Method for open-phase protection of an electric motor, controller and laundry appliance

Abstract

The application relates to a motor open-phase protection method, a controller and a washing equipment. The method samples three-phase currents of a motor, obtains a three-phase current data sequence of a current sampling window, calculates a current integral imbalance degree based on the three-phase current data sequence, performs phase mutation detection based on the three-phase current data sequence, generates a phase deviation amount, and then performs weighted fusion on the current integral imbalance degree and the phase deviation amount to obtain a fusion score. Finally, a hierarchical protection action is executed based on the fusion score. Therefore, the method can accurately detect an open-phase fault, has rapid response and high robustness, and meanwhile, the fusion score can directly reflect the fault severity, facilitating the execution of subsequent protection actions, and realizing the cooperative optimization of rapidity and reliability.

Description

Technical Field

[0001] This application relates to the field of motor phase loss protection, and in particular to a motor phase loss protection method, controller, and washing equipment. Background Technology

[0002] In high-speed spin-drying conditions of washing machines, rapid and accurate detection of motor phase loss faults is crucial for system safety. While existing methods based on current amplitude analysis can reuse sampling circuits and are relatively low-cost, they are affected by rotor inertia, resulting in a delayed current amplitude drop during phase loss, leading to electrical anomalies. This causes significant detection delays and susceptibility to interference from normal current fluctuations caused by clothing eccentricity. Voltage detection methods require additional hardware and are susceptible to PWM noise interference, while model observer methods are highly dependent on motor parameters, have complex algorithms, and suffer from blind spots at low speeds. Furthermore, under phase loss fault conditions, the motor exhibits a phase change that precedes the amplitude drop, rendering the aforementioned phase loss fault detection methods inaccurate and exhibiting slow response and low reliability. Summary of the Invention

[0003] This application addresses at least one of the aforementioned technical problems to a certain extent. To this end, this application provides a motor phase loss protection method, controller, and washing equipment, which can accurately detect motor phase loss faults through the coordinated detection of current integral imbalance and phase change, and can achieve high robustness while responding extremely quickly.

[0004] In a first aspect, embodiments of this application provide a method for protecting a motor from phase loss, the method comprising: The three-phase current of the motor is sampled to obtain the three-phase current data sequence of the current sampling window; The current integral unbalance is calculated based on the three-phase current data sequence, wherein the value of the current integral unbalance characterizes the uniformity of the three-phase current amplitude distribution of the motor. Phase abrupt change detection is performed based on the three-phase current data sequence to generate phase deviation. The current integral imbalance and the phase deviation are weighted and fused to obtain a fusion score; Based on the fusion score, hierarchical protection actions are performed.

[0005] In some embodiments, calculating the current integral unbalance based on the three-phase current data sequence includes: The integral value of the absolute value of each phase current is calculated using the following formula: ; in, The integral value of the absolute value of the x-th phase current. Let N be the instantaneous value of the x-th phase current at the nth sampling point, and N be the number of sampling points within the sampling window; The current integral unbalance is calculated using the following formula: ; in, The integral unbalance of the current. The integral value of the absolute value of the U-th phase current. This is the integral value of the absolute value of the current in phase V. This is the integral value of the absolute value of the current in phase W.

[0006] In some embodiments, the step of detecting a phase abrupt change based on the three-phase current data sequence and generating a phase deviation includes: The three-phase current data sequence is processed to obtain a first complex spectrum sequence, wherein the first complex spectrum sequence includes M complex spectrum values, the three-phase current data sequence includes N current data, and M is greater than N; The fundamental phase of the current sampling window is extracted based on the first complex spectrum sequence; Based on the fundamental phase of the current sampling window and the fundamental phase of the previous sampling window, the actual phase increment of each phase is obtained; Obtain the theoretical phase increment; The phase deviation of each phase is calculated based on the actual phase increment and the theoretical phase increment of each phase.

[0007] In some embodiments, extracting the fundamental phase of the current sampling window based on the first complex spectral sequence includes: Determine a first set of frequency points, wherein the first set of frequency points is an integer set of frequency points centered on the theoretical frequency point corresponding to the fundamental frequency; Obtain a target complex spectrum sequence from the first complex spectrum sequence, wherein the target complex spectrum sequence includes the complex spectrum values ​​corresponding to each frequency point in the first frequency point set; Find the first complex spectrum value corresponding to the first integer frequency point and the second complex spectrum value corresponding to the second integer frequency point from the target complex spectrum sequence, wherein the first integer frequency point is the integer frequency point with the largest amplitude in the target complex spectrum sequence, and the second integer frequency point is the next integer frequency point after the first integer frequency point; Determine the first frequency correction amount; Based on the first frequency correction, the first integer spectrum value and the second complex spectrum value are linearly interpolated to obtain the fundamental spectrum value, wherein the fundamental spectrum value is the complex spectrum value corresponding to the fundamental frequency. The fundamental phase of the current sampling window is determined based on the fundamental frequency spectrum value.

[0008] In some embodiments, determining the first frequency correction amount includes: Find the third complex spectrum value corresponding to the third integer frequency point from the target complex spectrum sequence, wherein the third integer frequency point is the previous integer frequency point of the first integer frequency point; Calculate the first amplitude of the first complex spectrum value, the second amplitude of the second complex spectrum value, and the third amplitude of the third complex spectrum value; The first frequency correction amount is determined based on the first amplitude, the second amplitude, and the third amplitude.

[0009] In some embodiments, determining the first frequency correction amount based on the first amplitude, the second amplitude, and the third amplitude includes: The first frequency correction amount is calculated using the following formula: ; in, The first complex spectrum value, The second complex spectrum value, The third complex spectrum value, For the first integer frequency point, The second integer frequency point, The third integer frequency point, This is the first frequency correction amount.

[0010] In some embodiments, calculating the phase deviation of each phase based on the actual phase increment and the theoretical phase increment of each phase includes: The phase deviation of each phase is calculated using the following formula: ; in, The phase deviation amount, The actual phase increment, The fundamental phase of the current sampling window. The fundamental phase of the previous sampling window is denoted by Δt, where Δt is the time interval between adjacent sampling windows. This is the theoretical phase increment.

[0011] In some embodiments, the method further includes: Based on the first complex spectrum sequence, the fundamental amplitude and the odd harmonic amplitude are calculated, wherein the fundamental amplitude is the amplitude corresponding to the fundamental spectrum value, the odd harmonic amplitude is the amplitude corresponding to the harmonic spectrum value, and the harmonic spectrum value is the complex spectrum value corresponding to the odd harmonic. The harmonic distortion rate is calculated based on the fundamental frequency amplitude and the odd harmonic amplitude. The current integral imbalance, the phase deviation, and the harmonic distortion rate are weighted and fused to obtain the fusion score.

[0012] In some embodiments, the weighted fusion of the current integral imbalance, the phase deviation, and the harmonic distortion rate to obtain the fusion score includes: A current integration flag is determined, wherein if the current integration imbalance is greater than a first preset threshold, the current integration flag is determined to be 1; otherwise, the current integration flag is determined to be 0. A phase change flag is determined, wherein if the maximum value of the phase change amount of each phase is greater than a second preset threshold, the phase change flag is determined to be 1; otherwise, the phase change flag is determined to be 0. Determine a harmonic auxiliary flag, wherein if the harmonic distortion rate is greater than a third preset threshold, the harmonic auxiliary flag is determined to be 1; otherwise, the harmonic auxiliary flag is determined to be 0. The fusion score is calculated using the following formula: ; in, The fusion score is given by w1, w2, and w3, which are weighting coefficients that satisfy w1 + w2 + w3 = 1. For the current integral flag, This is the phase abrupt change flag. This is the harmonic auxiliary symbol.

[0013] In some embodiments, the weighted fusion of the current integral imbalance and the phase deviation to obtain a fusion score includes: A current integration flag is determined, wherein if the current integration imbalance is greater than a first preset threshold, the current integration flag is determined to be 1; otherwise, the current integration flag is determined to be 0. A phase change flag is determined, wherein if the maximum value of the phase change amount of each phase is greater than a second preset threshold, the phase change flag is determined to be 1; otherwise, the phase change flag is determined to be 0. The fusion score is calculated using the following formula: ; in, The fusion score is given by w1 and w2, which are weighting coefficients that satisfy w1 + w2 = 1. For the current integral flag, This is the phase abrupt change flag.

[0014] In some embodiments, the method further includes: The load compensation item is determined based on the load rate and the high load rate threshold. The first preset threshold is dynamically adjusted based on the current motor speed and the load compensation item.

[0015] In some embodiments, dynamically adjusting the first preset threshold based on the current motor speed and the load compensation term includes: The first preset threshold is calculated using the following formula: ; in, The first preset threshold, Based on the basic imbalance threshold, For speed coefficient, This is the current motor speed. This is the reference value for rotational speed. For load compensation items; The load compensation term is calculated using the following formula:

[0016] in, For load compensation items,

[0017] In some embodiments, performing graded protection actions based on the fusion score includes: If the fusion score reaches or exceeds the first score threshold for the first time, the driving power of the motor will be reduced to a predetermined percentage of the rated power. If the fusion score of two consecutive sampling windows reaches or exceeds the first score threshold, then each power transistor in the inverter unit is turned off and the fault state is locked.

[0018] In some embodiments, the method further includes: The integral value of the absolute value of the three-phase current is determined based on the three-phase current data sequence; The phase with a phase loss fault is determined based on the integral value of the absolute value of the three-phase current and the phase deviation of the three-phase current, wherein the phase with a phase loss fault has the smallest integral value of the absolute value of the current and the largest phase deviation.

[0019] Secondly, embodiments of this application provide a controller, the controller including at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the motor phase loss protection method as described above.

[0020] Thirdly, embodiments of this application provide a washing device, including a motor and a controller as described above, wherein the controller is connected to the motor.

[0021] Compared with the prior art, this application has at least the following advantages: The motor phase loss protection method first samples the three-phase current of the motor to obtain the three-phase current data sequence of the current sampling window, calculates the current integral unbalance based on the three-phase current data sequence, performs phase change detection based on the three-phase current data sequence to generate the phase deviation, then performs weighted fusion of the current integral unbalance and the phase deviation to obtain the fusion score, and finally executes graded protection actions based on the fusion score.

[0022] Therefore, this method, by weightedly fusing current integral imbalance and phase change detection, utilizes for the first time the physical characteristic that phase change precedes amplitude drop under phase loss faults, achieving millisecond-level rapid response and enabling fault identification before thermal damage to power devices. Specifically, the current integral characteristic is sensitive to amplitude imbalance, compensating for the uncertainty of phase detection under steady-state conditions, while the phase change characteristic responds rapidly to instantaneous faults, compensating for the lag of current integral under load disturbances. The weighted fusion of these two significantly improves the robustness of diagnosis. Thus, this method can accurately detect phase loss faults, combining rapid response and high robustness. Furthermore, the fused score directly reflects the severity of the fault, facilitating subsequent protection actions, achieving a synergistic optimization of speed and reliability. Attached Figure Description

[0023] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0024] Figure 1 This is a schematic diagram of the structure of a laundry device provided in an embodiment of this application; Figure 2 This is a schematic diagram of a controller structure provided in an embodiment of this application; Figure 3 This is a schematic flowchart of one of the motor phase loss protection methods provided in the embodiments of this application; Figure 4 This is a schematic diagram of one type of sampling window provided in an embodiment of this application; Figure 5 yes Figure 3 A flowchart illustrating step S103; Figure 6 yes Figure 5 A flowchart illustrating step S1032; Figure 7 This is a schematic flowchart of one of the motor phase loss protection methods provided in the embodiments of this application; Figure 8 This is a schematic flowchart of one of the motor phase loss protection methods provided in the embodiments of this application; Figure 9 This is a schematic diagram of the structure of a motor phase loss protection device provided in an embodiment of this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0026] It should be noted that, unless there is a conflict, the various features in the embodiments of this application can be combined with each other, all of which are within the protection scope of this application. Furthermore, although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than the module division in the device or the order in the flowchart. Moreover, the terms "first," "second," and "third" used in this application do not limit the data or execution order, but only distinguish identical or similar items with essentially the same function and effect.

[0027] Please see Figure 1 , Figure 1 This is a schematic diagram of the structure of a laundry device provided in an embodiment of this application. Figure 1 As shown, the washing equipment 100 includes a motor 10 and a controller 20, with the controller 20 electrically connected to the motor 10. The motor 10 is a three-phase brushless DC motor or a permanent magnet synchronous motor, and the washing equipment 100 can specifically be a drum washing machine, a pulsator washing machine, or a washer-dryer combo.

[0028] The controller 20 receives external commands (such as dehydration speed and start / stop commands) and generates PWM signals based on real-time motor phase loss diagnosis methods to drive the motor 10. The controller 20 integrates a processor (MCU), memory, a three-phase current sampling circuit, and a power inverter circuit. The three-phase current sampling circuit can use sampling resistors and operational amplifier circuits to sample the U, V, and W phase currents in real time. The sampling frequency can be set as needed, such as 20kHz. When the processor executes the computer program stored in the memory, it can perform current sampling, fault diagnosis, and protection actions.

[0029] In related technologies, the diagnosis and protection of motor phase loss faults under high-speed spin-drying conditions in washing machines generally adopts a detection method based on current imbalance. This method calculates the effective or integral values ​​of the three-phase currents and compares the amplitude differences between the three phases. When the current of one phase is significantly lower than that of the other two phases and exceeds a preset threshold, it is determined to be a phase loss fault. However, under high-speed spin-drying conditions, when a phase loss fault occurs, due to the huge mechanical inertia of the motor rotor, the significant drop in current amplitude often lags behind the electrical abnormality by about 5-10 ms, i.e., there is a current amplitude response lag characteristic. Therefore, the detection method based solely on current imbalance has the disadvantage of detection delay, resulting in protection delay in the early stage of high-speed phase loss, which puts the inverter bridge power devices (MOSFET / IGBT) at risk of damage due to instantaneous overcurrent or voltage spikes.

[0030] In related technologies, voltage detection and model observation methods can be used for the diagnosis and protection of motor phase loss faults under high-speed spin-drying conditions in washing equipment. However, voltage detection increases hardware costs, is susceptible to interference, and its protection is unreliable. Model observation is highly dependent on motor parameters (such as resistance and inductance parameters). Resistance drift caused by motor temperature rise and inductance changes caused by magnetic saturation under washing equipment operating conditions can lead to model mismatch, which in turn can result in unreliable phase loss fault diagnosis and protection.

[0031] Based on the above reasons, this application provides a motor phase loss protection method. The motor phase loss protection method first samples the three-phase current of the motor to obtain the three-phase current data sequence of the current sampling window, calculates the current integral unbalance based on the three-phase current data sequence, performs phase change detection based on the three-phase current data sequence to generate the phase deviation, then performs weighted fusion of the current integral unbalance and the phase deviation to obtain the fusion score, and finally executes graded protection actions based on the fusion score.

[0032] Therefore, this method, by weightedly fusing current integral imbalance and phase change detection, utilizes for the first time the physical characteristic that phase change precedes amplitude drop under phase loss faults, achieving millisecond-level rapid response and enabling fault identification before thermal damage to power devices. Specifically, the current integral characteristic is sensitive to amplitude imbalance, compensating for the uncertainty of phase detection under steady-state conditions, while the phase change characteristic responds rapidly to instantaneous faults, compensating for the lag of current integral under load disturbances. The weighted fusion of these two significantly improves the robustness of diagnosis. Thus, this method can accurately detect phase loss faults, combining rapid response and high robustness. Furthermore, the fused score directly reflects the severity of the fault, facilitating subsequent protection actions, achieving a synergistic optimization of speed and reliability.

[0033] Please refer to the following: Figure 2 , Figure 2This is a schematic diagram of the hardware structure of a controller provided in an embodiment of this application. The controller 20 includes at least one processor 21 that is communicatively connected via a system bus or other means. Figure 2 (Taking a processor as an example) and memory 22. The controller 20 can exist in the form of a chip.

[0034] The memory 22 stores instructions that can be executed by the at least one processor 21. The instructions are executed by the at least one processor 21, which provides computing and control capabilities to execute relevant commands, such as controlling the controller 20 to execute any of the motor phase loss protection methods provided in the following embodiments of this application.

[0035] The memory 22, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the motor phase loss protection method provided in the following embodiments of this application. The processor 21 can implement the motor phase loss protection method in any of the following method embodiments by running the non-transitory software programs, instructions, and modules stored in the memory 22. Specifically, the memory 22 may include high-speed random access memory and non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 22 may also include memories remotely located relative to the processor 21, which can be connected to the processor 21 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0036] In some embodiments, controller 20 may be a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), microcontroller, ARM (Acorn RISC Machine) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination of these components. Additionally, controller 20 may also be any conventional processor, controller, microcontroller, or state machine. Controller 20 may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP, and / or any other such configuration.

[0037] This application provides a method for motor phase loss protection. Please refer to [link / reference]. Figure 3 , Figure 3 This is a flowchart illustrating a motor phase loss protection method provided in an embodiment of this application. Method S100 includes, but is not limited to, the following steps: S101: Sample the three-phase current of the motor to obtain the three-phase current data sequence of the current sampling window; In this embodiment, the sampling of the three-phase current of the motor adopts a high-frequency synchronous sampling method. The three-phase current i is sampled at a sampling frequency fs. U i V i W Synchronous sampling is performed, and a set of instantaneous values ​​is obtained at each sampling moment.

[0038] To achieve real-time diagnostics, a sliding window is used for sampling, with a fixed length of N sampling points, corresponding to a time length Δt = N / fs. Data storage employs a circular buffer (i.e., a sliding window). Each time a new sampling point arrives, it overwrites the oldest data point in the window, thus ensuring that the window always contains data from the most recent N sampling points.

[0039] like Figure 4 As shown, the window slides forward over time, and there is a large amount of overlapping data between adjacent windows (the number of overlap points is N). 1) Only the latest sampling point is updated. Therefore, at each sampling moment, the three-phase current data sequence within the current sliding window can be obtained. , , , where n is any integer from 0 to N-1.

[0040] The sampling frequency and the number of sampling points can be set as needed. In this embodiment, the sampling frequency fs can be 20kHz, the number of sampling points N can be 100, and the corresponding window duration Δt = 5ms. The number of sampling points N can also be other values ​​(such as 64, 128, etc.) according to actual needs, and the window duration Δt will be adjusted accordingly. The circular buffer can be implemented using head and tail pointers to avoid data movement overhead.

[0041] It should be noted that, Figure 4 The example shown here uses a sampling frequency fs of 20kHz, a sampling point count N of 100, and a window duration Δt of 5ms. Figure 4 The specific values ​​in the figures do not limit the scope of this application.

[0042] S102: Calculate the current integral unbalance based on the three-phase current data sequence, wherein the value of the current integral unbalance characterizes the uniformity of the three-phase current amplitude distribution of the motor. The integral current imbalance (Kimb) is obtained based on the integral value of the absolute values ​​of the three-phase currents, and its value ranges from [0,1]. Its magnitude directly characterizes the uniformity of the three-phase current amplitude distribution in the motor. When Kimb = 0, it indicates that the integral values ​​of the absolute values ​​of the three-phase currents are completely equal, meaning the three-phase current amplitude distribution is absolutely uniform, and the motor is in an ideal symmetrical operating state. When Kimb approaches 1, it indicates that the integral value of one or two phases is much smaller than the other phases, the three-phase current amplitude distribution is extremely uneven, and the motor has a serious imbalance. The larger the value, the higher the three-phase current imbalance and the more uneven the amplitude distribution.

[0043] In actual phase loss fault scenarios, the faulty phase current will significantly decrease or even approach zero, while the non-faulty phase current may remain normal or slightly increase. At this time, the current integral imbalance Kimb will rapidly increase to a level close to 1. Therefore, the current integral imbalance Kimb, as one of the important characteristics of phase loss criteria, can effectively reflect the degree of damage to the electrical symmetry of the motor.

[0044] Specifically, the integral value of the absolute value of each phase current is calculated using the following formula: (1) Where Sx is the integral value of the absolute value of the x-th phase current. Let N be the instantaneous value of the x-th phase current at the nth sampling point, and N be the number of sampling points within the sampling window.

[0045] The current integral unbalance is then calculated using the following formula: (2) Where Kimb is the integral unbalance of the current, S U SV is the integral value of the absolute value of the current in phase U, SV is the integral value of the absolute value of the current in phase V, and SW is the integral value of the absolute value of the current in phase W.

[0046] S103: Based on the three-phase current data sequence, perform phase abrupt change detection to generate phase deviation; During the spin-drying process of a washing machine, normal load disturbances such as eccentric clothing can also cause imbalance in the three-phase current amplitude, leading to an increase in the integral current imbalance Kimb. The magnitude of this increase may overlap with the actual phase loss, making it difficult to reliably distinguish between them solely based on the amplitude or rate of change of the integral current imbalance Kimb. When a phase loss fault occurs, in addition to current amplitude imbalance, it also causes a sudden change in the fundamental current phase. This phase change precedes the amplitude drop, and the phase change is stable under normal load disturbances.

[0047] Therefore, this application embodiment introduces phase change detection and weighted fusion with current integral imbalance, utilizing the complementarity of the two features to achieve accurate identification of true phase loss.

[0048] Specifically, such as Figure 5 As shown, step S103 includes: S1031: The three-phase current data sequence is processed to obtain a first complex spectrum sequence, wherein the first complex spectrum sequence includes M complex spectrum values, the three-phase current data sequence includes N current data, and M is greater than N; For the three-phase current data sequence within the current sampling window (x∈{U,V,W}, n=0,1,…,N) 1) Preprocessing: First, a window function (such as the Hanning window) is applied to suppress spectral leakage. Then, zero-padding is performed on the windowed sequence to extend its length to M points (M>N). The purpose of zero-padding is to increase the frequency domain sampling density, making subsequent phase extraction more accurate. Subsequently, a Fast Fourier Transform (FFT) is performed on the zero-padding sequence to obtain the first complex spectral sequence. ,in, =0,1,…,M 1 represents the frequency index. This complex spectrum sequence contains M complex spectrum values, each consisting of a real part and an imaginary part, representing the amplitude of the cosine and sine components of the corresponding frequency component, respectively. Since M>N, the frequency domain sampling points are denser, and the frequency resolution Δf=fs / M is improved (where fs is the sampling frequency), thus laying the foundation for the accurate extraction of the fundamental phase and harmonic amplitude.

[0049] S1032: Extract the fundamental phase of the current sampling window based on the first complex spectrum sequence; The fundamental phase is the phase angle of the most important frequency component in the motor current signal, directly reflecting the electrical synchronization relationship between the rotor position and the current. During normal steady-state operation of the motor, the fundamental phase increases linearly with the electrical angle, and the theoretical phase increment between adjacent sampling windows is fixed. =360 f1 Δt). When a phase loss fault occurs, the sudden change in the current path causes an instantaneous jump in the fundamental phase, and this phase change precedes the significant drop in current amplitude (about 5~10 ms). Therefore, extracting the fundamental phase and performing abrupt change detection is the key to achieving millisecond-level rapid diagnosis.

[0050] Specifically, such as Figure 6 As shown, step S1032 includes: S10321: Determine a first set of frequency points, wherein the first set of frequency points is an integer set of frequency points centered on the theoretical frequency point corresponding to the fundamental frequency; First, estimate the fundamental frequency f1 based on the current motor speed and number of pole pairs. Then, combine the sampling frequency fs and the number of FFT points M to calculate the frequency resolution Δf = fs / M. The theoretical frequency point corresponding to the fundamental frequency is k. theory =f1 / Δf, which is usually a non-integer value.

[0051] With k theory Centered on the center, select R integer frequency points on the left and right (R is a preset positive integer, such as R=1 or R=2) to form the first frequency point set. Where k is an integer, that is, the first frequency point set is the set of frequencies with k as the base. theory Centered on the frequency point, expand to the left and right by R integer steps, and take all integers within the range as the first frequency point set. The elements in.

[0052] For example, if fs = 20kHz and M = 128, then Δf = 156.25 Hz. Assuming the fundamental frequency f1 = 80Hz, then... =80 / 156.25=0.512. Taking R=2, the theoretical set of the first frequency points is { If the negative frequency points are ignored, the actual value is {0,1,2}.

[0053] S10322: Obtain a target complex spectrum sequence from the first complex spectrum sequence, wherein the target complex spectrum sequence includes the complex spectrum values ​​corresponding to each frequency point in the first frequency point set; From the first complex spectrum sequence X[k] (k=0,1,…,M) In step 1), the complex spectrum values ​​corresponding to each integer frequency point in the first frequency point set K1 are extracted to form the target complex spectrum sequence Xtarget={X[k]|k∈K1}.

[0054] For example: K1={0,1,2}, then Xtarget={X[0],X[1],X[2]}.

[0055] S10323: Find the first complex spectrum value corresponding to the first integer frequency point and the second complex spectrum value corresponding to the second integer frequency point from the target complex spectrum sequence, wherein the first integer frequency point is the integer frequency point with the largest amplitude in the target complex spectrum sequence, and the second integer frequency point is the next integer frequency point after the first integer frequency point; In the target complex spectral sequence Xtarget, calculate the amplitude at each integer frequency point k. Find the integer frequency point with the largest amplitude and denote it as the first integer frequency point. Its corresponding complex spectrum value is the first complex spectrum value. Then, take The next integer frequency point As the second integer frequency point, its corresponding complex spectrum value is the second complex spectrum value. .

[0056] For example: Suppose that in K1={0,1,2}, the magnitude distribution is as follows: maximum, Secondly, The smallest, then the first integer frequency point =0, the first complex frequency value is X[0], and the second integer frequency point is +1=1, the second complex number spectrum value is X[1].

[0057] S10324: Determine the first frequency correction amount; Since the actual fundamental frequency f1 usually does not fall exactly at an integer frequency point. or +1, directly take the complex spectrum value at the integer frequency point. or Calculating the phase can produce a "fence effect," leading to phase estimation errors.

[0058] Therefore, in order to obtain the accurate complex spectrum value at the actual fundamental frequency, it is necessary to first determine the first frequency correction δ, which represents the actual fundamental frequency relative to the integer frequency point. The degree of offset.

[0059] Specifically, the first frequency correction δ is calculated using parabolic interpolation. First, the third complex spectrum value corresponding to the third integer frequency point is found from the target complex spectrum sequence, where the third integer frequency point is the integer frequency point preceding the first integer frequency point. Then, the first amplitude of the first complex spectrum value, the second amplitude of the second complex spectrum value, and the third amplitude of the third complex spectrum value are calculated. Finally, the first frequency correction is determined based on the first amplitude, the second amplitude, and the third amplitude.

[0060] If a third complex spectral value is found from the target complex spectral sequence Xtarget The third integer frequency point is Then calculate the first complex spectrum value respectively. The amplitude is called the first amplitude. | Calculate the spectrum value of the second complex number. The amplitude of this value is called the second amplitude. |and calculate the spectrum value of the third complex number. The amplitude of ] is called the third amplitude. Finally, based on the first amplitude... |Second Amplitude| |and the third value| Determine the first frequency correction amount δ, and calculate the first frequency correction amount using the following formula: (3) in, The first complex spectral value, The second complex spectral value, The third complex spectral value, For the first integer frequency point, The second integer frequency point, The third integer frequency point, This is the first frequency correction amount.

[0061] Therefore, in the embodiments of this application, the actual fundamental frequency is obtained relative to the integer frequency point. The offset provides weights for subsequent interpolation calculations of the fundamental frequency spectrum.

[0062] S10325: Based on the first frequency correction amount, perform linear interpolation on the first integer spectrum value and the second complex spectrum value to obtain the fundamental spectrum value, wherein the fundamental spectrum value is the complex spectrum value corresponding to the fundamental frequency; Based on the first frequency correction amount For the first complex spectral value With the second complex spectrum value Linear interpolation is performed to obtain the fundamental frequency spectrum. Linear interpolation is used because the actual fundamental frequency lies at an integer frequency point. and Non-integer positions between +1 + ,and It has been accurately obtained using parabolic interpolation and represents the actual fundamental frequency up to The offset weight. At this point, directly using... As a linear weight, the complex spectrum values ​​at two adjacent integer frequency points are weighted and averaged to obtain an approximate value of the complex spectrum value at the actual fundamental frequency. The calculation is simple and the accuracy meets the requirements.

[0063] Specifically, the fundamental frequency spectrum value is calculated using the following formula: (4) in, This is the fundamental frequency spectrum value. This is the first frequency correction amount. The first complex spectral value, This is the second complex number spectrum value.

[0064] Understandable, As a weight, when When the fundamental frequency spectrum value is close to 0, it is mainly composed of Contribution, when When it is close to 1, it is mainly composed of This contributes to a smooth transition to the complex spectrum value corresponding to the actual fundamental frequency, effectively eliminating the phase error caused by the picket fence effect.

[0065] S10326: Determine the fundamental phase of the current sampling window based on the fundamental frequency spectrum value.

[0066] Based on the fundamental frequency spectrum value Determine the fundamental phase of the current sampling window Fundamental frequency spectrum value It is a complex number, and its real part is Re{ } and imaginary part These represent the amplitudes of the cosine and sine components of the fundamental frequency component, respectively. Fundamental phase. This is the argument of the complex number in the complex plane, reflecting the initial phase angle of the fundamental signal within the sampling window. The relationship between the two conforms to the arctangent function.

[0067] The fundamental phase is calculated using the following formula: (5) in, The fundamental phase of the current sampling window. The fundamental frequency spectrum value The imaginary part, The fundamental frequency spectrum value The real part.

[0068] For each phase, the fundamental phase at time t of the corresponding pre-sampling window is calculated using formula (5). For example, for phase U, the fundamental phase of phase U at time t in the current sampling window is calculated using the following formula. : (6) in, The fundamental phase of phase U at time t in the current sampling window. This represents the fundamental frequency spectrum value of phase U.

[0069] Similarly, for phase V and phase W, the fundamental frequency spectrum values ​​are obtained respectively. and And calculate the corresponding fundamental phase. and The fundamental phase of the three phases , , They will be used to calculate the phase deviation between subsequent adjacent sampling windows to detect the characteristics of phase abrupt changes under phase loss faults.

[0070] In some embodiments, wavelet transform can also be used to process the three-phase current data sequence. Specifically, for the three-phase current data sequence ix[n] (x∈{U,V,W}, n=0,1,…,N) within the current sampling window... 1) Perform Discrete Wavelet Transform (DWT). Select a suitable wavelet basis function (such as the Daubechies 4th order wavelet, i.e., db4) to decompose the signal into multiple scales, with the number of decomposition levels set to L (e.g., L=5). After wavelet transform, the approximation coefficients and detail coefficients of each level are obtained. The detail coefficients of level d1 correspond to high-frequency components and can effectively capture the transient phase change characteristics caused by phase loss faults. The method for extracting the fundamental phase from the wavelet coefficients is as follows: select the scale level corresponding to the fundamental frequency, perform Hilbert transform or complex wavelet reconstruction on the coefficients of this level to obtain the analytic signal, and then calculate the instantaneous phase. Compared with FFT, wavelet transform has good time-frequency localization characteristics and can provide time and frequency information simultaneously, making it more suitable for analyzing non-stationary signals. In transient processes such as motor startup or load change, wavelet transform can more sensitively detect the moment of phase change.

[0071] S1033: Based on the fundamental phase of the current sampling window and the fundamental phase of the previous sampling window, obtain the actual phase increment of each phase; S1034: Obtain the theoretical phase increment; S1035: Calculate the phase deviation of each phase based on the actual phase increment and the theoretical phase increment of each phase.

[0072] The actual phase increment is calculated using the following formula: (7) in, This is the actual phase increment. The fundamental phase of the current sampling window. Δt represents the fundamental phase of the previous sampling window, and Δt represents the time interval between adjacent sampling windows.

[0073] The phase deviation of each phase is then calculated using the following formula: (8) in, This is the phase deviation. This represents the theoretical phase increment.

[0074] Theoretical phase increment Calculated based on fundamental frequency For example, if the fundamental frequency f1 = 80Hz, =0.005s, then .

[0075] For each phase, the phase deviation at time t corresponding to the pre-sampling window is calculated using formula (8). For example, the phase deviation of phase U at time t in the current sampling window can be calculated using the following formula. : ,in, , , This represents the actual phase increment of phase U. This represents the fundamental phase of phase U in the current sampling window. This is the fundamental phase of the U-phase in the previous sampling window.

[0076] Similarly, for phase V and phase W, calculate the actual phase increment of phase V respectively. The actual phase increment of phase W Then, the phase deviation of phase V is calculated using formula (8). Phase deviation of phase W .

[0077] Therefore, in this embodiment, the phase deviation of phase U, phase V, and phase W of the current sampling window are obtained by the above method.

[0078] S104: Weight the current integral imbalance and the phase deviation to obtain a fusion score; The current integral imbalance reflects the difference in three-phase amplitude, but it is sensitive to load disturbances (such as clothing eccentricity) and has a delayed response; the phase deviation reflects abrupt phase changes, responds quickly but is easily affected by noise. The two are naturally complementary in physical characteristics—a phase loss fault simultaneously causes amplitude imbalance and abrupt phase changes, while normal disturbances only cause amplitude fluctuations and stable phase changes.

[0079] Therefore, in this embodiment, the current integral imbalance and phase deviation are weighted and fused. By weighting and fusing, the robustness of the amplitude characteristics and the speed of the phase characteristics can be comprehensively utilized to achieve complementary advantages.

[0080] First, determine the current integration flag and the phase change flag, then calculate the fusion score using the following formula: (9) in, For the fusion score, w1 and w2 are weighting coefficients, satisfying w1 + w2 = 1. For current integral, This is a marker of a phase abrupt change.

[0081] The current integral flag is determined based on the current integral unbalance. If the current integral unbalance is greater than the first preset threshold, the current integral flag is set to 1, indicating that there is a serious imbalance in the three-phase current amplitude. Otherwise, the current integral flag is set to 0, indicating that the three-phase current amplitude distribution is basically symmetrical.

[0082] The phase change flag is determined based on the phase change amount of each phase. If the maximum value of the phase change amount of each phase is greater than the second preset threshold, the phase change flag is set to 1, indicating that a significant phase change has been detected. Otherwise, the phase change flag is set to 0, indicating that the phase change is normal.

[0083] The first and second preset thresholds can be flexibly set according to the motor type, operating conditions, and sampling parameters. In this embodiment, the first preset threshold can be 0.5 and adjusted according to the speed and load, and the second preset threshold is 30°, which can also be adjusted within the range of 20° to 40° according to the actual motor parameters.

[0084] During the spin-drying process of washing equipment, there is a common phenomenon of clothes being out of sync, which leads to an imbalance in the three-phase current under normal operating conditions. The phase loss protection method based on a fixed threshold is difficult to distinguish between a real phase loss and normal load disturbance.

[0085] To address this issue, in some embodiments, the first preset threshold is a dynamic threshold that can be adaptively adjusted based on motor speed and load, thereby accurately distinguishing between actual phase loss and current fluctuations caused by eccentric load, effectively preventing false phase loss alarms from occurring in the washing equipment during spin-drying.

[0086] First, the load compensation item is determined based on the load rate and the high load rate threshold. Then, the first preset threshold is dynamically adjusted according to the current motor speed and the load compensation item.

[0087] Specifically, the first preset threshold is calculated using the following formula: (10) in, The first preset threshold, Based on the basic imbalance threshold, For speed coefficient, This is the current motor speed. This is the reference value for rotational speed. This is a load compensation item.

[0088] The load compensation term is calculated using the following formula: (11) in, For load compensation items, This is the high load rate threshold.

[0089] Load rate represents the ratio of the motor's current actual output power (or effective value of current) to its rated power (or effective value of rated current), i.e., load rate = (actual effective value of current / effective value of rated current) × 100%, reflecting the mechanical load currently borne by the motor.

[0090] Furthermore, under high-speed dehydration conditions, the current amplitude is inherently large, and the current imbalance caused by phase loss is more pronounced. Appropriately increasing the threshold can avoid misjudging normal fluctuations; therefore, a speed term is introduced. Under heavy load, the current amplitude is large and stable, and the phase loss characteristic is obvious. Appropriately lowering the threshold ( = This can improve detection sensitivity.

[0091] Basic Imbalance Threshold A speed coefficient that can cover 95% of the imbalance under normal operating conditions can be determined through extensive experimental statistics. Speed ​​coefficient It can be obtained by fitting the current response characteristic curve. For example, if It is 0.002. It is 0.5. For 1000, 80%, If the value is 0.05, then when the load rate is greater than 80%, the load compensation item... = If the value is 0.05, then the first preset threshold is... .

[0092] Therefore, in this embodiment of the application, through the dual-factor adaptive adjustment of rotational speed and load, the first preset threshold is... It can dynamically optimize according to changes in operating conditions, thus accurately distinguishing between real phase loss and normal current fluctuations even under load disturbances such as clothing eccentricity, effectively reducing the false alarm rate.

[0093] S105: Execute hierarchical protection actions based on the fusion score.

[0094] The fusion score is a measure of the current integral. Phase change markers The weighted summation result ranges from [0,1]. The magnitude of the fusion score directly represents the overall confidence level of the motor experiencing a phase loss fault: the closer the fusion score is to 1, the higher the probability of a phase loss fault, indicating a severe imbalance in current amplitude and the simultaneous detection of a significant phase change; the closer the fusion score is to 0, the more likely the motor is operating normally or only experiences minor disturbances.

[0095] Therefore, graded protection actions can be performed based on the fusion score. Specifically, if the fusion score reaches or exceeds the first score threshold for the first time, the drive power of the motor is reduced to a predetermined proportion of the rated power. If the fusion scores of two consecutive sampling windows both reach or exceed the first score threshold, the power transistors in the inverter unit are turned off and the fault state is locked.

[0096] For example, when the fusion score reaches or exceeds the first score threshold S_thr (e.g., 0.8) for the first time, it is determined to be a suspected phase loss, triggering the first-level protection (soft degradation). The PWM duty cycle is reduced to a predetermined ratio (e.g., 50%) to reduce power and observe whether the fault persists. During this stage, the system is allowed to continue operating with reduced power to avoid water overflow in the washing machine due to sudden shutdown. If the fusion score reaches or exceeds the first score threshold S_thr for two consecutive sampling windows, a phase loss fault is confirmed, triggering the second-level protection (hard cut-off). All power transistors of the inverter bridge are immediately shut down and the fault state is locked. After the fault is locked, it can only be released by manual reset or power failure and restart.

[0097] Therefore, by integrating score-driven hierarchical protection, we can avoid accidental shutdowns caused by momentary interference and ensure rapid and safe disconnection under real fault conditions.

[0098] In summary, this motor phase loss protection method, by weightedly fusing current integral imbalance and phase change detection, utilizes for the first time the physical characteristic that phase change precedes amplitude drop under phase loss faults, achieving millisecond-level rapid response and enabling fault identification before thermal damage to power devices. Specifically, the current integral characteristic is sensitive to amplitude imbalance, compensating for the uncertainty of phase detection under steady-state conditions, while the phase change characteristic responds rapidly to instantaneous faults, compensating for the lag of current integral under load disturbances. The weighted fusion of these two significantly improves diagnostic robustness. Therefore, this method can accurately detect phase loss faults, combining rapid response and high robustness. Furthermore, the fused score directly reflects the severity of the fault, facilitating the execution of subsequent protection actions, achieving a synergistic optimization of speed and reliability.

[0099] A phase loss fault can cause severe distortion of the motor current waveform, generating abundant odd harmonic components, with the fifth harmonic showing the most significant amplitude increase. Relying solely on current integral imbalance and phase change detection still carries a small chance of misdiagnosis under conditions of severe load fluctuations or sensor noise interference. Introducing harmonic-assisted detection can serve as a third layer of verification, further improving diagnostic reliability.

[0100] Furthermore, the phase loss disrupts the symmetry of the stator winding, leading to distortion of the air gap magnetic field. The resulting harmonics are mainly odd harmonics such as the 5th, 7th, and 11th, while even harmonics have smaller amplitudes and are unstable. Among them, the 5th harmonic has the most significant amplitude variation and the highest signal-to-noise ratio, and its content is extremely low under normal operating conditions (usually below 5%).

[0101] For the reasons mentioned above, in some embodiments, the motor phase loss protection method, in addition to detecting current imbalance and phase change, also employs odd harmonic assistance.

[0102] Specifically, such as Figure 7 As shown, the method S100 further includes: S106: Based on the first complex spectrum sequence, calculate the fundamental amplitude and the odd harmonic amplitude, wherein the fundamental amplitude is the amplitude corresponding to the fundamental spectrum value, the odd harmonic amplitude is the amplitude corresponding to the harmonic spectrum value, and the harmonic spectrum value is the complex spectrum value corresponding to the odd harmonic. S107: Calculate the harmonic distortion rate based on the fundamental frequency amplitude and the odd harmonic amplitude; Based on the first complex spectrum sequence X[k], obtain the fundamental frequency spectrum value. Then calculate the fundamental frequency spectrum value. The amplitude of the fundamental frequency is called the fundamental frequency amplitude. Then, based on the first complex spectrum sequence X[k], the complex spectrum values ​​corresponding to the odd harmonics are obtained, which are called harmonic spectrum values. Calculate the harmonic spectrum value The amplitude of the odd harmonic is called the amplitude of the odd harmonic. .

[0103] Among them, the fundamental frequency spectrum value Having been obtained through the aforementioned linear interpolation, the formula for calculating the fundamental amplitude is: (12) The harmonic spectrum values ​​at the frequency points corresponding to the odd harmonics are obtained by using the same parabolic interpolation method as the fundamental frequency. Specifically, for odd harmonics, the frequency of the odd harmonics needs to be determined first. The corresponding non-integer frequency positions are determined, where h is an odd number, such as h=5, 7, 11, etc. Then, the theoretical frequency positions corresponding to the odd harmonics are calculated. Then, based on the theoretical frequency position Centered on the target frequency, select R integer frequency points (e.g., R=1 or 2) to the left and right to form the first frequency point set of the odd harmonics. Extract the complex spectrum values ​​corresponding to each integer frequency point in the first frequency point set from the first complex spectrum sequence X[k] to form the target complex spectrum sequence. Based on the target complex spectrum sequence, find the integer frequency point k0_h with the largest amplitude, whose corresponding complex spectrum value is X[k0_h]. At the same time, obtain the complex spectrum value X[k0_h-1] corresponding to the previous integer frequency point k0_h-1 and the complex spectrum value corresponding to the next integer frequency point k0_h+1. Calculate the first frequency correction δ_h of the odd harmonics using the parabolic interpolation formula. Then, perform linear interpolation on X[k0_h] and X[k0_h+1] based on the first frequency correction δ_h of the odd harmonics to obtain the harmonic spectrum values ​​at the odd harmonic frequency points. That is, the calculation method for harmonic spectrum values ​​is the same as that for fundamental frequency spectrum values; only the fundamental frequency f1 in the formula needs to be modified. That's all.

[0104] Next, calculate the amplitude of the odd harmonics: (13) in, For odd harmonic amplitude, This represents the harmonic spectrum value.

[0105] Then based on the fundamental amplitude With odd harmonic amplitude Calculate harmonic distortion rate The harmonic distortion rate is calculated using the following formula: (14) in, This represents the harmonic distortion rate.

[0106] Harmonic distortion rate This reflects the energy proportion of odd harmonic components relative to the fundamental component. When the motor is running normally and the three phases are balanced, the current waveform is close to sinusoidal, and the harmonic distortion rate... Generally small, but when a phase loss fault occurs, the current waveform distortion is severe, the amplitude of odd harmonics increases significantly, and the harmonic distortion rate increases. Significant increase.

[0107] In some embodiments, the 5th harmonic variation is most significant. When the motor is running normally and the three phases are balanced, the harmonic distortion rate (THD5) of the 5th harmonic is typically less than 5%. However, when a phase loss fault occurs, the THD5 of the 5th harmonic can rise to over 15%. Therefore, the THD5 of the 5th harmonic is used as an auxiliary criterion. When it exceeds a preset threshold (such as 15%), a phase loss fault can be further confirmed, improving the reliability of the diagnosis.

[0108] S108: The current integral unbalance, the phase deviation, and the harmonic distortion rate are weighted and fused to obtain the fusion score.

[0109] The current integral flag, phase abrupt change flag, and harmonic auxiliary flag are calculated based on the current integral unbalance, phase deviation, and harmonic distortion rate. Specifically, the current integral flag is determined based on the current integral unbalance. If the current integral unbalance is greater than a first preset threshold, the current integral flag is set to 1, indicating a severe imbalance in the three-phase current amplitude; otherwise, the current integral flag is set to 0, indicating that the three-phase current amplitude distribution is basically symmetrical. The phase abrupt change flag is determined based on the phase change of each phase. If the maximum value of the phase change among all phases is greater than a second preset threshold, the phase abrupt change flag is set to 1, indicating a significant phase abrupt change has been detected; otherwise, the phase abrupt change flag is set to 0, indicating that the phase change is normal. The harmonic auxiliary flag is determined based on the harmonic distortion rate. If the harmonic distortion rate is greater than a third preset threshold, the harmonic auxiliary flag is set to 1; otherwise, the harmonic auxiliary flag is set to 0.

[0110] The fusion score is calculated using the following formula: (15) in, For the fusion score, w1, w2, and w3 are weighting coefficients, satisfying w1 + w2 + w3 = 1. For current integral, As a marker of phase abrupt change, It serves as an auxiliary indicator for harmonics.

[0111] The weighting coefficients w1, w2, and w3 can be set as needed. In this embodiment, the weighting coefficients w1, w2, and w3 can be 0.7, 0.2, and 0.1, respectively.

[0112] The current integral imbalance, phase deviation, and harmonic distortion rate are weighted and fused to obtain a fusion score, which is used to execute graded protection actions. The current integral imbalance reflects the degree of three-phase amplitude imbalance; it is sensitive to steady-state phase loss but has a slow response and is easily affected by load disturbances. The phase deviation reflects the fundamental phase abrupt change; it responds quickly but may be affected by noise interference. The harmonic distortion rate reflects the degree of current waveform distortion; odd harmonics increase significantly under phase loss faults, exhibiting high fault specificity. These three factors characterize different physical features of phase loss faults in the amplitude, phase, and frequency domains, respectively, and achieve information complementarity through weighted fusion: the phase deviation provides millisecond-level rapid early warning, the current integral imbalance verifies amplitude imbalance, and the harmonic distortion rate assists in confirmation and suppresses false judgments.

[0113] Therefore, in this embodiment of the application, the fusion score is used as a comprehensive criterion, which has the advantages of fast response, high robustness and low false alarm rate. It can accurately distinguish between real phase loss and load disturbance and noise interference under complex working conditions, and significantly improve the overall performance of the diagnostic system.

[0114] In some embodiments, when a motor phase loss fault is diagnosed, it is also necessary to locate the faulty phase. For example... Figure 8 As shown, the motor phase loss protection method S100 also includes: S109: Determine the integral value of the absolute value of the three-phase current based on the three-phase current data sequence; S110: The phase with a phase loss fault is determined based on the integral value of the absolute value of the three-phase current and the phase deviation of the three-phase current, wherein the integral value of the absolute value of the current corresponding to the phase loss fault is the smallest and the phase deviation corresponding to the phase loss fault is the largest.

[0115] The phase with a phase loss fault is determined by the following formula: (16) in, This is the integral of the absolute values ​​of the three-phase currents. This represents the phase deviation of the three phases.

[0116] Therefore, this motor phase loss protection method uses a dual-criteria location strategy to determine the faulty phase. This strategy fully utilizes the physical characteristics of phase loss faults—the faulty phase current is significantly reduced (minimum integral value) while the phase undergoes a sudden change (maximum phase deviation). The intersection of these two factors ensures accurate location. After determining the faulty phase, the controller can record the fault information and report it to the washing machine's main control board or after-sales diagnostic equipment via the communication interface. Maintenance personnel can quickly locate the fault point (such as a broken phase harness, loose connector, or open circuit in a power transistor) without having to check each of the three phases individually, significantly shortening fault diagnosis and repair time, reducing after-sales maintenance costs, and improving user experience.

[0117] The phase loss protection method of this application is now used to conduct a phase loss protection experiment on the motor.

[0118] Experiment 1, under standard operating conditions, the experimental conditions are as follows: Three-phase BLDC motor, number of pole pairs P=4, rated power 500W, speed range 1000~1600 r / min, load is standard cotton clothing, rated capacity.

[0119] The experimental method is as follows: Phase loss fault simulation: Disconnect any phase harness by relay to simulate a loose harness fault.

[0120] Eccentric load simulation: 0.5kg, 1kg, and 1.5kg eccentric masses are placed inside the drum of the washing machine, with a distribution angle of 0° to 360°.

[0121] Temperature testing: Threshold stability verification at ambient temperatures of -10℃, 25℃, and 60℃.

[0122] The experimental data are shown in Table 1: Table 1. Experimental data on motor phase loss protection methods.

[0123] The motor phase loss protection method proposed in this application reduces the false alarm rate by two orders of magnitude while maintaining a sub-millisecond response speed, verifying the significant progress of dual-mode coupling. Experiments also simulated eccentric masses of 0.5 kg, 1 kg, and 1.5 kg and ambient temperatures ranging from -10℃ to 60℃. The results show that the dynamic threshold has good adaptability, and neither the false alarm rate nor the missed alarm rate exhibited significant fluctuations.

[0124] Experiment 2 replaces FFT with wavelet transform, using the Daubechies 4th order wavelet (db4) instead of FFT for phase detection. The three-phase current sequence within the sampling window is decomposed into 5 levels of discrete wavelet decomposition, extracting the detail coefficients of level d1 (corresponding to high-frequency components). The instantaneous phase is obtained through Hilbert transform, and then the phase deviation is calculated.

[0125] This method is suitable for applications using high-performance MCUs (such as ARM Cortex-M7 or DSP). Compared with FFT, wavelet transform has time-frequency localization characteristics, which can better capture the transient phase jump of phase loss faults, and has obvious advantages in non-stationary processes such as startup or load change; however, the computational complexity increases by about 30%, and the requirements for processor performance are higher.

[0126] Experiment 3 uses a simplified single-phase detection scheme, detecting only the current of one phase (e.g., phase U). Phase loss is determined by whether the integral value of this phase current is close to zero and whether there is a sudden change in phase. A phase loss is determined when both the integral value of the current is below a preset threshold and the phase deviation exceeds 30°.

[0127] This solution has extremely low hardware costs, requiring only one current sampling circuit; however, it cannot distinguish which phase is missing, and if the sampled phase itself is faulty or the phase is not actually missing, it may miss the detection. Its reliability is lower than that of a three-phase detection solution. It is suitable for low-end products where cost is extremely sensitive and fault location requirements are not high.

[0128] Experiment 4 uses different sampling parameters to verify the protection results of different sampling frequencies and sampling window lengths. The sampling parameters are shown in Table 2. Table 2 Sampling Parameters

[0129] The results show that the sampling frequency and window length can be dynamically adjusted according to the rated speed range of the motor. The motor phase loss protection method of this application can achieve good protection effect. The motor phase loss protection method of this application has universality and scalability.

[0130] In summary, this motor phase loss protection method, by weightedly fusing current integral imbalance and phase change detection, utilizes for the first time the physical characteristic that phase change precedes amplitude drop under phase loss faults, achieving millisecond-level rapid response and enabling fault identification before thermal damage to power devices. Specifically, the current integral characteristic is sensitive to amplitude imbalance, compensating for the uncertainty of phase detection under steady-state conditions, while the phase change characteristic responds rapidly to instantaneous faults, compensating for the lag of current integral under load disturbances. The weighted fusion of these two significantly improves diagnostic robustness. Therefore, this method can accurately detect phase loss faults, combining rapid response and high robustness. Furthermore, the fused score directly reflects the severity of the fault, facilitating the execution of subsequent protection actions, achieving a synergistic optimization of speed and reliability.

[0131] It should be noted that in the above embodiments, there is no necessarily a certain order between the above steps. Those skilled in the art can understand from the description of the embodiments of this application that the above steps may have different execution orders in different embodiments, that is, they may be executed in parallel or in interchange, etc.

[0132] As another aspect of the embodiments of this application, this application provides a motor phase loss protection device. The motor phase loss protection device can be a software module, which includes several instructions stored in the memory of a controller. The processor can access the memory, call the instructions, and execute them to complete the motor phase loss protection method described in the above embodiments.

[0133] In some embodiments, the motor phase loss protection device can also be constructed from hardware components. For example, the motor phase loss protection device can be constructed from one or more chips, and the chips can work in coordination to complete the motor phase loss protection method described in the above embodiments. As another example, the motor phase loss protection device can also be constructed from various logic devices, such as general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), microcontrollers, ARM (Acorn RISC Machine) or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination of these components.

[0134] Please see Figure 9 , Figure 9This application provides a motor phase loss protection device 200, which includes a sampling module 201, a calculation module 202, a generation module 203, a fusion module 204, and an execution module 205.

[0135] The sampling module 201 is used to sample the three-phase current of the motor to obtain the three-phase current data sequence of the current sampling window. The generation module 203 is used to calculate the current integral imbalance degree based on the three-phase current data sequence, wherein the value of the current integral imbalance degree characterizes the uniformity of the amplitude distribution of the three-phase current of the motor. The generation module 203 is used to perform phase change detection based on the three-phase current data sequence to generate a phase deviation. The fusion module 204 is used to perform weighted fusion of the current integral imbalance degree and the phase deviation to obtain a fusion score. The execution module 205 is used to execute graded protection actions based on the fusion score.

[0136] Therefore, the motor phase loss protection device can accurately detect phase loss faults, and has both fast response and high robustness. At the same time, the fusion score can directly reflect the severity of the fault, which facilitates the execution of subsequent protection actions, and achieves synergistic optimization of speed and reliability.

[0137] It should be noted that since the motor phase loss protection device 200 and the motor phase loss protection method in the above embodiments are based on the same application concept, the corresponding contents in the above method embodiments are also applicable to the device embodiments, and will not be described in detail here.

[0138] This application also provides a non-transitory computer-readable storage medium storing computer-executable instructions that are executed by one or more processors, for example... Figure 2 One of the processors 21 can enable the one or more processors to execute the motor phase loss protection method in any of the above method embodiments.

[0139] This application also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, which, when executed by a controller 20, cause the controller 20 to perform any of the motor phase loss protection methods described above.

[0140] In summary, this motor phase loss protection method can accurately detect phase loss faults, and has both fast response and high robustness. At the same time, the fusion score can directly reflect the severity of the fault, which facilitates the execution of subsequent protection actions, and achieves synergistic optimization of speed and reliability.

[0141] The device or equipment embodiments described above are merely illustrative. The unit modules described as separate components may or may not be physically separate. The components shown as module units may or may not be physical units; that is, they may be located in one place or distributed across multiple network module units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0142] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a general-purpose hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the motor phase loss protection method described in various embodiments or some parts of embodiments.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them; under the concept of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of this application as described above, which are not provided in detail for the sake of brevity; although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for protecting a motor from phase loss, characterized in that, The method includes: The three-phase current of the motor is sampled to obtain the three-phase current data sequence of the current sampling window; The current integral unbalance is calculated based on the three-phase current data sequence, wherein the value of the current integral unbalance characterizes the uniformity of the three-phase current amplitude distribution of the motor. Phase abrupt change detection is performed based on the three-phase current data sequence to generate phase deviation. The current integral imbalance and the phase deviation are weighted and fused to obtain a fusion score; Based on the fusion score, hierarchical protection actions are performed.

2. The method according to claim 1, characterized in that, The calculation of the current integral unbalance based on the three-phase current data sequence includes: The integral value of the absolute value of each phase current is calculated using the following formula: ； in, The integral value of the absolute value of the x-th phase current. Let N be the instantaneous value of the x-th phase current at the nth sampling point, and N be the number of sampling points within the sampling window; The current integral unbalance is calculated using the following formula: ； in, The integral unbalance of the current. The integral value of the absolute value of the U-th phase current. This is the integral value of the absolute value of the current in phase V. This is the integral value of the absolute value of the current in phase W.

3. The method according to claim 1, characterized in that, The step of detecting abrupt phase changes based on the three-phase current data sequence and generating a phase deviation includes: The three-phase current data sequence is processed to obtain a first complex spectrum sequence, wherein the first complex spectrum sequence includes M complex spectrum values, the three-phase current data sequence includes N current data, and M is greater than N; The fundamental phase of the current sampling window is extracted based on the first complex spectrum sequence; Based on the fundamental phase of the current sampling window and the fundamental phase of the previous sampling window, the actual phase increment of each phase is obtained; Obtain the theoretical phase increment; The phase deviation of each phase is calculated based on the actual phase increment and the theoretical phase increment of each phase.

4. The method according to claim 3, characterized in that, The step of extracting the fundamental phase of the current sampling window based on the first complex spectral sequence includes: Determine a first set of frequency points, wherein the first set of frequency points is an integer set of frequency points centered on the theoretical frequency point corresponding to the fundamental frequency; Obtain a target complex spectrum sequence from the first complex spectrum sequence, wherein the target complex spectrum sequence includes the complex spectrum values ​​corresponding to each frequency point in the first frequency point set; Find the first complex spectrum value corresponding to the first integer frequency point and the second complex spectrum value corresponding to the second integer frequency point from the target complex spectrum sequence, wherein the first integer frequency point is the integer frequency point with the largest amplitude in the target complex spectrum sequence, and the second integer frequency point is the next integer frequency point after the first integer frequency point; Determine the first frequency correction amount; Based on the first frequency correction, the first integer spectrum value and the second complex spectrum value are linearly interpolated to obtain the fundamental spectrum value, wherein the fundamental spectrum value is the complex spectrum value corresponding to the fundamental frequency. The fundamental phase of the current sampling window is determined based on the fundamental frequency spectrum value.

5. The method according to claim 4, characterized in that, Determining the first frequency correction amount includes: Find the third complex spectrum value corresponding to the third integer frequency point from the target complex spectrum sequence, wherein the third integer frequency point is the previous integer frequency point of the first integer frequency point; Calculate the first amplitude of the first complex spectrum value, the second amplitude of the second complex spectrum value, and the third amplitude of the third complex spectrum value; The first frequency correction amount is determined based on the first amplitude, the second amplitude, and the third amplitude.

6. The method according to claim 5, characterized in that, Determining the first frequency correction amount based on the first amplitude, the second amplitude, and the third amplitude includes: The first frequency correction amount is calculated using the following formula: ； in, The first complex spectrum value, The second complex spectrum value, The third complex spectrum value, For the first integer frequency point, The second integer frequency point, The third integer frequency point, This is the first frequency correction amount.

7. The method according to claim 3, characterized in that, The calculation of the phase deviation of each phase based on the actual phase increment and the theoretical phase increment of each phase includes: The phase deviation of each phase is calculated using the following formula: ； in, The phase deviation amount, The actual phase increment, The fundamental phase of the current sampling window. The fundamental phase of the previous sampling window is denoted by Δt, where Δt is the time interval between adjacent sampling windows. This is the theoretical phase increment.

8. The method according to claim 3, characterized in that, The method further includes: Based on the first complex spectrum sequence, the fundamental amplitude and the odd harmonic amplitude are calculated, wherein the fundamental amplitude is the amplitude corresponding to the fundamental spectrum value, the odd harmonic amplitude is the amplitude corresponding to the harmonic spectrum value, and the harmonic spectrum value is the complex spectrum value corresponding to the odd harmonic. The harmonic distortion rate is calculated based on the fundamental frequency amplitude and the odd harmonic amplitude. The current integral imbalance, the phase deviation, and the harmonic distortion rate are weighted and fused to obtain the fusion score.

9. The method according to claim 8, characterized in that, The weighted fusion of the current integral unbalance, the phase deviation, and the harmonic distortion rate to obtain the fusion score includes: A current integration flag is determined, wherein if the current integration imbalance is greater than a first preset threshold, the current integration flag is determined to be 1; otherwise, the current integration flag is determined to be 0. A phase change flag is determined, wherein if the maximum value of the phase change amount of each phase is greater than a second preset threshold, the phase change flag is determined to be 1; otherwise, the phase change flag is determined to be 0. Determine a harmonic auxiliary flag, wherein if the harmonic distortion rate is greater than a third preset threshold, the harmonic auxiliary flag is determined to be 1; otherwise, the harmonic auxiliary flag is determined to be 0. The fusion score is calculated using the following formula: ； in, The fusion score is given by w1, w2, and w3, which are weighting coefficients that satisfy w1 + w2 + w3 = 1. For the current integral flag, This is the phase abrupt change flag. This is the harmonic auxiliary symbol.

10. The method according to claim 1, characterized in that, The weighted fusion of the current integral imbalance and the phase deviation to obtain a fusion score includes: A current integration flag is determined, wherein if the current integration imbalance is greater than a first preset threshold, the current integration flag is determined to be 1; otherwise, the current integration flag is determined to be 0. A phase change flag is determined, wherein if the maximum value of the phase change amount of each phase is greater than a second preset threshold, the phase change flag is determined to be 1; otherwise, the phase change flag is determined to be 0. The fusion score is calculated using the following formula: ； in, The fusion score is given by w1 and w2, which are weighting coefficients that satisfy w1 + w2 = 1. For the current integral flag, This is the phase abrupt change flag.

11. The method according to claim 9 or 10, characterized in that, The method further includes: The load compensation item is determined based on the load rate and the high load rate threshold. The first preset threshold is dynamically adjusted based on the current motor speed and the load compensation item.

12. The method according to claim 11, characterized in that, The step of dynamically adjusting the first preset threshold based on the current motor speed and the load compensation term includes: The first preset threshold is calculated using the following formula: ； in, The first preset threshold, Based on the basic imbalance threshold, For speed coefficient, This is the current motor speed. This is the reference value for rotational speed. For load compensation items; The load compensation term is calculated using the following formula: in, For load compensation items, This is the high load rate threshold.

13. The method according to claim 1, characterized in that, The step of performing graded protection actions based on the fusion score includes: If the fusion score reaches or exceeds the first score threshold for the first time, the driving power of the motor will be reduced to a predetermined percentage of the rated power. If the fusion score of two consecutive sampling windows reaches or exceeds the first score threshold, then each power transistor in the inverter unit is turned off and the fault state is locked.

14. The method according to claim 1, characterized in that, The method further includes: The integral value of the absolute value of the three-phase current is determined based on the three-phase current data sequence; The phase with a phase loss fault is determined based on the integral value of the absolute value of the three-phase current and the phase deviation of the three-phase current, wherein the phase with a phase loss fault has the smallest integral value of the absolute value of the current and the largest phase deviation.

15. A controller, characterized in that, The controller includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the motor phase loss protection method as described in any one of claims 1-14.

16. A washing machine, characterized in that, It includes a motor and a controller as described in claim 15, wherein the controller is connected to the motor.

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