A voltage deviation calculation method of an inverter and a related device

Abstract

The embodiment of the application discloses a voltage deviation calculation method of an inverter, which comprises the following steps: obtaining a stator resistance value of a motor; calculating a sampling voltage deviation according to a sampling voltage, a sampling current and the stator resistance value; obtaining a segmented node vector, which is a vector of nodes of a preset equation after segmentation; calculating a slope increment according to the sampling voltage deviation, the sampling current and the segmented node vector, wherein the slope increment is a slope change amount of each segmented equation after the preset equation is segmented; and obtaining a target voltage deviation based on the preset equation, the slope increment, a target current and the segmented node vector.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the field of power electronics, and particularly relate to a voltage deviation calculation method of an inverter and related devices. BACKGROUND

[0002] In the field of power electronics, in order to ensure the reliable operation of the power device of the inverter, a dead time is inserted when the upper and lower switching tubes are alternately switched, thereby causing the theoretical output voltage of the inverter at a certain time point to have a voltage deviation from the actual output voltage. However, the voltage deviation has a nonlinear relationship with the current flowing through the inverter, which makes it very difficult to compensate for the voltage deviation.

[0003] Currently, in the frequency converter industry, the developer usually gives the inverter a nonlinear voltage deviation compensation curve according to his own experience, and determines the voltage deviation through the compensation curve when the inverter is working, so as to compensate for the actual output voltage. However, the voltage deviation obtained according to experience is not accurate enough, and the data fitting accuracy is not enough, which further affects the control performance of the inverter and brings certain inconvenience to the user. SUMMARY

[0004] Embodiments of the present application provide a voltage deviation calculation method of an inverter and related devices.

[0005] A voltage deviation calculation method of an inverter, comprising:

[0006] obtaining a stator resistance value of a motor;

[0007] calculating a sampling voltage deviation according to a sampling voltage, a sampling current and the stator resistance value, the sampling voltage deviation being a difference between the sampling voltage and a product of the sampling current and the stator resistance value;

[0008] obtaining a segmented node vector, the segmented node vector being a vector of nodes of a segmented preset equation;

[0009] calculating a slope increment according to the sampling voltage deviation, the sampling current and the segmented node vector, the slope increment being a slope change of each segmented equation of the segmented preset equation;

[0010] obtaining a target voltage deviation based on the preset equation, the slope increment, a target current and the segmented node vector, the target voltage deviation being a correction value of an actual output voltage of the inverter.

[0011] Optionally, the obtaining of the segmented node vector comprises:

[0012] giving the segmented node vector according to the sampling voltage deviation and the sampling current.

[0013] Optionally, the obtaining the segment node vector comprises:

[0014] setting an initial solution vector group, the initial solution vector group comprising a plurality of solution vectors, each solution vector being an array of a preset dimension, each item of the array being a random number in a preset range;

[0015] taking the initial solution vector group as a first solution vector group;

[0016] obtaining a first optimization vector through the first solution vector group and a differential amplification coefficient;

[0017] judging whether each item in the first optimization vector satisfies a boundary condition;

[0018] if yes, determining a target vector through the first optimization vector, the target vector being a solution vector corresponding to a comparison vector in a second solution vector group, the second solution vector group being a solution vector group obtained by performing one iteration on the first solution vector group, and the comparison vector being a solution vector corresponding to the first optimization vector in the first solution vector group;

[0019] if no, returning to the obtaining of the first optimization vector through the first solution vector group and the differential amplification coefficient until the determining of the target vector through the first optimization vector if yes;

[0020] judging whether an iteration termination condition is met;

[0021] if yes, determining a solution vector in the target vector that makes a value of an objective function minimum as the segment node vector, the objective function being a sum of squares of errors function about the preset equation;

[0022] if no, taking the second solution vector group as a new first solution vector group, and returning to the obtaining of the first optimization vector through the first solution vector group and the differential amplification coefficient until the determining of the solution vector in the target vector that makes the value of the objective function minimum as the segment node vector if yes.

[0023] Optionally, the determining of the target vector through the first optimization vector comprises:

[0024] judging whether a value of the objective function with the first optimization vector substituted thereinto is greater than a value of the objective function with the comparison vector substituted thereinto;

[0025] if no, determining the first optimization vector as the target vector;

[0026] if yes, determining the comparison vector as the target vector.

[0027] Optionally, the determining of the target vector through the first optimization vector comprises:

[0028] determining whether a random probability is greater than a cross probability, the random probability being a probability randomly selected, and the cross probability being a pre-set value;

[0029] if less than or equal to, determining the first optimization vector as a second optimization vector;

[0030] if greater than, determining a comparison vector as the second optimization vector, the comparison vector being a solution vector corresponding to the first optimization vector in the first solution vector group;

[0031] determining whether a value of the objective function with the second optimization vector is greater than a value of the objective function with the comparison vector;

[0032] if less than or equal to the value of the objective function with the comparison vector, determining the second optimization vector as the target vector;

[0033] if greater than the value of the objective function with the comparison vector, determining the comparison vector as the target vector.

[0034] Optionally, determining whether an iteration termination condition is met, comprising:

[0035] calculating a difference between a value of the objective function with the target vector and a value of the objective function with the comparison vector;

[0036] determining whether a maximum value of the difference in a preset number of continuous iterations is less than a preset threshold value;

[0037] if less than the preset threshold value, the iteration termination condition is met;

[0038] if greater than or equal to the preset threshold value, the iteration termination condition is not met;

[0039] or,

[0040] determining whether a number of iterations is less than a preset number;

[0041] if less than the preset number, the iteration termination condition is not met;

[0042] if greater than or equal to the preset number, the iteration termination condition is met.

[0043] Optionally, based on the preset equation, obtaining a target voltage deviation through the slope increment, a target current and the segmented node vector, comprising:

[0044] obtaining the target voltage deviation through the following formula:

[0045] y = b1 + (x - b1) * k1 * b2 + (x - b2) * k2 * b3 +... + (x - bn-1) * kn-1 * bn m-1 )*k m-1 *β m ;

[0046]

[0047] y is the target voltage deviation;

[0048] x is the target current;

[0049] b i is the horizontal coordinate of the ith segment node, also representing the ith term of the segment node vector;

[0050] β m is the change in the slope of the segment equation corresponding to the mth segment node.

[0051] A chip system comprises:

[0052] An acquisition unit configured to acquire a stator resistance value of a motor;

[0053] A calculation unit configured to calculate a sampling voltage deviation based on a sampling voltage, a sampling current, and the stator resistance value, the sampling voltage deviation being the difference between the sampling voltage and the product of the sampling current and the stator resistance value;

[0054] The acquisition unit is configured to acquire a segment node vector, the segment node vector being a vector of nodes of a preset equation after being segmented;

[0055] The calculation unit is configured to calculate a slope increment based on the sampling voltage deviation, the sampling current, and the segment node vector, the slope increment being the change in the slope of each segment equation after the preset equation is segmented;

[0056] A processing unit configured to obtain a target voltage deviation based on the preset equation, the slope increment, a target current, and the segment node vector, the target voltage deviation being a correction value for an actual output voltage of an inverter.

[0057] A chip system comprises:

[0058] A central processing unit, a memory, and an input / output interface;

[0059] The memory is a transitory storage memory or a persistent storage memory;

[0060] The central processing unit is configured to communicate with the memory and execute instruction operations in the memory to perform the foregoing method.

[0061] A computer-readable storage medium comprising instructions which, when executed on a computer, cause the computer to carry out the preceding method.

[0062] From the above technical solutions, the embodiments of the application have the following advantages:

[0063] After obtaining the stator resistance value of the motor, the sampling voltage deviation is calculated by combining the sampling voltage and the sampling current. Then, the slope increment is obtained by combining the sampling current and the segmented node vector. Finally, the target voltage deviation is obtained based on a preset equation, through the slope increment, the target current and the segmented node vector. The target voltage deviation is obtained through the stator resistance value, the actually measured sampling voltage and sampling current and the segmented node vector. The calculated voltage deviation value is accurate, the data fitting accuracy is high, the inverter can exert the due control performance, and the user can have a better experience. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1 An embodiment of the voltage deviation calculation method of the application is shown in the figure;

[0065] Figure 2 Another embodiment of the voltage deviation calculation method of the application is shown in the figure;

[0066] Figure 3 Another embodiment of the voltage deviation calculation method of the application is shown in the figure;

[0067] Figure 4 An embodiment of the chip system of the application is shown in the figure;

[0068] Figure 5 Another embodiment of the chip system of the application is shown in the figure. DETAILED DESCRIPTION

[0069] The embodiments of the application provide a voltage deviation calculation method of an inverter and related devices.

[0070] The given voltage deviation of the inverter according to the experience of the person himself is not accurate enough, and the compensation effect on the actual output voltage of the inverter is not satisfactory enough. To solve the above problems, the application provides a voltage deviation calculation method of an inverter and related devices to bring a better experience to the user.

[0071] Please refer to Figure 1 An embodiment of the voltage deviation calculation method of the application includes:

[0072] 101. Obtain the stator resistance value of the motor;

[0073] The stator resistance value of the motor is obtained. Specifically, the stator resistance value of the motor can be obtained by a direct current-voltage method. The first direct current and the second direct current injected into the motor are obtained, and the first direct current voltage and the second direct current voltage are obtained, and the stator resistance value is calculated by the following formula:

[0074]

[0075] Wherein, R is the stator resistance value, I1 is the first direct current, I2 is the second direct current, U1 is the first direct current voltage, U2 is the second direct current voltage, U err1 is the voltage deviation caused by the non-linear factor when the first direct current flows through the inverter, U err2 is the voltage deviation caused by the non-linear factor when the second direct current flows through the inverter. When I1 and I2 are large, U err1 ≈U err2 , for example, when I1 is half of the rated current of the motor, and I2 is the rated current of the motor, the formula can be simplified as:

[0076]

[0077] It can be understood that the stator resistance value of the motor can also be calculated in advance and saved, and the stator resistance value is obtained when needed. The specific is not limited here.

[0078] 102. Calculate the sampling voltage deviation according to the sampling voltage, the sampling current and the stator resistance value;

[0079] Calculate the sampling voltage deviation according to the sampling voltage, the sampling current and the stator resistance value, wherein the sampling voltage deviation is the difference between the sampling voltage and the product of the sampling current and the stator resistance value. Specifically, the sampling current injected into the motor is obtained, wherein the value of each sampling current approximates t*I min , t=1,2,3...N, N is the sampling current injected into the motor. Here, it can be set according to the demand, and

[0080]

[0081] Wherein, I n is the rated current of the motor. After obtaining the sampling voltage corresponding to the sampling current, the sampling voltage deviation can be calculated according to the following formula:

[0082] U err =U-I*R;

[0083] Wherein, U err is the sampling voltage deviation, U is the sampling voltage, I is the sampling current, and R is the stator resistance value.

[0084] 103. Obtain the segmented node vector;

[0085] The segmented node vector is obtained. The segmented node vector is a vector of nodes segmented from the preset equation. Specifically, the segmented node vector can be obtained according to the data characteristics by sampling the voltage deviation and the sampling current, for example, artificially averaging a plurality of groups of data composed of the sampling voltage deviation and the sampling current. In addition, the segmented node vector can also be obtained by algorithm optimization.

[0086] 104. A slope increment is calculated according to the sampling voltage deviation, the sampling current and the segmented node vector;

[0087] A slope increment is calculated according to the sampling voltage deviation, the sampling current and the segmented node vector, wherein the slope increment is a slope change of each segmented equation after the preset equation is segmented.

[0088] Specifically, the number of injection motors of the sampling current to be fitted is set as n, the number of segmented nodes is set as m, and the multi-segment linear fitting equation is:

[0089]

[0090]

[0091] y is the sampling voltage deviation;

[0092] x is the sampling current;

[0093] b i is the horizontal coordinate of the i-th segmented node, and also represents the i-th item of the segmented node vector;

[0094] β m is the change of the slope of the segmented equation corresponding to the m-th segmented node.

[0095] Wherein, the sampling current deviation, the sampling current and the segmented node vector are known, and the slope increment is unknown.

[0096] The predicted value of the multi-segment linear fitting equation It can be calculated by the following formula:

[0097]

[0098] The objective function, i.e. the error sum of squares function, is:

[0099]

[0100] Wherein, ESS is the error sum of squares.

[0101] The fitting objective is to find the β that makes ESS minimum. This is a problem of finding the least squares solution of an overdetermined linear equation system, and the β can be solved by using the Gauss-Seidel iteration algorithm.

[0102] 105. Based on the preset equation, the target voltage deviation is obtained through the slope increment, target current and segmented node vector.

[0103] Specifically, the target voltage deviation is calculated using the following formula:

[0104] y=β1+(x-b1)*k1*β2+(x-b2)*k2*β3+...+(xb m-1 )*k m-1 *β m ;

[0105]

[0106] y represents the target voltage deviation, which is a correction value for the actual output voltage of the inverter;

[0107] x is the target current;

[0108] b i It represents the x-coordinate of the i-th segment node, and also the i-th term of the segment node vector;

[0109] β m This represents the change in the slope of the piecewise equation corresponding to the m-th segment node.

[0110] In this embodiment, after obtaining the stator resistance value of the motor, the sampled voltage deviation is calculated by combining it with the sampled voltage and sampled current. Next, the slope increment is obtained by combining the sampled current and the segmented node vector. Finally, based on a preset equation, the target voltage deviation is obtained through the slope increment, target current, and segmented node vector. By obtaining the target voltage deviation using the stator resistance value, the actually measured sampled voltage and current, and the segmented node vector, the calculated voltage deviation value is accurate, and the data fitting accuracy is high, enabling the inverter to perform its intended control function and providing a better user experience.

[0111] The voltage deviation calculation method of this application is described in detail below. Please refer to... Figure 2 Another embodiment of the voltage deviation calculation method of this application includes:

[0112] 201. Obtain the stator resistance value of the motor;

[0113] Obtain the stator resistance value of the motor. Specifically, the stator resistance value can be obtained using the DC volt-ampere method. Obtain the first and second DC currents injected into the motor, and obtain the first and second DC voltages. The stator resistance value can then be calculated using the following formula:

[0114]

[0115] Wherein, R is the stator resistance value, I1 is the first DC current, I2 is the second DC current, U1 is the first DC voltage, U2 is the second DC voltage, U err1 is the voltage deviation caused by the non-linear factor when the first DC current flows through the inverter, U err2 is the voltage deviation caused by the non-linear factor when the second DC current flows through the inverter. When I1 and I2 are large, U err1 ≈U err2 For example, when I1 is half of the rated current of the motor and I2 is the rated current of the motor, the formula can be simplified as:

[0116]

[0117] It can be understood that the stator resistance value of the motor can also be calculated in advance and saved, and the stator resistance value is obtained when needed, which is not limited here.

[0118] 202, calculate the sampling voltage deviation according to the sampling voltage, the sampling current and the stator resistance value;

[0119] Calculate the sampling voltage deviation according to the sampling voltage, the sampling current and the stator resistance value, wherein the sampling voltage deviation is the difference between the sampling voltage and the product of the sampling current and the stator resistance value. Specifically, the sampling current injected into the motor is obtained, wherein the value of each sampling current approximates t*I min , t = 1, 2, 3...N, N is the number of sampling current injected into the motor, which can be set according to requirements, and

[0120]

[0121] Wherein, I n is the rated current of the motor. After obtaining the sampling voltage corresponding to the sampling current, the sampling voltage deviation can be calculated according to the following formula:

[0122] U err = U - I * R;

[0123] Wherein, U err is the sampling voltage deviation, U is the sampling voltage, I is the sampling current, and R is the stator resistance value.

[0124] The sampling voltage deviation is the difference between the sampling voltage and the product of the sampling current and the stator resistance value;

[0125] 203, give the segmented node vector according to the sampling voltage deviation and the sampling current;

[0126] The segmented node vector is obtained by sampling the voltage deviation and the sampling current, wherein the segmented node vector is a vector of nodes segmented by the preset equation. Specifically, the segmented node vector can be directly given according to the data characteristics by sampling the voltage deviation and the sampling current, for example, a plurality of groups of data composed of the sampling voltage deviation and the sampling current are divided according to the user's setting average to obtain the segmented node vector.

[0127] 204. calculating the slope increment according to the sampling voltage deviation, the sampling current and the segmented node vector;

[0128] calculating the slope increment according to the sampling voltage deviation, the sampling current and the segmented node vector, wherein the slope increment is the slope change of each segmented equation after the preset equation is segmented.

[0129] Specifically, the number of injection motors of the sampling current to be fitted is set as n, the number of segmented nodes is set as m, and the multi-segment linear fitting equation is:

[0130]

[0131]

[0132] y is the sampling voltage deviation;

[0133] x is the sampling current;

[0134] b i is the horizontal coordinate of the i th segmented node, and also represents the i th item of the segmented node vector;

[0135] β m is the change of the slope of the segmented equation corresponding to the m th segmented node.

[0136] wherein the sampling current deviation, the sampling current and the segmented node vector are known, and the slope increment is unknown.

[0137] The predicted value of the multi-segment linear fitting equation which can be calculated by the following formula:

[0138]

[0139] The objective function, i.e. the error sum of squares function, is:

[0140]

[0141] wherein ESS is the error sum of squares.

[0142] The fitting objective is to find the β that makes ESS minimum. This is a problem of finding the least squares solution of an overdetermined linear equation system, and the β can be found by using the Gauss-Seidel iterative algorithm.

[0143] 205. Based on the preset equation, the target voltage deviation is obtained through the slope increment, target current and segmented node vector.

[0144] Specifically, the target voltage deviation is calculated using the following formula:

[0145] y=β1+(x-b1)*k1*β2+(x-b2)*k2*β3+...+(xb m-1 )*k m-1 *β m ;

[0146]

[0147] y represents the target voltage deviation, which is a correction value for the actual output voltage of the inverter;

[0148] x is the target current;

[0149] b i It represents the x-coordinate of the i-th segment node, and also the i-th term of the segment node vector;

[0150] β m This represents the change in the slope of the piecewise equation corresponding to the m-th segment node.

[0151] In this embodiment, after obtaining the stator resistance value of the motor, the sampled voltage deviation is calculated by combining it with the sampled voltage and sampled current. A segmented node vector is obtained from the sampled voltage deviation and sampled current. Then, the slope increment is obtained by combining the sampled current and the segmented node vector. Finally, based on a preset equation, the target voltage deviation is obtained using the slope increment, target current, and segmented node vector. By obtaining the target voltage deviation using the stator resistance value, the actually measured sampled voltage and current, and the segmented node vector, the calculated voltage deviation value is accurate, and the data fitting accuracy is high, enabling the inverter to perform its intended control function and providing a better user experience.

[0152] Please see Figure 3 Another embodiment of the voltage deviation calculation method of this application includes:

[0153] 301. Obtain the stator resistance value of the motor;

[0154] 302. The sampling voltage deviation is calculated based on the sampling voltage, sampling current, and stator resistance value.

[0155] Steps 301 to 302 in this embodiment are the same as those described above. Figure 2 Steps 201 to 202 in the illustrated embodiment are similar and will not be repeated here.

[0156] 303. Set the initial solution vector set;

[0157] Set up an initial solution vector set. The initial solution vector set consists of multiple solution vectors, each of which is an array of preset dimensions, where each element of the array is a random number within a preset range. Specifically, if the piecewise linear fitting equation has s segments, then s-1 segment nodes need to be determined. The solution vector set is defined as the g-th generation solution vector set, containing NP solution vectors, each of which is an s-1 dimensional array, where each element is from α to I. max A random number within a range, i.e.:

[0158] b ij (g)=α+rand(0,1)*(I max -α),i=0,1,2,...,NP,j=0,1,2,...,n-1,α<I max ;

[0159] Among them, b ij (g) represents the j-th term of the i-th solution vector in the g-th iteration. NP can be set according to requirements, but is generally set to 50. I n represents the rated current of the motor. The initial solution vector set is the 0th generation solution vector set.

[0160] 304. Use the initial solution vector set as the first solution vector set;

[0161] The initial solution vector set is taken as the first solution vector set. The first solution vector set is the solution vector set before each iteration in the iterative process. The second solution vector set mentioned in the following steps is the solution vector set obtained in each iteration. The initial solution vector set marks the beginning of the iteration.

[0162] 305. Obtain the first optimization vector through the first solution vector set and the difference amplification coefficient;

[0163] The first optimization vector is obtained by using the first solution vector set and the difference amplification coefficient. Specifically, the first optimization vector is obtained by the following formula:

[0164] u i (g+1)=b r1 (g)+F*(b r2 (g)-b r3 (g));

[0165] Among them, u i (g+1) is the first optimization vector, r1, r2 and r3 are integers randomly generated in the range of 0 to NP, and r1≠r2≠r3≠i. F is the differential amplification coefficient, which is given by the user and is generally set to 0.4.

[0166] 306, judging whether each item in the first optimization vector satisfies the boundary condition, if not, executing step 305, if yes, executing step 307;

[0167] judging whether each item in the first optimization vector satisfies the boundary condition, specifically, judging whether 0 ij <I max if not, executing step 305 to regenerate the first optimization vector, if yes, executing step 307.

[0168] 307, determining the target vector through the first optimization vector;

[0169] determining the target vector through the first optimization vector. The target vector is the solution vector corresponding to the comparison vector in the second solution vector group, and the second solution vector group is the solution vector group obtained by iterating the first solution vector group once.

[0170] Specifically, judging whether the value of the objective function with the first optimization vector is greater than the value of the objective function with the comparison vector, if not, determining the first optimization vector as the target vector, if yes, determining the comparison vector as the target vector. For example, when the target vector is b i (g+1), the first optimization vector is u i (g+1), and the comparison vector is b i (g), the target vector can be obtained by the following formula:

[0171]

[0172] In addition, the target vector can also be determined by another method. Specifically, first judging whether the random probability is greater than the crossover probability, wherein the random probability is the randomly selected probability, and the crossover probability is the pre-set constant value. When the random probability is less than or equal to the crossover probability, the first optimization vector is determined as the second optimization vector, and when the random probability is greater than the crossover probability, the comparison vector is determined as the second optimization vector. Then, judging whether the value of the objective function with the second optimization vector is greater than the value of the objective function with the comparison vector, if the value of the objective function with the second optimization vector is less than or equal to the value of the objective function with the comparison vector, the second optimization vector is determined as the target vector, if the value of the objective function with the second optimization vector is greater than the value of the objective function with the comparison vector, the comparison vector is determined as the target vector. For example, when the second optimization vector is v i (g+1), the first optimization vector is u i (g+1), the comparison vector is b i (g), and CR is the crossover probability, the second optimization vector can be obtained by the following formula:

[0173]

[0174] Wherein, CR can be set by user according to requirement, generally set as 0.7.

[0175] The target vector is obtained by the following formula:

[0176]

[0177] 308, judge whether to meet the iteration termination condition, if yes, execute step 309, if no, execute step 310 and then execute 305;

[0178] Judge whether to meet the iteration termination condition, specifically, first calculate the difference value of the value of the target function substituted by the target vector and the value of the target function substituted by the contrast vector, and then judge whether the maximum value of the difference value of the continuous preset iteration number is less than the preset threshold value, if less than the preset threshold value, the iteration termination condition is met, if greater than or equal to the preset threshold value, the iteration termination condition is not met. For example, first calculate the difference value by the following formula:

[0179]

[0180] The preset iteration number is set as 3, and the preset threshold value is set as 1, so the maximum value of each generation difference value obtained by the continuous 3 generations (i.e. the g+1 generation, the g generation and the g-1 generation) is less than 1, and the iteration termination condition is met, if greater than or equal to 1, the iteration is continued.

[0181] In addition, another method can also be used for judgment. Specifically, judge whether the iteration number is less than the preset number; if less than the preset number, the iteration termination condition is not met; if greater than or equal to the preset number, the iteration termination condition is met. For example, the preset number is 100, when the iteration number is less than 100, the iteration is continued, when the iteration number is greater than or equal to 100, the iteration is stopped.

[0182] 309, determine the solution vector in the target vector which makes the value of the target function minimum as the segmentation node vector;

[0183] Determine the solution vector in the target vector which makes the value of the target function minimum as the segmentation node vector, wherein the target function is the error square sum function about the preset equation. The minimum value can be obtained by solving the target function substituted by the target vector and comparing the results, and then the segmentation node vector is determined.

[0184] 310, take the second solution vector group as the new first solution vector group;

[0185] If the iteration termination condition is not met, take the second solution vector group as the new first solution vector group, continue the iteration of the next round, so as to improve the fitting accuracy.

[0186] 311、calculate the slope increment according to the sampling voltage deviation, the sampling current and the segmented node vector;

[0187] 312、obtain the target voltage deviation based on the preset equation, the slope increment, the target current and the segmented node vector.

[0188] The steps 311 to 312 in the embodiment are similar to the steps 204 to 205 in the foregoing Figure 2 The steps 204 to 205 in the embodiment are similar to the steps 204 to 205 in the foregoing

[0189] In the embodiment, after obtaining the stator resistance value of the motor, the sampling voltage deviation is calculated by combining the sampling voltage and the sampling current. The segmented node vector is obtained by iterative optimization, and then the slope increment is obtained by combining the sampling current and the segmented node vector. Finally, the target voltage deviation is obtained based on the preset equation, the slope increment, the target current and the segmented node vector. The target voltage deviation is obtained by the stator resistance value, the actually measured sampling voltage and sampling current and the segmented node vector. The calculated voltage deviation value is accurate, the data fitting precision is high, and the inverter can play the due control performance. In addition, the iterative optimization ensures the high precision of data fitting, and brings better experience to the user.

[0190] The chip system of the present application is described below. Please refer to Figure 4 An embodiment of the chip system of the present application includes:

[0191] The acquisition unit 401 is configured to obtain the stator resistance value of the motor;

[0192] The calculation unit 402 is configured to calculate the sampling voltage deviation according to the sampling voltage, the sampling current and the stator resistance value, the sampling voltage deviation being the difference between the sampling voltage and the product of the sampling current and the stator resistance value;

[0193] The acquisition unit 401 is further configured to obtain the segmented node vector, the segmented node vector being the vector of the nodes of the segmented preset equation;

[0194] The calculation unit 402 is further configured to calculate the slope increment according to the sampling voltage deviation, the sampling current and the segmented node vector, the slope increment being the slope change of each segmented equation after the preset equation is segmented;

[0195] The processing unit 403 is configured to obtain the target voltage deviation based on the preset equation, the slope increment, the target current and the segmented node vector, the target voltage deviation being the correction value of the actual output voltage of the inverter.

[0196] In the embodiment of the present application, after the acquisition unit 401 acquires the stator resistance value of the motor, the calculation unit 402 calculates the sampling voltage deviation by combining the sampling voltage and the sampling current. Then, the slope increment is obtained by combining the sampling current and the segmented node vector. Finally, the processing unit 403 obtains the target voltage deviation based on the preset equation by using the slope increment, the target current and the segmented node vector. The target voltage deviation is obtained by using the stator resistance value, the actually measured sampling voltage and sampling current and the segmented node vector. The calculated voltage deviation value is accurate, the data fitting accuracy is high, the inverter can exert the control performance, and the user can have a better experience.

[0197] The functions and processes performed by the units in the chip system in the embodiment are similar to the functions and processes performed by the chip system in the foregoing Figures 1 to 3 embodiment, and details are not repeated here.

[0198] Figure 5 FIG. 5 is a schematic diagram of a chip system structure according to an embodiment of the present application. The chip system 500 can include one or more central processing units (CPUs) 501 and a memory 505, and the memory 505 stores one or more application programs or data.

[0199] The memory 505 can be volatile storage or persistent storage. The programs stored in the memory 505 can include one or more modules, and each module can include a series of instruction operations in the chip system. Further, the central processing unit 501 can be configured to communicate with the memory 505 and execute the series of instruction operations in the memory 505 on the chip system 500.

[0200] The chip system 500 can further include one or more power supplies 502, one or more wired or wireless network interfaces 503, and one or more input / output interfaces 504.

[0201] The central processing unit 501 can perform the operations of the chip system in the embodiments shown in the foregoing Figures 1 to 2 embodiment, and details are not repeated here.

[0202] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which are not repeated here.

[0203] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the described device embodiments are merely schematic. The division of the units is merely a logical function division. There can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between the units can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0204] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments of the present application.

[0205] In addition, each functional unit in the embodiments of the present application can be integrated in one processing unit, or each unit can exist physically as a separate unit, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware, or in the form of a software functional unit.

[0206] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such an understanding, the technical solutions of the present application essentially or substantially, or all or part of the technical solutions, can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application. The foregoing storage medium includes: U disk, mobile hard disk, read-only memory (ROM, read-only memory), random access memory (RAM, random access memory), magnetic disk or optical disk, and various other media that can store program codes.

Claims

1. A voltage deviation calculation method of an inverter, characterized by, The method comprises the following steps: obtaining a stator resistance value of a motor; calculating a sampling voltage deviation based on a sampling voltage, a sampling current and the stator resistance value, the sampling voltage deviation being a difference between the sampling voltage and a product of the sampling current and the stator resistance value; obtaining a segmented node vector, the segmented node vector being a vector of nodes of a preset equation after being segmented; calculating a slope increment based on the sampling voltage deviation, the sampling current and the segmented node vector, the slope increment being a slope change of each segment of the preset equation after being segmented; obtaining a target voltage deviation based on the slope increment, a target current and the segmented node vector, the target voltage deviation being a correction value of an actual output voltage of an inverter.

2. The voltage deviation calculation method according to claim 1, characterized by, The method of obtaining the segmented node vector comprises the following steps: defining the segmented node vector based on the sampling voltage deviation and the sampling current.

3. The voltage deviation calculation method of claim 1, wherein The method of obtaining the segmented node vector comprises the following steps: setting an initial solution vector group, the initial solution vector group comprising a plurality of solution vectors, each solution vector being an array of a preset dimension, each item of the array being a random number in a preset range; taking the initial solution vector group as a first solution vector group; obtaining a first optimization vector based on the first solution vector group and a differential amplification coefficient; judging whether each item in the first optimization vector satisfies a boundary condition; if yes, determining a target vector based on the first optimization vector, the target vector being a solution vector corresponding to a comparison vector in a second solution vector group, the second solution vector group being a solution vector group obtained by performing one iteration on the first solution vector group, and the comparison vector being a solution vector corresponding to the first optimization vector in the first solution vector group; if no, returning to the step of obtaining the first optimization vector based on the first solution vector group and the differential amplification coefficient until the step of determining the target vector based on the first optimization vector is performed; judging whether an iteration termination condition is met; if yes, determining a solution vector in the target vector that makes a value of a target function minimum as the segmented node vector, the target function being a sum of squares of errors function about the preset equation; if no, taking the second solution vector group as a new first solution vector group and returning to the step of obtaining the first optimization vector based on the first solution vector group and the differential amplification coefficient until the step of determining the solution vector in the target vector that makes the value of the target function minimum is performed.

4. The voltage deviation calculation method according to claim 3, characterized by, The method of determining the target vector based on the first optimization vector comprises the following steps: judging whether a value of the target function into which the first optimization vector is substituted is greater than a value of the target function into which the comparison vector is substituted; if no, determining the first optimization vector as the target vector; if yes, determining the comparison vector as the target vector.

5. The voltage deviation calculation method according to claim 3, characterized by, The method of determining the target vector based on the first optimization vector comprises the following steps: judging whether a random probability is greater than a crossover probability, the random probability being a randomly selected probability, and the crossover probability being a preset constant; if less than or equal to, determining the first optimization vector as a second optimization vector; if greater than, determining the comparison vector as the second optimization vector. determining whether a value of the objective function substituted by the second optimization vector is greater than a value of the objective function substituted by the contrast vector; if less than or equal to the value of the objective function substituted by the contrast vector, determining the second optimization vector as the target vector; if greater than the value of the objective function substituted by the contrast vector, determining the contrast vector as the target vector.

6. The voltage deviation calculation method according to any one of claims 4 or 5, characterized by, determining whether an iteration termination condition is met, comprising: calculating a difference between a value of the objective function substituted by the target vector and a value of the objective function substituted by the contrast vector; determining whether a maximum value of the difference in a preset number of continuous iterations is less than a preset threshold value; if less than the preset threshold value, the iteration termination condition is met; if greater than or equal to the preset threshold value, the iteration termination condition is not met; or, determining whether the number of iterations is less than a preset number; if less than the preset number, the iteration termination condition is not met; if greater than or equal to the preset number, the iteration termination condition is met.

7. The voltage deviation calculation method of claim 1, wherein based on the preset equation, obtaining a target voltage deviation through the slope increment, a target current and the segmented node vector, comprising: obtaining the target voltage deviation through the following formula: y = b1 + (x - b1) * k1 * b2 + (x - b2) * k2 * b3 +... + (x - bn) * kn * bn+1 m-1 * k m-1 * b m n+1 ; y is the target voltage deviation; x is the target current; b i x-coordinate of the i-th segment node, also denoting the i-th entry of the segment node vector; β m is the change of the slope of the segment equation corresponding to the mth segment node.

8. A chip system, characterized by comprising: an acquisition unit configured to acquire a stator resistance value of a motor; a calculation unit configured to calculate a sampling voltage deviation through a sampling voltage, a sampling current and the stator resistance value, the sampling voltage deviation being a difference between the sampling voltage and a product of the sampling current and the stator resistance value; the acquisition unit is configured to acquire a segmented node vector, the segmented node vector being a vector of nodes segmented from a preset equation; the calculation unit is configured to calculate a slope increment through the sampling voltage deviation, the sampling current and the segmented node vector, the slope increment being a slope change amount of each segmented equation segmented from the preset equation; a processing unit configured to obtain a target voltage deviation through the slope increment, a target current and the segmented node vector based on the preset equation, the target voltage deviation being a correction value of an actual output voltage of an inverter.

9. A chip system, characterized by comprising: a central processing unit, a memory and an input-output interface; the memory is a transitory storage memory or a persistent storage memory; the central processing unit is configured to communicate with the memory and execute instruction operations in the memory to perform the method in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, instructions, when executed on a computer, cause the computer to perform the method in any one of claims 1 to 7.

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