Control method of motor for inverter compressor

Abstract

The invention belongs to the technical field of motor control, and discloses a control method of a motor for an inverter compressor, which comprises the following steps: embedding a distributed thermocouple array in a motor stator slot to acquire temperature data of the motor, synchronously acquiring operation parameters of the motor, generating a dynamic thermal field characteristic spectrum through a data fusion algorithm, coordinates of the hot spot areas with the temperature abnormally increased are marked, a dynamic correlation model is established, and heat conduction characteristic parameters in the dynamic correlation model are updated through a sliding window algorithm; outputting a predicted temperature rise curve through the dynamic correlation model, adjusting a PWM control strategy of the inverter, inputting the PWM control strategy to a motor driving unit to execute preliminary regulation and control, collecting feedback data to generate a multi-dimensional feedback feature vector, iteratively optimizing a combined weight of a switch interval compensation amount and a harmonic injection strategy through a gradient descent algorithm, and performing harmonic injection control. And a global optimal strategy considering the heat dissipation efficiency and the operation stability is formed, and the operation stability and the energy efficiency level of the inverter compressor under various working conditions are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of motor control, and in particular to a control method for a motor used in a variable frequency compressor. Background Art

[0002] As the core power unit of refrigeration equipment, variable frequency compressors are widely used in household air conditioners, cold chain logistics, industrial refrigeration systems and other fields. With the continuous advancement of energy conservation and emission reduction policies, the efficient speed regulation and thermal management capabilities of motors have become the key to improving system energy efficiency. Under harsh working conditions such as high temperature and airtightness, frequent start and stop, the problems of local overheating of motor windings and electromagnetic parameter mismatch are particularly prominent. Traditional heat dissipation design is difficult to balance the contradiction between rapid temperature rise suppression and dynamic load response, resulting in energy efficiency loss and even device failure risks.

[0003] In the prior art, the control methods for variable frequency compressor motors mostly adopt temperature threshold triggered heat dissipation strategies, such as adjusting the PWM switching frequency or forcibly starting an auxiliary heat dissipation device through a preset temperature threshold. Although such methods can temporarily alleviate overheating, they have the core defect of decoupling heat dissipation regulation from electromagnetic control: their temperature feedback is based only on limited measurement point data, and cannot accurately identify the thermal field distribution characteristics inside the winding, resulting in a disconnect between the global heat dissipation strategy and local hot spot suppression; at the same time, the single-dimensional adjustment of PWM parameters often causes secondary problems such as increased torque pulsation and worsening current harmonics, forming a vicious cycle of "heat dissipation optimization-stability degradation". The essence of this defect lies in the failure to establish a dynamic coupling mechanism between the thermal field evolution and the electromagnetic control parameters, resulting in the inability to coordinately optimize the heat dissipation efficiency and operational stability.

[0004] Therefore, an intelligent control method capable of achieving coordinated regulation is required to solve the technical problem that the existing variable frequency compressor motor has a single heat dissipation regulation capability and cannot establish coordinated optimization between heat dissipation and stability. Summary of the invention

[0005] The object of the present invention is to provide a control method for a motor for a variable frequency compressor to solve the above technical problems.

[0006] To achieve this object, the present invention adopts the following technical solutions: A method for controlling a motor for a variable frequency compressor, comprising: A distributed thermocouple array is embedded in the stator slot of the motor to collect the temperature data of the motor and the motor operating parameters simultaneously. The temperature data and the motor operating parameters are combined through a data fusion algorithm to generate a dynamic thermal field characteristic map, and the coordinates of the hot spot area where the temperature rises abnormally are marked; the motor operating parameters include the real-time temperature sequence of each winding segment and the three-phase current spectrum when the motor is running; Based on the dynamic thermal field characteristic map, a dynamic correlation model between winding temperature and motor operating parameters is established, and the heat conduction characteristic parameters in the dynamic correlation model are updated through a sliding window algorithm; The predicted temperature rise curve is output by the dynamic correlation model to adjust the PWM control strategy of the inverter; the PWM control strategy includes: For the marked hotspot area, increase the switch interval compensation of the corresponding phase winding according to the temperature rise gradient ratio; According to the energy ratio of the fundamental wave and the third harmonic of the three-phase current spectrum, the harmonic injection strategy of the voltage vector is adjusted; The adjusted PWM control strategy is input into the motor drive unit to perform preliminary control, and the temperature change rate and torque fluctuation data of the regulated winding are collected to generate a multi-dimensional feedback feature vector; Based on the multi-dimensional feedback feature vector, the combined weight of the switching interval compensation amount and the harmonic injection strategy is iteratively optimized through the gradient descent algorithm, and the motor control strategy matrix adapted to the current working condition is output and loaded into the control system in real time.

[0007] Optionally, the process of embedding the distributed thermocouple array in the stator slot of the motor is specifically as follows: During the design phase of the motor stator slots, a laser micromachining process is used to form an array of equally spaced micro grooves in the stator slots; A flexible thin film thermocouple is embedded in the groove in a serpentine routing manner, wherein the flexible thin film thermocouple comprises a nickel-chromium-nickel-silicon composite layer and a polyimide insulation layer; wherein the node spacing between adjacent flexible thin film thermocouples is consistent with the groove spacing, forming a distributed temperature measurement network covering the circumference of the stator.

[0008] Optionally, the temperature data and the motor operating parameters are combined through a data fusion algorithm to generate a dynamic thermal field characteristic map, and the coordinates of hotspot areas where the temperature rises abnormally are marked, specifically including: Extracting the current harmonic features of the three-phase current spectrum by fast Fourier transform, wherein the current harmonic features include the spectrum energy of the fundamental wave, the third harmonic and the fifth harmonic components; Performing joint preprocessing in the time and space domains on the temperature data, using a wavelet threshold denoising algorithm, selecting the sym4 wavelet basis for 5-layer decomposition, and setting an adaptive threshold function to eliminate electromagnetic interference noise of the temperature data; A three-dimensional polar coordinate system is established based on the geometric parameters of the motor, and the discrete temperature points are mapped to the spatial topological structure of the stator winding to generate an initial temperature field distribution cloud map.

[0009] Optionally, the step of mapping the discrete temperature points to the spatial topological structure of the stator winding to generate an initial temperature field distribution cloud map further includes: The initial temperature field distribution cloud map and the three-phase current spectrum are input into the DS evidence theory fusion model, where the temperature data is used as the first evidence body, the third harmonic energy ratio is used as the second evidence body, and the fifth harmonic energy ratio is used as the third evidence body; The credibility weight of each evidence body is calculated through the correction algorithm, and the thermal field confidence distribution matrix containing the temperature-current coupling characteristics is output; The thermal field confidence distribution matrix is ​​interpolated to reconstruct the global temperature gradient surface of the winding. When the local temperature confidence is ≥0.92 and the gradient change rate is >3℃ / mm 2 When an abnormal hot spot is detected, its polar coordinate position (r, θ, z) and confidence level are recorded to calibrate the hot spot area.

[0010] Optionally, the polar coordinate position (r, θ, z) and confidence level are recorded to mark the hotspot area, and then the following is further included: The calibrated hot spot area coordinates are aligned with the current harmonic characteristics in time and space to establish a three-dimensional feature vector set including temperature amplitude, gradient vector, and harmonic energy weight; Through the PCA dimensionality reduction processing of the three-dimensional feature vector set, the first three principal components are extracted to form the encoding matrix of the thermal field feature map.

[0011] Optionally, based on the dynamic thermal field characteristic map, a dynamic correlation model between winding temperature and motor operating parameters is established, and the heat conduction characteristic parameters in the dynamic correlation model are updated by a sliding window algorithm, specifically including: Analyze the encoding matrix of the dynamic thermal field characteristic map, extract the temperature amplitude principal component vector T_pca, the harmonic energy weight vector H_wei and the gradient vector field G_xyz as the model input data set; The winding temperature rise rate prediction model is constructed as a dynamic correlation model. The model input variables are defined as: the temperature amplitude T_pca(k) at the current moment, the harmonic energy vector of the three historical cycles [H_wei(k-1), H_wei(k-2), H_wei(k-3)], the modulus value ||G|| of the gradient vector field G_xyz(k), and the model output is the temperature rise rate ΔT_pred of the next cycle. Initial heat conduction characteristic parameters, the heat conduction characteristic parameters including heat conduction coefficient α_t, harmonic influence factor β_t and gradient weight δ_t; Set the sliding window duration and the number of sampling groups within the window duration, and update the thermal conductivity characteristic parameters of the winding temperature rise rate prediction model through the recursive least squares method based on the residual between the measured temperature rise rate ΔT_meas and the predicted value ΔT_pred within the window; The residual root mean square error (RMSE) in the calculation window is used. When RMSE is less than 0.12°C / s, the updated thermal conductivity characteristic parameters are accepted. If RMSE is greater than or equal to 0.2°C / s after two consecutive updates, the parameter set is returned to the previous valid parameter set.

[0012] Optionally, the adjusted PWM control strategy is input into the motor drive unit to perform preliminary regulation, and the regulated winding temperature change rate and torque fluctuation data are collected to generate a multi-dimensional feedback feature vector, specifically including: The adjusted PWM control parameter set is loaded into the FPGA control unit of the motor driver to perform preliminary control, where the actual dead time T_phase of each phase is written into the timer register in 16 bits through the SPI interface, and the composite voltage vector waveform data is stored in the dual-port RAM buffer; The distributed thermocouple array sampling rate is used to capture the temperature sequence of each winding section after regulation, and the temperature change rate ΔT_rate=ΔT / Δt is calculated, with a time window Δt=100ms; The motor rotor angular displacement signal is collected by a magnetic encoder, and the second-order derivative is obtained after Savitzky-Golay filtering to obtain the torque fluctuation component ΔTe.

[0013] Optionally, the second-order derivative is obtained after Savitzky-Golay filtering to obtain the torque fluctuation component ΔTe, and then the following is further included: Extracting temperature feedback indicators, the temperature feedback indicators include: maximum temperature rise rate ΔT_max in the hot spot area, average temperature rise gradient T_avg and temperature distribution uniformity index U_T=σ(T) / μ(T); Extracting electrical parameter indicators, the electrical parameter indicators include: current total harmonic distortion THD, third harmonic distortion H3%; The temperature feedback index and the electrical parameter index are normalized and encoded into a 5-dimensional feature vector in the order of [ΔT_max, T_avg, U_T, THD, H3%] to construct a multi-dimensional feedback feature vector.

[0014] Optionally, based on the multi-dimensional feedback feature vector, the combined weight of the switching interval compensation amount and the harmonic injection strategy is iteratively optimized by a gradient descent algorithm, and a motor control strategy matrix adapted to the current working condition is output and loaded into the control system in real time, specifically including: Analyze the multi-dimensional feedback feature vector, extract the maximum temperature rise rate ΔT_max, the current total harmonic distortion rate THD and the torque fluctuation peak ΔTe_pp as optimization targets, and construct a normalized cost function; Define the optimization variables and constraints: the switching interval compensation weight k_comp ranges from [0.1, 0.4], the harmonic injection ratio weight k_h3 ranges from [0.05, 0.25], the learning rate α is set to 0.02, the maximum number of iterations is 50, and the convergence threshold ΔJ is less than 0.5%.

[0015] Optionally, the step of defining optimization variables and constraints further includes: Calculate the cost function value J_current based on the current weight combination (k_comp, k_h3); The partial derivatives are calculated by perturbation analysis: a ±0.02 perturbation is applied to k_comp to obtain the J change ΔJ_comp; a ±0.005 perturbation is applied to k_h3 to obtain the J change ΔJ_h3; Update the current weight combination (k_comp, k_h3) through the calculated partial derivatives. If k_comp exceeds the range, take the boundary value. If k_h3 exceeds the limit, retreat by 0.005 steps to perform gradient descent iterative optimization. When the decrease of J is less than 0.5% for three consecutive iterations, the current weight combination is extracted; Check the constraints of ΔT_max≤5℃ / s, THD≤5%, and ΔTe_pp≤2N·m. If any indicator exceeds the limit, reiterate according to the weight priority of the cost function component; Map the optimization weights to the actual control parameters, generate 12 sets of discrete parameter combinations, and store them in the strategy matrix in ascending order of J value; The strategy matrix is ​​transmitted to the DSP coprocessor of the motor controller via the PCIe bus. At the beginning of the next control cycle, the strategy group with the smallest J value is selected for activation and execution.

[0016] Compared with the prior art, the present invention has the following beneficial effects: the method is constructed based on the principle of multi-physical field coupling control, realizes synchronous perception of temperature and electrical parameters through a distributed thermocouple array, and uses a data fusion algorithm to establish a dynamic thermal field feature map to locate hot spots; combines the dynamic correlation model of the sliding window algorithm to form a closed-loop mapping of thermal field characteristics and PWM control parameters; realizes preliminary adaptation of thermal-electric parameters by predicting the temperature rise curve to drive the dual-path control of switch interval compensation and harmonic injection; constructs a coupling evaluation index of thermal control effect and mechanical performance based on multi-dimensional feedback data, and uses a gradient descent algorithm to optimize the weights of multi-objective conflict parameters to form a global optimal strategy that takes into account both heat dissipation efficiency and operation stability; the method upgrades the traditional single electronic control logic to an intelligent control system that coordinates thermal-electric-mechanical multi-physical fields, which significantly improves the operating stability and energy efficiency of the variable frequency compressor under various working conditions while ensuring efficient heat dissipation of the motor. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0018] The structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0019] Figure 1 Schematic diagram of the flow of the control method of the motor for the variable frequency compressor of this embodiment. DETAILED DESCRIPTION

[0020] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below 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 creative work are within the scope of protection of the present invention.

[0021] In the description of the present invention, it should be understood that the terms "upper", "lower", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. It should be noted that when a component is considered to be "connected" to another component, it may be directly connected to the other component or there may be a centrally arranged component at the same time.

[0022] The technical solution of the present invention is further described below with reference to the accompanying drawings and through specific implementation methods.

[0023] Combination Figure 1 As shown, an embodiment of the present invention provides a method for controlling a motor for a variable frequency compressor, comprising: S1, embed a distributed thermocouple array in the stator slot of the motor to collect the temperature data of the motor, synchronously collect the motor operating parameters, generate a dynamic thermal field characteristic map through the data fusion algorithm of the temperature data and the motor operating parameters, and mark the coordinates of the hot spot area where the temperature rises abnormally; the motor operating parameters include the real-time temperature sequence of each winding segment and the three-phase current spectrum when the motor is running.

[0024] S2, based on the dynamic thermal field characteristic map, establish a dynamic correlation model between winding temperature and motor operating parameters, and update the thermal conduction characteristic parameters in the dynamic correlation model through the sliding window algorithm; the dynamic correlation model includes the mapping relationship between current harmonic components, PWM switching frequency and local temperature rise rate.

[0025] S3, predicting the temperature rise curve through the dynamic correlation model output, and adjusting the PWM control strategy of the inverter; the PWM control strategy includes: For the marked hotspot area, increase the switch interval compensation of the corresponding phase winding according to the temperature rise gradient ratio; According to the energy ratio of the fundamental wave and the third harmonic of the three-phase current spectrum, the harmonic injection strategy of the voltage vector is adjusted.

[0026] S4, inputting the adjusted PWM control strategy into the motor drive unit to perform preliminary regulation, and collecting the winding temperature change rate and torque fluctuation data after regulation to generate a multi-dimensional feedback feature vector.

[0027] S5, based on the multi-dimensional feedback feature vector, iteratively optimizes the combined weights of the switching interval compensation and the harmonic injection strategy through the gradient descent algorithm, outputs the motor control strategy matrix adapted to the current working conditions and loads it into the control system in real time.

[0028] The working principle of the present invention is as follows: the control method is constructed based on the principle of multi-physical field coupling control, and synchronous perception of temperature and electrical parameters is achieved through a distributed thermocouple array. A dynamic thermal field feature map is established using a data fusion algorithm to locate hot spots. The dynamic correlation model of the sliding window algorithm is combined to form a closed-loop mapping of thermal field characteristics and PWM control parameters. The dual-path control of switch interval compensation and harmonic wave injection is driven by predicting the temperature rise curve to achieve preliminary adaptation of thermal-electrical parameters. A coupling evaluation index of thermal control effect and mechanical performance is constructed based on multi-dimensional feedback data, and a gradient descent algorithm is used to optimize the weights of multi-objective conflict parameters to form a global optimal strategy that takes into account both heat dissipation efficiency and operational stability. The method upgrades the traditional single electronic control logic to an intelligent control system that coordinates thermal-electrical-mechanical multi-physical fields, which significantly improves the operational stability and energy efficiency of the variable frequency compressor under various working conditions while ensuring efficient heat dissipation of the motor.

[0029] In this embodiment, it is specifically described that the process of embedding the distributed thermocouple array in the stator slot of the motor is specifically as follows: During the design stage of the motor stator slot, a laser micromachining process is used to form an array of equally spaced micro grooves in the stator slot; preferably, the groove spacing is set to 2.8±0.1 mm, and the depth is 1 / 3 of the stator slot depth.

[0030] A flexible thin-film thermocouple is embedded in the groove in a serpentine routing manner, wherein the flexible thin-film thermocouple comprises a nickel-chromium-nickel-silicon composite layer and a polyimide insulation layer; wherein the node spacing between adjacent flexible thin-film thermocouples is consistent with the groove spacing, forming a distributed temperature measurement network covering the circumference of the stator.

[0031] In this embodiment, it is specifically explained that step S1 specifically includes: S11, collecting temperature data of the motor through a distributed thermocouple array and synchronously collecting operating parameters of the motor; A high-density layout strategy (4 measurement points per slot) is adopted to synchronously collect temperature data and three-phase current signals of each winding segment at a sampling rate of 1kHz. The temperature data is eliminated from common-mode interference through an isolation amplifier, and the current signal is converted by a 16-bit ADC and cached in the FPGA to ensure strict alignment of time and space data.

[0032] S12, extracting current harmonic features of the three-phase current spectrum by fast Fourier transform, where the current harmonic features include spectrum energy of fundamental wave, third harmonic and fifth harmonic components; Fast Fourier transform analysis is performed on the three-phase current signal to calculate the energy proportion of the fundamental wave, the third harmonic, and the fifth harmonic. A bandpass filter (150±5Hz) is set especially for the third harmonic to suppress noise interference near the switching frequency (15kHz). The extracted harmonic feature vectors [H3%, H5%] reflect the electromagnetic loss distribution characteristics and have a strong correlation with the temperature field evolution.

[0033] S13 performs joint preprocessing in the time and space domains on the temperature data, adopts the wavelet threshold denoising algorithm, selects the sym4 wavelet basis for 5-layer decomposition, and sets an adaptive threshold function to eliminate the electromagnetic interference noise of the temperature data; performs soft threshold processing on the coefficients exceeding the threshold, and the signal-to-noise ratio after reconstruction is improved by ≥18dB, effectively filtering out the pulse noise caused by PWM switching.

[0034] S14, establishing a three-dimensional polar coordinate system based on the geometric parameters of the motor, mapping the discrete temperature points to the spatial topological structure of the stator winding, and generating an initial temperature field distribution cloud map.

[0035] Based on the three-dimensional polar coordinate parameters of the motor stator, a non-uniform grid mapping model is established. The discrete temperature measurement points are interpolated into a grid system containing multiple units (432 units) to generate an initial temperature field cloud map with a resolution of 0.5°C, which intuitively presents the thermal distribution of the winding.

[0036] S15, input the initial temperature field distribution cloud map and the three-phase current spectrum into the DS evidence theory fusion model, where the temperature data is used as the first evidence body, the third harmonic energy ratio is used as the second evidence body, and the fifth harmonic energy ratio is used as the third evidence body; the joint confidence is calculated through the evidence synthesis rule to break through the limitations of a single data source.

[0037] S16, calculates the credibility weight of each evidence body through the correction algorithm, and outputs the thermal field confidence distribution matrix containing the temperature-current coupling characteristics; ensures the dominance of temperature evidence in high-conflict scenarios (w1≥0.6), and outputs the thermal field confidence matrix.

[0038] S17, the interpolation algorithm is used to reconstruct the global temperature gradient surface of the winding using the thermal field confidence distribution matrix. When the local temperature confidence is ≥ 0.92 and the gradient change rate is > 3℃ / mm 2 When an abnormal hot spot is detected, its polar coordinate position (r, θ, z) and confidence level are recorded to calibrate the hot spot area.

[0039] S18, align the calibrated hot spot area coordinates with the current harmonic characteristics in time and space, and establish a three-dimensional feature vector set including temperature amplitude, gradient vector, and harmonic energy weight; ensure data causality through a timestamp synchronization mechanism to form a time series feature library.

[0040] S19, through the PCA dimensionality reduction processing of the three-dimensional feature vector set, the first three principal components are extracted to form the encoding matrix of the thermal field feature map as the digital representation of the thermal field feature map.

[0041] In this embodiment, it is specifically described that step S2 specifically includes: S21, parse the encoding matrix of the dynamic thermal field characteristic map, extract the temperature amplitude principal component vector T_pca, the harmonic energy weight vector H_wei and the gradient vector field G_xyz as the model input data set; where the dimension of T_pca is [1×64], and H_wei contains the energy proportion coefficients of the fundamental wave, the third harmonic and the fifth harmonic. Through data standardization, eliminate the dimensional difference and form the model input data set.

[0042] Temperature amplitude principal component vector T_pca: The dimension is [1×64], representing 64 standardized temperature principal components, reflecting the dimensionality reduction characteristics of the global temperature distribution of the winding; Harmonic energy weight vector H_wei: contains the normalized energy weight coefficients of the fundamental wave (accounting for 65%-85%), the third harmonic (8%-25%) and the fifth harmonic (2%-10%), which characterizes the electromagnetic loss distribution; Gradient vector field G_xyz: a three-dimensional matrix structure, each unit stores a gradient vector, and quantifies the spatial variation trend of the temperature field.

[0043] S22, construct a winding temperature rise rate prediction model as a dynamic correlation model, and define the model input variables as: the temperature amplitude T_pca (k) at the current moment, the harmonic energy vector of the three historical cycles [H_wei (k-1), H_wei (k-2), H_wei (k-3)], the modulus value ||G|| of the gradient vector field G_xyz (k), and the model output is the temperature rise rate ΔT_pred of the next cycle; the winding temperature rise rate prediction model reveals the dynamic correlation between temperature evolution and multi-physical fields through linear combination.

[0044] S23, initial heat conduction characteristic parameters, the heat conduction characteristic parameters include heat conduction coefficient α_t, harmonic influence factor β_t and gradient weight δ_t; When the initial heat conduction characteristic parameters are set, preferably, the initial heat conduction coefficient α_0=0.75 W / (m·K), the harmonic influence factor β_0=0.02, the gradient weight δ_0=0.15, and the historical harmonic attenuation coefficients γ_1=0.5, γ_2=0.3, and γ_3=0.2 are defined.

[0045] S24, setting the sliding window duration and the number of sampling groups within the window duration, and updating the thermal conductivity characteristic parameters of the winding temperature rise rate prediction model by recursive least squares method based on the residual between the measured temperature rise rate ΔT_meas and the predicted value ΔT_pred within the window; Preferably, the sliding window duration is set to 300 ms, including 20 consecutive groups of sampled data, with an interval of 15 ms between each group.

[0046] S25, calculating the residual root mean square error RMSE in the window, and accepting the updated heat conduction characteristic parameters when RMSE < 0.12°C / s; if RMSE ≥ 0.2°C / s after two consecutive updates, then returning to the previous valid parameter set.

[0047] Calculate the prediction error RMSE within the window = √(Σ(ΔT_meas-ΔT_pred) 2 / N), the threshold RMSE < 0.12℃ / s corresponds to a temperature rise prediction accuracy of ±1.2℃ / min.

[0048] Abnormal processing: When RMSE ≥ 0.2℃ / s for two consecutive times, it is judged as a model mismatch (such as cooling system failure), and it automatically falls back to the mean parameters of the previous 10 windows (α_avg, β_avg, δ_avg), and triggers an early warning signal.

[0049] In this embodiment, it is specifically described that step S4 specifically includes: S41, loading the adjusted PWM control parameter set into the FPGA control unit of the motor driver to perform preliminary control, wherein the actual dead time T_phase of each phase is written into the timer register in 16 bits via the SPI interface, and the composite voltage vector waveform data is stored in the dual-port RAM buffer; The adjusted PWM control parameters are written to the motor driver's FPGA control unit with 16-bit precision via the SPI interface to ensure that the timer register receives accurate dead time parameters (T_phase). The actual dead time of each phase is configured differently according to the spatial angle of the hot spot area; for example, when the U phase hot spot is at θ=120°: T_phase_U=T_dead+ΔT_comp·cos (120°); The composite voltage vector waveform data (including fundamental and third harmonic components) is transferred to the dual-port RAM buffer through the DMA channel. The dual-port parallel access feature is used to achieve zero-delay switching of waveform data, ensuring seamless update of the modulation strategy at the beginning of the next PWM cycle (usually 50μs) to avoid control interruption.

[0050] S42, using a distributed thermocouple array sampling rate to capture the temperature sequence of each winding segment after regulation, and calculating the temperature change rate ΔT_rate=ΔT / Δt, with a time window Δt=100ms; Real-time monitoring of overheating risks. When the ΔT_rate in a certain area exceeds the limit for three consecutive times, a warning signal is triggered, providing key input for subsequent optimization.

[0051] S43, the motor rotor angular displacement signal is collected through a magnetic encoder, and the second-order derivative is obtained after Savitzky-Golay filtering to obtain the torque fluctuation component ΔTe, and its spectrum analysis adopts STFT transformation with a resolution of 0.5 Hz; The rotor angular displacement signal is collected at a frequency of 20kHz by a magnetic encoder, and the 5th-order Savitzky-Golay filter (window length 201 points) is used to smooth the angle data to eliminate the slight oscillation caused by the cogging torque. The second-order derivative (d 2 θ / dt 2 ), combined with the motor moment of inertia J = 0.02kg·m 2 Calculate the torque fluctuation component ΔTe=J·d 2 θ / dt 2 The short-time Fourier transform (STFT, window length 4096 points, overlap rate 75%) was used to analyze the ΔTe spectrum, extract the main frequency energy distribution (such as 150Hz switching frequency sideband) with an accuracy of 0.5Hz, and quantify the mechanical vibration characteristics.

[0052] S44, extracting temperature feedback indicators, which include: maximum temperature rise rate ΔT_max in the hot spot area, average temperature rise gradient T_avg, and temperature distribution uniformity index U_T=σ(T) / μ(T); Three core indicators are extracted from the temperature data: the maximum temperature rise rate ΔT_max in the hot spot area (taking the 95% percentile value of ΔT_rate of all measuring points), the average temperature rise gradient T_avg (calculating the arithmetic mean of the global gradient modulus ||G||), and the temperature distribution uniformity index U_T=σ(T) / μ(T) (ratio of standard deviation to mean). For example, when U_T>0.15, it is determined that the temperature distribution is uneven, and the heat dissipation strategy needs to be adjusted first. ΔT_max reflects the severity of local overheating, T_avg represents the overall heat dissipation efficiency, and U_T evaluates the thermal field balance. The three together constitute the heat dissipation performance evaluation system.

[0053] S45, extracting electrical parameter indicators, the electrical parameter indicators include: current total harmonic distortion rate THD, third harmonic distortion rate H3%; Perform harmonic analysis on the current signal and calculate the total harmonic distortion THD = √(ΣH_n 2 ) / H_1×100% (n=2~50), focusing on the third harmonic distortion rate H3%=H_3 / H_1×100%. FFT (65536 points) with Blackman-Harris window is used to improve the spectrum resolution and accurately separate the dense harmonic components. The THD safety threshold is set to 5%. When THD exceeds the limit, the system automatically records the number of harmonics exceeding the limit and the amplitude and phase information to provide data support for the optimization of harmonic suppression strategy.

[0054] S46, normalizing the temperature feedback index and the electrical parameter index and encoding them into a 5-dimensional feature vector in the order of [ΔT_max, T_avg, U_T, THD, H3%] to construct a multi-dimensional feedback feature vector.

[0055] The numerical range of each dimension is compressed to the interval [0, 1]. For example, the characteristic vector under a certain working condition is [0.8, 0.35, 0.12, 0.28, 0.18], reflecting that the current heat dissipation pressure is large (ΔT_max_norm=0.8) but the electromagnetic interference is controllable (THD_norm=0.28). This vector is used as a multi-dimensional feedback signal to input the subsequent optimization algorithm to drive the dynamic tuning of the control strategy.

[0056] In this embodiment, it is specifically described that step S5 specifically includes: S51, analyzing the multi-dimensional feedback feature vector, extracting the maximum temperature rise rate ΔT_max, the current total harmonic distortion rate THD and the torque fluctuation peak ΔTe_pp as optimization targets, and constructing a normalized cost function; By analyzing the multi-dimensional feedback feature vector, the maximum temperature rise rate ΔT_max (reflecting the risk of local overheating), the current total harmonic distortion rate THD (characterizing the electromagnetic interference level) and the torque fluctuation peak ΔTe_pp (indicating mechanical stability) are extracted as optimization targets. When constructing the normalized cost function, each indicator is scaled according to the safety threshold: ΔT_max is divided by 5℃ / s (maximum allowable temperature rise rate), THD is divided by 5% (limit), ΔTe_pp is divided by 2N·m (mechanical tolerance threshold), and is linearly combined at a weight ratio of 0.6:0.3:0.1 to reflect the principle of heat dissipation priority. For example, when ΔT_max=4℃ / s, THD=3%, ΔTe_pp=1.5N·m, J=0.6*(4 / 5)+0.3*(3 / 5)+0.1*(1.5 / 2)=0.717 is calculated, providing a quantitative benchmark for subsequent optimization.

[0057] S52, define optimization variables and constraints: the value range of the switch interval compensation weight k_comp is [0.1, 0.4], the value range of the harmonic injection ratio weight k_h3 is [0.05, 0.25], set the learning rate α=0.02, the maximum number of iterations is 50 times, and the convergence threshold ΔJ<0.5%.

[0058] Define optimization variables and their physical constraints: The switch interval compensation weight k_comp (adjusting the dead time compensation strength) is limited to the range of [0.1, 0.4] to prevent overcompensation from causing a sharp increase in switching losses; the harmonic injection ratio weight k_h3 (controlling the third harmonic voltage amplitude) is limited to [0.05, 0.25] to avoid electromagnetic compatibility problems caused by excessive harmonics. Set the learning rate α=0.02 (calibrated through previous simulations) to ensure that the weight adjustment step size takes into account both convergence speed and stability; the combination of the maximum number of iterations of 50 times (taking about 10ms) and the convergence threshold ΔJ<0.5% (the objective function improves saturation) balances real-time requirements and optimization accuracy.

[0059] S53, calculating the cost function value J_current based on the current weight combination (k_comp, k_h3); For example, when k_comp=0.25 and k_h3=0.15, the system obtains ΔT_max=3.8℃ / s, THD=4.2%, and ΔTe_pp=1.2N·m based on the actual control effect, and substitutes into the cost function formula to obtain J_current=0.6*(3.8 / 5)+0.3*(4.2 / 5)+0.1*(1.2 / 2)=0.672. This value reflects the comprehensive performance of the current strategy and serves as the starting point for optimization.

[0060] S54, calculating partial derivatives by perturbation analysis method: applying ±0.02 perturbation to k_comp to obtain J change ΔJ_comp; applying ±0.005 perturbation to k_h3 to obtain J change ΔJ_h3; For example, if k_comp=0.25, J=0.672, after the disturbance, J_+=0.665, J_-=0.680, then ΔJ_comp=(0.665-0.680) / 0.04=-0.375. Similarly, apply ±0.005 disturbance to k_h3 to calculate ΔJ_h3.

[0061] S55, updating the current weight combination (k_comp, k_h3) by using the calculated partial derivatives. If k_comp exceeds the range, the boundary value is taken. If k_h3 exceeds the limit, the step length is withdrawn by 0.005 to perform gradient descent iterative optimization. The weight is adjusted according to the gradient direction. For example, when ΔJ_comp=-0.375, k_comp_new=0.25 - 0.02*(-0.375)=0.2575, which is rounded to 0.258. If k_comp exceeds the range of [0.1, 0.4], it is forced to be truncated (such as 0.405→0.4); when k_h3 exceeds the limit, it is withdrawn in steps of 0.005 (such as 0.26→0.255) to prevent oscillation caused by sudden changes in parameters. This process ensures that the optimization path is always within the feasible region.

[0062] S56, when the J decrease is less than 0.5% for three consecutive iterations, extract the current weight combination; when the J value decreases less than 0.5% in three consecutive iterations (such as 0.672→0.670→0.669→0.668), it is determined to be converged. Extract the current weight combination (such as k_comp=0.28, k_h3=0.12), and record its J value (such as 0.668) as a candidate solution.

[0063] S57, check the constraints of ΔT_max≤5℃ / s, THD≤5%, and ΔTe_pp≤2N·m. When any indicator exceeds the limit, reiterate according to the weight priority of the cost function component.

[0064] S58, mapping the optimization weights to actual control parameters, generating 12 sets of discrete parameter combinations, arranging them in ascending order of J values ​​and storing them in the strategy matrix; According to k_comp, the dead time of each phase is adjusted (such as T_phase_U=3μs*(1+0.28cos(120°))=3.42μs), and the harmonic voltage is generated according to k_h3 (such as V_h3=220V0.12*e^{-j60°}=26.4∠-60°V). 12 groups of discrete parameter combinations are generated (such as k_comp=0.25, 0.26, ..., 0.36; k_h3=0.10, 0.11, ..., 0.15), and they are arranged in ascending order of J value and stored in the matrix to form an "optimal-suboptimal" strategy sequence, which supports real-time fast switching.

[0065] S59, transmitting the strategy matrix to the DSP coprocessor of the motor controller via the PCIe bus, and selecting the strategy group with the smallest J value to activate and execute at the start of the next control cycle.

[0066] At the start of the next control cycle (every 200ms), select the strategy group with the smallest J value (such as index 1, J=0.652) to activate and execute. At the same time, monitor the ambient temperature change rate (dT_env / dt) and the load change rate (dLoad / dt). When dT_env / dt>2℃ / s or dLoad / dt>15%, immediately trigger strategy re-optimization to ensure continuous adaptability under dynamic conditions.

[0067] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A control method for a motor for a variable frequency compressor, characterized in that: include: A distributed thermocouple array is embedded in the stator slot of the motor to collect the temperature data of the motor and the motor operating parameters simultaneously. The temperature data and the motor operating parameters are combined through a data fusion algorithm to generate a dynamic thermal field characteristic map, and the coordinates of the hot spot area where the temperature rises abnormally are marked; the motor operating parameters include the real-time temperature sequence of each winding segment and the three-phase current spectrum when the motor is running; Based on the dynamic thermal field characteristic map, a dynamic correlation model between winding temperature and motor operating parameters is established, and the heat conduction characteristic parameters in the dynamic correlation model are updated through a sliding window algorithm; The dynamic correlation model is used to output a predicted temperature rise curve and adjust the PWM control strategy of the inverter; The PWM control strategy includes: For the marked hotspot area, increase the switch interval compensation of the corresponding phase winding according to the temperature rise gradient ratio; According to the energy ratio of the fundamental wave and the third harmonic of the three-phase current spectrum, the harmonic injection strategy of the voltage vector is adjusted; The adjusted PWM control strategy is input into the motor drive unit to perform preliminary control, and the temperature change rate and torque fluctuation data of the regulated winding are collected to generate a multi-dimensional feedback feature vector; Based on the multi-dimensional feedback feature vector, the combined weight of the switching interval compensation amount and the harmonic injection strategy is iteratively optimized through the gradient descent algorithm, and the motor control strategy matrix adapted to the current working condition is output and loaded into the control system in real time.

2. The control method for a motor for a variable frequency compressor according to claim 1, characterized in that: The process of embedding the distributed thermocouple array in the motor stator slot is specifically as follows: During the design phase of the motor stator slots, a laser micromachining process is used to form an array of equally spaced micro grooves in the stator slots; A flexible thin film thermocouple is embedded in the groove in a serpentine routing manner, wherein the flexible thin film thermocouple comprises a nickel-chromium-nickel-silicon composite layer and a polyimide insulation layer; wherein the node spacing between adjacent flexible thin film thermocouples is consistent with the groove spacing, forming a distributed temperature measurement network covering the circumference of the stator.

3. The control method for a motor for a variable frequency compressor according to claim 1, characterized in that: The temperature data and the motor operating parameters are combined through a data fusion algorithm to generate a dynamic thermal field characteristic map, and the coordinates of the hot spot area where the temperature is abnormally increased are marked, specifically including: Extracting the current harmonic features of the three-phase current spectrum by fast Fourier transform, wherein the current harmonic features include the spectrum energy of the fundamental wave, the third harmonic and the fifth harmonic components; Performing joint preprocessing in the time and space domains on the temperature data, using a wavelet threshold denoising algorithm, selecting the sym4 wavelet basis for 5-layer decomposition, and setting an adaptive threshold function to eliminate electromagnetic interference noise of the temperature data; A three-dimensional polar coordinate system is established based on the geometric parameters of the motor, and the discrete temperature points are mapped to the spatial topological structure of the stator winding to generate an initial temperature field distribution cloud map.

4. The control method for a motor for a variable frequency compressor according to claim 3, characterized in that: The discrete temperature points are mapped to the spatial topological structure of the stator winding to generate an initial temperature field distribution cloud map, and then the following steps are further included: The initial temperature field distribution cloud map and the three-phase current spectrum are input into the DS evidence theory fusion model, where the temperature data is used as the first evidence body, the third harmonic energy ratio is used as the second evidence body, and the fifth harmonic energy ratio is used as the third evidence body; The credibility weight of each evidence body is calculated through the correction algorithm, and the thermal field confidence distribution matrix containing the temperature-current coupling characteristics is output; The thermal field confidence distribution matrix is ​​interpolated to reconstruct the global temperature gradient surface of the winding. When the local temperature confidence is ≥0.92 and the gradient change rate is >3℃ / mm 2 When an abnormal hot spot is detected, its polar coordinate position (r, θ, z) and confidence level are recorded to calibrate the hot spot area.

5. The control method for a motor for a variable frequency compressor according to claim 4, characterized in that: The polar coordinate position (r, θ, z) and confidence level are recorded to mark the hotspot area, and then the following is included: The calibrated hot spot area coordinates are aligned with the current harmonic characteristics in time and space to establish a three-dimensional feature vector set including temperature amplitude, gradient vector, and harmonic energy weight; Through the PCA dimensionality reduction processing of the three-dimensional feature vector set, the first three principal components are extracted to form the encoding matrix of the thermal field feature map.

6. The control method for a motor for a variable frequency compressor according to claim 5, characterized in that: The method of establishing a dynamic correlation model between winding temperature and motor operating parameters based on the dynamic thermal field characteristic map and updating the heat conduction characteristic parameters in the dynamic correlation model by a sliding window algorithm specifically includes: Analyze the encoding matrix of the dynamic thermal field characteristic map, extract the temperature amplitude principal component vector T_pca, the harmonic energy weight vector H_wei and the gradient vector field G_xyz as the model input data set; The winding temperature rise rate prediction model is constructed as a dynamic correlation model. The model input variables are defined as: the temperature amplitude T_pca(k) at the current moment, the harmonic energy vector of the three historical cycles [H_wei(k-1), H_wei(k-2), H_wei(k-3)], the modulus value ||G|| of the gradient vector field G_xyz(k), and the model output is the temperature rise rate ΔT_pred of the next cycle. Initial heat conduction characteristic parameters, the heat conduction characteristic parameters including heat conduction coefficient α_t, harmonic influence factor β_t and gradient weight δ_t; Set the sliding window duration and the number of sampling groups within the window duration, and update the thermal conductivity characteristic parameters of the winding temperature rise rate prediction model through the recursive least squares method based on the residual between the measured temperature rise rate ΔT_meas and the predicted value ΔT_pred within the window; The residual root mean square error (RMSE) in the calculation window is used. When RMSE is less than 0.12°C / s, the updated thermal conductivity characteristic parameters are accepted. If RMSE is greater than or equal to 0.2°C / s after two consecutive updates, the parameter set is returned to the previous valid parameter set.

7. The control method for a motor for a variable frequency compressor according to claim 1, characterized in that: The adjusted PWM control strategy is input into the motor drive unit to perform preliminary regulation, and the regulated winding temperature change rate and torque fluctuation data are collected to generate a multi-dimensional feedback feature vector, specifically including: The adjusted PWM control parameter set is loaded into the FPGA control unit of the motor driver to perform preliminary control, where the actual dead time T_phase of each phase is written into the timer register in 16 bits through the SPI interface, and the composite voltage vector waveform data is stored in the dual-port RAM buffer; The distributed thermocouple array sampling rate is used to capture the temperature sequence of each winding section after regulation, and the temperature change rate ΔT_rate=ΔT / Δt is calculated, with a time window Δt=100ms; The motor rotor angular displacement signal is collected by a magnetic encoder, and the second-order derivative is obtained after Savitzky-Golay filtering to obtain the torque fluctuation component ΔTe.

8. The control method for a motor for a variable frequency compressor according to claim 7, characterized in that: The second-order derivative is obtained after Savitzky-Golay filtering to obtain the torque fluctuation component ΔTe, which then includes: Extracting temperature feedback indicators, the temperature feedback indicators include: maximum temperature rise rate ΔT_max in the hot spot area, average temperature rise gradient T_avg and temperature distribution uniformity index U_T=σ(T) / μ(T); Extracting electrical parameter indicators, the electrical parameter indicators include: current total harmonic distortion THD, third harmonic distortion H3%; The temperature feedback index and the electrical parameter index are normalized and encoded into a 5-dimensional feature vector in the order of [ΔT_max, T_avg, U_T, THD, H3%] to construct a multi-dimensional feedback feature vector.

9. The control method for a motor for a variable frequency compressor according to claim 1, characterized in that: Based on the multi-dimensional feedback feature vector, the combined weight of the switching interval compensation amount and the harmonic injection strategy is iteratively optimized through the gradient descent algorithm, and the motor control strategy matrix adapted to the current working condition is output and loaded into the control system in real time, specifically including: Analyze the multi-dimensional feedback feature vector, extract the maximum temperature rise rate ΔT_max, the current total harmonic distortion rate THD and the torque fluctuation peak ΔTe_pp as optimization targets, and construct a normalized cost function; Define the optimization variables and constraints: the switching interval compensation weight k_comp ranges from [0.1, 0.4], the harmonic injection ratio weight k_h3 ranges from [0.05, 0.25], the learning rate α is set to 0.02, the maximum number of iterations is 50, and the convergence threshold ΔJ is less than 0.5%.

10. The control method for a motor for a variable frequency compressor according to claim 9, characterized in that: The definition of optimization variables and constraints further includes: Calculate the cost function value J_current based on the current weight combination (k_comp, k_h3); The partial derivatives are calculated by perturbation analysis: a ±0.02 perturbation is applied to k_comp to obtain the J change ΔJ_comp; a ±0.005 perturbation is applied to k_h3 to obtain the J change ΔJ_h3; Update the current weight combination (k_comp, k_h3) through the calculated partial derivatives. If k_comp exceeds the range, take the boundary value. If k_h3 exceeds the limit, retreat by 0.005 steps to perform gradient descent iterative optimization. When the decrease of J is less than 0.5% for three consecutive iterations, the current weight combination is extracted; Check the constraints of ΔT_max≤5℃ / s, THD≤5%, and ΔTe_pp≤2N·m. If any indicator exceeds the limit, reiterate according to the weight priority of the cost function component; Map the optimization weights to the actual control parameters, generate 12 sets of discrete parameter combinations, and store them in the strategy matrix in ascending order of J value; The strategy matrix is ​​transmitted to the DSP coprocessor of the motor controller via the PCIe bus. At the beginning of the next control cycle, the strategy group with the smallest J value is selected for activation and execution.

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