively calculated to obtain the winding short-circuit coefficient. The specific method for obtaining the winding short-circuit coefficient is: ； in, Indicates the winding short-circuit coefficient, represents the temperature anomaly coefficient, Indicates the inductance anomaly coefficient, Indicates the amount of charge at the leakage point.

2. A zero voltage vector multi-pulse belt speed re-casting method according to claim 1, characterized in that: In step 2, the inter-turn short-circuit interval is set according to historical experimental data. If the winding short-circuit coefficient is less than the lower limit of the inter-turn short-circuit interval, the motor is judged to be normal. If the winding short-circuit coefficient is within the range of the winding short-circuit coefficient, it is judged to be an inter-turn short-circuit. If the winding short-circuit coefficient is greater than the upper limit of the inter-turn short-circuit interval, it is judged to be a phase short-circuit.

3. The method for re-investing a belt with a zero voltage vector multi-pulse according to claim 1, characterized in that: The specific steps of dynamically compensating the real-time speed and rotor position of the motor with inter-turn short circuit by using the adaptive pulse width dynamic compensation algorithm are as follows: The inductances of the three phases are compared separately, and the phase with reduced inductance is judged as the fault phase according to the inductance phase difference method. The voltage of this phase is increased in real time for the fault phase. The current data is calculated by the variance method to obtain the current stability value. The current data of the normal operation of the motor is summarized to obtain the current stability dynamic threshold. The current stability value is compared with the current stability dynamic threshold. If the current stability value is within the current stability dynamic threshold, the voltage of this phase is stopped from being increased. If the current stability value is not within the current stability dynamic threshold, the voltage of this phase is continued to be increased until the current stability value is within the current stability dynamic threshold.

4. The method for re-investing a belt with a zero voltage vector multi-pulse according to claim 1, characterized in that: The specific method of obtaining the evolution value is as follows: According to the characteristic of the accelerated decreasing trend of the inductance, mathematical modeling is performed to obtain an inductance trend model. The second-order derivative of the inductance trend model is taken, and when the second-order derivative of the model is equal to zero, the evolution value is obtained.

5. The method for re-investing a belt with zero voltage vector multi-pulse according to claim 1, characterized in that: In step three, a real-time comparison is performed based on the current inductance and the evolution value. If the current inductance is less than the evolution value, the predicted trend is turn-to-turn short circuit. If the current inductance is greater than or equal to the evolution value, the predicted trend is phase-to-phase short circuit.

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